Intelligent Trading Model

This repository demonstrates how to train a reinforcement learning (RL) agent using Stable-Baselines3 for trading on hourly candlestick data. The project consists of:

• A custom Gymnasium environment (env/hourly_trading_env.py) that simulates trading with:

  • Multi-bar holding (positions can stay open across multiple time steps).
  • Stop-Loss and Take-Profit checks at each bar.
  • Commission and slippage modeling.
  • Partial position closing (to support scaling out of positions).
  • Detailed logging of each trade for analysis.

• A training script (train_rl.py) that:

  • Loads real historical data from a CSV file (historical_data_1h.csv).
  • Creates and wraps the environment.
  • Trains a PPO agent.
  • Runs a quick test and prints final results.
  • Stores checkpoints, logs and TensorBoard data under runs/.

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Features

• ATR-based Stop Loss and Take Profit for risk management.
• Partial Position Closing to scale out of trades.
• Simulated Trading with commission and slippage modelling.
• Detailed Trade Logging for analysis.

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How It Works

1. Configuration

The model parameters are defined in config.json:

• window_size: Number of data points in a sequence.
• model_name: File name for saving and loading the model.
• data_file: CSV file containing historical data.
• initial_balance, trade_risk, min_balance, etc.: Parameters for simulated trading.
• data_top_n, data_interval, data_start_date, data_end_date, data_output_file: Parameters for downloading market history for the most traded pairs.

2. Data Preparation

The model processes historical market data:

• Calculate Indicators: Computes ATR for each bar.
• Normalization: Scales data for efficient training.
• Sequence Slicing: Creates windows of historical data for input to the agent.

3. Training

• Uses the PPO algorithm from Stable-Baselines3.
• Simulates trades, updates balances and logs results.

4. Reports

• Detailed Report: Logs individual trades, balances, thresholds, and outcomes.
• Summary Report: Tracks overall performance metrics, such as success rates and total trades.

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Installation

1. Clone the Repository:

   git clone https://github.com/yourusername/intelligent-trading-model.git
   cd intelligent-trading-model

2. Install Dependencies:

   pip install -r requirements.txt

3. Prepare the Configuration: Edit config.json to specify your desired parameters.
4. Prepare Historical Data: Ensure you have a CSV file (historical_data_1h.csv) with the required columns: open, high, low, close, volume.

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Usage

Run Training

python train_rl.py

• If ppo_hourly_model.zip is found, training continues from the saved weights.
• Otherwise, a new model is created and trained.

Monitor Logs

• Check training progress in the console or via TensorBoard:

  tensorboard --logdir ./runs/tensorboard/

Analyze Results

• Evaluate the model's decisions and performance using the generated reports (detailed_report_file and report_file).

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Output

• Trained Model: Saved to runs/ppo_hourly_model.zip for future use.
• Reports:
  • Detailed Report: Logs individual trade metrics, including timestamps, thresholds, positions, and balances.
  • Summary Report: Aggregates metrics like total trades, success rates, and final balance.

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Example Workflow

1. Collect hourly historical data for a trading pair (e.g., BTC/USDT) or use get_data.py to download top trading pairs from Binance.
2. Configure the model with window_size = 168.
3. Train the model using train_rl.py.
4. Analyze trading performance using the detailed and summary reports.

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Key Advantages

• Automation: End-to-end pipeline from data ingestion to trading simulation.
• Dynamic Learning: Adapts thresholds and parameters for evolving market conditions.
• Comprehensive Reporting: Provides deep insights into trading strategy effectiveness.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Author

Developed by Ktyby21. Feel free to contact me for suggestions or collaboration opportunities.