Research
Security News
Malicious npm Packages Inject SSH Backdoors via Typosquatted Libraries
Socket’s threat research team has detected six malicious npm packages typosquatting popular libraries to insert SSH backdoors.
pip install ejtraderMT -U
for developers attention may contain countless bugs
git clone https://github.com/ejtraderLabs/ejtraderMT
cd ejtraderMT
python setup.py install
or
pip install git+https://github.com/ejtraderLabs/ejtraderMT.git
docker volume create ejtraderMT
docker run -d --restart=always -p 5900:5900 -p 15555:15555 -p 15556:15556 -p 15557:15557 -p 15558:15558 --name ejtraderMT -v ejtraderMT:/data ejtrader/metatrader:5
or docker compose
version: '3.8'
services:
app:
container_name: metatrader
image: ejtrader/metatrader:5
restart: unless-stopped
ports:
- '5900:5900'
- '15555:15555'
- '15556:15556'
- '15557:15557'
- '15558:15558'
volumes:
- ejtraderMT:/data
volumes:
ejtraderMT: {}
Dockerfile and source for Docker wine vnc github
download VNC VIEWER or any other vnc client of your preference: Download
username: root
password: root
First download MQL5 source code and install it on the Metatrader 5 github
second download and install Microsoft Visual C++ 2015 Download
from ejtraderMT import Metatrader
make sure ejtraderMT expert are load on the chart
'''
to change the host IP example Metatrader("192.168.1.100") ou
you can use doman example "metatraderserverdomain.com"
for you local time on the Dataframe Metatrader(tz_local=True)
attention utc time is the default for Dataframe index "date"
for real volume for active like WIN futures ou centralized market use Metatrader(real_volume=True)
attention tick volume is the default
to use more than one option just use , example Metatrader(host='hostIP',tz_local=True)
'''
api = Metatrader()
accountInfo = api.accountInfo()
print(accountInfo)
print(accountInfo['broker'])
print(accountInfo['balance'])
symbol = "EURUSD"
fromDate = "20/08/2021"
toDate = "24/08/2022"
calendar = api.calendar(symbol,fromDate,toDate)
print(calendar)
currency impact event country actual forecast previous
date
2021-08-20 06:00:00 EUR 2 PPI m/m(ppi-mm) Germany(DE) 1.9 0.9 1.3
2021-08-20 06:00:00 EUR 1 PPI y/y(ppi-yy) Germany(DE) 10.4 9.4 8.5
2021-08-20 17:00:00 USD 2 Baker Hughes US Oil Rig Count(baker-hughes-us-... United States(US) 405 None 397
2021-08-20 17:00:00 USD 2 Baker Hughes US Total Rig Count(baker-hughes-u... United States(US) 503 None 500
2021-08-20 19:30:00 EUR 1 CFTC EUR Non-Commercial Net Positions(cftc-eur... European Union(EU) 57.6 K None 33.9 K
... ... ... ... ... ... ... ...
2022-08-24 14:30:00 USD 1 EIA Heating Oil Stocks Change(eia-heating-oil-... United States(US) 0.845 M -0.013 M 0.249 M
2022-08-24 14:30:00 USD 1 EIA Gasoline Stocks Change(eia-gasoline-stocks... United States(US) -0.027 M -1.829 M -4.642 M
2022-08-24 14:30:00 USD 1 EIA Refinery Crude Oil Daily Inputs Change(eia... United States(US) -0.168 M None -0.158 M
2022-08-24 14:30:00 USD 1 EIA Refinery Utilization Rate Change(eia-refin... United States(US) 0.3 None -0.8
2022-08-24 17:00:00 USD 1 5-Year Note Auction(5-year-note-auction) United States(US) 3.23 None 2.86
# you can add unlimited actives to list ["EURUSD","GBPUSD","AUDUSD"]
symbol = "EURUSD"
timeframe = "M1"
fromDate = "20/02/2021"
toDate = "24/02/2021"
history = api.history(symbol,timeframe,fromDate,toDate)
print(history)
open high low close volume spread
date
2021-02-21 23:00:00 1.21135 1.21138 1.21131 1.21134 7.0 35
2021-02-21 23:01:00 1.21130 1.21135 1.21130 1.21135 6.0 43
2021-02-21 23:04:00 1.21150 1.21184 1.21134 1.21184 13.0 31
2021-02-21 23:05:00 1.21163 1.21207 1.21148 1.21181 39.0 42
2021-02-21 23:06:00 1.21189 1.21193 1.21182 1.21182 17.0 64
... ... ... ... ... ... ...
