Turning Natural-Language Trading Rules into QMT Backtests

Summary

The article describes a workflow in which a trader states a strategy in everyday language and a tool generates runnable QMT code for backtesting. Its example uses a Chinese stock, buying when the 10-day moving average crosses above the 60-day average and selling when it crosses below, with adjusted historical prices. The author says the generated strategy can be loaded into QMT to produce a backtest, reducing the time and coding effort needed to test ideas.

The piece presents this primarily as a productivity aid for exploring low- and medium-frequency price-volume or fundamental-factor strategies. It supplies no backtest results or independent assessment of the generated code's correctness. It also acknowledges that complex models, high-frequency strategies, and strategies needing specialized data still require deeper development, so generated code and test assumptions need review before relying on them.

Key ideas

• A natural-language description can be used to generate QMT strategy code for testing.\nThe example trades moving-average crossovers using adjusted prices.\nThe author presents code generation as a way to shorten the idea-to-backtest workflow.\nThe article provides no performance evidence or validation of generated code.\nComplex, high-frequency, and specialized-data strategies may require further development.

Tags

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