Developed and Optimized Trading Strategies in Backtrader

📅 2024-01-16 — Session: Developed and Optimized Trading Strategies in Backtrader

🕒 00:00–23:55
🏷️ Labels: Backtrader, Python, Trading, Data Analysis, Visualization
📂 Project: Dev
⭐ Priority: MEDIUM

Session Goal:

The goal of this session was to enhance and optimize trading strategies using the Backtrader framework, focusing on data visualization, indicator plotting, and trading logic implementation.

Key Activities:

• Investment Strategy Overview: Reflected on combining technical analysis with systematic trading rules to enhance investment strategies.
• Data Processing Expansion: Developed Python code for weekly data resampling using the yfinance library, improving stock data analysis.
• Model Fitting Techniques: Transitioned from exponential model fitting to linear fit on log-transformed stock prices for better residual analysis.
• Backtrader Enhancements:
  • Optimized plotting of ‘OLSResiduals’ indicator and modified the plotinfo attribute in CustomCashObserver.
  • Implemented trading logic in Backtrader strategies using the next method, including buy and sell orders based on residuals.
  • Modified OLSResiduals indicator to ensure unique naming and correct plotting for each ticker.

Achievements:

• Successfully integrated and optimized multiple components of the Backtrader framework to improve trading strategy execution and visualization.
• Enhanced data processing capabilities and model fitting techniques for better financial analysis.

Pending Tasks:

• Further testing and validation of the implemented strategies to ensure robustness and efficiency in different market conditions.