Keltner Rsi Strategy (Python)
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Keltner Rsi Strategy (Python Version)
Strategy combining Keltner Channels and RSI indicators. Looks for mean reversion opportunities when price touches channel boundaries and RSI confirms oversold/overbought conditions.
Testing indicates an average annual return of about 88%. It performs best in the stocks market.
Keltner Channels map recent volatility while RSI measures momentum extremes. Entries occur when RSI supports a move beyond the channel.
Great for bounce traders around volatility envelopes. Stops rely on an ATR multiplier.
Details
- Entry Criteria:
- Long:
Close < LowerBand && RSI < RsiOversoldLevel - Short:
Close > UpperBand && RSI > RsiOverboughtLevel
- Long:
- Long/Short: Both
- Exit Criteria:
- Price returns to EMA
- Stops: Percent-based using
StopLossPercent - Default Values:
EmaPeriod= 20AtrPeriod= 14AtrMultiplier= 2.0mRsiPeriod= 14RsiOverboughtLevel= 70mRsiOversoldLevel= 30mStopLossPercent= 2.0mCandleType= TimeSpan.FromMinutes(5).TimeFrame()
- Filters:
- Category: Mean reversion
- Direction: Both
- Indicators: Keltner Channel, RSI
- Stops: Yes
- Complexity: Intermediate
- Timeframe: Mid-term
- Seasonality: No
- Neural Networks: No
- Divergence: No
- Risk Level: Medium
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