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The "CustomStoplossWithPSAR" strategy is an example class that demonstrates a trailing stop loss based on the Parabolic SAR (PSAR) indicator. It is designed to be adapted to your own trading strategy. Here are the important parts of the strategy:
Timeframe: The strategy operates on the 1-hour timeframe.
Stoploss: The stop loss percentage is set to -0.2, meaning a 20% loss will trigger a stop loss.
Custom Info: A dictionary to store custom information specific to each trading pair.
Use Custom Stoploss: A flag indicating whether to use the custom stop loss. Custom Stoploss Function: This function calculates a custom stop loss based on the PSAR indicator. It takes the current pair, trade information, current time, current rate, and current profit as input and returns the new stop loss as a percentage relative to the current rate. Populate Indicators Function: This function populates the "sar" column in the dataframe with the PSAR values. It also stores the PSAR values in the custom_info dictionary for backtesting and hyperparameter optimization. Populate Buy Trend Function: This function generates buy signals when the PSAR value is smaller than the PSAR value of the previous candle. Populate Sell Trend Function: This function does nothing and keeps the sell signal deactivated. Note: The provided description is a summary of the strategy and may not include all the details or nuances. It is recommended to review the code and customize it according to your specific requirements.
Stoploss: The stop loss percentage is set to -0.2, meaning a 20% loss will trigger a stop loss.
Custom Info: A dictionary to store custom information specific to each trading pair.
Use Custom Stoploss: A flag indicating whether to use the custom stop loss. Custom Stoploss Function: This function calculates a custom stop loss based on the PSAR indicator. It takes the current pair, trade information, current time, current rate, and current profit as input and returns the new stop loss as a percentage relative to the current rate. Populate Indicators Function: This function populates the "sar" column in the dataframe with the PSAR values. It also stores the PSAR values in the custom_info dictionary for backtesting and hyperparameter optimization. Populate Buy Trend Function: This function generates buy signals when the PSAR value is smaller than the PSAR value of the previous candle. Populate Sell Trend Function: This function does nothing and keeps the sell signal deactivated. Note: The provided description is a summary of the strategy and may not include all the details or nuances. It is recommended to review the code and customize it according to your specific requirements.
stoploss: -0.2 timeframe: 1h hash(sha256): b41b75c998b0b5c102648c3cf92b55f80061033b1d42eceb3652c4c5f649a610 indicators: date sar date sar
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last change: 2022-07-24 11:07:20