# source: https://raw.githubusercontent.com/remiotore/freqtrade/44beaeb6a420cd8e9f2e4ea93e11d6cfa192ee03/strategies/Discord_candlestickSD.py from freqtrade.strategy import IStrategy import pandas as pd import ta # Import the 'ta' module for technical indicators import numpy as np class Github_remiotore_freqtrade__Discord_candlestickSD__20260111_210550(IStrategy): timeframe = '1m' tol = 0.003 A=0.3 B=2 C=2 cross_candle_length = 5 # Minimum candles for a cross cross_tolerance = 0.003 # Cross tolerance take_profit = 0.0075 # Take profit percentage stop_loss = -0.005 # Stop loss percentage stoploss = stop_loss def populate_indicators(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: Tol=dataframe['close'][1]*0.003 dataframe['Candle'] = 0 dataframe['Type'] = 0 dataframe['SD']=0 SD=[] for i in range(len(dataframe)): if abs(dataframe['close'][i]-dataframe['high'][i])<(self.A): if abs(dataframe['open'][i]-dataframe['low'][i])Tol: dataframe['Candle'][i] = 'Doji' if dataframe['close'][i]>dataframe['open'][i]: dataframe['Type'][i] = 'G' else: dataframe['Type'][i] = 'R' if abs(dataframe['close'][i]-dataframe['high'][i])Tol: dataframe['Candle'][i]='Hammer' if dataframe['close'][i]>dataframe['open'][i]: dataframe['Type'][i] = 'G' else: dataframe['Type'][i] = 'R' if abs(dataframe['open'][i]-dataframe['high'][i])Tol: dataframe['Candle'][i]='Hammer' if dataframe['close'][i]>dataframe['open'][i]: dataframe['Type'][i] = 'G' else: dataframe['Type'][i] = 'R' if abs(dataframe['close'][i]-dataframe['low'][i])Tol: dataframe['Candle'][i]='InHammer' if dataframe['close'][i]>dataframe['open'][i]: dataframe['Type'][i] = 'G' else: dataframe['Type'][i] = 'R' if abs(dataframe['open'][i]-dataframe['low'][i])Tol: dataframe['Candle'][i]='InHammer' if dataframe['close'][i]>dataframe['open'][i]: dataframe['Type'][i] = 'G' else: dataframe['Type'][i] = 'R' for i in range(24*60,len(dataframe)): SD=[] A1=np.max(dataframe['close'][i-15:i]) A2=np.min(dataframe['close'][i-15:i]) A3=np.max(dataframe['close'][i-60:i]) A4=np.min(dataframe['close'][i-60:i]) A5=np.max(dataframe['close'][i-240:i]) A6=np.min(dataframe['close'][i-240:i]) A7=np.max(dataframe['close'][i-24*60:i]) A8=np.min(dataframe['close'][i-24*60:i]) B1=np.argmax(dataframe['close'][i-15:i]) B2=np.argmin(dataframe['close'][i-15:i]) B3=np.argmax(dataframe['close'][i-60:i]) B4=np.argmin(dataframe['close'][i-60:i]) B5=np.argmax(dataframe['close'][i-240:i]) B6=np.argmin(dataframe['close'][i-240:i]) B7=np.argmax(dataframe['close'][i-24*60:i]) B8=np.argmin(dataframe['close'][i-24*60:i]) if 24*60-B7>64*self.C and B7>64*self.C: if i==1: SDt.append(A7) dataframe['SD'][i-24*60+B7]=1 else: SD.append(A7) dataframe['SD'][i-24*60+B7]=1 if 24*60-B8>64*self.C and B8>64*self.C: if i==1: SDt.append(A8) dataframe['SD'][i-24*60+B8]=1 else: SD.append(A8) dataframe['SD'][i-24*60+B8]=1 if 240-B5>16*self.C and B5>16*self.C: SD.append(A5) dataframe['SD'][i-240+B5]=2 if 240-B6>16*self.C and B6>16*self.C: SD.append(A6) dataframe['SD'][i-240+B6]=2 if 60-B3>4*self.C and B3>4*self.C: SD.append(A3) dataframe['SD'][i-60+B3]=3 if 60-B4>4*self.C and B4>4*self.C: SD.append(A4) dataframe['SD'][i-60+B4]=3 if 15-B1>self.C and B1>self.C: SD.append(A1) dataframe['SD'][i-15+B1]=4 if 15-B2>self.C and B2>self.C: SD.append(A2) dataframe['SD'][i-15+B2]=4 return dataframe def populate_entry_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: SD = [index for index, arr in enumerate(dataframe['SD']) if np.any(arr != 0)] SDt=[] for j in range(len(SD)): flag=0 for k in range(len(SDt)): if abs(SD[j]-SDt[k])= dataframe['Median2']) else