Русский 中文 Español Deutsch 日本語 Português
MetaTrader 5 and Python integration: receiving and sending data

MetaTrader 5 and Python integration: receiving and sending data

MetaTrader 5Trading | 28 March 2019, 11:25
80 525 36
Maxim Dmitrievsky
Maxim Dmitrievsky

Why integrate MQL5 and Python?

Comprehensive data processing requires extensive tools and is often beyond the sandbox of one single application. Specialized programming languages are used for processing and analyzing data, statistics and machine learning. One of the leading programming languages for data processing is Python. A very effective solution is to use the power of the language and included libraries for the development of trading systems.

There are different solutions for implementing the interaction of two or more programs. Sockets are one the fastest and most flexible solutions.

A network socket is the endpoint of interprocess communication over a computer network. The MQL5 Standard Library includes a group of Socket functions, which provide a low-level interface for working on the Internet. This is a common interface for different programming languages, as it uses system calls at the operating system level.

Data exchange between the prices is implemented over TCP/IP (Transmission Control Protocol/Internet Protocol). Thus, processes can interact within a single computer and over a local network or the Internet.

To establish a connection, it is necessary to create and initialize a TCP server to which the client process will connect. Once the interaction of processes is completed, the connection must be forcibly closed. Data in a TCP exchange is a stream of bytes.

When creating a server, we need to associate a socket with one or more hosts (IP addresses) and an unused port. If the list of hosts is not set or is specified as "", the socket will listen to all hosts. If you specify "" or 'localhost', connection will be possible only within the "internal loop", i.e. only within one computer.

Since only the client is available in MQL5, we will create a server in Python.

Creating a socket server in Python

The purpose of the article is not to teach the Python programming basics. It is therefore assumed that the reader is familiar with this language. 

We will use version 3.7.2 and the built-in socket package. Please read related documentation for more details.

We will write a simple program which will create a socket server and receive the necessary information from the client (the MQL5 program), handle it and send back the result. This seems to be the most efficient interaction method. Suppose we need to use a machine learning library, such as for example scikit learn, which will calculate linear regression using prices and return coordinates, based on which a line can be drawn in the MetaTrader 5 terminal. This is the basic example. However, such interaction can also be used for training a neural network, for sending to it data from the terminal (quotes), learning and returning the result to the terminal.

Let us create the program and import the libraries described above:

import socket, numpy as np
from sklearn.linear_model import LinearRegression

Now we can proceed to creating a class responsible for socket manipulation:

class socketserver:
    def __init__(self, address = '', port = 9090):
        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        self.address = address
        self.port = port
        self.sock.bind((self.address, self.port))
        self.cummdata = ''
    def recvmsg(self):
        self.conn, self.addr = self.sock.accept()
        print('connected to', self.addr)
        self.cummdata = ''

        while True:
            data = self.conn.recv(10000)
            if not data:
            self.conn.send(bytes(calcregr(self.cummdata), "utf-8"))
            return self.cummdata
    def __del__(self):

When creating a class object, the constructor gets the host name (IP address) and port number. Then the sock object is created, which is associated with the address and port sock.bind().

The recvmsg method listens for the incoming connection sock.listen(1). When an incoming client connection arrives, the server accepts it self.sock.accept().

Then the server waits in an infinite loop for an incoming client message, which arrives as a stream of bytes. Since the message length is not known in advance, the server receives this message in parts, say 1 Kbytes at a time, until the whole message is read self.conn.recv(10000). Received piece of data is converted to a string data.decode("utf-8") and is added to the rest of the string summdata.

Once all the data have been received (if not data:), the server sends to the client a string containing the rightmost and leftmost coordinates of the calculated regression line. The string is preliminary converted to a byte array conn.send(bytes(calcregr(self.cummdata), "utf-8")).

At the end, the method returns the string received from the client. It can be used for the visualization of received quotes, among others.

A destructor closes the socket once the Python program execution is completed.

Please note that this is not the only possible implementation of the class. Alternatively, you can separate the methods for receiving and sending messages and use it in different ways at different points in time. I have only described the basic technology for creating a connection. You can implement your own solutions.

