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The Heracles Strategy is a trading strategy implemented in Python. It is designed to be used for backtesting on a trading platform. Here is a brief description of what the strategy does:
The strategy calculates various indicators based on price data and uses them to determine buy signals.
It employs the Keltner Channel and Donchian Channel indicators to assess market volatility.
The strategy looks for a specific relationship between these indicators to generate buy signals.
The buy signals are generated when a certain condition is met, which involves comparing the values of the indicators with predefined thresholds. If the conditions are satisfied, the strategy marks the corresponding data points as buy signals. The strategy does not implement any specific sell conditions, as indicated by the populate_sell_trend function, which sets the sell signal to 0 for all data points. The strategy also provides hyperparameters that can be tuned for optimization purposes. These hyperparameters include buy_div_min, buy_div_max, buy_indicator_shift, and buy_crossed_indicator_shift. They control the thresholds and shifts used in the buy signal generation process. The strategy's overall performance is evaluated using a minimal return on investment (ROI) table, defined by the minimal_roi dictionary. It specifies the ROI percentages at different time intervals after the buy signal. Please note that the strategy requires the installation of the TA-Lib library (pip install ta) for calculating the indicators. For more details and the complete implementation, you can refer to the GitHub repository provided by the author: https://github.com/mablue/
It employs the Keltner Channel and Donchian Channel indicators to assess market volatility.
The strategy looks for a specific relationship between these indicators to generate buy signals.
The buy signals are generated when a certain condition is met, which involves comparing the values of the indicators with predefined thresholds. If the conditions are satisfied, the strategy marks the corresponding data points as buy signals. The strategy does not implement any specific sell conditions, as indicated by the populate_sell_trend function, which sets the sell signal to 0 for all data points. The strategy also provides hyperparameters that can be tuned for optimization purposes. These hyperparameters include buy_div_min, buy_div_max, buy_indicator_shift, and buy_crossed_indicator_shift. They control the thresholds and shifts used in the buy signal generation process. The strategy's overall performance is evaluated using a minimal return on investment (ROI) table, defined by the minimal_roi dictionary. It specifies the ROI percentages at different time intervals after the buy signal. Please note that the strategy requires the installation of the TA-Lib library (pip install ta) for calculating the indicators. For more details and the complete implementation, you can refer to the GitHub repository provided by the author: https://github.com/mablue/
stoploss: -0.256 timeframe: 4h hash(sha256): 8039952f7ce86bfbe955482dca98d1ff89e33e3fb6037bdf99dc53265459fe0b indicators: high close volatility_kcw volatility_dcp low
No similar strategies found. (based on used indicators)
last change: 2022-07-11 15:27:06