Mean Reversion Strategy (Python)

This statistical approach looks for short-term extremes in price relative to its recent average. The strategy uses a moving average to define fair value and measures the deviation from that mean through a standard deviation calculation. Trades are opened when price pushes a set distance from the average. A dip below the lower band triggers a long entry, anticipating a rebound toward the mean, while a rally above the upper band prompts a short. Once price touches the moving average again, any open position is closed. The method appeals to traders who prefer a contrarian style and want clearly defined entry and exit zones. Because it relies on volatility-based bands, it adapts to quieter or more active markets while still keeping losses in check via a fixed stop-loss.

• Entry Criteria:

• Long: Price < MA - k*StdDev (below lower band)

• Short: Price > MA + kStdDev (above upper band) []Long/Short: Both sides. [*]Exit Criteria:

• Long: Exit when price crosses above the moving average

• Short: Exit when price crosses below the moving average []Stops: Yes. []Default Values:

• MovingAveragePeriod = 20

• DeviationMultiplier = 2.0m

• StopLossPercent = 2m

• CandleType = TimeSpan.FromMinutes(5) [*]Filters:

• Category: Mean Reversion

• Direction: Both

• Indicators: Mean Reversion

• Stops: Yes

• Complexity: Intermediate

• Timeframe: Intraday

• Seasonality: No

• Neural networks: No

• Divergence: No

• Risk Level: Medium