This project was developed to work as a server for 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:
- Historical data load speed
- Add error handling to docs
- Trades info
- Experation
- Devitation
- Netting/hedging mode switch
- Stop limit orders
- Install ZeroMQ for MQL5 https://github.com/dingmaotu/mql-zmq
- Put
include/Json.mqhfrom this repo to your MetaEditorincludedirectoty. - Download and compile
experts/JsonAPI.mq5script. - Check if Metatrader 5 automatic trading is allowed.
- Attach the script to a chart in Metatrader 5.
- Allow DLL import in dialog window.
- Check if the ports are free to use. (default:
15555,15556,15557,15558)
Tested on macOS Mojave / Windows 10 in Parallels Desktop container.
The script uses four ZeroMQ sockets:
System socket- recives requests from client and replies 'OK'Data socket- pushes data to client depending on request via System socket.Live socket- automatically pushes last candle when it closes.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 server sends data to theese sockets automatically. See examples in Usage section.
System socket request uses default JSON dictionary:
{
"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
}
Check out the available combinations of action and actionType:
| action | actionType | Description |
|---|---|---|
| CONFIG | None | Set script configuration |
| ACCOUNT | None | Get account settings |
| BALANCE | None | Get current balance |
| POSITIONS | None | Get current open positions |
| ORDERS | None | 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 |
Example Python API class:
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)
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)
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):
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("Binding ports ERROR")
return socket
def streaming_socket(self, context=None):
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("Binding ports ERROR")
return socket
def construct_and_send(self, **kwargs) -> dict:
""" Construct request dictionary from default """
# 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()All examples will be on Python 3. Lets create an instance of MetaTrader API class:
api = MTraderAPI()First of all we should configure 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. 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 sockettimeout. It depends on your broker. Second request will be handeled quickly. - Historical data processing code is not optimal. It takes too much time to process more than
50000candles. Under refactoring now.
rep = api.construct_and_send(action="HISTORY", actionType="DATA", symbol="EURUSD", chartTF="M5", fromDate=1555555555)
print(rep)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)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 i in range(3):
t = threading.Thread(target=_t_livedata, daemon=True)
t.start()
for i in range(3):
t = threading.Thread(target=_t_streaming_events, daemon=True)
t.start()
while True:
passThis 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.
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