CodeMot MOT v2: A Multi-Model AI Framework for Quantitative Trading
MOT is CodeMot’s second-generation AI quantitative trading engine, designed to integrate multiple machine learning and deep learning models with reinforcement learning for adaptive trading decisions.
- Multi-model architecture (LSTM, Transformer, XGBoost, CNN, RL)
- Multi-source data fusion
- Market regime detection
- Reinforcement learning–based strategy optimization
- Modular and extensible design
Data Flow:
Data Layer
→ Feature Engineering
→ Model Pool
→ RL Policy Engine
→ Trade Execution
→ Feedback Loop
| Model | Purpose |
|---|---|
| LSTM | Time-series trend & volatility |
| Transformer | Multi-dimensional feature prediction |
| XGBoost | Structured feature stability |
| CNN | Chart pattern recognition |
| RL | Strategy optimization & execution |
- Market data (OHLCV, order book)
- News & sentiment
- Macro indicators
- Historical market data
- Financial statements
- Alternative business data
- Trade signal (long / short / no trade)
- Position size
- Risk parameters (SL / TP)
This project is for research and educational purposes only. It does not constitute financial advice.