StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py
5.0.1
Prefix Reserved
dotnet add package StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py --version 5.0.1
NuGet\Install-Package StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py -Version 5.0.1
<PackageReference Include="StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py" Version="5.0.1" />
<PackageVersion Include="StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py" Version="5.0.1" />
<PackageReference Include="StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py" />
paket add StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py --version 5.0.1
#r "nuget: StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py, 5.0.1"
#:package StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py@5.0.1
#addin nuget:?package=StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py&version=5.0.1
#tool nuget:?package=StockSharp.Strategies.0343_Keltner_Reinforcement_Learning_Signal.py&version=5.0.1
Keltner Reinforcement Learning Signal (Python Version)
The Keltner Reinforcement Learning Signal strategy is built around Keltner Reinforcement Learning Signal.
Testing indicates an average annual return of about 118%. It performs best in the stocks market.
Signals trigger when Keltner confirms trend changes on intraday (15m) data. This makes the method suitable for active traders.
Stops rely on ATR multiples and factors like EmaPeriod, AtrPeriod. Adjust these defaults to balance risk and reward.
Details
- Entry Criteria: see implementation for indicator conditions.
- Long/Short: Both directions.
- Exit Criteria: opposite signal or stop logic.
- Stops: Yes, using indicator-based calculations.
- Default Values:
EmaPeriod = 20
AtrPeriod = 14
AtrMultiplier = 2m
StopLossAtr = 2m
CandleType = TimeSpan.FromMinutes(15).TimeFrame()
- Filters:
- Category: Trend following
- Direction: Both
- Indicators: Keltner, Reinforcement
- Stops: Yes
- Complexity: Intermediate
- Timeframe: Intraday (15m)
- Seasonality: No
- Neural Networks: Yes
- 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.
GitHub repositories
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Refactor resets for strategies 342-344
Fix C# indentation and move remaining strategy resets