Genetic Algorithm for Trading Strategy Optimization

This project implements a genetic algorithm to optimize trading strategy parameters and trading pair selection. It supports multi-process parallel computation, dynamic generation of strategies, and saving the best strategy from each generation.

Features

• Genetic algorithm optimization for trading strategies
• Optimization of strategy parameters and trading pair selection
• Support for setting maximum open trades
• Multi-process parallel computation
• Dynamic strategy generation and evaluation
• Saving of best individuals from each generation
• Configurable optimization parameters
• Optional data downloading before running the algorithm
• Checkpointing and ability to resume from the latest checkpoint

Prerequisites

• Required libraries (specified in requirements.txt)

Installation

1. Clone the repository:

   git clone https://github.com/your-username/your-repo-name.git
   cd your-repo-name
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Create a configuration file:

   cp ga.json.example ga.json
   

4. Edit ga.json to configure the genetic algorithm parameters and other settings according to your needs.

Configuration

Edit ga.json (or your custom config file) to configure:

Basic Parameters

• population_size: Number of individuals in each generation
• generations: Total number of generations to run
• tournament_size: Number of individuals in tournament selection
• crossover_prob: Probability of crossover (0.0 to 1.0)
• mutation_prob: Probability of mutation (0.0 to 1.0)

Trading Parameters

• max_open_trades: Maximum number of trades that can be open simultaneously (default: 3)

  • Range: 1 to 6
  • Higher values allow more concurrent trades but require more capital
  • Lower values reduce risk but may miss opportunities

• timeframe: Trading interval for analysis and execution (default: "5m")

  • Available options: "1m", "5m", "15m", "30m", "1h", "4h"
  • Shorter timeframes (1m, 5m) provide more trading opportunities but may have higher noise
  • Longer timeframes (1h, 4h) may provide more reliable signals but fewer opportunities

Processing Options

• pool_processes: Number of parallel processes for backtesting
• checkpoint_frequency: How often to save checkpoints
• fix_pairs: Whether to use fixed trading pairs or allow optimization

File Paths

• strategy_dir: Directory for strategy files
• data_dir: Directory for historical data
• results_dir: Directory for optimization results

Example configuration:

{
    // ... other settings ...
    "max_open_trades": 3,
    "timeframe": "5m",
    // ... other settings ...
}

Tips for Parameter Selection:

• For max_open_trades:

  • Start with a lower value (2-3) for testing
  • Increase gradually based on available capital and risk tolerance
  • Consider exchange limits and liquidity

• For timeframe:

  • Lower timeframes (1m-5m): Suitable for scalping strategies
  • Medium timeframes (15m-30m): Balance between opportunities and noise
  • Higher timeframes (1h-4h): Better for trend-following strategies

Updating Trading Pairs

You can dynamically update the trading pairs in your configuration using the get_pairs.py script:

python scripts/get_pairs.py [--mode {volume,all}]

Options:

• --mode volume: Get top 100 pairs sorted by USDT trading volume
• --mode all: Get all available USDT trading pairs (default)

The script will:

1. Fetch available trading pairs from Binance
2. Filter out pairs based on a predefined blacklist
3. Save the pairs to a timestamped JSON file
4. Automatically update the pair_whitelist in your user_data/config.json

Example usage:

# Get top 100 pairs by trading volume
python scripts/get_pairs.py --mode volume

# Get all available USDT pairs
python scripts/get_pairs.py --mode all

Usage

Run the optimization script with the following command:

python main.py [options]

Available options:

• --config CONFIG_FILE: Specify a custom configuration file (default is 'ga.json')
• --download: Download data before running the algorithm
• --start-date YYYYMMDD: Start date for data download (default is '20240101')
• --end-date YYYYMMDD: End date for data download (default is today's date)
• --resume: Resume from the latest checkpoint

Examples:

1. Run with default settings:

   python main.py
   

2. Use a custom configuration file:

   python main.py --config my_custom_config.json
   

3. Download data before running the algorithm:

   python main.py --download
   

4. Download data for a specific date range:

   python main.py --download --start-date 20230101 --end-date 20231231
   

5. Resume from the latest checkpoint:

   python main.py --resume
   

Project Structure

• main.py: Main script to run the genetic algorithm
• config/settings.py: Settings class to load configuration
• utils/: Utility functions for logging and file operations
• genetic_algorithm/: Classes and functions for the genetic algorithm
• strategy/: Strategy-related code, including backtesting and template generation
• data/: Data handling, including the downloader module

