Metaquotes MQL5 - JSON - API

Development state: stable beta (code is stable)

Table of Contents

• About the Project
• Installation
• Documentation
• Usage
• Live data and streaming events
• License

About the Project

This project was developed to work as a server for the Backtrader Python trading framework. It is based on ZeroMQ sockets and uses JSON format to communicate. But now it has grown to the independent project. You can use it with any language that has ZeroMQ binding.

Backtrader Python client located here: Python Backtrader - Metaquotes MQL5

In development:

• Add error handling to docs
• Trades info
• Experation
• Devitation
• Netting/hedging mode switch
• Stop limit orders

Installation

1. Install ZeroMQ for MQL5 https://github.com/dingmaotu/mql-zmq
2. Put include/Json.mqh from this repo to your MetaEditor include directoty.
3. Download and compile experts/JsonAPI.mq5 script.
4. Check if Metatrader 5 automatic trading is allowed.
5. Attach the script to a chart in Metatrader 5.
6. Allow DLL import in dialog window.
7. Check if the ports are free to use. (default:15555,15556, 15557,15558)

Tested on macOS Mojave / Windows 10 in Parallels Desktop container.

Documentation

The script uses four ZeroMQ sockets:

1. System socket - recives requests from client and replies 'OK'
2. Data socket - pushes data to client depending on the request via System socket.
3. Live socket - automatically pushes last candle when it closes.
4. Streaming socket - automatically pushes last transaction info every time it happens.

The idea is to send requests via System socket and recieve results/errors via Data socket. Event handlers should be created for Live socket and Streaming socket because the server sends data to theese sockets automatically. See examples in Live data and streaming events section.

System socket request uses default JSON dictionary:

{
	"action": null,
	"actionType": null,
	"symbol": null,
	"chartTF": null,
	"fromDate": null,
	"toDate": null,
	"id": null,
	"magic": null,
	"volume": null,
	"price": null,
	"stoploss": null,
	"takeprofit": null,
	"expiration": null,
	"deviation": null,
	"comment": null
}

Check out the available combinations of action and actionType:

action	actionType	Description
CONFIG	null	Set script configuration
ACCOUNT	null	Get account settings
BALANCE	null	Get current balance
POSITIONS	null	Get current open positions
ORDERS	null	Get current open orders
HISTORY	DATA	Get data history
HISTORY	TRADES	Get trades history
TRADE	ORDER_TYPE_BUY	Buy market
TRADE	ORDER_TYPE_SELL	Sell market
TRADE	ORDER_TYPE_BUY_LIMIT	Buy limit
TRADE	ORDER_TYPE_SELL_LIMIT	Sell limit
TRADE	ORDER_TYPE_BUY_STOP	Buy stop
TRADE	ORDER_TYPE_SELL_STOP	Sell stop
TRADE	POSITION_MODIFY	Position modify
TRADE	POSITION_PARTIAL	Position close partial
TRADE	POSITION_CLOSE_ID	Position close by id
TRADE	POSITION_CLOSE_SYMBOL	Positions close by symbol
TRADE	ORDER_MODIFY	Order modify
TRADE	ORDER_CANCEL	Order cancel

Python 3 API class example:

import zmq

class MTraderAPI:
    def __init__(self, host=None):
        self.HOST = host or 'localhost'
        self.SYS_PORT = 15555       # REP/REQ port
        self.DATA_PORT = 15556      # PUSH/PULL port
        self.LIVE_PORT = 15557      # PUSH/PULL port
        self.EVENTS_PORT = 15558    # PUSH/PULL port

        # ZeroMQ timeout in seconds
        sys_timeout = 1
        data_timeout = 10

        # initialise ZMQ context
        context = zmq.Context()

        # connect to server sockets
        try:
            self.sys_socket = context.socket(zmq.REQ)
            # set port timeout
            self.sys_socket.RCVTIMEO = sys_timeout * 1000
            self.sys_socket.connect('tcp://{}:{}'.format(self.HOST, self.SYS_PORT))

            self.data_socket = context.socket(zmq.PULL)
            # set port timeout
            self.data_socket.RCVTIMEO = data_timeout * 1000
            self.data_socket.connect('tcp://{}:{}'.format(self.HOST, self.DATA_PORT))
        except zmq.ZMQError:
            raise zmq.ZMQBindError("Binding ports ERROR")

    def _send_request(self, data: dict) -> None:
        """Send request to server via ZeroMQ System socket"""
        try:
            self.sys_socket.send_json(data)
            msg = self.sys_socket.recv_string()
            # terminal received the request
            assert msg == 'OK', 'Something wrong on server side'
        except AssertionError as err:
            raise zmq.NotDone(err)
        except zmq.ZMQError:
            raise zmq.NotDone("Sending request ERROR")

    def _pull_reply(self):
        """Get reply from server via Data socket with timeout"""
        try:
            msg = self.data_socket.recv_json()
        except zmq.ZMQError:
            raise zmq.NotDone('Data socket timeout ERROR')
        return msg

    def live_socket(self, context=None):
        """Connect to socket in a ZMQ context"""
        try:
            context = context or zmq.Context.instance()
            socket = context.socket(zmq.PULL)
            socket.connect('tcp://{}:{}'.format(self.HOST, self.LIVE_PORT))
        except zmq.ZMQError:
            raise zmq.ZMQBindError("Live port connection ERROR")
        return socket

    def streaming_socket(self, context=None):
        """Connect to socket in a ZMQ context"""
        try:
            context = context or zmq.Context.instance()
            socket = context.socket(zmq.PULL)
            socket.connect('tcp://{}:{}'.format(self.HOST, self.EVENTS_PORT))
        except zmq.ZMQError:
            raise zmq.ZMQBindError("Data port connection ERROR")
        return socket

    def construct_and_send(self, **kwargs) -> dict:
        """Construct a request dictionary from default and send it to server"""