False # crossDown1 = True if (dataframe['Median1'].shift(1) > dataframe['Median2'].shift(1)) & (dataframe['Median1'] <= dataframe['Median2']) else False # crossUp2 = True if (dataframe['Median1'].shift(1) < dataframe['Median3'].shift(1)) & (dataframe['Median1'] >= dataframe['Median3']) else False # crossDown2 = True if (dataframe['Median1'].shift(1) > dataframe['Median3'].shift(1)) & (dataframe['Median1'] <= dataframe['Median2']) else False # # cross_tolerance = abs(dataframe['Median1'][i] - dataframe['Median2'][i]) / dataframe['Median1'][i] # buySignal = dataframe['close'].shift(1) < dataframe['Median1'].shift(1) & (dataframe['close'] >= dataframe['Median1'].shift(1)) & rsi_value > (rsi_value.shift(self.cross_candle_length).where(crossUp1).max()) & dataframe['close']< (rsi_value.shift(self.cross_candle_length).where(crossUp1).max()) for i in range(1,len(dataframe)): if dataframe['Candle'][i]=='Doji': print ("Long Signal Detected") dataframe.loc[i + self.cross_candle_length - 1, 'enter_long'] = 1 elif dataframe['Candle'][i]=='Hammer': print ("Short Signal Detected") dataframe.loc[i + self.cross_candle_length - 1, 'enter_short'] = 1 return dataframe def populate_exit_trend(self, dataframe: pd.DataFrame, metadata: dict) -> pd.DataFrame: dataframe['exit_long'] = 0 dataframe['exit_short'] = 0 tolerance=0.003 for idx, row in dataframe.iterrows(): if row['enter_long'] == 1: entry_price = row['close'] profit_target_price = entry_price * (1 + self.take_profit) stop_loss_price = entry_price * (1 + self.stop_loss) # Find the index of prices that are higher than entry price higher_prices_idx = dataframe[dataframe['close'] > profit_target_price].index higher_prices_idx1= dataframe[dataframe['close'] < stop_loss_price].index if higher_prices_idx.empty and higher_prices_idx1.empty: continue F=higher_prices_idx-idx F1=higher_prices_idx1-idx # Find the index of the price closest to the profit target price among higher prices T1=F[F > 0].min()+idx T2=F1[F1 > 0].min()+idx m=1 n=1 flag=1 if np.isnan(T1): flag=0 m=0 if np.isnan(T2): flag=0 n=0 if flag==0: if m==0 and n!=0: exit_idx=T2 elif n==0 and m!=0: exit_idx=T1 elif m==0 and n==0: exit_idx=idx+self.cross_candle_length else: exit_idx=min(T1,T2) if exit_idx>len(dataframe): exit_idx=len(dataframe) if exit_idx= profit_target_price : dataframe.loc[exit_idx, 'exit_long'] = 1 print(f"Long Exit signal Detected with Profit at index {idx}") elif dataframe['close'][exit_idx] <= stop_loss_price : dataframe.loc[idx, 'exit_long'] = 1 print(f"Long Exit signal Detected with StopLoss at index {idx}") elif row['enter_short'] == 1: entry_price = row['close'] profit_target_price = entry_price * (1 - self.take_profit) stop_loss_price = entry_price * (1 - self.stop_loss) # Find the index of prices that are lower than entry price lower_prices_idx = dataframe[dataframe['close'] < profit_target_price].index lower_prices_idx1= dataframe[dataframe['close'] > stop_loss_price].index if lower_prices_idx.empty and lower_prices_idx1.empty: continue F=lower_prices_idx-idx F1=lower_prices_idx1-idx # Find the index of the price closest to the profit target price among higher prices T1=F[F > 0].min()+idx T2=F1[F1 > 0].min()+idx m=1 n=1 flag=1 if np.isnan(T1): flag=0 m=0 if np.isnan(T2): flag=0 n=0 if flag==0: if m==0 and n!=0: exit_idx=T2 elif n==0 and m!=0: exit_idx=T1 elif m==0 and n==0: exit_idx=idx+self.cross_candle_length else: exit_idx=min(T1,T2) if exit_idx= stop_loss_price : dataframe.loc[idx, 'exit_long'] = 1 print(f"Short Exit signal Detected with StopLoss at index {idx}") return dataframe