Merge pull request #327 from kagari306/kagari306-patch-1

Refactor Supertrend method for efficiency

user_data/strategies/Supertrend.py

@@ -18,6 +18,7 @@ from freqtrade.strategy import IStrategy, IntParameter
 from pandas import DataFrame
 import talib.abstract as ta
 import numpy as np
+import pandas as pd
 
 class Supertrend(IStrategy):
     # Buy params, Sell params, ROI, Stoploss and Trailing Stop are values generated by 'freqtrade hyperopt --strategy Supertrend --hyperopt-loss ShortTradeDurHyperOptLoss --timerange=20210101- --timeframe=1h --spaces all'
@@ -80,30 +81,47 @@ class Supertrend(IStrategy):
     sell_p3 = IntParameter(7, 21, default=14)
 
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        new_cols = []
+    
         for multiplier in self.buy_m1.range:
             for period in self.buy_p1.range:
-                dataframe[f'supertrend_1_buy_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_1_buy_{multiplier}_{period}'})
+                new_cols.append(st)
+    
         for multiplier in self.buy_m2.range:
             for period in self.buy_p2.range:
-                dataframe[f'supertrend_2_buy_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_2_buy_{multiplier}_{period}'})
+                new_cols.append(st)
+    
         for multiplier in self.buy_m3.range:
             for period in self.buy_p3.range:
-                dataframe[f'supertrend_3_buy_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_3_buy_{multiplier}_{period}'})
+                new_cols.append(st)
+    
         for multiplier in self.sell_m1.range:
             for period in self.sell_p1.range:
-                dataframe[f'supertrend_1_sell_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_1_sell_{multiplier}_{period}'})
+                new_cols.append(st)
+    
         for multiplier in self.sell_m2.range:
             for period in self.sell_p2.range:
-                dataframe[f'supertrend_2_sell_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_2_sell_{multiplier}_{period}'})
+                new_cols.append(st)
+    
         for multiplier in self.sell_m3.range:
             for period in self.sell_p3.range:
-                dataframe[f'supertrend_3_sell_{multiplier}_{period}'] = self.supertrend(dataframe, multiplier, period)['STX']
+                st = self.supertrend(dataframe, multiplier, period)[['STX']].rename(
-
+                    columns={'STX': f'supertrend_3_sell_{multiplier}_{period}'})
+                new_cols.append(st)
+    
+        if new_cols:
+            dataframe = pd.concat([dataframe] + new_cols, axis=1)
+    
         return dataframe
 
     def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
@@ -136,42 +154,43 @@ class Supertrend(IStrategy):
         Supertrend Indicator; adapted for freqtrade
         from: https://github.com/freqtrade/freqtrade-strategies/issues/30
     """
-    def supertrend(self, dataframe: DataFrame, multiplier, period):
+    def supertrend(self, dataframe: pd.DataFrame, multiplier, period):
         df = dataframe.copy()
+        high = df['high'].values
+        low = df['low'].values
+        close = df['close'].values
+        length = len(df)
+    
+        # 1. TR and ATR
+        tr = ta.TRANGE(df['high'], df['low'], df['close'])
+        atr = pd.Series(tr).rolling(period).mean().to_numpy()
+    
+        # 2. basic upper / lower bands
+        basic_ub = (high + low) / 2 + multiplier * atr
+        basic_lb = (high + low) / 2 - multiplier * atr
+    
+        # 3. final upper / lower bands
+        final_ub = np.zeros(length)
+        final_lb = np.zeros(length)
+    
+        for i in range(period, length):
+            final_ub[i] = basic_ub[i] if basic_ub[i] < final_ub[i-1] or close[i-1] > final_ub[i-1] else final_ub[i-1]
+            final_lb[i] = basic_lb[i] if basic_lb[i] > final_lb[i-1] or close[i-1] < final_lb[i-1] else final_lb[i-1]
+    
+        # 4. ST calculation
+        st = np.zeros(length)
+        for i in range(period, length):
+            if st[i-1] == final_ub[i-1]:
+                st[i] = final_ub[i] if close[i] <= final_ub[i] else final_lb[i]
+            elif st[i-1] == final_lb[i-1]:
+                st[i] = final_lb[i] if close[i] >= final_lb[i] else final_ub[i]
+    
+        # 5. STX direction
+        stx = np.where(st > 0, np.where(close < st, 'down', 'up'), None)
+    
+        # 6. fillna
+        result = pd.DataFrame({'ST': st, 'STX': stx}, index=df.index)
+        result.fillna(0, inplace=True)
+      
+        return result
 
-        df['TR'] = ta.TRANGE(df)
-        df['ATR'] = ta.SMA(df['TR'], period)
-
-        st = 'ST_' + str(period) + '_' + str(multiplier)
-        stx = 'STX_' + str(period) + '_' + str(multiplier)
-
-        # Compute basic upper and lower bands
-        df['basic_ub'] = (df['high'] + df['low']) / 2 + multiplier * df['ATR']
-        df['basic_lb'] = (df['high'] + df['low']) / 2 - multiplier * df['ATR']
-
-        # Compute final upper and lower bands
-        df['final_ub'] = 0.00
-        df['final_lb'] = 0.00
-        for i in range(period, len(df)):
-            df['final_ub'].iat[i] = df['basic_ub'].iat[i] if df['basic_ub'].iat[i] < df['final_ub'].iat[i - 1] or df['close'].iat[i - 1] > df['final_ub'].iat[i - 1] else df['final_ub'].iat[i - 1]
-            df['final_lb'].iat[i] = df['basic_lb'].iat[i] if df['basic_lb'].iat[i] > df['final_lb'].iat[i - 1] or df['close'].iat[i - 1] < df['final_lb'].iat[i - 1] else df['final_lb'].iat[i - 1]
-
-        # Set the Supertrend value
-        df[st] = 0.00
-        for i in range(period, len(df)):
-            df[st].iat[i] = df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] <= df['final_ub'].iat[i] else \
-                            df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_ub'].iat[i - 1] and df['close'].iat[i] >  df['final_ub'].iat[i] else \
-                            df['final_lb'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] >= df['final_lb'].iat[i] else \
-                            df['final_ub'].iat[i] if df[st].iat[i - 1] == df['final_lb'].iat[i - 1] and df['close'].iat[i] <  df['final_lb'].iat[i] else 0.00
-        # Mark the trend direction up/down
-        df[stx] = np.where((df[st] > 0.00), np.where((df['close'] < df[st]), 'down',  'up'), None)
-
-        # Remove basic and final bands from the columns
-        df.drop(['basic_ub', 'basic_lb', 'final_ub', 'final_lb'], inplace=True, axis=1)
-
-        df.fillna(0, inplace=True)
-
-        return DataFrame(index=df.index, data={
-            'ST' : df[st],
-            'STX' : df[stx]
-        })