How a Retail Dev uses Cursor to Build Mean Reversion Algos

A&T: How has AI changed your development cycle?

Alex: It hasn't made me a better trader, but it has drastically reduced the time from "idea" to "backtest." I used to spend days wrestling with Pandas DataFrame alignments and datetime timezone bugs. Now, I highlight the block in Cursor, hit Cmd+K, and say "fix the tz-naive comparison," and it's done.

A&T: What's a specific workflow you rely on?

Alex: I use Claude 3.5 Sonnet within Cursor to write boilerplate for connecting to the IBKR TWS API. The official documentation is famously obtuse. I can drop a snippet of their docs into the chat context and ask for an asynchronous wrapper using `ib_insync`. It gets it right 90% of the time, saving me hours of trial and error.

// Example Prompt

"Given this IBKR API error: 'Error 200, reqId 1: No security definition has been found for the request', rewrite the contract definition for VIX futures expiring next month."

A&T: Where do the models fail you?

Alex: Logic errors in the actual trading strategy. If I ask it to write a mean reversion logic block (e.g., "buy if price is 2 standard deviations below the 20-day VWAP"), it often messes up lookahead bias. It will inadvertently use today's closing price to calculate the moving average before taking a trade on today's open. You absolutely cannot trust it to write the core logic without line-by-line review.

Key Takeaways

Use AI for infrastructure and data wrangling, not alpha generation.
Provide official API docs in the context window.
Beware of lookahead bias in LLM-generated backtesting code.