The GodStra strategy, which is implemented as a class that inherits from IStrategy, performs backtesting of trading strategies. Here's a brief description of what the strategy does:
populate_indicators: This method takes a DataFrame of price data and adds technical analysis indicators to it. It uses the add_all_ta_features function to calculate various indicators such as moving averages, relative strength index (RSI), and others.
The indicators are computed based on the columns of the DataFrame representing the open, high, low, close, and volume of the asset.
The resulting DataFrame with added indicators is returned.
populate_buy_trend: This method populates the "buy" column of the DataFrame with binary values indicating the buy signals generated by the strategy. It iterates over a set of buy parameters specified in the self.buy_params dictionary. For each parameter, it compares the values of certain indicators (IND and CRS) with predefined conditions (OPR) such as greater than (>), equal to (=), less than (<), crossed above (CA), or crossed below (CB). Additionally, it compares the indicators with integer values (INT) or real values (REAL). Based on these conditions, the method appends the corresponding Boolean expression to a list of conditions. Finally, it uses a bitwise AND operation to combine all the conditions and selects the rows in the DataFrame where all conditions are true. Those rows are marked with a value of 1 in the "buy" column. populate_sell_trend: This method is similar to populate_buy_trend but populates the "sell" column instead. It compares the values of indicators (IND and CRS) with conditions (OPR), integer values (INT), or real values (REAL). It appends the conditions to a list and selects the rows in the DataFrame where all conditions are true. The selected rows are marked with a value of 1 in the "sell" column. The overall purpose of the GodStra strategy is to generate buy and sell signals based on the specified conditions and indicators, allowing for backtesting and evaluation of the strategy's performance.
The indicators are computed based on the columns of the DataFrame representing the open, high, low, close, and volume of the asset.
The resulting DataFrame with added indicators is returned.
populate_buy_trend: This method populates the "buy" column of the DataFrame with binary values indicating the buy signals generated by the strategy. It iterates over a set of buy parameters specified in the self.buy_params dictionary. For each parameter, it compares the values of certain indicators (IND and CRS) with predefined conditions (OPR) such as greater than (>), equal to (=), less than (<), crossed above (CA), or crossed below (CB). Additionally, it compares the indicators with integer values (INT) or real values (REAL). Based on these conditions, the method appends the corresponding Boolean expression to a list of conditions. Finally, it uses a bitwise AND operation to combine all the conditions and selects the rows in the DataFrame where all conditions are true. Those rows are marked with a value of 1 in the "buy" column. populate_sell_trend: This method is similar to populate_buy_trend but populates the "sell" column instead. It compares the values of indicators (IND and CRS) with conditions (OPR), integer values (INT), or real values (REAL). It appends the conditions to a list and selects the rows in the DataFrame where all conditions are true. The selected rows are marked with a value of 1 in the "sell" column. The overall purpose of the GodStra strategy is to generate buy and sell signals based on the specified conditions and indicators, allowing for backtesting and evaluation of the strategy's performance.
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stoploss: -0.345 timeframe: 12h hash(sha256): dc5ea5d3ea64d18f799291363003b851f7952d67cc91ba2a15858588fb4708a2
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last change: 2024-08-03 18:17:24