2021-02-24 02:56:00 1.21629 1.21629 1.21590 1.21594 51.0 5
2021-02-24 02:57:00 1.21592 1.21592 1.21574 1.21574 34.0 5
2021-02-24 02:58:00 1.21574 1.21579 1.21572 1.21575 35.0 5
2021-02-24 02:59:00 1.21576 1.21588 1.21573 1.21582 55.0 5
2021-02-24 03:00:00 1.21583 1.21601 1.21578 1.21598 80.0 5
[3104 rows x 6 columns]
# you can add unlimited actives to list ["EURUSD","GBPUSD","AUDUSD"]
symbol = "EURUSD"
timeframe = "M1"
fromDate = 27
history = api.history(symbol,timeframe,fromDate)
print(history)
open high low close volume spread
date
2021-02-26 19:23:00 1.20846 1.20857 1.20837 1.20856 84.0 5
2021-02-26 19:24:00 1.20855 1.20858 1.20842 1.20847 71.0 5
2021-02-26 19:25:00 1.20846 1.20849 1.20832 1.20845 69.0 5
2021-02-26 19:26:00 1.20844 1.20845 1.20823 1.20833 64.0 5
2021-02-26 19:27:00 1.20833 1.20836 1.20821 1.20834 53.0 5
... ... ... ... ... ... ...
2021-02-26 22:55:00 1.20721 1.20730 1.20718 1.20719 46.0 13
2021-02-26 22:56:00 1.20718 1.20738 1.20718 1.20731 39.0 12
2021-02-26 22:57:00 1.20730 1.20731 1.20716 1.20717 45.0 18
2021-02-26 22:58:00 1.20716 1.20731 1.20694 1.20704 77.0 16
2021-02-26 22:59:00 1.20702 1.20705 1.20702 1.20704 16.0 37
# you can add unlimited actives to list ["EURUSD","GBPUSD","AUDUSD"]
symbol = "EURUSD"
timeframe = "M1"
fromDate = 27
history = api.history(symbol,timeframe)
print(history)
open high low close volume spread
date
2021-02-26 19:23:00 1.20846 1.20857 1.20837 1.20856 84.0 5
2021-02-26 19:24:00 1.20855 1.20858 1.20842 1.20847 71.0 5
2021-02-26 19:25:00 1.20846 1.20849 1.20832 1.20845 69.0 5
2021-02-26 19:26:00 1.20844 1.20845 1.20823 1.20833 64.0 5
2021-02-26 19:27:00 1.20833 1.20836 1.20821 1.20834 53.0 5
# you can add unlimited actives to list ["EURUSD","GBPUSD","AUDUSD"] etc
symbol = ["EURUSD","GBPUSD"]
timeframe = "M1"
fromDate = "20/02/2021"
toDate = "24/02/2021"
history = api.history(symbol,timeframe,fromDate,toDate)
print(history)
open high low close volume spread gbpusd_open gbpusd_high gbpusd_low gbpusd_close gbpusd_volume gbpusd_spread
date
2021-02-21 23:00:00 1.21135 1.21138 1.21131 1.21134 7.0 35 1.40113 1.40113 1.40110 1.40110 2.0 130
2021-02-21 23:04:00 1.21150 1.21184 1.21134 1.21184 13.0 31 1.40119 1.40119 1.40119 1.40119 1.0 102
2021-02-21 23:05:00 1.21163 1.21207 1.21148 1.21181 39.0 42 1.40174 1.40174 1.40167 1.40168 11.0 61
2021-02-21 23:06:00 1.21189 1.21193 1.21182 1.21182 17.0 64 1.40156 1.40170 1.40132 1.40155 10.0 46
2021-02-21 23:07:00 1.21181 1.21182 1.21180 1.21182 4.0 82 1.40156 1.40156 1.40156 1.40156 1.0 63
... ... ... ... ... ... ... ... ... ... ... ... ...
