StockSharp.Strategies.0393_ROAEffect_Stocks.py
5.0.0
Prefix Reserved
dotnet add package StockSharp.Strategies.0393_ROAEffect_Stocks.py --version 5.0.0
NuGet\Install-Package StockSharp.Strategies.0393_ROAEffect_Stocks.py -Version 5.0.0
<PackageReference Include="StockSharp.Strategies.0393_ROAEffect_Stocks.py" Version="5.0.0" />
<PackageVersion Include="StockSharp.Strategies.0393_ROAEffect_Stocks.py" Version="5.0.0" />
<PackageReference Include="StockSharp.Strategies.0393_ROAEffect_Stocks.py" />
paket add StockSharp.Strategies.0393_ROAEffect_Stocks.py --version 5.0.0
#r "nuget: StockSharp.Strategies.0393_ROAEffect_Stocks.py, 5.0.0"
#:package StockSharp.Strategies.0393_ROAEffect_Stocks.py@5.0.0
#addin nuget:?package=StockSharp.Strategies.0393_ROAEffect_Stocks.py&version=5.0.0
#tool nuget:?package=StockSharp.Strategies.0393_ROAEffect_Stocks.py&version=5.0.0
ROA Effect Stocks (Python Version)
The ROA Effect Stocks strategy targets equities with high returns on assets. An external fundamental data feed supplies the ROA values for the trading universe. At the beginning of each month, the stocks are ranked by ROA, and the portfolio goes long the top decile and short the bottom decile.
Positions are sized equally and rebalanced monthly, capturing the tendency for profitable firms to outperform.
Details
- Entry Criteria: Monthly ranking by external ROA data.
- Long/Short: Both directions.
- Exit Criteria: Monthly rebalance.
- Stops: No explicit stop.
- Default Values:
Decile = 10
MinTradeUsd = 200
CandleType = TimeSpan.FromDays(1).TimeFrame()
- Filters:
- Category: Fundamental
- Direction: Both
- Indicators: Fundamentals
- Stops: No
- Complexity: Intermediate
- Timeframe: Daily
- Seasonality: Yes
- 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.
GitHub repositories
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Version | Downloads | Last Updated |
---|---|---|
5.0.0 | 227 | 8/7/2025 |
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