Let us consider in more detail the linear regression learning method within the current implementation:

def calcregr(msg = ''):
    chartdata = np.fromstring(msg, dtype=float, sep= ' ') 
    Y = np.array(chartdata).reshape(-1,1)
    X = np.array(np.arange(len(chartdata))).reshape(-1,1)
    lr = LinearRegression(), Y)
    Y_pred = lr.predict(X)
    P = Y_pred.astype(str).item(-1) + ' ' + Y_pred.astype(str).item(0)
    return str(P)

The received byte stream is converted to a utf-8 string, which is then accepted by the calcregr(msg = ' ') method. Since the string contains a sequence of prices separated by spaces (as implemented in the client), it is converted to a NumPy array of the float type. After that the price array is converted to a column (the data receiving format is sclearn) Y = np.array(chartdata).reshape(-1,1). The predictor for the model is the linear time (a sequence of values; its size is equal to the length of the training sample) X = np.array(np.arange(len(chartdata))).reshape(-1,1)

This is followed by training and model prediction, while the first and the last values of the line (the edges of the segment) are written to the "P" variable, converted to a string and passed to the client in byte form.

Now, we only need to create the class object and call the recvmsg() method in a loop:

serv = socketserver('', 9090)

while True:  
    msg = serv.recvmsg()

Creating a socket client in MQL5

Let us create a simple Expert Advisor, which can connect to the server, pass the specified number of recent Close prices, get back the coordinates of the regression line and draw it on the chart. 

The socksend() function will pass data to the server:

bool socksend(int sock,string request) 
   char req[];
   int  len=StringToCharArray(request,req)-1;
   if(len<0) return(false);

It receives the string, converts to a byte array and sends to a server.

The socketreceive() function listens on the port. Once a server response is received, the function returns it as a string:

string socketreceive(int sock,int timeout)
   char rsp[];
   string result="";
   uint len;
   uint timeout_check=GetTickCount()+timeout;
         int rsp_len;
   while((GetTickCount()<timeout_check) && !IsStopped());
   return result;

The last function drawlr() receives a string, in which the left and right line coordinates are written, then parses the string to a string array and draws the linear regression line on a chart:

void drawlr(string points) 
   string res[];
   StringSplit(points,' ',res);

      datetime temp[];

The function is implemented in the OnTick() handler.

void OnTick() {
 if(socket!=INVALID_HANDLE) {
  if(SocketConnect(socket,"localhost",9090,1000)) {
   Print("Connected to "," localhost",":",9090);
   double clpr[];
   int copyed = CopyClose(_Symbol,PERIOD_CURRENT,0,lrlenght,clpr);
   string tosend;
   for(int i=0;i<ArraySize(clpr);i++) tosend+=(string)clpr[i]+" ";       
   string received = socksend(socket, tosend) ? socketreceive(socket, 10) : ""; 
   drawlr(recieved); }
  else Print("Connection ","localhost",":",9090," error ",GetLastError());
  SocketClose(socket); }
 else Print("Socket creation error ",GetLastError()); }

Testing the MQL5-Python Client-Server application

To run the application, you need to have the Python interpreter installed. You can download it from the official website.

Then run the server application It creates a socket and listens for new connections from the MQL5 program socketclientEA.mq5.

After a successful connection, the connection process and regression line prices are displayed in the program window. The prices are sent back to the client:

The connection activity and regression line prices are also displayed in the MetaTrader 5 terminal. Also the regression line is drawn on a chart and is further updated at each new tick:

We have considered the implementation of direct interaction of two programs via a socket connection. At the same time, MetaQuotes has developed a Python package, which allows receiving data directly from the terminal. For more details, please see the forum discussion related to the use of Python in MetaTrader (in Russian, so use the auto translation option).

Let us create a script to demonstrate how to receive quotes from the terminal.

