StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py
5.0.0
Prefix Reserved
dotnet add package StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py --version 5.0.0
NuGet\Install-Package StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py -Version 5.0.0
<PackageReference Include="StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py" Version="5.0.0" />
<PackageVersion Include="StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py" Version="5.0.0" />
<PackageReference Include="StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py" />
paket add StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py --version 5.0.0
#r "nuget: StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py, 5.0.0"
#:package StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py@5.0.0
#addin nuget:?package=StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py&version=5.0.0
#tool nuget:?package=StockSharp.Strategies.0158_Parabolic_SAR_Stochastic.py&version=5.0.0
Parabolic Sar Stochastic Strategy (Python Version)
Implementation of strategy - Parabolic SAR + Stochastic. Buy when price is above SAR and Stochastic %K is below 20 (oversold). Sell when price is below SAR and Stochastic %K is above 80 (overbought).
Parabolic SAR supplies the trend and Stochastic refines entry on pullbacks. Signals flip when SAR changes side.
A straightforward trend strategy with built-in SAR stops. ATR settings handle additional risk control.
Details
- Entry Criteria:
- Long:
Close > SAR && StochK < StochOversold
- Short:
Close < SAR && StochK > StochOverbought
- Long:
- Long/Short: Both
- Exit Criteria:
- Parabolic SAR flip in opposite direction
- Stops: Dynamic SAR based
- Default Values:
AccelerationFactor
= 0.02mMaxAccelerationFactor
= 0.2mStochK
= 3StochD
= 3StochPeriod
= 14StochOversold
= 20mStochOverbought
= 80mCandleType
= TimeSpan.FromMinutes(5).TimeFrame()
- Filters:
- Category: Mean reversion
- Direction: Both
- Indicators: Parabolic SAR, Parabolic SAR, Stochastic Oscillator
- Stops: Yes
- Complexity: Intermediate
- Timeframe: Mid-term
- Seasonality: No
- Neural Networks: No
- Divergence: No
- Risk Level: Medium
Learn more about Target Frameworks and .NET Standard.
This package has no dependencies.
NuGet packages
This package is not used by any NuGet packages.
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Version | Downloads | Last Updated |
---|---|---|
5.0.0 | 18 | 7/19/2025 |