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TPTBusiness/README.md

Trading Prediction Technology · TPTBusiness

Building AI-powered trading systems — from autonomous factor discovery to local LLM inference.
Supporter of open-source software and the communities that build it.

🚀 Main Project — Predix

Predix is an autonomous AI agent for quantitative EUR/USD forex trading. It automates the full research and development cycle — from factor discovery to backtesting — using a multi-agent LLM framework on 1-minute data.

What makes it different:

  • 🧠 Autonomous factor evolution — the agent proposes, codes, and validates its own alpha signals
  • 🛡️ Built-in risk management — drawdown protection, cooldown periods, stoploss clustering detection
  • 🔄 Walk-forward validation — avoids overfitting across 2020–2026 EUR/USD data
  • 🖥️ Real-time dashboard — Streamlit UI for monitoring factor performance and model evolution
  • 🔒 60 integration tests — every commit is checked before it lands
 
Market Data (CCXT / yfinance)
         ↓
  Feature Engineering
   (TA-Lib, pandas)
         ↓
  LLM Agent Loop
  (factor proposal
   + code generation)
         ↓
   Backtesting
   (Qlib, 1-min)
         ↓
  Risk Management
  (drawdown / cooldown)
         ↓
  Portfolio Output

GitHub License Last Commit Stars

🧠 What I build

Autonomous Trading Agents — Multi-agent LLM frameworks that discover, evolve, and validate trading strategies end-to-end
Local LLM Integration — Running AI systems fully offline with llama.cpp (no cloud dependency)
Open-Source Tools — Pine Script strategies and Python frameworks for the trading community
Full Trading Pipelines — From raw kline data to live execution, built and maintained independently

🗺️ Predix Roadmap

Status Phase Feature
✅ Done P0 Data Loader — OHLCV loading, HDF5 caching, thread-safe feature matrix builder
✅ Done P1 Strategy Worker — LLM call wrapper, backtest engine, FTMO compliance gate
✅ Done P2 Strategy Orchestrator — multi-process pool, LLM semaphore, result deduplication
✅ Done P3 Optuna Optimizer — TPE sampler, FTMO penalty logic, 20–50 trials per strategy
✅ Done P4 CLI Commands — generate_strategies, Rich console output
✅ Done P5 ML Training Pipeline — LightGBM, time-series split, feature importance analysis
✅ Done P6 fin_quant Feedback Loop — ML feature importance → LLM prompt feedback
✅ Done P7 Portfolio Optimizer — mean-variance, risk parity, Black-Litterman with LLM views
✅ Done P8 Integration Tests — end-to-end pipeline, parallelisation, FTMO compliance
✅ Done P9 Documentation — architecture diagrams, setup guide, data flow
🔄 Next P10a Kronos-mini — OHLCV foundation model inference on EUR/USD (4.1M params, AAAI 2026, MIT)
📋 Planned P10b Kronos as factor generator — kronos_predicted_return_96, volatility, momentum, uncertainty
📋 Planned P10c Kronos + LLM Ensemble — Optuna-optimized weighting of DL + LLM alpha signals
📋 Planned P10d Kronos fine-tuning on EUR/USD 1-min custom tokenizer (optional)
📋 Planned P11 Live execution — offline/online split, Telegram signal alerts

🛠️ Stack

Core & AI

Python PyTorch LightGBM XGBoost scikit-learn LangChain

Model Architectures

Transformer LSTM PPO Stable-Baselines3

Data & Finance

pandas NumPy TA-Lib CCXT Qlib yfinance Binance API

Local LLM & Inference

llama.cpp Ollama CUDA OpenRouter

UI & Infra

Streamlit Docker Linux Pine Script

🌍 Open-Source Contributions

Project Contribution
TradingAgents ⭐ 34k Added llama.cpp local LLM support — run multi-agent stock analysis fully offline via .env config
OpenStock ⭐ 9.9k Updated deps, fixed Inngest v4 API, force-dynamic for auth routes — resolved 28 vulnerabilities, migrated Inngest v3→v4

📊 Activity

GitHub followers GitHub stars Predix commits Predix issues closed

📬 Contact

Premium models & collaborations → tpt.requests@pm.me
Mastodon → @TPTBusiness@mastodon.social

⚠️ All content is for educational purposes only. Past performance does not guarantee future results.

Pinned Loading

  1. Predix Predix Public

    Autonomous AI agent for quantitative EUR/USD forex trading — automates factor discovery, model evolution, and backtesting on 1-minute data using a multi-agent LLM framework.

    Python 2

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