Open-source Grid Trading Bot implemented in Python, allowing you to backtest and execute grid trading strategies on cryptocurrency markets. The bot is highly customizable and works with various exchanges using the CCXT library.
- Grid Trading Bot
- Features
- π€ What is Grid Trading?
- π₯οΈ Installation
- π Configuration
- π Running the Bot
- π Docker Compose for Logs Management
- π€ Contributing
- πΈ Donations
- π License
- π¨ Disclaimer
- Backtesting: Simulate your grid trading strategy using historical data.
- Live Trading: Execute trades on live markets using real funds, supported by robust configurations and risk management.
- Paper Trading: Test strategies in a simulated live market environment without risking actual funds.
- Multiple Grid Trading Strategies: Implement different grid trading strategies to match market conditions.
- Customizable Configurations: Use a JSON file to define grid levels, strategies, and risk settings.
- Support for Multiple Exchanges: Seamless integration with multiple cryptocurrency exchanges via the CCXT library.
- Take Profit & Stop Loss: Safeguard your investments with configurable take profit and stop loss thresholds.
- Performance Metrics: Gain insights with comprehensive metrics like ROI, max drawdown, run-up, and more.
- HealthCheck: Continuously monitor the botβs performance and system resource usage to ensure stability.
- CLI BotController: Control and interact with the bot in real time using intuitive commands.
- Logging with Grafana: Centralized logging system for monitoring bot activity and debugging, enhanced with visual dashboards.
Grid trading is a trading strategy that places buy and sell orders at predefined intervals above and below a set price. The goal is to capitalize on market volatility by buying low and selling high at different price points. There are two primary types of grid trading: arithmetic and geometric.
In an arithmetic grid, the grid levels (price intervals) are spaced equally. The distance between each buy and sell order is constant, providing a more straightforward strategy for fluctuating markets.
Suppose the price of a cryptocurrency is $3000, and you set up a grid with the following parameters:
- Grid levels: $2900, $2950, $3000, $3050, $3100
- Buy orders: Set at $2900 and $2950
- Sell orders: Set at $3050 and $3100
As the price fluctuates, the bot will automatically execute buy orders as the price decreases and sell orders as the price increases. This method profits from small, predictable price fluctuations, as the intervals between buy/sell orders are consistent (in this case, $50).
In a geometric grid, the grid levels are spaced proportionally or by a percentage. The intervals between price levels increase or decrease exponentially based on a set percentage, making this grid type more suited for assets with higher volatility.
Suppose the price of a cryptocurrency is $3000, and you set up a geometric grid with a 5% spacing between levels. The price intervals will not be equally spaced but will grow or shrink based on the percentage.
- Grid levels: $2700, $2835, $2975, $3125, $3280
- Buy orders: Set at $2700 and $2835
- Sell orders: Set at $3125 and $3280
As the price fluctuates, buy orders are executed at lower levels and sell orders at higher levels, but the grid is proportional. This strategy is better for markets that experience exponential price movements.
- Arithmetic grids are ideal for assets with more stable, linear price fluctuations.
- Geometric grids are better for assets with significant, unpredictable volatility, as they adapt more flexibly to market swings.
- Simple Grid: Independent buy and sell grids. Profits from each grid level are standalone.
- Hedged Grid: Pairs buy and sell levels dynamically, balancing risk and reward for higher volatility markets.
Ensure you have Conda installed on your machine.
- Clone the repository:
git clone https://github.com/jordantete/grid_trading_bot.git
cd grid_trading_bot
- Create the Conda environment:
conda env create -f environment.yml
- Activate the environment:
conda activate grid_trading_bot
The bot is configured via a JSON file config/config.json
to suit your trading needs, alongside a .env
file to securely store sensitive credentials and environment variables. Below is an example configuration file and a breakdown of all parameters.
{
"exchange": {
"name": "binance",
"trading_fee": 0.001,
"trading_mode": "backtest"
},
"pair": {
"base_currency": "SOL",
"quote_currency": "USDT"
},
"trading_settings": {
"timeframe": "1m",
"period": {
"start_date": "2024-08-01T00:00:00Z",
"end_date": "2024-10-20T00:00:00Z"
},
"initial_balance": 10000,
"historical_data_file": "data/SOL_USDT/2024/1m.csv"
},
"grid_strategy": {
"type": "simple_grid",
"spacing": "geometric",
"num_grids": 8,
"range": {
"top": 200,
"bottom": 250
}
},
"risk_management": {
"take_profit": {
"enabled": false,
"threshold": 300
},
"stop_loss": {
"enabled": false,
"threshold": 150
}
},
"logging": {
"log_level": "INFO",
"log_to_file": true
}
}
-
exchange: Defines the exchange and trading fee to be used.
- name: The name of the exchange (e.g., binance).
- trading_fee: The trading fee should be in decimal format (e.g., 0.001 for 0.1%).
