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Mean Reversion

Mean Reversion Backtesting: validate price deviation and reversion logic

A mean-reversion strategy looks simple, but a real backtest needs a clear moving-average window, deviation threshold, holding period, stop-loss and trading cost. FiClaw structures these rules and uses real history to check whether the strategy only works in specific regimes.

Search intent

The user is looking for how to backtest a mean-reversion strategy, its parameter settings and an AI-assisted implementation path.

Sample prompts

“A Bollinger-band mean-reversion strategy: buy below the lower band, sell at the middle band.”

“Compare 10-, 20- and 40-day moving-average deviation thresholds on win rate and drawdown.”

“Add a maximum holding period and stop-loss to a mean-reversion strategy and output a backtest diagnosis.”

Reviewable metrics

Core signal

Price deviation

Moving-average or Bollinger convention

Key metrics

Win rate / drawdown

Never read win rate alone

Risk control

Stop-loss

Contain failure in trending regimes

The deviation signal must be executable

A mean-reversion strategy needs a clear definition of how price deviation is computed and what triggers buy, sell, stop-loss and exit.

  • Moving-average window, Bollinger-band window or standard-deviation threshold
  • Entry, exit, stop-loss and maximum holding days
  • Trading cost, slippage and suspended-sample handling

Focus on drawdown and tail risk

Mean reversion can have a high win rate, but a single failure can cause a large loss, so tail risk and stop-loss design need special attention.

  • Watch win rate, profit-loss ratio and max drawdown together
  • Compare stability across thresholds and holding periods
  • Check whether it keeps averaging down in trending markets

Workflow

How FiClaw handles this

1

Define deviation

Enter the moving-average, Bollinger-band or standard-deviation rule.

2

Generate trading rules

Form entry, exit, stop-loss and holding-period constraints.

3

Submit the backtest

Generate Python code and validate against historical data.

4

Check tail risk

Diagnose drawdown, profit-loss ratio and failure in trending regimes.

FAQ

Frequently asked questions

Why can't a mean-reversion strategy be judged on win rate alone?

Mean reversion can show a high win rate but a low profit-loss ratio, where a single failure eats many wins, so you must watch max drawdown, profit-loss ratio and tail risk together.

Can FiClaw tune parameters for a mean-reversion strategy?

Yes. FiClaw can run a parameter search over window length, deviation threshold, stop-loss and holding period, but the final parameters still need human confirmation with out-of-sample validation.

Boundaries

Where it applies

A financial AI tool needs clear boundaries. FiClaw is here to speed up strategy research; its output still needs team review.

  • Mean reversion can keep failing in trending markets.
  • A high win rate does not mean low risk; check profit-loss ratio and max drawdown.
  • FiClaw backtest results are for research evaluation and are not investment advice.

Put the idea into a real backtest loop

If you are evaluating how AI fits into quant strategy research, start by running your first reviewable backtest report with FiClaw.

Download FiClawSee parameter optimization