低頻數量化交易模型 QuantitativeTrading : QuantitativeTradingJulia , QuantitativeTradingPython

Julia , Python

可擴展的數量化交易模型框架 ( scalable quantitative trading model frame ) , 分別使用 Julia 或 Python 程式設計語言 ( computer programming language ) 各自獨立實現 Julia , Pyhton 兩套方案序列遍歷 ( sequence traversal ) 動作 , 借用 Julia 或 Python 的第三方擴展包 ( third-party extensions ( libraries or modules ) ) 實現 Julia , Python 兩套方案優化算法 ( parameters optimization ) 參數最優化.

可自定義擴展新增適配演算法模組.

---

一. 其中「QuantitativeTradingJulia」項目，使用 Julia 程式設計語言 ( computer programming language )，借用第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「Optim」「JSON」「CSV」「XLSX」「JLD」「DataFrames」實現 .

1. 借用「HTTP」模組，實現 http 協議 web 伺服器 ( Server ) 功能.

2. 借用「Optim」模組的「optimize」函數，實現優化算法 ( parameters optimization ) 即通用形式優化問題求解 ( optimization ) 功能，最優化此數量化交易模型「QuantitativeTradingJulia」參數 .

3. 借用「JSON」模組，實現 Julia 原生數據類型字典 ( Base.Dict ) 對象 ( Object ) 與 JSON 字符串 ( String ) 對象 ( Object ) 之間，數據類型相互轉換 .

4. 借用「CSV」模組，實現程式設計語言 ( computer programming language ) : Julia 操作逗號 ( Comma ) 分隔符檔 ( .csv ) 讀 (read) , 寫 ( write ) 功能 .

5. 借用「XLSX」模組，實現程式設計語言 ( computer programming language ) : Julia 操作微軟電子表格 ( Windows - Office - Excel ) 檔 ( .xlsx ) 讀 (read) , 寫 ( write ) 功能 .

6. 借用「JLD」模組，實現程式設計語言 ( computer programming language ) : Julia 操作 Hierarchical Data Format version 5 , HDF5 格式的數據 ( Julia data format , JLD ) 持久化存儲檔 ( .jld ) 讀 (read) , 寫 ( write ) 功能 .

7. 借用「DataFrames」模組，實現程式設計語言 ( computer programming language ) : Julia 處理數據框 ( Julia - DataFrame ) 類型的數據 .

8. 也可以自定義修改代碼，借用第三方「LsqFit」模組，實現自定義任意形式初等函數 ( Elementary Function ) 方程擬合 ( Fit ) 運算.

9. 也可以自定義修改代碼，借用第三方「Interpolations」和「DataInterpolations」模組，實現插值 ( Interpolation ) 運算.

10. 也可以自定義修改代碼，借用第三方「Roots」模組，實現任意形式自定義初等函數一元方程求根 ( Solving Equation )，即求解反函數 ( Inverse ) .

11. 也可以自定義修改代碼，借用第三方「JuMP」模組選擇調用第三方「Gurobi」,「Ipopt」,「Cbc」,「GLPK」等算法模組，實現優化算法 ( parameters optimization ) 即通用形式優化問題求解 ( optimization ) 功能，最優化此數量化交易模型「QuantitativeTradingJulia」參數 .

二. 其中「QuantitativeTradingPython」項目，使用 Python3 程式設計語言 ( computer programming language )，借用第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「numpy」「scipy」「pandas」實現 .

1. 借用「numpy」模組，實現基礎數學向量 ( Vector ) 運算 .

2. 借用「scipy」項目的優化模組「optimize」的「minimize」函數，實現優化算法 ( parameters optimization ) 即通用形式優化問題求解 ( optimization ) 功能，最優化此數量化交易模型「QuantitativeTradingPython」參數 .

3. 借用「pandas」模組，實現程式設計語言 ( computer programming language ) : Python 操作逗號 ( Comma ) 分隔符檔 ( .csv ) 和微軟電子表格 ( Windows - Office - Excel ) 檔 ( .xlsx ) 讀 (read) , 寫 ( write ) 功能 .

4. 借用「pandas」模組，實現程式設計語言 ( computer programming language ) : Python 處理數據框 ( Python - DataFrame ) 類型的數據 .

5. 使用實現程式設計語言 ( computer programming language ) : Python 的原生「json」模組，實現 Python 原生數據類型字典 ( dict ) 對象 ( Object ) 與 JSON 字符串 ( String ) 對象 ( Object ) 之間，數據類型相互轉換 .

6. 使用實現程式設計語言 ( computer programming language ) : Python 的原生「csv」模組，實現程式設計語言 ( computer programming language ) : Python 操作逗號 ( Comma ) 分隔符檔 ( .csv ) 讀 (read) , 寫 ( write ) 功能 .

7. 使用實現程式設計語言 ( computer programming language ) : Python 的原生「pickle」模組，實現數據持久化存儲序列化 ( pickling ) 二進位字節流 ( bytes ) 檔 ( .pickle ) 讀 (read) , 寫 ( write ) 功能 .

8. 也可以自定義修改代碼，借用第三方「scipy」項目的優化模組「optimize」的「curve_fit」函數，實現自定義任意形式初等函數 ( Elementary Function ) 方程擬合 ( Fit ) 運算.

9. 也可以自定義修改代碼，借用第三方「scipy」項目的插值模組「interpolate」的「make_interp_spline」「BSpline」「interp1d」「UnivariateSpline」「lagrange」函數，實現插值 ( Interpolation ) 運算.

10. 也可以自定義修改代碼，借用第三方「scipy」項目的優化模組「optimize」的「root」函數，實現任意形式自定義初等函數一元方程求根 ( Solving Equation )，即求解反函數 ( Inverse ) .

---

一. 代碼脚本檔 ( script file ) : QuantitativeTrading/QuantitativeTradingJulia/src/Interface.jl , QuantitativeTrading/QuantitativeTradingPython/src/Interface.py

代碼脚本 ( Script ) 檔 : Interface.jl 或 Interface.py 是伺服器 ( Server ) 函數 ( Function ) , 具體功能是實現: 讀入 ( read ) 數據, 寫出 ( write ) 結果.

二. 代碼脚本檔 ( script file ) : QuantitativeTrading/QuantitativeTradingJulia/src/Router.jl , QuantitativeTrading/QuantitativeTradingPython/src/Router.py

代碼脚本 ( Script ) 檔 : Router.jl 或 Router.py 引用 ( Import ) 檔 Interpolation_Fitting.jl 或 Interpolation_Fitting.py 裏的執行自定義運算規則的函數 ( Function )，並將計算結果返回 ( return ) 至檔 Interface 的伺服器 ( Server ) 函數.

其中, 檔 Router.jl 或 Router.py 裏的 : do_data 函數 ( Function ) 是執行文檔 ( file ) 監聽伺服器 ( file_Monitor ) 讀入的數據分發路由 ( Router ) 功能.

其中, 檔 Router.jl 或 Router.py 裏的 : do_Request 函數 ( Function ) 是執行網路 ( web ) 伺服器 ( http_Server ) 讀入的從用戶端 ( http_Client ) 發送的請求 ( Request ) 數據的分發路由 ( Router ) 功能.

其中, 檔 Router.jl 或 Router.py 裏的 : do_Response 函數 ( Function ) 是執行網路 ( web ) 用戶端鏈接器 ( http_Client ) 接收到從伺服器 ( http_Server ) 回饋的響應 ( Response ) 數據 ( 運算處理結果 ) 的分發路由 ( Router ) 功能.

可自行修改行使路由 (Router) 功能的代碼脚本 ( script file ) 檔「Router.jl」「Router.py」内的 Julia 或 Python 代碼，同時需自行修改行使具體算法 ( Algorithm ) 功能的代碼脚本 ( script file ) 檔内的 Julia 或 Python 代碼，如此例的「Interpolation_Fitting.jl」「Interpolation_Fitting.py」檔，使二者相互因應協調，即可自定義擴展此數量化交易運算伺服器「QuantitativeTrading」所能提供的計算方法 ( Server Respond ) 的選項.

三. 代碼脚本檔 ( script file ) : QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl , QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py

代碼脚本 ( Script ) 檔 : QuantitativeTradingServer.jl 或 QuantitativeTradingServer.py 是伺服器（Server）啓動入口，引用 ( Import ) 檔 Interface.jl 或 Interface.py 裏的伺服器 ( Server ) 讀入 ( read ) 待處理的原始數據, 然後, 實現數據分發路由 ( Router ) 功能, 可通過修改代碼脚本 ( Script ) 檔 : Router.jl 或 Router.py 裏的 : do_data 和 do_Request 兩個函數 ( Function ) , 實現自定義規則的數據分發運算處理並返回 ( return ) 運算結果, 然後再將運算結果, 通過引用 ( Import ) 檔 Interface.jl 或 Interface.py 裏的伺服器 ( Server ) 回饋寫出 ( write ) 結果返回 ( return ) 至用戶端 ( Client ) .

四. 代碼脚本檔 ( script file ) : QuantitativeTrading/QuantitativeTradingJulia/src/Interpolation_Fitting.jl , QuantitativeTrading/QuantitativeTradingPython/src/Interpolation_Fitting.py

代碼脚本 ( Script ) 檔 : Interpolation_Fitting.jl 和 Interpolation_Fitting.py 裏，可創建執行自定義運算規則的函數 ( Function )，用以執行讀入 ( read ) 數據具體的運算處理 ( calculator ) 功能, 即本例擬合（Fit）運算、插值（Interpolation）運算等，並返回 ( return ) 處理結果至檔 Router 的路由函數.

五. 代碼脚本檔 ( script file ) : QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Indicators.jl , QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_Indicators.py

代碼脚本 ( Script ) 檔 : Quantitative_Indicators.jl 或 Quantitative_Indicators.py 是此「QuantitativeTrading」數量化交易模型的指標模組，計算日棒缐 ( K - Line ) 數據趨勢强度自定義示意指標的模組，實現從日棒缐 ( K - Line ) 原始數據計算抽象獲取自定義示意指標值的功能 .

六. 代碼脚本檔 ( script file ) : QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Data_Cleaning.jl , QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_Data_Cleaning.py

代碼脚本 ( Script ) 檔 : Quantitative_Data_Cleaning.jl 或 Quantitative_Data_Cleaning.py 是此「QuantitativeTrading」數量化交易模型的數據初加工模組，初步清理日棒缐 ( K - Line ) 原始數據，並引用「Quantitative_Indicators.jl」或「Quantitative_Indicators.py」模組，計算趨勢强度自定義示意指標，然後使計算結果標準化日棒缐 ( K - Line ) 數據，輸出至字典類型 ( Julia - Base.Dict 或 Python - dict ) 數據變量「stepping_data」存儲，可進一步寫入自定義本地檔 ( .jld 或 .pickle , .csv , .xlsx ) 持久化存儲 .

七. 代碼脚本檔 ( script file ) : QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_MarketTiming.jl , QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_MarketTiming.py

代碼脚本 ( Script ) 檔 : Quantitative_MarketTiming.jl 或 Quantitative_MarketTiming.py 是此「QuantitativeTrading」數量化交易模型擇時 ( market timing ) 規則模組，使用「Quantitative_Data_Cleaning.jl」或「Quantitative_Data_Cleaning.py」模組初步清理日棒缐 ( K - Line ) 原始數據獲得標準化日棒缐 ( K - Line ) 數據，引用「Quantitative_Indicators.jl」或「Quantitative_Indicators.py」模組計算趨勢强度自定義示意指標，然後操作標準化日棒缐 ( K - Line ) 數據，計算擇時 ( market timing ) 判斷及優化 ( optimization ) 擇時 ( market timing ) 規則參數 ( parameters ) 依據，函數計算結果返回字典類型 ( Julia - Base.Dict 或 Python - dict ) 數據變量存儲 .

八. 代碼脚本檔 ( script file ) : QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_PickStock.jl , QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_PickStock.py

代碼脚本 ( Script ) 檔 : Quantitative_PickStock.jl 或 Quantitative_PickStock.py 是此「QuantitativeTrading」數量化交易模型選股 ( pick stock ) 規則模組，使用「Quantitative_Data_Cleaning.jl」或「Quantitative_Data_Cleaning.py」模組初步清理日棒缐 ( K - Line ) 原始數據獲得標準化日棒缐 ( K - Line ) 數據，引用「Quantitative_Indicators.jl」或「Quantitative_Indicators.py」模組計算趨勢强度自定義示意指標，操作標準化日棒缐 ( K - Line ) 數據，引用「Quantitative_MarketTiming.jl」或「Quantitative_MarketTiming.py」模組計算擇時 ( market timing ) 操作，再計算選股 ( pick stock ) 判斷及優化 ( optimization ) 選股 ( pick stock ) 規則參數 ( parameters ) 依據，函數計算結果返回字典類型 ( Julia - Base.Dict 或 Python - dict ) 數據變量存儲 .

九. 代碼脚本檔 ( script file ) : QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_SizePosition.jl , QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_SizePosition.py

代碼脚本 ( Script ) 檔 : Quantitative_SizePosition.jl 或 Quantitative_SizePosition.py 是此「QuantitativeTrading」數量化交易模型倉位 ( size position ) 規則模組，使用「Quantitative_Data_Cleaning.jl」或「Quantitative_Data_Cleaning.py」模組初步清理日棒缐 ( K - Line ) 原始數據獲得標準化日棒缐 ( K - Line ) 數據，引用「Quantitative_Indicators.jl」或「Quantitative_Indicators.py」模組計算趨勢强度自定義示意指標，操作標準化日棒缐 ( K - Line ) 數據，引用「Quantitative_MarketTiming.jl」或「Quantitative_MarketTiming.py」模組計算擇時 ( market timing ) 操作，引用「Quantitative_PickStock.jl」或「Quantitative_PickStock.py」模組計算選股 ( pick stock ) 操作，再計算倉位 ( size position ) 判斷及優化 ( optimization ) 倉位 ( size position ) 規則參數 ( parameters ) 依據，函數計算結果返回字典類型 ( Julia - Base.Dict 或 Python - dict ) 數據變量存儲 .

十. 代碼脚本檔 ( script file ) : QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_BackTesting.jl , QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_BackTesting.py

代碼脚本 ( Script ) 檔 : Quantitative_BackTesting.jl 或 Quantitative_BackTesting.py 是此「QuantitativeTrading」數量化交易模型回測 ( back testing ) 模組，採取推進分析 ( stepper movement ) ( propulsion analysis ) 的方法，使用「Quantitative_Data_Cleaning.jl」或「Quantitative_Data_Cleaning.py」模組初步清理日棒缐 ( K - Line ) 原始數據獲得標準化日棒缐 ( K - Line ) 數據，引用「Quantitative_Indicators.jl」或「Quantitative_Indicators.py」模組計算趨勢强度自定義示意指標，操作標準化日棒缐 ( K - Line ) 數據，引用「Quantitative_MarketTiming.jl」或「Quantitative_MarketTiming.py」模組計算擇時 ( market timing ) 操作，引用「Quantitative_PickStock.jl」或「Quantitative_PickStock.py」模組計算選股 ( pick stock ) 操作，引用「Quantitative_SizePosition.jl」或「Quantitative_SizePosition.py」模組計算倉位 ( size position ) 操作，推進分析 ( stepper movement ) ( propulsion analysis ) 遍歷數據序列計算纍計盈虧額，函數計算結果返回字典類型 ( Julia - Base.Dict 或 Python - dict ) 數據變量存儲 .

十一. 純文本文檔 ( .txt ) : QuantitativeTrading/QuantitativeTradingJulia/config.txt , QuantitativeTrading/QuantitativeTradingPython/config.txt

純文本文檔 ( .txt ) : QuantitativeTrading/QuantitativeTradingJulia/config.txt , QuantitativeTrading/QuantitativeTradingPython/config.txt 是「QuantitativeTradingJulia」和「QuantitativeTradingPython」數量化交易模型的參數配置文檔 ( config file ) 行使運行參數傳入職能 .

十二. 檔案夾 ( folder ) : QuantitativeTrading/Julia/ , QuantitativeTrading/Python/

檔案夾 ( folder )「QuantitativeTrading/Julia/」爲微軟視窗系統 ( Windows10 x86_64 ) 程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 二進位可執行檔 ( julia.exe ) 的儲存位置，需自行下載後，將其解壓縮，保存至「C:/QuantitativeTrading/Julia/Julia-1.10.10/bin/julia.exe」路徑.

檔案夾 ( folder )「QuantitativeTrading/Python/」爲微軟視窗系統 ( Windows10 x86_64 ) 程式設計語言 ( Python3 ) 解釋器 ( Interpreter ) 二進位可執行檔 ( python.exe ) 的儲存位置，需自行下載後，將其解壓縮，保存至「C:/QuantitativeTrading/Python/Python311/python.exe」路徑.

十三. 檔案夾 ( folder ) : QuantitativeTrading/Data/

檔案夾 ( folder )「QuantitativeTrading/Data/」爲日棒缐 ( K - Line ) 數據檔的保存位置 .

其中 :

1. 檔案夾 ( folder )「QuantitativeTrading/Data/K-Day-source/」爲日棒缐 ( K - Line ) 原始數據逗號 ( Comma ) 分隔符檔 ( .csv ) 的保存位置，如此例通過深圳市招商證券股份有限公司 ( CHINA MERCHANTS SECURITIES CO., LTD. ) 證券交易服務用戶端 ( zyyht.exe ) 下載得到中華人民共和國人民幣認購和交易的普通股票 ( A shares ) 數據逗號 ( Comma ) 分隔符檔 ( .csv ) 示例 .

2. 程式設計語言 ( computer programming language ) : Julia 數據檔 ( .jld ) 「QuantitativeTrading/Data/steppingData.jld」是使用「Quantitative_Data_Cleaning.jl」模組初步清理日棒缐 ( K - Line ) 原始數據獲得標準化日棒缐 ( K - Line ) 數據本地存儲爲程式設計語言 ( computer programming language ) : Julia 數據持久化存儲 Hierarchical Data Format version 5 , HDF5 格式的數據 ( Julia data format , JLD ) 檔 ( .jld ) 示例 .

3. 程式設計語言 ( computer programming language ) : Python 數據檔 ( .pickle ) 「QuantitativeTrading/Data/steppingData.pickle」是使用「Quantitative_Data_Cleaning.py」模組初步清理日棒缐 ( K - Line ) 原始數據獲得標準化日棒缐 ( K - Line ) 數據本地存儲爲程式設計語言 ( computer programming language ) : Python 數據持久化存儲序列化 ( pickling ) 二進位字節流 ( bytes ) 檔 ( .pickle ) 示例 .

4. 逗號 ( Comma ) 分隔符檔 ( .csv ) 「QuantitativeTrading/Data/SZ#002611.csv」是使用「Quantitative_Data_Cleaning.jl」或「Quantitative_Data_Cleaning.py」模組初步清理日棒缐 ( K - Line ) 原始數據獲得標準化日棒缐 ( K - Line ) 數據持久化存儲爲本地逗號 ( Comma ) 分隔符檔 ( .csv ) 示例 .

5. 微軟電子表格 ( Windows - Office - Excel ) 檔 ( .xlsx ) 「QuantitativeTrading/Data/SZ#002611.xlsx」是使用「Quantitative_Data_Cleaning.jl」或「Quantitative_Data_Cleaning.py」模組初步清理日棒缐 ( K - Line ) 原始數據獲得標準化日棒缐 ( K - Line ) 數據持久化存儲爲本地微軟電子表格 ( Windows - Office - Excel ) 檔 ( .xlsx ) 示例 .

十四. 代碼脚本檔 ( script file ) : QuantitativeTrading/TradingAlgorithmModule.bas , 微軟電子表格應用檔 ( Windows - Office - Excel - Visual Basic for Applications ) : QuantitativeTrading/Client.xlsm

微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用檔「Client.xlsm」可作爲用戶端 ( Client ) 連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 計算.

代碼脚本檔「TradingAlgorithmModule.bas」是微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用檔「Client.xlsm」運行時，需導入的標準模組 ( Module ) 代碼（必須），可在此代碼脚本檔内，自行修改 Visual Basic for Applications , VBA 代碼，擴展運算方法的連接 ( Client Request ) 項.

十五. 檔案夾 ( folder ) : QuantitativeTrading/html/

檔案夾 ( folder )「QuantitativeTrading/html/」爲使用瀏覽器 ( Browser ) 作爲用戶端 ( Client ) 時，數量化交易運算伺服器「QuantitativeTrading」向用戶端瀏覽器 ( Browser ) 發送 ( Respond ) 的標準通用標記語言代碼脚本 ( .html ) 檔.

其中 :

1. 代碼脚本檔「index.html」爲應用交互頁面，因應網址 ( Uniform Resource Locator , URL ) 爲 : http://[::1]:10001/index.html

2. 代碼脚本檔「administrator.html」爲管理頁面，因應網址 ( Uniform Resource Locator , URL ) 爲 : http://[::1]:10001/administrator.html

3. 逗號 ( Comma ) 分隔符檔 ( .csv )「calculated.csv」爲計算結果數據示例，是用戶端瀏覽器 ( Browser ) 應用交互頁面「index.html」計算結果表格内數據，單擊「保存運算結果數據文檔」按鈕 ( Button ) 後，從瀏覽器 ( Browser ) 應用交互頁面「index.html」計算結果表格内導出至本地硬盤 ( Disk , Read-Only Memory ) 存儲的數據文檔示例.

4. 逗號 ( Comma ) 分隔符檔 ( .csv )「LogisticLog5PInputData.csv」爲待計算的原數據示例，是用戶端瀏覽器 ( Browser ) 應用交互頁面「index.html」待計算表格内的原數據，單擊「讀取待處理的數據文檔」按鈕 ( Button ) 後，從本地硬盤 ( Disk , Read-Only Memory ) 導入至瀏覽器 ( Browser ) 應用交互頁面「index.html」待計算表格内的數據文檔示例.

可自行修改標準通用標記語言代碼脚本 ( .html ) 檔「index.html」「SelectStatisticalAlgorithms.html」「InputHTML.html」「OutputHTML.html」内的 HTML , JavaScript , CSS 代碼，擴展交互頁面「index.html」内數量化交易方法的連接 ( Browser Client Request ) 選項.

可自定義修改代碼脚本 ( Script ) 檔「Quantitative_Indicators.jl」或「Quantitative_Indicators.py」模組内函數，個性化調整擴展此量化交易模型日棒缐 ( K - Line ) 數據趨勢强度示意指標，並輔以調整代碼脚本 ( Script ) 檔「Quantitative_MarketTiming.jl」「Quantitative_PickStock.jl」「Quantitative_SizePosition.jl」「Quantitative_BackTesting.jl」或「Quantitative_MarketTiming.py」「Quantitative_PickStock.py」「Quantitative_SizePosition.py」「Quantitative_BackTesting.py」内函數 ( Function ) 因應協調，即可實現個性化擴展此量化交易模型效果 .

此量化交易模型「QuantitativeTradingJulia」的優化器 ( optimization ) 借用程式設計語言 ( computer programming language ) : Julia 第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「Optim」模組的「optimize」函數 ( Function ) 實現，未做計算效率優化，可自行變更調整優化器，並自行調整代碼脚本 ( Script ) 檔「Quantitative_MarketTiming.jl」「Quantitative_PickStock.jl」「Quantitative_SizePosition.jl」内函數 ( Function ) 因應協調，可實現優化效率提速 .

此量化交易模型「QuantitativeTradingPython」的優化器 ( optimization ) 借用程式設計語言 ( computer programming language ) : Python 第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「scipy」項目内優化模組「optimize」内「minimize」函數 ( Function ) 實現，未做計算效率優化，可自行變更調整優化器，並自行調整代碼脚本 ( Script ) 檔「Quantitative_MarketTiming.py」「Quantitative_PickStock.py」「Quantitative_SizePosition.py」内函數 ( Function ) 因應協調，可實現優化效率提速 .

注意，現時此兩處尚不具實用性，需自行視具體問題個性化修整精進，使之具備實用性 :

1. 代碼脚本 ( Script ) 檔「Quantitative_Indicators.jl」「Quantitative_Indicators.py」計算趨勢强度示意值，從日棒缐 ( K - Line ) 數據抽象，即所謂交易策略，尚不具備實用性，需個性化自行調整完備，使之具有實用性 .

2. 量化交易模型「QuantitativeTradingJulia」「QuantitativeTradingPython」參數優化器 ( optimization ) 選用未做計算效率考量，尚不具備實用性，需酌情調整，可自行選用更換第三方優化器 ( optimization ) 提升計算效率，使之具有實用性 .

---

Python3 Explain : Interface.py , Router.py , QuantitativeTradingServer.py , Interpolation_Fitting.py , Quantitative_Indicators.py , Quantitative_Data_Cleaning.py , Quantitative_MarketTiming.py , Quantitative_PickStock.py , Quantitative_SizePosition.py , Quantitative_BackTesting.py

計算機程式設計語言 ( Python ) 解釋器 ( Interpreter ) 與作業系統 ( Operating System ) 環境配置釋明 :

Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30

Interpreter: python-3.11.2-amd64.exe

Interpreter: Python-3.12.4-tar.xz

Operating System: Google-Pixel-7 Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 MSM8998-Snapdragon835-Qualcomm®-Kryo™-280

Interpreter: Python-3.12.4-tar.xz

微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 配置程式設計語言 ( Python ) 解釋器 ( Interpreter ) 隔離運行環境 :

1. 首先，在微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 創建程式設計語言 ( Python ) 解釋器 ( Interpreter ) 隔離運行環境「C:/QuantitativeTrading/QuantitativeTradingPython/」 :

C:\QuantitativeTrading> C:/QuantitativeTrading/Python/Python311/python.exe -m venv C:/QuantitativeTrading/QuantitativeTradingPython/

運行結束後，可以看到在「C:/QuantitativeTrading/」路徑下已經多了一個「QuantitativeTradingPython」文件夾，就是新創建成功的「C:/QuantitativeTrading/QuantitativeTradingPython/」項目的工作空間 .

2. 微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 進入新創建的「C:/QuantitativeTrading/QuantitativeTradingPython/」項目工作空間 :

C:\QuantitativeTrading> cd C:/QuantitativeTrading/QuantitativeTradingPython/

3. 微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 運行啓動指令，激活進入程式設計語言 ( Python ) 解釋器 ( Interpreter ) 隔離運行環境「C:/QuantitativeTrading/QuantitativeTradingPython/」 :

C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/activate.bat

4. 最後, 在程式設計語言 ( Python ) 解釋器 ( Interpreter ) 隔離運行環境「C:/QuantitativeTrading/QuantitativeTradingPython/」下, 安裝配置第三方擴展包 ( packages ) :

安裝配置程式設計語言 ( Python ) 的第三方擴展模組「numpy」 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/pip.exe install numpy

安裝配置程式設計語言 ( Python ) 的第三方擴展模組「scipy」 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/pip.exe install scipy

安裝配置程式設計語言 ( Python ) 的第三方擴展模組「pyarrow」 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/pip.exe install pyarrow

安裝配置程式設計語言 ( Python ) 的第三方擴展模組「pillow」 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/pip.exe install pillow

安裝配置程式設計語言 ( Python ) 的第三方擴展模組「openpyxl」 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/pip.exe install openpyxl

安裝配置程式設計語言 ( Python ) 的第三方擴展模組「xlrd」 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/pip.exe install xlrd

安裝配置程式設計語言 ( Python ) 的第三方擴展模組「pandas」 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/pip.exe install pandas

安裝配置程式設計語言 ( Python ) 的第三方擴展模組「matplotlib」 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/pip.exe install matplotlib

安裝配置程式設計語言 ( Python ) 的第三方擴展模組「statsmodels」 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/pip.exe install statsmodels

安裝配置程式設計語言 ( Python ) 的第三方擴展模組「scikit-learn」 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/pip.exe install sklearn

5. 從已激活的程式設計語言 ( Python ) 解釋器 ( Interpreter ) 隔離運行環境「C:/QuantitativeTrading/QuantitativeTradingPython/」退出返回至微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 環境，使用如下指令 :

(QuantitativeTradingPython) C:\QuantitativeTrading\QuantitativeTradingPython> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/deactivate.bat

即可 .

Interpreter :

python - 3.12.4

程式設計 Python 語言解釋器 ( Interpreter ) 官方網站: https://www.python.org/

程式設計 Python 語言解釋器 ( Interpreter ) 官方下載頁: https://www.python.org/downloads/

程式設計 Python 語言解釋器 ( Interpreter ) 官方 GitHub 網站賬戶: https://github.com/python

程式設計 Python 語言解釋器 ( Interpreter ) 官方 GitHub 網站倉庫頁: https://github.com/python/cpython.git

程式設計 Python 語言統計算法 ( algorithm ) 借用第三方擴展模組 ( third-party extensions ( libraries or modules ) ) 説明 :

Python - numpy 官方網站: https://numpy.org/

Python - numpy 官方手冊: https://numpy.org/doc/stable/

Python - numpy 官方 GitHub 網站倉庫頁: https://github.com/numpy/numpy.git

Python - scipy 官方網站: https://scipy.org/

Python - scipy 官方手冊: https://docs.scipy.org/doc/scipy/

Python - scipy 官方 GitHub 網站倉庫頁: https://github.com/scipy/scipy.git

Python - pandas 官方網站: https://pandas.pydata.org/

Python - pandas 官方手冊: https://pandas.pydata.org/docs/

Python - pandas 官方 GitHub 網站倉庫頁: https://github.com/pandas-dev/pandas.git

Python - openpyxl 官方網站: https://www.python-excel.org/

Python - openpyxl 官方手冊: https://openpyxl.readthedocs.io/en/stable/

Python - openpyxl 官方 PyPi 組織倉庫頁: https://pypi.org/project/openpyxl/

Python - openpyxl 發佈頁: https://foss.heptapod.net/openpyxl/openpyxl

Python - xlrd 官方手冊: https://xlrd.readthedocs.io/en/latest/?badge=latest

Python - xlrd 官方 GitHub 網站倉庫頁: https://github.com/python-excel/xlrd.git

Python - pillow 官方手冊: https://pillow.readthedocs.io/en/stable/?badge=latest

Python - pillow 官方 PyPi 組織倉庫頁: https://pypi.org/project/pillow/

Python - pillow 官方 GitHub 網站倉庫頁: https://github.com/python-pillow/Pillow.git

Python - pyarrow 官方手冊: https://arrow.apache.org/docs/3.0/_modules/pyarrow.html

Python - pyarrow 官方 PyPi 組織倉庫頁: https://pypi.org/project/pyarrow/

Python - matplotlib 官方網站: https://matplotlib.org/

Python - matplotlib 官方手冊: https://matplotlib.org/stable/

Python - matplotlib 官方 GitHub 網站倉庫頁: https://github.com/matplotlib/matplotlib.git

Python - statsmodels 官方手冊: https://www.statsmodels.org/stable/index.html

Python - statsmodels 官方 GitHub 網站倉庫頁: https://github.com/statsmodels/statsmodels.git

Python - sklearn 官方網站: https://scikit-learn.org/stable/

Python - sklearn 官方手冊: https://scikit-learn.org/stable/user_guide.html

Python - sklearn 官方 GitHub 網站倉庫頁: https://github.com/scikit-learn/scikit-learn.git

Python - sympy 官方網站: https://www.sympy.org/en/index.html

Python - sympy 官方手冊: https://docs.sympy.org/latest/index.html

Python - sympy 官方 GitHub 網站倉庫頁: https://github.com/sympy/sympy.git

使用説明:

谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 )

控制臺命令列 ( bash ) 運行啓動指令 :

root@localhost:~# /usr/bin/python3 /home/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py configFile=/home/QuantitativeTrading/QuantitativeTradingPython/config.txt interface_Function=http_Server webPath=/home/QuantitativeTrading/html/ host=::0 port=10001 Key=username:password Is_multi_thread=False number_Worker_process=0

微軟視窗系統 ( Window10 x86_64 )

控制臺命令列 ( cmd ) 運行啓動指令 :

C:\QuantitativeTrading> C:/QuantitativeTrading/Python/Python311/python.exe C:/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py configFile=C:/QuantitativeTrading/QuantitativeTradingPython/config.txt interface_Function=http_Server webPath=C:/QuantitativeTrading/html/ host=::0 port=10001 Key=username:password Is_multi_thread=False number_Worker_process=0

控制臺啓動傳參釋意, 各參數之間以一個空格字符 ( SPACE ) ( 00100000 ) 分隔, 鍵(Key) ~ 值(Value) 之間以一個等號字符 ( = ) 連接, 即類比 Key=Value 的形式 :

1. (必), (自定義), 安裝配置的程式設計語言 ( Python ) 解釋器 ( Interpreter ) 環境的二進制可執行檔啓動存儲路徑全名, 預設值爲 : C:/QuantitativeTrading/Python/Python311/python.exe

2. (必), (自定義), 語言 ( Python ) 程式代碼脚本 ( Script ) 檔 ( QuantitativeTradingServer.py ) 的存儲路徑全名, 預設值爲 : C:/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py

   注意, 因爲「QuantitativeTradingServer.py」檔中脚本代碼需要加載引入「Interface.py」檔, 所以需要保持「QuantitativeTradingServer.py」檔與「Interface.py」檔在相同目錄下, 不然就需要手動修改「QuantitativeTradingServer.py」檔中有關引用「Interface.py」檔的加載路徑代碼, 以確保能正確引入「Interface.py」檔.

3. (選), (鍵 configFile 固定, 值 C:/QuantitativeTrading/QuantitativeTradingPython/config.txt 自定義), 用於傳入配置文檔的保存路徑全名, 預設值爲 : configFile=C:/QuantitativeTrading/QuantitativeTradingPython/config.txt

4. (選), (鍵 interface_Function 固定, 值 file_Monitor 自定義, [ file_Monitor, http_Server, http_Client ] 取其一), 用於傳入選擇啓動哪一種接口服務, 外設硬盤 ( Hard Disk ) 文檔 ( File ) 作橋, 外設網卡 ( Network Interface Card ) 埠 ( Port ) 作橋, 預設值爲 : interface_Function=file_Monitor

以下是當參數 : interface_Function 取 : http_Server 值時, 可在控制臺命令列傳入的參數 :

14. (選), (鍵 host 固定, 值 ::0 自定義, 例如 [ ::0, ::1, 0.0.0.0, 127.0.0.1 ] 取其一), 用於傳入伺服器 ( http_Server ) 監聽的外設網卡 ( Network Interface Card ) 地址 ( IPv6 , IPv4 ) 或域名, 預設值爲 : host=::0

15. (選), (鍵 port 固定, 值 10001 自定義), 用於傳入伺服器 ( http_Server ) 監聽的外設網卡 ( Network Interface Card ) 自定義設定的埠號 ( 1 ~ 65535 ), 預設值爲 : port=10001

16. (選), (鍵 Key 固定, 賬號密碼連接符 : 固定, 值 username 和 password 自定義), 用於傳入自定義的訪問網站驗證 ( Authorization ) 用戶名和密碼, 預設值爲 : Key=username:password

17. (選), (鍵 Is_multi_thread 固定, 值 False 自定義, 例如 [ True, False ] 取其一), 用於判斷是否開啓多缐程 ( Threading ) 並發, 預設值爲 : Is_multi_thread=False

18. (選), (鍵 number_Worker_process 固定, 值 0 自定義), 用於傳入創建並發數目, 子進程 ( Sub Process ) 並發, 或者, 子缐程 ( Sub Threading ) 並發, 即, 可以設爲等於物理中央處理器 ( Central Processing Unit ) 的數目, 取 0 值表示不開啓並發架構, 預設值爲 : number_Worker_process=0

19. (選), (鍵 webPath 固定, 值 C:/QuantitativeTrading/html/ 自定義), 用於傳入伺服器 ( http_Server ) 啓動運行的自定義的根目錄 (項目空間) 路徑全名, 預設值爲 : webPath=C:/QuantitativeTrading/html/

以下是當參數 : interface_Function 取 : http_Client 值時, 可在控制臺命令列傳入的參數 :

14. (選), (鍵 host 固定, 值 ::1 自定義, 例如 [ ::1, 127.0.0.1, localhost ] 取其一), 用於傳入用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的地址 ( IPv6 , IPv4 ) 或域名, 預設值爲 : host=::1

15. (選), (鍵 port 固定, 值 10001 自定義), 用於傳入用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的埠號 ( 1 ~ 65535 ), 預設值爲 : port=10001

16. (選), (鍵 URL 固定, 值 / 自定義, 例如配置爲 http://[::1]:10001/index.html 值), 用於傳入用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的地址, 萬維網統一資源定位系統 ( Uniform Resource Locator ) 地址字符串, 預設值爲 : URL=/

17. (選), (鍵 Method 固定, 值 POST 自定義, 例如 [ POST, GET ] 取其一), 用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的類型, 預設值爲 : Method=POST

18. (選), (鍵 time_out 固定, 值 0.5 自定義), 用於傳入設置鏈接超時自動中斷的時長，單位 ( Unit ) 爲秒 ( Second ), 預設值爲 : time_out=0.5

19. (選), (鍵 request_Auth 固定, 賬號密碼連接符 : 固定, 值 username 和 password 自定義), 用於傳入用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的驗證 ( Authorization ) 的賬號密碼字符串, 預設值爲 : request_Auth=username:password

20. (選), (鍵 request_Cookie 固定, 其中 Cookie 名稱 Session_ID 可以設計爲固定, Cookie 值 request_Key->username:password 可以設計爲自定義), 用於傳入用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的 Cookies 值字符串, 預設值爲 : request_Cookie=Session_ID=request_Key->username:password

量化交易運算模組説明 :

1. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_Data_Cleaning.py」運行示例 :

谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 )

控制臺命令列 ( bash ) 運行啓動指令 :

root@localhost:~# /usr/bin/python3 /home/QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_Data_Cleaning.py configFile=/home/QuantitativeTrading/QuantitativeTradingPython/config.txt input_K_Line=/home/QuantitativeTrading/Data/K-Day-source/ is_save_pickle=True output_pickle_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle is_save_csv=False output_csv_K_Line=/home/QuantitativeTrading/Data/K-Day/ is_save_xlsx=False output_xlsx_K_Line=/home/QuantitativeTrading/Data/K-Day/

微軟視窗系統 ( Window10 x86_64 )

控制臺命令列 ( cmd ) 運行啓動指令 :

C:\QuantitativeTrading> C:/QuantitativeTrading/Python/Python311/python.exe C:/QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_Data_Cleaning.py configFile=C:/QuantitativeTrading/QuantitativeTradingPython/config.txt input_K_Line=C:/QuantitativeTrading/Data/K-Day-source/ is_save_pickle=True output_pickle_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle is_save_csv=False output_csv_K_Line=C:/QuantitativeTrading/Data/K-Day/ is_save_xlsx=False output_xlsx_K_Line=C:/QuantitativeTrading/Data/K-Day/

2. 標準化日棒缐 ( K - Line ) 數據，以程式設計語言 ( computer programming language ) : Python 字典類型 ( Python - dict ) 數據傳入，數據格式可類比如下 :

training_data =

{
    str("002607") : {
        str("date_transaction") : [ datetime.date("2022-03-14") , datetime.date("2022-03-15") , datetime.date("2022-03-16") , ... ],
        str("turnover_volume") : [ int(10002) , int(10003) , int(10001) , ... ],
        str("opening_price") : [ float(1.81) , float(1.52) , float(1.23) , ... ],
        str("close_price") : [ float(1.21) , float(1.52) , float(1.83) , ... ],
        str("low_price") : [ float(1.11) , float(1.42) , float(1.13) , ... ],
        str("high_price") : [ float(1.91) , float(1.62) , float(1.93) , ... ],
        ...
    },
    str("002608") : {
        str("date_transaction") : [ datetime.date("2022-03-14") , datetime.date("2022-03-15") , datetime.date("2022-03-16") , ... ],
        str("turnover_volume") : [ int(10002) , int(10003) , int(10001) , ... ],
        str("opening_price") : [ float(1.81) , float(1.52) , float(1.23) , ... ],
        str("close_price") : [ float(1.21) , float(1.52) , float(1.83) , ... ],
        str("low_price") : [ float(1.11) , float(1.42) , float(1.13) , ... ],
        str("high_price") : [ float(1.91) , float(1.62) , float(1.93) , ... ],
        ...
    },
    str("002609") : {
        str("date_transaction") : [ datetime.date("2022-03-14") , datetime.date("2022-03-15") , datetime.date("2022-03-16") , ... ],
        str("turnover_volume") : [ int(10002) , int(10003) , int(10001) , ... ],
        str("opening_price") : [ float(1.81) , float(1.52) , float(1.23) , ... ],
        str("close_price") : [ float(1.21) , float(1.52) , float(1.83) , ... ],
        str("low_price") : [ float(1.11) , float(1.42) , float(1.13) , ... ],
        str("high_price") : [ float(1.91) , float(1.62) , float(1.93) , ... ],
        ...
    },
    ...
}

3. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_Indicators.py」内函數 ( Function ) 運行示例 :

return_Intuitive_Momentum = Intuitive_Momentum(
    training_data["002611"]["close_price"],  # [],  # 時間序列 ( time series ) 數據一維數組 ( Python - list ) ;
    int(3),  # Parameter-1,  # 觀察收益率歷史向前推的交易日長度;
    y_P_Positive = None,  # float(1.0),  # 增長率（正）的可能性（頻率）示意;
    y_P_Negative = None,  # float(1.0),  # 衰退率（負）的可能性（頻率）示意;
    weight = None  # []  # [float(int(int(i) + int(1)) / int(Parameter-1)) for i in range(Parameter-1)]  # 每計增長率的權重（weight）值，距離當下時長的倒數（直覺推理有效性示意）;
)
print("closing price growth rate :\n", return_Intuitive_Momentum)

return_Intuitive_Momentum_KLine = Intuitive_Momentum_KLine(
    {
        "date_transaction": training_data["002611"]["date_transaction"],  # 交易日期;
        "turnover_volume": training_data["002611"]["turnover_volume"],  # 成交量;
        "opening_price": training_data["002611"]["opening_price"],  # 開盤成交價;
        "close_price": training_data["002611"]["close_price"],  # 收盤成交價;
        "low_price": training_data["002611"]["low_price"],  # 最低成交價;
        "high_price": training_data["002611"]["high_price"],  # 最高成交價;
        "focus": training_data["002611"]["focus"],  # 當日成交價重心;
        "amplitude": training_data["002611"]["amplitude"],  # 當日成交價絕對振幅;
        "amplitude_rate": training_data["002611"]["amplitude_rate"],  # 當日成交價相對振幅（%）;
        "opening_price_Standardization": training_data["002611"]["opening_price_Standardization"],  # 日棒缐（K Line Daily）數據交易日首筆成交價（開盤價）標準化值;
        "closing_price_Standardization": training_data["002611"]["closing_price_Standardization"],  # 日棒缐（K Line Daily）數據交易日尾筆成交價（收盤價）標準化值;
        "low_price_Standardization": training_data["002611"]["low_price_Standardization"],  # 日棒缐（K Line Daily）數據交易日最低成交價標準化值;
        "high_price_Standardization": training_data["002611"]["high_price_Standardization"],  # 日棒缐（K Line Daily）數據交易日最高成交價標準化值;
        "turnover_volume_growth_rate": training_data["002611"]["turnover_volume_growth_rate"],  # 成交量的成長率;
        "opening_price_growth_rate": training_data["002611"]["opening_price_growth_rate"],  # 開盤價的成長率;
        "closing_price_growth_rate": training_data["002611"]["closing_price_growth_rate"],  # 收盤價的成長率;
        "closing_minus_opening_price_growth_rate": training_data["002611"]["closing_minus_opening_price_growth_rate"],  # 收盤價減開盤價的成長率;
        "high_price_proportion": training_data["002611"]["high_price_proportion"],  # 收盤價和開盤價裏的最大值占最高價的比例;
        "low_price_proportion": training_data["002611"]["low_price_proportion"],  # 最低價占收盤價和開盤價裏的最小值的比例;
        "moving_average_3": training_data["002611"]["moving_average_3"],  # 日棒缐（K Line Daily）數據交易日尾筆成交價（收盤價）三日移動平均缐值;
        "moving_average_5": training_data["002611"]["moving_average_5"],  # 日棒缐（K Line Daily）數據交易日尾筆成交價（收盤價）五日移動平均缐值;
        "moving_average_10": training_data["002611"]["moving_average_10"],  # 日棒缐（K Line Daily）數據交易日尾筆成交價（收盤價）十日移動平均缐值;
        "turnover_rate": training_data["002611"]["turnover_rate"]  # 成交量換手率;
    },  # {}  # 標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    int(3),  # 觀察收益率歷史向前推的交易日長度;
    y_P_Positive = None,  # float(1.0),  # 增長率（正）的可能性（頻率）示意;
    y_P_Negative = None,  # float(1.0),  # 衰退率（負）的可能性（頻率）示意;
    weight = None,  # [],  # [float(int(int(i) + int(1)) / int(Parameter-1)) for i in range(Parameter-1)]  # 每計增長率的權重（weight）值，距離當下時長的倒數（直覺推理有效性示意）;
    Intuitive_Momentum = Intuitive_Momentum  # lambda argument : argument
)
print("turnover volume growth rate :\n", return_Intuitive_Momentum_KLine["P1_turnover_volume_growth_rate"])
print("opening price growth rate :\n", return_Intuitive_Momentum_KLine["P1_opening_price_growth_rate"])
print("closing price growth rate :\n", return_Intuitive_Momentum_KLine["P1_closing_price_growth_rate"])
print("closing minus opening price growth rate :\n", return_Intuitive_Momentum_KLine["P1_closing_minus_opening_price_growth_rate"])
print("high price proportion :\n", return_Intuitive_Momentum_KLine["P1_high_price_proportion"])
print("low price proportion :\n", return_Intuitive_Momentum_KLine["P1_low_price_proportion"])
print("intuitive momentum indicator :\n", return_Intuitive_Momentum_KLine["P1_Intuitive_Momentum"])

4. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_MarketTiming.py」内函數 ( Function ) 運行示例 :

return_MarketTiming_fit_model = MarketTiming_fit_model(
    {"002611": training_data["002611"]},  # {}  # 標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    int(10),  # Parameter-1,  # 觀察收益率歷史向前推的交易日長度;
    float(+0.58),  # Parameter-2  # 買入閾值;
    float(-0.02),  # Parameter-3  # 賣出閾值;
    float(0.0),  # Parameter-4,  # risk threshold drawdown loss;  # 風險控制閾值，强制平倉，可接受的最大回撤比例 : Long_Position = sell_price ÷ buy_price , Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
    Intuitive_Momentum_KLine,  # lambda argument : argument,
    "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
)
print("y_profit = ", return_MarketTiming_fit_model["002611"]["y_profit"])  # 每兩次對衝交易利潤 × 頻率 × 權重，加權纍加總計;
print("y_Long_Position_profit = ", return_MarketTiming_fit_model["002611"]["y_Long_Position_profit"])  # 每兩次對衝交易利潤 × 頻率 × 權重，加權纍加總計;
print("y_Short_Selling_profit = ", return_MarketTiming_fit_model["002611"]["y_Short_Selling_profit"])  # 每兩次對衝交易利潤 × 頻率 × 權重，加權纍加總計;
print("y_loss = ", return_MarketTiming_fit_model["002611"]["y_loss"])  # 每兩次對衝交易最大回撤 × 頻率 × 權重，加權取極值總計;
print("y_Long_Position_loss = ", return_MarketTiming_fit_model["002611"]["y_Long_Position_loss"])  # 每兩次對衝交易最大回撤 × 頻率 × 權重，加權取極值總計;
print("y_Short_Selling_loss = ", return_MarketTiming_fit_model["002611"]["y_Short_Selling_loss"])  # 每兩次對衝交易最大回撤 × 頻率 × 權重，加權取極值總計;
print("profit_total = ", return_MarketTiming_fit_model["002611"]["profit_total"])  # 每兩次對衝交易利潤 × 頻率，纍加總計;
print("profit_Positive = ", return_MarketTiming_fit_model["002611"]["profit_Positive"])  # 每兩次對衝交易收益纍加總計;
print("profit_Negative = ", return_MarketTiming_fit_model["002611"]["profit_Negative"])  # 每兩次對衝交易損失纍加總計;
print("Long_Position_profit_total = ", return_MarketTiming_fit_model["002611"]["Long_Position_profit_total"])  # 每兩次對衝交易利潤 × 頻率，纍加總計;
print("Long_Position_profit_Positive = ", return_MarketTiming_fit_model["002611"]["Long_Position_profit_Positive"])  # 每兩次對衝交易收益纍加總計;
print("Long_Position_profit_Negative = ", return_MarketTiming_fit_model["002611"]["Long_Position_profit_Negative"])  # 每兩次對衝交易損失纍加總計;
print("Short_Selling_profit_total = ", return_MarketTiming_fit_model["002611"]["Short_Selling_profit_total"])  # 每兩次對衝交易利潤 × 頻率，纍加總計;
print("Short_Selling_profit_Positive = ", return_MarketTiming_fit_model["002611"]["Short_Selling_profit_Positive"])  # 每兩次對衝交易收益纍加總計;
print("Short_Selling_profit_Negative = ", return_MarketTiming_fit_model["002611"]["Short_Selling_profit_Negative"])  # 每兩次對衝交易損失纍加總計;
print("profit_Positive_probability = ", return_MarketTiming_fit_model["002611"]["profit_Positive_probability"])  # 每兩次對衝交易正利潤概率;
print("profit_Negative_probability = ", return_MarketTiming_fit_model["002611"]["profit_Negative_probability"])  # 每兩次對衝交易負利潤概率;
print("Long_Position_profit_Positive_probability = ", return_MarketTiming_fit_model["002611"]["Long_Position_profit_Positive_probability"])  # 每兩次對衝交易正利潤概率;
print("Long_Position_profit_Negative_probability = ", return_MarketTiming_fit_model["002611"]["Long_Position_profit_Negative_probability"])  # 每兩次對衝交易負利潤概率;
print("Short_Selling_profit_Positive_probability = ", return_MarketTiming_fit_model["002611"]["Short_Selling_profit_Positive_probability"])  # 每兩次對衝交易正利潤概率;
print("Short_Selling_profit_Negative_probability = ", return_MarketTiming_fit_model["002611"]["Short_Selling_profit_Negative_probability"])  # 每兩次對衝交易負利潤概率;
print(return_MarketTiming_fit_model["002611"]["Long_Position_profit_date_transaction"])  # 每兩次對衝交易利潤，向量;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_profit_date_transaction"])  # 每兩次對衝交易利潤，向量;
print("maximum_drawdown = ", return_MarketTiming_fit_model["002611"]["maximum_drawdown"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum_drawdown_Long_Position = ", return_MarketTiming_fit_model["002611"]["maximum_drawdown_Long_Position"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum_drawdown_Short_Selling = ", return_MarketTiming_fit_model["002611"]["maximum_drawdown_Short_Selling"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("Long_Position_drawdown_date_transaction = ", return_MarketTiming_fit_model["002611"]["Long_Position_drawdown_date_transaction"])  # 向量，記錄做多模式每組對衝交易日的回撤值序列，風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
print("Short_Selling_drawdown_date_transaction = ", return_MarketTiming_fit_model["002611"]["Short_Selling_drawdown_date_transaction"])  # 向量，記錄做多模式每組對衝交易日的回撤值序列，風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
print("average_price_amplitude_date_transaction = ", return_MarketTiming_fit_model["002611"]["average_price_amplitude_date_transaction"])  # 兩兩次對衝交易日成交價振幅平方和，均值;
print("Long_Position_average_price_amplitude_date_transaction = ", return_MarketTiming_fit_model["002611"]["Long_Position_average_price_amplitude_date_transaction"])  # 兩兩次對衝交易日成交價振幅平方和，均值;
print("Short_Selling_average_price_amplitude_date_transaction = ", return_MarketTiming_fit_model["002611"]["Short_Selling_average_price_amplitude_date_transaction"])  # 兩兩次對衝交易日成交價振幅平方和，均值;
print(return_MarketTiming_fit_model["002611"]["Long_Position_price_amplitude_date_transaction"])  # 兩次對衝交易日成交價振幅平方和，向量;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_price_amplitude_date_transaction"])  # 兩次對衝交易日成交價振幅平方和，向量;
print("average_volume_turnover_date_transaction = ", return_MarketTiming_fit_model["002611"]["average_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）均值;
print("Long_Position_average_volume_turnover_date_transaction = ", return_MarketTiming_fit_model["002611"]["Long_Position_average_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）均值;
print("Short_Selling_average_volume_turnover_date_transaction = ", return_MarketTiming_fit_model["002611"]["Short_Selling_average_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）均值;
print(return_MarketTiming_fit_model["002611"]["Long_Position_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）向量;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）向量;
print("average_date_transaction_between = ", return_MarketTiming_fit_model["002611"]["average_date_transaction_between"])  # 兩次交易間隔日長，均值;
print("Long_Position_average_date_transaction_between = ", return_MarketTiming_fit_model["002611"]["Long_Position_average_date_transaction_between"])  # 兩次對衝交易間隔日長，均值;
print("Short_Selling_average_date_transaction_between = ", return_MarketTiming_fit_model["002611"]["Short_Selling_average_date_transaction_between"])  # 兩次對衝交易間隔日長，均值;
print("weight_MarketTiming = ", return_MarketTiming_fit_model["002611"]["weight_MarketTiming"])  # 擇時權重，每兩次對衝交易的盈利概率占比;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction_between"])  # 兩次對衝交易間隔日長，向量;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction_between"])  # 兩次對衝交易間隔日長，向量;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"])  # 按規則執行交易的日期，向量;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][0])  # 交易規則自動選取的交易日期;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][1])  # 交易規則自動選取的買入或賣出;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][2])  # 交易規則自動選取的成交價;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][3])  # 交易規則自動選取的成交量;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][4])  # 交易規則自動選取的成交次數記錄;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][5])  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][6])  # 交易日（Dates.Date 類型）;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][7])  # 當日總成交量（turnover volume）;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][8])  # 當日開盤（opening）成交價;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][9])  # 當日收盤（closing）成交價;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][10])  # 當日最低（low）成交價;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][11])  # 當日最高（high）成交價;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][12])  # 當日總成交金額（turnover amount）;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][13])  # 當日成交量（turnover volume）換手率（turnover rate）;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][14])  # 當日每股收益（price earnings）;
print(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][15])  # 當日每股净值（book value per share）;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"])  # 按規則執行交易的日期，向量;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][0])  # 交易規則自動選取的交易日期;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][1])  # 交易規則自動選取的買入或賣出;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][2])  # 交易規則自動選取的成交價;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][3])  # 交易規則自動選取的成交量;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][4])  # 交易規則自動選取的成交次數記錄;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][5])  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][6])  # 交易日（Dates.Date 類型）;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][7])  # 當日總成交量（turnover volume）;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][8])  # 當日開盤（opening）成交價;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][9])  # 當日收盤（closing）成交價;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][10])  # 當日最低（low）成交價;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][11])  # 當日最高（high）成交價;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][12])  # 當日總成交金額（turnover amount）;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][13])  # 當日成交量（turnover volume）換手率（turnover rate）;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][14])  # 當日每股收益（price earnings）;
print(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][15])  # 當日每股净值（book value per share）;
print(return_MarketTiming_fit_model["002611"]["revenue_and_expenditure_records_date_transaction"])  # 每次交易的收支記錄序列，不區分做多（Long Position）或做空（Short Selling），向量;
print(return_MarketTiming_fit_model["002611"]["P1_Array"])  # 依照擇時規則計算得到參數 P1 值的序列存儲數組;

result = MarketTiming(
    training_data = {"002611": training_data["002611"]},  # {}  # 訓練集，標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    testing_data = {"002611": testing_data["002611"]},  # {}  # 測試集，標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    Pdata_0 = [int(3), float(+0.1), float(-0.1), float(0.0)],  # training_data["002611"]["Pdata_0"],  # 優化迭代參數初值;
    weight = []  # training_data["002611"]["weight"],  # 優化迭代數據權重值;
    Plower = [-math.inf, -math.inf, -math.inf, -math.inf],  # training_data["002611"]["Plower"],  # 優化迭代參數值約束下限;
    Pupper = [+math.inf, +math.inf, +math.inf, +math.inf],  # training_data["002611"]["Pupper"],  # 優化迭代參數值約束上限;
    MarketTiming_fit_model = MarketTiming_fit_model,  # lambda argument : argument,
    Quantitative_Indicators_Function = Intuitive_Momentum_KLine,  # lambda argument : argument,
    investment_method = "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
)
print("Coefficient : ", result["002611"]["Coefficient"])  # 優化得到的參數;
print(result["002611"]["P1_Array"])  # 依照擇時規則計算得到參數 P1 值的序列存儲數組;
print("profit total per share : ", result["002611"]["testData"]["profit_total"])
print("profit positive per share : ", result["002611"]["testData"]["profit_Positive"])
print("profit negative per share : ", result["002611"]["testData"]["profit_Negative"])
print("Long Position profit total per share : ", result["002611"]["testData"]["Long_Position_profit_total"])
print("Long Position profit positive per share : ", result["002611"]["testData"]["Long_Position_profit_Positive"])
print("Long Position profit negative per share : ", result["002611"]["testData"]["Long_Position_profit_Negative"])
print("Short Selling profit total per share : ", result["002611"]["testData"]["Short_Selling_profit_total"])
print("Short Selling profit positive per share : ", result["002611"]["testData"]["Short_Selling_profit_Positive"])
print("Short Selling profit negative per share : ", result["002611"]["testData"]["Short_Selling_profit_Negative"])
print("maximum drawdown per share : ", result["002611"]["testData"]["maximum_drawdown"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum drawdown Long Position per share : ", result["002611"]["testData"]["maximum_drawdown_Long_Position"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum drawdown Short Selling per share : ", result["002611"]["testData"]["maximum_drawdown_Short_Selling"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("Long Position drawdown date transaction : ", result["002611"]["testData"]["Long_Position_drawdown_date_transaction"])  # 向量，記錄做多模式每組對衝交易日的回撤值序列，風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
print("Short Selling drawdown date transaction : ", result["002611"]["testData"]["Short_Selling_drawdown_date_transaction"])  # 向量，記錄做多模式每組對衝交易日的回撤值序列，風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
print("profit positive probability : ", result["002611"]["testData"]["profit_Positive_probability"])
print("profit negative probability : ", result["002611"]["testData"]["profit_Negative_probability"])
print("Long Position profit positive probability : ", result["002611"]["testData"]["Long_Position_profit_Positive_probability"])
print("Long Position profit negative probability : ", result["002611"]["testData"]["Long_Position_profit_Negative_probability"])
print("Short Selling profit positive probability : ", result["002611"]["testData"]["Short_Selling_profit_Positive_probability"])
print("Short Selling profit negative probability : ", result["002611"]["testData"]["Short_Selling_profit_Negative_probability"])
print("average date transaction between : ", result["002611"]["testData"]["average_date_transaction_between"])
print("Long Position average date transaction between : ", result["002611"]["testData"]["Long_Position_average_date_transaction_between"])
print("Short Selling average date transaction between : ", result["002611"]["testData"]["Short_Selling_average_date_transaction_between"])
print("number Long Position date transaction : ", len(result["002611"]["testData"]["Long_Position_date_transaction"]))
print("number Short Selling date transaction : ", len(result["002611"]["testData"]["Short_Selling_date_transaction"]))
print("weight MarketTiming : ", result["002611"]["testData"]["weight_MarketTiming"])  # 擇時權重，每兩次對衝交易的盈利概率占比;
print(result["002611"]["testData"]["P1_Array"])
print(result["002611"]["testData"]["Long_Position_date_transaction"])
print(result["002611"]["testData"]["Short_Selling_date_transaction"])
print(result["002611"]["testData"])

5. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_PickStock.py」内函數 ( Function ) 運行示例 :

return_PickStock_fit_model = PickStock_fit_model(
    {
        "600118": training_data["600118"],
        "600119": training_data["600119"],
        "600120": training_data["600120"],
        "002607": training_data["002607"],
        "002608": training_data["002608"],
        "002609": training_data["002609"],
        "002611": training_data["002611"]
    },  # {}  # 標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    int(3),  # Parameter-1,  # 觀察收益率歷史向前推的交易日長度;
    int(10),  # Parameter-2  # 依據市值高低分組選股的分類數目;
    MarketTiming_Parameter,  # {}  # 按照擇時規則優化之後的參數字典;
    MarketTiming,  # lambda argument : argument,
    MarketTiming_fit_model,  # lambda argument : argument,
    Intuitive_Momentum_KLine,  # lambda argument : argument,
    "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
)
print("y_profit = ", return_PickStock_fit_model["y_profit"])  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
print("y_Long_Position_profit = ", return_PickStock_fit_model["y_Long_Position_profit"])  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
print("y_Short_Selling_profit = ", return_PickStock_fit_model["y_Short_Selling_profit"])  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
print("y_loss = ", return_PickStock_fit_model["y_loss"])  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
print("y_Long_Position_loss = ", return_PickStock_fit_model["y_Long_Position_loss"])  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
print("y_Short_Selling_loss = ", return_PickStock_fit_model["y_Short_Selling_loss"])  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
print("maximum_drawdown = ", return_PickStock_fit_model["maximum_drawdown"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum_drawdown_Long_Position = ", return_PickStock_fit_model["maximum_drawdown_Long_Position"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum_drawdown_Short_Selling = ", return_PickStock_fit_model["maximum_drawdown_Short_Selling"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("profit_total = ", return_PickStock_fit_model["profit_total"])  # 每兩次對衝交易利潤 × 權重，纍加總計;
print("Long_Position_profit_total = ", return_PickStock_fit_model["Long_Position_profit_total"])  # 每兩次對衝交易利潤 × 權重，纍加總計;
print("Short_Selling_profit_total = ", return_PickStock_fit_model["Short_Selling_profit_total"])  # 每兩次對衝交易利潤 × 權重，纍加總計;
print("profit_Positive = ", return_PickStock_fit_model["profit_Positive"])  # 每兩次對衝交易收益纍加總計;
print("profit_Negative = ", return_PickStock_fit_model["profit_Negative"])  # 每兩次對衝交易損失纍加總計;
print("Long_Position_profit_Positive = ", return_PickStock_fit_model["Long_Position_profit_Positive"])  # 每兩次對衝交易收益纍加總計;
print("Long_Position_profit_Negative = ", return_PickStock_fit_model["Long_Position_profit_Negative"])  # 每兩次對衝交易損失纍加總計;
print("Short_Selling_profit_Positive = ", return_PickStock_fit_model["Short_Selling_profit_Positive"])  # 每兩次對衝交易收益纍加總計;
print("Short_Selling_profit_Negative = ", return_PickStock_fit_model["Short_Selling_profit_Negative"])  # 每兩次對衝交易損失纍加總計;
print("profit_Positive_probability = ", return_PickStock_fit_model["profit_Positive_probability"])  # 每兩次對衝交易正利潤概率;
print("profit_Negative_probability = ", return_PickStock_fit_model["profit_Negative_probability"])  # 每兩次對衝交易負利潤概率;
print("Long_Position_profit_Positive_probability = ", return_PickStock_fit_model["Long_Position_profit_Positive_probability"])  # 每兩次對衝交易正利潤概率;
print("Long_Position_profit_Negative_probability = ", return_PickStock_fit_model["Long_Position_profit_Negative_probability"])  # 每兩次對衝交易負利潤概率;
print("Short_Selling_profit_Positive_probability = ", return_PickStock_fit_model["Short_Selling_profit_Positive_probability"])  # 每兩次對衝交易正利潤概率;
print("Short_Selling_profit_Negative_probability = ", return_PickStock_fit_model["Short_Selling_profit_Negative_probability"])  # 每兩次對衝交易負利潤概率;
print("average_price_amplitude_date_transaction = ", return_PickStock_fit_model["average_price_amplitude_date_transaction"])  # 兩兩次對衝交易日成交價振幅平方和，均值;
print("Long_Position_average_price_amplitude_date_transaction = ", return_PickStock_fit_model["Long_Position_average_price_amplitude_date_transaction"])  # 兩兩次對衝交易日成交價振幅平方和，均值;
print("Short_Selling_average_price_amplitude_date_transaction = ", return_PickStock_fit_model["Short_Selling_average_price_amplitude_date_transaction"])  # 兩兩次對衝交易日成交價振幅平方和，均值;
print("average_volume_turnover_date_transaction = ", return_PickStock_fit_model["average_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）均值;
print("Long_Position_average_volume_turnover_date_transaction = ", return_PickStock_fit_model["Long_Position_average_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）均值;
print("Short_Selling_average_volume_turnover_date_transaction = ", return_PickStock_fit_model["Short_Selling_average_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）均值;
print("average_date_transaction_between = ", return_PickStock_fit_model["average_date_transaction_between"])  # 兩次交易間隔日長，均值;
print("Long_Position_average_date_transaction_between = ", return_PickStock_fit_model["Long_Position_average_date_transaction_between"])  # 兩次對衝交易間隔日長，均值;
print("Short_Selling_average_date_transaction_between = ", return_PickStock_fit_model["Short_Selling_average_date_transaction_between"])  # 兩次對衝交易間隔日長，均值;
print("number_PickStock_transaction = ", return_PickStock_fit_model["number_PickStock_transaction"])  # 交易過股票的總隻數;
print("weight_PickStock = ", return_PickStock_fit_model["weight_PickStock"])  # 選股權重，每隻股票的盈利概率占比;
print(return_PickStock_fit_model["PickStock_sort"])  # 依照選股規則排序篩選出的股票代碼字符串和得分存儲字典（Dict）;
print(return_PickStock_fit_model["PickStock_sort"]["ticker_symbol"])  # 依照選股規則排序篩選出的股票代碼字符串存儲數組;
print(return_PickStock_fit_model["PickStock_sort"]["score"])  # 依照選股規則排序篩選出的股票代碼字符串存儲數組;
print(return_PickStock_fit_model["PickStock_transaction_sequence"])
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"])
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_profit_date_transaction"])  # 每兩次對衝交易利潤，向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_profit_date_transaction"])  # 每兩次對衝交易利潤，向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_price_amplitude_date_transaction"])  # 兩次對衝交易日成交價振幅平方和，向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_price_amplitude_date_transaction"])  # 兩次對衝交易日成交價振幅平方和，向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction_between"])  # 兩次對衝交易間隔日長，向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction_between"])  # 兩次對衝交易間隔日長，向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"])  # 按規則執行交易的日期，向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][0])  # 交易規則自動選取的交易日期;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][1])  # 交易規則自動選取的買入或賣出;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][2])  # 交易規則自動選取的成交價;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][3])  # 交易規則自動選取的成交量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][4])  # 交易規則自動選取的成交次數記錄;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][5])  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][6])  # 交易日（Dates.Date 類型）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][7])  # 當日總成交量（turnover volume）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][8])  # 當日開盤（opening）成交價;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][9])  # 當日收盤（closing）成交價;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][10])  # 當日最低（low）成交價;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][11])  # 當日最高（high）成交價;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][12])  # 當日總成交金額（turnover amount）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][13])  # 當日成交量（turnover volume）換手率（turnover rate）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][14])  # 當日每股收益（price earnings）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][15])  # 當日每股净值（book value per share）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"])  # 按規則執行交易的日期，向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][0])  # 交易規則自動選取的交易日期;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][1])  # 交易規則自動選取的買入或賣出;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][2])  # 交易規則自動選取的成交價;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][3])  # 交易規則自動選取的成交量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][4])  # 交易規則自動選取的成交次數記錄;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][5])  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][6])  # 交易日（Dates.Date 類型）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][7])  # 當日總成交量（turnover volume）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][8])  # 當日開盤（opening）成交價;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][9])  # 當日收盤（closing）成交價;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][10])  # 當日最低（low）成交價;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][11])  # 當日最高（high）成交價;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][12])  # 當日總成交金額（turnover amount）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][13])  # 當日成交量（turnover volume）換手率（turnover rate）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][14])  # 當日每股收益（price earnings）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][15])  # 當日每股净值（book value per share）;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["revenue_and_expenditure_records_date_transaction"])  # 每次交易的收支記錄序列，不區分做多（Long Position）或做空（Short Selling），向量;
print(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["P1_Array"])  # 依照擇時規則計算得到參數 P1 值的序列存儲數組;

result = PickStock(
    training_data = {
        "600118": training_data["600118"],
        "600119": training_data["600119"],
        "600120": training_data["600120"],
        "002607": training_data["002607"],
        "002608": training_data["002608"],
        "002609": training_data["002609"],
        "002611": training_data["002611"]
    },  # {}  # 訓練集，標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    testing_data = {
        "600118": testing_data["600118"],
        "600119": testing_data["600119"],
        "600120": testing_data["600120"],
        "002607": testing_data["002607"],
        "002608": testing_data["002608"],
        "002609": testing_data["002609"],
        "002611": testing_data["002611"]
    },  # {}  # 測試集，標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    Pdata_0 = [int(3), int(5)],  # training_data["002611"]["Pdata_0"],  # 優化迭代參數初值;
    weight = []  # training_data["002611"]["weight"],  # 優化迭代數據權重值;
    Plower = [-math.inf, -math.inf],  # training_data["002611"]["Plower"],  # 優化迭代參數值約束下限;
    Pupper = [+math.inf, +math.inf],  # training_data["002611"]["Pupper"],  # 優化迭代參數值約束上限;
    MarketTiming_Parameter = MarketTiming_Parameter,  # {}  # 按照擇時規則優化之後的參數字典;
    PickStock_fit_model = PickStock_fit_model,  # lambda argument : argument,
    MarketTiming = MarketTiming,  # lambda argument : argument,
    MarketTiming_fit_model = MarketTiming_fit_model,  # lambda argument : argument,
    Quantitative_Indicators_Function = Intuitive_Momentum_KLine,  # lambda argument : argument,
    investment_method = "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
)
print("Coefficient : ", result["Coefficient"])  # 優化得到的參數;
print(result["PickStock_sort"]["ticker_symbol"])  # 依照選股規則排序篩選出的股票代碼字符串存儲數組;
print(result["PickStock_sort"]["score"])  # 依照選股規則排序篩選出的股票得分值存儲數組;
print("maximum drawdown per share : ", result["testData"]["maximum_drawdown"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum drawdown Long Position per share : ", result["testData"]["maximum_drawdown_Long_Position"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum drawdown Short Selling per share : ", result["testData"]["maximum_drawdown_Short_Selling"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("profit total per share : ", result["testData"]["profit_total"])
print("Long Position profit total per share : ", result["testData"]["Long_Position_profit_total"])
print("Short Selling profit total per share : ", result["testData"]["Short_Selling_profit_total"])
print("profit positive per share : ", result["testData"]["profit_Positive"])
print("profit negative per share : ", result["testData"]["profit_Negative"])
print("Long Position profit positive per share : ", result["testData"]["Long_Position_profit_Positive"])
print("Long Position profit negative per share : ", result["testData"]["Long_Position_profit_Negative"])
print("Short Selling profit positive per share : ", result["testData"]["Short_Selling_profit_Positive"])
print("Short Selling profit negative per share : ", result["testData"]["Short_Selling_profit_Negative"])
print("profit positive probability : ", result["testData"]["profit_Positive_probability"])
print("profit negative probability : ", result["testData"]["profit_Negative_probability"])
print("Long Position profit positive probability : ", result["testData"]["Long_Position_profit_Positive_probability"])
print("Long Position profit negative probability : ", result["testData"]["Long_Position_profit_Negative_probability"])
print("Short Selling profit positive probability : ", result["testData"]["Short_Selling_profit_Positive_probability"])
print("Short Selling profit negative probability : ", result["testData"]["Short_Selling_profit_Negative_probability"])
print("average date transaction between : ", result["testData"]["average_date_transaction_between"])
print("Long Position average date transaction between : ", result["testData"]["Long_Position_average_date_transaction_between"])
print("Short Selling average date transaction between : ", result["testData"]["Short_Selling_average_date_transaction_between"])
print("number_PickStock_transaction : ", result["testData"]["number_PickStock_transaction"])  # 交易過股票的總隻數;
print("weight_PickStock : ", result["testData"]["weight_PickStock"])  # 選股權重，每隻股票的盈利概率占比;
print(result["testData"]["PickStock_transaction_sequence"]["002611"]["P1_Array"])
print(result["testData"]["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"])
print(result["testData"]["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"])
print(result["testData"]["PickStock_transaction_sequence"]["002611"])

6. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_SizePosition.py」内函數 ( Function ) 運行示例 :

return_SizePosition_fit_model = SizePosition_fit_model(
    {
        "600118": training_data["600118"],
        "600119": training_data["600119"],
        "600120": training_data["600120"],
        "002607": training_data["002607"],
        "002608": training_data["002608"],
        "002609": training_data["002609"],
        "002611": training_data["002611"]
    },  # {}  # 標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    weight_MarketTiming_Dict,  # {},  # 股票擇時交易倉位占比;
    weight_PickStock_Dict,  # {},  # 選股組合占比;
    MarketTiming_Parameter,  # {},  # 按照擇時規則優化之後的參數字典;
    PickStock_Parameter,  # {},  # 按照選股規則優化之後的參數字典;
    PickStock_ticker_symbol,  # [[str()]],  # 依照選股規則排序篩選出的股票代碼字符串存儲數組;
    PickStock,  # lambda argument : argument,
    PickStock_fit_model,  # lambda argument : argument,
    MarketTiming,  # lambda argument : argument,
    MarketTiming_fit_model,  # lambda argument : argument,
    Intuitive_Momentum_KLine,  # lambda argument : argument,
    "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
)
print("y_profit = ", return_SizePosition_fit_model["y_profit"])  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
print("y_Long_Position_profit = ", return_SizePosition_fit_model["y_Long_Position_profit"])  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
print("y_Short_Selling_profit = ", return_SizePosition_fit_model["y_Short_Selling_profit"])  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
print("y_loss = ", return_SizePosition_fit_model["y_loss"])  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
print("y_Long_Position_loss = ", return_SizePosition_fit_model["y_Long_Position_loss"])  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
print("y_Short_Selling_loss = ", return_SizePosition_fit_model["y_Short_Selling_loss"])  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
print("maximum_drawdown = ", return_SizePosition_fit_model["maximum_drawdown"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum_drawdown_Long_Position = ", return_SizePosition_fit_model["maximum_drawdown_Long_Position"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum_drawdown_Short_Selling = ", return_SizePosition_fit_model["maximum_drawdown_Short_Selling"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("profit_total = ", return_SizePosition_fit_model["profit_total"])  # 每兩次對衝交易利潤 × 頻率，纍加總計;
print("Long_Position_profit_total = ", return_SizePosition_fit_model["Long_Position_profit_total"])  # 每兩次對衝交易利潤 × 頻率，纍加總計;
print("Short_Selling_profit_total = ", return_SizePosition_fit_model["Short_Selling_profit_total"])  # 每兩次對衝交易利潤 × 頻率，纍加總計;
print("profit_Positive = ", return_SizePosition_fit_model["profit_Positive"])  # 每兩次對衝交易收益纍加總計;
print("profit_Negative = ", return_SizePosition_fit_model["profit_Negative"])  # 每兩次對衝交易損失纍加總計;
print("Long_Position_profit_Positive = ", return_SizePosition_fit_model["Long_Position_profit_Positive"])  # 每兩次對衝交易收益纍加總計;
print("Long_Position_profit_Negative = ", return_SizePosition_fit_model["Long_Position_profit_Negative"])  # 每兩次對衝交易損失纍加總計;
print("Short_Selling_profit_Positive = ", return_SizePosition_fit_model["Short_Selling_profit_Positive"])  # 每兩次對衝交易收益纍加總計;
print("Short_Selling_profit_Negative = ", return_SizePosition_fit_model["Short_Selling_profit_Negative"])  # 每兩次對衝交易損失纍加總計;
print("profit_Positive_probability = ", return_SizePosition_fit_model["profit_Positive_probability"])  # 每兩次對衝交易正利潤概率;
print("profit_Negative_probability = ", return_SizePosition_fit_model["profit_Negative_probability"])  # 每兩次對衝交易負利潤概率;
print("Long_Position_profit_Positive_probability = ", return_SizePosition_fit_model["Long_Position_profit_Positive_probability"])  # 每兩次對衝交易正利潤概率;
print("Long_Position_profit_Negative_probability = ", return_SizePosition_fit_model["Long_Position_profit_Negative_probability"])  # 每兩次對衝交易負利潤概率;
print("Short_Selling_profit_Positive_probability = ", return_SizePosition_fit_model["Short_Selling_profit_Positive_probability"])  # 每兩次對衝交易正利潤概率;
print("Short_Selling_profit_Negative_probability = ", return_SizePosition_fit_model["Short_Selling_profit_Negative_probability"])  # 每兩次對衝交易負利潤概率;
print("average_price_amplitude_date_transaction = ", return_SizePosition_fit_model["average_price_amplitude_date_transaction"])  # 兩兩次對衝交易日成交價振幅平方和，均值;
print("Long_Position_average_price_amplitude_date_transaction = ", return_SizePosition_fit_model["Long_Position_average_price_amplitude_date_transaction"])  # 兩兩次對衝交易日成交價振幅平方和，均值;
print("Short_Selling_average_price_amplitude_date_transaction = ", return_SizePosition_fit_model["Short_Selling_average_price_amplitude_date_transaction"])  # 兩兩次對衝交易日成交價振幅平方和，均值;
print("average_volume_turnover_date_transaction = ", return_SizePosition_fit_model["average_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）均值;
print("Long_Position_average_volume_turnover_date_transaction = ", return_SizePosition_fit_model["Long_Position_average_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）均值;
print("Short_Selling_average_volume_turnover_date_transaction = ", return_SizePosition_fit_model["Short_Selling_average_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）均值;
print("average_date_transaction_between = ", return_SizePosition_fit_model["average_date_transaction_between"])  # 兩次交易間隔日長，均值;
print("Long_Position_average_date_transaction_between = ", return_SizePosition_fit_model["Long_Position_average_date_transaction_between"])  # 兩次對衝交易間隔日長，均值;
print("Short_Selling_average_date_transaction_between = ", return_SizePosition_fit_model["Short_Selling_average_date_transaction_between"])  # 兩次對衝交易間隔日長，均值;
print("number_SizePosition_transaction = ", return_SizePosition_fit_model["number_PickStock_transaction"])  # 交易過股票的總隻數;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"])
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_profit_date_transaction"])  # 每兩次對衝交易利潤，向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_profit_date_transaction"])  # 每兩次對衝交易利潤，向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_price_amplitude_date_transaction"])  # 兩次對衝交易日成交價振幅平方和，向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_price_amplitude_date_transaction"])  # 兩次對衝交易日成交價振幅平方和，向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_volume_turnover_date_transaction"])  # 兩次對衝交易日成交量（換手率）向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction_between"])  # 兩次對衝交易間隔日長，向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction_between"])  # 兩次對衝交易間隔日長，向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"])  # 按規則執行交易的日期，向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][0])  # 交易規則自動選取的交易日期;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][1])  # 交易規則自動選取的買入或賣出;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][2])  # 交易規則自動選取的成交價;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][3])  # 交易規則自動選取的成交量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][4])  # 交易規則自動選取的成交次數記錄;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][5])  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][6])  # 交易日（Dates.Date 類型）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][7])  # 當日總成交量（turnover volume）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][8])  # 當日開盤（opening）成交價;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][9])  # 當日收盤（closing）成交價;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][10])  # 當日最低（low）成交價;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][11])  # 當日最高（high）成交價;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][12])  # 當日總成交金額（turnover amount）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][13])  # 當日成交量（turnover volume）換手率（turnover rate）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][14])  # 當日每股收益（price earnings）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][15])  # 當日每股净值（book value per share）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"])  # 按規則執行交易的日期，向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][0])  # 交易規則自動選取的交易日期;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][1])  # 交易規則自動選取的買入或賣出;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][2])  # 交易規則自動選取的成交價;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][3])  # 交易規則自動選取的成交量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][4])  # 交易規則自動選取的成交次數記錄;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][5])  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][6])  # 交易日（Dates.Date 類型）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][7])  # 當日總成交量（turnover volume）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][8])  # 當日開盤（opening）成交價;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][9])  # 當日收盤（closing）成交價;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][10])  # 當日最低（low）成交價;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][11])  # 當日最高（high）成交價;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][12])  # 當日總成交金額（turnover amount）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][13])  # 當日成交量（turnover volume）換手率（turnover rate）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][14])  # 當日每股收益（price earnings）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][15])  # 當日每股净值（book value per share）;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["revenue_and_expenditure_records_date_transaction"])  # 每次交易的收支記錄序列，不區分做多（Long Position）或做空（Short Selling），向量;
print(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["P1_Array"])  # 依照擇時規則計算得到參數 P1 值的序列存儲數組;

result = SizePosition(
    training_data = {
        "600118": training_data["600118"],
        "600119": training_data["600119"],
        "600120": training_data["600120"],
        "002607": training_data["002607"],
        "002608": training_data["002608"],
        "002609": training_data["002609"],
        "002611": training_data["002611"]
    },  # {}  # 訓練集，標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    testing_data = {
        "600118": testing_data["600118"],
        "600119": testing_data["600119"],
        "600120": testing_data["600120"],
        "002607": testing_data["002607"],
        "002608": testing_data["002608"],
        "002609": testing_data["002609"],
        "002611": testing_data["002611"]
    },  # {}  # 測試集，標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    Pdata_0 = [weight_MarketTiming_Dict, weight_PickStock_Dict],  # training_data["002611"]["Pdata_0"],  # 優化迭代參數初值;
    weight = [],  # training_data["002611"]["weight"],  # 優化迭代數據權重值;
    Plower = [Plower_weight_MarketTiming_Dict, Plower_weight_PickStock_Dict],  # 優化迭代參數值約束下限;
    Pupper = [Pupper_weight_MarketTiming_Dict, Pupper_weight_PickStock_Dict],  # 優化迭代參數值約束上限;
    MarketTiming_Parameter = MarketTiming_Parameter,  # {},  # 按照擇時規則優化之後的參數字典;
    PickStock_Parameter = PickStock_Parameter,  # {},  # 按照選股規則優化之後的參數字典;
    PickStock_ticker_symbol = PickStock_ticker_symbol,  # [[str()]],  # 依照選股規則排序篩選出的股票代碼字符串存儲數組;
    SizePosition_fit_model = SizePosition_fit_model,  # lambda argument : argument,
    PickStock = PickStock,  # lambda argument : argument,
    PickStock_fit_model = PickStock_fit_model,  # lambda argument : argument,
    MarketTiming = MarketTiming,  # lambda argument : argument,
    MarketTiming_fit_model = MarketTiming_fit_model,  # lambda argument : argument,
    Quantitative_Indicators_Function = Intuitive_Momentum_KLine,  # lambda argument : argument,
    investment_method = "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
)
print("Coefficient 1 weight_MarketTiming : ", "\n", result["Coefficient"][0])  # 優化得到的參數;
print("Coefficient 2 weight_PickStock : ", "\n", result["Coefficient"][1])  # 優化得到的參數;
print("maximum drawdown per share : ", result["testData"]["maximum_drawdown"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum drawdown Long Position per share : ", result["testData"]["maximum_drawdown_Long_Position"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("maximum drawdown Short Selling per share : ", result["testData"]["maximum_drawdown_Short_Selling"])  # 兩次對衝交易之間的最大回撤值，取極值統計;
print("profit total per share : ", result["testData"]["profit_total"])
print("Long Position profit total per share : ", result["testData"]["Long_Position_profit_total"])
print("Short Selling profit total per share : ", result["testData"]["Short_Selling_profit_total"])
print("profit positive per share : ", result["testData"]["profit_Positive"])
print("profit negative per share : ", result["testData"]["profit_Negative"])
print("Long Position profit positive per share : ", result["testData"]["Long_Position_profit_Positive"])
print("Long Position profit negative per share : ", result["testData"]["Long_Position_profit_Negative"])
print("Short Selling profit positive per share : ", result["testData"]["Short_Selling_profit_Positive"])
print("Short Selling profit negative per share : ", result["testData"]["Short_Selling_profit_Negative"])
print("profit positive probability : ", result["testData"]["profit_Positive_probability"])
print("profit negative probability : ", result["testData"]["profit_Negative_probability"])
print("Long Position profit positive probability : ", result["testData"]["Long_Position_profit_Positive_probability"])
print("Long Position profit negative probability : ", result["testData"]["Long_Position_profit_Negative_probability"])
print("Short Selling profit positive probability : ", result["testData"]["Short_Selling_profit_Positive_probability"])
print("Short Selling profit negative probability : ", result["testData"]["Short_Selling_profit_Negative_probability"])
print("average date transaction between : ", result["testData"]["average_date_transaction_between"])
print("Long Position average date transaction between : ", result["testData"]["Long_Position_average_date_transaction_between"])
print("Short Selling average date transaction between : ", result["testData"]["Short_Selling_average_date_transaction_between"])
print("number_PickStock_transaction : ", result["testData"]["number_PickStock_transaction"])  # 交易過股票的總隻數;
print(result["testData"]["SizePosition_transaction_sequence"]["002611"]["P1_Array"])
print(result["testData"]["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"])
print(result["testData"]["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"])
print(result["testData"]["SizePosition_transaction_sequence"]["002611"])

7. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingPython/src/Quantitative_BackTesting.py」内函數 ( Function ) 運行示例 :

return_stepping_Dict = BackTesting_Stepper(
    steppingData = {
        "600118": stepping_data["600118"],
        "600119": stepping_data["600119"],
        "600120": stepping_data["600120"],
        "002607": stepping_data["002607"],
        "002608": stepping_data["002608"],
        "002609": stepping_data["002609"],
        "002611": stepping_data["002611"]
    },  # 回測數據集，標準化日棒缐（K Line Daily）數據字典 ( Python - dict ) ;
    risk_threshold = float(0.8),  # risk_threshold_drawdown_loss,  # 自定義的風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
    training_sequence_length = int(60),  # 推進分析（Stepper movement）（propulsion analysis）每一次步進，訓練集數據自定義的交易日截取長度;
    training_ticker_symbol = [str(item) for item in stepping_data.keys()]  # training_data_ticker_symbol_Array,  # 訓練集納入股票代碼字符串的一維數組，函數 dict.keys() 表示獲取字典的所有 key 值，返回值爲字符串列表（list）;
    testing_ticker_symbol = [str(item) for item in stepping_data.keys()]  # testing_data_ticker_symbol_Array,  # 訓練集納入股票代碼字符串的一維數組，函數 dict.keys() 表示獲取字典的所有 key 值，返回值爲字符串列表（list）;
    testing_sequence_length = int(1),  # 推進分析（Stepper movement）（propulsion analysis）每一次步進，測試集數據自定義的交易日截取長度;
    SizePosition = SizePosition,  # lambda argument : argument,
    SizePosition_fit_model = SizePosition_fit_model,  # lambda argument : argument,
    SizePosition_Pdata_0 = SizePosition_Parameter_Array,  # 倉位優化迭代參數初值;
    SizePosition_weight = [],  # [float(1.0) for i in 1:len(steppingData)],  # 倉位優化迭代數據權重值;
    SizePosition_Plower = [Plower_weight_MarketTiming_Dict, Plower_weight_PickStock_Dict],  # [-math.inf, -math.inf],  # 倉位優化迭代參數值約束下限;
    SizePosition_Pupper = [Pupper_weight_MarketTiming_Dict, Pupper_weight_PickStock_Dict],  # [+math.inf, +math.inf],  # 倉位優化迭代參數值約束上限;
    PickStock = PickStock,  # lambda argument : argument,
    PickStock_fit_model = PickStock_fit_model,  # lambda argument : argument,
    PickStock_Pdata_0 = [],  # [5, 3],  # PickStock_Parameter,  # 選股優化迭代參數初值;
    PickStock_weight = [],  # [float(1.0) for i in 1:len(steppingData)],  # 選股優化迭代數據權重值;
    PickStock_Plower = [int(1), int(1)],  # [-math.inf, -math.inf],  # 選股優化迭代參數值約束下限;
    PickStock_Pupper = [int([int(maximum_stepping_data) if int(maximum_stepping_data) > int(0) else int(1) for i in range(int(0), int(1), int(1))][int(0)]), int([int(maximum_ticker_symbol_stepping_data) if int(maximum_ticker_symbol_stepping_data) > int(0) else int(1) for i in range(int(0), int(1), int(1))][int(0)])],  # [+math.inf, +math.inf],  # 選股優化迭代參數值約束上限;
    MarketTiming = MarketTiming,  # lambda argument : argument,
    MarketTiming_fit_model = MarketTiming_fit_model,  # lambda argument : argument,
    MarketTiming_Pdata_0 = [],  # [5, 0.1, -0.1, 0.0],  # MarketTiming_Parameter,  # 擇時優化迭代參數初值;
    MarketTiming_weight = [],  # [float(1.0) for i in 1:len(steppingData)],  # 擇時優化迭代數據權重值;
    MarketTiming_Plower = [int(1), -math.inf, -math.inf, -math.inf],  # [-math.inf, -math.inf, -math.inf, -math.inf],  # 擇時優化迭代參數值約束下限;
    MarketTiming_Pupper = [int([int(maximum_stepping_data) if int(maximum_stepping_data) > int(0) else int(1) for i in range(int(0), int(1), int(1))][int(0)]), +math.inf, +math.inf, +math.inf],  # [+math.inf, +math.inf, +math.inf, +math.inf],  # 擇時優化迭代參數值約束上限;
    Quantitative_Indicators_Function = Intuitive_Momentum_KLine,  # lambda argument : argument,
    investment_method = investment_method  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
)
print("number PickStock : ", return_stepping_Dict["number_PickStock"])
print("number PickStock Long Position : ", return_stepping_Dict["number_PickStock_Long_Position"])
print("number PickStock Short Selling : ", return_stepping_Dict["number_PickStock_Short_Selling"])
print("number transaction : ", return_stepping_Dict["number_transaction_total"])
print("number transaction Long Position : ", return_stepping_Dict["number_transaction_total_Long_Position"])
print("number transaction Short Selling : ", return_stepping_Dict["number_transaction_total_Short_Selling"])
print("maximum drawdown : ", return_stepping_Dict["maximum_drawdown"])
print("maximum drawdown Long Position : ", return_stepping_Dict["maximum_drawdown_Long_Position"])
print("maximum drawdown Short Selling : ", return_stepping_Dict["maximum_drawdown_Short_Selling"])
print("profit total : ", return_stepping_Dict["profit_total"])
print("Long Position profit total : ", return_stepping_Dict["Long_Position_profit_total"])
print("Short Selling profit total : ", return_stepping_Dict["Short_Selling_profit_total"])
print("profit Positive : ", return_stepping_Dict["profit_Positive"])
print("profit Negative : ", return_stepping_Dict["profit_Negative"])
print("Long Position profit Positive : ", return_stepping_Dict["Long_Position_profit_Positive"])
print("Long Position profit Negative : ", return_stepping_Dict["Long_Position_profit_Negative"])
print("Short Selling profit Positive : ", return_stepping_Dict["Short_Selling_profit_Positive"])
print("Short Selling profit Negative : ", return_stepping_Dict["Short_Selling_profit_Negative"])
print("profit Positive probability : ", return_stepping_Dict["profit_Positive_probability"])
print("profit Negative probability : ", return_stepping_Dict["profit_Negative_probability"])
print("Long Position profit Positive probability : ", return_stepping_Dict["Long_Position_profit_Positive_probability"])
print("Long Position profit Negative probability : ", return_stepping_Dict["Long_Position_profit_Negative_probability"])
print("Short Selling profit Positive probability : ", return_stepping_Dict["Short_Selling_profit_Positive_probability"])
print("Short Selling profit Negative probability : ", return_stepping_Dict["Short_Selling_profit_Negative_probability"])
print("average price amplitude date transaction : ", return_stepping_Dict["average_price_amplitude_date_transaction"])
print("Long Position average price amplitude date transaction : ", return_stepping_Dict["Long_Position_average_price_amplitude_date_transaction"])
print("Short Selling average price amplitude date transaction : ", return_stepping_Dict["Short_Selling_average_price_amplitude_date_transaction"])
print("average volume turnover date transaction : ", return_stepping_Dict["average_volume_turnover_date_transaction"])
print("Long Position average volume turnover date transaction : ", return_stepping_Dict["Long_Position_average_volume_turnover_date_transaction"])
print("Short Selling average volume turnover date transaction : ", return_stepping_Dict["Short_Selling_average_volume_turnover_date_transaction"])
print("average date transaction between : ", return_stepping_Dict["average_date_transaction_between"])
print("Long Position average date transaction between : ", return_stepping_Dict["Long_Position_average_date_transaction_between"])
print("Short Selling average date transaction between : ", return_stepping_Dict["Short_Selling_average_date_transaction_between"])
print("PickStock Long Position Array :", "\n", return_stepping_Dict["PickStock_Long_Position"])
print("PickStock Short Selling Array :", "\n", return_stepping_Dict["PickStock_Short_Selling"])
print("PickStock Array :", "\n", return_stepping_Dict["PickStock"])
print("profit paired transaction Dict :", "\n", return_stepping_Dict["profit_paired_transaction"])
print("transaction sequence Dict :", "\n", return_stepping_Dict["transaction_sequence"])
print("stepping sequence Array :", "\n", return_stepping_Dict["stepping_sequence"])

---

Julia Explain : Interface.jl , Router.jl , QuantitativeTradingServer.jl , Interpolation_Fitting.jl , Quantitative_Indicators.jl , Quantitative_Data_Cleaning.jl , Quantitative_MarketTiming.jl , Quantitative_PickStock.jl , Quantitative_SizePosition.jl , Quantitative_BackTesting.jl

計算機程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 與作業系統 ( Operating System ) 環境配置釋明 :

Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30

Interpreter: julia-1.10.10-win64.exe

Interpreter: julia-1.10.10-linux-x86_64.tar.gz

Operating System: Google-Pixel-7 Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 MSM8998-Snapdragon835-Qualcomm®-Kryo™-280

Interpreter: julia-1.10.10-linux-aarch64.tar.gz

注意,

程式代碼脚本檔 Interface.jl 裏, 函數 http_Server, http_Client 使用了第三方模組 HTTP.jl , JSON.jl 擴展包 ( packages ) ,

程式代碼脚本檔 QuantitativeTradingServer.jl 和 Router.jl 裏, 函數 do_data, do_Request, do_Response 使用了第三方模組 JSON.jl 擴展包 ( packages ) ,

所以, 需事先安裝配置成功, 加載導入之後, 才能正常運行.

首先在作業系統 ( Operating System ) 控制臺命令列窗口 ( bash, cmd ) 啓動程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 進入語言 ( Julia ) 的運行環境 :

谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 ) 控制臺命令列 ( bash ) 運行啓動指令 :

root@localhost:~# /usr/julia/julia-1.10.10/bin/julia --project=/home/QuantitativeTrading/QuantitativeTradingJulia/

微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 運行啓動指令 :

C:\QuantitativeTrading> C:/QuantitativeTrading/Julia/Julia-1.10.10/bin/julia.exe --project=C:/QuantitativeTrading/QuantitativeTradingJulia/

然後, 在程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 運行環境下, 安裝配置第三方擴展包 ( packages ) :

程式設計語言 ( Julia ) 的第三方擴展模組 HTTP.jl 安裝配置説明 :

julia> using Pkg
julia> Pkg.add("HTTP")

程式設計語言 ( Julia ) 的第三方擴展模組 HTTP.jl 加載導入説明 :

julia> using HTTP

程式設計語言 ( Julia ) 的第三方擴展模組 JSON.jl 安裝配置説明 :

julia> using Pkg
julia> Pkg.add("JSON")

程式設計語言 ( Julia ) 的第三方擴展模組 JSON.jl 加載導入説明 :

julia> using JSON

也可以自定義從新創建隔離運行環境 , 創建過程如下 ：

微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 配置程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 隔離運行環境 :

1. 首先，微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 啓動程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 進入語言 ( Julia ) 的運行環境 :

C:\QuantitativeTrading> C:/QuantitativeTrading/Julia/Julia-1.10.10/bin/julia.exe

2. 然後，創建程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 隔離運行環境「C:/QuantitativeTrading/QuantitativeTradingJulia/」 :

從程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行進入程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行的擴展包安裝環境 :

julia> ]

在程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行的擴展包安裝環境在當前目錄創建隔離運行環境「QuantitativeTradingJulia」項目 :

(@v1.9) pkg> generate ./QuantitativeTradingJulia

運行結束後，可以看到在「C:/QuantitativeTrading/」路徑下已經多了一個「QuantitativeTradingJulia」文件夾，就是新創建成功的「C:/QuantitativeTrading/QuantitativeTradingJulia/」項目的工作空間 . 鍵盤按下退格鍵「Backspce」退出程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行的擴展包安裝環境，返回到程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行環境 . 退出程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行環境返回至微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 環境，使用如下指令 :

julia> exit()

微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 進入新創建的「C:/QuantitativeTrading/QuantitativeTradingJulia/」項目工作空間 :

C:\QuantitativeTrading> cd C:/QuantitativeTrading/QuantitativeTradingJulia/

3. 微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 運行啓動指令，進入程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 隔離運行環境「C:/QuantitativeTrading/QuantitativeTradingJulia/」 :

C:\QuantitativeTrading\QuantitativeTradingJulia> C:/QuantitativeTrading/Julia/Julia-1.10.10/bin/julia.exe --project=C:/QuantitativeTrading/QuantitativeTradingJulia/

4. 最後, 在程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 隔離運行環境「C:/QuantitativeTrading/QuantitativeTradingJulia/」下, 安裝配置第三方擴展包 ( packages ) :

從程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行進入程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行的擴展包安裝環境 :

julia> ]

安裝配置程式設計語言 ( Julia ) 的第三方擴展模組「JSON」 :

(QuantitativeTradingJulia) pkg> add JSON

安裝配置程式設計語言 ( Julia ) 的第三方擴展模組「Optim」 :

(QuantitativeTradingJulia) pkg> add Optim

安裝配置程式設計語言 ( Julia ) 的第三方擴展模組「JLD」 :

(QuantitativeTradingJulia) pkg> add JLD

安裝配置程式設計語言 ( Julia ) 的第三方擴展模組「DataFrames」 :

(QuantitativeTradingJulia) pkg> add DataFrames

安裝配置程式設計語言 ( Julia ) 的第三方擴展模組「CSV」 :

(QuantitativeTradingJulia) pkg> add CSV

安裝配置程式設計語言 ( Julia ) 的第三方擴展模組「XLSX」 :

(QuantitativeTradingJulia) pkg> add XLSX

安裝配置程式設計語言 ( Julia ) 的第三方擴展模組「HTTP」 :

(QuantitativeTradingJulia) pkg> add HTTP

安裝配置程式設計語言 ( Julia ) 的第三方擴展模組「Gadfly」 :

(QuantitativeTradingJulia) pkg> add Gadfly

安裝配置程式設計語言 ( Julia ) 的第三方擴展模組「Plots」 :

(QuantitativeTradingJulia) pkg> add Plots

鍵盤按下退格鍵「Backspce」退出程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行的擴展包安裝環境，返回到程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行環境 . 退出程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 控制臺命令行環境返回至微軟視窗系統 ( Window10 x86_64 ) 控制臺命令列 ( cmd ) 環境，使用如下指令 :

julia> exit()

即可 .

Interpreter :

julia - 1.10.10

程式設計 Julia 語言解釋器 ( Interpreter ) 官方網站: https://julialang.org/

程式設計 Julia 語言解釋器 ( Interpreter ) 官方下載頁: https://julialang.org/downloads/

程式設計 Julia 語言解釋器 ( Interpreter ) 官方 GitHub 網站賬戶: https://github.com/JuliaLang

程式設計 Julia 語言解釋器 ( Interpreter ) 官方 GitHub 網站倉庫頁: https://github.com/JuliaLang/julia.git

程式設計 Julia 語言解釋器 ( Interpreter ) 第三方擴展模組 ( module ) ( packages ) 托管網站官方手冊: https://julialang.org/packages/

程式設計 Julia 語言解釋器 ( Interpreter ) 官方 General.jl 模組 GitHub 網站倉庫頁: https://github.com/JuliaRegistries/General.git

程式設計 Julia 語言統計算法 ( algorithm ) 借用第三方擴展模組 ( third-party extensions ( libraries or modules ) ) 説明 :

Julia - JSON 官方 GitHub 網站倉庫頁: https://github.com/JuliaIO/JSON.jl.git

Julia - Optim 官方手冊: https://julianlsolvers.github.io/Optim.jl/stable/

Julia - Optim 官方 GitHub 網站倉庫頁: https://github.com/JuliaNLSolvers/Optim.jl

Julia - JLD 官方 GitHub 網站倉庫頁: https://github.com/JuliaIO/JLD.jl.git

Julia - DataFrames 官方 GitHub 網站倉庫頁: https://github.com/JuliaData/DataFrames.jl.git

Julia - CSV 官方 GitHub 網站倉庫頁: https://github.com/JuliaData/CSV.jl.git

Julia - XLSX 官方手冊: https://felipenoris.github.io/XLSX.jl/stable/

Julia - XLSX 官方 GitHub 網站倉庫頁: https://github.com/felipenoris/XLSX.jl.git

Julia - HTTP 官方 GitHub 網站倉庫頁: https://github.com/JuliaWeb/HTTP.jl.git

Julia - JuMP 官方網站: https://jump.dev/

Julia - JuMP 官方手冊: https://jump.dev/JuMP.jl/stable/

Julia - JuMP 官方 GitHub 網站倉庫頁: https://github.com/jump-dev/JuMP.jl.git

Julia - LsqFit 官方手冊: https://julianlsolvers.github.io/LsqFit.jl/latest/

Julia - LsqFit 官方 GitHub 網站倉庫頁: https://github.com/JuliaNLSolvers/LsqFit.jl.git

Julia - Interpolations 官方手冊: https://juliamath.github.io/Interpolations.jl/stable/

Julia - Interpolations 官方 GitHub 網站倉庫頁: https://github.com/JuliaMath/Interpolations.jl.git

Julia - DataInterpolations 官方手冊: https://docs.sciml.ai/DataInterpolations/stable/

Julia - DataInterpolations 官方 GitHub 網站倉庫頁: https://github.com/SciML/DataInterpolations.jl.git

Julia - Roots 官方手冊: https://juliamath.github.io/Roots.jl/stable/

Julia - Roots 官方 GitHub 網站倉庫頁: https://github.com/JuliaMath/Roots.jl.git

Julia - ForwardDiff 官方手冊: https://juliadiff.org/ForwardDiff.jl/stable/

Julia - ForwardDiff 官方 GitHub 網站倉庫頁: https://github.com/JuliaDiff/ForwardDiff.jl.git

Julia - Symbolics 官方手冊: https://docs.sciml.ai/Symbolics/stable/

Julia - Symbolics 官方 GitHub 網站倉庫頁: https://github.com/JuliaSymbolics/Symbolics.jl.git

Julia - Gadfly 官方手冊: https://gadflyjl.org/stable/

Julia - Gadfly 官方 GitHub 網站倉庫頁: https://github.com/GiovineItalia/Gadfly.jl.git

Julia - Plots 官方手冊: https://docs.juliaplots.org/stable/

Julia - Plots 官方 GitHub 網站倉庫頁: https://github.com/JuliaPlots/Plots.jl.git

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「JuliaIO」項目官方 GitHub 網站賬戶: https://github.com/JuliaIO

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「JuliaWeb」項目官方 GitHub 網站賬戶: https://github.com/JuliaWeb

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「JuliaData」項目官方 GitHub 網站賬戶: https://github.com/JuliaData

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「JuliaPlots」項目官方 GitHub 網站賬戶: https://github.com/JuliaPlots

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「GiovineItalia」項目官方 GitHub 網站賬戶: https://github.com/GiovineItalia

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「JuliaMath」項目官方 GitHub 網站賬戶: https://github.com/JuliaMath

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「JuliaNLSolvers」項目官方 GitHub 網站賬戶: https://github.com/JuliaNLSolvers

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「SciML」項目官方 GitHub 網站賬戶: https://github.com/SciML

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「JuliaDiff」項目官方 GitHub 網站賬戶: https://github.com/JuliaDiff

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「JuMP」項目官方 GitHub 網站賬戶: https://github.com/jump-dev

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「JuliaSymbolics」項目官方 GitHub 網站賬戶: https://github.com/JuliaSymbolics

程式設計 Julia 語言第三方擴展模組 ( third-party extensions ( libraries or modules ) ) : 「JuliaHub」項目官方 GitHub 網站賬戶: https://juliahub.com/

使用説明:

谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 )

控制臺命令列 ( bash ) 運行啓動指令 :

root@localhost:~# /usr/julia/julia-1.10.10/bin/julia -p 4 --project=/home/QuantitativeTrading/QuantitativeTradingJulia/ /home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt interface_Function=http_Server webPath=/home/QuantitativeTrading/html/ host=::0 port=10001 key=username:password number_Worker_threads=1 isConcurrencyHierarchy=Tasks readtimeout=0 connecttimeout=0

微軟視窗系統 ( Window10 x86_64 )

控制臺命令列 ( cmd ) 運行啓動指令 :

C:\QuantitativeTrading> C:/QuantitativeTrading/Julia/Julia-1.10.10/bin/julia.exe -p 4 --project=C:/QuantitativeTrading/QuantitativeTradingJulia/ C:/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configFile=C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt interface_Function=http_Server webPath=C:/QuantitativeTrading/html/ host=::0 port=10001 key=username:password number_Worker_threads=1 isConcurrencyHierarchy=Tasks readtimeout=0 connecttimeout=0

控制臺啓動傳參釋意, 各參數之間以一個空格字符 ( SPACE ) ( 00100000 ) 分隔, 鍵(Key) ~ 值(Value) 之間以一個等號字符 ( = ) 連接, 即類比 Key=Value 的形式 :

1. (必), (自定義), 安裝配置的程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 環境的二進制可執行檔啓動存儲路徑全名, 預設值爲 : C:/QuantitativeTrading/Julia/Julia-1.10.10/bin/julia.exe

2. (必), (自定義), 語言 ( Julia ) 程式代碼脚本 ( Script ) 檔 ( QuantitativeTradingServer.jl ) 的存儲路徑全名, 預設值爲 : C:/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl

   注意, 因爲「QuantitativeTradingServer.jl」檔中脚本代碼需要加載引入「Interface.jl」檔, 所以需要保持「QuantitativeTradingServer.jl」檔與「Interface.jl」檔在相同目錄下, 不然就需要手動修改「QuantitativeTradingServer.jl」檔中有關引用「Interface.jl」檔的加載路徑代碼, 以確保能正確引入「Interface.jl」檔.

3. (選), (鍵 configFile 固定, 值 C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt 自定義), 用於傳入配置文檔的保存路徑全名, 預設值爲 : configFile=C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt

4. (選), (鍵 interface_Function 固定, 值 file_Monitor 自定義, [ file_Monitor, http_Server, http_Client ] 取其一), 用於傳入選擇啓動哪一種接口服務, 外設硬盤 ( Hard Disk ) 文檔 ( File ) 作橋, 外設網卡 ( Network Interface Card ) 埠 ( Port ) 作橋, 預設值爲 : interface_Function=file_Monitor

以下是當參數 : interface_Function 取 : http_Server 值時, 可在控制臺命令列傳入的參數 :

7. (選), (鍵 number_Worker_threads 固定, 值 0 自定義), 用於傳入創建並發數目, 子進程 ( Sub Process ) 並發, 或者, 子缐程 ( Sub Threading ) 並發, 即, 可以設爲等於物理中央處理器 ( Central Processing Unit ) 的數目, 取 0 值表示不開啓並發架構, 預設值爲 : number_Worker_threads=0

8. (選), (鍵 host 固定, 值 ::0 自定義, 例如 [ ::0, ::1, 0.0.0.0, 127.0.0.1, localhost ] 取其一), 用於傳入伺服器 ( http_Server ) 監聽的外設網卡 ( Network Interface Card ) 地址 ( IPv6 , IPv4 ) 或域名, 預設值爲 : host=::0

9. (選), (鍵 port 固定, 值 10001 自定義), 用於傳入伺服器 ( http_Server ) 監聽的外設網卡 ( Network Interface Card ) 自定義設定的埠號 ( 1 ~ 65535 ), 預設值爲 : port=10001

10. (選), (鍵 key 固定, 賬號密碼連接符 : 固定, 值 username 和 password 自定義), 用於傳入自定義的訪問網站驗證 ( Authorization ) 用戶名和密碼, 預設值爲 : key=username:password

11. (選), (鍵 isConcurrencyHierarchy 固定, 值 Tasks 自定義, 例如 [ Tasks, Multi-Threading, Multi-Processes ] 取其一), 用於選擇並發種類, 多進程 ( Process ) 並發, 或者, 多缐程 ( Threading ) 並發, 或者, 多協程 ( Tasks ) 並發, 當取值為多缐程 Multi-Threading 時，必須在啓動 Julia 解釋器之前，在控制臺命令行修改環境變量 : export JULIA_NUM_THREADS=4(Linux OSX) 或 set JULIA_NUM_THREADS=4(Windows) 來設置預創建多個缐程, 預設值爲 : isConcurrencyHierarchy=Tasks

12. (選), (鍵 webPath 固定, 值 C:/QuantitativeTrading/html/ 自定義), 用於傳入伺服器 ( http_Server ) 啓動運行的自定義的根目錄 (項目空間) 路徑全名, 預設值爲 : webPath=C:/QuantitativeTrading/html/

13. (選), (鍵 readtimeout 固定, 值 0 自定義), 用於傳入客戶端請求數據讀取超時中止時長，單位 ( Unit ) 爲秒 ( Second ), 取 0 值表示不做判斷是否超時, 預設值爲 : readtimeout=0

以下是當參數 : interface_Function 取 : http_Client 值時, 可在控制臺命令列傳入的參數 :

15. (選), (鍵 host 固定, 值 ::1 自定義, 例如 [ ::1, 127.0.0.1, localhost ] 取其一), 用於傳入用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的地址 ( IPv6 , IPv4 ) 或域名, 預設值爲 : host=::1

16. (選), (鍵 port 固定, 值 10001 自定義), 用於傳入用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的埠號 ( 1 ~ 65535 ), 預設值爲 : port=10001

17. (選), (鍵 URL 固定, 取值自定義, 例如配置爲 http://[::1]:10001/index.html 值), 用於傳入用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的地址, 萬維網統一資源定位系統 ( Uniform Resource Locator ) 地址字符串, 預設值爲 : URL=""

18. (選), (鍵 proxy 固定, 取值自定義, 例如配置爲 http://[::1]:10001/index.html 值), 當用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求時, 若需要代理轉發, 用於傳入轉發代理服務器的地址, 萬維網統一資源定位系統 ( Uniform Resource Locator ) 地址字符串, 預設值爲 : proxy=""

19. (選), (鍵 requestMethod 固定, 值 POST 自定義, 例如 [ POST, GET ] 取其一), 用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的類型, 預設值爲 : requestMethod=POST

20. (選), (鍵 readtimeout 固定, 值 0 自定義), 用於傳入服務端響應數據讀取超時中止時長，單位 ( Unit ) 爲秒 ( Second ), 取 0 值表示不做判斷是否超時, 預設值爲 : readtimeout=0

21. (選), (鍵 connecttimeout 固定, 值 0 自定義), 用於傳入客戶端請求鏈接超時中止時長，單位 ( Unit ) 爲秒 ( Second ), 取 0 值表示不做判斷是否超時, 預設值爲 : connecttimeout=0

22. (選), (鍵 Authorization 固定, 賬號密碼連接符 : 固定, 值 username 和 password 自定義), 用於傳入用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的驗證 ( Authorization ) 的賬號密碼字符串, 預設值爲 : Authorization=username:password

23. (選), (鍵 Cookie 固定, 其中 Cookie 名稱 Session_ID 可以設計爲固定, Cookie 值 request_Key->username:password 可以設計爲自定義), 用於傳入用戶端連接器 ( http_Client ) 向外設網卡 ( Network Interface Card ) 發送請求的 Cookies 值字符串, 預設值爲 : Cookie=Session_ID=request_Key->username:password

量化交易運算模組説明 :

1. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Data_Cleaning.jl」運行示例 :

谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 )

控制臺命令列 ( bash ) 運行啓動指令 :

root@localhost:~# /usr/julia/julia-1.10.10/bin/julia -p 4 --project=/QuantitativeTrading/QuantitativeTradingJulia/ /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Data_Cleaning.jl configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt input_K_Line=/home/QuantitativeTrading/Data/K-Day-source/ is_save_JLD=false output_jld_K_Line=/home/QuantitativeTrading/Data/steppingData.jld is_save_csv=false output_csv_K_Line=/home/QuantitativeTrading/Data/K-Day/ is_save_xlsx=false output_xlsx_K_Line=/home/QuantitativeTrading/Data/K-Day/

微軟視窗系統 ( Window10 x86_64 )

控制臺命令列 ( cmd ) 運行啓動指令 :

C:\QuantitativeTrading> C:/QuantitativeTrading/Julia/Julia-1.10.10/bin/julia.exe -p 4 --project=C:/QuantitativeTrading/QuantitativeTradingJulia/ C:/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Data_Cleaning.jl configFile=C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt input_K_Line=C:/QuantitativeTrading/Data/K-Day-source/ is_save_JLD=true output_jld_K_Line=C:/QuantitativeTrading/Data/steppingData.jld is_save_csv=false output_csv_K_Line=C:/QuantitativeTrading/Data/K-Day/ is_save_xlsx=false output_xlsx_K_Line=C:/QuantitativeTrading/Data/K-Day/

2. 標準化日棒缐 ( K - Line ) 數據以程式設計語言 ( computer programming language ) : Julia 字典類型 ( Julia - Base.Dict ) 數據傳入，數據格式可類比如下 :

training_data =

Base.Dict{Core.String, Core.Any}(
    Base.string("002607") => Base.Dict{Core.String, Core.Any}(
        Base.string("date_transaction") : Core.Array{Core.Union{Dates.Date, Dates.DateTime, Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Dates.Date("2022-03-14") , Dates.Date("2022-03-15") , Dates.Date("2022-03-16") , ... ],
        Base.string("turnover_volume") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Int64(10002) , Core.Int64(10003) , Core.Int64(10001) , ... ],
        Base.string("opening_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.81) , Core.Float64(1.52) , Core.Float64(1.23) , ... ],
        Base.string("close_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.21) , Core.Float64(1.52) , Core.Float64(1.83) , ... ],
        Base.string("low_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.11) , Core.Float64(1.42) , Core.Float64(1.13) , ... ],
        Base.string("high_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.91) , Core.Float64(1.62) , Core.Float64(1.93) , ... ],
        ...
    ),
    Base.string("002608") => Base.Dict{Core.String, Core.Any}(
        Base.string("date_transaction") : Core.Array{Core.Union{Dates.Date, Dates.DateTime, Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Dates.Date("2022-03-14") , Dates.Date("2022-03-15") , Dates.Date("2022-03-16") , ... ],
        Base.string("turnover_volume") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Int64(10002) , Core.Int64(10003) , Core.Int64(10001) , ... ],
        Base.string("opening_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.81) , Core.Float64(1.52) , Core.Float64(1.23) , ... ],
        Base.string("close_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.21) , Core.Float64(1.52) , Core.Float64(1.83) , ... ],
        Base.string("low_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.11) , Core.Float64(1.42) , Core.Float64(1.13) , ... ],
        Base.string("high_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.91) , Core.Float64(1.62) , Core.Float64(1.93) , ... ],
        ...
    ),
    Base.string("002609") => Base.Dict{Core.String, Core.Any}(
        Base.string("date_transaction") : Core.Array{Core.Union{Dates.Date, Dates.DateTime, Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Dates.Date("2022-03-14") , Dates.Date("2022-03-15") , Dates.Date("2022-03-16") , ... ],
        Base.string("turnover_volume") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Int64(10002) , Core.Int64(10003) , Core.Int64(10001) , ... ],
        Base.string("opening_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.81) , Core.Float64(1.52) , Core.Float64(1.23) , ... ],
        Base.string("close_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.21) , Core.Float64(1.52) , Core.Float64(1.83) , ... ],
        Base.string("low_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.11) , Core.Float64(1.42) , Core.Float64(1.13) , ... ],
        Base.string("high_price") : Core.Array{Core.Union{Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.String, Core.Nothing, Base.Missing}, 1}()[ Core.Float64(1.91) , Core.Float64(1.62) , Core.Float64(1.93) , ... ],
        ...
    ),
    ...
)

3. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Indicators.jl」内函數 ( Function ) 運行示例 :

return_Intuitive_Momentum = Intuitive_Momentum(
    training_data["002611"]["close_price"],  # 時間序列 ( time series ) 數據一維數組 ( Julia - Base.Dict ) ;  # ::Core.Array{Core.Union{Core.String, Core.Float64, Core.Int64, Core.UInt64, Core.Bool, Core.Nothing, Base.Missing}, 1},
    Core.Int64(3);  # 觀察收益率歷史向前推的交易日長度;  # Parameter-1;
    y_P_Positive = Core.Float64(1.0),  # Core.nothing,  # ::Core.Float64  # 增長率（正）的可能性（頻率）示意;
    y_P_Negative = Core.Float64(1.0),  # Core.nothing,  # ::Core.Float64  # 衰退率（負）的可能性（頻率）示意;
    weight = Core.Array{Core.Float64, 1}()  # Core.nothing  # [Core.Float64(Core.Int64(i) / Core.Int64(Parameter-1)) for i in 1:Core.Int64(Parameter-1)]  # 每計增長率的權重（weight）值，距離當下時長的倒數（直覺推理有效性示意）;
);
println("closing price growth rate", return_Intuitive_Momentum);

return_Intuitive_Momentum_KLine = Intuitive_Momentum_KLine(
    Base.Dict{Core.String, Core.Any}(
        "date_transaction" => training_data["002611"]["date_transaction"],  # 交易日期;
        "turnover_volume" => training_data["002611"]["turnover_volume"],  # 成交量;
        "opening_price" => training_data["002611"]["opening_price"],  # 開盤成交價;
        "close_price" => training_data["002611"]["close_price"],  # 收盤成交價;
        "low_price" => training_data["002611"]["low_price"],  # 最低成交價;
        "high_price" => training_data["002611"]["high_price"],  # 最高成交價;
        "focus" => training_data["002611"]["focus"],  # 當日成交價重心;
        "amplitude" => training_data["002611"]["amplitude"],  # 當日成交價絕對振幅;
        "amplitude_rate" => training_data["002611"]["amplitude_rate"],  # 當日成交價相對振幅（%）;
        "opening_price_Standardization" => training_data["002611"]["opening_price_Standardization"],  # 日棒缐（K Line Daily）數據交易日首筆成交價（開盤價）標準化值;
        "closing_price_Standardization" => training_data["002611"]["closing_price_Standardization"],  # 日棒缐（K Line Daily）數據交易日尾筆成交價（收盤價）標準化值;
        "low_price_Standardization" => training_data["002611"]["low_price_Standardization"],  # 日棒缐（K Line Daily）數據交易日最低成交價標準化值;
        "high_price_Standardization" => training_data["002611"]["high_price_Standardization"],  # 日棒缐（K Line Daily）數據交易日最高成交價標準化值;
        "turnover_volume_growth_rate" => training_data["002611"]["turnover_volume_growth_rate"],  # 成交量的成長率;
        "opening_price_growth_rate" => training_data["002611"]["opening_price_growth_rate"],  # 開盤價的成長率;
        "closing_price_growth_rate" => training_data["002611"]["closing_price_growth_rate"],  # 收盤價的成長率;
        "closing_minus_opening_price_growth_rate" => training_data["002611"]["closing_minus_opening_price_growth_rate"],  # 收盤價減開盤價的成長率;
        "high_price_proportion" => training_data["002611"]["high_price_proportion"],  # 收盤價和開盤價裏的最大值占最高價的比例;
        "low_price_proportion" => training_data["002611"]["low_price_proportion"],  # 最低價占收盤價和開盤價裏的最小值的比例;
        "moving_average_3" => training_data["002611"]["moving_average_3"],  # 日棒缐（K Line Daily）數據交易日尾筆成交價（收盤價）三日移動平均缐值;
        "moving_average_5" => training_data["002611"]["moving_average_5"],  # 日棒缐（K Line Daily）數據交易日尾筆成交價（收盤價）五日移動平均缐值;
        "moving_average_10" => training_data["002611"]["moving_average_10"],  # 日棒缐（K Line Daily）數據交易日尾筆成交價（收盤價）十日移動平均缐值;
        "turnover_rate" => training_data["002611"]["turnover_rate"]  # 成交量換手率;
    ),  # Base.Dict{Core.String, Core.Any}(),  # 標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    Core.Int64(3);  # 觀察收益率歷史向前推的交易日長度;  # Parameter-1;
    y_P_Positive = Core.nothing,  # ::Core.Float64 = Core.Float64(1.0),  # 增長率（正）的可能性（頻率）示意;
    y_P_Negative = Core.nothing,  # ::Core.Float64 = Core.Float64(1.0),  # 衰退率（負）的可能性（頻率）示意;
    weight = Core.nothing,  # Core.Array{Core.Float64, 1}()  # [Core.Float64(Core.Int64(i) / Core.Int64(Parameter-1)) for i in 1:Core.Int64(Parameter-1)],  # 每計增長率的權重（weight）值，距離當下時長的倒數（直覺推理有效性示意）;
    Intuitive_Momentum = Intuitive_Momentum  # (arguments) -> begin return arguments; end
);
println("turnover volume growth rate", return_Intuitive_Momentum_KLine["P1_turnover_volume_growth_rate"]);
println("opening price growth rate", return_Intuitive_Momentum_KLine["P1_opening_price_growth_rate"]);
println("closing price growth rate", return_Intuitive_Momentum_KLine["P1_closing_price_growth_rate"]);
println("closing minus opening price growth rate", return_Intuitive_Momentum_KLine["P1_closing_minus_opening_price_growth_rate"]);
println("high price proportion", return_Intuitive_Momentum_KLine["P1_high_price_proportion"]);
println("low price proportion", return_Intuitive_Momentum_KLine["P1_low_price_proportion"]);
println("intuitive momentum indicator", return_Intuitive_Momentum_KLine["P1_Intuitive_Momentum"]);

4. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_MarketTiming.jl」内函數 ( Function ) 運行示例 :

return_MarketTiming_fit_model = MarketTiming_fit_model(
    Base.Dict{Core.String, Core.Any}("002611" => training_data["002611"]),  # Base.Dict{Core.String, Core.Any}(),  # 標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    Core.Int64(10),  # 觀察收益率歷史向前推的交易日長度;  # Parameter-1;
    Core.Float64(+0.58),  # 買入閾值;  # Parameter-2;
    Core.Float64(-0.02),  # 賣出閾值;  # Parameter-3;
    Core.Float64(0.0),  # risk threshold drawdown loss;  # 風險控制閾值 Parameter-4;  # 强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
    Intuitive_Momentum_KLine,  # (arguments) -> begin return arguments; end,
    "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
);
println("y_profit = ", return_MarketTiming_fit_model["002611"]["y_profit"]);  # 每兩次對衝交易利潤 × 頻率 × 權重，加權纍加總計;
println("y_Long_Position_profit = ", return_MarketTiming_fit_model["002611"]["y_Long_Position_profit"]);  # 每兩次對衝交易利潤 × 頻率 × 權重，加權纍加總計;
println("y_Short_Selling_profit = ", return_MarketTiming_fit_model["002611"]["y_Short_Selling_profit"]);  # 每兩次對衝交易利潤 × 頻率 × 權重，加權纍加總計;
println("y_loss = ", return_MarketTiming_fit_model["002611"]["y_loss"]);  # 每兩次對衝交易最大回撤 × 頻率 × 權重，加權取極值總計;
println("y_Long_Position_loss = ", return_MarketTiming_fit_model["002611"]["y_Long_Position_loss"]);  # 每兩次對衝交易最大回撤 × 頻率 × 權重，加權取極值總計;
println("y_Short_Selling_loss = ", return_MarketTiming_fit_model["002611"]["y_Short_Selling_loss"]);  # 每兩次對衝交易最大回撤 × 頻率 × 權重，加權取極值總計;
println("profit_total = ", return_MarketTiming_fit_model["002611"]["profit_total"]);  # 每兩次對衝交易利潤 × 頻率，纍加總計;
println("profit_Positive = ", return_MarketTiming_fit_model["002611"]["profit_Positive"]);  # 每兩次對衝交易收益纍加總計;
println("profit_Negative = ", return_MarketTiming_fit_model["002611"]["profit_Negative"]);  # 每兩次對衝交易損失纍加總計;
println("Long_Position_profit_total = ", return_MarketTiming_fit_model["002611"]["Long_Position_profit_total"]);  # 每兩次對衝交易利潤 × 頻率，纍加總計;
println("Long_Position_profit_Positive = ", return_MarketTiming_fit_model["002611"]["Long_Position_profit_Positive"]);  # 每兩次對衝交易收益纍加總計;
println("Long_Position_profit_Negative = ", return_MarketTiming_fit_model["002611"]["Long_Position_profit_Negative"]);  # 每兩次對衝交易損失纍加總計;
println("Short_Selling_profit_total = ", return_MarketTiming_fit_model["002611"]["Short_Selling_profit_total"]);  # 每兩次對衝交易利潤 × 頻率，纍加總計;
println("Short_Selling_profit_Positive = ", return_MarketTiming_fit_model["002611"]["Short_Selling_profit_Positive"]);  # 每兩次對衝交易收益纍加總計;
println("Short_Selling_profit_Negative = ", return_MarketTiming_fit_model["002611"]["Short_Selling_profit_Negative"]);  # 每兩次對衝交易損失纍加總計;
println("profit_Positive_probability = ", return_MarketTiming_fit_model["002611"]["profit_Positive_probability"]);  # 每兩次對衝交易正利潤概率;
println("profit_Negative_probability = ", return_MarketTiming_fit_model["002611"]["profit_Negative_probability"]);  # 每兩次對衝交易負利潤概率;
println("Long_Position_profit_Positive_probability = ", return_MarketTiming_fit_model["002611"]["Long_Position_profit_Positive_probability"]);  # 每兩次對衝交易正利潤概率;
println("Long_Position_profit_Negative_probability = ", return_MarketTiming_fit_model["002611"]["Long_Position_profit_Negative_probability"]);  # 每兩次對衝交易負利潤概率;
println("Short_Selling_profit_Positive_probability = ", return_MarketTiming_fit_model["002611"]["Short_Selling_profit_Positive_probability"]);  # 每兩次對衝交易正利潤概率;
println("Short_Selling_profit_Negative_probability = ", return_MarketTiming_fit_model["002611"]["Short_Selling_profit_Negative_probability"]);  # 每兩次對衝交易負利潤概率;
println(return_MarketTiming_fit_model["002611"]["Long_Position_profit_date_transaction"]);  # 每兩次對衝交易利潤，向量;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_profit_date_transaction"]);  # 每兩次對衝交易利潤，向量;
println("maximum_drawdown = ", return_MarketTiming_fit_model["002611"]["maximum_drawdown"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum_drawdown_Long_Position = ", return_MarketTiming_fit_model["002611"]["maximum_drawdown_Long_Position"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum_drawdown_Short_Selling = ", return_MarketTiming_fit_model["002611"]["maximum_drawdown_Short_Selling"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("Long_Position_drawdown_date_transaction = ", return_MarketTiming_fit_model["002611"]["Long_Position_drawdown_date_transaction"]);  # 向量，記錄做多模式每組對衝交易日的回撤值序列，風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
println("Short_Selling_drawdown_date_transaction = ", return_MarketTiming_fit_model["002611"]["Short_Selling_drawdown_date_transaction"]);  # 向量，記錄做多模式每組對衝交易日的回撤值序列，風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
println("average_price_amplitude_date_transaction = ", return_MarketTiming_fit_model["002611"]["average_price_amplitude_date_transaction"]);  # 兩兩次對衝交易日成交價振幅平方和，均值;
println("Long_Position_average_price_amplitude_date_transaction = ", return_MarketTiming_fit_model["002611"]["Long_Position_average_price_amplitude_date_transaction"]);  # 兩兩次對衝交易日成交價振幅平方和，均值;
println("Short_Selling_average_price_amplitude_date_transaction = ", return_MarketTiming_fit_model["002611"]["Short_Selling_average_price_amplitude_date_transaction"]);  # 兩兩次對衝交易日成交價振幅平方和，均值;
println(return_MarketTiming_fit_model["002611"]["Long_Position_price_amplitude_date_transaction"]);  # 兩次對衝交易日成交價振幅平方和，向量;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_price_amplitude_date_transaction"]);  # 兩次對衝交易日成交價振幅平方和，向量;
println("average_volume_turnover_date_transaction = ", return_MarketTiming_fit_model["002611"]["average_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）均值;
println("Long_Position_average_volume_turnover_date_transaction = ", return_MarketTiming_fit_model["002611"]["Long_Position_average_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）均值;
println("Short_Selling_average_volume_turnover_date_transaction = ", return_MarketTiming_fit_model["002611"]["Short_Selling_average_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）均值;
println(return_MarketTiming_fit_model["002611"]["Long_Position_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）向量;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）向量;
println("average_date_transaction_between = ", return_MarketTiming_fit_model["002611"]["average_date_transaction_between"]);  # 兩次交易間隔日長，均值;
println("Long_Position_average_date_transaction_between = ", return_MarketTiming_fit_model["002611"]["Long_Position_average_date_transaction_between"]);  # 兩次對衝交易間隔日長，均值;
println("Short_Selling_average_date_transaction_between = ", return_MarketTiming_fit_model["002611"]["Short_Selling_average_date_transaction_between"]);  # 兩次對衝交易間隔日長，均值;
println("weight_MarketTiming = ", return_MarketTiming_fit_model["002611"]["weight_MarketTiming"]);  # 擇時權重，每兩次對衝交易的盈利概率占比;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction_between"]);  # 兩次對衝交易間隔日長，向量;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction_between"]);  # 兩次對衝交易間隔日長，向量;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"]);  # 按規則執行交易的日期，向量;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][1]);  # 交易規則自動選取的交易日期;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][2]);  # 交易規則自動選取的買入或賣出;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][3]);  # 交易規則自動選取的成交價;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][4]);  # 交易規則自動選取的成交量;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][5]);  # 交易規則自動選取的成交次數記錄;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][6]);  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][7]);  # 交易日（Dates.Date 類型）;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][8]);  # 當日總成交量（turnover volume）;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][9]);  # 當日開盤（opening）成交價;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][10]);  # 當日收盤（closing）成交價;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][11]);  # 當日最低（low）成交價;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][12]);  # 當日最高（high）成交價;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][13]);  # 當日總成交金額（turnover amount）;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][14]);  # 當日成交量（turnover volume）換手率（turnover rate）;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][15]);  # 當日每股收益（price earnings）;
println(return_MarketTiming_fit_model["002611"]["Long_Position_date_transaction"][16]);  # 當日每股净值（book value per share）;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"]);  # 按規則執行交易的日期，向量;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][1]);  # 交易規則自動選取的交易日期;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][2]);  # 交易規則自動選取的買入或賣出;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][3]);  # 交易規則自動選取的成交價;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][4]);  # 交易規則自動選取的成交量;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][5]);  # 交易規則自動選取的成交次數記錄;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][6]);  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][7]);  # 交易日（Dates.Date 類型）;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][8]);  # 當日總成交量（turnover volume）;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][9]);  # 當日開盤（opening）成交價;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][10]);  # 當日收盤（closing）成交價;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][11]);  # 當日最低（low）成交價;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][12]);  # 當日最高（high）成交價;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][13]);  # 當日總成交金額（turnover amount）;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][14]);  # 當日成交量（turnover volume）換手率（turnover rate）;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][15]);  # 當日每股收益（price earnings）;
println(return_MarketTiming_fit_model["002611"]["Short_Selling_date_transaction"][16]);  # 當日每股净值（book value per share）;
println(return_MarketTiming_fit_model["002611"]["revenue_and_expenditure_records_date_transaction"]);  # 每次交易的收支記錄序列，不區分做多（Long Position）或做空（Short Selling），向量;
println(return_MarketTiming_fit_model["002611"]["P1_Array"]);  # 依照擇時規則計算得到參數 P1 值的序列存儲數組;