Getting and analyzing quotes using MetaTrader 5 Python API

First you need to install the MetaTrader5 Python module (the summary of Python discussions is available here). 

pip install MetaTrader5

Import it to the program and initialize connection to the terminal:

from MetaTrader5 import *
from datetime import date
import pandas as pd 
import matplotlib.pyplot as plt 

# Initializing MT5 connection 


After that create the list of desired symbols and successively request Close prices for each currency pair from the terminal to pandas dataframe:

# Create currency watchlist for which correlation matrix is to be plotted

# Copying data to dataframe
d = pd.DataFrame()
for i in sym:
     rates = MT5CopyRatesFromPos(i, MT5_TIMEFRAME_M1, 0, 1000)
     d[i] = [y.close for y in rates]

Now we can disconnect from the terminal and then represent currency pair prices as percentage changes by calculating the correlation matrix and displaying it on the screen:

# Deinitializing MT5 connection

# Compute Percentage Change
rets = d.pct_change()

# Compute Correlation
corr = rets.corr()

# Plot correlation matrix
plt.figure(figsize=(10, 10))
plt.imshow(corr, cmap='RdYlGn', interpolation='none', aspect='auto')
plt.xticks(range(len(corr)), corr.columns, rotation='vertical')
plt.yticks(range(len(corr)), corr.columns);
plt.suptitle('FOREX Correlations Heat Map', fontsize=15, fontweight='bold')

We can see a good correlation between GBPUSD and GBPJPY in the above heat map. Then we can test the co-integration by importing the statmodels library:

# Importing statmodels for cointegration test
import statsmodels
from statsmodels.tsa.stattools import coint

x = d['GBPUSD']
y = d['GBPJPY']
x = (x-min(x))/(max(x)-min(x))
y = (y-min(y))/(max(y)-min(y))

score = coint(x, y)
print('t-statistic: ', score[0], ' p-value: ', score[1])

The relationship between two currency pairs can be displayed as a Z score:

# Plotting z-score transformation
diff_series = (x - y)
zscore = (diff_series - diff_series.mean()) / diff_series.std()

plt.axhline(2.0, color='red', linestyle='--')
plt.axhline(-2.0, color='green', linestyle='--')

Visualizing market data using the Plotly library

It is often needed to visualize quotes in a convenient form. This can be implemented using the Plotly library, which also allows saving charts in the interactive .html format.

Let us download EURUSD quotes and display them in a candlestick chart:

# -*- coding: utf-8 -*-
Created on Thu Mar 14 16:13:03 2019

@author: dmitrievsky
from MetaTrader5 import *
from datetime import datetime
import pandas as pd
# Initializing MT5 connection 


# Copying data to pandas data frame
stockdata = pd.DataFrame()
rates = MT5CopyRatesFromPos("EURUSD", MT5_TIMEFRAME_M1, 0, 5000)
# Deinitializing MT5 connection

stockdata['Open'] = [ for y in rates]
stockdata['Close'] = [y.close for y in rates]
stockdata['High'] = [y.high for y in rates]
stockdata['Low'] = [y.low for y in rates]
stockdata['Date'] = [y.time for y in rates]

import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

trace = go.Ohlc(x=stockdata['Date'],

data = [trace]

It is also possible to download and display any depth of the Bid and Ask history:

# -*- coding: utf-8 -*-
Created on Thu Mar 14 16:13:03 2019

@author: dmitrievsky
from MetaTrader5 import *
from datetime import datetime

# Initializing MT5 connection 


# Copying data to list
rates = MT5CopyTicksFrom("EURUSD", datetime(2019,3,14,13), 1000, MT5_COPY_TICKS_ALL)
bid = [ for x in rates]
ask = [x.ask for x in rates]
time = [x.time for x in rates]

# Deinitializing MT5 connection

import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
data = [go.Scatter(x=time, y=bid), go.Scatter(x=time, y=ask)]



In this article, we considered options for implementing communication between the terminal and a program written in Python, via sockets and directly using MetaQuotes' specialized library. Unfortunately, the current implementation of the socket client in MetaTrader 5 is not suitable for running in the Strategy Tester, so no full testing and measurement of the solution performance were performed. Let us wait for further updates of the socket functionality.

Translated from Russian by MetaQuotes Ltd.
Original article:

Attached files |
Last comments | Go to discussion (36)
Janet Sheao Mae Wen
Janet Sheao Mae Wen | 13 Apr 2021 at 09:02

Hi, interesting article. Thanks for that.

