📅 2024-01-16 — Session: Developed and Implemented Algorithmic Trading Strategies
🕒 04:25–06:15
🏷️ Labels: Trading, Algorithmic Trading, Backtrader, Pyalgotrade, Zipline
📂 Project: Business
⭐ Priority: MEDIUM
Session Goal
The session aimed to develop and implement various algorithmic trading strategies using different frameworks such as Backtrader, PyAlgoTrade, and Zipline.
Key Activities
- Strategy Planning: Outlined a comprehensive guide for developing trading algorithms, including strategy definition, historical data handling, backtesting frameworks, and compliance considerations.
- Quantitative Analysis: Analyzed a quantitative trading strategy using concepts like mean reversion and statistical arbitrage.
- Backtrader Implementation: Implemented custom trading strategies in Backtrader, focusing on linear fits, entry and exit criteria, and residuals-based logic.
- PyAlgoTrade Implementation: Developed trading strategies in PyAlgoTrade, addressing residuals calculation, entry/exit criteria, and error resolution for data feed setup.
- Zipline Setup: Created and troubleshooted trading strategies in Zipline, including resolving compatibility issues with Python environments.
Achievements
- Successfully developed templates and code for implementing trading strategies across multiple platforms.
- Resolved technical errors related to data feed setup and compatibility issues in PyAlgoTrade and Zipline.
Pending Tasks
- Further testing and optimization of implemented trading strategies to ensure robustness and efficiency.
- Exploration of additional trading concepts and frameworks to enhance strategy performance.