result = MarketTiming(
    training_data = Base.Dict{Core.String, Core.Any}("002611" => training_data["002611"]),  # Base.Dict{Core.String, Core.Any}(),  # 訓練集，標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    testing_data = Base.Dict{Core.String, Core.Any}("002611" => testing_data["002611"]),  # Base.Dict{Core.String, Core.Any}(),  # 測試集，標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    Pdata_0 = [Core.Int64(3), Core.Float64(+0.1), Core.Float64(-0.1), Core.Float64(0.0)],  #training_data["002611"]["Pdata_0"],  # 優化迭代參數初值;
    weight = Core.Array{Core.Float64, 1}(),  # training_data["002611"]["weight"],  # 優化迭代數據權重值;
    Plower = [-Base.Inf, -Base.Inf, -Base.Inf, -Base.Inf],  # training_data["002611"]["Plower"],  # 優化迭代參數值約束下限;
    Pupper = [+Base.Inf, +Base.Inf, +Base.Inf, +Base.Inf],  # training_data["002611"]["Pupper"],  # 優化迭代參數值約束上限;
    MarketTiming_fit_model = MarketTiming_fit_model,  # (arguments) -> begin return arguments; end,
    Quantitative_Indicators_Function = Intuitive_Momentum_KLine,  # (arguments) -> begin return arguments; end,
    investment_method = "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
);
println("Coefficient : ", result["002611"]["Coefficient"]);  # 優化得到的參數;
println(result["002611"]["P1_Array"]);  # 依照擇時規則計算得到參數 P1 值的序列存儲數組;
println("profit total per share : ", result["002611"]["testData"]["profit_total"]);
println("profit positive per share : ", result["002611"]["testData"]["profit_Positive"]);
println("profit negative per share : ", result["002611"]["testData"]["profit_Negative"]);
println("Long Position profit total per share : ", result["002611"]["testData"]["Long_Position_profit_total"]);
println("Long Position profit positive per share : ", result["002611"]["testData"]["Long_Position_profit_Positive"]);
println("Long Position profit negative per share : ", result["002611"]["testData"]["Long_Position_profit_Negative"]);
println("Short Selling profit total per share : ", result["002611"]["testData"]["Short_Selling_profit_total"]);
println("Short Selling profit positive per share : ", result["002611"]["testData"]["Short_Selling_profit_Positive"]);
println("Short Selling profit negative per share : ", result["002611"]["testData"]["Short_Selling_profit_Negative"]);
println("maximum drawdown per share : ", result["002611"]["testData"]["maximum_drawdown"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum drawdown Long Position per share : ", result["002611"]["testData"]["maximum_drawdown_Long_Position"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum drawdown Short Selling per share : ", result["002611"]["testData"]["maximum_drawdown_Short_Selling"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("Long Position drawdown date transaction : ", result["002611"]["testData"]["Long_Position_drawdown_date_transaction"]);  # 向量，記錄做多模式每組對衝交易日的回撤值序列，風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
println("Short Selling drawdown date transaction : ", result["002611"]["testData"]["Short_Selling_drawdown_date_transaction"]);  # 向量，記錄做多模式每組對衝交易日的回撤值序列，風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
println("profit positive probability : ", result["002611"]["testData"]["profit_Positive_probability"]);
println("profit negative probability : ", result["002611"]["testData"]["profit_Negative_probability"]);
println("Long Position profit positive probability : ", result["002611"]["testData"]["Long_Position_profit_Positive_probability"]);
println("Long Position profit negative probability : ", result["002611"]["testData"]["Long_Position_profit_Negative_probability"]);
println("Short Selling profit positive probability : ", result["002611"]["testData"]["Short_Selling_profit_Positive_probability"]);
println("Short Selling profit negative probability : ", result["002611"]["testData"]["Short_Selling_profit_Negative_probability"]);
println("average date transaction between : ", result["002611"]["testData"]["average_date_transaction_between"]);
println("Long Position average date transaction between : ", result["002611"]["testData"]["Long_Position_average_date_transaction_between"]);
println("Short Selling average date transaction between : ", result["002611"]["testData"]["Short_Selling_average_date_transaction_between"]);
println("number Long Position date transaction : ", Base.length(result["002611"]["testData"]["Long_Position_date_transaction"]));
println("number Short Selling date transaction : ", Base.length(result["002611"]["testData"]["Short_Selling_date_transaction"]));
println("weight MarketTiming : ", result["002611"]["testData"]["weight_MarketTiming"]);  # 擇時權重，每兩次對衝交易的盈利概率占比;
println(result["002611"]["testData"]["P1_Array"]);
println(result["002611"]["testData"]["Long_Position_date_transaction"]);
println(result["002611"]["testData"]["Short_Selling_date_transaction"]);
println(result["002611"]["testData"]);

5. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_PickStock.jl」内函數 ( Function ) 運行示例 :

return_PickStock_fit_model = PickStock_fit_model(
    Base.Dict{Core.String, Core.Any}(
        "600118" => training_data["600118"],
        "600119" => training_data["600119"],
        "600120" => training_data["600120"],
        "002607" => training_data["002607"],
        "002608" => training_data["002608"],
        "002609" => training_data["002609"],
        "002611" => training_data["002611"]
    ),  Base.Dict{Core.String, Core.Any}(),  # 標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    Core.Int64(3),  # 觀察收益率歷史向前推的交易日長度;  # Parameter-1;
    Core.Int64(10),  # 依據市值高低分組選股的分類數目;  # Parameter-2;
    MarketTiming_Parameter,  # Base.Dict{Core.String, Core.Any}(),  # 按照擇時規則優化之後的參數字典;
    MarketTiming,  # (arguments) -> begin return arguments; end,
    MarketTiming_fit_model,  # (arguments) -> begin return arguments; end,
    Intuitive_Momentum_KLine,  # (arguments) -> begin return arguments; end,
    "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
);
println("y_profit = ", return_PickStock_fit_model["y_profit"]);  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
println("y_Long_Position_profit = ", return_PickStock_fit_model["y_Long_Position_profit"]);  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
println("y_Short_Selling_profit = ", return_PickStock_fit_model["y_Short_Selling_profit"]);  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
println("y_loss = ", return_PickStock_fit_model["y_loss"]);  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
println("y_Long_Position_loss = ", return_PickStock_fit_model["y_Long_Position_loss"]);  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
println("y_Short_Selling_loss = ", return_PickStock_fit_model["y_Short_Selling_loss"]);  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
println("maximum_drawdown = ", return_PickStock_fit_model["maximum_drawdown"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum_drawdown_Long_Position = ", return_PickStock_fit_model["maximum_drawdown_Long_Position"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum_drawdown_Short_Selling = ", return_PickStock_fit_model["maximum_drawdown_Short_Selling"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("profit_total = ", return_PickStock_fit_model["profit_total"]);  # 每兩次對衝交易利潤 × 權重，纍加總計;
println("Long_Position_profit_total = ", return_PickStock_fit_model["Long_Position_profit_total"]);  # 每兩次對衝交易利潤 × 權重，纍加總計;
println("Short_Selling_profit_total = ", return_PickStock_fit_model["Short_Selling_profit_total"]);  # 每兩次對衝交易利潤 × 權重，纍加總計;
println("profit_Positive = ", return_PickStock_fit_model["profit_Positive"]);  # 每兩次對衝交易收益纍加總計;
println("profit_Negative = ", return_PickStock_fit_model["profit_Negative"]);  # 每兩次對衝交易損失纍加總計;
println("Long_Position_profit_Positive = ", return_PickStock_fit_model["Long_Position_profit_Positive"]);  # 每兩次對衝交易收益纍加總計;
println("Long_Position_profit_Negative = ", return_PickStock_fit_model["Long_Position_profit_Negative"]);  # 每兩次對衝交易損失纍加總計;
println("Short_Selling_profit_Positive = ", return_PickStock_fit_model["Short_Selling_profit_Positive"]);  # 每兩次對衝交易收益纍加總計;
println("Short_Selling_profit_Negative = ", return_PickStock_fit_model["Short_Selling_profit_Negative"]);  # 每兩次對衝交易損失纍加總計;
println("profit_Positive_probability = ", return_PickStock_fit_model["profit_Positive_probability"]);  # 每兩次對衝交易正利潤概率;
println("profit_Negative_probability = ", return_PickStock_fit_model["profit_Negative_probability"]);  # 每兩次對衝交易負利潤概率;
println("Long_Position_profit_Positive_probability = ", return_PickStock_fit_model["Long_Position_profit_Positive_probability"]);  # 每兩次對衝交易正利潤概率;
println("Long_Position_profit_Negative_probability = ", return_PickStock_fit_model["Long_Position_profit_Negative_probability"]);  # 每兩次對衝交易負利潤概率;
println("Short_Selling_profit_Positive_probability = ", return_PickStock_fit_model["Short_Selling_profit_Positive_probability"]);  # 每兩次對衝交易正利潤概率;
println("Short_Selling_profit_Negative_probability = ", return_PickStock_fit_model["Short_Selling_profit_Negative_probability"]);  # 每兩次對衝交易負利潤概率;
println("average_price_amplitude_date_transaction = ", return_PickStock_fit_model["average_price_amplitude_date_transaction"]);  # 兩兩次對衝交易日成交價振幅平方和，均值;
println("Long_Position_average_price_amplitude_date_transaction = ", return_PickStock_fit_model["Long_Position_average_price_amplitude_date_transaction"]);  # 兩兩次對衝交易日成交價振幅平方和，均值;
println("Short_Selling_average_price_amplitude_date_transaction = ", return_PickStock_fit_model["Short_Selling_average_price_amplitude_date_transaction"]);  # 兩兩次對衝交易日成交價振幅平方和，均值;
println("average_volume_turnover_date_transaction = ", return_PickStock_fit_model["average_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）均值;
println("Long_Position_average_volume_turnover_date_transaction = ", return_PickStock_fit_model["Long_Position_average_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）均值;
println("Short_Selling_average_volume_turnover_date_transaction = ", return_PickStock_fit_model["Short_Selling_average_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）均值;
println("average_date_transaction_between = ", return_PickStock_fit_model["average_date_transaction_between"]);  # 兩次交易間隔日長，均值;
println("Long_Position_average_date_transaction_between = ", return_PickStock_fit_model["Long_Position_average_date_transaction_between"]);  # 兩次對衝交易間隔日長，均值;
println("Short_Selling_average_date_transaction_between = ", return_PickStock_fit_model["Short_Selling_average_date_transaction_between"]);  # 兩次對衝交易間隔日長，均值;
println("number_PickStock_transaction = ", return_PickStock_fit_model["number_PickStock_transaction"]);  # 交易過股票的總隻數;
println("weight_PickStock = ", return_PickStock_fit_model["weight_PickStock"]);  # 選股權重，每隻股票的盈利概率占比;
println(return_PickStock_fit_model["PickStock_sort"]);  # 依照選股規則排序篩選出的股票代碼字符串和得分存儲字典（Dict）;
println(return_PickStock_fit_model["PickStock_sort"]["ticker_symbol"]);  # 依照選股規則排序篩選出的股票代碼字符串存儲數組;
println(return_PickStock_fit_model["PickStock_sort"]["score"]);  # 依照選股規則排序篩選出的股票代碼字符串存儲數組;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]);
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]);
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_profit_date_transaction"]);  # 每兩次對衝交易利潤，向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_profit_date_transaction"]);  # 每兩次對衝交易利潤，向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_price_amplitude_date_transaction"]);  # 兩次對衝交易日成交價振幅平方和，向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_price_amplitude_date_transaction"]);  # 兩次對衝交易日成交價振幅平方和，向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction_between"]);  # 兩次對衝交易間隔日長，向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction_between"]);  # 兩次對衝交易間隔日長，向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"]);  # 按規則執行交易的日期，向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][1]);  # 交易規則自動選取的交易日期;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][2]);  # 交易規則自動選取的買入或賣出;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][3]);  # 交易規則自動選取的成交價;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][4]);  # 交易規則自動選取的成交量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][5]);  # 交易規則自動選取的成交次數記錄;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][6]);  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][7]);  # 交易日（Dates.Date 類型）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][8]);  # 當日總成交量（turnover volume）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][9]);  # 當日開盤（opening）成交價;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][10]);  # 當日收盤（closing）成交價;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][11]);  # 當日最低（low）成交價;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][12]);  # 當日最高（high）成交價;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][13]);  # 當日總成交金額（turnover amount）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][14]);  # 當日成交量（turnover volume）換手率（turnover rate）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][15]);  # 當日每股收益（price earnings）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"][16]);  # 當日每股净值（book value per share）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"]);  # 按規則執行交易的日期，向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][1]);  # 交易規則自動選取的交易日期;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][2]);  # 交易規則自動選取的買入或賣出;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][3]);  # 交易規則自動選取的成交價;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][4]);  # 交易規則自動選取的成交量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][5]);  # 交易規則自動選取的成交次數記錄;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][6]);  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][7]);  # 交易日（Dates.Date 類型）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][8]);  # 當日總成交量（turnover volume）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][9]);  # 當日開盤（opening）成交價;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][10]);  # 當日收盤（closing）成交價;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][11]);  # 當日最低（low）成交價;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][12]);  # 當日最高（high）成交價;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][13]);  # 當日總成交金額（turnover amount）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][14]);  # 當日成交量（turnover volume）換手率（turnover rate）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][15]);  # 當日每股收益（price earnings）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][16]);  # 當日每股净值（book value per share）;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["revenue_and_expenditure_records_date_transaction"]);  # 每次交易的收支記錄序列，不區分做多（Long Position）或做空（Short Selling），向量;
println(return_PickStock_fit_model["PickStock_transaction_sequence"]["002611"]["P1_Array"]);  # 依照擇時規則計算得到參數 P1 值的序列存儲數組;

result = PickStock(
    training_data = Base.Dict{Core.String, Core.Any}(
        "600118" => training_data["600118"],
        "600119" => training_data["600119"],
        "600120" => training_data["600120"],
        "002607" => training_data["002607"],
        "002608" => training_data["002608"],
        "002609" => training_data["002609"],
        "002611" => training_data["002611"]
    ),  # Base.Dict{Core.String, Core.Any}(),  # 訓練集，標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    testing_data = Base.Dict{Core.String, Core.Any}(
        "600118" => testing_data["600118"],
        "600119" => testing_data["600119"],
        "600120" => testing_data["600120"],
        "002607" => testing_data["002607"],
        "002608" => testing_data["002608"],
        "002609" => testing_data["002609"],
        "002611" => testing_data["002611"]
    ),  # Base.Dict{Core.String, Core.Any}(),  # 測試集，標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    Pdata_0 = [Core.Int64(3), Core.Int64(5)],  #training_data["002611"]["Pdata_0"],  # 優化迭代參數初值;
    weight = Core.Array{Core.Float64, 1}(),  # training_data["002611"]["weight"],  # 優化迭代數據權重值;
    Plower = [-Base.Inf, -Base.Inf],  # training_data["002611"]["Plower"],  # 優化迭代參數值約束下限;
    Pupper = [+Base.Inf, +Base.Inf],  # training_data["002611"]["Pupper"],  # 優化迭代參數值約束上限;
    MarketTiming_Parameter = MarketTiming_Parameter,  # Base.Dict{Core.String, Core.Any}(),  # 按照擇時規則優化之後的參數字典;
    PickStock_fit_model = PickStock_fit_model,  # (arguments) -> begin return arguments; end,
    MarketTiming = MarketTiming,  # (arguments) -> begin return arguments; end,
    MarketTiming_fit_model = MarketTiming_fit_model,  # (arguments) -> begin return arguments; end,
    Quantitative_Indicators_Function = Intuitive_Momentum_KLine,  # (arguments) -> begin return arguments; end,
    investment_method = "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
);
println("Coefficient : ", result["Coefficient"]);  # 優化得到的參數;
println(result["PickStock_sort"]["ticker_symbol"]);  # 依照選股規則排序篩選出的股票代碼字符串存儲數組;
println(result["PickStock_sort"]["score"]);  # 依照選股規則排序篩選出的股票得分值存儲數組;
println("maximum drawdown per share : ", result["testData"]["maximum_drawdown"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum drawdown Long Position per share : ", result["testData"]["maximum_drawdown_Long_Position"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum drawdown Short Selling per share : ", result["testData"]["maximum_drawdown_Short_Selling"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("profit total per share : ", result["testData"]["profit_total"]);
println("Long Position profit total per share : ", result["testData"]["Long_Position_profit_total"]);
println("Short Selling profit total per share : ", result["testData"]["Short_Selling_profit_total"]);
println("profit positive per share : ", result["testData"]["profit_Positive"]);
println("profit negative per share : ", result["testData"]["profit_Negative"]);
println("Long Position profit positive per share : ", result["testData"]["Long_Position_profit_Positive"]);
println("Long Position profit negative per share : ", result["testData"]["Long_Position_profit_Negative"]);
println("Short Selling profit positive per share : ", result["testData"]["Short_Selling_profit_Positive"]);
println("Short Selling profit negative per share : ", result["testData"]["Short_Selling_profit_Negative"]);
println("profit positive probability : ", result["testData"]["profit_Positive_probability"]);
println("profit negative probability : ", result["testData"]["profit_Negative_probability"]);
println("Long Position profit positive probability : ", result["testData"]["Long_Position_profit_Positive_probability"]);
println("Long Position profit negative probability : ", result["testData"]["Long_Position_profit_Negative_probability"]);
println("Short Selling profit positive probability : ", result["testData"]["Short_Selling_profit_Positive_probability"]);
println("Short Selling profit negative probability : ", result["testData"]["Short_Selling_profit_Negative_probability"]);
println("average date transaction between : ", result["testData"]["average_date_transaction_between"]);
println("Long Position average date transaction between : ", result["testData"]["Long_Position_average_date_transaction_between"]);
println("Short Selling average date transaction between : ", result["testData"]["Short_Selling_average_date_transaction_between"]);
println("number_PickStock_transaction : ", result["testData"]["number_PickStock_transaction"]);  # 交易過股票的總隻數;
println("weight_PickStock : ", result["testData"]["weight_PickStock"]);  # 選股權重，每隻股票的盈利概率占比;
println(result["testData"]["PickStock_transaction_sequence"]["002611"]["P1_Array"]);
println(result["testData"]["PickStock_transaction_sequence"]["002611"]["Long_Position_date_transaction"]);
println(result["testData"]["PickStock_transaction_sequence"]["002611"]["Short_Selling_date_transaction"]);
println(result["testData"]["PickStock_transaction_sequence"]["002611"]);

6. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_SizePosition.jl」内函數 ( Function ) 運行示例 :

return_SizePosition_fit_model = SizePosition_fit_model(
    Base.Dict{Core.String, Core.Any}(
        "600118" => training_data["600118"],
        "600119" => training_data["600119"],
        "600120" => training_data["600120"],
        "002607" => training_data["002607"],
        "002608" => training_data["002608"],
        "002609" => training_data["002609"],
        "002611" => training_data["002611"]
    ),  Base.Dict{Core.String, Core.Any}(),  # 標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    weight_MarketTiming_Dict,  # Base.Dict{Core.String, Core.Any}(),  # 股票擇時交易倉位占比;
    weight_PickStock_Dict,  # Base.Dict{Core.String, Core.Any}(),  # 選股組合占比;
    MarketTiming_Parameter,  # Base.Dict{Core.String, Core.Any}(),  # 按照擇時規則優化之後的參數字典;
    PickStock_Parameter,  # Base.Dict{Core.String, Core.Any}(),  # 按照選股規則優化之後的參數字典;
    PickStock_ticker_symbol,  # ::Core.Array{Core.Array{Core.String, 1}, 1} = Core.Array{Core.Array{Core.String, 1}, 1}(),  # 依照選股規則排序篩選出的股票代碼字符串存儲數組;
    PickStock,  # (arguments) -> begin return arguments; end,
    PickStock_fit_model,  # (arguments) -> begin return arguments; end,
    MarketTiming,  # (arguments) -> begin return arguments; end,
    MarketTiming_fit_model,  # (arguments) -> begin return arguments; end,
    Intuitive_Momentum_KLine,  # (arguments) -> begin return arguments; end,
    "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
);
println("y_profit = ", return_SizePosition_fit_model["y_profit"]);  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
println("y_Long_Position_profit = ", return_SizePosition_fit_model["y_Long_Position_profit"]);  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
println("y_Short_Selling_profit = ", return_SizePosition_fit_model["y_Short_Selling_profit"]);  # 每兩次對衝交易利潤 × 權重，加權纍加總計;
println("y_loss = ", return_SizePosition_fit_model["y_loss"]);  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
println("y_Long_Position_loss = ", return_SizePosition_fit_model["y_Long_Position_loss"]);  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
println("y_Short_Selling_loss = ", return_SizePosition_fit_model["y_Short_Selling_loss"]);  # 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
println("maximum_drawdown = ", return_SizePosition_fit_model["maximum_drawdown"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum_drawdown_Long_Position = ", return_SizePosition_fit_model["maximum_drawdown_Long_Position"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum_drawdown_Short_Selling = ", return_SizePosition_fit_model["maximum_drawdown_Short_Selling"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("profit_total = ", return_SizePosition_fit_model["profit_total"]);  # 每兩次對衝交易利潤 × 頻率，纍加總計;
println("Long_Position_profit_total = ", return_SizePosition_fit_model["Long_Position_profit_total"]);  # 每兩次對衝交易利潤 × 頻率，纍加總計;
println("Short_Selling_profit_total = ", return_SizePosition_fit_model["Short_Selling_profit_total"]);  # 每兩次對衝交易利潤 × 頻率，纍加總計;
println("profit_Positive = ", return_SizePosition_fit_model["profit_Positive"]);  # 每兩次對衝交易收益纍加總計;
println("profit_Negative = ", return_SizePosition_fit_model["profit_Negative"]);  # 每兩次對衝交易損失纍加總計;
println("Long_Position_profit_Positive = ", return_SizePosition_fit_model["Long_Position_profit_Positive"]);  # 每兩次對衝交易收益纍加總計;
println("Long_Position_profit_Negative = ", return_SizePosition_fit_model["Long_Position_profit_Negative"]);  # 每兩次對衝交易損失纍加總計;
println("Short_Selling_profit_Positive = ", return_SizePosition_fit_model["Short_Selling_profit_Positive"]);  # 每兩次對衝交易收益纍加總計;
println("Short_Selling_profit_Negative = ", return_SizePosition_fit_model["Short_Selling_profit_Negative"]);  # 每兩次對衝交易損失纍加總計;
println("profit_Positive_probability = ", return_SizePosition_fit_model["profit_Positive_probability"]);  # 每兩次對衝交易正利潤概率;
println("profit_Negative_probability = ", return_SizePosition_fit_model["profit_Negative_probability"]);  # 每兩次對衝交易負利潤概率;
println("Long_Position_profit_Positive_probability = ", return_SizePosition_fit_model["Long_Position_profit_Positive_probability"]);  # 每兩次對衝交易正利潤概率;
println("Long_Position_profit_Negative_probability = ", return_SizePosition_fit_model["Long_Position_profit_Negative_probability"]);  # 每兩次對衝交易負利潤概率;
println("Short_Selling_profit_Positive_probability = ", return_SizePosition_fit_model["Short_Selling_profit_Positive_probability"]);  # 每兩次對衝交易正利潤概率;
println("Short_Selling_profit_Negative_probability = ", return_SizePosition_fit_model["Short_Selling_profit_Negative_probability"]);  # 每兩次對衝交易負利潤概率;
println("average_price_amplitude_date_transaction = ", return_SizePosition_fit_model["average_price_amplitude_date_transaction"]);  # 兩兩次對衝交易日成交價振幅平方和，均值;
println("Long_Position_average_price_amplitude_date_transaction = ", return_SizePosition_fit_model["Long_Position_average_price_amplitude_date_transaction"]);  # 兩兩次對衝交易日成交價振幅平方和，均值;
println("Short_Selling_average_price_amplitude_date_transaction = ", return_SizePosition_fit_model["Short_Selling_average_price_amplitude_date_transaction"]);  # 兩兩次對衝交易日成交價振幅平方和，均值;
println("average_volume_turnover_date_transaction = ", return_SizePosition_fit_model["average_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）均值;
println("Long_Position_average_volume_turnover_date_transaction = ", return_SizePosition_fit_model["Long_Position_average_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）均值;
println("Short_Selling_average_volume_turnover_date_transaction = ", return_SizePosition_fit_model["Short_Selling_average_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）均值;
println("average_date_transaction_between = ", return_SizePosition_fit_model["average_date_transaction_between"]);  # 兩次交易間隔日長，均值;
println("Long_Position_average_date_transaction_between = ", return_SizePosition_fit_model["Long_Position_average_date_transaction_between"]);  # 兩次對衝交易間隔日長，均值;
println("Short_Selling_average_date_transaction_between = ", return_SizePosition_fit_model["Short_Selling_average_date_transaction_between"]);  # 兩次對衝交易間隔日長，均值;
println("number_SizePosition_transaction = ", return_SizePosition_fit_model["number_PickStock_transaction"]);  # 交易過股票的總隻數;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]);
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_profit_date_transaction"]);  # 每兩次對衝交易利潤，向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_profit_date_transaction"]);  # 每兩次對衝交易利潤，向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_price_amplitude_date_transaction"]);  # 兩次對衝交易日成交價振幅平方和，向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_price_amplitude_date_transaction"]);  # 兩次對衝交易日成交價振幅平方和，向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_volume_turnover_date_transaction"]);  # 兩次對衝交易日成交量（換手率）向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction_between"]);  # 兩次對衝交易間隔日長，向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction_between"]);  # 兩次對衝交易間隔日長，向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"]);  # 按規則執行交易的日期，向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][1]);  # 交易規則自動選取的交易日期;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][2]);  # 交易規則自動選取的買入或賣出;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][3]);  # 交易規則自動選取的成交價;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][4]);  # 交易規則自動選取的成交量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][5]);  # 交易規則自動選取的成交次數記錄;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][6]);  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][7]);  # 交易日（Dates.Date 類型）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][8]);  # 當日總成交量（turnover volume）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][9]);  # 當日開盤（opening）成交價;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][10]);  # 當日收盤（closing）成交價;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][11]);  # 當日最低（low）成交價;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][12]);  # 當日最高（high）成交價;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][13]);  # 當日總成交金額（turnover amount）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][14]);  # 當日成交量（turnover volume）換手率（turnover rate）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][15]);  # 當日每股收益（price earnings）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"][16]);  # 當日每股净值（book value per share）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"]);  # 按規則執行交易的日期，向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][1]);  # 交易規則自動選取的交易日期;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][2]);  # 交易規則自動選取的買入或賣出;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][3]);  # 交易規則自動選取的成交價;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][4]);  # 交易規則自動選取的成交量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][5]);  # 交易規則自動選取的成交次數記錄;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][6]);  # 交易規則自動選取的交易日期的序列號，用於繪圖可視化;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][7]);  # 交易日（Dates.Date 類型）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][8]);  # 當日總成交量（turnover volume）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][9]);  # 當日開盤（opening）成交價;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][10]);  # 當日收盤（closing）成交價;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][11]);  # 當日最低（low）成交價;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][12]);  # 當日最高（high）成交價;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][13]);  # 當日總成交金額（turnover amount）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][14]);  # 當日成交量（turnover volume）換手率（turnover rate）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][15]);  # 當日每股收益（price earnings）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"][16]);  # 當日每股净值（book value per share）;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["revenue_and_expenditure_records_date_transaction"]);  # 每次交易的收支記錄序列，不區分做多（Long Position）或做空（Short Selling），向量;
println(return_SizePosition_fit_model["SizePosition_transaction_sequence"]["002611"]["P1_Array"]);  # 依照擇時規則計算得到參數 P1 值的序列存儲數組;

result = SizePosition(
    training_data = Base.Dict{Core.String, Core.Any}(
        "600118" => training_data["600118"],
        "600119" => training_data["600119"],
        "600120" => training_data["600120"],
        "002607" => training_data["002607"],
        "002608" => training_data["002608"],
        "002609" => training_data["002609"],
        "002611" => training_data["002611"]
    ),  Base.Dict{Core.String, Core.Any}(),  # 訓練集，標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    testing_data = Base.Dict{Core.String, Core.Any}(
        "600118" => testing_data["600118"],
        "600119" => testing_data["600119"],
        "600120" => testing_data["600120"],
        "002607" => testing_data["002607"],
        "002608" => testing_data["002608"],
        "002609" => testing_data["002609"],
        "002611" => testing_data["002611"]
    ),  Base.Dict{Core.String, Core.Any}(),  # 測試集，標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    Pdata_0 = [weight_MarketTiming_Dict, weight_PickStock_Dict],  # training_data["002611"]["Pdata_0"],  # 優化迭代參數初值;
    weight = Core.Array{Core.Float64, 1}(),  # training_data["002611"]["weight"],  # 優化迭代數據權重值;
    Plower = [Plower_weight_MarketTiming_Dict, Plower_weight_PickStock_Dict],  # 優化迭代參數值約束下限;
    Pupper = [Pupper_weight_MarketTiming_Dict, Pupper_weight_PickStock_Dict],  # 優化迭代參數值約束上限;
    MarketTiming_Parameter = MarketTiming_Parameter,  # Base.Dict{Core.String, Core.Any}(),  # 按照擇時規則優化之後的參數字典;
    PickStock_Parameter = PickStock_Parameter,  # Base.Dict{Core.String, Core.Any}(),  # 按照選股規則優化之後的參數字典;
    PickStock_ticker_symbol = PickStock_ticker_symbol,  # ::Core.Array{Core.Array{Core.String, 1}, 1} = Core.Array{Core.Array{Core.String, 1}, 1}(),  # 依照選股規則排序篩選出的股票代碼字符串存儲數組;
    SizePosition_fit_model = SizePosition_fit_model,  # (arguments) -> begin return arguments; end,
    PickStock = PickStock,  # (arguments) -> begin return arguments; end,
    PickStock_fit_model = PickStock_fit_model,  # (arguments) -> begin return arguments; end,
    MarketTiming = MarketTiming,  # (arguments) -> begin return arguments; end,
    MarketTiming_fit_model = MarketTiming_fit_model,  # (arguments) -> begin return arguments; end,
    Quantitative_Indicators_Function = Intuitive_Momentum_KLine,  # (arguments) -> begin return arguments; end,
    investment_method = "Long_Position_and_Short_Selling"  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
);
println("Coefficient 1 weight_MarketTiming : ", "\n", result["Coefficient"][1]);  # 優化得到的參數;
println("Coefficient 2 weight_PickStock : ", "\n", result["Coefficient"][2]);  # 優化得到的參數;
println("maximum drawdown per share : ", result["testData"]["maximum_drawdown"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum drawdown Long Position per share : ", result["testData"]["maximum_drawdown_Long_Position"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("maximum drawdown Short Selling per share : ", result["testData"]["maximum_drawdown_Short_Selling"]);  # 兩次對衝交易之間的最大回撤值，取極值統計;
println("profit total per share : ", result["testData"]["profit_total"]);
println("Long Position profit total per share : ", result["testData"]["Long_Position_profit_total"]);
println("Short Selling profit total per share : ", result["testData"]["Short_Selling_profit_total"]);
println("profit positive per share : ", result["testData"]["profit_Positive"]);
println("profit negative per share : ", result["testData"]["profit_Negative"]);
println("Long Position profit positive per share : ", result["testData"]["Long_Position_profit_Positive"]);
println("Long Position profit negative per share : ", result["testData"]["Long_Position_profit_Negative"]);
println("Short Selling profit positive per share : ", result["testData"]["Short_Selling_profit_Positive"]);
println("Short Selling profit negative per share : ", result["testData"]["Short_Selling_profit_Negative"]);
println("profit positive probability : ", result["testData"]["profit_Positive_probability"]);
println("profit negative probability : ", result["testData"]["profit_Negative_probability"]);
println("Long Position profit positive probability : ", result["testData"]["Long_Position_profit_Positive_probability"]);
println("Long Position profit negative probability : ", result["testData"]["Long_Position_profit_Negative_probability"]);
println("Short Selling profit positive probability : ", result["testData"]["Short_Selling_profit_Positive_probability"]);
println("Short Selling profit negative probability : ", result["testData"]["Short_Selling_profit_Negative_probability"]);
println("average date transaction between : ", result["testData"]["average_date_transaction_between"]);
println("Long Position average date transaction between : ", result["testData"]["Long_Position_average_date_transaction_between"]);
println("Short Selling average date transaction between : ", result["testData"]["Short_Selling_average_date_transaction_between"]);
println("number_PickStock_transaction : ", result["testData"]["number_PickStock_transaction"]);  # 交易過股票的總隻數;
println(result["testData"]["SizePosition_transaction_sequence"]["002611"]["P1_Array"]);
println(result["testData"]["SizePosition_transaction_sequence"]["002611"]["Long_Position_date_transaction"]);
println(result["testData"]["SizePosition_transaction_sequence"]["002611"]["Short_Selling_date_transaction"]);
println(result["testData"]["SizePosition_transaction_sequence"]["002611"]);

7. 代碼脚本檔 ( script file ) 「QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_BackTesting.jl」内函數 ( Function ) 運行示例 :

return_stepping_Dict = BackTesting_Stepper(
    steppingData = Base.Dict{Core.String, Core.Any}(
        "600118" => stepping_data["600118"],
        "600119" => stepping_data["600119"],
        "600120" => stepping_data["600120"],
        "002607" => stepping_data["002607"],
        "002608" => stepping_data["002608"],
        "002609" => stepping_data["002609"],
        "002611" => stepping_data["002611"]
    ),  Base.Dict{Core.String, Core.Any}(),  # 回測數據集，標準化日棒缐（K Line Daily）數據字典 ( Julia - Base.Dict ) ;
    risk_threshold = Core.Float64(0.8),  # risk_threshold_drawdown_loss,  # 自定義的風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
    training_sequence_length = Core.Int64(60),  # 推進分析（Stepper movement）（propulsion analysis）每一次步進，訓練集數據自定義的交易日截取長度;
    training_ticker_symbol = [Base.string(item) for item in Base.keys(stepping_data)],  # training_data_ticker_symbol_Array,  # 訓練集納入股票代碼字符串的一維數組，函數 Base.keys(Dict) 表示獲取字典的所有 key 值，返回值爲字符串向量（Base.Vector）;
    testing_ticker_symbol = [Base.string(item) for item in Base.keys(stepping_data)],  # testing_data_ticker_symbol_Array,  # 訓練集納入股票代碼字符串的一維數組，函數 Base.keys(Dict) 表示獲取字典的所有 key 值，返回值爲字符串向量（Base.Vector）;
    testing_sequence_length = Core.Int64(1),  # 推進分析（Stepper movement）（propulsion analysis）每一次步進，測試集數據自定義的交易日截取長度;
    SizePosition = SizePosition,  # (arguments) -> begin return arguments; end,
    SizePosition_fit_model = SizePosition_fit_model,  # (arguments) -> begin return arguments; end,
    SizePosition_Pdata_0 = Core.Array{Core.Any, 1}(),  # SizePosition_Parameter_Array,  # 倉位優化迭代參數初值;  # ::Core.Array{Core.Any, 1}[::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64())), ::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64()))],
    SizePosition_weight = Core.Array{Core.Any, 1}(),  # [Core.Float64(1.0) for i in 1:Base.length(stepping_data)],  # 倉位優化迭代數據權重值;
    SizePosition_Plower = [Plower_weight_MarketTiming_Dict, Plower_weight_PickStock_Dict],  # [-Base.Inf, -Base.Inf],  # 倉位優化迭代參數值約束下限;  # ::Core.Array{Core.Any, 1}[::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(0.0), "Short_Selling" => ::Core.Float64 = Core.Float64(0.0))), ::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64()))],
    SizePosition_Pupper = [Pupper_weight_MarketTiming_Dict, Pupper_weight_PickStock_Dict],  # [+Base.Inf, +Base.Inf],  # 倉位優化迭代參數值約束上限;  # ::Core.Array{Core.Any, 1}[::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(1.0), "Short_Selling" => ::Core.Float64 = Core.Float64(1.0))), ::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64()))],
    PickStock = PickStock,  # (arguments) -> begin return arguments; end,
    PickStock_fit_model = PickStock_fit_model,  # (arguments) -> begin return arguments; end,
    PickStock_Pdata_0 = Core.Array{Core.Float64, 1}(),  # [5, 3],  # PickStock_Parameter,  # 選股優化迭代參數初值;  # ::Core.Array{Core.Any, 1}[::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64())), ::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64()))],
    PickStock_weight = Core.Array{Core.Float64, 1}(),  # [Core.Float64(1.0) for i in 1:Base.length(stepping_data)],  # 選股優化迭代數據權重值;
    PickStock_Plower = [Core.Int64(1), Core.Int64(1)],  # [-Base.Inf, -Base.Inf],  # 選股優化迭代參數值約束下限;  # ::Core.Array{Core.Any, 1}[::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(0.0), "Short_Selling" => ::Core.Float64 = Core.Float64(0.0))), ::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64()))],
    PickStock_Pupper = [Core.Int64([if (Core.Int64(maximum_stepping_data) > Core.Int64(0)) Core.Int64(maximum_stepping_data) else Core.Int64(1) end for i in Core.Int64(1):Core.Int64(1)][Core.Int64(1)]), Core.Int64([if (Core.Int64(maximum_ticker_symbol_stepping_data) > Core.Int64(0)) Core.Int64(maximum_ticker_symbol_stepping_data) else Core.Int64(1) end for i in Core.Int64(1):Core.Int64(1)][Core.Int64(1)])],  # [+Base.Inf, +Base.Inf],  # 選股優化迭代參數值約束上限;  # ::Core.Array{Core.Any, 1}[::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(1.0), "Short_Selling" => ::Core.Float64 = Core.Float64(1.0))), ::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64()))],
    MarketTiming = MarketTiming,  # (arguments) -> begin return arguments; end,
    MarketTiming_fit_model = MarketTiming_fit_model,  # (arguments) -> begin return arguments; end,
    MarketTiming_Pdata_0 = Core.Array{Core.Float64, 1}(),  # [5, 0.1, -0.1, 0.0],  # MarketTiming_Parameter,  # 擇時優化迭代參數初值;  # ::Core.Array{Core.Any, 1}[::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64())), ::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64()))],
    MarketTiming_weight = Core.Array{Core.Float64, 1}(),  # [Core.Float64(1.0) for i in 1:Base.length(stepping_data)],  # 擇時優化迭代數據權重值;
    MarketTiming_Plower = [Core.Int64(1), -Base.Inf, -Base.Inf, -Base.Inf],  # [-Base.Inf, -Base.Inf, -Base.Inf, -Base.Inf],  # 擇時優化迭代參數值約束下限;  # ::Core.Array{Core.Any, 1}[::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(0.0), "Short_Selling" => ::Core.Float64 = Core.Float64(0.0))), ::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64()))],
    MarketTiming_Pupper = [Core.Int64([if (Core.Int64(maximum_stepping_data) > Core.Int64(0)) Core.Int64(maximum_stepping_data) else Core.Int64(1) end for i in Core.Int64(1):Core.Int64(1)][Core.Int64(1)]), +Base.Inf, +Base.Inf, +Base.Inf],  # [+Base.Inf, +Base.Inf, +Base.Inf, +Base.Inf],  # 擇時優化迭代參數值約束上限;  # ::Core.Array{Core.Any, 1}[::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(1.0), "Short_Selling" => ::Core.Float64 = Core.Float64(1.0))), ::Base.Dict{Core.String, Core.Any} = Base.Dict{Core.String, Core.Any}("ticker_symbol" => Base.Dict{Core.String, Core.Any}("Long_Position" => ::Core.Float64 = Core.Float64(), "Short_Selling" => ::Core.Float64 = Core.Float64()))],
    Quantitative_Indicators_Function = Intuitive_Momentum_KLine,  # (arguments) -> begin return arguments; end,
    investment_method = investment_method  # "Long_Position_and_Short_Selling" , "Long_Position" , "Short_Selling" ;  # 選擇是否允許「賣空」交易;
);
println("number PickStock : ", return_stepping_Dict["number_PickStock"]);
println("number PickStock Long Position : ", return_stepping_Dict["number_PickStock_Long_Position"]);
println("number PickStock Short Selling : ", return_stepping_Dict["number_PickStock_Short_Selling"]);
println("number transaction : ", return_stepping_Dict["number_transaction_total"]);
println("number transaction Long Position : ", return_stepping_Dict["number_transaction_total_Long_Position"]);
println("number transaction Short Selling : ", return_stepping_Dict["number_transaction_total_Short_Selling"]);
println("maximum drawdown : ", return_stepping_Dict["maximum_drawdown"]);
println("maximum drawdown Long Position : ", return_stepping_Dict["maximum_drawdown_Long_Position"]);
println("maximum drawdown Short Selling : ", return_stepping_Dict["maximum_drawdown_Short_Selling"]);
println("profit total : ", return_stepping_Dict["profit_total"]);
println("Long Position profit total : ", return_stepping_Dict["Long_Position_profit_total"]);
println("Short Selling profit total : ", return_stepping_Dict["Short_Selling_profit_total"]);
println("profit Positive : ", return_stepping_Dict["profit_Positive"]);
println("profit Negative : ", return_stepping_Dict["profit_Negative"]);
println("Long Position profit Positive : ", return_stepping_Dict["Long_Position_profit_Positive"]);
println("Long Position profit Negative : ", return_stepping_Dict["Long_Position_profit_Negative"]);
println("Short Selling profit Positive : ", return_stepping_Dict["Short_Selling_profit_Positive"]);
println("Short Selling profit Negative : ", return_stepping_Dict["Short_Selling_profit_Negative"]);
println("profit Positive probability : ", return_stepping_Dict["profit_Positive_probability"]);
println("profit Negative probability : ", return_stepping_Dict["profit_Negative_probability"]);
println("Long Position profit Positive probability : ", return_stepping_Dict["Long_Position_profit_Positive_probability"]);
println("Long Position profit Negative probability : ", return_stepping_Dict["Long_Position_profit_Negative_probability"]);
println("Short Selling profit Positive probability : ", return_stepping_Dict["Short_Selling_profit_Positive_probability"]);
println("Short Selling profit Negative probability : ", return_stepping_Dict["Short_Selling_profit_Negative_probability"]);
println("average price amplitude date transaction : ", return_stepping_Dict["average_price_amplitude_date_transaction"]);
println("Long Position average price amplitude date transaction : ", return_stepping_Dict["Long_Position_average_price_amplitude_date_transaction"]);
println("Short Selling average price amplitude date transaction : ", return_stepping_Dict["Short_Selling_average_price_amplitude_date_transaction"]);
println("average volume turnover date transaction : ", return_stepping_Dict["average_volume_turnover_date_transaction"]);
println("Long Position average volume turnover date transaction : ", return_stepping_Dict["Long_Position_average_volume_turnover_date_transaction"]);
println("Short Selling average volume turnover date transaction : ", return_stepping_Dict["Short_Selling_average_volume_turnover_date_transaction"]);
println("average date transaction between : ", return_stepping_Dict["average_date_transaction_between"]);
println("Long Position average date transaction between : ", return_stepping_Dict["Long_Position_average_date_transaction_between"]);
println("Short Selling average date transaction between : ", return_stepping_Dict["Short_Selling_average_date_transaction_between"]);
println("PickStock Long Position Array :", "\n", return_stepping_Dict["PickStock_Long_Position"]);
println("PickStock Short Selling Array :", "\n", return_stepping_Dict["PickStock_Short_Selling"]);
println("PickStock Array :", "\n", return_stepping_Dict["PickStock"]);
println("profit paired transaction Dict :", "\n", return_stepping_Dict["profit_paired_transaction"]);
println("transaction sequence Dict :", "\n", return_stepping_Dict["transaction_sequence"]);
println("stepping sequence Array :", "\n", return_stepping_Dict["stepping_sequence"]);

