Algorithmic Trading in Python

Decompose a trading bot concept (e.g., Moving Average Crossover) into core components: data, strategy, and execution.

Define the interfaces and data flow between components to create a project blueprint.

Formulate a detailed prompt for a code function, specifying project context, language, inputs, and desired behavior.

Refine prompts with constraints to enforce specific library usage, code style, and error handling.

Use incremental prompting to build a complex function step-by-step rather than in a single request.

Use an AI assistant to research and identify suitable Forex data APIs and their documentation.

Generate a Python project structure with a virtual environment and secure API keys using environment variables.

Generate a data-fetching module to connect to a Forex API using prompts based on documentation snippets.

Debug the AI-generated API code by interpreting error messages and creating follow-up clarification prompts.

Write a focused prompt to generate the core trading strategy logic (e.g., calculate moving averages and identify crossovers).

Verify the correctness of the AI-generated strategy logic by prompting for explanations and code comments.

Integrate the strategy logic component with the data connector, ensuring data flows correctly between them.

Refine the strategy module by prompting the AI to add functions for managing trade state (e.g., holding a position).