trading-ig 0.0.5

How To Use The Library

Using this library to connect to the IG Markets API is extremely easy. All you need to do is import the IGService class, create an instance, and call the methods you wish to use. There is a method for each endpoint exposed by their API. The code sample below shows you how to connect to the API, switch to a secondary account and retrieve all open positions for the active account.

Note: The secure session with IG is established when you create an instance of the IGService class.

from trading_ig import IGService
from trading_ig_config import config

ig_service = IGService(config.username, config.password, config.api_key, config.acc_type)
ig_service.create_session()

account_info = ig_service.switch_account(config.acc_number, False) # not necessary
print(account_info)

open_positions = ig_service.fetch_open_positions()
print("open_positions:\n%s" % open_positions)

print("")

epic = 'CS.D.EURUSD.MINI.IP'
resolution = 'D'
num_points = 10
response = ig_service.fetch_historical_prices_by_epic_and_num_points(epic, resolution, num_points)
df_ask = response['prices']['ask']
print("ask prices:\n%s" % df_ask)

with trading_ig_config.py

class config(object):
    username = "YOUR_USERNAME"
    password = "YOUR_PASSWORD"
    api_key = "YOUR_API_KEY"
    acc_type = "DEMO" # LIVE / DEMO
    acc_number = "ABC123"

Config can also be set as environment variable

export IG_SERVICE_USERNAME="..."
export IG_SERVICE_PASSWORD="..."
export IG_SERVICE_API_KEY="..."
export IG_SERVICE_ACC_TYPE="DEMO" # LIVE / DEMO
export IG_SERVICE_ACC_NUMBER="..."

and we can get it using

from trading_ig import ConfigEnvVar
config = ConfigEnvVar("IG_SERVICE")

it should display:

open_positions:
Empty DataFrame
Columns: []
Index: []

ask prices:
                        Open     High      Low    Close
DateTime
2014:11:18-00:00:00  1.24510  1.25465  1.24442  1.25330
2014:11:19-00:00:00  1.25332  1.26013  1.25127  1.25461
2014:11:20-00:00:00  1.25463  1.25760  1.25048  1.25427
2014:11:21-00:00:00  1.25428  1.25689  1.23755  1.23924
2014:11:23-00:00:00  1.23640  1.23770  1.23607  1.23725
2014:11:24-00:00:00  1.23864  1.24453  1.23830  1.24390
2014:11:25-00:00:00  1.24389  1.24877  1.24026  1.24743
2014:11:26-00:00:00  1.24744  1.25322  1.24443  1.25077
2014:11:27-00:00:00  1.25078  1.25244  1.24569  1.24599
2014:11:28-00:00:00  1.24598  1.24909  1.24269  1.24505

Many IGService methods return Python Pandas DataFrame, Series or Panel.

from datetime import datetime, timedelta
import requests_cache
session = requests_cache.CachedSession(cache_name='cache', backend='sqlite', expire_after=timedelta(hours=1))
# set expire_after=None if you don't want cache expiration
# set expire_after=0 if you don't want to cache queries

ig_service = IGService(config.username, config.password, config.api_key, config.acc_type, session)
ig_service.create_session()

epic = 'CS.D.EURUSD.MINI.IP'
resolution = 'D'
start_date = '2014-12-15'
end_date = '2014-12-20'
response = ig_service.fetch_historical_prices_by_epic_and_date_range(epic, resolution, start_date, end_date, session)

Install

$ pip install trading_ig

$ git clone https://github.com/femtotrader/ig-markets-rest-api-python-library.git
$ cd ig-markets-rest-api-python-library
$ python setup.py install