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The Heracles strategy is a trading strategy implemented in Python for backtesting purposes. It is designed to be used with the Freqtrade platform. Here is a brief description of what the strategy does:
The strategy aims to generate profits by identifying buying opportunities based on specific indicators.
It uses the following indicators: Keltner Channel Width Band (KCW) and Donchian Channel P-Value (DCP).
These indicators help determine the volatility of the market.
The strategy sets certain parameters for buying. It calculates a value based on the indicators and their respective shifts. This value is then compared to a minimum and maximum value specified by the parameters buy_div_min and buy_div_max. If the calculated value falls within this range, a buy signal is generated. The strategy also includes a stop-loss parameter (stoploss) that defines the maximum acceptable loss before selling the asset. The strategy operates on a 4-hour timeframe and provides a predefined ROI (Return on Investment) table that specifies the expected returns at different time intervals. To use the strategy, you need to install the TA (Technical Analysis) library before running it. Note: This description is based on the provided code and comments. For more detailed information about the strategy's logic and implementation, you can refer to the author's GitHub repository mentioned in the code.
It uses the following indicators: Keltner Channel Width Band (KCW) and Donchian Channel P-Value (DCP).
These indicators help determine the volatility of the market.
The strategy sets certain parameters for buying. It calculates a value based on the indicators and their respective shifts. This value is then compared to a minimum and maximum value specified by the parameters buy_div_min and buy_div_max. If the calculated value falls within this range, a buy signal is generated. The strategy also includes a stop-loss parameter (stoploss) that defines the maximum acceptable loss before selling the asset. The strategy operates on a 4-hour timeframe and provides a predefined ROI (Return on Investment) table that specifies the expected returns at different time intervals. To use the strategy, you need to install the TA (Technical Analysis) library before running it. Note: This description is based on the provided code and comments. For more detailed information about the strategy's logic and implementation, you can refer to the author's GitHub repository mentioned in the code.
startup_candle_count : 50 Recursive Analysis found no issues while using 50 startup_candle_count
stoploss: -0.256 timeframe: 4h hash(sha256): a958a54bf9fffd62e8af9cb61109853d8a56bd1e0d090f4a9fc11d44b56878a5 indicators: high close volatility_kcw volatility_dcp low
No similar strategies found. (based on used indicators)
last change: 2023-12-12 22:33:51