- trading_mode: The trading mode of operation (backtest, live or paper trading).
-
pair: Specifies the trading pair.
- base_currency: The base currency (e.g., ETH).
- quote_currency: The quote currency (e.g., USDT).
-
trading_settings: General trading settings.
- timeframe: Time interval for the data (e.g.,
1m
for one minute). - period: The start and end dates for the backtest or trading period.
- start_date: The start date of the trading or backtest period.
- end_date: The end date of the trading or backtest period.
- initial_balance: Starting balance for the bot.
- historical_data_file: Path to a local historical data file for offline testing (optional).
- timeframe: Time interval for the data (e.g.,
-
grid_strategy: Defines the grid trading parameters.
- type: Type of grid strategy:
- simple_grid: Independent buy/sell levels.
- hedged_grid: Dynamically paired buy/sell levels for risk balancing.
- spacing: Grid spacing type:
- arithmetic: Equal price intervals.
- geometric: Proportional price intervals based on percentage.
- num_grids: The total number of grid levels.
- range: Defines the price range of the grid.
- top: The upper price limit of the grid.
- bottom: The lower price limit of the grid.
- type: Type of grid strategy:
-
risk_management: Configurations for risk management.
- take_profit: Settings for taking profit.
- enabled: Whether the take profit is active.
- threshold: The price at which to take profit.
- stop_loss: Settings for stopping loss.
- enabled: Whether the stop loss is active.
- threshold: The price at which to stop loss.
- take_profit: Settings for taking profit.
-
logging: Configures logging settings.
- log_level: The logging level (e.g.,
INFO
,DEBUG
). - log_to_file: Enables logging to a file.
- log_level: The logging level (e.g.,
The .env
file securely stores sensitive data like API keys and credentials. Below is an example:
# Exchange API credentials
EXCHANGE_API_KEY=YourExchangeAPIKeyHere
EXCHANGE_SECRET_KEY=YourExchangeSecretKeyHere
# Notification URLs for Apprise
APPRISE_NOTIFICATION_URLS=
# Grafana Admin Access
GRAFANA_ADMIN_USER=admin
GRAFANA_ADMIN_PASSWORD=YourGrafanaPasswordHere
Environment Variables Breakdown
- EXCHANGE_API_KEY: Your API key for the exchange.
- EXCHANGE_SECRET_KEY: Your secret key for the exchange.
- APPRISE_NOTIFICATION_URLS: URLs for notifications (e.g., Telegram bot, Discord Server). For detailed setup instructions, visit the Apprise GitHub repository.
- GRAFANA_ADMIN_USER: Admin username for Grafana.
- GRAFANA_ADMIN_PASSWORD: Admin password for Grafana.
To run the bot, use the following command:
grid_trading_bot --config config/config.json
If you want to run the bot with multiple configuration files simultaneously, you can specify them all:
grid_trading_bot --config config/config1.json config/config2.json config/config3.json
To save the performance results to a file, use the --save_performance_results option:
grid_trading_bot --config config/config.json --save_performance_results results.json
To run the bot without displaying the end-of-simulation plots, use the --no-plot flag:
grid_trading_bot --config config/config.json --no-plot
You can combine multiple options to customize how the bot runs. For example:
grid_trading_bot --config config/config1.json config/config2.json --save_performance_results combined_results.json --no-plot
A docker-compose.yml
file is included to set up centralized logging using Grafana, Loki, and Promtail. This allows you to monitor and analyze the bot's logs efficiently.
-
Ensure Docker and Docker Compose Are Installed
Verify that Docker and Docker Compose are installed on your system. If not, follow the official Docker installation guide. -
Start the Services
Run the following command to spin up Grafana, Loki, and Promtail:
docker-compose up -d
- Access Grafana Dashboards
Navigate to http://localhost:3000 in your browser to access the Grafana dashboard. Use the following default credentials to log in:
- Username: admin
- Password: YourGrafanaPasswordHere (as defined in the .env file)
- Import Dashboards
Go to the Dashboards section in Grafana and click Import. Use the provided JSON file for predefined dashboards. This file can be found in the project directory: grafana/dashboards/grid_trading_bot_dashboard.json
Contributions are welcome! If you have suggestions or want to improve the bot, feel free to fork the repository and submit a pull request.
If you encounter any issues or have feature requests, please create a new issue on the GitHub Issues page.
If you find this project helpful and would like to support its development, consider buying me a coffee! Your support is greatly appreciated and motivates me to continue improving and adding new features.
Thank you for your support!
This project is licensed under the MIT License. See the LICENSE file for more details.
This project is intended for educational purposes only. The authors and contributors are not responsible for any financial losses incurred while using this bot. Trading cryptocurrencies involves significant risk and can result in the loss of all invested capital. Please do your own research and consult with a licensed financial advisor before making any trading decisions. Use this software at your own risk.