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The "GodStraNew" strategy is implemented as a class that inherits from the "IStrategy" interface. It consists of three main methods: "populate_indicators," "populate_buy_trend," and "populate_sell_trend." Here's a breakdown of what each method does:
populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
This method is responsible for calculating indicators and optimizing the strategy. It is mentioned that calculating all indicators in all time periods here would take a long time and may not be used in the strategy's optimization.
Instead, specific indicators are calculated within the buy and sell strategy populator methods if needed.
This method also calculates default values for hyperoptable parameters.
The main benefit of using this method is to calculate default values, but it doesn't provide significant advantages compared to calculating usable things inside the buy and sell trend populators. The method returns the updated dataframe. populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: This method populates the buy trend by generating conditions based on specified indicators and parameters. It initializes an empty list called "conditions" to store the generated conditions. Multiple sets of indicators and corresponding parameters are used to generate conditions. For each set of indicators, the method calls a "condition_generator" function with the appropriate parameters to generate a condition and update the dataframe accordingly. The generated condition is appended to the list of conditions. If there are any conditions in the list, the dataframe is updated by setting the "buy" column to 1 where all conditions are satisfied. The method returns the updated dataframe. populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: This method is similar to the "populate_buy_trend" method but is responsible for populating the sell trend. It follows the same structure, generating conditions based on specified indicators and parameters, appending them to a list, and updating the dataframe accordingly. If there are any conditions in the list, the dataframe is updated by setting the "sell" column to 1 where all conditions are satisfied. The method returns the updated dataframe. Overall, this strategy calculates indicators, generates buy and sell conditions based on specified indicators and parameters, and updates the dataframe accordingly to indicate buy and sell signals.
Instead, specific indicators are calculated within the buy and sell strategy populator methods if needed.
This method also calculates default values for hyperoptable parameters.
The main benefit of using this method is to calculate default values, but it doesn't provide significant advantages compared to calculating usable things inside the buy and sell trend populators. The method returns the updated dataframe. populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: This method populates the buy trend by generating conditions based on specified indicators and parameters. It initializes an empty list called "conditions" to store the generated conditions. Multiple sets of indicators and corresponding parameters are used to generate conditions. For each set of indicators, the method calls a "condition_generator" function with the appropriate parameters to generate a condition and update the dataframe accordingly. The generated condition is appended to the list of conditions. If there are any conditions in the list, the dataframe is updated by setting the "buy" column to 1 where all conditions are satisfied. The method returns the updated dataframe. populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame: This method is similar to the "populate_buy_trend" method but is responsible for populating the sell trend. It follows the same structure, generating conditions based on specified indicators and parameters, appending them to a list, and updating the dataframe accordingly. If there are any conditions in the list, the dataframe is updated by setting the "sell" column to 1 where all conditions are satisfied. The method returns the updated dataframe. Overall, this strategy calculates indicators, generates buy and sell conditions based on specified indicators and parameters, and updates the dataframe accordingly to indicate buy and sell signals.
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stoploss: -1 timeframe: 1h hash(sha256): 3df10f3f3c2699c7f7aabcd506cb49a167542a8a660174e1e171f219577d6990 indicators: volume indicator_trend_sma crossed_indicator indicator sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-07-27 09:37:23