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The Heracles strategy is a trading strategy designed for backtesting on a trading platform. It aims to identify buying opportunities in the market based on certain indicators. The strategy uses the Keltner Channel and Donchian Channel indicators to determine market volatility.
Here's a breakdown of how the strategy works: The strategy calculates the Keltner Channel Width Band (volatility_kcw) and Donchian Channel Percentage Band (volatility_dcp) indicators using the high, low, and close prices of the trading data.
It sets specific parameters for buying based on the calculated indicators.
These parameters include the minimum and maximum values for a divergence ratio (buy_div_min and buy_div_max), as well as the shifts applied to the indicators (buy_indicator_shift and buy_crossed_indicator_shift). The strategy then applies these parameters to determine buying conditions. It checks if the divergence ratio falls within the specified range. If the conditions are met, the strategy marks the corresponding data points as a buy signal by setting the 'buy' column to 1 in the DataFrame. The strategy does not implement a specific selling strategy, as indicated by the 'populate_sell_trend' function, which sets the 'sell' column to 0 for all data points. It's worth noting that the strategy provides predefined buy_params and minimal_roi (Return on Investment) values, as well as a stoploss parameter to manage risk. Please ensure that you have the necessary libraries installed (TA-Lib, freqtrade, qtpylib) and configure the pairlists in the config.json file accordingly before running the strategy.
Here's a breakdown of how the strategy works: The strategy calculates the Keltner Channel Width Band (volatility_kcw) and Donchian Channel Percentage Band (volatility_dcp) indicators using the high, low, and close prices of the trading data.
It sets specific parameters for buying based on the calculated indicators.
These parameters include the minimum and maximum values for a divergence ratio (buy_div_min and buy_div_max), as well as the shifts applied to the indicators (buy_indicator_shift and buy_crossed_indicator_shift). The strategy then applies these parameters to determine buying conditions. It checks if the divergence ratio falls within the specified range. If the conditions are met, the strategy marks the corresponding data points as a buy signal by setting the 'buy' column to 1 in the DataFrame. The strategy does not implement a specific selling strategy, as indicated by the 'populate_sell_trend' function, which sets the 'sell' column to 0 for all data points. It's worth noting that the strategy provides predefined buy_params and minimal_roi (Return on Investment) values, as well as a stoploss parameter to manage risk. Please ensure that you have the necessary libraries installed (TA-Lib, freqtrade, qtpylib) and configure the pairlists in the config.json file accordingly before running the strategy.
stoploss: -0.256 timeframe: 4h hash(sha256): 3cc9e2ea74dd78fce958c08fcf69dc9cf07497d9e2575177af4a015ff9c8341c indicators: high close volatility_kcw volatility_dcp low
No similar strategies found. (based on used indicators)
last change: 2022-07-02 19:54:08