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custom_stoploss_with_psar.py

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+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# isort: skip_file
+# --- Do not remove these libs ---
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+
+from freqtrade.strategy.interface import IStrategy
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from datetime import datetime
+from freqtrade.persistence import Trade
+
+
+class CustomStoplossWithPSAR(IStrategy):
+    """
+    this is an example class, implementing a PSAR based trailing stop loss
+    you are supposed to take the `custom_stoploss()` and `populate_indicators()`
+    parts and adapt it to your own strategy
+
+    the populate_buy_trend() function is pretty nonsencial
+    """
+    timeframe = '1h'
+    stoploss = -0.2
+    custom_info = {}
+    use_custom_stoploss = True
+
+    def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
+                        current_rate: float, current_profit: float, **kwargs) -> float:
+
+        result = 1
+        if self.custom_info and pair in self.custom_info and trade:
+            # using current_time directly (like below) will only work in backtesting/hyperopt.
+            # in live / dry-run, it'll be really the current time
+            relative_sl = None
+            if self.dp:
+                # so we need to get analyzed_dataframe from dp
+                dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
+                # only use .iat[-1] in callback methods, never in "populate_*" methods.
+                # see: https://www.freqtrade.io/en/latest/strategy-customization/#common-mistakes-when-developing-strategies
+                last_candle = dataframe.iloc[-1].squeeze()
+                relative_sl = last_candle['sar']
+
+            if (relative_sl is not None):
+                # print("custom_stoploss().relative_sl: {}".format(relative_sl))
+                # calculate new_stoploss relative to current_rate
+                new_stoploss = (current_rate - relative_sl) / current_rate
+                # turn into relative negative offset required by `custom_stoploss` return implementation
+                result = new_stoploss - 1
+
+        # print("custom_stoploss() -> {}".format(result))
+        return result
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe['sar'] = ta.SAR(dataframe)
+        if self.dp.runmode.value in ('backtest', 'hyperopt'):
+            self.custom_info[metadata['pair']] = dataframe[['date', 'sar']].copy().set_index('date')
+
+        # all "normal" indicators:
+        # e.g.
+        # dataframe['rsi'] = ta.RSI(dataframe)
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Placeholder Strategy: buys when SAR is smaller then candle before
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe['sar'] < dataframe['sar'].shift())
+            ),
+            'buy'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Placeholder Strategy: does nothing
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame
+        :return: DataFrame with buy column
+        """
+        # Deactivated sell signal to allow the strategy to work correctly
+        dataframe.loc[:, 'sell'] = 0
+        return dataframe