Trading Bot

A serverless cryptocurrency trading bot that predicts price movements using deep learning models and executes trades

Features

• Support for multiple cryptocurrency exchanges via CCXT library
• Use pre-trained deep learning models for price prediction
• Provide backtesting capabilities for trading strategy evaluation
• Utilize serverless architecture with AWS Lambda
• Continuous deployment with GitHub Actions

Prerequisites

You need a pre-trained deep learning model and scalers. For more information, see here.

Usage

You can implement your own trading strategy by modifying MyStrategy class, or adding a new class that inherits from Strategy class.

class Strategy(ABC):
    @abstractmethod
    def should_open_position(self, *args, **kwargs) -> bool:
        pass

    @abstractmethod
    def should_close_position(self, *args, **kwargs) -> bool:
        pass

Backtesting

Set up the configuration in config.yaml.

default:
  EXCHANGE_ID: 'binanceusdm'
  INTERVAL: '1d'
  LEVERAGE: 1
  MIN_ORDER_AMOUNT: 0.002
  MODEL_KEY: 'model.keras'
  SCALERS_KEY: 'scalers.pkl'
  STOP_LOSS: 0.00         # 0.01 = 1%
  SYMBOL: 'BTC/USDT:USDT'
  THRESHOLD: 0.00         # 0.01 = 1%
  WINDOW_SIZE: 5

simulation:
  BUCKET_NAME: ''                     # No need to change
  DOWNLOAD_DIR: '../downloads'        # Path to model and scalers
  START_TIME: '2024-02-12T00:00:00Z'  # ISO 8601

When setting START_TIME, please note that backtesting should be performed on data not used for model fitting to avoid overfitting.

Now run the simulator.

(env) user@host:~/trading-bot$ pip install -r requirements-local.txt
(env) user@host:~/trading-bot$ export ENVIRONMENT=simulation
(env) user@host:~/trading-bot$ cd src
(env) user@host:~/trading-bot/src$ python simulator.py

---------------------------  ------
           Number of trades      64
                   Win rate   51.6%
                  P&L ratio    1.72
Max profit rate (per trade)    9.5%
  Max loss rate (per trade)   -5.1%
              Final balance  1524.2
---------------------------  ------

Live Trading

To deploy to AWS Lambda for serverless live trading:

1. Create an S3 bucket to store the model and scalers.
2. Create an ECR Repository to store Docker images.
3. Build and push the Docker image.
   • Can be done by manually running the workflow 'Manual deploy to Amazon ECR' in GitHub Actions.
4. Create a Lambda function using the Docker image pushed to ECR.
   • Please grant S3 access to the IAM role.
5. Set the configuration of your Lambda function.
   • Recommended general configuration
     • Memory: 1~2 GB
     • Timeout: 2 min
   • Required environment variables
     • BUCKET_NAME
     • EXCHANGE_API_KEY
     • EXCHANGE_API_SECRET
6. Create an EventBridge schedule to periodically invoke the Lambda function.

Continuous Deployment

To set up CD:

1. Fork this repository.
2. Set up repository secrets for GitHub Actions in your repository settings.
   • AWS_ACCESS_KEY_ID
   • AWS_SECRET_ACCESS_KEY
3. Update cd.yml with your AWS region, ECR repository name, and Lambda function name.
4. Push changes to main branch and activate the workflow.