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The "GodStraNew" strategy is a backtesting strategy implemented in a class that extends the "IStrategy" interface. It consists of the following main methods:
populate_indicators: This method is responsible for calculating indicators in different time periods to optimize the strategy. However, in this particular strategy, the indicators are calculated inside the buy and sell strategy populator methods as needed.
The populate_indicators method only calculates default values of hyperoptable parameters, providing limited benefits compared to calculating usable things inside the buy and sell trend populators.
populate_buy_trend: This method populates the buy signals in the dataframe based on specified conditions.
It retrieves the values of different indicators, crossed indicators, operators, and real numbers associated with the buy signals. For each set of indicators, it generates a condition using a helper function called condition_generator and appends it to a list of conditions. Finally, if there are any conditions, it applies the logical AND operation on them using the reduce function and assigns the value of 1 to the 'buy' column in the dataframe where the conditions are satisfied. populate_sell_trend: Similar to the populate_buy_trend method, this method populates the sell signals in the dataframe based on specified conditions. It retrieves the values of different sell indicators, crossed indicators, operators, and real numbers. It generates conditions using the condition_generator helper function and appends them to a list of conditions. If there are any conditions, it applies the logical AND operation on them and assigns the value of 1 to the 'sell' column in the dataframe where the conditions are satisfied. Overall, the strategy calculates indicators, generates buy and sell conditions based on indicator values and other parameters, and populates the corresponding signals in the dataframe. It uses a combination of indicators, crossed indicators, operators, and real numbers to determine the buy and sell signals.
The populate_indicators method only calculates default values of hyperoptable parameters, providing limited benefits compared to calculating usable things inside the buy and sell trend populators.
populate_buy_trend: This method populates the buy signals in the dataframe based on specified conditions.
It retrieves the values of different indicators, crossed indicators, operators, and real numbers associated with the buy signals. For each set of indicators, it generates a condition using a helper function called condition_generator and appends it to a list of conditions. Finally, if there are any conditions, it applies the logical AND operation on them using the reduce function and assigns the value of 1 to the 'buy' column in the dataframe where the conditions are satisfied. populate_sell_trend: Similar to the populate_buy_trend method, this method populates the sell signals in the dataframe based on specified conditions. It retrieves the values of different sell indicators, crossed indicators, operators, and real numbers. It generates conditions using the condition_generator helper function and appends them to a list of conditions. If there are any conditions, it applies the logical AND operation on them and assigns the value of 1 to the 'sell' column in the dataframe where the conditions are satisfied. Overall, the strategy calculates indicators, generates buy and sell conditions based on indicator values and other parameters, and populates the corresponding signals in the dataframe. It uses a combination of indicators, crossed indicators, operators, and real numbers to determine the buy and sell signals.
Unable to parse Traceback (Logfile Exceeded Limit)
stoploss: -0.99 timeframe: 5m hash(sha256): 746b84b948ed482c175f17cab5c515ed3fb409cda8f7a07bc7e5eabb7830662f indicators: volume indicator_trend_sma crossed_indicator indicator sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-07-28 12:49:23