trading-ig Documentation

trading-ig is a lightweight Python wrapper designed to simplify access to the IG Markets API. It provides programmatic access to both the IG REST and Streaming APIs, allowing developers to retrieve live and historical data, automate trades, and build custom trading applications.

Important Note: This is not an official IG project. Use it at your own risk. For official API details and support, refer to the IG Labs website.

The trading-ig library supports the IG Markets STREAM API, which allows you to receive real-time data such as prices and account balances.

For detailed information regarding the underlying API specifications and instructions on how to open an IG account to access these streams, refer to the official IG Labs documentation at: http://labs.ig.com/

The library uses optional dependencies in pyproject.toml to provide flexibility depending on your environment:

• pandas: If pandas is installed, the library will automatically convert time series data (like historical price data or account activity) into a pandas.DataFrame. If not installed, it returns a standard Python dict.
• munch: If munch is installed, market info for a given epic will be returned as a munch object. Otherwise, it returns a dict.
• tenacity: This is used for handling IG rate limits via a retry mechanism. It is optional because while it works for non-latency-sensitive tasks (like nightly tests), it may not be suitable for high-speed trading applications.

You can install the core library using pip. If you require advanced functionality like data manipulation with pandas, or specific utility libraries like munch and tenacity, you should install the package with the optional dependencies included.

# Install core library
$ pip install trading-ig

# Install with all optional dependencies (pandas, munch, tenacity)
$ pip install "trading-ig[pandas, munch, tenacity]"

To view log messages from the ig_trading library, configure the standard Python logging module in your application. This is useful for debugging and monitoring the rate limiter's activity.

import logging

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s %(levelname)s %(message)s',
    datefmt='%Y-%m-%d %H:%M:%S'
)

logging.info("Log something")

import logging

logging.basicConfig(
        level=logging.INFO,
        format='%(asctime)s %(levelname)s %(message)s',
        datefmt='%Y-%m-%d %H:%M:%S')

logging.info("Log something")

You can configure your credentials using either a Python config file or environment variables.

Using a Config File

Copy trading_ig_config.default.py to trading_ig_config.py and populate the config class with your credentials:

Using Environment Variables

If the config file fails to load, IGService will look for these specific environment variables:

• IG_SERVICE_USERNAME
• IG_SERVICE_PASSWORD
• IG_SERVICE_API_KEY
• IG_SERVICE_ACC_NUMBER
• IG_SERVICE_ACC_TYPE

class config(object):
    username = "your_username"
    password = "your_password"
    api_key = "your_api_key"
    acc_type = "DEMO"
    acc_number = "your_account_number"

To interact with the REST API, instantiate IGService with your credentials and then call create_session() to get an active session object.

from trading_ig.rest import IGService
from trading_ig.config import config

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

An 'epic' is a unique identifier for a market. You can find it using several methods:

1. IGService.search_markets(): Use this method within the library to search for markets by name.
2. IG API Companion: Use the Search Markets tool in the REST API Companion.
3. Browser Developer Tools (Recommended):
   • Log in to the IG website.
   • Open your browser's Developer Tools (Network tab).
   • Navigate to the desired market.
   • Inspect the network requests. The epic is typically part of the URL. For example, in https://deal.ig.com/nwtpdeal/v2/markets/details/CS.D.USCGC.TODAY.IP?_=1626527237228, the epic is CS.D.USCGC.TODAY.IP.
4. Navigation Tree: You can use fetch_top_level_navigation_nodes() and fetch_sub_nodes_by_node() to traverse the market tree, though this is slow due to rate limits.

You can configure the library using a Python configuration object or environment variables.

Using a Python config object

Create a trading_ig_config.py file with a config class containing your credentials:

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

Using Environment Variables

Alternatively, set the following environment variables:

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

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