https://github.com/inevolin/interactivebrokers-backtesting

This repository provides a simple Python3 template for creating and running backtesting algorithms on a historical dataset of a specific security.
https://github.com/inevolin/interactivebrokers-backtesting

backtesting interactive-brokers interactivebrokers python3 trading trading-algorithms

Last synced: 3 days ago
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This repository provides a simple Python3 template for creating and running backtesting algorithms on a historical dataset of a specific security.

• Host: GitHub
• URL: https://github.com/inevolin/interactivebrokers-backtesting
• Owner: inevolin
• License: mit
• Created: 2023-10-08T10:09:41.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-10-08T10:36:42.000Z (about 1 year ago)
• Last Synced: 2023-10-08T11:23:38.735Z (about 1 year ago)
• Topics: backtesting, interactive-brokers, interactivebrokers, python3, trading, trading-algorithms
• Language: Python
• Homepage:
• Size: 22.5 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# InteractiveBrokers Backtesting

This repository provides a simple Python3 template for creating, running and visualizing backtesting algorithms on a traded security.

![tsla](https://github.com/inevolin/InteractiveBrokers-backtesting/assets/53948000/ab29e8d1-ed60-4fcf-b336-7ed28e402880)

## Getting started

1. Download and run the [IB Client Portal](https://www.interactivebrokers.com/en/trading/ib-api.php) : `./bin/run.sh root/conf.yaml`

2. Authenticate through the local portal: `https://localhost:5000`

3. Once authenticated you can make HTTP requests to obtain historical data: `https://localhost:5000/v1/api/iserver/marketdata/history?conid=76792991&period=14d&bar=1h&outsideRth=true` (the `conid` is for TSLA)

## Code

In the code example I obtain data for Tesla Inc. and generate buy/sell signals based on a simple heuristic.

1. Ensure the Client Portal up and authenticated.

2. Install the required Python libs `pip install -r requirements.txt`

3. Run `python3 backtest.py` (this script acts as a template).

4. A new browser tab will open with the candlestick chart and buy/sell signals.

5. The script will output the ROI of the buy/sell signals (excluding transaction fees) `ROI: 5.140722` (in %).

### Contact

- Name: [Ilya Nevolin](https://www.linkedin.com/in/iljanevolin/)

- Email: [email protected]