You Want to Backtest a Strategy. Claude Wants to Start a Software Company.

You’re using an LLM to help you with your trading.

You come across an idea you think might lead to a profitable trading strategy.

But you’re not quite sure where to start, so you ask your friend Claude:

“Can you help me backtest this opening range breakout strategy?”

Claude responds:

“Absolutely. This is a great strategy to backtest! Let’s build a comprehensive Python backtesting framework that downloads historical minute data, identifies the opening range, generates entry and exit signals, calculates position sizing, models slippage and commissions, and produces a full performance report with Sharpe ratio, maximum drawdown, win rate, profit factor, and Monte Carlo analysis.”

Wow - it sounds like exactly what you need.

But here's where the rabbit hole begins.

AI is like an overeager new employee on their first day, desperate to prove they belong.

But they don’t know what they don’t know, which is always dangerous when allocating resources to projects.

(Ever notice how software projects always take longer than estimated, even in the AI era? This is why!)

Just because Claude says it’s a good idea for you to become a backtest software engineer doesn’t mean it’s the right move.

If you want to be a successful trader, being the lead engineer on a complex software project to build a backtesting engine will get in the way.

But there’s a much better way.

Instead of building from scratch, you can have Claude work with existing backtesting platforms that have spent decades fixing hidden edge cases you don’t know about.

It’s the best of both worlds: Claude can focus on trading strategies instead of tracking down an endless stream of software bugs you don’t have to.

The goal is to build better trading strategies, not better backtesting software.

The best backtesting engine is the one you don’t have to build.

-Dave

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