Was wondering. Why did you decide to implement the server in Python and the EA as the client in MT5?

Wouldnt it be more natural to have the server as an EA in MT5 providing data and executing traders and the client(s) in python, processing the data and making the trade decisions that are sent to the server?

Is there some obvious advantage to using the client as an EA and the server in python, that I am missing?

If i remember correctly, from past experience with ZeroMQ the server was as an EA in MT5 and the client in python.

Laishram Rama
Laishram Rama | 26 Aug 2021 at 06:33

@Maxim Dmitrievsky

I just wanted to say thank you for making this available. I always wanted to be able to code my indicators in Python and since you began this thread I've been able to access realtime futures data and use it to develop and run my indicators. I use my Python to plot and analyze the live feed and I place trades through the MT5 platform - it works great. Please keep up the good work and I truly hope Python will be full embraced my the MT community.

Thank you and best regards!

Hi, I want to connect mt5 with live data using an api. Because in my country, there are no authorised broker or data vendor that support mt5. Can you pls help me. 
utsaeed21 | 1 Apr 2022 at 19:32

Thank you for complete description. 

i have a question:

is it possible for us to run a script of .ex5 type by a command in python environment?

i wanna run it through python, and the script save the results in an excel file, so can i use the excel data again in my website or android program.

Thanks a lot.

Sioux Gougui
Sioux Gougui | 26 Oct 2023 at 21:39

Hello - Can someone please help me!!!

I'm trying to execute this code in Python and keep getting an error:

Below is my code and it results in the error:

>>> # Copying data to dataframe

>>> d = pd.DataFrame()

>>> for i in sym:

... rates = mt.copy_rates_from_pos(i, mt.TIMEFRAME_M1, 0, 1000)

  File "<stdin>", line 2

    rates = mt.copy_rates_from_pos(i, mt.TIMEFRAME_M1, 0, 1000)


IndentationError: expected an indented block

>>> d[i] = [y.close for y in rates]

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

  File "<stdin>", line 1, in <listcomp>

AttributeError: 'numpy.void' object has no attribute 'close'

# sbcapitalfx Heat Map - version 1.0

import MetaTrader5 as mt

import pandas as pd

from datetime import datetime

import matplotlib.pyplot as plt

# Initializing MT5 connection 


login = ************

password = ' ************ '

server = 'MetaQuotes-Demo'


# Create currency watchlist for which correlation matrix is to be plotted


# Copying data to dataframe

d = pd.DataFrame()

for i in sym:

        rates = mt.copy_rates_from_pos(i, mt.TIMEFRAME_M1, 0, 1000)

        d[i] = [y.close for y in rates]
atemosfair | 6 Mar 2024 at 11:36
Thnks bro. This article is greate. You help me a lot. :*
Color optimization of trading strategies Color optimization of trading strategies
In this article we will perform an experiment: we will color optimization results. The color is determined by three parameters: the levels of red, green and blue (RGB). There are other color coding methods, which also use three parameters. Thus, three testing parameters can be converted to one color, which visually represents the values. Read this article to find out if such a representation can be useful.
Studying candlestick analysis techniques (Part II): Auto search for new patterns Studying candlestick analysis techniques (Part II): Auto search for new patterns
In the previous article, we analyzed 14 patterns selected from a large variety of existing candlestick formations. It is impossible to analyze all the patterns one by one, therefore another solution was found. The new system searches and tests new candlestick patterns based on known candlestick types.
Developing graphical interfaces for Expert Advisors and indicators based on .Net Framework and C# Developing graphical interfaces for Expert Advisors and indicators based on .Net Framework and C#
The article presents a simple and fast method of creating graphical windows using Visual Studio with subsequent integration into the Expert Advisor's MQL code. The article is meant for non-specialist audiences and does not require any knowledge of C# and .Net technology.
MQL Parsing by Means of MQL MQL Parsing by Means of MQL
The article describes a preprocessor, a scanner, and a parser to be used in parsing the MQL-based source codes. MQL implementation is attached.