The GodStraNewOptQuick strategy is designed to backtest trading strategies. Here is a short description of what it does:
This strategy has two main functions: populate_indicators and populate_buy_trend/populate_sell_trend. In the populate_indicators function, it calculates various indicators for the given DataFrame and metadata.
However, it only calculates default values of hyperoptable parameters and doesn't provide significant benefits apart from that.
The strategy prefers to calculate specific indicators within the buy and sell trend population methods if they are needed.
The populate_buy_trend function populates the buy trend based on certain conditions. It retrieves indicator values and other parameters for buy signals from the strategy's internal variables. It then uses a condition_generator function to generate a condition based on the specified indicators, operators, and numerical values. The generated conditions are appended to a list. Finally, if there are any conditions in the list, the DataFrame is updated to mark the corresponding rows as a buy signal. Similarly, the populate_sell_trend function populates the sell trend based on specified conditions. It retrieves indicator values and other parameters for sell signals from the strategy's internal variables. It uses the condition_generator function to generate conditions based on the specified indicators, operators, and numerical values. 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 a sell signal. Overall, the strategy calculates indicators, generates buy and sell conditions based on specified indicators and parameters, and marks the DataFrame accordingly to indicate buy and sell signals.
However, it only calculates default values of hyperoptable parameters and doesn't provide significant benefits apart from that.
The strategy prefers to calculate specific indicators within the buy and sell trend population methods if they are needed.
The populate_buy_trend function populates the buy trend based on certain conditions. It retrieves indicator values and other parameters for buy signals from the strategy's internal variables. It then uses a condition_generator function to generate a condition based on the specified indicators, operators, and numerical values. The generated conditions are appended to a list. Finally, if there are any conditions in the list, the DataFrame is updated to mark the corresponding rows as a buy signal. Similarly, the populate_sell_trend function populates the sell trend based on specified conditions. It retrieves indicator values and other parameters for sell signals from the strategy's internal variables. It uses the condition_generator function to generate conditions based on the specified indicators, operators, and numerical values. 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 a sell signal. Overall, the strategy calculates indicators, generates buy and sell conditions based on specified indicators and parameters, and marks the DataFrame accordingly to indicate buy and sell signals.
stoploss: -1 timeframe: 4h hash(sha256): 9d07b87b2992c45203f1ae83ced2eabc6feb2a14adc0867a5f08df43a36be77c indicators: volume indicator_trend_sma crossed_indicator indicator sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-04-28 05:33:06