Developed and Implemented Algorithmic Trading Strategies

• Day: 2024-01-16
• Time: 04:25 to 06:15
• Project: Business
• Workspace: WP 1: Strategic / Growth & Development
• Status: In Progress
• Priority: MEDIUM
• Assignee: Matías Nehuen Iglesias
• Tags: Trading, Algorithmic Trading, Backtrader, Pyalgotrade, Zipline

Description

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.

Evidence

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