The Cryptocurrency Algorithmic Trading GitHub repository is a project developed as part of the Holistic Data Science Bootcamp. This repository includes code and resources for building and testing algorithmic trading strategies for cryptocurrencies using Python and various data science libraries. The project involves analyzing historical price data, identifying patterns and trends, and using technical indicators to identify patterns and forecast future market movements. The code includes various trading strategies and backtesting tools to evaluate their effectiveness. The project is designed to provide an introduction to algorithmic trading and cryptocurrency markets while also demonstrating the use of data science techniques in financial applications.
This project explores algorithmic trading strategies for 12 selected cryptocurrencies, including Bitcoin and Ethereum. The Jupyter notebook (final_strategy.ipynb) contains code for a combined Bollinger Bands, RSI, ADX, and Chaikin Money Flow strategy, as well as a MACD strategy. The project aims to identify effective trading strategies for these cryptocurrencies and evaluate their performance through backtesting.
Disclaimer Please note that this project is for educational purposes only and should not be considered financial advice. Trading cryptocurrencies involves significant risk, and you should only invest what you can afford to lose. The author of this project is not responsible for any financial loss resulting from the use of these strategies.
- Combined Bollinger Bands, RSI, ADX, and Chaikin Money Flow strategy
- MACD strategy
- Backtesting of both strategies for 12 selected cryptocurrencies
- Analysis of performance and potential profitability of each strategy for each coin
- Discussion of the impact of transaction fees on profitability
The backtesting results show that the MACD strategy works well for some of the selected coins but not as well for others. The combined strategy did not perform as well overall. It's important to note that the performance of these strategies may vary based on market conditions and other factors, so caution should always be exercised when implementing them.
Additionally, the analysis revealed that transaction fees and other costs can have a significant impact on profitability. Going forward, it's important to consider these fees and costs when developing and evaluating trading strategies.
This project provides insights into the performance and potential profitability of algorithmic trading strategies for selected cryptocurrencies. The code is provided in the Jupyter notebook, making it easy to replicate and modify the strategies for other cryptocurrencies. However, please note that the strategies are for educational purposes only, and any actual trading should be done with caution and careful consideration of the risks involved. It's also important to consider the impact of transaction fees and other costs on profitability when developing future trading strategies.