Autonomous trading combining deep learning, reinforcement learning, and explainable AI for transparent, risk-managed crypto trading.
Technical Specifications
Multi-Exchange
Binance, Coinbase, Kraken via CCXT
Order Types
Market, Limit, Stop-Loss, TWAP
Paper Trading
Full simulation mode
Low Latency
Sub-50ms execution
LSTM Networks
Pattern recognition
Transformer
Multi-horizon forecasting
PPO Agent
Adaptive position sizing
Ensemble
Meta-learning
Dynamic Sizing
Kelly criterion
Drawdown Protection
Circuit breakers
VaR & CVaR
Real-time metrics
SHAP Values
Feature attribution
LIME
Local explanations
Confidence
Uncertainty quantification
Architecture
Python backend with FastAPI. TensorFlow and PyTorch for neural networks. Next.js dashboard.
TimescaleDB for time-series. Redis for caching. Docker orchestration. Prometheus + Grafana monitoring.
TTM/ ├── src/ │ ├── trading/ │ │ ├── execution_engine.py │ │ ├── risk_manager.py │ │ └── portfolio_optimizer.py │ ├── models/ │ │ ├── lstm_model.py │ │ ├── cnn_lstm.py │ │ ├── transformer.py │ │ ├── tft_model.py │ │ └── ppo_agent.py │ ├── xai/ │ │ ├── shap_explainer.py │ │ ├── lime_explainer.py │ │ └── confidence_scorer.py │ ├── data/ │ │ ├── multi_exchange.py │ │ └── preprocessor.py │ └── api/ │ └── server.py ├── web/ │ ├── app/ │ │ ├── dashboard/ │ │ └── api/ │ └── components/ ├── scripts/ │ ├── backtest.py │ └── train_models.py ├── docker-compose.yml ├── requirements.txt └── main.py
Roadmap
Phase 01
Done
Core Engine
Phase 02
Done
ML Models
Phase 03
Done
XAI
Phase 04
Active
Production