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title: DRL Trading Bot
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: streamlit
sdk_version: 1.42.0
app_file: src/ui/app.py
pinned: false
---
# DRL Trading System
An autonomous Deep Reinforcement Learning trading system using PPO-LSTM for Binance Testnet trading with real-time Streamlit monitoring.
## Features
- 🧠 **PPO-LSTM Agent**: Captures time-series dependencies for smarter trading decisions
- 🔄 **Self-Improvement Loop**: Automatically fine-tunes on successful trades every 24 hours
- 📊 **Real-time Dashboard**: TradingView-style charts with live Buy/Sell signals
- 🛡️ **Risk Management**: Circuit breaker stops trading at 5% daily loss
- 📈 **Backtesting**: Validated on 2024-2025 historical data
## Quick Start
```bash
# 1. Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
# 2. Install dependencies
pip install -r requirements.txt
# 3. Configure API keys
cp .env.example .env
# Edit .env with your Binance Testnet credentials
# 4. Run backtest first
python -m src.backtest.engine
# 5. Launch UI
streamlit run src/ui/app.py
```
## Project Structure
```
drl-trading-system/
├── config/
│ └── config.yaml # All configuration
├── src/
│ ├── env/ # Gymnasium environment
│ ├── brain/ # PPO-LSTM agent
│ ├── api/ # Binance connector
│ ├── backtest/ # Backtesting engine
│ └── ui/ # Streamlit dashboard
├── data/
│ ├── historical/ # Cached OHLCV data
│ └── models/ # Saved checkpoints
└── tests/
```
## Architecture
The system uses a Sharpe/Sortino ratio reward function to prioritize risk-adjusted returns over raw profit.
## License
MIT
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