---

微軟視窗系統 ( Windows10 x86_64 ) 使用 Batchfile 代碼脚本檔「startServer.bat」啓動數量化交易運算伺服器「QuantitativeTrading」 :

使用説明:

Window-cmd : startServer.bat

微軟視窗系統 ( Windows10 x86_64 )

控制臺命令列 ( cmd ) 運行啓動指令 :

C:\QuantitativeTrading> C:/Windows/System32/cmd.exe C:/QuantitativeTrading/startServer.bat C:/QuantitativeTrading/config.txt

控制臺啓動傳參釋意 :

1. (必), (固定), 微軟視窗作業系統 ( Window10 x86_64 ) 控制臺命令列窗口的二進制可執行檔 ( cmd.exe ) 啓動存儲路徑全名, 作業系統 ( Window10 x86_64 ) 固定存儲在路徑爲 : C:/Windows/System32/cmd.exe

2. (必), (自定義), 微軟視窗系統 ( Windows10 x86_64 ) 批處理程式代碼脚本 ( .bat ) 檔 ( startServer.bat ) 的存儲路徑全名, 預設值爲 : C:/QuantitativeTrading/startServer.bat

3. (選) (值 C:/QuantitativeTrading/config.txt 自定義), 用於傳入配置文檔的保存路徑全名, 配置文檔裏的橫向列首可用一個井號字符 ( # ) 注釋掉, 使用井號字符 ( # ) 注釋掉之後，該橫向列的參數即不會傳入從而失效, 若需啓用可刪除橫向列首的井號字符 ( # ) 即可, 注意橫向列首的空格也要刪除, 每一個橫向列的參數必須頂格書寫, 預設值爲 : C:/QuantitativeTrading/config.txt

谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 ) 使用 Shell 代碼脚本檔「startServer.sh」啓動數量化交易運算伺服器「QuantitativeTrading」 :

使用説明:

Android-Termux-Ubuntu-bash : startServer.sh

谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 )

控制臺命令列 ( bash ) 運行啓動指令 :

root@localhost:~# /bin/bash /home/QuantitativeTrading/startServer.sh configFile=/home/QuantitativeTrading/config.txt executableFile=/bin/julia interpreterFile=-p,4,--project=/home/QuantitativeTrading/QuantitativeTradingJulia/ scriptFile=/home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configInstructions=configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt,interface_Function=http_Server,webPath=/home/QuantitativeTrading/html/,host=::0,port=10001,key=username:password,number_Worker_threads=1,isConcurrencyHierarchy=Tasks

控制臺啓動傳參釋意, 各參數之間以一個逗號 ( Comma ) 字符 ( , ) 分隔, 鍵(Key) ~ 值(Value) 之間以一個等號字符 ( = ) 連接, 即類比 Key=Value 的形式 :

1. (必), (固定), 谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 ) 控制臺命令列窗口的二進制可執行檔 ( bash ) 啓動存儲路徑全名, 作業系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 ) 固定存儲在路徑爲 : /bin/bash

2. (必), (自定義), 谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 ) 批處理程式代碼脚本 ( .sh ) 檔 ( startServer.sh ) 的存儲路徑全名, 預設值爲 : C:/QuantitativeTrading/startServer.sh

3. (選), (鍵 configFile 固定, 值 /home/QuantitativeTrading/config.txt 自定義), 用於傳入配置文檔的保存路徑全名, 配置文檔裏的橫向列首可用一個井號字符 ( # ) 注釋掉, 使用井號字符 ( # ) 注釋掉之後，該橫向列的參數即不會傳入從而失效, 若需啓用可刪除橫向列首的井號字符 ( # ) 即可, 注意橫向列首的空格也要刪除, 每一個橫向列的參數必須頂格書寫, 預設值爲 : configFile=/home/QuantitativeTrading/config.txt

4. (選), (鍵 executableFile 固定, 值 /bin/julia 自定義, 例如 [ /bin/julia, /bin/python3 ] 可自定義取其一配置), 用於傳入選擇啓動哪一種程式語言編寫的接口服務, 計算機 ( Computer ) 程式 ( Programming ) 設計 Julia 語言, 計算機 ( Computer ) 程式 ( Programming ) 設計 Python 語言, 預設值爲 : executableFile=/bin/julia

5. (選), (鍵 interpreterFile 固定, 值 -p,4,--project=/home/QuantitativeTrading/QuantitativeTradingJulia/ 自定義, 且可爲空, 即取 interpreterFile= 的形式, 亦可不傳入該參數), 用於傳入程式設計語言 ( Julia, Python3 ) 解釋器 ( Interpreter ) 環境的二進制可執行檔, 於作業系統控制臺命令列 ( Operating System Console Command ) 使用指令啓動時傳入的運行參數, 若爲多參數, 則各參數之間用一個逗號 ( Comma ) 字符 ( , ) 連接, 批處理程式脚本 startServer.sh 已設計爲可自動將逗號 ( Comma ) 字符 ( , ) 替換爲空格字符 ( SPACE ) ( 00100000 ), 然後再傳入程式設計語言 ( Julia, Python3 ) 解釋器 ( Interpreter ) 的運行環境, 預設值爲 : interpreterFile=-p,4,--project=/home/QuantitativeTrading/QuantitativeTradingJulia/

6. (選), (鍵 scriptFile 固定, 值 /home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl 自定義, 例如 [ /home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl, /home/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py ] 可自定義取其一配置), 用於傳入程式 ( Programming ) 設計語言 ( Julia, Python3 ) 代碼脚本 ( Script ) 檔 ( QuantitativeTradingServer.jl, QuantitativeTradingServer.py ) 的存儲路徑全名, 預設值爲 : scriptFile=/home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl

7. (選), (鍵 configInstructions 固定, 取值自定義, 且可爲空, 即取 configInstructions= 的形式, 亦可不傳入該參數), 用於傳入程式 ( Programming ) 設計語言 ( Julia, Python3 ) 代碼脚本 ( Script ) 檔 ( QuantitativeTradingServer.jl, QuantitativeTradingServer.py ) 的運行參數, 若爲多參數, 則各參數之間用一個逗號 ( Comma ) 字符 ( , ) 連接, 批處理程式脚本 startServer.sh 已設計爲可自動將逗號 ( Comma ) 字符 ( , ) 替換爲空格字符 ( SPACE ) ( 00100000 ), 然後再傳入代碼脚本 ( Script ) 檔 ( QuantitativeTradingServer.jl, QuantitativeTradingServer.py ) 的運行環境, 預設值爲 : configInstructions=configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt,interface_Function=http_Server,webPath=/home/QuantitativeTrading/html/,host=::0,port=10001,key=username:password,number_Worker_threads=1,isConcurrencyHierarchy=Tasks

---

c2exe.c

程式設計 C 語言, 使用 FILE *fstream = popen("shell Code Script", "r") 函數, 創建子進程 ( Sub Process ), 並在子進程 ( Sub Process ) 運行外部二進制可執行檔 ( julia.exe, python.exe, ), 功能與批處理檔 startServer.sh 類似.

使用説明:

微軟視窗系統 ( Windows10 x86_64 ) 使用二進位可執行檔「QuantitativeTrading.exe」啓動數量化交易運算伺服器「QuantitativeTrading」 :

微軟視窗系統 ( Windows10 x86_64 )

Windows10 x86_64 Compiler :

Minimalist GNU on Windows ( MinGW-w64 ) mingw64-8.1.0-release-posix-seh-rt_v6-rev0

控制臺命令列 ( cmd ) 運行編譯指令 :

C:\QuantitativeTrading> C:\MinGW64\bin\gcc.exe C:/QuantitativeTrading/c/c2exe.c -o C:/QuantitativeTrading/QuantitativeTrading.exe

控制臺命令列 ( cmd ) 運行顯示中文字符指令 :

C:\QuantitativeTrading> chcp 65001

控制臺命令列 ( cmd ) 運行啓動指令 :

C:\QuantitativeTrading> C:/QuantitativeTrading/QuantitativeTrading.exe configFile=C:/QuantitativeTrading/config.txt executableFile=C:/QuantitativeTrading/Julia/Julia-1.10.10/julia.exe interpreterFile=-p,4,--project=C:/QuantitativeTrading/QuantitativeTradingJulia/ scriptFile=C:/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configInstructions=configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt,interface_Function=http_Server,webPath=C:/QuantitativeTrading/html/,host=::0,port=10001,key=username:password,number_Worker_threads=1,isConcurrencyHierarchy=Tasks

谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 ) 使用二進位可執行檔「QuantitativeTrading.exe」啓動數量化交易運算伺服器「QuantitativeTrading」 :

谷歌安卓系統 之 Termux 系統 之 烏班圖系統 ( Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 )

Android-11 Termux-0.118 Ubuntu-22.04 Arm64-aarch64 Compiler :

gcc v9.3.0 , g++ v9.3.0

控制臺命令列 ( bash ) 運行編譯指令 :

root@localhost:~# /bin/gcc /home/QuantitativeTrading/c/c2exe.c -o /home/QuantitativeTrading/QuantitativeTrading.exe

控制臺命令列 ( bash ) 運行啓動指令 :

root@localhost:~# /home/QuantitativeTrading/QuantitativeTrading.exe configFile=/home/QuantitativeTrading/config.txt executableFile=/bin/julia interpreterFile=-p,4,--project=/home/QuantitativeTrading/QuantitativeTradingJulia/ scriptFile=/home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configInstructions=configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt,interface_Function=http_Server,webPath=/home/QuantitativeTrading/html/,host=::0,port=10001,key=username:password,number_Worker_threads=1,isConcurrencyHierarchy=Tasks

控制臺啓動傳參釋意, 各參數之間以一個逗號 ( Comma ) 字符 ( , ) 分隔, 鍵(Key) ~ 值(Value) 之間以一個等號字符 ( = ) 連接, 即類比 Key=Value 的形式 :

1. (必), (自定義), 計算機 C 語言 ( Computer Programming C Language ) 程式設計 ( Programming ) 代碼檔 ( c2exe.c ), 使用編譯器 ( Compiler ), 經過編譯之後, 轉換爲二進制可執行檔 ( .exe ), 啓動運行指令存儲路徑全名, 例如可自定義配置爲 : C:/QuantitativeTrading/QuantitativeTrading.exe

2. (選) (值 C:/QuantitativeTrading/config.txt 自定義), 用於傳入配置文檔的保存路徑全名, 配置文檔裏的橫向列首可用一個井號字符 ( # ) 注釋掉, 使用井號字符 ( # ) 注釋掉之後，該橫向列的參數即不會傳入從而失效, 若需啓用可刪除橫向列首的井號字符 ( # ) 即可, 注意橫向列首的空格也要刪除, 每一個橫向列的參數必須頂格書寫, 預設值爲 : C:/QuantitativeTrading/config.txt

3. (選), (鍵 executableFile 固定, 值 /bin/julia 自定義, 例如 [ /bin/julia, /bin/python3 ] 可自定義取其一配置), 用於傳入選擇啓動哪一種程式語言編寫的接口服務, 計算機 ( Computer ) 程式 ( Programming ) 設計 Julia 語言, 計算機 ( Computer ) 程式 ( Programming ) 設計 Python 語言, 預設值爲 : executableFile=/bin/julia

4. (選), (鍵 interpreterFile 固定, 值 -p,4,--project=/home/QuantitativeTrading/QuantitativeTradingJulia/ 自定義, 且可爲空, 即取 interpreterFile= 的形式, 亦可不傳入該參數), 用於傳入程式設計語言 ( Julia, Python3 ) 解釋器 ( Interpreter ) 環境的二進制可執行檔, 於作業系統控制臺命令列 ( Operating System Console Command ) 使用指令啓動時傳入的運行參數, 若爲多參數, 則各參數之間用一個逗號 ( Comma ) 字符 ( , ) 連接, 代碼文檔 c2exe.c 已設計爲可自動將逗號 ( Comma ) 字符 ( , ) 替換爲空格字符 ( SPACE ) ( 00100000 ), 然後再傳入程式設計語言 ( Julia, Python3 ) 解釋器 ( Interpreter ) 的運行環境, 預設值爲 : interpreterFile=-p,4,--project=/home/QuantitativeTrading/QuantitativeTradingJulia/

5. (選), (鍵 scriptFile 固定, 值 /home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl 自定義, 例如 [ /home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl, /home/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py ] 可自定義取其一配置), 用於傳入程式 ( Programming ) 設計語言 ( Julia, Python3 ) 代碼脚本 ( Script ) 檔 ( QuantitativeTradingServer.jl, QuantitativeTradingServer.py ) 的存儲路徑全名, 預設值爲 : scriptFile=/home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl

6. (選), (鍵 configInstructions 固定, 取值自定義, 且可爲空, 即取 configInstructions= 的形式, 亦可不傳入該參數), 用於傳入程式 ( Programming ) 設計語言 ( Julia, Python3 ) 代碼脚本 ( Script ) 檔 ( QuantitativeTradingServer.jl, QuantitativeTradingServer.py ) 的運行參數, 若爲多參數, 則各參數之間用一個逗號 ( Comma ) 字符 ( , ) 連接, 代碼文檔 c2exe.c 已設計爲可自動將逗號 ( Comma ) 字符 ( , ) 替換爲空格字符 ( SPACE ) ( 00100000 ), 然後再傳入代碼脚本 ( Script ) 檔 ( QuantitativeTradingServer.jl, QuantitativeTradingServer.py ) 的運行環境, 預設值爲 : configInstructions=configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt,interface_Function=http_Server,webPath=/home/QuantitativeTrading/html/,host=::0,port=10001,key=username:password,number_Worker_threads=1,isConcurrencyHierarchy=Tasks

Compiler :

Minimalist GNU on Windows ( MinGW-w64 ) : mingw64-8.1.0-release-posix-seh-rt_v6-rev0

程式設計 C 語言 gcc, g++ 編譯器 ( Compiler ) 之 MinGW-w64 官方網站: https://www.mingw-w64.org/

程式設計 C 語言 gcc, g++ 編譯器 ( Compiler ) 之 MinGW-w64 官方下載頁: https://www.mingw-w64.org/downloads/

程式設計 C 語言 gcc, g++ 編譯器 ( Compiler ) 之 MinGW-w64 作者官方 GitHub 網站賬戶: https://github.com/niXman

程式設計 C 語言 gcc, g++ 編譯器 ( Compiler ) 之 MinGW-w64 官方 GitHub 網站倉庫: https://github.com/nixman/mingw-builds.git

程式設計 C 語言 gcc, g++ 編譯器 ( Compiler ) 之 MinGW-w64 官方 GitHub 網站倉庫預編譯二進制檔下載頁: https://github.com/niXman/mingw-builds-binaries/releases

程式設計 C 語言 gcc, g++ 編譯器 ( Compiler ) 之 MinGW-w64 預編譯二進制檔下載頁: https://sourceforge.net/projects/mingw-w64/

---

一. 可使用谷歌 ( Google - Chromium ) 或火狐 ( Mozilla - Firefox ) 瀏覽器 ( Browser ) 作爲用戶端 ( Client ) 連接數量化交易運算伺服器「QuantitativeTrading」打開交互介面.

打開應用頁面「index.html」可在地址欄 ( Browser address bar ) 輸入網址 ( Uniform Resource Locator , URL ) :

http://username:password@[::1]:10001/index.html?Key=username:password

打開管理頁面「administrator.html」可在地址欄 ( Browser address bar ) 輸入網址 ( Uniform Resource Locator , URL ) :

http://username:password@[::1]:10001/administrator.html?Key=username:password

交互頁面「index.html」可視化數據圖表，使用第三方擴展包，百度 ( Baidu ) 公司開發的基於 JavaScript 程式設計語言的開源可視化圖表庫「Apache - ECharts」實現.

可自行修改標準通用標記語言代碼脚本 ( .html ) 檔「index.html」「SelectStatisticalAlgorithms.html」「InputHTML.html」「OutputHTML.html」内的 HTML , JavaScript , CSS 代碼，擴展交互頁面「index.html」内統計方法的連接 ( Browser Client Request ) 選項.

二. 可使用項目空間内的微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用檔「Client.xlsm」作爲用戶端 ( Client ) 連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 計算.

微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用檔「Client.xlsm」打開之後，菜單欄 ( Excel menu bar ) 之 :

1. 「加載項 ( Excel Add-in )」 → 「數量化交易 ( Quantitative Trading )」 → 「人機交互介面 ( operation panel )」，爲連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 計算的操作面板.

2. 「加載項 ( Excel Add-in )」 → 「數量化交易 ( Quantitative Trading )」 → 「數量化交易運算伺服器 ( Quantitative Trading server )」，爲從微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用檔 ( Client.xlsm ) 内，通過創建子進程 ( Sub Process ) 調用微軟視窗系統 ( Windows10 x86_64 ) 控制臺命令列 ( cmd.exe ) 應用，啓動數量化交易運算伺服器「QuantitativeTrading」的運行指令.

其中，項目空間内的代碼脚本檔「TradingAlgorithmModule.bas」是微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用檔「Client.xlsm」運行時，需導入的標準模組 ( Module ) 代碼（必須），可在此代碼脚本檔内，自行修改 Visual Basic for Applications , VBA 代碼，擴展數量化交易方法的連接 ( Client Request ) 項.

微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用檔「Client.xlsm」轉換 JSON 字符串類型的變量 ( JSON - String Object ) 與微軟電子表格字典類型的變量 ( Windows - Office - Excel - Visual Basic for Applications - Dict Object ) 數據類型，借用微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用的第三方擴展類模組「VBA-JSON : JsonConverter.bas」實現.

三. 也可自行選擇其他程式設計語言編寫用戶端鏈接器 ( Client ) 應用，連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 計算，比如，使用 JavaScript 語言的 NW.js , Electron 解析器等製作用戶端交互介面，或使用 C 語言的 GNU Image Manipulation Program - GIMP Toolkit , GTK+ 圖形框架等製作用戶端交互介面，然後，使用 Julia 或 Python 語言的數量化交易運算伺服器「QuantitativeTrading」作爲後端行使數據計算功能，這樣即可實現類似跨語言混合編程的效果.

使用自行製作的用戶端鏈接器 ( Client ) 時，連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 計算，可使用如下網址 ( Uniform Resource Locator , URL ) :

1. 連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 插值 ( Interpolation ) 計算，使用網址 ( Uniform Resource Locator , URL ) :

http://[::1]:10001/Interpolation?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=BSpline(Cubic)&algorithmLambda=0.0&algorithmKei=2.0&algorithmDi=1.0&algorithmEith=1.0

2. 連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 多項式 ( 3 階 ) 方程 ( Polynomial ( Cubic ) ) 模型擬合 ( Fit ) 計算，使用網址 ( Uniform Resource Locator , URL ) :

http://[::1]:10001/Polynomial3Fit?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=Polynomial3Fit

3. 連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 邏輯 4 , 5 參數模型 ( 4 , 5 - parameter logistic curve ) 擬合 ( Fit ) 計算，使用網址 ( Uniform Resource Locator , URL ) :

http://[::1]:10001/LC5PFit?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=LC5PFit

用戶端 ( Client - Request ) 發送 POST 請求的數據爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Request - POST =

{
    "trainXdata" : [
        0.0,
        1.0,
        2.0,
        3.0,
        4.0,
        5.0,
        6.0,
        7.0,
        8.0,
        9.0,
        10.0,
    ],
    "trainYdata_1" : [
        100.0,
        200.0,
        300.0,
        400.0,
        500.0,
        600.0,
        700.0,
        800.0,
        900.0,
        1000.0,
        1100.0
    ],
    "trainYdata_2" : [
        98.0,
        198.0,
        298.0,
        398.0,
        498.0,
        598.0,
        698.0,
        798.0,
        898.0,
        998.0,
        1098.0
    ],
    "trainYdata_3" : [
        102.0,
        202.0,
        302.0,
        402.0,
        502.0,
        602.0,
        702.0,
        802.0,
        902.0,
        1002.0,
        1102.0
    ],
    "weight" : [
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0,
        1.0
    ],
    "Pdata_0" : [
        90.0,
        4.0,
        1.0,
        1210.0,
        1.0
    ],
    "Plower" : [
        "-Infinity",
        "-Infinity",
        "-Infinity",
        "-Infinity",
        "-Infinity"
    ],
    "Pupper" : [
        "+Infinity",
        "+Infinity",
        "+Infinity",
        "+Infinity",
        "+Infinity"
    ],
    "testYdata_1" : [
        150.0,
        200.0,
        250.0,
        350.0,
        450.0,
        550.0,
        650.0,
        750.0,
        850.0,
        950.0,
        1050.0
    ],
    "testYdata_2" : [
        148.0,
        198.0,
        248.0,
        348.0,
        448.0,
        548.0,
        648.0,
        748.0,
        848.0,
        948.0,
        1048.0
    ],
    "testYdata_3" : [
        152.0,
        202.0,
        252.0,
        352.0,
        452.0,
        552.0,
        652.0,
        752.0,
        852.0,
        952.0,
        1052.0
    ],
    "testXdata" : [
        0.5,
        1.0,
        1.5,
        2.5,
        3.5,
        4.5,
        5.5,
        6.5,
        7.5,
        8.5,
        9.5
    ],
    "trainYdata" : [
        [100.0, 98.0, 102.0],
        [200.0, 198.0, 202.0],
        [300.0, 298.0, 302.0],
        [400.0, 398.0, 402.0],
        [500.0, 498.0, 502.0],
        [600.0, 598.0, 602.0],
        [700.0, 698.0, 702.0],
        [800.0, 798.0, 802.0],
        [900.0, 898.0, 902.0],
        [1000.0, 998.0, 1002.0],
        [1100.0, 1098.0, 1102.0]
    ],
    "testYdata" : [
        [150.0, 148.0, 152.0],
        [200.0, 198.0, 202.0],
        [250.0, 248.0, 252.0],
        [350.0, 348.0, 352.0],
        [450.0, 448.0, 452.0],
        [550.0, 548.0, 552.0],
        [650.0, 648.0, 652.0],
        [750.0, 748.0, 752.0],
        [850.0, 848.0, 852.0],
        [950.0, 948.0, 952.0],
        [1050.0, 1048.0, 1052.0]
    ]
}

伺服器 ( Server - Respond ) 響應 POST 請求的數據格式爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Respond - body =

{
    "Coefficient" : [
        100.007982422761,
        42148.4577551448,
        1.0001564001486,
        4221377.92224082
    ],
    "Coefficient-StandardDeviation" : [
        0.00781790123184812,
        2104.76673086505,
        0.0000237490808220821,
        210359.023599377
    ],
    "Coefficient-Confidence-Lower-95%" : [
        99.9908250045862,
        37529.2688077105,
        1.0001042796499,
        3759717.22485611
    ],
    "Coefficient-Confidence-Upper-95%" : [
        100.025139840936,
        46767.6467025791,
        1.00020852064729,
        4683038.61962554
    ],
    "Yfit" : [
        100.008980483748,
        199.99155580718,
        299.992070696316,
        399.99603100866,
        500.000567344017,
        600.00431688223,
        700.006476967595,
        800.006517272442,
        900.004060927778,
        999.998826196417,
        1099.99059444852
    ],
    "Yfit-Uncertainty-Lower" : [
        99.0089499294379,
        198.991136273453,
        298.990136898385,
        398.991624763274,
        498.99282487668,
        598.992447662226,
        698.989753032473,
        798.984266632803,
        898.975662941844,
        998.963708008532,
        1098.94822805642
    ],
    "Yfit-Uncertainty-Upper" : [
        101.00901103813,
        200.991951293373,
        300.993902825086,
        401.000210884195,
        501.007916682505,
        601.015588680788,
        701.022365894672,
        801.027666045591,
        901.031064750697,
        1001.0322361364,
        1101.0309201882
    ],
    "Residual" : [
        0.00898048374801874,
        -0.00844419281929731,
        -0.00792930368334055,
        -0.00396899133920669,
        0.000567344017326831,
        0.00431688223034143,
        0.00647696759551763,
        0.00651727244257926,
        0.00406092777848243,
        -0.00117380358278751,
        -0.00940555147826671
    ],
    "testData" : {
        "Ydata" : [
            [150.0, 148.0, 152.0],
            [200.0, 198.0, 202.0],
            [250.0, 248.0, 252.0],
            [350.0, 348.0, 352.0],
            [450.0, 448.0, 452.0],
            [550.0, 548.0, 552.0],
            [650.0, 648.0, 652.0],
            [750.0, 748.0, 752.0],
            [850.0, 848.0, 852.0],
            [950.0, 948.0, 952.0],
            [1050.0, 1048.0, 1052.0]
        ],
        "test-Xvals" : [
            0.500050586546119,
            1.00008444458554,
            1.50008923026377,
            2.50006143908055,
            3.50001668919562,
            4.49997400999207,
            5.49994366811569,
            6.49993211621922,
            7.49994379302719,
            8.49998194168741,
            9.50004903674755
        ],
        "test-Xvals-Uncertainty-Lower" : [
            0.499936310423273,
            0.999794808816128,
            1.49963107921017,
            2.49927920023971,
            3.49892261926065,
            4.49857747071072,
            5.4982524599721,
            6.4979530588239,
            7.49768303155859,
            8.49744512880161,
            9.49724144950174
        ],
        "test-Xvals-Uncertainty-Upper" : [
            0.500160692642957,
            1.00036584601127,
            1.50053513648402,
            2.5008235803856,
            3.50108303720897,
            4.50133543331854,
            5.50159259771137,
            6.50186196458511,
            7.50214864756277,
            8.50245638268284,
            9.50278802032924
        ],
        "Xdata" : [
            0.5,
            1.0,
            1.5,
            2.5,
            3.5,
            4.5,
            5.5,
            6.5,
            7.5,
            8.5,
            9.5
        ],
        "test-Yfit" : [
            149.99283432168886,
            199.98780598165467,
            249.98704946506768,
            349.9910371559672,
            449.9975369446911,
            550.0037557953037,
            650.0081868763082,
            750.0098833059892,
            850.0081939375959,
            950.002643218264,
            1049.9928684998304
        ],
        "test-Yfit-Uncertainty-Lower" : [],
        "test-Yfit-Uncertainty-Upper" : [],
        "test-Residual" : [
            [0.000050586546119],
            [0.00008444458554],
            [0.00008923026377],
            [0.00006143908055],
            [0.00001668919562],
            [-0.00002599000793],
            [-0.0000563318843],
            [-0.00006788378077],
            [-0.0000562069728],
            [-0.00001805831259],
            [0.00004903674755]
        ]
    }
}

4. 連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 原始日棒缐 ( K-Line day ) 數據清洗 ( Preprocessing ) 運算，使用網址 ( Uniform Resource Locator , URL ) :

http://[::1]:10001/KLineCleaning?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=KLineCleaning&configFile=C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt&input_K_Line=C:/QuantitativeTrading/Data/K-Day-source/&is_save_JLD=true&output_jld_K_Line=C:/QuantitativeTrading/Data/steppingData.jld&is_save_pickle=True&output_pickle_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle&is_save_csv=true&output_csv_K_Line=C:/QuantitativeTrading/Data/K-Day/&is_save_xlsx=true&output_xlsx_K_Line=C:/QuantitativeTrading/Data/K-Day/

用戶端 ( Client - Request ) 發送 POST 請求的數據爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Request - POST =

{
    "configFile" : ["C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt"],
    // "configFile" : ["C:/QuantitativeTrading/QuantitativeTradingPython/config.txt"],
    "input_K_Line" : ["C:/QuantitativeTrading/Data/K-Day-source/"],
    "is_save_JLD" : ["true"],
    "output_jld_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.jld"],
    "is_save_pickle" : ["True"],
    "output_pickle_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.pickle"],
    "is_save_csv" : ["true"],
    "output_csv_K_Line" : ["C:/QuantitativeTrading/Data/K-Day/"],
    "is_save_xlsx" : ["true"],
    "output_xlsx_K_Line" : ["C:/QuantitativeTrading/Data/K-Day/"],
    "Cleaned_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.jld"],
    // "Cleaned_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.pickle"],
    "source_data" : {
        "002611" : {
            "date_transaction": [
                "2019-1-2",
                "2019-1-3",
                "2019-1-4",
                "2019-1-7",
                "2019-1-8",
                "2019-1-9",
                "2019-1-10",
                "2019-1-11",
                "2019-1-14",
                "2019-1-15",
                "2019-1-16"
            ],
            "turnover_volume": [
                7385675,
                33846475,
                27957054,
                25700917,
                56678844,
                39931296,
                21818792,
                15243953,
                46110961,
                29099424,
                58411670
            ],
            "opening_price": [
                3.75,
                3.76,
                3.84,
                3.99,
                4.04,
                4.24,
                4.12,
                4.13,
                4.14,
                4.27,
                4.32
            ],
            "close_price": [
                3.76,
                3.94,
                3.98,
                4.05,
                4.27,
                4.18,
                4.14,
                4.13,
                4.31,
                4.34,
                4.48
            ],
            "low_price": [
                3.73,
                3.72,
                3.8,
                3.92,
                3.98,
                4.16,
                4.11,
                4.09,
                4.12,
                4.24,
                4.29
            ],
            "high_price": [
                3.8,
                4.14,
                4.0,
                4.07,
                4.46,
                4.36,
                4.21,
                4.17,
                4.45,
                4.36,
                4.67
            ]
        },
        ...
    }
}

伺服器 ( Server - Respond ) 響應 POST 請求的數據格式爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Respond - body =

{
    "request_Url" : "/KLineCleaning?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=KLineCleaning",
    "request_Authorization" : "Basic dXNlcm5hbWU6cGFzc3dvcmQ=",
    "request_Cookie" : "session_id=cmVxdWVzdF9LZXktPnVzZXJuYW1lOnBhc3N3b3Jk",
    "time" : "2024-02-03 17:59:58.239794",
    "Server_say" : "",
    "error" : "",
    "configFile" : "C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt",
    // "configFile" : "C:/QuantitativeTrading/QuantitativeTradingPython/config.txt",
    "input_K_Line" : "C:/QuantitativeTrading/Data/K-Day-source/",
    "is_save_JLD" : "true",
    "output_jld_K_Line" : "C:/QuantitativeTrading/Data/steppingData.jld",
    "is_save_pickle" : "True",
    "output_pickle_K_Line" : "C:/QuantitativeTrading/Data/steppingData.pickle",
    "is_save_csv" : "true",
    "output_csv_K_Line" : "C:/QuantitativeTrading/Data/K-Day/",
    "is_save_xlsx" : "true",
    "output_xlsx_K_Line" : "C:/QuantitativeTrading/Data/K-Day/",
    "Cleaned_K_Line" : "C:/QuantitativeTrading/Data/steppingData.jld",
    // "Cleaned_K_Line" : "C:/QuantitativeTrading/Data/steppingData.pickle",
    "return_KLineCleaning" : {
        "KLineCleaned" : {
            "002611" : {
                "date_transaction": [
                    "2019-1-2",
                    "2019-1-3",
                    "2019-1-4",
                    "2019-1-7",
                    "2019-1-8",
                    "2019-1-9",
                    "2019-1-10",
                    "2019-1-11",
                    "2019-1-14",
                    "2019-1-15",
                    "2019-1-16"
                ],
                "turnover_volume": [
                    7385675,
                    33846475,
                    27957054,
                    25700917,
                    56678844,
                    39931296,
                    21818792,
                    15243953,
                    46110961,
                    29099424,
                    58411670
                ],
                "turnover_amount": [
                    27770014,
                    135627968,
                    109496376,
                    103257416,
                    242614176,
                    170208784,
                    90564944,
                    62862520,
                    197721472,
                    125331136,
                    260167456
                ],
                "opening_price": [
                    3.75,
                    3.76,
                    3.84,
                    3.99,
                    4.04,
                    4.24,
                    4.12,
                    4.13,
                    4.14,
                    4.27,
                    4.32
                ],
                "close_price": [
                    3.76,
                    3.94,
                    3.98,
                    4.05,
                    4.27,
                    4.18,
                    4.14,
                    4.13,
                    4.31,
                    4.34,
                    4.48
                ],
                "low_price": [
                    3.73,
                    3.72,
                    3.8,
                    3.92,
                    3.98,
                    4.16,
                    4.11,
                    4.09,
                    4.12,
                    4.24,
                    4.29
                ],
                "high_price": [
                    3.8,
                    4.14,
                    4.0,
                    4.07,
                    4.46,
                    4.36,
                    4.21,
                    4.17,
                    4.45,
                    4.36,
                    4.67
                ],
                "focus": [
                    3.76,
                    3.89,
                    3.905,
                    4.0075,
                    4.1875,
                    4.235,
                    4.145,
                    4.13,
                    4.255,
                    4.3025,
                    4.44
                ],
                "amplitude": [
                    0.029439203,
                    0.192180471,
                    0.099833194,
                    0.06751543,
                    0.220510771,
                    0.09,
                    0.045092498,
                    0.032659863,
                    0.155456318,
                    0.056789083,
                    0.174547033
                ],
                "amplitude_rate": [
                    0.007829575,
                    0.04940372,
                    0.025565479,
                    0.016847269,
                    0.052659289,
                    0.021251476,
                    0.010878769,
                    0.007907957,
                    0.036534975,
                    0.01319909,
                    0.039312395
                ],
                "opening_price_Standardization": [
                    -0.33968311,
                    -0.676447505,
                    -0.651086049,
                    -0.259200007,
                    -0.668901567,
                    0.055555556,
                    -0.554415953,
                    0.0,
                    -0.739757649,
                    -0.572293089,
                    -0.687493784
                ],
                "closing_price_Standardization": [
                    0.0,
                    0.260172117,
                    0.751253134,
                    0.629485731,
                    0.374131385,
                    -0.611111111,
                    -0.110883191,
                    0.0,
                    0.353797136,
                    0.66033818,
                    0.229164595
                ],
                "low_price_Standardization": [
                    -1.019049331,
                    -0.884585199,
                    -1.051754387,
                    -1.296000034,
                    -0.940997119,
                    -0.833333333,
                    -0.776182335,
                    -1.224744871,
                    -0.868411153,
                    -1.100563633,
                    -0.859367229
                ],
                "high_price_Standardization": [
                    1.358732441,
                    1.300860587,
                    0.951587303,
                    0.92571431,
                    1.235767301,
                    1.388888889,
                    1.441481478,
                    1.224744871,
                    1.254371666,
                    1.012518542,
                    1.317696418
                ],
                "turnover_volume_growth_rate": [
                    0.0,
                    3.58271925,
                    -0.174003969,
                    -0.080700098,
                    1.205323802,
                    -0.295481467,
                    -0.453591689,
                    -0.30133836,
                    2.024869009,
                    -0.3689261,
                    1.007313616
                ],
                "opening_price_growth_rate": [
                    0.0,
                    0.002666667,
                    0.021276596,
                    0.0390625,
                    0.012531328,
                    0.04950495,
                    -0.028301887,
                    0.002427184,
                    0.002421308,
                    0.031400966,
                    0.011709602
                ],
                "closing_price_growth_rate": [
                    0.0,
                    0.04787234,
                    0.010152284,
                    0.01758794,
                    0.054320988,
                    -0.021077283,
                    -0.009569378,
                    -0.002415459,
                    0.043583535,
                    0.006960557,
                    0.032258065
                ],
                "high_price_proportion": [
                    0.989473684,
                    0.951690821,
                    0.995,
                    0.995085995,
                    0.957399103,
                    0.972477064,
                    0.983372922,
                    0.990407674,
                    0.968539326,
                    0.995412844,
                    0.959314775
                ],
                "low_price_proportion": [
                    0.994666667,
                    0.989361702,
                    0.989583333,
                    0.98245614,
                    0.985148515,
                    0.995215311,
                    0.997572816,
                    0.99031477,
                    0.995169082,
                    0.992974239,
                    0.993055556
                ],
                "closing_minus_opening_price_growth_rate": [
                    0.002666667,
                    0.04787234,
                    0.036458333,
                    0.015037594,
                    0.056930693,
                    -0.014150943,
                    0.004854369,
                    0.0,
                    0.041062802,
                    0.016393443,
                    0.037037037
                ],
                "sum_2_turnover_volume_growth_rate": [
                    "null",
                    1.791359625,
                    0.808677828,
                    -0.167702083,
                    0.582486877,
                    0.153590217,
                    -0.601332423,
                    -0.528134205,
                    0.937099914,
                    0.321754202,
                    0.411425283
                ],
                "sum_2_opening_price_growth_rate": [
                    "null",
                    0.001333333,
                    0.022609929,
                    0.049700798,
                    0.032062578,
                    0.055770615,
                    -0.001774706,
                    -0.005861879,
                    0.0036349,
                    0.03261162,
                    0.027410085
                ],
                "sum_2_closing_price_growth_rate": [
                    "null",
                    0.02393617,
                    0.034088454,
                    0.022664082,
                    0.063114958,
                    0.003041605,
                    -0.02010802,
                    -0.007200148,
                    0.021187903,
                    0.028752324,
                    0.035738343
                ],
                "sum_2_closing_minus_opening_price_growth_rate": [
                    "null",
                    0.049205674,
                    0.060394504,
                    0.033266761,
                    0.06444949,
                    0.007157202,
                    -0.001110551,
                    0.001213592,
                    0.020531401,
                    0.036924844,
                    0.045233758
                ],
                "sum_2_high_price_proportion": [
                    "null",
                    1.446427663,
                    1.470845411,
                    1.492585995,
                    1.454942101,
                    1.451176616,
                    1.469611454,
                    1.482094135,
                    1.463743163,
                    1.479682507,
                    1.457021197
                ],
                "sum_2_low_price_proportion": [
                    "null",
                    1.486695035,
                    1.484264184,
                    1.477247807,
                    1.476376585,
                    1.487789568,
                    1.495180471,
                    1.489101178,
                    1.490326467,
                    1.49055878,
                    1.489542675
                ],
                "sum_2_KLine_Intuitive_Momentum": [
                    "null",
                    1.794033435,
                    0.816153433,
                    -0.162394081,
                    0.595663278,
                    0.154499026,
                    -0.601279023,
                    -0.52816919,
                    0.938211686,
                    0.326751665,
                    0.417624603
                ],
                "sum_3_turnover_volume_growth_rate": [
                    "null",
                    "null",
                    0.73815851,
                    0.266944754,
                    0.327240342,
                    0.052928734,
                    -0.299794689,
                    -0.702226642,
                    0.440230023,
                    0.137056078,
                    1.039529723
                ],
                "sum_3_opening_price_growth_rate": [
                    "null",
                    "null",
                    0.015369582,
                    0.054135786,
                    0.045665194,
                    0.070880003,
                    0.015352978,
                    0.006329915,
                    -0.0004517,
                    0.033824233,
                    0.033450682
                ],
                "sum_3_closing_price_growth_rate": [
                    "null",
                    "null",
                    0.028044785,
                    0.040313576,
                    0.069430376,
                    0.021025331,
                    -0.009711602,
                    -0.015820805,
                    0.011327779,
                    0.023742447,
                    0.051426281
                ],
                "sum_3_closing_minus_opening_price_growth_rate": [
                    "null",
                    "null",
                    0.069262116,
                    0.055300596,
                    0.079108534,
                    0.023927237,
                    0.012742857,
                    -0.000493578,
                    0.02845395,
                    0.029179096,
                    0.061653599
                ],
                "sum_3_high_price_proportion": [
                    "null",
                    "null",
                    1.959285109,
                    1.975649602,
                    1.952456433,
                    1.942438465,
                    1.950823999,
                    1.970148643,
                    1.956602082,
                    1.971241619,
                    1.94576978
                ],
                "sum_3_low_price_proportion": [
                    "null",
                    "null",
                    1.980713357,
                    1.971965597,
                    1.969980386,
                    1.979466368,
                    1.989432528,
                    1.987101751,
                    1.987903201,
                    1.986525217,
                    1.986761409
                ],
                "sum_3_KLine_Intuitive_Momentum": [
                    "null",
                    "null",
                    0.74982791,
                    0.287293521,
                    0.362261072,
                    0.061384027,
                    -0.299515692,
                    -0.702208303,
                    0.441433708,
                    0.143633821,
                    1.059759237
                ],
                "sum_5_turnover_volume_growth_rate": [
                    "null",
                    "null",
                    "null",
                    "null",
                    0.987779617,
                    0.424219288,
                    -0.461007301,
                    -0.589686585,
                    0.527522535,
                    -0.308230627,
                    0.585057271
                ],
                "sum_5_opening_price_growth_rate": [
                    "null",
                    "null",
                    "null",
                    "null",
                    0.046091162,
                    0.092011485,
                    0.047942084,
                    0.031435847,
                    0.017940814,
                    0.033492099,
                    0.030271151
                ],
                "sum_5_closing_price_growth_rate": [
                    "null",
                    "null",
                    "null",
                    "null",
                    0.074905317,
                    0.049900492,
                    0.014422453,
                    -0.003532006,
                    0.012116149,
                    0.011035464,
                    0.037233955
                ],
                "sum_5_closing_minus_opening_price_growth_rate": [
                    "null",
                    "null",
                    "null",
                    "null",
                    0.110518038,
                    0.060149741,
                    0.03959144,
                    0.016099861,
                    0.032084862,
                    0.030145221,
                    0.060608277
                ],
                "sum_5_high_price_proportion": [
                    "null",
                    "null",
                    "null",
                    "null",
                    2.929038965,
                    2.923786108,
                    2.932828433,
                    2.94256909,
                    2.931359864,
                    2.952333491,
                    2.9296063
                ],
                "sum_5_low_price_proportion": [
                    "null",
                    "null",
                    "null",
                    "null",
                    2.959541441,
                    2.966513481,
                    2.975733296,
                    2.976052843,
                    2.981080415,
                    2.981370555,
                    2.980176867
                ],
                "sum_5_KLine_Intuitive_Momentum": [
                    "null",
                    "null",
                    "null",
                    "null",
                    1.103698843,
                    0.498255593,
                    -0.439458671,
                    -0.585752412,
                    0.535949828,
                    -0.296415761,
                    0.620777963
                ],
                "average_5_closing_price": [
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    4.0,
                    4.084,
                    4.124,
                    4.154,
                    4.206,
                    4.22,
                    4.28
                ],
                "average_10_closing_price": [
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    4.11,
                    4.182
                ]
            },
            ...
        }
    }
}

