How to Get Historical Market Data with Interactive Brokers and Python
🚀 Master Quantitative Skills with Quant Guild
https://quantguild.com
📈 Interactive Brokers for Algorithmic Trading
https://www.interactivebrokers.com/mk...
👾 Join the Quant Guild Discord server here
/ discord
___________________________________________
🪐 Source Code
https://github.com/romanmichaelpaoluc...
📑 TL;DW Executive Summary
Account Prerequisites: You must have an Interactive Brokers account and maintain a minimum equity of $500 to access market data subscriptions
Connection Setup: Install Trader Workstation (TWS) or IB Gateway and enable ActiveX and Socket Clients in the API settings to allow Python connectivity
API Architecture: The Python code requires a class that inherits from both EWrapper (to receive data) and EClient (to send requests)
Contract Specification: You must define a Contract object specifying the ticker symbol, security type (STK), exchange (SMART), and currency (USD)
Data Retrieval: Use the reqHistoricalData method to specify the request ID, look-back period (e.g., 1 year), and bar size (e.g., 1 day)
Data Processing: Data is collected in a list via the historicalData callback and then converted into a Pandas DataFrame for CSV export once the request is complete
I hope you enjoyed, and I hope you learned something!
Roman
___________________________________________
📖 Chapters:
00:00 - Historical Data with Interactive Brokers
01:15 - Getting Started with Interactive Brokers
03:28 - Setting Up Trader Workstation
04:28 - Building a Historical Data Request in Python
07:08 - IBKR API Implementation (EWrapper and EClient)
10:33 - Requesting and Processing Historical Data
19:47 - Debugging and Querying Historical Data
___________________________________________
🗣️ Shout Outs
A special thank you to my members on YouTube for supporting my channel and enabling me to continue to create videos just like this one!
⭐ Quant Guild Directors
Dr. Jason Pirozzolo
___________________________________________
▶️ Related Videos
Quant Builds 🔨
How to Build a Live Volatility Surface in Python (Interactive Brokers)
• How to Build a Live Volatility Surface in ...
Statistics and Trading Profitability Over Time (Edge) 📈
Time Series Analysis for Quant Finance
• Time Series Analysis for Quant Finance
Quant Trader on Retail vs Institutional Trading
• Quant Trader on Retail vs. Institutional T...
Quant on Trading and Investing
• Quant on Trading and Investing
Why Poker Pros Make the Best Traders (It's NOT Luck)
• Video
Quant vs. Discretionary Trading
• Quant vs. Discretionary Trading
___________________________________________
🗂️ Resources
📚 Quant Guild Library:
https://github.com/romanmichaelpaoluc...
🌎 GitHub:
https://github.com/RomanMichaelPaolucci
https://github.com/Quant-Guild
📝 Medium (Blog):
/ quantguild
/ quant
___________________________________________
🛠️ Projects
The Gaussian Cookbook:
https://gaussiancookbook.com
Recipes for simulating stochastic processes:
https://papers.ssrn.com/sol3/papers.c...
___________________________________________
💬 Socials
TikTok: / quantguild
Instagram: / quantguild
X/Twitter: https://x.com/quantguild/
LinkedIn (personal): / rmp99
LinkedIn (company): / quant-guild
___________________________________________