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The Heracles Strategy, named after the strongest son of God, is a trading strategy designed for backtesting. It utilizes a single genome, which in this case represents a bacteria. The strategy is authored by @Mablue (Masoud Azizi) and can be found on GitHub.
Before running the strategy, it is important to add it to the pairlists inside the config.json file under the StaticPairList section.
Additionally, the TA (Technical Analysis) library needs to be installed using the command "pip install ta." The strategy uses various indicators and functions from different libraries, including pandas, ta, qtpylib, and numpy.
These libraries provide the necessary tools for analyzing and processing trading data. The HeraclesBTCBULL strategy is optimized with specific configuration settings, such as a maximum of 100 open trades and an unlimited stake amount. These settings can be adjusted based on individual preferences. The strategy employs a set of buy and sell hyperspace parameters to determine entry and exit points. The buy parameters include indicators like volatility_kcw and volatility_dcp, while the sell parameters include indicators like trend_macd_signal and trend_ema_fast. The strategy incorporates a minimal ROI (Return on Investment) table, which outlines the expected returns at different stages of the trade. It also includes a stop loss and a trailing stop mechanism to manage risk and maximize profits. The timeframe used for analysis is 4 hours, and the strategy calculates various indicators using the populate_indicators() function. These indicators include volatility_kcw, volatility_dcp, trend_macd_signal, and trend_ema_fast. The populate_buy_trend() function determines the conditions for buying, based on the buy hyperspace parameters. If the specified indicators meet the defined criteria, a buy signal is generated. Conversely, the populate_sell_trend() function defines the conditions for selling. If the sell hyperspace parameters are met, a sell signal is generated. Overall, the Heracles Strategy is a backtesting strategy that utilizes a combination of indicators and parameters to determine optimal entry and exit points for trading.
Before running the strategy, it is important to add it to the pairlists inside the config.json file under the StaticPairList section.
Additionally, the TA (Technical Analysis) library needs to be installed using the command "pip install ta." The strategy uses various indicators and functions from different libraries, including pandas, ta, qtpylib, and numpy.
These libraries provide the necessary tools for analyzing and processing trading data. The HeraclesBTCBULL strategy is optimized with specific configuration settings, such as a maximum of 100 open trades and an unlimited stake amount. These settings can be adjusted based on individual preferences. The strategy employs a set of buy and sell hyperspace parameters to determine entry and exit points. The buy parameters include indicators like volatility_kcw and volatility_dcp, while the sell parameters include indicators like trend_macd_signal and trend_ema_fast. The strategy incorporates a minimal ROI (Return on Investment) table, which outlines the expected returns at different stages of the trade. It also includes a stop loss and a trailing stop mechanism to manage risk and maximize profits. The timeframe used for analysis is 4 hours, and the strategy calculates various indicators using the populate_indicators() function. These indicators include volatility_kcw, volatility_dcp, trend_macd_signal, and trend_ema_fast. The populate_buy_trend() function determines the conditions for buying, based on the buy hyperspace parameters. If the specified indicators meet the defined criteria, a buy signal is generated. Conversely, the populate_sell_trend() function defines the conditions for selling. If the sell hyperspace parameters are met, a sell signal is generated. Overall, the Heracles Strategy is a backtesting strategy that utilizes a combination of indicators and parameters to determine optimal entry and exit points for trading.
stoploss: -0.346 timeframe: 4h hash(sha256): 59c539552f93f5bd508a40564a08337e5a6198f0176fc928049ddfdc029f7212 indicators: high buyindicator0 trend_macd_signal close volatility_kcw volatility_dcp sellindicator0 low trend_ema_fast buycross0 sellcross0
No similar strategies found. (based on used indicators)
last change: 2023-06-24 09:48:58