5. 連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 依據數量化指標擇時 ( market timing ) 運算，使用網址 ( Uniform Resource Locator , URL ) :

http://[::1]:10001/MarketTiming?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=MarketTiming&trading_direction=Long_Position_and_Short_Selling&ticker_symbol=["all"]&is_Optimize=false&MarketTiming_Pdata_0=[3,+0.1,-0.1,0.0]&MarketTiming_Plower=["-Infinity","-Infinity","-Infinity","-Infinity"]&MarketTiming_Pupper=["+Infinity","+Infinity","+Infinity","+Infinity"]&MarketTiming_weight=[]&Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.jld&training_data_file=C:/QuantitativeTrading/Data/trainingData.jld&testing_data_file=C:/QuantitativeTrading/Data/testingData.jld&stepping_data_file=C:/QuantitativeTrading/Data/steppingData.jld

用戶端 ( Client - Request ) 發送 POST 請求的數據爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Request - POST =

{
    "configFile" : ["C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt"],
    // "configFile" : ["C:/QuantitativeTrading/QuantitativeTradingPython/config.txt"],
    "Cleaned_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.jld"],
    // "Cleaned_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.pickle"],
    "trading_direction" : ["Long_Position_and_Short_Selling"],
    "ticker_symbol" : ["all"],
    "is_Optimize" : ["false"],
    "MarketTiming_Pdata_0" : [3, +0.1, -0.1, 0.0],
    "MarketTiming_Plower" : ["-Infinity", "-Infinity", "-Infinity", "-Infinity"],
    "MarketTiming_Pupper" : ["+Infinity", "+Infinity", "+Infinity", "+Infinity"],
    "MarketTiming_weight" : [],
    "training_data_file" : ["C:/QuantitativeTrading/Data/trainingData.jld"],
    // "training_data_file" : ["C:/QuantitativeTrading/Data/trainingData.pickle"],
    "testing_data_file" : ["C:/QuantitativeTrading/Data/testingData.jld"],
    // "testing_data_file" : ["C:/QuantitativeTrading/Data/testingData.pickle"],
    "training_data" : {
        "002611" : {
            "date_transaction": [
                "2019-1-2",
                "2019-1-3",
                "2019-1-4",
                "2019-1-7",
                "2019-1-8",
                "2019-1-9",
                "2019-1-10",
                "2019-1-11",
                "2019-1-14",
                "2019-1-15",
                "2019-1-16"
            ],
            "turnover_volume": [
                7385675,
                33846475,
                27957054,
                25700917,
                56678844,
                39931296,
                21818792,
                15243953,
                46110961,
                29099424,
                58411670
            ],
            "turnover_amount": [
                27770014,
                135627968,
                109496376,
                103257416,
                242614176,
                170208784,
                90564944,
                62862520,
                197721472,
                125331136,
                260167456
            ],
            "opening_price": [
                3.75,
                3.76,
                3.84,
                3.99,
                4.04,
                4.24,
                4.12,
                4.13,
                4.14,
                4.27,
                4.32
            ],
            "close_price": [
                3.76,
                3.94,
                3.98,
                4.05,
                4.27,
                4.18,
                4.14,
                4.13,
                4.31,
                4.34,
                4.48
            ],
            "low_price": [
                3.73,
                3.72,
                3.8,
                3.92,
                3.98,
                4.16,
                4.11,
                4.09,
                4.12,
                4.24,
                4.29
            ],
            "high_price": [
                3.8,
                4.14,
                4.0,
                4.07,
                4.46,
                4.36,
                4.21,
                4.17,
                4.45,
                4.36,
                4.67
            ],
            "focus": [
                3.76,
                3.89,
                3.905,
                4.0075,
                4.1875,
                4.235,
                4.145,
                4.13,
                4.255,
                4.3025,
                4.44
            ],
            "amplitude": [
                0.029439203,
                0.192180471,
                0.099833194,
                0.06751543,
                0.220510771,
                0.09,
                0.045092498,
                0.032659863,
                0.155456318,
                0.056789083,
                0.174547033
            ],
            "amplitude_rate": [
                0.007829575,
                0.04940372,
                0.025565479,
                0.016847269,
                0.052659289,
                0.021251476,
                0.010878769,
                0.007907957,
                0.036534975,
                0.01319909,
                0.039312395
            ],
            "opening_price_Standardization": [
                -0.33968311,
                -0.676447505,
                -0.651086049,
                -0.259200007,
                -0.668901567,
                0.055555556,
                -0.554415953,
                0.0,
                -0.739757649,
                -0.572293089,
                -0.687493784
            ],
            "closing_price_Standardization": [
                0.0,
                0.260172117,
                0.751253134,
                0.629485731,
                0.374131385,
                -0.611111111,
                -0.110883191,
                0.0,
                0.353797136,
                0.66033818,
                0.229164595
            ],
            "low_price_Standardization": [
                -1.019049331,
                -0.884585199,
                -1.051754387,
                -1.296000034,
                -0.940997119,
                -0.833333333,
                -0.776182335,
                -1.224744871,
                -0.868411153,
                -1.100563633,
                -0.859367229
            ],
            "high_price_Standardization": [
                1.358732441,
                1.300860587,
                0.951587303,
                0.92571431,
                1.235767301,
                1.388888889,
                1.441481478,
                1.224744871,
                1.254371666,
                1.012518542,
                1.317696418
            ],
            "turnover_volume_growth_rate": [
                0.0,
                3.58271925,
                -0.174003969,
                -0.080700098,
                1.205323802,
                -0.295481467,
                -0.453591689,
                -0.30133836,
                2.024869009,
                -0.3689261,
                1.007313616
            ],
            "opening_price_growth_rate": [
                0.0,
                0.002666667,
                0.021276596,
                0.0390625,
                0.012531328,
                0.04950495,
                -0.028301887,
                0.002427184,
                0.002421308,
                0.031400966,
                0.011709602
            ],
            "closing_price_growth_rate": [
                0.0,
                0.04787234,
                0.010152284,
                0.01758794,
                0.054320988,
                -0.021077283,
                -0.009569378,
                -0.002415459,
                0.043583535,
                0.006960557,
                0.032258065
            ],
            "high_price_proportion": [
                0.989473684,
                0.951690821,
                0.995,
                0.995085995,
                0.957399103,
                0.972477064,
                0.983372922,
                0.990407674,
                0.968539326,
                0.995412844,
                0.959314775
            ],
            "low_price_proportion": [
                0.994666667,
                0.989361702,
                0.989583333,
                0.98245614,
                0.985148515,
                0.995215311,
                0.997572816,
                0.99031477,
                0.995169082,
                0.992974239,
                0.993055556
            ],
            "closing_minus_opening_price_growth_rate": [
                0.002666667,
                0.04787234,
                0.036458333,
                0.015037594,
                0.056930693,
                -0.014150943,
                0.004854369,
                0.0,
                0.041062802,
                0.016393443,
                0.037037037
            ],
            "sum_2_turnover_volume_growth_rate": [
                "null",
                1.791359625,
                0.808677828,
                -0.167702083,
                0.582486877,
                0.153590217,
                -0.601332423,
                -0.528134205,
                0.937099914,
                0.321754202,
                0.411425283
            ],
            "sum_2_opening_price_growth_rate": [
                "null",
                0.001333333,
                0.022609929,
                0.049700798,
                0.032062578,
                0.055770615,
                -0.001774706,
                -0.005861879,
                0.0036349,
                0.03261162,
                0.027410085
            ],
            "sum_2_closing_price_growth_rate": [
                "null",
                0.02393617,
                0.034088454,
                0.022664082,
                0.063114958,
                0.003041605,
                -0.02010802,
                -0.007200148,
                0.021187903,
                0.028752324,
                0.035738343
            ],
            "sum_2_closing_minus_opening_price_growth_rate": [
                "null",
                0.049205674,
                0.060394504,
                0.033266761,
                0.06444949,
                0.007157202,
                -0.001110551,
                0.001213592,
                0.020531401,
                0.036924844,
                0.045233758
            ],
            "sum_2_high_price_proportion": [
                "null",
                1.446427663,
                1.470845411,
                1.492585995,
                1.454942101,
                1.451176616,
                1.469611454,
                1.482094135,
                1.463743163,
                1.479682507,
                1.457021197
            ],
            "sum_2_low_price_proportion": [
                "null",
                1.486695035,
                1.484264184,
                1.477247807,
                1.476376585,
                1.487789568,
                1.495180471,
                1.489101178,
                1.490326467,
                1.49055878,
                1.489542675
            ],
            "sum_2_KLine_Intuitive_Momentum": [
                "null",
                1.794033435,
                0.816153433,
                -0.162394081,
                0.595663278,
                0.154499026,
                -0.601279023,
                -0.52816919,
                0.938211686,
                0.326751665,
                0.417624603
            ],
            "sum_3_turnover_volume_growth_rate": [
                "null",
                "null",
                0.73815851,
                0.266944754,
                0.327240342,
                0.052928734,
                -0.299794689,
                -0.702226642,
                0.440230023,
                0.137056078,
                1.039529723
            ],
            "sum_3_opening_price_growth_rate": [
                "null",
                "null",
                0.015369582,
                0.054135786,
                0.045665194,
                0.070880003,
                0.015352978,
                0.006329915,
                -0.0004517,
                0.033824233,
                0.033450682
            ],
            "sum_3_closing_price_growth_rate": [
                "null",
                "null",
                0.028044785,
                0.040313576,
                0.069430376,
                0.021025331,
                -0.009711602,
                -0.015820805,
                0.011327779,
                0.023742447,
                0.051426281
            ],
            "sum_3_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                0.069262116,
                0.055300596,
                0.079108534,
                0.023927237,
                0.012742857,
                -0.000493578,
                0.02845395,
                0.029179096,
                0.061653599
            ],
            "sum_3_high_price_proportion": [
                "null",
                "null",
                1.959285109,
                1.975649602,
                1.952456433,
                1.942438465,
                1.950823999,
                1.970148643,
                1.956602082,
                1.971241619,
                1.94576978
            ],
            "sum_3_low_price_proportion": [
                "null",
                "null",
                1.980713357,
                1.971965597,
                1.969980386,
                1.979466368,
                1.989432528,
                1.987101751,
                1.987903201,
                1.986525217,
                1.986761409
            ],
            "sum_3_KLine_Intuitive_Momentum": [
                "null",
                "null",
                0.74982791,
                0.287293521,
                0.362261072,
                0.061384027,
                -0.299515692,
                -0.702208303,
                0.441433708,
                0.143633821,
                1.059759237
            ],
            "sum_5_turnover_volume_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.987779617,
                0.424219288,
                -0.461007301,
                -0.589686585,
                0.527522535,
                -0.308230627,
                0.585057271
            ],
            "sum_5_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.046091162,
                0.092011485,
                0.047942084,
                0.031435847,
                0.017940814,
                0.033492099,
                0.030271151
            ],
            "sum_5_closing_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.074905317,
                0.049900492,
                0.014422453,
                -0.003532006,
                0.012116149,
                0.011035464,
                0.037233955
            ],
            "sum_5_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.110518038,
                0.060149741,
                0.03959144,
                0.016099861,
                0.032084862,
                0.030145221,
                0.060608277
            ],
            "sum_5_high_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.929038965,
                2.923786108,
                2.932828433,
                2.94256909,
                2.931359864,
                2.952333491,
                2.9296063
            ],
            "sum_5_low_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.959541441,
                2.966513481,
                2.975733296,
                2.976052843,
                2.981080415,
                2.981370555,
                2.980176867
            ],
            "sum_5_KLine_Intuitive_Momentum": [
                "null",
                "null",
                "null",
                "null",
                1.103698843,
                0.498255593,
                -0.439458671,
                -0.585752412,
                0.535949828,
                -0.296415761,
                0.620777963
            ],
            "average_5_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                4.0,
                4.084,
                4.124,
                4.154,
                4.206,
                4.22,
                4.28
            ],
            "average_10_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                4.11,
                4.182
            ]
        },
        ...
    },
    "testing_data" : {
        "002611" : {
            "date_transaction": [
                "2019-1-2",
                "2019-1-3",
                "2019-1-4",
                "2019-1-7",
                "2019-1-8",
                "2019-1-9",
                "2019-1-10",
                "2019-1-11",
                "2019-1-14",
                "2019-1-15",
                "2019-1-16"
            ],
            "turnover_volume": [
                7385675,
                33846475,
                27957054,
                25700917,
                56678844,
                39931296,
                21818792,
                15243953,
                46110961,
                29099424,
                58411670
            ],
            "turnover_amount": [
                27770014,
                135627968,
                109496376,
                103257416,
                242614176,
                170208784,
                90564944,
                62862520,
                197721472,
                125331136,
                260167456
            ],
            "opening_price": [
                3.75,
                3.76,
                3.84,
                3.99,
                4.04,
                4.24,
                4.12,
                4.13,
                4.14,
                4.27,
                4.32
            ],
            "close_price": [
                3.76,
                3.94,
                3.98,
                4.05,
                4.27,
                4.18,
                4.14,
                4.13,
                4.31,
                4.34,
                4.48
            ],
            "low_price": [
                3.73,
                3.72,
                3.8,
                3.92,
                3.98,
                4.16,
                4.11,
                4.09,
                4.12,
                4.24,
                4.29
            ],
            "high_price": [
                3.8,
                4.14,
                4.0,
                4.07,
                4.46,
                4.36,
                4.21,
                4.17,
                4.45,
                4.36,
                4.67
            ],
            "focus": [
                3.76,
                3.89,
                3.905,
                4.0075,
                4.1875,
                4.235,
                4.145,
                4.13,
                4.255,
                4.3025,
                4.44
            ],
            "amplitude": [
                0.029439203,
                0.192180471,
                0.099833194,
                0.06751543,
                0.220510771,
                0.09,
                0.045092498,
                0.032659863,
                0.155456318,
                0.056789083,
                0.174547033
            ],
            "amplitude_rate": [
                0.007829575,
                0.04940372,
                0.025565479,
                0.016847269,
                0.052659289,
                0.021251476,
                0.010878769,
                0.007907957,
                0.036534975,
                0.01319909,
                0.039312395
            ],
            "opening_price_Standardization": [
                -0.33968311,
                -0.676447505,
                -0.651086049,
                -0.259200007,
                -0.668901567,
                0.055555556,
                -0.554415953,
                0.0,
                -0.739757649,
                -0.572293089,
                -0.687493784
            ],
            "closing_price_Standardization": [
                0.0,
                0.260172117,
                0.751253134,
                0.629485731,
                0.374131385,
                -0.611111111,
                -0.110883191,
                0.0,
                0.353797136,
                0.66033818,
                0.229164595
            ],
            "low_price_Standardization": [
                -1.019049331,
                -0.884585199,
                -1.051754387,
                -1.296000034,
                -0.940997119,
                -0.833333333,
                -0.776182335,
                -1.224744871,
                -0.868411153,
                -1.100563633,
                -0.859367229
            ],
            "high_price_Standardization": [
                1.358732441,
                1.300860587,
                0.951587303,
                0.92571431,
                1.235767301,
                1.388888889,
                1.441481478,
                1.224744871,
                1.254371666,
                1.012518542,
                1.317696418
            ],
            "turnover_volume_growth_rate": [
                0.0,
                3.58271925,
                -0.174003969,
                -0.080700098,
                1.205323802,
                -0.295481467,
                -0.453591689,
                -0.30133836,
                2.024869009,
                -0.3689261,
                1.007313616
            ],
            "opening_price_growth_rate": [
                0.0,
                0.002666667,
                0.021276596,
                0.0390625,
                0.012531328,
                0.04950495,
                -0.028301887,
                0.002427184,
                0.002421308,
                0.031400966,
                0.011709602
            ],
            "closing_price_growth_rate": [
                0.0,
                0.04787234,
                0.010152284,
                0.01758794,
                0.054320988,
                -0.021077283,
                -0.009569378,
                -0.002415459,
                0.043583535,
                0.006960557,
                0.032258065
            ],
            "high_price_proportion": [
                0.989473684,
                0.951690821,
                0.995,
                0.995085995,
                0.957399103,
                0.972477064,
                0.983372922,
                0.990407674,
                0.968539326,
                0.995412844,
                0.959314775
            ],
            "low_price_proportion": [
                0.994666667,
                0.989361702,
                0.989583333,
                0.98245614,
                0.985148515,
                0.995215311,
                0.997572816,
                0.99031477,
                0.995169082,
                0.992974239,
                0.993055556
            ],
            "closing_minus_opening_price_growth_rate": [
                0.002666667,
                0.04787234,
                0.036458333,
                0.015037594,
                0.056930693,
                -0.014150943,
                0.004854369,
                0.0,
                0.041062802,
                0.016393443,
                0.037037037
            ],
            "sum_2_turnover_volume_growth_rate": [
                "null",
                1.791359625,
                0.808677828,
                -0.167702083,
                0.582486877,
                0.153590217,
                -0.601332423,
                -0.528134205,
                0.937099914,
                0.321754202,
                0.411425283
            ],
            "sum_2_opening_price_growth_rate": [
                "null",
                0.001333333,
                0.022609929,
                0.049700798,
                0.032062578,
                0.055770615,
                -0.001774706,
                -0.005861879,
                0.0036349,
                0.03261162,
                0.027410085
            ],
            "sum_2_closing_price_growth_rate": [
                "null",
                0.02393617,
                0.034088454,
                0.022664082,
                0.063114958,
                0.003041605,
                -0.02010802,
                -0.007200148,
                0.021187903,
                0.028752324,
                0.035738343
            ],
            "sum_2_closing_minus_opening_price_growth_rate": [
                "null",
                0.049205674,
                0.060394504,
                0.033266761,
                0.06444949,
                0.007157202,
                -0.001110551,
                0.001213592,
                0.020531401,
                0.036924844,
                0.045233758
            ],
            "sum_2_high_price_proportion": [
                "null",
                1.446427663,
                1.470845411,
                1.492585995,
                1.454942101,
                1.451176616,
                1.469611454,
                1.482094135,
                1.463743163,
                1.479682507,
                1.457021197
            ],
            "sum_2_low_price_proportion": [
                "null",
                1.486695035,
                1.484264184,
                1.477247807,
                1.476376585,
                1.487789568,
                1.495180471,
                1.489101178,
                1.490326467,
                1.49055878,
                1.489542675
            ],
            "sum_2_KLine_Intuitive_Momentum": [
                "null",
                1.794033435,
                0.816153433,
                -0.162394081,
                0.595663278,
                0.154499026,
                -0.601279023,
                -0.52816919,
                0.938211686,
                0.326751665,
                0.417624603
            ],
            "sum_3_turnover_volume_growth_rate": [
                "null",
                "null",
                0.73815851,
                0.266944754,
                0.327240342,
                0.052928734,
                -0.299794689,
                -0.702226642,
                0.440230023,
                0.137056078,
                1.039529723
            ],
            "sum_3_opening_price_growth_rate": [
                "null",
                "null",
                0.015369582,
                0.054135786,
                0.045665194,
                0.070880003,
                0.015352978,
                0.006329915,
                -0.0004517,
                0.033824233,
                0.033450682
            ],
            "sum_3_closing_price_growth_rate": [
                "null",
                "null",
                0.028044785,
                0.040313576,
                0.069430376,
                0.021025331,
                -0.009711602,
                -0.015820805,
                0.011327779,
                0.023742447,
                0.051426281
            ],
            "sum_3_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                0.069262116,
                0.055300596,
                0.079108534,
                0.023927237,
                0.012742857,
                -0.000493578,
                0.02845395,
                0.029179096,
                0.061653599
            ],
            "sum_3_high_price_proportion": [
                "null",
                "null",
                1.959285109,
                1.975649602,
                1.952456433,
                1.942438465,
                1.950823999,
                1.970148643,
                1.956602082,
                1.971241619,
                1.94576978
            ],
            "sum_3_low_price_proportion": [
                "null",
                "null",
                1.980713357,
                1.971965597,
                1.969980386,
                1.979466368,
                1.989432528,
                1.987101751,
                1.987903201,
                1.986525217,
                1.986761409
            ],
            "sum_3_KLine_Intuitive_Momentum": [
                "null",
                "null",
                0.74982791,
                0.287293521,
                0.362261072,
                0.061384027,
                -0.299515692,
                -0.702208303,
                0.441433708,
                0.143633821,
                1.059759237
            ],
            "sum_5_turnover_volume_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.987779617,
                0.424219288,
                -0.461007301,
                -0.589686585,
                0.527522535,
                -0.308230627,
                0.585057271
            ],
            "sum_5_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.046091162,
                0.092011485,
                0.047942084,
                0.031435847,
                0.017940814,
                0.033492099,
                0.030271151
            ],
            "sum_5_closing_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.074905317,
                0.049900492,
                0.014422453,
                -0.003532006,
                0.012116149,
                0.011035464,
                0.037233955
            ],
            "sum_5_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.110518038,
                0.060149741,
                0.03959144,
                0.016099861,
                0.032084862,
                0.030145221,
                0.060608277
            ],
            "sum_5_high_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.929038965,
                2.923786108,
                2.932828433,
                2.94256909,
                2.931359864,
                2.952333491,
                2.9296063
            ],
            "sum_5_low_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.959541441,
                2.966513481,
                2.975733296,
                2.976052843,
                2.981080415,
                2.981370555,
                2.980176867
            ],
            "sum_5_KLine_Intuitive_Momentum": [
                "null",
                "null",
                "null",
                "null",
                1.103698843,
                0.498255593,
                -0.439458671,
                -0.585752412,
                0.535949828,
                -0.296415761,
                0.620777963
            ],
            "average_5_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                4.0,
                4.084,
                4.124,
                4.154,
                4.206,
                4.22,
                4.28
            ],
            "average_10_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                4.11,
                4.182
            ]
        },
        ...
    }
}

伺服器 ( Server - Respond ) 響應 POST 請求的數據格式爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Respond - body =

{
    "request_Url" : "/MarketTiming?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=MarketTiming",
    "request_Authorization" : "Basic dXNlcm5hbWU6cGFzc3dvcmQ=",
    "request_Cookie" : "session_id=cmVxdWVzdF9LZXktPnVzZXJuYW1lOnBhc3N3b3Jk",
    "time" : "2024-02-03 17:59:58.239794",
    "Server_say" : "",
    "error" : "",
    "configFile" : "C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt",
    // "configFile" : "C:/QuantitativeTrading/QuantitativeTradingPython/config.txt",
    "trading_direction" : "Long_Position_and_Short_Selling",
    "ticker_symbol" : ["002611", ... ],
    "is_Optimize" : "false",
    "MarketTiming_Pdata_0" : [3, +0.1, -0.1, 0.0],
    "return_MarketTiming" : {
        "002611" : {
            "Coefficient" : {
                "Long_Position" : [Integer, Floating-Point, Floating-Point, Floating-Point],
                "Short_Selling" : [Integer, Floating-Point, Floating-Point, Floating-Point]
            },
            "y_profit" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，加權纍加總計;
            "y_Long_Position_profit" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，加權纍加總計;
            "y_Short_Selling_profit" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，加權纍加總計;
            "y_loss" : Floating-Point,  // 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
            "y_Long_Position_loss" : Floating-Point,  // 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
            "y_Short_Selling_loss" : Floating-Point,  // 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
            "maximum_drawdown" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
            "maximum_drawdown_Long_Position" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
            "maximum_drawdown_Short_Selling" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
            "profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
            "Long_Position_profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
            "Short_Selling_profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
            "profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
            "profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
            "Long_Position_profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
            "Long_Position_profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
            "Short_Selling_profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
            "Short_Selling_profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
            "profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
            "profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
            "Long_Position_profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
            "Long_Position_profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
            "Short_Selling_profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
            "Short_Selling_profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
            "average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
            "Long_Position_average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
            "Short_Selling_average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
            "average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
            "Long_Position_average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
            "Short_Selling_average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
            "average_date_transaction_between" : Integer,  // 兩次交易間隔日長，均值;
            "Long_Position_average_date_transaction_between" : Integer,  // 兩次對衝交易間隔日長，均值;
            "Short_Selling_average_date_transaction_between" : Integer,  // 兩次對衝交易間隔日長，均值;
            "Long_Position_drawdown_date_transaction" : [Floating-Point, Floating-Point, Floating-Point, ... ],  // 向量，記錄做多模式每組對衝交易日的回撤值序列，風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
            "Short_Selling_drawdown_date_transaction" : [Floating-Point, Floating-Point, Floating-Point, ... ],  // 向量，記錄做多模式每組對衝交易日的回撤值序列，風險控制閾值，强制平倉，可接受的最大回撤比例：Long_Position = sell_price ÷ buy_price、Short_Selling = 1 + ((sell_price - buy_price) ÷ sell_price) ;
            "Long_Position_profit_date_transaction" : [Floating-Point, Floating-Point, Floating-Point, ... ],  // 每兩次對衝交易利潤，向量;
            "Short_Selling_profit_date_transaction" : [Floating-Point, Floating-Point, Floating-Point, ... ],  // 每兩次對衝交易利潤，向量;
            "Long_Position_price_amplitude_date_transaction" : [Floating-Point, Floating-Point, Floating-Point, ... ],  // 兩次對衝交易日成交價振幅平方和，向量;
            "Short_Selling_price_amplitude_date_transaction" : [Floating-Point, Floating-Point, Floating-Point, ... ],  // 兩次對衝交易日成交價振幅平方和，向量;
            "Long_Position_volume_turnover_date_transaction" : [Floating-Point, Floating-Point, Floating-Point, ... ],  // 兩次對衝交易日成交量（換手率）向量;
            "Short_Selling_volume_turnover_date_transaction" : [Floating-Point, Floating-Point, Floating-Point, ... ],  // 兩次對衝交易日成交量（換手率）向量;
            "Long_Position_date_transaction_between" : [Floating-Point, Floating-Point, Floating-Point, ... ],  // 兩次對衝交易間隔日長，向量;
            "Short_Selling_date_transaction_between" : [Floating-Point, Floating-Point, Floating-Point, ... ],  // 兩次對衝交易間隔日長，向量;
            "Long_Position_date_transaction" : [
                [
                    "2019-01-11",  // 依照擇時交易規則選取的交易日期;
                    "sell" or "buy",  // 依照擇時交易規則選取的交易方向（買入或賣出）標示字符串;
                    Floating-Point,  // 依照擇時交易規則選取的交易價格;
                    "null" or Integer or Floating-Point,  // 依照擇時交易規則選取的交易量配置;
                    Integer,  // 記錄依照擇時交易規則選取的每組對衝交易 ( paired transaction ) 的序號;
                    Integer,  // 記錄依照擇時交易規則選取的交易日期的序列號，用於繪圖可視化;
                    "2019-01-11",  // K-Line day，當日棒缐的日期;
                    Integer,  // K-Line turnover volume，當日棒缐的總成交量;
                    Floating-Point,  // K-Line opening price，當日棒缐的開盤成交價格;
                    Floating-Point,  // K-Line closed price，當日棒缐的收盤成交價格;
                    Floating-Point,  // K-Line low price，當日棒缐的最低成交價格;
                    Floating-Point,  // K-Line high price，當日棒缐的最高成交價格;
                    "null" or Floating-Point,  // K-Line turnover amount，當日棒缐的總成交金額;
                    "null" or Floating-Point,  // K-Line turnover rate，當日棒缐的成交換手率;
                    "null" or Floating-Point,  // K-Line price earnings，當日棒缐的每股收益;
                    "null" or Floating-Point,  // K-Line book value per share，當日棒缐的每股票净值;
                ],
                ...
            ],  // 按擇時規則選取執行的每組對衝交易 ( paired transaction ) 信息記錄，向量;
            "Short_Selling_date_transaction" : [
                [
                    "2019-01-11",  // 依照擇時交易規則選取的交易日期;
                    "sell" or "buy",  // 依照擇時交易規則選取的交易方向（買入或賣出）標示字符串;
                    Floating-Point,  // 依照擇時交易規則選取的交易價格;
                    "null" or Integer or Floating-Point,  // 依照擇時交易規則選取的交易量配置;
                    Integer,  // 記錄依照擇時交易規則選取的每組對衝交易 ( paired transaction ) 的序號;
                    Integer,  // 記錄依照擇時交易規則選取的交易日期的序列號，用於繪圖可視化;
                    "2019-01-11",  // K-Line day，當日棒缐的日期;
                    Integer,  // K-Line turnover volume，當日棒缐的總成交量;
                    Floating-Point,  // K-Line opening price，當日棒缐的開盤成交價格;
                    Floating-Point,  // K-Line closed price，當日棒缐的收盤成交價格;
                    Floating-Point,  // K-Line low price，當日棒缐的最低成交價格;
                    Floating-Point,  // K-Line high price，當日棒缐的最高成交價格;
                    "null" or Floating-Point,  // K-Line turnover amount，當日棒缐的總成交金額;
                    "null" or Floating-Point,  // K-Line turnover rate，當日棒缐的成交換手率;
                    "null" or Floating-Point,  // K-Line price earnings，當日棒缐的每股收益;
                    "null" or Floating-Point,  // K-Line book value per share，當日棒缐的每股票净值;
                ],
                ...
            ],  // 按擇時規則選取執行的每組對衝交易 ( paired transaction ) 信息記錄，向量;
            "weight_MarketTiming" : {
                "Long_Position" : Floating-Point,
                "Short_Selling" : Floating-Point
            },  // 擇時權重，每兩次對衝交易的盈利概率占比，字典，"weight_MarketTiming" : {"Long_Position" : 1.0, "Short_Selling" : 1.0} 形式;
            "P1_Array" : [
                "null" or Floating-Point,  // 照擇時規則計算得到標示分值;
                ...
            ],  // 依照擇時規則計算得到參數 P1 標示分值序列的存儲數組，向量;
        },
        ...
    }
}

6. 連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 依據數量化指標選股 ( pick stock ) 運算，使用網址 ( Uniform Resource Locator , URL ) :

http://[::1]:10001/PickStock?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=PickStock&trading_direction=Long_Position_and_Short_Selling&ticker_symbol=["all"]&is_Optimize=false&MarketTiming_Pdata_0=[3,+0.1,-0.1,0.0]&MarketTiming_Plower=["-Infinity","-Infinity","-Infinity","-Infinity"]&MarketTiming_Pupper=["+Infinity","+Infinity","+Infinity","+Infinity"]&MarketTiming_weight=[]&PickStock_Pdata_0=[3,5]&PickStock_Plower=["-Infinity","-Infinity"]&PickStock_Pupper=["+Infinity","+Infinity"]&PickStock_weight=[]&Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.jld&training_data_file=C:/QuantitativeTrading/Data/trainingData.jld&testing_data_file=C:/QuantitativeTrading/Data/testingData.jld&stepping_data_file=C:/QuantitativeTrading/Data/steppingData.jld

用戶端 ( Client - Request ) 發送 POST 請求的數據爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Request - POST =

{
    "configFile" : ["C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt"],
    // "configFile" : ["C:/QuantitativeTrading/QuantitativeTradingPython/config.txt"],
    "Cleaned_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.jld"],
    // "Cleaned_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.pickle"],
    "trading_direction" : ["Long_Position_and_Short_Selling"],
    "ticker_symbol" : ["all"],
    "is_Optimize" : ["false"],
    "MarketTiming_Pdata_0" : [3, +0.1, -0.1, 0.0],
    "MarketTiming_Plower" : ["-Infinity", "-Infinity", "-Infinity", "-Infinity"],
    "MarketTiming_Pupper" : ["+Infinity", "+Infinity", "+Infinity", "+Infinity"],
    "MarketTiming_weight" : [],
    "PickStock_Pdata_0" : [3, 5],
    "PickStock_Plower" : ["-Infinity", "-Infinity"],
    "PickStock_Pupper" : ["+Infinity", "+Infinity"],
    "PickStock_weight" : [],
    "training_data_file" : ["C:/QuantitativeTrading/Data/trainingData.jld"],
    // "training_data_file" : ["C:/QuantitativeTrading/Data/trainingData.pickle"],
    "testing_data_file" : ["C:/QuantitativeTrading/Data/testingData.jld"],
    // "testing_data_file" : ["C:/QuantitativeTrading/Data/testingData.pickle"],
    "training_data" : {
        "002611" : {
            "date_transaction": [
                "2019-1-2",
                "2019-1-3",
                "2019-1-4",
                "2019-1-7",
                "2019-1-8",
                "2019-1-9",
                "2019-1-10",
                "2019-1-11",
                "2019-1-14",
                "2019-1-15",
                "2019-1-16"
            ],
            "turnover_volume": [
                7385675,
                33846475,
                27957054,
                25700917,
                56678844,
                39931296,
                21818792,
                15243953,
                46110961,
                29099424,
                58411670
            ],
            "turnover_amount": [
                27770014,
                135627968,
                109496376,
                103257416,
                242614176,
                170208784,
                90564944,
                62862520,
                197721472,
                125331136,
                260167456
            ],
            "opening_price": [
                3.75,
                3.76,
                3.84,
                3.99,
                4.04,
                4.24,
                4.12,
                4.13,
                4.14,
                4.27,
                4.32
            ],
            "close_price": [
                3.76,
                3.94,
                3.98,
                4.05,
                4.27,
                4.18,
                4.14,
                4.13,
                4.31,
                4.34,
                4.48
            ],
            "low_price": [
                3.73,
                3.72,
                3.8,
                3.92,
                3.98,
                4.16,
                4.11,
                4.09,
                4.12,
                4.24,
                4.29
            ],
            "high_price": [
                3.8,
                4.14,
                4.0,
                4.07,
                4.46,
                4.36,
                4.21,
                4.17,
                4.45,
                4.36,
                4.67
            ],
            "focus": [
                3.76,
                3.89,
                3.905,
                4.0075,
                4.1875,
                4.235,
                4.145,
                4.13,
                4.255,
                4.3025,
                4.44
            ],
            "amplitude": [
                0.029439203,
                0.192180471,
                0.099833194,
                0.06751543,
                0.220510771,
                0.09,
                0.045092498,
                0.032659863,
                0.155456318,
                0.056789083,
                0.174547033
            ],
            "amplitude_rate": [
                0.007829575,
                0.04940372,
                0.025565479,
                0.016847269,
                0.052659289,
                0.021251476,
                0.010878769,
                0.007907957,
                0.036534975,
                0.01319909,
                0.039312395
            ],
            "opening_price_Standardization": [
                -0.33968311,
                -0.676447505,
                -0.651086049,
                -0.259200007,
                -0.668901567,
                0.055555556,
                -0.554415953,
                0.0,
                -0.739757649,
                -0.572293089,
                -0.687493784
            ],
            "closing_price_Standardization": [
                0.0,
                0.260172117,
                0.751253134,
                0.629485731,
                0.374131385,
                -0.611111111,
                -0.110883191,
                0.0,
                0.353797136,
                0.66033818,
                0.229164595
            ],
            "low_price_Standardization": [
                -1.019049331,
                -0.884585199,
                -1.051754387,
                -1.296000034,
                -0.940997119,
                -0.833333333,
                -0.776182335,
                -1.224744871,
                -0.868411153,
                -1.100563633,
                -0.859367229
            ],
            "high_price_Standardization": [
                1.358732441,
                1.300860587,
                0.951587303,
                0.92571431,
                1.235767301,
                1.388888889,
                1.441481478,
                1.224744871,
                1.254371666,
                1.012518542,
                1.317696418
            ],
            "turnover_volume_growth_rate": [
                0.0,
                3.58271925,
                -0.174003969,
                -0.080700098,
                1.205323802,
                -0.295481467,
                -0.453591689,
                -0.30133836,
                2.024869009,
                -0.3689261,
                1.007313616
            ],
            "opening_price_growth_rate": [
                0.0,
                0.002666667,
                0.021276596,
                0.0390625,
                0.012531328,
                0.04950495,
                -0.028301887,
                0.002427184,
                0.002421308,
                0.031400966,
                0.011709602
            ],
            "closing_price_growth_rate": [
                0.0,
                0.04787234,
                0.010152284,
                0.01758794,
                0.054320988,
                -0.021077283,
                -0.009569378,
                -0.002415459,
                0.043583535,
                0.006960557,
                0.032258065
            ],
            "high_price_proportion": [
                0.989473684,
                0.951690821,
                0.995,
                0.995085995,
                0.957399103,
                0.972477064,
                0.983372922,
                0.990407674,
                0.968539326,
                0.995412844,
                0.959314775
            ],
            "low_price_proportion": [
                0.994666667,
                0.989361702,
                0.989583333,
                0.98245614,
                0.985148515,
                0.995215311,
                0.997572816,
                0.99031477,
                0.995169082,
                0.992974239,
                0.993055556
            ],
            "closing_minus_opening_price_growth_rate": [
                0.002666667,
                0.04787234,
                0.036458333,
                0.015037594,
                0.056930693,
                -0.014150943,
                0.004854369,
                0.0,
                0.041062802,
                0.016393443,
                0.037037037
            ],
            "sum_2_turnover_volume_growth_rate": [
                "null",
                1.791359625,
                0.808677828,
                -0.167702083,
                0.582486877,
                0.153590217,
                -0.601332423,
                -0.528134205,
                0.937099914,
                0.321754202,
                0.411425283
            ],
            "sum_2_opening_price_growth_rate": [
                "null",
                0.001333333,
                0.022609929,
                0.049700798,
                0.032062578,
                0.055770615,
                -0.001774706,
                -0.005861879,
                0.0036349,
                0.03261162,
                0.027410085
            ],
            "sum_2_closing_price_growth_rate": [
                "null",
                0.02393617,
                0.034088454,
                0.022664082,
                0.063114958,
                0.003041605,
                -0.02010802,
                -0.007200148,
                0.021187903,
                0.028752324,
                0.035738343
            ],
            "sum_2_closing_minus_opening_price_growth_rate": [
                "null",
                0.049205674,
                0.060394504,
                0.033266761,
                0.06444949,
                0.007157202,
                -0.001110551,
                0.001213592,
                0.020531401,
                0.036924844,
                0.045233758
            ],
            "sum_2_high_price_proportion": [
                "null",
                1.446427663,
                1.470845411,
                1.492585995,
                1.454942101,
                1.451176616,
                1.469611454,
                1.482094135,
                1.463743163,
                1.479682507,
                1.457021197
            ],
            "sum_2_low_price_proportion": [
                "null",
                1.486695035,
                1.484264184,
                1.477247807,
                1.476376585,
                1.487789568,
                1.495180471,
                1.489101178,
                1.490326467,
                1.49055878,
                1.489542675
            ],
            "sum_2_KLine_Intuitive_Momentum": [
                "null",
                1.794033435,
                0.816153433,
                -0.162394081,
                0.595663278,
                0.154499026,
                -0.601279023,
                -0.52816919,
                0.938211686,
                0.326751665,
                0.417624603
            ],
            "sum_3_turnover_volume_growth_rate": [
                "null",
                "null",
                0.73815851,
                0.266944754,
                0.327240342,
                0.052928734,
                -0.299794689,
                -0.702226642,
                0.440230023,
                0.137056078,
                1.039529723
            ],
            "sum_3_opening_price_growth_rate": [
                "null",
                "null",
                0.015369582,
                0.054135786,
                0.045665194,
                0.070880003,
                0.015352978,
                0.006329915,
                -0.0004517,
                0.033824233,
                0.033450682
            ],
            "sum_3_closing_price_growth_rate": [
                "null",
                "null",
                0.028044785,
                0.040313576,
                0.069430376,
                0.021025331,
                -0.009711602,
                -0.015820805,
                0.011327779,
                0.023742447,
                0.051426281
            ],
            "sum_3_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                0.069262116,
                0.055300596,
                0.079108534,
                0.023927237,
                0.012742857,
                -0.000493578,
                0.02845395,
                0.029179096,
                0.061653599
            ],
            "sum_3_high_price_proportion": [
                "null",
                "null",
                1.959285109,
                1.975649602,
                1.952456433,
                1.942438465,
                1.950823999,
                1.970148643,
                1.956602082,
                1.971241619,
                1.94576978
            ],
            "sum_3_low_price_proportion": [
                "null",
                "null",
                1.980713357,
                1.971965597,
                1.969980386,
                1.979466368,
                1.989432528,
                1.987101751,
                1.987903201,
                1.986525217,
                1.986761409
            ],
            "sum_3_KLine_Intuitive_Momentum": [
                "null",
                "null",
                0.74982791,
                0.287293521,
                0.362261072,
                0.061384027,
                -0.299515692,
                -0.702208303,
                0.441433708,
                0.143633821,
                1.059759237
            ],
            "sum_5_turnover_volume_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.987779617,
                0.424219288,
                -0.461007301,
                -0.589686585,
                0.527522535,
                -0.308230627,
                0.585057271
            ],
            "sum_5_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.046091162,
                0.092011485,
                0.047942084,
                0.031435847,
                0.017940814,
                0.033492099,
                0.030271151
            ],
            "sum_5_closing_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.074905317,
                0.049900492,
                0.014422453,
                -0.003532006,
                0.012116149,
                0.011035464,
                0.037233955
            ],
            "sum_5_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.110518038,
                0.060149741,
                0.03959144,
                0.016099861,
                0.032084862,
                0.030145221,
                0.060608277
            ],
            "sum_5_high_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.929038965,
                2.923786108,
                2.932828433,
                2.94256909,
                2.931359864,
                2.952333491,
                2.9296063
            ],
            "sum_5_low_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.959541441,
                2.966513481,
                2.975733296,
                2.976052843,
                2.981080415,
                2.981370555,
                2.980176867
            ],
            "sum_5_KLine_Intuitive_Momentum": [
                "null",
                "null",
                "null",
                "null",
                1.103698843,
                0.498255593,
                -0.439458671,
                -0.585752412,
                0.535949828,
                -0.296415761,
                0.620777963
            ],
            "average_5_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                4.0,
                4.084,
                4.124,
                4.154,
                4.206,
                4.22,
                4.28
            ],
            "average_10_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                4.11,
                4.182
            ]
        },
        ...
    },
    "testing_data" : {
        "002611" : {
            "date_transaction": [
                "2019-1-2",
                "2019-1-3",
                "2019-1-4",
                "2019-1-7",
                "2019-1-8",
                "2019-1-9",
                "2019-1-10",
                "2019-1-11",
                "2019-1-14",
                "2019-1-15",
                "2019-1-16"
            ],
            "turnover_volume": [
                7385675,
                33846475,
                27957054,
                25700917,
                56678844,
                39931296,
                21818792,
                15243953,
                46110961,
                29099424,
                58411670
            ],
            "turnover_amount": [
                27770014,
                135627968,
                109496376,
                103257416,
                242614176,
                170208784,
                90564944,
                62862520,
                197721472,
                125331136,
                260167456
            ],
            "opening_price": [
                3.75,
                3.76,
                3.84,
                3.99,
                4.04,
                4.24,
                4.12,
                4.13,
                4.14,
                4.27,
                4.32
            ],
            "close_price": [
                3.76,
                3.94,
                3.98,
                4.05,
                4.27,
                4.18,
                4.14,
                4.13,
                4.31,
                4.34,
                4.48
            ],
            "low_price": [
                3.73,
                3.72,
                3.8,
                3.92,
                3.98,
                4.16,
                4.11,
                4.09,
                4.12,
                4.24,
                4.29
            ],
            "high_price": [
                3.8,
                4.14,
                4.0,
                4.07,
                4.46,
                4.36,
                4.21,
                4.17,
                4.45,
                4.36,
                4.67
            ],
            "focus": [
                3.76,
                3.89,
                3.905,
                4.0075,
                4.1875,
                4.235,
                4.145,
                4.13,
                4.255,
                4.3025,
                4.44
            ],
            "amplitude": [
                0.029439203,
                0.192180471,
                0.099833194,
                0.06751543,
                0.220510771,
                0.09,
                0.045092498,
                0.032659863,
                0.155456318,
                0.056789083,
                0.174547033
            ],
            "amplitude_rate": [
                0.007829575,
                0.04940372,
                0.025565479,
                0.016847269,
                0.052659289,
                0.021251476,
                0.010878769,
                0.007907957,
                0.036534975,
                0.01319909,
                0.039312395
            ],
            "opening_price_Standardization": [
                -0.33968311,
                -0.676447505,
                -0.651086049,
                -0.259200007,
                -0.668901567,
                0.055555556,
                -0.554415953,
                0.0,
                -0.739757649,
                -0.572293089,
                -0.687493784
            ],
            "closing_price_Standardization": [
                0.0,
                0.260172117,
                0.751253134,
                0.629485731,
                0.374131385,
                -0.611111111,
                -0.110883191,
                0.0,
                0.353797136,
                0.66033818,
                0.229164595
            ],
            "low_price_Standardization": [
                -1.019049331,
                -0.884585199,
                -1.051754387,
                -1.296000034,
                -0.940997119,
                -0.833333333,
                -0.776182335,
                -1.224744871,
                -0.868411153,
                -1.100563633,
                -0.859367229
            ],
            "high_price_Standardization": [
                1.358732441,
                1.300860587,
                0.951587303,
                0.92571431,
                1.235767301,
                1.388888889,
                1.441481478,
                1.224744871,
                1.254371666,
                1.012518542,
                1.317696418
            ],
            "turnover_volume_growth_rate": [
                0.0,
                3.58271925,
                -0.174003969,
                -0.080700098,
                1.205323802,
                -0.295481467,
                -0.453591689,
                -0.30133836,
                2.024869009,
                -0.3689261,
                1.007313616
            ],
            "opening_price_growth_rate": [
                0.0,
                0.002666667,
                0.021276596,
                0.0390625,
                0.012531328,
                0.04950495,
                -0.028301887,
                0.002427184,
                0.002421308,
                0.031400966,
                0.011709602
            ],
            "closing_price_growth_rate": [
                0.0,
                0.04787234,
                0.010152284,
                0.01758794,
                0.054320988,
                -0.021077283,
                -0.009569378,
                -0.002415459,
                0.043583535,
                0.006960557,
                0.032258065
            ],
            "high_price_proportion": [
                0.989473684,
                0.951690821,
                0.995,
                0.995085995,
                0.957399103,
                0.972477064,
                0.983372922,
                0.990407674,
                0.968539326,
                0.995412844,
                0.959314775
            ],
            "low_price_proportion": [
                0.994666667,
                0.989361702,
                0.989583333,
                0.98245614,
                0.985148515,
                0.995215311,
                0.997572816,
                0.99031477,
                0.995169082,
                0.992974239,
                0.993055556
            ],
            "closing_minus_opening_price_growth_rate": [
                0.002666667,
                0.04787234,
                0.036458333,
                0.015037594,
                0.056930693,
                -0.014150943,
                0.004854369,
                0.0,
                0.041062802,
                0.016393443,
                0.037037037
            ],
            "sum_2_turnover_volume_growth_rate": [
                "null",
                1.791359625,
                0.808677828,
                -0.167702083,
                0.582486877,
                0.153590217,
                -0.601332423,
                -0.528134205,
                0.937099914,
                0.321754202,
                0.411425283
            ],
            "sum_2_opening_price_growth_rate": [
                "null",
                0.001333333,
                0.022609929,
                0.049700798,
                0.032062578,
                0.055770615,
                -0.001774706,
                -0.005861879,
                0.0036349,
                0.03261162,
                0.027410085
            ],
            "sum_2_closing_price_growth_rate": [
                "null",
                0.02393617,
                0.034088454,
                0.022664082,
                0.063114958,
                0.003041605,
                -0.02010802,
                -0.007200148,
                0.021187903,
                0.028752324,
                0.035738343
            ],
            "sum_2_closing_minus_opening_price_growth_rate": [
                "null",
                0.049205674,
                0.060394504,
                0.033266761,
                0.06444949,
                0.007157202,
                -0.001110551,
                0.001213592,
                0.020531401,
                0.036924844,
                0.045233758
            ],
            "sum_2_high_price_proportion": [
                "null",
                1.446427663,
                1.470845411,
                1.492585995,
                1.454942101,
                1.451176616,
                1.469611454,
                1.482094135,
                1.463743163,
                1.479682507,
                1.457021197
            ],
            "sum_2_low_price_proportion": [
                "null",
                1.486695035,
                1.484264184,
                1.477247807,
                1.476376585,
                1.487789568,
                1.495180471,
                1.489101178,
                1.490326467,
                1.49055878,
                1.489542675
            ],
            "sum_2_KLine_Intuitive_Momentum": [
                "null",
                1.794033435,
                0.816153433,
                -0.162394081,
                0.595663278,
                0.154499026,
                -0.601279023,
                -0.52816919,
                0.938211686,
                0.326751665,
                0.417624603
            ],
            "sum_3_turnover_volume_growth_rate": [
                "null",
                "null",
                0.73815851,
                0.266944754,
                0.327240342,
                0.052928734,
                -0.299794689,
                -0.702226642,
                0.440230023,
                0.137056078,
                1.039529723
            ],
            "sum_3_opening_price_growth_rate": [
                "null",
                "null",
                0.015369582,
                0.054135786,
                0.045665194,
                0.070880003,
                0.015352978,
                0.006329915,
                -0.0004517,
                0.033824233,
                0.033450682
            ],
            "sum_3_closing_price_growth_rate": [
                "null",
                "null",
                0.028044785,
                0.040313576,
                0.069430376,
                0.021025331,
                -0.009711602,
                -0.015820805,
                0.011327779,
                0.023742447,
                0.051426281
            ],
            "sum_3_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                0.069262116,
                0.055300596,
                0.079108534,
                0.023927237,
                0.012742857,
                -0.000493578,
                0.02845395,
                0.029179096,
                0.061653599
            ],
            "sum_3_high_price_proportion": [
                "null",
                "null",
                1.959285109,
                1.975649602,
                1.952456433,
                1.942438465,
                1.950823999,
                1.970148643,
                1.956602082,
                1.971241619,
                1.94576978
            ],
            "sum_3_low_price_proportion": [
                "null",
                "null",
                1.980713357,
                1.971965597,
                1.969980386,
                1.979466368,
                1.989432528,
                1.987101751,
                1.987903201,
                1.986525217,
                1.986761409
            ],
            "sum_3_KLine_Intuitive_Momentum": [
                "null",
                "null",
                0.74982791,
                0.287293521,
                0.362261072,
                0.061384027,
                -0.299515692,
                -0.702208303,
                0.441433708,
                0.143633821,
                1.059759237
            ],
            "sum_5_turnover_volume_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.987779617,
                0.424219288,
                -0.461007301,
                -0.589686585,
                0.527522535,
                -0.308230627,
                0.585057271
            ],
            "sum_5_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.046091162,
                0.092011485,
                0.047942084,
                0.031435847,
                0.017940814,
                0.033492099,
                0.030271151
            ],
            "sum_5_closing_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.074905317,
                0.049900492,
                0.014422453,
                -0.003532006,
                0.012116149,
                0.011035464,
                0.037233955
            ],
            "sum_5_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.110518038,
                0.060149741,
                0.03959144,
                0.016099861,
                0.032084862,
                0.030145221,
                0.060608277
            ],
            "sum_5_high_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.929038965,
                2.923786108,
                2.932828433,
                2.94256909,
                2.931359864,
                2.952333491,
                2.9296063
            ],
            "sum_5_low_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.959541441,
                2.966513481,
                2.975733296,
                2.976052843,
                2.981080415,
                2.981370555,
                2.980176867
            ],
            "sum_5_KLine_Intuitive_Momentum": [
                "null",
                "null",
                "null",
                "null",
                1.103698843,
                0.498255593,
                -0.439458671,
                -0.585752412,
                0.535949828,
                -0.296415761,
                0.620777963
            ],
            "average_5_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                4.0,
                4.084,
                4.124,
                4.154,
                4.206,
                4.22,
                4.28
            ],
            "average_10_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                4.11,
                4.182
            ]
        },
        ...
    }
}

