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The GodStraNewOpt500016 strategy is a backtesting strategy that calculates indicators and generates buy and sell signals based on specified conditions. Here is a breakdown of how the strategy works:
populate_indicators method:
This method calculates indicators for the given dataframe and metadata. It is mainly used for optimizing the strategy but doesn't provide significant benefits for generating usable signals.
Default values of hyperoptable parameters are calculated in this method.
populate_buy_trend method: This method populates the buy signals for the dataframe based on specified conditions.
Multiple sets of conditions are defined using different indicators, crossed indicators, operators, and real numbers. For each set of conditions, the condition_generator function is called to generate a condition and modify the dataframe accordingly. The generated conditions are appended to a list. If there are any conditions in the list, the dataframe is updated to mark the corresponding rows as buy signals. populate_sell_trend method: This method populates the sell signals for the dataframe based on specified conditions. Similar to the populate_buy_trend method, it defines multiple sets of conditions using different indicators, crossed indicators, operators, and real numbers. The condition_generator function is called for each set of conditions to generate a condition and modify the dataframe. The generated conditions are appended to a list. If there are any conditions in the list, the dataframe is updated to mark the corresponding rows as sell signals. Overall, the strategy calculates indicators, generates buy and sell signals based on specified conditions, and updates the dataframe accordingly. The specific indicators and conditions used for generating signals can be customized using the strategy's parameters.
Default values of hyperoptable parameters are calculated in this method.
populate_buy_trend method: This method populates the buy signals for the dataframe based on specified conditions.
Multiple sets of conditions are defined using different indicators, crossed indicators, operators, and real numbers. For each set of conditions, the condition_generator function is called to generate a condition and modify the dataframe accordingly. The generated conditions are appended to a list. If there are any conditions in the list, the dataframe is updated to mark the corresponding rows as buy signals. populate_sell_trend method: This method populates the sell signals for the dataframe based on specified conditions. Similar to the populate_buy_trend method, it defines multiple sets of conditions using different indicators, crossed indicators, operators, and real numbers. The condition_generator function is called for each set of conditions to generate a condition and modify the dataframe. The generated conditions are appended to a list. If there are any conditions in the list, the dataframe is updated to mark the corresponding rows as sell signals. Overall, the strategy calculates indicators, generates buy and sell signals based on specified conditions, and updates the dataframe accordingly. The specific indicators and conditions used for generating signals can be customized using the strategy's parameters.
stoploss: -1 timeframe: 4h hash(sha256): 054e70da10c19c84a0303dc6ffcfc2f2782ab1369a6fd9804e74d7e8c1879707 indicators: volume indicator_trend_sma crossed_indicator indicator sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-04-28 14:49:41