#!/usr/bin/env python # coding: utf-8 # In[114]: #Import MetaTrader5 package import MetaTrader5 as mt5 #Import datetime for selecting data from datetime import datetime #Keeping track of time import time #Import matplotlib import matplotlib.pyplot as plt #Intepret glass box model from interpret.glassbox import ExplainableBoostingClassifier #Intepret GUI dashboard utility from interpret import show #Pandas for handling data import pandas as pd #Pandas-ta for calculating technical indicators import pandas_ta as ta #Scoring metric to assess model accuracy from sklearn.metrics import precision_score # In[115]: #Our login credentials login = 30516497 password = '**dARERECHIMURENGA__2008' server = 'Deriv-Demo' # In[116]: #Initializing out MT5 terminal and logging in if mt5.initialize(login=login,password=password,server=server): print('Logged in successfully') else: print('Failed to login') # In[117]: #We need to specify the dates we want to use in our dataset date_from = datetime(2019,4,17) date_to = datetime.now() # In[118]: #Fetching historical data data = pd.DataFrame(mt5.copy_rates_range('Boom 1000 Index',mt5.TIMEFRAME_D1,date_from,date_to)) # In[119]: #Let's convert the time from seconds to year-month-date data['time'] = pd.to_datetime(data['time'],unit='s') data # In[120]: #Let's create a function to preprocess our data def preprocess(df): #All values of real_volume are 0 in this dataset df.drop(columns={'real_volume'},inplace=True) #Calculating 14 period ATR df.ta.atr(length=14,append=True) #Calculating the growth in the value of the ATR, the second difference df['ATR Growth'] = df['ATRr_14'].diff().diff() #Calculating 14 period RSI df.ta.rsi(length=14,append=True) #Calculating the rolling standard deviation of the RSI df['RSI Stdv'] = df['RSI_14'].rolling(window=14).std() #Calculating the mid point of the high and low price df['mid_point'] = ( ( df['high'] + df['low'] ) / 2 ) #We will keep track of the midpoint value of the previous day df['mid_point - 1'] = df['mid_point'].shift(1) #How far is our price from the midpoint? df['height'] = df['close'] - df['mid_point'] #Drop any rows that have missing values df.dropna(axis=0,inplace=True) # In[121]: preprocess(data) data # In[122]: data['target'] = (data['close'].shift(-1) > data['close']).astype(int) data # In[123]: predictors = ['open','high','low','close','tick_volume','spread','ATRr_14','ATR Growth','RSI_14','RSI Stdv','mid_point','mid_point - 1','height'] target = ['target'] train_start = 27 train_end = 1000 test_start = 1001 # In[124]: #Train set train_x = data.loc[train_start:train_end,predictors] train_y = data.loc[train_start:train_end,target] #Test set test_x = data.loc[test_start:,predictors] test_y = data.loc[test_start:,target] # In[125]: #Let us fit our glass box model glass_box = ExplainableBoostingClassifier() glass_box.fit(train_x,train_y) # In[126]: show(glass_box.explain_global()) # In[127]: #Obtaining glass box predictions glass_box_predictions = pd.DataFrame(glass_box.predict(test_x)) # In[128]: glass_box_score = precision_score(test_y,glass_box_predictions) glass_box_score # In[129]: #We can also obtain individual explanations for each prediction show(glass_box.explain_local(test_x,test_y)) # In[130]: #Fetching account Info account_info = mt5.account_info() # getting specific account data initial_balance = account_info.balance initial_equity = account_info.equity print('balance: ', initial_balance) print('equity: ', initial_equity) # In[131]: symbols = mt5.symbols_get() # In[132]: #Trading global variables #The symbol we want to trade MARKET_SYMBOL = 'Boom 1000 Index' #This data frame will store the most recent price update last_close = pd.DataFrame() #We may not always enter at the price we