伺服器 ( Server - Respond ) 響應 POST 請求的數據格式爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Respond - body =

{
    "request_Url" : "/PickStock?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=PickStock",
    "request_Authorization" : "Basic dXNlcm5hbWU6cGFzc3dvcmQ=",
    "request_Cookie" : "session_id=cmVxdWVzdF9LZXktPnVzZXJuYW1lOnBhc3N3b3Jk",
    "time" : "2024-02-03 17:59:58.239794",
    "Server_say" : "",
    "error" : "",
    "configFile" : "C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt",
    // "configFile" : "C:/QuantitativeTrading/QuantitativeTradingPython/config.txt",
    "trading_direction" : "Long_Position_and_Short_Selling",
    "ticker_symbol" : ["002611", ... ],
    "is_Optimize" : "false",
    "MarketTiming_Pdata_0" : [3, +0.1, -0.1, 0.0],
    "PickStock_Pdata_0" : [3, 5],
    "return_PickStock" : {
        "Coefficient" : {
            "Long_Position" : [Integer, Integer],
            "Short_Selling" : [Integer, Integer]
        },
        "y_profit" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，加權纍加總計;
        "y_Long_Position_profit" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，加權纍加總計;
        "y_Short_Selling_profit" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，加權纍加總計;
        "y_loss" : Floating-Point,  // 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
        "y_Long_Position_loss" : Floating-Point,  // 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
        "y_Short_Selling_loss" : Floating-Point,  // 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
        "maximum_drawdown" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
        "maximum_drawdown_Long_Position" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
        "maximum_drawdown_Short_Selling" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
        "profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
        "Long_Position_profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
        "Short_Selling_profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
        "profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
        "profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
        "Long_Position_profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
        "Long_Position_profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
        "Short_Selling_profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
        "Short_Selling_profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
        "profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
        "profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
        "Long_Position_profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
        "Long_Position_profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
        "Short_Selling_profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
        "Short_Selling_profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
        "average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
        "Long_Position_average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
        "Short_Selling_average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
        "average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
        "Long_Position_average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
        "Short_Selling_average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
        "average_date_transaction_between" : Integer,  // 兩次交易間隔日長，均值;
        "Long_Position_average_date_transaction_between" : Integer,  // 兩次對衝交易間隔日長，均值;
        "Short_Selling_average_date_transaction_between" : Integer,  // 兩次對衝交易間隔日長，均值;
        "number_PickStock_transaction" : Integer,
        "PickStock_sort_ticker" : [
            "null" or [String, ... ],  // 每個交易日依照選股規則篩選出的股票代碼字符串按標示分值排序數組;
            ...
        ],  // 依照選股規則排序篩選出的股票代碼字符串存儲數組;
        "PickStock_sort_score" : [
            "null" or [Floating-Point, ... ],  // 每個交易日依照選股規則篩選出的標示分值排序數組;
            ...
        ]  // 依照選股規則排序篩選出的股票代碼字符串存儲數組;
    }
}

7. 連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 依據數量化指標倉位配比 ( size position ) 運算，使用網址 ( Uniform Resource Locator , URL ) :

http://[::1]:10001/SizePosition?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=SizePosition&trading_direction=Long_Position_and_Short_Selling&ticker_symbol=["all"]&is_Optimize=false&MarketTiming_Pdata_0=[3,+0.1,-0.1,0.0]&MarketTiming_Plower=["-Infinity","-Infinity","-Infinity","-Infinity"]&MarketTiming_Pupper=["+Infinity","+Infinity","+Infinity","+Infinity"]&MarketTiming_weight=[]&PickStock_Pdata_0=[3,5]&PickStock_Plower=["-Infinity","-Infinity"]&PickStock_Pupper=["+Infinity","+Infinity"]&PickStock_weight=[]&SizePosition_Pdata_0=[1.0,"average"]&SizePosition_Plower=[0.0,0.0]&SizePosition_Pupper=[1.0,1.0]&SizePosition_weight=[]&Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.jld&training_data_file=C:/QuantitativeTrading/Data/trainingData.jld&testing_data_file=C:/QuantitativeTrading/Data/testingData.jld&stepping_data_file=C:/QuantitativeTrading/Data/steppingData.jld

用戶端 ( Client - Request ) 發送 POST 請求的數據爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Request - POST =

{
    "configFile" : ["C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt"],
    // "configFile" : ["C:/QuantitativeTrading/QuantitativeTradingPython/config.txt"],
    "Cleaned_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.jld"],
    // "Cleaned_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.pickle"],
    "trading_direction" : ["Long_Position_and_Short_Selling"],
    "ticker_symbol" : ["all"],
    "is_Optimize" : ["false"],
    "MarketTiming_Pdata_0" : [3, +0.1, -0.1, 0.0],
    "MarketTiming_Plower" : ["-Infinity", "-Infinity", "-Infinity", "-Infinity"],
    "MarketTiming_Pupper" : ["+Infinity", "+Infinity", "+Infinity", "+Infinity"],
    "MarketTiming_weight" : [],
    "PickStock_Pdata_0" : [3, 5],
    "PickStock_Plower" : ["-Infinity", "-Infinity"],
    "PickStock_Pupper" : ["+Infinity", "+Infinity"],
    "PickStock_weight" : [],
    "SizePosition_Pdata_0" : [1.0, "average"],
    "SizePosition_Plower" : [0.0, 0.0],
    "SizePosition_Pupper" : [1.0, 1.0],
    "SizePosition_weight" : [],
    "training_data_file" : ["C:/QuantitativeTrading/Data/trainingData.jld"],
    // "training_data_file" : ["C:/QuantitativeTrading/Data/trainingData.pickle"],
    "testing_data_file" : ["C:/QuantitativeTrading/Data/testingData.jld"],
    // "testing_data_file" : ["C:/QuantitativeTrading/Data/testingData.pickle"],
    "training_data" : {
        "002611" : {
            "date_transaction": [
                "2019-1-2",
                "2019-1-3",
                "2019-1-4",
                "2019-1-7",
                "2019-1-8",
                "2019-1-9",
                "2019-1-10",
                "2019-1-11",
                "2019-1-14",
                "2019-1-15",
                "2019-1-16"
            ],
            "turnover_volume": [
                7385675,
                33846475,
                27957054,
                25700917,
                56678844,
                39931296,
                21818792,
                15243953,
                46110961,
                29099424,
                58411670
            ],
            "turnover_amount": [
                27770014,
                135627968,
                109496376,
                103257416,
                242614176,
                170208784,
                90564944,
                62862520,
                197721472,
                125331136,
                260167456
            ],
            "opening_price": [
                3.75,
                3.76,
                3.84,
                3.99,
                4.04,
                4.24,
                4.12,
                4.13,
                4.14,
                4.27,
                4.32
            ],
            "close_price": [
                3.76,
                3.94,
                3.98,
                4.05,
                4.27,
                4.18,
                4.14,
                4.13,
                4.31,
                4.34,
                4.48
            ],
            "low_price": [
                3.73,
                3.72,
                3.8,
                3.92,
                3.98,
                4.16,
                4.11,
                4.09,
                4.12,
                4.24,
                4.29
            ],
            "high_price": [
                3.8,
                4.14,
                4.0,
                4.07,
                4.46,
                4.36,
                4.21,
                4.17,
                4.45,
                4.36,
                4.67
            ],
            "focus": [
                3.76,
                3.89,
                3.905,
                4.0075,
                4.1875,
                4.235,
                4.145,
                4.13,
                4.255,
                4.3025,
                4.44
            ],
            "amplitude": [
                0.029439203,
                0.192180471,
                0.099833194,
                0.06751543,
                0.220510771,
                0.09,
                0.045092498,
                0.032659863,
                0.155456318,
                0.056789083,
                0.174547033
            ],
            "amplitude_rate": [
                0.007829575,
                0.04940372,
                0.025565479,
                0.016847269,
                0.052659289,
                0.021251476,
                0.010878769,
                0.007907957,
                0.036534975,
                0.01319909,
                0.039312395
            ],
            "opening_price_Standardization": [
                -0.33968311,
                -0.676447505,
                -0.651086049,
                -0.259200007,
                -0.668901567,
                0.055555556,
                -0.554415953,
                0.0,
                -0.739757649,
                -0.572293089,
                -0.687493784
            ],
            "closing_price_Standardization": [
                0.0,
                0.260172117,
                0.751253134,
                0.629485731,
                0.374131385,
                -0.611111111,
                -0.110883191,
                0.0,
                0.353797136,
                0.66033818,
                0.229164595
            ],
            "low_price_Standardization": [
                -1.019049331,
                -0.884585199,
                -1.051754387,
                -1.296000034,
                -0.940997119,
                -0.833333333,
                -0.776182335,
                -1.224744871,
                -0.868411153,
                -1.100563633,
                -0.859367229
            ],
            "high_price_Standardization": [
                1.358732441,
                1.300860587,
                0.951587303,
                0.92571431,
                1.235767301,
                1.388888889,
                1.441481478,
                1.224744871,
                1.254371666,
                1.012518542,
                1.317696418
            ],
            "turnover_volume_growth_rate": [
                0.0,
                3.58271925,
                -0.174003969,
                -0.080700098,
                1.205323802,
                -0.295481467,
                -0.453591689,
                -0.30133836,
                2.024869009,
                -0.3689261,
                1.007313616
            ],
            "opening_price_growth_rate": [
                0.0,
                0.002666667,
                0.021276596,
                0.0390625,
                0.012531328,
                0.04950495,
                -0.028301887,
                0.002427184,
                0.002421308,
                0.031400966,
                0.011709602
            ],
            "closing_price_growth_rate": [
                0.0,
                0.04787234,
                0.010152284,
                0.01758794,
                0.054320988,
                -0.021077283,
                -0.009569378,
                -0.002415459,
                0.043583535,
                0.006960557,
                0.032258065
            ],
            "high_price_proportion": [
                0.989473684,
                0.951690821,
                0.995,
                0.995085995,
                0.957399103,
                0.972477064,
                0.983372922,
                0.990407674,
                0.968539326,
                0.995412844,
                0.959314775
            ],
            "low_price_proportion": [
                0.994666667,
                0.989361702,
                0.989583333,
                0.98245614,
                0.985148515,
                0.995215311,
                0.997572816,
                0.99031477,
                0.995169082,
                0.992974239,
                0.993055556
            ],
            "closing_minus_opening_price_growth_rate": [
                0.002666667,
                0.04787234,
                0.036458333,
                0.015037594,
                0.056930693,
                -0.014150943,
                0.004854369,
                0.0,
                0.041062802,
                0.016393443,
                0.037037037
            ],
            "sum_2_turnover_volume_growth_rate": [
                "null",
                1.791359625,
                0.808677828,
                -0.167702083,
                0.582486877,
                0.153590217,
                -0.601332423,
                -0.528134205,
                0.937099914,
                0.321754202,
                0.411425283
            ],
            "sum_2_opening_price_growth_rate": [
                "null",
                0.001333333,
                0.022609929,
                0.049700798,
                0.032062578,
                0.055770615,
                -0.001774706,
                -0.005861879,
                0.0036349,
                0.03261162,
                0.027410085
            ],
            "sum_2_closing_price_growth_rate": [
                "null",
                0.02393617,
                0.034088454,
                0.022664082,
                0.063114958,
                0.003041605,
                -0.02010802,
                -0.007200148,
                0.021187903,
                0.028752324,
                0.035738343
            ],
            "sum_2_closing_minus_opening_price_growth_rate": [
                "null",
                0.049205674,
                0.060394504,
                0.033266761,
                0.06444949,
                0.007157202,
                -0.001110551,
                0.001213592,
                0.020531401,
                0.036924844,
                0.045233758
            ],
            "sum_2_high_price_proportion": [
                "null",
                1.446427663,
                1.470845411,
                1.492585995,
                1.454942101,
                1.451176616,
                1.469611454,
                1.482094135,
                1.463743163,
                1.479682507,
                1.457021197
            ],
            "sum_2_low_price_proportion": [
                "null",
                1.486695035,
                1.484264184,
                1.477247807,
                1.476376585,
                1.487789568,
                1.495180471,
                1.489101178,
                1.490326467,
                1.49055878,
                1.489542675
            ],
            "sum_2_KLine_Intuitive_Momentum": [
                "null",
                1.794033435,
                0.816153433,
                -0.162394081,
                0.595663278,
                0.154499026,
                -0.601279023,
                -0.52816919,
                0.938211686,
                0.326751665,
                0.417624603
            ],
            "sum_3_turnover_volume_growth_rate": [
                "null",
                "null",
                0.73815851,
                0.266944754,
                0.327240342,
                0.052928734,
                -0.299794689,
                -0.702226642,
                0.440230023,
                0.137056078,
                1.039529723
            ],
            "sum_3_opening_price_growth_rate": [
                "null",
                "null",
                0.015369582,
                0.054135786,
                0.045665194,
                0.070880003,
                0.015352978,
                0.006329915,
                -0.0004517,
                0.033824233,
                0.033450682
            ],
            "sum_3_closing_price_growth_rate": [
                "null",
                "null",
                0.028044785,
                0.040313576,
                0.069430376,
                0.021025331,
                -0.009711602,
                -0.015820805,
                0.011327779,
                0.023742447,
                0.051426281
            ],
            "sum_3_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                0.069262116,
                0.055300596,
                0.079108534,
                0.023927237,
                0.012742857,
                -0.000493578,
                0.02845395,
                0.029179096,
                0.061653599
            ],
            "sum_3_high_price_proportion": [
                "null",
                "null",
                1.959285109,
                1.975649602,
                1.952456433,
                1.942438465,
                1.950823999,
                1.970148643,
                1.956602082,
                1.971241619,
                1.94576978
            ],
            "sum_3_low_price_proportion": [
                "null",
                "null",
                1.980713357,
                1.971965597,
                1.969980386,
                1.979466368,
                1.989432528,
                1.987101751,
                1.987903201,
                1.986525217,
                1.986761409
            ],
            "sum_3_KLine_Intuitive_Momentum": [
                "null",
                "null",
                0.74982791,
                0.287293521,
                0.362261072,
                0.061384027,
                -0.299515692,
                -0.702208303,
                0.441433708,
                0.143633821,
                1.059759237
            ],
            "sum_5_turnover_volume_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.987779617,
                0.424219288,
                -0.461007301,
                -0.589686585,
                0.527522535,
                -0.308230627,
                0.585057271
            ],
            "sum_5_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.046091162,
                0.092011485,
                0.047942084,
                0.031435847,
                0.017940814,
                0.033492099,
                0.030271151
            ],
            "sum_5_closing_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.074905317,
                0.049900492,
                0.014422453,
                -0.003532006,
                0.012116149,
                0.011035464,
                0.037233955
            ],
            "sum_5_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.110518038,
                0.060149741,
                0.03959144,
                0.016099861,
                0.032084862,
                0.030145221,
                0.060608277
            ],
            "sum_5_high_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.929038965,
                2.923786108,
                2.932828433,
                2.94256909,
                2.931359864,
                2.952333491,
                2.9296063
            ],
            "sum_5_low_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.959541441,
                2.966513481,
                2.975733296,
                2.976052843,
                2.981080415,
                2.981370555,
                2.980176867
            ],
            "sum_5_KLine_Intuitive_Momentum": [
                "null",
                "null",
                "null",
                "null",
                1.103698843,
                0.498255593,
                -0.439458671,
                -0.585752412,
                0.535949828,
                -0.296415761,
                0.620777963
            ],
            "average_5_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                4.0,
                4.084,
                4.124,
                4.154,
                4.206,
                4.22,
                4.28
            ],
            "average_10_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                4.11,
                4.182
            ]
        },
        ...
    },
    "testing_data" : {
        "002611" : {
            "date_transaction": [
                "2019-1-2",
                "2019-1-3",
                "2019-1-4",
                "2019-1-7",
                "2019-1-8",
                "2019-1-9",
                "2019-1-10",
                "2019-1-11",
                "2019-1-14",
                "2019-1-15",
                "2019-1-16"
            ],
            "turnover_volume": [
                7385675,
                33846475,
                27957054,
                25700917,
                56678844,
                39931296,
                21818792,
                15243953,
                46110961,
                29099424,
                58411670
            ],
            "turnover_amount": [
                27770014,
                135627968,
                109496376,
                103257416,
                242614176,
                170208784,
                90564944,
                62862520,
                197721472,
                125331136,
                260167456
            ],
            "opening_price": [
                3.75,
                3.76,
                3.84,
                3.99,
                4.04,
                4.24,
                4.12,
                4.13,
                4.14,
                4.27,
                4.32
            ],
            "close_price": [
                3.76,
                3.94,
                3.98,
                4.05,
                4.27,
                4.18,
                4.14,
                4.13,
                4.31,
                4.34,
                4.48
            ],
            "low_price": [
                3.73,
                3.72,
                3.8,
                3.92,
                3.98,
                4.16,
                4.11,
                4.09,
                4.12,
                4.24,
                4.29
            ],
            "high_price": [
                3.8,
                4.14,
                4.0,
                4.07,
                4.46,
                4.36,
                4.21,
                4.17,
                4.45,
                4.36,
                4.67
            ],
            "focus": [
                3.76,
                3.89,
                3.905,
                4.0075,
                4.1875,
                4.235,
                4.145,
                4.13,
                4.255,
                4.3025,
                4.44
            ],
            "amplitude": [
                0.029439203,
                0.192180471,
                0.099833194,
                0.06751543,
                0.220510771,
                0.09,
                0.045092498,
                0.032659863,
                0.155456318,
                0.056789083,
                0.174547033
            ],
            "amplitude_rate": [
                0.007829575,
                0.04940372,
                0.025565479,
                0.016847269,
                0.052659289,
                0.021251476,
                0.010878769,
                0.007907957,
                0.036534975,
                0.01319909,
                0.039312395
            ],
            "opening_price_Standardization": [
                -0.33968311,
                -0.676447505,
                -0.651086049,
                -0.259200007,
                -0.668901567,
                0.055555556,
                -0.554415953,
                0.0,
                -0.739757649,
                -0.572293089,
                -0.687493784
            ],
            "closing_price_Standardization": [
                0.0,
                0.260172117,
                0.751253134,
                0.629485731,
                0.374131385,
                -0.611111111,
                -0.110883191,
                0.0,
                0.353797136,
                0.66033818,
                0.229164595
            ],
            "low_price_Standardization": [
                -1.019049331,
                -0.884585199,
                -1.051754387,
                -1.296000034,
                -0.940997119,
                -0.833333333,
                -0.776182335,
                -1.224744871,
                -0.868411153,
                -1.100563633,
                -0.859367229
            ],
            "high_price_Standardization": [
                1.358732441,
                1.300860587,
                0.951587303,
                0.92571431,
                1.235767301,
                1.388888889,
                1.441481478,
                1.224744871,
                1.254371666,
                1.012518542,
                1.317696418
            ],
            "turnover_volume_growth_rate": [
                0.0,
                3.58271925,
                -0.174003969,
                -0.080700098,
                1.205323802,
                -0.295481467,
                -0.453591689,
                -0.30133836,
                2.024869009,
                -0.3689261,
                1.007313616
            ],
            "opening_price_growth_rate": [
                0.0,
                0.002666667,
                0.021276596,
                0.0390625,
                0.012531328,
                0.04950495,
                -0.028301887,
                0.002427184,
                0.002421308,
                0.031400966,
                0.011709602
            ],
            "closing_price_growth_rate": [
                0.0,
                0.04787234,
                0.010152284,
                0.01758794,
                0.054320988,
                -0.021077283,
                -0.009569378,
                -0.002415459,
                0.043583535,
                0.006960557,
                0.032258065
            ],
            "high_price_proportion": [
                0.989473684,
                0.951690821,
                0.995,
                0.995085995,
                0.957399103,
                0.972477064,
                0.983372922,
                0.990407674,
                0.968539326,
                0.995412844,
                0.959314775
            ],
            "low_price_proportion": [
                0.994666667,
                0.989361702,
                0.989583333,
                0.98245614,
                0.985148515,
                0.995215311,
                0.997572816,
                0.99031477,
                0.995169082,
                0.992974239,
                0.993055556
            ],
            "closing_minus_opening_price_growth_rate": [
                0.002666667,
                0.04787234,
                0.036458333,
                0.015037594,
                0.056930693,
                -0.014150943,
                0.004854369,
                0.0,
                0.041062802,
                0.016393443,
                0.037037037
            ],
            "sum_2_turnover_volume_growth_rate": [
                "null",
                1.791359625,
                0.808677828,
                -0.167702083,
                0.582486877,
                0.153590217,
                -0.601332423,
                -0.528134205,
                0.937099914,
                0.321754202,
                0.411425283
            ],
            "sum_2_opening_price_growth_rate": [
                "null",
                0.001333333,
                0.022609929,
                0.049700798,
                0.032062578,
                0.055770615,
                -0.001774706,
                -0.005861879,
                0.0036349,
                0.03261162,
                0.027410085
            ],
            "sum_2_closing_price_growth_rate": [
                "null",
                0.02393617,
                0.034088454,
                0.022664082,
                0.063114958,
                0.003041605,
                -0.02010802,
                -0.007200148,
                0.021187903,
                0.028752324,
                0.035738343
            ],
            "sum_2_closing_minus_opening_price_growth_rate": [
                "null",
                0.049205674,
                0.060394504,
                0.033266761,
                0.06444949,
                0.007157202,
                -0.001110551,
                0.001213592,
                0.020531401,
                0.036924844,
                0.045233758
            ],
            "sum_2_high_price_proportion": [
                "null",
                1.446427663,
                1.470845411,
                1.492585995,
                1.454942101,
                1.451176616,
                1.469611454,
                1.482094135,
                1.463743163,
                1.479682507,
                1.457021197
            ],
            "sum_2_low_price_proportion": [
                "null",
                1.486695035,
                1.484264184,
                1.477247807,
                1.476376585,
                1.487789568,
                1.495180471,
                1.489101178,
                1.490326467,
                1.49055878,
                1.489542675
            ],
            "sum_2_KLine_Intuitive_Momentum": [
                "null",
                1.794033435,
                0.816153433,
                -0.162394081,
                0.595663278,
                0.154499026,
                -0.601279023,
                -0.52816919,
                0.938211686,
                0.326751665,
                0.417624603
            ],
            "sum_3_turnover_volume_growth_rate": [
                "null",
                "null",
                0.73815851,
                0.266944754,
                0.327240342,
                0.052928734,
                -0.299794689,
                -0.702226642,
                0.440230023,
                0.137056078,
                1.039529723
            ],
            "sum_3_opening_price_growth_rate": [
                "null",
                "null",
                0.015369582,
                0.054135786,
                0.045665194,
                0.070880003,
                0.015352978,
                0.006329915,
                -0.0004517,
                0.033824233,
                0.033450682
            ],
            "sum_3_closing_price_growth_rate": [
                "null",
                "null",
                0.028044785,
                0.040313576,
                0.069430376,
                0.021025331,
                -0.009711602,
                -0.015820805,
                0.011327779,
                0.023742447,
                0.051426281
            ],
            "sum_3_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                0.069262116,
                0.055300596,
                0.079108534,
                0.023927237,
                0.012742857,
                -0.000493578,
                0.02845395,
                0.029179096,
                0.061653599
            ],
            "sum_3_high_price_proportion": [
                "null",
                "null",
                1.959285109,
                1.975649602,
                1.952456433,
                1.942438465,
                1.950823999,
                1.970148643,
                1.956602082,
                1.971241619,
                1.94576978
            ],
            "sum_3_low_price_proportion": [
                "null",
                "null",
                1.980713357,
                1.971965597,
                1.969980386,
                1.979466368,
                1.989432528,
                1.987101751,
                1.987903201,
                1.986525217,
                1.986761409
            ],
            "sum_3_KLine_Intuitive_Momentum": [
                "null",
                "null",
                0.74982791,
                0.287293521,
                0.362261072,
                0.061384027,
                -0.299515692,
                -0.702208303,
                0.441433708,
                0.143633821,
                1.059759237
            ],
            "sum_5_turnover_volume_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.987779617,
                0.424219288,
                -0.461007301,
                -0.589686585,
                0.527522535,
                -0.308230627,
                0.585057271
            ],
            "sum_5_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.046091162,
                0.092011485,
                0.047942084,
                0.031435847,
                0.017940814,
                0.033492099,
                0.030271151
            ],
            "sum_5_closing_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.074905317,
                0.049900492,
                0.014422453,
                -0.003532006,
                0.012116149,
                0.011035464,
                0.037233955
            ],
            "sum_5_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.110518038,
                0.060149741,
                0.03959144,
                0.016099861,
                0.032084862,
                0.030145221,
                0.060608277
            ],
            "sum_5_high_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.929038965,
                2.923786108,
                2.932828433,
                2.94256909,
                2.931359864,
                2.952333491,
                2.9296063
            ],
            "sum_5_low_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.959541441,
                2.966513481,
                2.975733296,
                2.976052843,
                2.981080415,
                2.981370555,
                2.980176867
            ],
            "sum_5_KLine_Intuitive_Momentum": [
                "null",
                "null",
                "null",
                "null",
                1.103698843,
                0.498255593,
                -0.439458671,
                -0.585752412,
                0.535949828,
                -0.296415761,
                0.620777963
            ],
            "average_5_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                4.0,
                4.084,
                4.124,
                4.154,
                4.206,
                4.22,
                4.28
            ],
            "average_10_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                4.11,
                4.182
            ]
        },
        ...
    }
}

伺服器 ( Server - Respond ) 響應 POST 請求的數據格式爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Respond - body =

{
    "request_Url" : "/SizePosition?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=SizePosition",
    "request_Authorization" : "Basic dXNlcm5hbWU6cGFzc3dvcmQ=",
    "request_Cookie" : "session_id=cmVxdWVzdF9LZXktPnVzZXJuYW1lOnBhc3N3b3Jk",
    "time" : "2024-02-03 17:59:58.239794",
    "Server_say" : "",
    "error" : "",
    "configFile" : "C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt",
    // "configFile" : "C:/QuantitativeTrading/QuantitativeTradingPython/config.txt",
    "trading_direction" : "Long_Position_and_Short_Selling",
    "ticker_symbol" : ["002611", ... ],
    "is_Optimize" : "false",
    "MarketTiming_Pdata_0" : [3, +0.1, -0.1, 0.0],
    "PickStock_Pdata_0" : [3, 5],
    "SizePosition_Pdata_0" : [1.0, 1.0],
    "return_SizePosition" : {
        "Coefficient" : {
            "002611" : {
                "Long_Position" : [Floating-Point, Floating-Point],
                "Short_Selling" : [Floating-Point, Floating-Point]
            },
            ...
        },
        "y_profit" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，加權纍加總計;
        "y_Long_Position_profit" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，加權纍加總計;
        "y_Short_Selling_profit" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，加權纍加總計;
        "y_loss" : Floating-Point,  // 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
        "y_Long_Position_loss" : Floating-Point,  // 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
        "y_Short_Selling_loss" : Floating-Point,  // 每兩次對衝交易最大回撤 × 權重，加權取極值總計;
        "maximum_drawdown" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
        "maximum_drawdown_Long_Position" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
        "maximum_drawdown_Short_Selling" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
        "profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
        "Long_Position_profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
        "Short_Selling_profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
        "profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
        "profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
        "Long_Position_profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
        "Long_Position_profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
        "Short_Selling_profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
        "Short_Selling_profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
        "profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
        "profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
        "Long_Position_profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
        "Long_Position_profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
        "Short_Selling_profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
        "Short_Selling_profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
        "average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
        "Long_Position_average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
        "Short_Selling_average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
        "average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
        "Long_Position_average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
        "Short_Selling_average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
        "average_date_transaction_between" : Integer,  // 兩次交易間隔日長，均值;
        "Long_Position_average_date_transaction_between" : Integer,  // 兩次對衝交易間隔日長，均值;
        "Short_Selling_average_date_transaction_between" : Integer,  // 兩次對衝交易間隔日長，均值;
        "number_PickStock_transaction" : Integer
    }
}

8. 連接數量化交易運算伺服器「QuantitativeTrading」做 ( Client - Request ) 回測 ( back testing ) 如：步進分析 ( stepper movement , propulsion analysis ) 運算，使用網址 ( Uniform Resource Locator , URL ) :

http://[::1]:10001/BackTesting?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=BackTesting&trading_direction=Long_Position_and_Short_Selling&ticker_symbol=["all"]&is_Optimize=true&MarketTiming_Pdata_0=[3,+0.1,-0.1,0.0]&MarketTiming_Plower=["-Infinity","-Infinity","-Infinity","-Infinity"]&MarketTiming_Pupper=["+Infinity","+Infinity","+Infinity","+Infinity"]&MarketTiming_weight=[]&PickStock_Pdata_0=[3,5]&PickStock_Plower=["-Infinity","-Infinity"]&PickStock_Pupper=["+Infinity","+Infinity"]&PickStock_weight=[]&SizePosition_Pdata_0=[1.0,"average"]&SizePosition_Plower=[0.0,0.0]&SizePosition_Pupper=[1.0,1.0]&SizePosition_weight=[]&risk_threshold=0.0&training_sequence_length=60&training_ticker_symbol=["all"]&testing_sequence_length=1&testing_ticker_symbol=["all"]&Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.jld&training_data_file=C:/QuantitativeTrading/Data/trainingData.jld&testing_data_file=C:/QuantitativeTrading/Data/testingData.jld&stepping_data_file=C:/QuantitativeTrading/Data/steppingData.jld

用戶端 ( Client - Request ) 發送 POST 請求的數據爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Request - POST =

