Developed and Optimized Trading Strategies in Backtrader

• Day: 2024-01-16
• Time: 00:00 to 23:55
• Project: Dev
• Workspace: WP 2: Operational
• Status: Completed
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Backtrader, Python, Trading, Data Analysis, Visualization

Description

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.

Evidence

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• event_ids: []