Project Overview

This project is focused on developing a statistical arbitrage strategy for cryptocurrencies using Python. The main objective of this project is to use mean-reversion trading and portfolio optimization to generate alpha and minimize risk in cryptocurrency trading. The project retrieves historical price data for Bitcoin (BTC) and Ethereum (ETH) from the CoinGecko API, and uses the pandas library to manipulate the data. The trading strategy is implemented in Python and involves executing trades based on the calculated daily returns. The portfolio optimization functions are used to minimize risk and maximize returns. The results of the trading strategy are plotted and saved in the results directory. The project can be easily extended by adding more cryptocurrencies to the portfolio or by using more sophisticated trading strategies.

Data

We will use historical price data for Bitcoin (BTC) and Ethereum (ETH) obtained from the CoinGecko API. We will use the requests library to make API requests and the pandas library to manipulate the data.

Requirements

This project requires Python 3.7 or later, as well as the following Python packages:

• requests
• pandas
• numpy
• matplotlib
• scipy
• datetime
• json

To install these packages, run the following command:

pip install requests pandas numpy matplotlib scipy datetime json

Usage

1. Clone the repository:

git clone https://github.com/yourusername/statistical-arbitrage-cryptocurrencies.git

1. Navigate to the project directory:

cd statistical-arbitrage-cryptocurrencies

1. Open the config.py file and enter your CoinGecko API key:

COINGECKO_API_KEY = "your-api-key"

1. Run the main.py file:

python main.py

This will retrieve the price data, calculate the daily returns, and execute the statistical arbitrage strategy. The results will be plotted and saved in the results directory.

Files

• config.py: Configuration file for the project.
• data.py: Data retrieval and manipulation functions.
• portfolio.py: Portfolio optimization functions.
• trading.py: Trading strategy functions.
• main.py: Main script to run the project.
• results/: Directory to store the results of the project.

Example Results

License

This project is licensed under the terms of the MIT license. See the [LICENSE](LICENSE" target="_new) file for details.

Conclusion

This project demonstrates how to develop a statistical arbitrage strategy for cryptocurrencies using Python. By combining mean-reversion trading and portfolio optimization, we were able to generate alpha and minimize risk. This project can be extended by adding more cryptocurrencies to the portfolio or by using more sophisticated trading strategies.