2021-02-24 02:56:00 1.21629 1.21629 1.21590 1.21594 51.0 5 1.41833 1.41835 1.41786 1.41800 62.0 8
2021-02-24 02:57:00 1.21592 1.21592 1.21574 1.21574 34.0 5 1.41798 1.41801 1.41765 1.41766 54.0 8
2021-02-24 02:58:00 1.21574 1.21579 1.21572 1.21575 35.0 5 1.41767 1.41789 1.41767 1.41768 64.0 8
2021-02-24 02:59:00 1.21576 1.21588 1.21573 1.21582 55.0 5 1.41769 1.41782 1.41764 1.41769 42.0 9
2021-02-24 03:00:00 1.21583 1.21601 1.21578 1.21598 80.0 5 1.41770 1.41797 1.41746 1.41784 95.0 8
[3097 rows x 12 columns]
from ejtraderMT import Metatrader
api = Metatrader()
symbols = ["EURUSD","GBPUSD","AUDUSD"]
timeframe = "TICK"
# stream price
while True:
price = api.price(symbols,timeframe)
print(price)
from ejtraderMT import Metatrader
api = Metatrader()
symbols = ["EURUSD","GBPUSD","AUDUSD"]
timeframe = "TICK"
# stream event
while True:
event = api.event(symbols,timeframe)
print(event)
# symbol, volume, stoploss, takeprofit, deviation
api.buy("EURUSD", 0.01, 1.18, 1.19, 5)
api.sell("EURUSD", 0.01, 1.18, 1.19, 5)
# symbol, volume, stoploss, takeprofit, price, deviation
api.buyLimit("EURUSD", 0.01, 1.17, 1.19, 1.18, 5)
api.sellLimit("EURUSD", 0.01, 1.20, 1.17, 1.19, 5)
#symbol, volume, stoploss, takeprofit, price, deviation
api.buyStop("EURUSD", 0.01, 1.18, 1.20, 1.19, 5)
api.sellStop("EURUSD", 0.01, 1.19, 1.17, 1.18, 5)
positions = api.positions()
if 'positions' in positions:
for position in positions['positions']:
api.CloseById(position['id'])
orders = api.orders()
if 'orders' in orders:
for order in orders['orders']:
api.CancelById(order['id'])
api.positionModify( id, stoploss, takeprofit)
api.orderModify( id, stoploss, takeprofit, price)
api.CloseBySymbol("EURUSD")
# id , volume
api.ClosePartial( id, volume)
api.cancel_all()
api.close_all()
# Project Based and reference thanks for
Ding Li @dingmaotu
https://github.com/dingmaotu/mql-zmq
Nikolai khramkov @khramkov
https://github.com/khramkov/MQL5-JSON-API
from ejtraderMT import Metatrader
api = Metatrader()
symbols = ["EURUSD"] # you can also use combind dataframe = ["EURUSD","GBPUSD","AUDUSD"]
timeframe = "M1"
# saving 20 years of OHLC
fromDate = "01/01/2001"
toDate = "01/01/2021"
api.history(symbol,timeframe,fromDate,toDate,database=True)
# or you could only pass from Date you want to start
"""
you can pull the history and save using only fromDate
its will pull history fromDate till now
api.history(symbol,timeframe,fromDate,database=True)
"""
# example of saving 20 years of M1 OHLC takes around 3 minutes on a 4 core CPU
30%|█████████████████████████████████▋ | 2174/7305 [01:10<02:28, 34.60it/s]
from ejtraderMT import Metatrader
api = Metatrader()
symbol = ["EURUSD"]
data = api.history(symbol)
# example reading 20 year of M1 OHLC takes around 2 seconds read more than 7 million canldes
Elapsed run time: 2.041501855 seconds
date open high low close volume spread
0 2001-01-01 04:02:00 0.94220 0.94220 0.94220 0.94220 1.0 50
1 2001-01-01 04:03:00 0.94240 0.94240 0.94240 0.94240 1.0 50
2 2001-01-01 10:47:00 0.94250 0.94250 0.94250 0.94250 1.0 50
3 2001-01-01 11:40:00 0.94190 0.94190 0.94190 0.94190 1.0 50
4 2001-01-01 14:45:00 0.93970 0.93990 0.93970 0.93990 3.0 50
... ... ... ... ... ... ... ...
7286195 2020-12-31 17:56:00 1.22147 1.22152 1.22147 1.22152 20.0 8
7286196 2020-12-31 17:57:00 1.22152 1.22162 1.22148 1.22157 58.0 8
7286197 2020-12-31 17:58:00 1.22157 1.22167 1.22152 1.22166 77.0 9
7286198 2020-12-31 17:59:00 1.22167 1.22177 1.22154 1.22154 129.0 8
7286199 2020-12-31 18:00:00 1.22156 1.22156 1.22155 1.22155 2.0 11
[7286200 rows x 7 columns]
economic calendar
level 1 for futures only
level 2 for futures only
FAQs
Metatrader API
We found that ejtraderMT demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
Security News
Socket’s threat research team has detected six malicious npm packages typosquatting popular libraries to insert SSH backdoors.
Security News
MITRE's 2024 CWE Top 25 highlights critical software vulnerabilities like XSS, SQL Injection, and CSRF, reflecting shifts due to a refined ranking methodology.
Security News
In this segment of the Risky Business podcast, Feross Aboukhadijeh and Patrick Gray discuss the challenges of tracking malware discovered in open source softare.