AI Quant Base

Write the experiment first

Record the hypothesis, asset universe, dates, data fields and signal timing. Decide which result would challenge the idea. Keep a benchmark that uses compatible dates and return conventions.

Model more than entry signals

Specify orders, fills, position sizes, fees and slippage. Inspect a small set of trades by hand before reading aggregate results. Record how corporate actions and missing observations are handled.

Read the report in context

Review the equity path, drawdown, exposure, turnover and distribution of outcomes. Preserve the configuration and a later evaluation period. A historical result is not a guarantee that the same rule will perform similarly in future markets.

Sources & references

References support specific definitions or methodological context. Examples and research checklists are explanatory; no performance result is claimed.

Educational guide. No backtest or live trading result is reported on this page.

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Backtesting & risk

Look-ahead bias and information timing

Look-ahead bias occurs when a historical decision uses information that would not yet have been available at that decision time.

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Backtesting & risk

Out-of-sample testing and strategy changes

Out-of-sample testing evaluates a frozen research specification on observations that were not used to choose it. The split and the change history matter.

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