Using an AI Assistant to Prototype and Backtest a Moving-Average Strategy

Summary

The article describes an experiment using an AI computer-operating assistant with a quantitative trading platform. The task begins with a dual moving-average crossover strategy, then proceeds through code generation, configuration of a Bitcoin market backtest, debugging, and report creation. The author says the assistant repaired a syntax issue and summarized performance measures and losing trades. The initial result was described as poor, without giving numerical findings in the text.

For a later iteration, the assistant changed the exchange, asset, and moving-average periods, and added an ATR volatility filter and dynamic stop-loss. The author reports a smoother return curve and positive results in that test, then notes that the system produced version comparisons and development notes. This is a single platform demonstration, not evidence that AI-generated strategies generalize or are profitable. The article warns that clear instructions and human review remain necessary, and that coding ability does not amount to deep understanding of market conditions. No detailed backtest settings or independently verifiable performance data are supplied.

Key ideas

• The demonstration uses an AI assistant to generate and edit a moving-average crossover strategy.
• The workflow includes backtesting, error correction, performance reporting, and strategy iteration.
• The revised example adds ATR-based volatility filtering and a dynamic stop-loss.
• A reported improvement in one backtest does not establish out-of-sample robustness or profitability.
• The author recommends human review because generated code and market reasoning can be flawed.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.