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The GodStraQuick strategy is designed for backtesting trading strategies. Here's a brief description of what the strategy does:
The strategy has two main methods: populate_indicators and populate_buy_trend (along with populate_sell_trend). The populate_indicators method calculates various indicators for the given data frame and time period.
However, this strategy optimizes its calculations by only computing specific indicators within the buy and sell strategy population methods when needed.
This method also calculates default values for hyperoptable parameters, but it doesn't provide significant benefits beyond that.
The populate_buy_trend method generates conditions for buying based on the specified indicators and their corresponding values. It iterates through three sets of indicators and their associated parameters, generating conditions using a condition_generator function. If any of the conditions are met, the 'buy' column in the data frame is set to 1. The populate_sell_trend method is similar to populate_buy_trend, but it generates conditions for selling instead. It also iterates through three sets of indicators and their parameters, generates conditions, and sets the 'sell' column in the data frame to 1 if any of the conditions are met. Overall, the strategy calculates indicators, generates buy and sell conditions based on these indicators, and marks the corresponding rows in the data frame for buying or selling.
However, this strategy optimizes its calculations by only computing specific indicators within the buy and sell strategy population methods when needed.
This method also calculates default values for hyperoptable parameters, but it doesn't provide significant benefits beyond that.
The populate_buy_trend method generates conditions for buying based on the specified indicators and their corresponding values. It iterates through three sets of indicators and their associated parameters, generating conditions using a condition_generator function. If any of the conditions are met, the 'buy' column in the data frame is set to 1. The populate_sell_trend method is similar to populate_buy_trend, but it generates conditions for selling instead. It also iterates through three sets of indicators and their parameters, generates conditions, and sets the 'sell' column in the data frame to 1 if any of the conditions are met. Overall, the strategy calculates indicators, generates buy and sell conditions based on these indicators, and marks the corresponding rows in the data frame for buying or selling.
stoploss: -0.128 timeframe: 5m hash(sha256): e8b49e3af14b4a218a2a61a5c94542e70b555a1e211bba47a6d2a44f86976127 indicators: Momentum Indicators volume Statistic Functions Overlap Studies Pattern Recognition indicator_trend_sma crossed_indicator Price Transform indicator Volume Indicators sharp_indicator Cycle Indicators Volatility Indicators
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last change: 2024-04-28 23:10:24