# Load pandas import pandas as pd import talib as ta import numpy as np # Read CSV file into DataFrame df df = pd.read_csv('EURUSD_Data.csv',parse_dates=['time'], index_col="time") #df=df.loc['2021-11'] # Show dataframe df.head()

#main strategy dataframe = df fee_rate= 0.0001 #0.01% def rsi_bb(data=df,period_bb=30, period_rsi=9, n_std=2): #needed for optimizing df['RSI'] = ta.RSI(data['close'], timeperiod=period_rsi) df['upperbb'], data['middlebb'], data['lowerbb'] = ta.BBANDS(data["close"], timeperiod=period_bb) df['close_yesterday'] = df.close.shift(1) df['close_m2'] = df.close.shift(2) df['lowerbb_y'] = df.lowerbb.shift(1) df['middlebb_y'] = df.middlebb.shift(1) df['RSI_y']=df.RSI.shift(1) def long_position(dataframe): dataframe.loc[ ( (dataframe['RSI_y'] < 35) & (dataframe["close"] > dataframe['lowerbb']) & (dataframe['close_yesterday'] < dataframe['lowerbb_y']) # confirmation ), 'buy'] = 1 return dataframe def exit(dataframe): dataframe.loc[ ( ((dataframe["close"] > dataframe['middlebb'])& (dataframe["close_yesterday"] < dataframe['middlebb_y'])) | (dataframe['RSI'] > 75) ), 'exit_buy'] = 1 dataframe.loc[ ( ((dataframe["close"] < dataframe['middlebb'])& (dataframe["close_yesterday"] > dataframe['middlebb_y']))| (dataframe['RSI'] > 35) ), 'exit_sell'] = 1 return dataframe def short_position(dataframe): dataframe.loc[ ( (dataframe['RSI_y'] > 65) & (dataframe["close"] < dataframe['upperbb'])& (dataframe['close_yesterday'] > dataframe['upperbb']) ), 'sell'] = 1 return dataframe dataframe.fillna(0, inplace=True) # بعد از بک1 تست و اپتیمایز همین استراتژی میشه یه اندیکاتور دیگه هم اضافه کرد برای بهبود استراتژی

import matplotlib.pyplot as plt # test Profit_array=np.array([[0,0,0]]) #for l in [9,14,22,34]: l=14 for k in range (10): rsi_bb(df,period_bb=30,period_rsi=14) long_position(df) exit(df) short_position(df) stoploss=0.0005 df["stoploss_buy"] = df['close'] * (1-stoploss) * df['buy'] df["stoploss_sell"] = df['close'] * (1+stoploss) * df['sell'] riskratio=(k+4) #min pos_now=False # False: close position, True: open position pos_type=False # True: there is an open long position, False: there is an open short position positions=np.array([[0,0]]) stop_pos=0 for i in df.index: if pos_now == False: if df.loc[i].buy==1: reward=(df.loc[i].middlebb-df.loc[i].close)/df.loc[i].close rr=reward/stoploss # print("long",rr) if rr>riskratio: positions = np.append(positions,[np.array([i,1])],axis= 0) pos_type=True pos_now=True stop_pos=float(df.loc[i].stoploss_buy) elif df.loc[i].sell==1: reward=(-df.loc[i].middlebb+df.loc[i].close)/df.loc[i].close rr=reward/stoploss # print('short',rr) if rr>riskratio: positions = np.append(positions,[np.array([i,2])],axis= 0) pos_now=True pos_type=False stop_pos=float(df.loc[i].stoploss_sell) elif pos_now == True: if pos_type==True: if df.loc[i].close<stop_pos: positions = np.append(positions,[np.array([i,-1])],axis= 0) pos_now=False if df.loc[i].exit_buy ==1: positions = np.append(positions,[np.array([i,-1])],axis= 0) pos_now=False elif pos_type==False: if df.loc[i].close>stop_pos: positions = np.append(positions,[np.array([i,-2])],axis= 0) pos_now=False if df.loc[i].exit_sell ==1: positions = np.append(positions,[np.array([i,-2])],axis= 0) print() pos_now=False long_idx=[] short_idx=[] end_long_idx=[] end_short_idx=[] for i in range(np.shape(positions)[0]): if positions[i][1]==1: long_idx=np.append(long_idx,positions[i][0]) elif positions[i][1]==2: short_idx=np.append(short_idx,positions[i][0]) elif positions[i][1]==-1: end_long_idx=np.append(end_long_idx,positions[i][0]) elif positions[i][1]==-2: end_short_idx=np.append(end_short_idx,positions[i][0]) profit = [0] * ((np.shape(long_idx)[0]-1)+np.shape(short_idx)[0]) for i in (range(np.shape(long_idx)[0]-1)): start_p=df.loc[long_idx[i]].close close_p=df.loc[end_long_idx[i]].close prf=-fee_rate+float((1+(close_p-start_p)/start_p)) profit[i]=prf for i in (range(np.shape(short_idx)[0])): start_p=df.loc[short_idx[i]].close close_p=df.loc[end_short_idx[i]].close prf=-fee_rate+float((1+(-close_p+start_p)/start_p)) profit[i+np.shape(long_idx)[0]-1]=prf profit_net=np.prod(profit) Profit_array=np.append(Profit_array,[np.array([l,riskratio,profit_net])],axis= 0) print("all positions", np.shape(end_long_idx)+ np.shape(end_short_idx)) print("final profit for if min riskratio= ",riskratio,"and rsi_period=",l," = ",profit_net) if k==4: plt.figure(figsize = (20,8)) plt.plot(df['close'],alpha=0.25) plt.scatter(long_idx, df.loc[long_idx]["close"], marker = '^', color='green') plt.scatter(short_idx, df.loc[short_idx]["close"], marker = 'v', color='red') plt.scatter(end_long_idx, df.loc[end_long_idx]["close"], marker = '+', color='m') plt.scatter(end_short_idx, df.loc[end_short_idx]["close"], marker = '+', color='m') #plt.axis(['2021-05' '2021-07' 1.2 1.3]) plt.show() print(Profit_array)