How It Works

1. The script loads configuration settings from a JSON file.
2. It generates a dynamic strategy template and extracts parameters.
3. If requested, it downloads historical data for backtesting.
4. The genetic algorithm creates an initial population of trading strategies or loads the latest checkpoint if resuming.
5. For each generation:
   • Strategies are evaluated in parallel using backtesting.
   • The best strategies are selected for the next generation.
   • Crossover and mutation operations are applied to create new strategies.
   • The best individual from each generation is saved.
   • A checkpoint is saved at regular intervals.
6. After all generations, the overall best strategy is reported.

Checkpointing

The algorithm saves checkpoints at regular intervals (configurable in the settings). These checkpoints allow you to resume the optimization process from where it left off in case of interruption. Use the --resume option to start from the latest checkpoint.

Contributing

Contributions are welcome! Please submit issues or pull requests.

License

This project is licensed under the MIT License.

Disclaimer

This project is for educational and research purposes only. It does not constitute investment advice. Users are responsible for any risks associated with using this project for actual trading.

Contact

For questions or suggestions, please open an issue on GitHub.

Community

Stay updated and engage with our community:

• GeneTrader Telegram Channel - For updates and announcements
• GeneTrader Algorithm Group - For discussions and support

Disclaimer

This project is for educational and research purposes only. It does not constitute investment advice. Users are responsible for any risks associated with using this project for actual trading.

Optimization Summary

Below is an example of optimization progress over 20 generations:

Generation	Trades	Avg Profit %	Tot Profit %	Win Draw Loss	Drawdown %
GeneTrader_gen1_1729489793_8197	257	0.21	36.228	176 0 81	54.53%
GeneTrader_gen2_1729489952_1439	569	0.49	243.621	397 0 172	24.59%
GeneTrader_gen3_1729490009_4737	354	0.31	145.164	256 0 98	51.58%
GeneTrader_gen4_1729490112_3632	358	0.37	202.592	266 0 92	38.83%
GeneTrader_gen5_1729490284_5784	322	0.27	97.633	264 0 58	43.65%
GeneTrader_gen6_1729490446_1924	376	0.73	1048.377	299 0 77	36.71%
GeneTrader_gen7_1729490545_9113	565	0.30	365.679	484 0 81	34.48%
GeneTrader_gen8_1729490992_8083	473	0.66	1695.533	412 0 61	12.59%
GeneTrader_gen9_1729491090_6216	421	0.55	669.485	360 0 61	8.35%
GeneTrader_gen10_1729491271_1982	587	0.13	62.295	494 0 93	57.37%
GeneTrader_gen11_1729491369_5745	471	0.58	1039.757	408 0 63	43.23%
GeneTrader_gen12_1729491539_3502	457	0.57	952.518	398 0 59	8.35%
GeneTrader_gen13_1729491663_2560	505	0.65	1974.439	439 0 66	8.35%
GeneTrader_gen14_1729491872_5826	460	0.77	2567.816	408 0 52	8.35%
GeneTrader_gen15_1729492004_9419	403	0.69	1209.204	354 0 49	26.96%
GeneTrader_gen16_1729492145_6860	393	0.64	894.785	340 0 53	41.60%
GeneTrader_gen17_1729492441_5355	360	0.62	637.224	310 0 50	40.51%
GeneTrader_gen18_1729492588_1685	382	0.47	373.272	325 0 57	43.14%
GeneTrader_gen19_1729492645_2977	384	0.53	519.371	335 0 49	36.79%
GeneTrader_gen20_1729492730_4647	335	0.48	309.840	289 0 46	47.06%

This table shows the progress of the genetic algorithm optimization over 20 generations. Key metrics include:

• Trades: The number of trades executed by the strategy.
• Avg Profit %: The average profit percentage per trade.
• Tot Profit %: The total cumulative profit percentage.
• Win Draw Loss: The number of winning, draw, and losing trades.
• Drawdown %: The maximum observed loss from a peak to a trough, expressed as a percentage.

The optimization process aims to improve these metrics over time, finding strategies with higher profitability, better risk management, and improved overall performance. As seen in the table, there's a general trend of improvement in key metrics such as Win Rate, Total Profit, and Sharpe Ratio as the generations progress, although there are some fluctuations due to the nature of genetic algorithms and market complexity.