mean-reversion-engine

To identify mean-reversion opportunities in ranging markets, you can apply Bollinger, RSI, and Z-score methods to price history DataFrames using pandas and numpy to generate structured trading signals.

The best way to detect Bollinger bounce signals for range-bound assets is to run batch analysis on price data, which returns a consolidated signal map with target and risk metrics for the given symbol.

Yes, you can use RSI extreme fade with divergence checks to confirm price reversals, generating structured signal dictionaries that indicate when a ranging market asset is likely to revert to its mean.

You calculate Z-score reversion signals by analyzing price histories with numpy and pandas, which outputs structured dictionaries containing the signal, mean, and distance-to-mean metrics for the asset.

Mean-reversion trading is designed for ranging regimes and may underperform in trending markets. These templates exploit range-bound moves, so you should avoid applying them when price data shows a clear directional trend.

Yes, you need pandas and numpy to generate mean-reversion signals, as the static methods processing price histories depend on these libraries to return structured dictionaries with signal and risk metrics.