Backtesting Equity Investment Strategies in Python | Zerodha Kite API

This session provides a technical approach to validating investment strategies using Python through broker API. We will try to establish a robust, objective backtesting framework.
Every strategy considered for a trading setup should be rigorously tested across multiple stocks and diverse historical timelines to assess profitability and loss. Only after this analysis and refinement should one proceed to live trading.
In previous sessions, we explored backtesting simple strategies in Google Sheets. Today, we will transition to Python through broker API to overcome the limitations of spreadsheet-based analysis.

### Session Objectives: The Strategy Workflow

Our goal is to build the core script necessary for comprehensive backtesting. We will focus on:

1. Data Retrieval: Retrieving necessary historical data via the broker API and saving it into a structured format (e.g., a CSV file).
2. Strategy Design: Defining a strategy by creating specific trigger conditions (e.g., conditions for buy entry, target profit/sell, and stop-loss/exit).
3. Code Generation: Creating an effective prompt for an AI tool (like ChatGPT/Gemini) to generate the necessary Python code based on the described strategy conditions.
4. Execution & Analysis: Running the generated code in Visual Studio Code to obtain the backtesting results.

### The Code & Environment: AI-Assisted Development

We will not focus on learning to write code manually. Instead, we will focus on creating and refining specific prompts to generate the functional Python code needed for automation, and the code is generated by AI tools like ChatGPT/Gemini.
We will walk through the resulting pre-written, fully functional code to understand the workflow. The script is tested in Visual Studio Code and is ready to perform:

• Retrieval of historical stock data from the API.
• Simple strategy backtesting with clear performance outputs.