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The "GodStraNewOpt3" strategy is a backtesting strategy implemented in a class that inherits from the "IStrategy" class. Here's a breakdown of what the strategy does:
populate_indicators method:
This method is responsible for calculating indicators in different time periods and optimizing the strategy. However, this particular strategy optimizes specific indicators within the buy and sell strategy population methods instead of calculating all indicators here.
It also calculates default values for hyperoptable parameters.
The method returns the updated DataFrame.
populate_buy_trend method: This method populates the buy trend by generating conditions based on indicator values. It retrieves indicator values and related parameters from instance variables. It calls the condition_generator function to generate a condition based on the specified operator, indicators, and numerical values. The generated condition is added to a list of conditions. The conditions are combined using the logical AND operation. If there are any conditions, the DataFrame is updated by setting the 'buy' column to 1 where all conditions are met. The method returns the updated DataFrame. populate_sell_trend method: This method populates the sell trend by generating conditions based on indicator values. Similar to the populate_buy_trend method, it retrieves indicator values and related parameters from instance variables. It calls the condition_generator function to generate a condition based on the specified operator, indicators, and numerical values. The generated condition is added to a list of conditions. The conditions are combined using the logical AND operation. If there are any conditions, the DataFrame is updated by setting the 'sell' column to 1 where all conditions are met. The method returns the updated DataFrame. Overall, the strategy calculates indicators, generates conditions based on indicators and parameters, and updates the buy and sell columns of the DataFrame accordingly. It follows a modular approach by separating indicator calculation, buy trend population, and sell trend population into separate methods.
It also calculates default values for hyperoptable parameters.
The method returns the updated DataFrame.
populate_buy_trend method: This method populates the buy trend by generating conditions based on indicator values. It retrieves indicator values and related parameters from instance variables. It calls the condition_generator function to generate a condition based on the specified operator, indicators, and numerical values. The generated condition is added to a list of conditions. The conditions are combined using the logical AND operation. If there are any conditions, the DataFrame is updated by setting the 'buy' column to 1 where all conditions are met. The method returns the updated DataFrame. populate_sell_trend method: This method populates the sell trend by generating conditions based on indicator values. Similar to the populate_buy_trend method, it retrieves indicator values and related parameters from instance variables. It calls the condition_generator function to generate a condition based on the specified operator, indicators, and numerical values. The generated condition is added to a list of conditions. The conditions are combined using the logical AND operation. If there are any conditions, the DataFrame is updated by setting the 'sell' column to 1 where all conditions are met. The method returns the updated DataFrame. Overall, the strategy calculates indicators, generates conditions based on indicators and parameters, and updates the buy and sell columns of the DataFrame accordingly. It follows a modular approach by separating indicator calculation, buy trend population, and sell trend population into separate methods.
stoploss: -0.99 timeframe: 1h hash(sha256): f007c48544c79a938cb7c451e3e54889f673003fc9bc75fd6aaaab4ea7ef424e indicators: volume indicator_trend_sma crossed_indicator indicator sharp_indicator
No similar strategies found. (based on used indicators)
last change: 2024-04-28 23:48:05