        # default dictionary
        request = {
            "action": None,
            "actionType": None,
            "symbol": None,
            "chartTF": None,
            "fromDate": None,
            "toDate": None,
            "id": None,
            "magic": None,
            "volume": None,
            "price": None,
            "stoploss": None,
            "takeprofit": None,
            "expiration": None,
            "deviation": None,
            "comment": None
        }

        # update dict values if exist
        for key, value in kwargs.items():
            if key in request:
                request[key] = value
            else:
                raise KeyError('Unknown key in **kwargs ERROR')

        # send dict to server
        self._send_request(request)

        # return server reply
        return self._pull_reply()

Usage

All examples will be on Python 3. Lets create an instance of MetaTrader API class:

api = MTraderAPI()

First of all we should configure the script symbol and timeframe. Live data stream will be configured to the seme params.

rep = api.construct_and_send(action="CONFIG", symbol="EURUSD", chartTF="M5")
print(rep)

Get information about trading account.

rep = api.construct_and_send(action="ACCOUNT")
print(rep)

Get historical data. fromDate should be in timestamp format. The data will be loaded to the last candle if toDate is None. Notice, that the script sends the last unclosed candle too. You should delete it manually.

There are some issues:

• MetaTrader keeps historical data in cache. But when you make a request for the first time, MetaTrader downloads data from a broker. This operation can exceed Data socket timeout. It depends on your broker. Second request will be handeled quickly.
• It takes 6-7 seconds to process 50000 M1 candles. It was tested on Windows 10 in Parallels Desktop container with 4 cores and 4GB RAM. So if you need more data there are three ways to handle it. 1) Increase Data socket timeout. 2) You can load data partially using fromDate and toDate. 3) You can use more powerfull hardware.

rep = api.construct_and_send(action="HISTORY", actionType="DATA", symbol="EURUSD", chartTF="M5", fromDate=1555555555)
print(rep)

History data reply example:

{'data': [[1560782340, 1.12271, 1.12288, 1.12269, 1.12277, 46.0],[1560782400, 1.12278, 1.12299, 1.12276, 1.12297, 43.0],[1560782460, 1.12296, 1.12302, 1.12293, 1.123, 23.0]]}

Buy market order.

rep = api.construct_and_send(action="TRADE", actionType="ORDER_TYPE_BUY", symbol="EURUSD", "volume": 0.1, "stoploss": 1.1, "takeprofit": 1.3)
print(rep)

Sell limit order. Remember to switch SL/TP depending on BUY/SELL, or you will get invalid stops error.

• BUY: SL < price < TP
• SELL: SL > price > TP

rep = api.construct_and_send(action="TRADE", actionType="ORDER_TYPE_SELL_LIMIT", symbol="EURUSD", "volume": 0.1, "price": 1.2, "stoploss": 1.3, "takeprofit": 1.1)
print(rep)

Live data and streaming events

Event handler example for Live socket and Data socket.

import zmq
import threading

api = MTraderAPI()


def _t_livedata():
    socket = api.live_socket()
    while True:
        try:
            last_candle = socket.recv_json()
        except zmq.ZMQError:
            raise zmq.NotDone("Live data ERROR")
        print(last_candle)


def _t_streaming_events():
    socket = api.streaming_socket()
    while True:
        try:
            trans = socket.recv_json()
            request, reply = trans.values()
        except zmq.ZMQError:
            raise zmq.NotDone("Streaming data ERROR")
        print(request)
        print(reply)


for _ in range(3):
    t = threading.Thread(target=_t_livedata, daemon=True)
    t.start()

for _ in range(3):
    t = threading.Thread(target=_t_streaming_events, daemon=True)
    t.start()

while True:
    pass

There are only two variants of Live socket data. When everything is ok, the script sends candle data on close:

{"status":"CONNECTED","data":[1560780120,1.12186,1.12194,1.12186,1.12191,15.00000]}

If the terminal has lost connection to the market:

{"status":"DISCONNECTED"}

When the terminal reconnects to the market, it sends the last closed candle again. So you should update your historical data. Make the action="HISTORY" request with fromDate equal to your last candle timestamp before disconnect.

OnTradeTransaction function is called when the trade transaction event occurs. Streaming socket sends TRADE_TRANSACTION_REQUEST data every time it happens. Try to create and modify orders in the MQL5 terminal manually and check the expert logging tab for better understanding. Also see MQL5 docs.

TRADE_TRANSACTION_REQUEST request data:

{
	'action': 'TRADE_ACTION_DEAL', 
	'order': 501700843, 
	'symbol': 'EURUSD', 
	'volume': 0.1, 
	'price': 1.12181, 
	'stoplimit': 0.0, 
	'sl': 1.1, 
	'tp': 1.13, 
	'deviation': 10, 
	'type': 'ORDER_TYPE_BUY', 
	'type_filling': 'ORDER_FILLING_FOK', 
	'type_time': 'ORDER_TIME_GTC', 
	'expiration': 0, 
	'comment': None, 
	'position': 0, 
	'position_by': 0
}

TRADE_TRANSACTION_REQUEST result data:

{
	'retcode': 10009, 
	'result': 'TRADE_RETCODE_DONE', 
	'deal': 501700843, 
	'order': 501700843, 
	'volume': 0.1, 
	'price': 1.12181, 
	'comment': None, 
	'request_id': 8, 
	'retcode_external': 0
}

License

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See LICENSE for more information.