Hydra Strategy

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  A tearsheet showing the potential of the strategy in real life

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  A plot of the strategy showing the portfolio value of the account after a period of 7 days

Inspiration

Hydra Strategy was inspired by the need for a robust, automated trading system capable of navigating the volatility of cryptocurrency markets using technical analysis. We aimed to harness well-established indicators like EMA and RSI to create a rule-based strategy that could adapt to changing market conditions.

What it does

Hydra Strategy leverages Exponential Moving Averages (EMA) and the Relative Strength Index (RSI) to generate trading signals. It executes buy orders when a "golden cross" is detected and sell orders when a "death cross" is observed, all while incorporating risk management by monitoring cash levels relative to an initial budget.

How we built it

We developed the strategy using Python, integrating libraries such as pandas for data manipulation, ta for technical analysis, and requests for API calls to the Orderly API. The core structure was built atop the lumibot framework, which provides essential tools for live trading and backtesting, enabling a smooth transition from historical testing to real market execution.

Challenges we ran into

One challenge was ensuring that the technical indicators provided accurate readings using real-time data, especially considering the inherent delays and data quality concerns from external APIs. Balancing the risk management rules with trade execution timing also required careful tuning to prevent overtrading while still capturing timely opportunities.

Accomplishments that we're proud of

We successfully implemented an end-to-end solution that fetches historical data, processes it into actionable signals, and simulates or executes trades based on predefined parameters. The modular design allows for easy tweaks to indicators and risk parameters, demonstrating a flexible yet effective approach to algorithmic trading.

What we learned

The development of Hydra Strategy reinforced the importance of accurate data handling and the interplay between various technical indicators in a live trading environment. We also gained insights into integrating third-party APIs and the delicate balance required between strategy aggressiveness and risk management.

What's next for Hydra Strategy

Moving forward, we plan to incorporate advanced machine learning models to further refine signal accuracy and explore additional indicators. Enhancements in order execution, such as adaptive trade sizing and more dynamic risk controls, are also on the roadmap, ensuring that Hydra Strategy remains

Built With

• lumibot
• pandas
• python
• ta