want, how much deviation can we tolerate? DEVIATION = 100 #For demonstrational purposes we will always enter at the minimum volume #However,we will not hardcode the minimum volume, we will fetch it dynamically VOLUME = 0 #What timeframe are we working on? TIMEFRAME = mt5.TIMEFRAME_D1 # In[133]: for index,symbol in enumerate(symbols): if symbol.name == MARKET_SYMBOL: print(f"{symbol.name} has minimum volume: {symbol.volume_min}") VOLUME = symbol.volume_min # In[134]: # function to send a market order def market_order(symbol, volume, order_type, **kwargs): #Fetching the current bid and ask prices tick = mt5.symbol_info_tick(symbol) #Creating a dictionary to keep track of order direction order_dict = {'buy': 0, 'sell': 1} price_dict = {'buy': tick.ask, 'sell': tick.bid} request = { "action": mt5.TRADE_ACTION_DEAL, "symbol": symbol, "volume": volume, "type": order_dict[order_type], "price": price_dict[order_type], "deviation": DEVIATION, "magic": 100, "comment": "Glass Box Market Order", "type_time": mt5.ORDER_TIME_GTC, "type_filling": mt5.ORDER_FILLING_FOK, } order_result = mt5.order_send(request) print(order_result) return order_result # In[135]: # Close an order based on ticket id def close_order(ticket): positions = mt5.positions_get() for pos in positions: tick = mt5.symbol_info_tick(pos.symbol) #validating that the order is for this symbol type_dict = {0: 1, 1: 0} # 0 represents buy, 1 represents sell - inverting order_type to close the position price_dict = {0: tick.ask, 1: tick.bid} #bid ask prices if pos.ticket == ticket: request = { "action": mt5.TRADE_ACTION_DEAL, "position": pos.ticket, "symbol": pos.symbol, "volume": pos.volume, "type": type_dict[pos.type], "price": price_dict[pos.type], "deviation": DEVIATION, "magic": 100, "comment": "Glass Box Close Order", "type_time": mt5.ORDER_TIME_GTC, "type_filling": mt5.ORDER_FILLING_FOK, } order_result = mt5.order_send(request) print(order_result) return order_result return 'Ticket does not exist' # In[136]: #Update our date from and date to date_from = datetime(2023,11,1) date_to = datetime.now() # In[139]: #Get signals from our glass box model def ai_signal(): #Fetch OHLC data df = pd.DataFrame(mt5.copy_rates_range(MARKET_SYMBOL,TIMEFRAME,date_from,date_to)) #Process the data df['time'] = pd.to_datetime(df['time'],unit='s') df['target'] = (df['close'].shift(-1) > df['close']).astype(int) preprocess(df) #Select the last row last_close = df.iloc[-1:,1:] #Remove the target column last_close.pop('target') #Use the last row to generate a forecast from our glass box model #Remember 1 means buy and 0 means sell forecast = glass_box.predict(last_close) return forecast[0] # In[140]: #Now we define the main body of our Python Glass Box Trading Bot if __name__ == '__main__': while True: #Fetching model prediction signal = ai_signal() #Decoding model prediction into an action if signal == 1: direction = 'buy' elif signal == 0: direction = 'sell' print(f'AI Forecast: {direction}') #Opening A Buy Trade #But first we need to ensure there are no opposite trades open on the same symbol if direction == 'buy': #Close any sell positions for pos in mt5.positions_get(): if pos.type == 1: #This is an open sell order, and we need to close it close_order(pos.ticket) if not mt5.positions_totoal(): #We have no open positions market_order(MARKET_SYMBOL,VOLUME,direction) #Opening A Sell Trade elif direction == 'sell': #Close any buy positions for pos in mt5.positions_get(): if pos.type == 0: #This is an open buy order, and we need to close it close_order(pos.ticket) if not mt5.positions_get(): #We have no open positions market_order(MARKET_SYMBOL,VOLUME,direction) print('time: ', datetime.now()) print('-------\n') time.sleep(60) # In[ ]: