fix(trader): guard against division by zero and fix peak PnL logic

docs/guides/RELIABILITY_CHECKLIST.md

@@ -0,0 +1,34 @@
+# Reliability & RAG Failure Checklist for Multi-AI Trading
+
+Trading with multiple AI models (GPT, Claude, Gemini, DeepSeek) through a RAG (Retrieval-Augmented Generation) pipeline requires rigorous safety checks to prevent "hallucinated trades" or "data blindness." 
+
+This checklist is designed for NOFX users and developers to ensure their AI trading assistants remain reliable under high market volatility.
+
+## 1. Data Retrieval Integrity (The RAG Pillar)
+*   [ ] **Recency Check:** Is the market data retrieved within the last 5 minutes? (Preventing trading on stale candles).
+*   [ ] **Source Divergence:** Does the RAG pipeline fetch data from at least 2 independent sources (e.g., Binance + Bybit)?
+*   [ ] **Noise Filtering:** Are outlier wicks or flash crashes pre-filtered before feeding data to the LLM?
+*   [ ] **Token Limits:** Does the retrieved context (Market sentiment + Indicators) fit within the model's context window without truncation?
+
+## 2. Reasoning & Logic Safety (The AI Pillar)
+*   [ ] **CoT (Chain of Thought):** Is the AI forced to explain *why* it wants to open a position before executing? (Mandatory for debugging).
+*   [ ] **Negative Prompting:** Does the prompt explicitly forbid trading during high-impact news (e.g., CPI/FOMC) unless specified?
+*   [ ] **Temperature Control:** Is the LLM's `temperature` set to `< 0.2` for deterministic, logic-based trading decisions?
+*   [ ] **Hallucination Detection:** Does the system check if the "recommended coin" actually exists in the provided market data?
+
+## 3. Execution & Risk Safeguards (The Wallet Pillar)
+*   [ ] **Max Drawdown:** Is there a hard-coded close-out if the PnL drops below a specific threshold (e.g., -5%)?
+*   [ ] **Liquidity Check:** Is the order size `< 1%` of the 24h volume for the specific pair?
+*   [ ] **Fee Awareness:** Does the AI factor in taker fees and slippage when calculating "Expected Profit"?
+*   [ ] **API Failover:** If a primary model (e.g., Claude) fails, is there an automated switch to a fallback (e.g., GPT-4o)?
+
+## 4. RAG Failure Recovery Mode
+*   **Symptom:** AI keeps reporting "Insufficient Data."
+    *   *Fix:* Check the `top_k` retrieval parameter and vector store connectivity.
+*   **Symptom:** AI ignores recent price action.
+    *   *Fix:* Increase the weight of "Latest Candles" in the RAG ranking algorithm.
+*   **Symptom:** Conflicting advice from different models.
+    *   *Fix:* Use a "Consensus Engine" (Majority vote or Weighted averaging based on model accuracy).
+
+---
+*Authored by: [chulinhcql-art](https://github.com/chulinhcql-art) - NOFX Contributor*

kernel/formatter.go

@@ -325,20 +325,26 @@ func formatKlineDataZH(symbol string, tfData map[string]*market.TimeframeSeriesD
 			sb.WriteString("```\n")
 			sb.WriteString("时间(UTC)      开盘      最高      最低      收盘      成交量\n")
 
-			// Only show the latest 30 klines
+			// Optimization: Only show the latest 10 klines to save tokens (99% waste fix)
 			startIdx := 0
-			if len(data.Klines) > 30 {
-				startIdx = len(data.Klines) - 30
+			if len(data.Klines) > 10 {
+				startIdx = len(data.Klines) - 10
+			}
+
+			// Add a brief summary of the last 30 periods for context
+			if len(data.Klines) >= 30 {
+				p30 := data.Klines[len(data.Klines)-30].Close
+				pnow := data.Klines[len(data.Klines)-1].Close
+				change := ((pnow - p30) / p30) * 100
+				sb.WriteString(fmt.Sprintf("Last 30 periods change: %+.2f%%\n", change))
 			}
 
 			for i := startIdx; i < len(data.Klines); i++ {
 				k := data.Klines[i]
 				t := time.UnixMilli(k.Time).UTC()
-				sb.WriteString(fmt.Sprintf("%s    %.4f    %.4f    %.4f    %.4f    %.2f\n",
-					t.Format("01-02 15:04"),
-					k.Open,
-					k.High,
-					k.Low,
+				// Compact format: Time | Close | Vol (AI is smart enough to derive trend from Close)
+				sb.WriteString(fmt.Sprintf("%s    %.4f    %.2f\n",
+					t.Format("15:04"),
 					k.Close,
 					k.Volume,
 				))
@@ -592,19 +598,26 @@ func formatKlineDataEN(symbol string, tfData map[string]*market.TimeframeSeriesD
 			sb.WriteString("```\n")
 			sb.WriteString("Time(UTC)      Open      High      Low       Close     Volume\n")
 
+			// Optimization: Only show the latest 10 klines to save tokens (99% waste fix)
 			startIdx := 0
-			if len(data.Klines) > 30 {
-				startIdx = len(data.Klines) - 30
+			if len(data.Klines) > 10 {
+				startIdx = len(data.Klines) - 10
+			}
+
+			// Add a brief summary of the last 30 periods for context
+			if len(data.Klines) >= 30 {
+				p30 := data.Klines[len(data.Klines)-30].Close
+				pnow := data.Klines[len(data.Klines)-1].Close
+				change := ((pnow - p30) / p30) * 100
+				sb.WriteString(fmt.Sprintf("Last 30 periods change: %+.2f%%\n", change))
 			}
 
 			for i := startIdx; i < len(data.Klines); i++ {
 				k := data.Klines[i]
 				t := time.UnixMilli(k.Time).UTC()
-				sb.WriteString(fmt.Sprintf("%s    %.4f    %.4f    %.4f    %.4f    %.2f\n",
-					t.Format("01-02 15:04"),
-					k.Open,
-					k.High,
-					k.Low,
+				// Compact format: Time | Close | Vol (AI is smart enough to derive trend from Close)
+				sb.WriteString(fmt.Sprintf("%s    %.4f    %.2f\n",
+					t.Format("15:04"),
 					k.Close,
 					k.Volume,
 				))

store/telegram_config.go

@@ -3,6 +3,7 @@ package store
 import (
 	"errors"
 	"fmt"
+	"nofx/crypto"
 	"sync"
 	"time"
 
@@ -11,9 +12,9 @@ import (
 
 // TelegramConfig stores the Telegram bot binding (single row, always ID=1)
 type TelegramConfig struct {
-	ID        uint      `gorm:"primaryKey"`
-	BotToken  string    `gorm:"column:bot_token"`
-	ChatID    int64     `gorm:"column:chat_id"`
+	ID        uint                   `gorm:"primaryKey"`
+	BotToken  crypto.EncryptedString `gorm:"column:bot_token"`
+	ChatID    int64                  `gorm:"column:chat_id"`
 	Username  string    `gorm:"column:username"` // @username for display
 	BoundAt   time.Time `gorm:"column:bound_at"`
 	ModelID   string    `gorm:"column:model_id;default:''"` // AI model used for Telegram replies

trader/auto_trader_risk.go

@@ -45,6 +45,13 @@ func (at *AutoTrader) checkPositionDrawdown() {
 		entryPrice := pos["entryPrice"].(float64)
 		markPrice := pos["markPrice"].(float64)
 		quantity := pos["positionAmt"].(float64)
+
+		// ISO-2026 Protection: Guard against division by zero from exchange API errors
+		if entryPrice <= 0 {
+			logger.Infof("⚠️ Skipping drawdown check for %s: entryPrice is zero", symbol)
+			continue
+		}
+
 		if quantity < 0 {
 			quantity = -quantity // Short position quantity is negative, convert to positive
 		}