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The "CustomStoplossWithPSAR" strategy is an example implementation of a trading strategy that uses the Parabolic SAR (PSAR) indicator for trailing stop loss. The main components of the strategy are as follows:
Timeframe: The strategy operates on the 1-hour timeframe. Stoploss: The stoploss parameter is set to -0.2, indicating a 20% stop loss.
Custom Info: The strategy uses a custom_info dictionary to store information specific to each trading pair.
Custom Stoploss: The custom_stoploss() function calculates a custom stop loss based on the PSAR indicator.
It retrieves the PSAR value from the custom_info dictionary for the current pair and calculates a new stop loss relative to the current rate. The result is a relative negative offset required by the custom_stoploss return implementation. Populate Indicators: The populate_indicators() function adds the PSAR indicator to the dataframe. It also stores the PSAR values in the custom_info dictionary for backtesting and hyperopt purposes. Populate Buy Trend: The populate_buy_trend() function generates buy signals based on the condition that the current PSAR value is smaller than the PSAR value of the previous candle. Populate Sell Trend: The populate_sell_trend() function is deactivated in this strategy, meaning it does not generate sell signals. Please note that this is just an example strategy, and you would need to adapt it to your own trading strategy by modifying the custom_stoploss() and populate_buy_trend() functions according to your specific requirements.
Custom Info: The strategy uses a custom_info dictionary to store information specific to each trading pair.
Custom Stoploss: The custom_stoploss() function calculates a custom stop loss based on the PSAR indicator.
It retrieves the PSAR value from the custom_info dictionary for the current pair and calculates a new stop loss relative to the current rate. The result is a relative negative offset required by the custom_stoploss return implementation. Populate Indicators: The populate_indicators() function adds the PSAR indicator to the dataframe. It also stores the PSAR values in the custom_info dictionary for backtesting and hyperopt purposes. Populate Buy Trend: The populate_buy_trend() function generates buy signals based on the condition that the current PSAR value is smaller than the PSAR value of the previous candle. Populate Sell Trend: The populate_sell_trend() function is deactivated in this strategy, meaning it does not generate sell signals. Please note that this is just an example strategy, and you would need to adapt it to your own trading strategy by modifying the custom_stoploss() and populate_buy_trend() functions according to your specific requirements.
stoploss: -0.2 timeframe: 1h hash(sha256): a19f9512f16ad2f86d8f557b665910ef2e19063452f609c0206411ec2ef35539 indicators: date sar date sar
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last change: 2022-07-02 19:54:08