{
    "configFile" : ["C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt"],
    // "configFile" : ["C:/QuantitativeTrading/QuantitativeTradingPython/config.txt"],
    "Cleaned_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.jld"],
    // "Cleaned_K_Line" : ["C:/QuantitativeTrading/Data/steppingData.pickle"],
    "trading_direction" : ["Long_Position_and_Short_Selling"],
    "is_Optimize" : ["true"],
    "risk_threshold" : [0.0],
    "training_ticker_symbol" : ["all"],
    "training_sequence_length" : [60],
    "testing_ticker_symbol" : ["all"],
    "testing_sequence_length" : [1],
    "MarketTiming_Pdata_0" : [3, +0.1, -0.1, 0.0],
    "MarketTiming_Plower" : ["-Infinity", "-Infinity", "-Infinity", "-Infinity"],
    "MarketTiming_Pupper" : ["+Infinity", "+Infinity", "+Infinity", "+Infinity"],
    "MarketTiming_weight" : [],
    "PickStock_Pdata_0" : [3, 5],
    "PickStock_Plower" : ["-Infinity", "-Infinity"],
    "PickStock_Pupper" : ["+Infinity", "+Infinity"],
    "PickStock_weight" : [],
    "SizePosition_Pdata_0" : [1.0, "average"],
    "SizePosition_Plower" : [0.0, 0.0],
    "SizePosition_Pupper" : [1.0, 1.0],
    "SizePosition_weight" : [],
    "stepping_data_file" : ["C:/QuantitativeTrading/Data/steppingData.jld"],
    // "stepping_data_file" : ["C:/QuantitativeTrading/Data/steppingData.pickle"],
    "stepping_data" : {
        "002611" : {
            "date_transaction": [
                "2019-1-2",
                "2019-1-3",
                "2019-1-4",
                "2019-1-7",
                "2019-1-8",
                "2019-1-9",
                "2019-1-10",
                "2019-1-11",
                "2019-1-14",
                "2019-1-15",
                "2019-1-16"
            ],
            "turnover_volume": [
                7385675,
                33846475,
                27957054,
                25700917,
                56678844,
                39931296,
                21818792,
                15243953,
                46110961,
                29099424,
                58411670
            ],
            "turnover_amount": [
                27770014,
                135627968,
                109496376,
                103257416,
                242614176,
                170208784,
                90564944,
                62862520,
                197721472,
                125331136,
                260167456
            ],
            "opening_price": [
                3.75,
                3.76,
                3.84,
                3.99,
                4.04,
                4.24,
                4.12,
                4.13,
                4.14,
                4.27,
                4.32
            ],
            "close_price": [
                3.76,
                3.94,
                3.98,
                4.05,
                4.27,
                4.18,
                4.14,
                4.13,
                4.31,
                4.34,
                4.48
            ],
            "low_price": [
                3.73,
                3.72,
                3.8,
                3.92,
                3.98,
                4.16,
                4.11,
                4.09,
                4.12,
                4.24,
                4.29
            ],
            "high_price": [
                3.8,
                4.14,
                4.0,
                4.07,
                4.46,
                4.36,
                4.21,
                4.17,
                4.45,
                4.36,
                4.67
            ],
            "focus": [
                3.76,
                3.89,
                3.905,
                4.0075,
                4.1875,
                4.235,
                4.145,
                4.13,
                4.255,
                4.3025,
                4.44
            ],
            "amplitude": [
                0.029439203,
                0.192180471,
                0.099833194,
                0.06751543,
                0.220510771,
                0.09,
                0.045092498,
                0.032659863,
                0.155456318,
                0.056789083,
                0.174547033
            ],
            "amplitude_rate": [
                0.007829575,
                0.04940372,
                0.025565479,
                0.016847269,
                0.052659289,
                0.021251476,
                0.010878769,
                0.007907957,
                0.036534975,
                0.01319909,
                0.039312395
            ],
            "opening_price_Standardization": [
                -0.33968311,
                -0.676447505,
                -0.651086049,
                -0.259200007,
                -0.668901567,
                0.055555556,
                -0.554415953,
                0.0,
                -0.739757649,
                -0.572293089,
                -0.687493784
            ],
            "closing_price_Standardization": [
                0.0,
                0.260172117,
                0.751253134,
                0.629485731,
                0.374131385,
                -0.611111111,
                -0.110883191,
                0.0,
                0.353797136,
                0.66033818,
                0.229164595
            ],
            "low_price_Standardization": [
                -1.019049331,
                -0.884585199,
                -1.051754387,
                -1.296000034,
                -0.940997119,
                -0.833333333,
                -0.776182335,
                -1.224744871,
                -0.868411153,
                -1.100563633,
                -0.859367229
            ],
            "high_price_Standardization": [
                1.358732441,
                1.300860587,
                0.951587303,
                0.92571431,
                1.235767301,
                1.388888889,
                1.441481478,
                1.224744871,
                1.254371666,
                1.012518542,
                1.317696418
            ],
            "turnover_volume_growth_rate": [
                0.0,
                3.58271925,
                -0.174003969,
                -0.080700098,
                1.205323802,
                -0.295481467,
                -0.453591689,
                -0.30133836,
                2.024869009,
                -0.3689261,
                1.007313616
            ],
            "opening_price_growth_rate": [
                0.0,
                0.002666667,
                0.021276596,
                0.0390625,
                0.012531328,
                0.04950495,
                -0.028301887,
                0.002427184,
                0.002421308,
                0.031400966,
                0.011709602
            ],
            "closing_price_growth_rate": [
                0.0,
                0.04787234,
                0.010152284,
                0.01758794,
                0.054320988,
                -0.021077283,
                -0.009569378,
                -0.002415459,
                0.043583535,
                0.006960557,
                0.032258065
            ],
            "high_price_proportion": [
                0.989473684,
                0.951690821,
                0.995,
                0.995085995,
                0.957399103,
                0.972477064,
                0.983372922,
                0.990407674,
                0.968539326,
                0.995412844,
                0.959314775
            ],
            "low_price_proportion": [
                0.994666667,
                0.989361702,
                0.989583333,
                0.98245614,
                0.985148515,
                0.995215311,
                0.997572816,
                0.99031477,
                0.995169082,
                0.992974239,
                0.993055556
            ],
            "closing_minus_opening_price_growth_rate": [
                0.002666667,
                0.04787234,
                0.036458333,
                0.015037594,
                0.056930693,
                -0.014150943,
                0.004854369,
                0.0,
                0.041062802,
                0.016393443,
                0.037037037
            ],
            "sum_2_turnover_volume_growth_rate": [
                "null",
                1.791359625,
                0.808677828,
                -0.167702083,
                0.582486877,
                0.153590217,
                -0.601332423,
                -0.528134205,
                0.937099914,
                0.321754202,
                0.411425283
            ],
            "sum_2_opening_price_growth_rate": [
                "null",
                0.001333333,
                0.022609929,
                0.049700798,
                0.032062578,
                0.055770615,
                -0.001774706,
                -0.005861879,
                0.0036349,
                0.03261162,
                0.027410085
            ],
            "sum_2_closing_price_growth_rate": [
                "null",
                0.02393617,
                0.034088454,
                0.022664082,
                0.063114958,
                0.003041605,
                -0.02010802,
                -0.007200148,
                0.021187903,
                0.028752324,
                0.035738343
            ],
            "sum_2_closing_minus_opening_price_growth_rate": [
                "null",
                0.049205674,
                0.060394504,
                0.033266761,
                0.06444949,
                0.007157202,
                -0.001110551,
                0.001213592,
                0.020531401,
                0.036924844,
                0.045233758
            ],
            "sum_2_high_price_proportion": [
                "null",
                1.446427663,
                1.470845411,
                1.492585995,
                1.454942101,
                1.451176616,
                1.469611454,
                1.482094135,
                1.463743163,
                1.479682507,
                1.457021197
            ],
            "sum_2_low_price_proportion": [
                "null",
                1.486695035,
                1.484264184,
                1.477247807,
                1.476376585,
                1.487789568,
                1.495180471,
                1.489101178,
                1.490326467,
                1.49055878,
                1.489542675
            ],
            "sum_2_KLine_Intuitive_Momentum": [
                "null",
                1.794033435,
                0.816153433,
                -0.162394081,
                0.595663278,
                0.154499026,
                -0.601279023,
                -0.52816919,
                0.938211686,
                0.326751665,
                0.417624603
            ],
            "sum_3_turnover_volume_growth_rate": [
                "null",
                "null",
                0.73815851,
                0.266944754,
                0.327240342,
                0.052928734,
                -0.299794689,
                -0.702226642,
                0.440230023,
                0.137056078,
                1.039529723
            ],
            "sum_3_opening_price_growth_rate": [
                "null",
                "null",
                0.015369582,
                0.054135786,
                0.045665194,
                0.070880003,
                0.015352978,
                0.006329915,
                -0.0004517,
                0.033824233,
                0.033450682
            ],
            "sum_3_closing_price_growth_rate": [
                "null",
                "null",
                0.028044785,
                0.040313576,
                0.069430376,
                0.021025331,
                -0.009711602,
                -0.015820805,
                0.011327779,
                0.023742447,
                0.051426281
            ],
            "sum_3_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                0.069262116,
                0.055300596,
                0.079108534,
                0.023927237,
                0.012742857,
                -0.000493578,
                0.02845395,
                0.029179096,
                0.061653599
            ],
            "sum_3_high_price_proportion": [
                "null",
                "null",
                1.959285109,
                1.975649602,
                1.952456433,
                1.942438465,
                1.950823999,
                1.970148643,
                1.956602082,
                1.971241619,
                1.94576978
            ],
            "sum_3_low_price_proportion": [
                "null",
                "null",
                1.980713357,
                1.971965597,
                1.969980386,
                1.979466368,
                1.989432528,
                1.987101751,
                1.987903201,
                1.986525217,
                1.986761409
            ],
            "sum_3_KLine_Intuitive_Momentum": [
                "null",
                "null",
                0.74982791,
                0.287293521,
                0.362261072,
                0.061384027,
                -0.299515692,
                -0.702208303,
                0.441433708,
                0.143633821,
                1.059759237
            ],
            "sum_5_turnover_volume_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.987779617,
                0.424219288,
                -0.461007301,
                -0.589686585,
                0.527522535,
                -0.308230627,
                0.585057271
            ],
            "sum_5_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.046091162,
                0.092011485,
                0.047942084,
                0.031435847,
                0.017940814,
                0.033492099,
                0.030271151
            ],
            "sum_5_closing_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.074905317,
                0.049900492,
                0.014422453,
                -0.003532006,
                0.012116149,
                0.011035464,
                0.037233955
            ],
            "sum_5_closing_minus_opening_price_growth_rate": [
                "null",
                "null",
                "null",
                "null",
                0.110518038,
                0.060149741,
                0.03959144,
                0.016099861,
                0.032084862,
                0.030145221,
                0.060608277
            ],
            "sum_5_high_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.929038965,
                2.923786108,
                2.932828433,
                2.94256909,
                2.931359864,
                2.952333491,
                2.9296063
            ],
            "sum_5_low_price_proportion": [
                "null",
                "null",
                "null",
                "null",
                2.959541441,
                2.966513481,
                2.975733296,
                2.976052843,
                2.981080415,
                2.981370555,
                2.980176867
            ],
            "sum_5_KLine_Intuitive_Momentum": [
                "null",
                "null",
                "null",
                "null",
                1.103698843,
                0.498255593,
                -0.439458671,
                -0.585752412,
                0.535949828,
                -0.296415761,
                0.620777963
            ],
            "average_5_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                4.0,
                4.084,
                4.124,
                4.154,
                4.206,
                4.22,
                4.28
            ],
            "average_10_closing_price": [
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                0.0,
                4.11,
                4.182
            ]
        },
        ...
    }
}

伺服器 ( Server - Respond ) 響應 POST 請求的數據格式爲 JSON 字符串 ( JSON String ) 類型，數據格式可類比如下 :

Respond - body =

{
    "request_Url" : "/BackTesting?Key=username:password&algorithmUser=username&algorithmPass=password&algorithmName=BackTesting",
    "request_Authorization" : "Basic dXNlcm5hbWU6cGFzc3dvcmQ=",
    "request_Cookie" : "session_id=cmVxdWVzdF9LZXktPnVzZXJuYW1lOnBhc3N3b3Jk",
    "time" : "2024-02-03 17:59:58.239794",
    "Server_say" : "",
    "error" : "",
    "configFile" : "C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt",
    // "configFile" : "C:/QuantitativeTrading/QuantitativeTradingPython/config.txt",
    "trading_direction" : "Long_Position_and_Short_Selling",
    "is_Optimize" : "true",
    "training_ticker_symbol" : ["002611", ... ],
    "training_sequence_length" : 60,
    "testing_ticker_symbol" : ["002611", ... ],
    "testing_sequence_length" : 1,
    "risk_threshold" : 0.0,
    "MarketTiming_Pdata_0" : [3, +0.1, -0.1, 0.0],
    "PickStock_Pdata_0" : [3, 5],
    "SizePosition_Pdata_0" : [1.0, 1.0],
    "return_BackTesting" : {
        "number_PickStock" : Integer,  // 交易過股票的總隻數;
        "number_PickStock_Long_Position" : Integer,  // 做多（Long_Position）交易過股票的總隻數;
        "number_PickStock_Short_Selling" : Integer,  // 做空（Short_Selling）交易過股票的總隻數;
        "number_transaction_total" : Integer,  // 交易總次數（兩次對衝交易作爲一組配對交易）（paired_transaction）;
        "number_transaction_total_Long_Position" : Integer,  // 做多（Long_Position）交易總次數（兩次對衝交易作爲一組配對交易）（paired_transaction）;
        "number_transaction_total_Short_Selling" : Integer,  // 做空（Short_Selling）交易總次數（兩次對衝交易作爲一組配對交易）（paired_transaction）;
        "maximum_drawdown" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
        "maximum_drawdown_Long_Position" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
        "maximum_drawdown_Short_Selling" : Floating-Point,  // 兩次對衝交易之間的最大回撤值，取極值統計;
        "profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
        "Long_Position_profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
        "Short_Selling_profit_total" : Floating-Point,  // 每兩次對衝交易利潤 × 權重，纍加總計;
        "profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
        "profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
        "Long_Position_profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
        "Long_Position_profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
        "Short_Selling_profit_Positive" : Floating-Point,  // 每兩次對衝交易收益纍加總計;
        "Short_Selling_profit_Negative" : Floating-Point,  // 每兩次對衝交易損失纍加總計;
        "profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
        "profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
        "Long_Position_profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
        "Long_Position_profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
        "Short_Selling_profit_Positive_probability" : Floating-Point,  // 每兩次對衝交易正利潤概率;
        "Short_Selling_profit_Negative_probability" : Floating-Point,  // 每兩次對衝交易負利潤概率;
        "average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
        "Long_Position_average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
        "Short_Selling_average_price_amplitude_date_transaction" : Floating-Point,  // 兩兩次對衝交易日成交價振幅平方和，均值;
        "average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
        "Long_Position_average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
        "Short_Selling_average_volume_turnover_date_transaction" : Integer,  // 兩次對衝交易日成交量（換手率）均值;
        "average_date_transaction_between" : Integer,  // 兩次交易間隔日長，均值;
        "Long_Position_average_date_transaction_between" : Integer,  // 兩次對衝交易間隔日長，均值;
        "Short_Selling_average_date_transaction_between" : Integer  // 兩次對衝交易間隔日長，均值;
        "PickStock" : [String, String, String, ... ],  // 向量（Array），依照選股規則計算得到每次交易的股票代碼字符串數組序列;
        "PickStock_Long_Position" : [String, String, String, ... ],  // 向量（Array），做多（Long_Position）依照選股規則計算得到每次交易的股票代碼字符串數組序列;
        "PickStock_Short_Selling" : [String, String, String, ... ],  // 向量（Array），做空（Short_Selling）依照選股規則計算得到每次交易的股票代碼字符串數組序列;
        "transaction_sequence" : {
            "002611" : {
                "Long_Position" : {
                    "index" : [Integer, ... ],  // transaction index，依照交易規則執行交易的序號;
                    "date_transaction" : ["2019-01-11", ... ],  // 依照交易規則選取的交易日期;
                    "direction" : ["sell" or "buy", ... ],  // transaction direction，依照交易規則選取的交易方向（買入或賣出）;
                    "price" : [Floating-Point, ... ],  // transaction price，依照交易規則選取的成交價;
                    "SizePosition" : [Floating-Point, ... ],  // transaction size position，依照交易規則選取的交易比例;
                    "focus" : [Floating-Point, ... ],  // K-Line focus;
                    "amplitude" : [Floating-Point, ... ],  // K-Line amplitude;
                    "turnover_volume" : [Integer, ... ],  // K-Line turnover volume，當日總成交量（turnover volume）;
                    "opening_price" : [Floating-Point, ... ],  // K-Line opening price，當日開盤（opening）成交價;
                    "close_price" : [Floating-Point, ... ],  // K-Line close price，當日收盤（closing）成交價;
                    "low_price" : [Floating-Point, ... ],  // K-Line low price，當日最低（low）成交價;
                    "high_price" : [Floating-Point, ... ],  // K-Line high price，當日最高（high）成交價;
                    "MarketTiming_Parameter" : [[Integer, Floating-Point, Floating-Point, Floating-Point], ... ],
                    "PickStock_Parameter" : [[Integer, Integer], ... ],
                    "SizePosition_Parameter" : [[Floating-Point, Floating-Point], ... ]
                },
                "Short_Selling" : {
                    "index" : [Integer, ... ],  // transaction index，依照交易規則執行交易的序號;
                    "date_transaction" : ["2019-01-11", ... ],  // 依照交易規則選取的交易日期;
                    "direction" : ["sell" or "buy", ... ],  // transaction direction，依照交易規則選取的交易方向（買入或賣出）;
                    "price" : [Floating-Point, ... ],  // transaction price，依照交易規則選取的成交價;
                    "SizePosition" : [Floating-Point, ... ],  // transaction size position，依照交易規則選取的交易比例;
                    "focus" : [Floating-Point, ... ],  // K-Line focus;
                    "amplitude" : [Floating-Point, ... ],  // K-Line amplitude;
                    "turnover_volume" : [Integer, ... ],  // K-Line turnover volume，當日總成交量（turnover volume）;
                    "opening_price" : [Floating-Point, ... ],  // K-Line opening price，當日開盤（opening）成交價;
                    "close_price" : [Floating-Point, ... ],  // K-Line close price，當日收盤（closing）成交價;
                    "low_price" : [Floating-Point, ... ],  // K-Line low price，當日最低（low）成交價;
                    "high_price" : [Floating-Point, ... ],  // K-Line high price，當日最高（high）成交價;
                    "MarketTiming_Parameter" : [[Integer, Floating-Point, Floating-Point, Floating-Point], ... ],
                    "PickStock_Parameter" : [[Integer, Integer], ... ],
                    "SizePosition_Parameter" : [[Floating-Point, Floating-Point], ... ]
                }
            },
            ...
        },  // 字典（Dictionary）， 向量（Array），依照傳入的參數：training_data_sequence_length 和 testing_data_sequence_length 整數長度值對原始數據集：stepping_data 字典序列分割得到的測試集（testing）數據切片序列;
        // "profit_paired_transaction" : {},  // 字典（Dictionary），依照優化之後的擇時（MarketTiming）、選股（PickStock）、倉位（SizePosition）規則計算分割得到的每組對衝交易（paired_transaction）數據切片序列;
        // "stepping_sequence" : [{"002611" : {}, ... }, ... ],  // 向量（Array），依照傳入的參數：training_data_sequence_length 和 testing_data_sequence_length 整數長度值對原始數據集：stepping_data 字典序列分割得到的訓練集（training）和測試集（testing）數據切片序列，用於對擇時（MarketTiming）、選股（PickStock）、倉位（SizePosition）規則優化運算以及交易測試的數據切片序列;
    }
}

微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用，轉換 JSON 字符串類型的變量 ( JSON - String Object ) 與微軟電子表格字典類型的變量 ( Windows - Office - Excel - Visual Basic for Applications - Dict Object ) 時，使用的第三方擴展類模組「VBA-JSON」説明 :

微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用第三方擴展類模組 VBA-JSON 官方 GitHub 網站倉庫: https://github.com/VBA-tools/VBA-JSON.git

百度 ( Baidu ) 公司開發的基於 JavaScript 程式設計語言的開源可視化圖表庫「Apache - ECharts」説明 :

基於 JavaScript 程式設計語言的開源可視化圖表庫「Apache - ECharts」官方網站: https://echarts.apache.org/zh/index.html

基於 JavaScript 程式設計語言的開源可視化圖表庫「Apache - ECharts」官方手冊: https://echarts.apache.org/handbook/zh/get-started/

基於 JavaScript 程式設計語言的開源可視化圖表庫「Apache - ECharts」官方 GitHub 網站倉庫頁: https://github.com/apache/echarts.git

計算機程式設計 C 語言「GIMP Toolkit , GTK+」圖形框架説明 :

計算機程式設計 C 語言「GIMP Toolkit , GTK+」圖形框架官方網站: https://www.gtk.org/

計算機程式設計 C 語言「GIMP Toolkit , GTK+」圖形框架官方手冊: https://www.gtk.org/docs/

谷歌 ( Google - Chromium ) 或火狐 ( Mozilla - Firefox ) 瀏覽器 ( Browser ) 的官方網站 ( Uniform Resource Locator , URL ) 鏈接 :

火狐 ( Mozilla - Gecko - Firefox ) 瀏覽器官方網站: https://www.mozilla.org/zh-TW/

火狐 ( Mozilla - Gecko - Firefox ) 瀏覽器官方手冊: https://firefox-source-docs.mozilla.org/setup/windows_wsl_build.html

火狐 ( Mozilla - Gecko - Firefox ) 瀏覽器官方 GitHub 網站倉庫頁: https://github.com/mozilla/gecko-dev.git

谷歌 ( Google - Chromium ) 瀏覽器官方 GitHub 網站倉庫頁: https://github.com/chromium/chromium.git

微軟電子表格 ( Windows - Office - Excel - Visual Basic for Applications ) 應用 Microsoft Office Excel Professional 2019 的官方網站 ( Uniform Resource Locator , URL ) 鏈接 :

作業系統 ( Operating system ) 之 Microsoft Windows 官方網站: https://www.microsoft.com/zh-tw/windows

電子表格應用 Microsoft Office Excel 官方下載頁: https://www.microsoft.com/zh-tw/download/office

電子表格應用 Microsoft Office Excel 2019 官方説明頁: https://learn.microsoft.com/zh-tw/deployoffice/office2019/overview

---

深圳市招商證券股份有限公司 ( CHINA MERCHANTS SECURITIES CO., LTD. ) 證券交易服務用戶端 ( zyyht.exe ) 官方下載頁: https://yht.cmschina.com/download.html

---

開箱即用 ( out of the box ) ( portable application ) 已配置第三方擴展模組 ( third-party extensions ( libraries or modules ) ) 的程式設計語言 ( computer programming language ) : Julia 解釋器 ( Interpreter ) 和 Python 解釋器 ( Interpreter ) 運行環境的壓縮檔 ( .7z ) 的 百度網盤(pan.baidu.com) 下載頁: https://pan.baidu.com/s/16jdb-nX45cR5uZZKMItsjQ?pwd=kgbh

提取碼：kgbh

開箱即用 ( out of the box ) ( portable application ) 檔 :

1. 壓縮檔 : Julia-1.10.10-Window10-AMD_FX8800P_x86_64.7z

壓縮檔「Julia-1.10.10-Window10-AMD_FX8800P_x86_64.7z」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 程式設計語言 ( Julia ) 解釋器 ( Interpreter ) 二進位可執行檔 ( julia-1.10.10-win64.exe ) 開箱即用 ( out of the box ) ( portable application ) 免安裝版，需自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/Julia/ 内，最終完整路徑應爲「QuantitativeTrading/Julia/Julia-1.10.10/bin/julia.exe」

2. 壓縮檔 : Python-3.11.2-Window10-AMD_FX8800P_x86_64.7z

壓縮檔「Python-3.11.2-Window10-AMD_FX8800P_x86_64.7z」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 程式設計語言 ( Python ) 解釋器 ( Interpreter ) 二進位可執行檔 ( python-3.11.2-amd64.exe ) 開箱即用 ( out of the box ) ( portable application ) 免安裝版，需自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/Python/ 内，最終完整路徑應爲「QuantitativeTrading/Python/Python311/python.exe」

3. 壓縮檔 : Nodejs-22.20.0-Window10-AMD_FX8800P_x86_64.7z

壓縮檔「Nodejs-22.20.0-Window10-AMD_FX8800P_x86_64.7z」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 程式設計語言 ( JavaScript ) 解釋器 ( Interpreter ) 二進位可執行檔 ( node-v22.20.0-x64.msi ) 開箱即用 ( out of the box ) ( portable application ) 免安裝版，需自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/Nodejs/ 内，最終完整路徑應爲「QuantitativeTrading/Nodejs/Nodejs-22.20.0/node.exe」

4. 壓縮檔 : QuantitativeTradingJulia-Julia1.10.10-Window10-AMD_FX8800P_x86_64.7z

壓縮檔「QuantitativeTradingJulia-Julia1.10.10-Window10-AMD_FX8800P_x86_64.7z」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 數量化交易模型 'QuantitativeTradingJulia' 開箱即用 ( out of the box ) ( portable application ) 版，已配置計算機程式設計語言 ( computer programming language ) : Julia 解釋器 ( Interpreter ) 運行此數量化交易模型 'QuantitativeTradingJulia' 項目所需的第三方擴展模組 ( third-party extensions ( libraries or modules ) ) 的運行環境，可自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/QuantitativeTradingJulia/ 内，再因應協調配置壓縮檔「Julia-1.10.10-Window10-AMD_FX8800P_x86_64.7z」之後，即可使用如下指令啓動運行數量化交易模型「QuantitativeTradingJulia」項目 :

C:\QuantitativeTrading> C:/QuantitativeTrading/Julia/Julia-1.10.10/bin/julia.exe -p 4 --project=C:/QuantitativeTrading/QuantitativeTradingJulia/ C:/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configFile=C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt interface_Function=http_Server webPath=C:/QuantitativeTrading/html/ host=::0 port=10001 key=username:password number_Worker_threads=1 isConcurrencyHierarchy=Tasks readtimeout=0 connecttimeout=0 input_K_Line=C:/QuantitativeTrading/Data/K-Day-source/ is_save_JLD=true output_jld_K_Line=C:/QuantitativeTrading/Data/steppingData.jld is_save_csv=false output_csv_K_Line=C:/QuantitativeTrading/Data/K-Day/ is_save_xlsx=false output_xlsx_K_Line=C:/QuantitativeTrading/Data/K-Day/ Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.jld

5. 壓縮檔 : QuantitativeTradingPython-Python3.11.2-Window10-AMD_FX8800P_x86_64.7z

壓縮檔「QuantitativeTradingPython-Python3.11.2-Window10-AMD_FX8800P_x86_64.7z」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 數量化交易模型 'QuantitativeTradingPython' 開箱即用 ( out of the box ) ( portable application ) 版，已配置計算機程式設計語言 ( computer programming language ) : Python 解釋器 ( Interpreter ) 運行此數量化交易模型 'QuantitativeTradingPython' 項目所需的第三方擴展模組 ( third-party extensions ( libraries or modules ) ) 的運行環境，可自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/QuantitativeTradingPython/ 内，再因應協調配置壓縮檔「Python-3.11.2-Window10-AMD_FX8800P_x86_64.7z」之後，即可使用如下指令啓動運行數量化交易模型「'QuantitativeTradingPython`」項目 :

C:\QuantitativeTrading> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/python.exe C:/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py configFile=C:/QuantitativeTrading/QuantitativeTradingPython/config.txt interface_Function=http_Server webPath=C:/QuantitativeTrading/html/ host=::0 port=10001 Key=username:password Is_multi_thread=False number_Worker_process=0 input_K_Line=C:/QuantitativeTrading/Data/K-Day-source/ is_save_pickle=True output_pickle_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle is_save_csv=False output_csv_K_Line=C:/QuantitativeTrading/Data/K-Day/ is_save_xlsx=False output_xlsx_K_Line=C:/QuantitativeTrading/Data/K-Day/ Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle

或者 :

C:\QuantitativeTrading> C:/QuantitativeTrading/Python/Python311/python.exe C:/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py configFile=C:/QuantitativeTrading/QuantitativeTradingPython/config.txt interface_Function=http_Server webPath=C:/QuantitativeTrading/html/ host=::0 port=10001 Key=username:password Is_multi_thread=False number_Worker_process=0 input_K_Line=C:/QuantitativeTrading/Data/K-Day-source/ is_save_pickle=True output_pickle_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle is_save_csv=False output_csv_K_Line=C:/QuantitativeTrading/Data/K-Day/ is_save_xlsx=False output_xlsx_K_Line=C:/QuantitativeTrading/Data/K-Day/ Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle

6. 壓縮檔 : QuantitativeTrading-Window10-AMD_FX8800P_x86_64.7z

壓縮檔「QuantitativeTrading-Window10-AMD_FX8800P_x86_64.7z」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 數量化交易模型 'QuantitativeTrading' 開箱即用 ( out of the box ) ( portable application ) 版，已配置計算機程式設計語言 ( computer programming language ) : Julia 解釋器 ( Interpreter ) 和 Python 解釋器 ( Interpreter ) 運行此數量化交易模型 'QuantitativeTrading' 項目所需的第三方擴展模組 ( third-party extensions ( libraries or modules ) ) 的運行環境，可自行下載解壓縮，將其保存至根目錄 ( Root Directory ) : C: 内，即可使用如下指令啓動運行數量化交易模型「'QuantitativeTrading'」項目 :

程式設計語言 ( computer programming language ) : Julia 實現，使用如下指令:

C:\QuantitativeTrading> C:/QuantitativeTrading/Julia/Julia-1.10.10/bin/julia.exe -p 4 --project=C:/QuantitativeTrading/QuantitativeTradingJulia/ C:/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configFile=C:/QuantitativeTrading/QuantitativeTradingJulia/config.txt interface_Function=http_Server webPath=C:/QuantitativeTrading/html/ host=::0 port=10001 key=username:password number_Worker_threads=1 isConcurrencyHierarchy=Tasks readtimeout=0 connecttimeout=0 input_K_Line=C:/QuantitativeTrading/Data/K-Day-source/ is_save_JLD=true output_jld_K_Line=C:/QuantitativeTrading/Data/steppingData.jld is_save_csv=false output_csv_K_Line=C:/QuantitativeTrading/Data/K-Day/ is_save_xlsx=false output_xlsx_K_Line=C:/QuantitativeTrading/Data/K-Day/ Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.jld

程式設計語言 ( computer programming language ) : Python 實現，使用如下指令:

C:\QuantitativeTrading> C:/QuantitativeTrading/QuantitativeTradingPython/Scripts/python.exe C:/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py configFile=C:/QuantitativeTrading/QuantitativeTradingPython/config.txt interface_Function=http_Server webPath=C:/QuantitativeTrading/html/ host=::0 port=10001 Key=username:password Is_multi_thread=False number_Worker_process=0 input_K_Line=C:/QuantitativeTrading/Data/K-Day-source/ is_save_pickle=True output_pickle_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle is_save_csv=False output_csv_K_Line=C:/QuantitativeTrading/Data/K-Day/ is_save_xlsx=False output_xlsx_K_Line=C:/QuantitativeTrading/Data/K-Day/ Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle

或者 :

C:\QuantitativeTrading> C:/QuantitativeTrading/Python/Python311/python.exe C:/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py configFile=C:/QuantitativeTrading/QuantitativeTradingPython/config.txt interface_Function=http_Server webPath=C:/QuantitativeTrading/html/ host=::0 port=10001 Key=username:password Is_multi_thread=False number_Worker_process=0 input_K_Line=C:/QuantitativeTrading/Data/K-Day-source/ is_save_pickle=True output_pickle_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle is_save_csv=False output_csv_K_Line=C:/QuantitativeTrading/Data/K-Day/ is_save_xlsx=False output_xlsx_K_Line=C:/QuantitativeTrading/Data/K-Day/ Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.pickle

使用批處理脚本「startServer.bat」啓動，使用如下指令 :

C:\QuantitativeTrading> C:/Windows/System32/cmd.exe C:/QuantitativeTrading/startServer.bat C:/QuantitativeTrading/config.txt

使用二進位可執行檔「QuantitativeTrading.exe」啓動，使用如下指令 :

C:\QuantitativeTrading> C:/QuantitativeTrading/QuantitativeTrading.exe configFile=C:/QuantitativeTrading/config.txt executableFile=C:/QuantitativeTrading/Julia/Julia-1.10.10/julia.exe interpreterFile=-p,4,--project=C:/QuantitativeTrading/QuantitativeTradingJulia/ scriptFile=C:/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configInstructions=configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt,interface_Function=http_Server,webPath=C:/QuantitativeTrading/html/,host=::0,port=10001,key=username:password,number_Worker_threads=1,isConcurrencyHierarchy=Tasks,input_K_Line=C:/QuantitativeTrading/Data/K-Day-source/,is_save_JLD=true,output_jld_K_Line=C:/QuantitativeTrading/Data/steppingData.jld,is_save_csv=false,output_csv_K_Line=C:/QuantitativeTrading/Data/K-Day/,is_save_xlsx=false,output_xlsx_K_Line=C:/QuantitativeTrading/Data/K-Day/,Cleaned_K_Line=C:/QuantitativeTrading/Data/steppingData.jld

7. 壓縮檔 : google-pixel-2_android-11_termux-0.118_arm64_ubuntu-22.04_arm64_QuantitativeTrading.tar.gz

壓縮檔「google-pixel-2_android-11_termux-0.118_arm64_ubuntu-22.04_arm64_QuantitativeTrading.tar.gz」爲谷歌安卓作業系統 ( Operating System: Google-Pixel-7 Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 MSM8998-Snapdragon835-Qualcomm®-Kryo™-280 ) 數量化交易運算伺服器「'QuantitativeTrading'」項目源代碼脚本，可自行下載將其移動至 Android-Termux-Ubuntu 系統的檔案夾 ( folder ) : /home/ 内，然後再使用如下指令解壓縮 :

root@localhost:~# tar -zxvf /home/google-pixel-2_android-11_termux-0.118_arm64_ubuntu-22.04_arm64_QuantitativeTrading.tar.gz

最終應保存爲檔案夾 ( folder ) : /home/QuantitativeTrading/ 形式.

然後，再使用如下指令修改批處理 ( Bash ) 脚本「startServer.sh」和二進位可執行檔「QuantitativeTrading.exe」的權限爲所有用戶可運行 :

root@localhost:~# chmod 777 /home/QuantitativeTrading/startServer.sh

root@localhost:~# chmod 777 /home/QuantitativeTrading/QuantitativeTrading.exe

使用如下指令修改參數配置文檔「/home/QuantitativeTrading/config.txt」「/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt」「/home/QuantitativeTrading/QuantitativeTradingPython/config.txt」和代碼脚本檔 ( Script file ) 「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Interface.jl」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Router.jl」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Interpolation_Fitting.jl」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Indicators.jl」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Data_Cleaning.jl」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_MarketTiming.jl」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_PickStock.jl」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_SizePosition.jl」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_BackTesting.jl」「/home/QuantitativeTrading/QuantitativeTradingPython/src/Interface.py」「/home/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py」「/home/QuantitativeTrading/QuantitativeTradingPython/src/Router.py」「/home/QuantitativeTrading/QuantitativeTradingPython/src/Interpolation_Fitting.py」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Indicators.py」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Data_Cleaning.py」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_MarketTiming.py」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_PickStock.py」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_SizePosition.py」「/home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_BackTesting.py」的權限爲所有用戶可讀可寫 :

root@localhost:~# chmod 666 /home/QuantitativeTrading/config.txt

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/config.txt

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingPython/config.txt

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Interface.jl

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Router.jl

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Interpolation_Fitting.jl

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Indicators.jl

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Data_Cleaning.jl

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_MarketTiming.jl

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_PickStock.jl

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_SizePosition.jl

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_BackTesting.jl

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingPython/src/Interface.py

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingPython/src/Router.py

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingPython/src/Interpolation_Fitting.py

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Indicators.py

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_Data_Cleaning.py

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_MarketTiming.py

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_PickStock.py

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_SizePosition.py

root@localhost:~# chmod 666 /home/QuantitativeTrading/QuantitativeTradingJulia/src/Quantitative_BackTesting.py

然後，即可使用如下指令啓動運行數量化交易運算伺服器「'QuantitativeTrading'」項目 :

程式設計語言 ( computer programming language ) : Julia 實現，使用如下指令:

root@localhost:~# /usr/julia/julia-1.10.10/bin/julia -p 4 --project=/home/QuantitativeTrading/QuantitativeTradingJulia/ /home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt interface_Function=http_Server webPath=/home/QuantitativeTrading/html/ host=::0 port=10001 key=username:password number_Worker_threads=1 isConcurrencyHierarchy=Tasks readtimeout=0 connecttimeout=0

程式設計語言 ( computer programming language ) : Python 實現，使用如下指令:

root@localhost:~# /usr/bin/python3 /home/QuantitativeTrading/QuantitativeTradingPython/src/QuantitativeTradingServer.py configFile=/home/QuantitativeTrading/QuantitativeTradingPython/config.txt interface_Function=http_Server webPath=/home/QuantitativeTrading/html/ host=::0 port=10001 Key=username:password Is_multi_thread=False number_Worker_process=0

使用 Shell 語言脚本「startServer.sh」啓動，使用如下指令 :

root@localhost:~# /bin/bash /home/QuantitativeTrading/startServer.sh configFile=/home/QuantitativeTrading/config.txt executableFile=/bin/julia interpreterFile=-p,4,--project=/home/QuantitativeTrading/QuantitativeTradingJulia/ scriptFile=/home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configInstructions=configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt,interface_Function=http_Server,webPath=/home/QuantitativeTrading/html/,host=::0,port=10001,key=username:password,number_Worker_threads=1,isConcurrencyHierarchy=Tasks

使用二進位可執行檔「QuantitativeTrading.exe」啓動，使用如下指令 :

root@localhost:~# /home/QuantitativeTrading/QuantitativeTrading.exe configFile=/home/QuantitativeTrading/config.txt executableFile=/bin/julia interpreterFile=-p,4,--project=/home/QuantitativeTrading/QuantitativeTradingJulia/ scriptFile=/home/QuantitativeTrading/QuantitativeTradingJulia/src/QuantitativeTradingServer.jl configInstructions=configFile=/home/QuantitativeTrading/QuantitativeTradingJulia/config.txt,interface_Function=http_Server,webPath=/home/QuantitativeTrading/html/,host=::0,port=10001,key=username:password,number_Worker_threads=1,isConcurrencyHierarchy=Tasks

8. 二進位可執行檔 : QuantitativeTrading-Window10-AMD_FX8800P_x86_64.exe

二進位可執行檔「QuantitativeTrading-Window10-AMD_FX8800P_x86_64.exe」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 數量化交易運算伺服器「'QuantitativeTrading'」項目内 C 語言源代碼檔「'QuantitativeTrading/c/c2exe.c'」使用 Window10 - MinGW-w64 - gcc 編譯器，編譯之後得到的二進位可執行檔，可自行下載保存至檔案夾 ( folder ) : C:/QuantitativeTrading/ 内，使用如下指令將其重命名 :

C:\QuantitativeTrading> rename C:/QuantitativeTrading/QuantitativeTrading-Window10-AMD_FX8800P_x86_64.exe C:/QuantitativeTrading/QuantitativeTrading.exe

9. 二進位可執行檔 : QuantitativeTrading_google-pixel-2_android-11_termux-0.118_ubuntu-22.04-LTS-rootfs_arm64.exe

二進位可執行檔「QuantitativeTrading_google-pixel-2_android-11_termux-0.118_ubuntu-22.04-LTS-rootfs_arm64.exe」爲谷歌安卓作業系統 ( Operating System: Google-Pixel-7 Android-11 Termux-0.118 Ubuntu-22.04-LTS-rootfs Arm64-aarch64 MSM8998-Snapdragon835-Qualcomm®-Kryo™-280 ) 數量化交易運算伺服器「'QuantitativeTrading'」項目内 C 語言源代碼檔「'QuantitativeTrading/c/c2exe.c'」使用 Ubuntu 22.04 - gcc 編譯器，編譯之後得到的二進位可執行檔，可自行下載保存至檔案夾 ( folder ) : /home/QuantitativeTrading/ 内，使用如下指令將其重命名 :

root@localhost:~# /home/QuantitativeTrading/QuantitativeTrading_google-pixel-2_android-11_termux-0.118_ubuntu-22.04-LTS-rootfs_arm64.exe /home/QuantitativeTrading/QuantitativeTrading.exe

再使用如下指令修改其權限爲所有用戶可運行 :

root@localhost:~# chmod 777 /home/QuantitativeTrading/QuantitativeTrading.exe

10. 壓縮檔 : NodejsToMongoDB-MongoDB_8.2.3-Window10-AMD_FX8800P_x86_64.zip

壓縮檔「NodejsToMongoDB-MongoDB_8.2.3-Window10-AMD_FX8800P_x86_64.zip」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 使用程式設計語言 ( computer programming language ) : JavaScript 鏈接操作 MongoDB 資料庫的伺服器 'NodejsToMongoDB' 開箱即用 ( out of the box ) ( portable application ) 版，已配置計算機程式設計語言 ( computer programming language ) : JavaScript 解釋器 ( Interpreter ) 運行此資料庫伺服器 'NodejsToMongoDB' 項目所需的第三方擴展模組 ( third-party extensions ( libraries or modules ) ) 的運行環境，可自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/Data/MongoDB/NodejsToMongoDB/ 内，再因應協調配置壓縮檔「Nodejs-22.20.0-Window10-AMD_FX8800P_x86_64.7z」之後，即可使用如下指令啓動運行資料庫伺服器「NodejsToMongoDB」項目 :

C:\QuantitativeTrading\Data\MongoDB> C:/QuantitativeTrading/Nodejs/Nodejs-22.20.0/node.exe C:/QuantitativeTrading/Data/MongoDB/NodejsToMongoDB/Nodejs2MongodbServer.js host=::0 port=27016 number_cluster_Workers=0 MongodbHost=[::1] MongodbPort=27017 dbUser=admin_Database1 dbPass=admin dbName=Database1

11. 壓縮檔 : PythonToMariaDB-MariaDB10.11-Window10-AMD_FX8800P_x86_64.zip

壓縮檔「PythonToMariaDB-MariaDB10.11-Window10-AMD_FX8800P_x86_64.zip」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 使用程式設計語言 ( computer programming language ) : Python 鏈接操作 MariaDB 資料庫的伺服器 'PythonToMariaDB' 開箱即用 ( out of the box ) ( portable application ) 版，已配置計算機程式設計語言 ( computer programming language ) : Python 解釋器 ( Interpreter ) 運行此資料庫伺服器 'PythonToMariaDB' 項目所需的第三方擴展模組 ( third-party extensions ( libraries or modules ) ) 的運行環境，可自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/Data/MariaDB/PythonToMariaDB/ 内，再因應協調配置壓縮檔「Python-3.11.2-Window10-AMD_FX8800P_x86_64.7z」之後，即可使用如下指令啓動運行統計運算伺服器「'PythonToMariaDB`」項目 :

C:\QuantitativeTrading\Data\MariaDB> C:/QuantitativeTrading/Data/MariaDB/PythonToMariaDB/Scripts/python.exe C:/QuantitativeTrading/Data/MariaDB/PythonToMariaDB/src/Python2MariaDBServer.py host=::0 port=27016 Is_multi_thread=False number_Worker_process=0 MongodbHost=[::1] MongodbPort=27017 dbUser=admin_Database1 dbPass=admin dbName=Database1

或者 :

C:\QuantitativeTrading\Data\MariaDB> C:/QuantitativeTrading/Python/Python311/python.exe C:/QuantitativeTrading/Data/MariaDB/PythonToMariaDB/src/Python2MariaDBServer.py host=::0 port=27016 Is_multi_thread=False number_Worker_process=0 MongodbHost=[::1] MongodbPort=27017 dbUser=admin_Database1 dbPass=admin dbName=Database1

12. 壓縮檔 : Server-MongoDB_8.2.3-Window10-AMD_FX8800P_x86_64.zip

壓縮檔「Server-MongoDB_8.2.3-Window10-AMD_FX8800P_x86_64.zip」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 資料庫應用 MongoDB 伺服器端二進位可執行啓動檔 'mongod.exe' 開箱即用 ( out of the box ) ( portable application ) 版運行環境，可自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/MongoDB/Server/ 内，最終完整路徑應爲「QuantitativeTrading/MongoDB/Server/8.2/bin/mongod.exe」，即可使用如下指令啓動運行資料庫 MongoDB 伺服器應用 :

C:\QuantitativeTrading\Data\MongoDB> C:/QuantitativeTrading/MongoDB/Server/8.2/bin/mongod.exe --config=C:/QuantitativeTrading/Data/MongoDB/NodejsToMongoDB/mongod.cfg

13. 壓縮檔 : mongosh_2.6.0-Window10-AMD_FX8800P_x86_64.zip

壓縮檔「mongosh_2.6.0-Window10-AMD_FX8800P_x86_64.zip」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 資料庫應用 MongoDB 用戶端二進位可執行啓動檔 'mongosh.exe' 開箱即用 ( out of the box ) ( portable application ) 版運行環境，可自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/MongoDB/mongosh/ 内，最終完整路徑應爲「QuantitativeTrading/MongoDB/mongosh/mongosh.exe」，即可使用如下指令啓動運行資料庫 MongoDB 用戶端應用 :

C:\QuantitativeTrading\Data\MongoDB> C:/QuantitativeTrading/MongoDB/mongosh/mongosh.exe mongodb://username:password@[::1]:27017/Database1

14. 壓縮檔 : data-MongoDB_8.2.3-Window10-AMD_FX8800P_x86_64.zip

壓縮檔「data-MongoDB_8.2.3-Window10-AMD_FX8800P_x86_64.zip」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 資料庫應用 MongoDB 伺服器端自定義創建的名爲 'Database1' 資料庫 ( Database ) , 内含名爲 'Collection1' 自定義數據集 ( Collection/Table ) , 開箱即用 ( out of the box ) ( portable application ) 版運行環境，可自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/Data/MongoDB/data/ 内，可使用資料庫 MongoDB 用戶端應用鏈接伺服器之後，操作處理增、刪、改、查資料集合.

15. 壓縮檔 : MariaDB10.11-Window10-AMD_FX8800P_x86_64.zip

壓縮檔「MariaDB10.11-Window10-AMD_FX8800P_x86_64.zip」爲微軟視窗作業系統 ( Operating System: Acer-NEO-2023 Windows10 x86_64 Inter(R)-Core(TM)-m3-6Y30 ) 資料庫應用 MariaDB 伺服器端二進位可執行啓動檔 'mysqld.exe' 開箱即用 ( out of the box ) ( portable application ) 版運行環境，可自行下載解壓縮，將其保存至檔案夾 ( folder ) : QuantitativeTrading/MariaDB/MariaDB10.11/ 内，最終完整路徑應爲「QuantitativeTrading/MariaDB/MariaDB10.11/bin/mysqld.exe」，即可使用如下指令啓動運行資料庫 MongoDB 伺服器應用 :

C:\QuantitativeTrading\Data\MariaDB> C:/QuantitativeTrading/MariaDB/MariaDB10.11/bin/mysqld.exe

即可.