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Algorithmic Trading using Interactive Broker's Python API

Mayank Rasu and RASUQUANT LTD

Design and deploy trading strategies on Interactive Broker's platform. Automate every step of your strategy including, extracting data (stock data and fundamental data), performing technical/fundamental analysis, generating signals, placing trades, risk management etc. Gain a thorough understanding of native interactive broker's API.

You can expect to gain the following skills from this course

Read more

Design and deploy trading strategies on Interactive Broker's platform. Automate every step of your strategy including, extracting data (stock data and fundamental data), performing technical/fundamental analysis, generating signals, placing trades, risk management etc. Gain a thorough understanding of native interactive broker's API.

You can expect to gain the following skills from this course

  • API trading

  • Advanced python concepts (OOP concepts, multi-threading etc.)

  • Extracting historical data

  • Extracting fundamental data

  • Harnessing streaming tick level data

  • Incorporating technical indicators using python

  • End to End strategy design and deployment

  • Handling asynchronous calls

  • Sqlite database management

  • Interactive Broker's TWS terminal

  • Relevant account settings in IB

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Important note - Course prerequisites:

Please note that this course requires basic python proficiency. At the minimum, you should be comfortable with:

  • basic python data types and format

  • basic python data structures such as list, dictionary, tuple etc.

  • how to create python functions

  • how to implement loops in python

  • installing and importing libraries 

Basic python proficiency is mandatory because Interactive Broker API's python client uses advanced OOP and asynchronous programming concepts. While, I have devoted an entire section explaining these concepts, students with no python knowledge will really struggle to follow along.

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Enroll now

What's inside

Learning objectives

  • Algorithmic trading
  • Interactive broker api
  • Quantitative finance
  • Advanced python concepts

Syllabus

Introduction to Interactive Brokers & its API
IBAPI Courses
IB TWS Introduction
IB API Architecture
Read more
Anaconda Distribution Intro
Creating Virtual Environment (Optional)
Installing IB Python Client
Installing IBAPI using PIP
API Configuration Settings
Advanced Python Concepts
OOP Basics (Class - I)
OOP Basics (Class - II)
OOP Basics (Inheritance)
Threads in Python
Turning "Daemon" Threads into Your Angel
Multi threading using Event object
Websocket Intro
Understanding IB API Python Wrapper
Eclient and Ewrapper Class Intro
Important - Debugging Errors Arising from Function Signature Changes
IBAPI Function Change Catalogue
Getting Contract Info
Asynchronous Implementation Intro
Asynchronous Implementation Using Event
Historical Data
Market Data Subscription
Important Note: Please Read
Getting Historical Data Using IBAPI
Getting Historical Data (multiple tickers) using IBAPI
Storing Historical Data in Dataframes
Storing Historical Data in Dataframes - II
Extracting Historical Data Iteratively
Storing Historical Data of Stocks from Different Exchanges
Order Management
Placing a Simple Limit Order Using IBAPI
Placing Order - Reusable Code
Cancelling Orders
Cancelling Orders - Important Update
Modifying Orders
Other Important Order Types
Other Important API Calls
Getting Open Orders Information
Getting Position Details
Homework - Getting Account Summary & PnL Details
Homework Solution
Technical Indicators in IB
Technical Indicators Intro
TWS Terminal - Technical Indicators
MACD Overview
MACD Implementation Using IBAPI
ATR and Bollinger Bands Overview
Bollinger Bands Implementation Using IBAPI
ATR Implementation Using IBAPI
RSI Overview and Excel Implementation
RSI Implementation Using IBAPI
ADX Overview
ADX Implementation in Excel
ADX Implementation Using IBAPI
Stochastic Oscillator Overview
Stochastic Oscillator Implementation Using IBAPI
Backtesting Strategies
Backtesting Intro
CAGR Implementation using IBAPI
Volatility & Sharpe Implementation using IBAPI
Maximum Drawdown Implementation
KPIs for Intraday Strategies
Backtesting Sample Strategy (MACD+Stochastic)
Backtesting Strategy - Extracting Data
Backtesting Strategy - Signal Generation & Return Calculation
Backtesting Strategy - KPI Calculation
Homework - Implement Intraday KPIs
Designing & Deploying Strategies on IB
Strategy Implementation - Blueprint
Strategy Implementation - Data Preparation
Strategy Implementation - Signal
Strategy Execution Demo
Closing All Positions Programatically
Streaming Market Data
Streaming Tick Level Data
Streaming Aggregated Snapshot Data - I
Streaming Aggregated Snapshot Data - II
Storing Tick Data in SQL DB - I
Storing Tick Data in SQL DB - II
Storing Tick Data in SQL DB - III
Accessing Data in DB
Converting Ticks to Candles
Extracting Fundamental Data
Fundamental Data API Basics
Storing Fundamental Data in XML File
Parsing XML Data - I
Parsing XML Data - II
Parsing XML Data - III
Handling Multiple Fundamental Data Files
Getting Corporate Events Data
What Why and How of Corporate Events Data
Getting Events Data Using WSH API
Parsing Events Data
Storing Events Data for Multiple Stocks in Required Data Structure
Backtesting Assignment

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Algorithmic Trading using Interactive Broker's Python API with these activities:
Review Python Fundamentals
Strengthen your understanding of Python basics, including data types, data structures, functions, and loops, to prepare for the advanced Python concepts used in the course.
Show steps
  • Review Python documentation on data types and structures.
  • Practice writing simple functions and loops.
  • Complete online Python tutorials for beginners.
Practice Object-Oriented Programming (OOP) in Python
Reinforce your OOP skills in Python, focusing on classes, inheritance, and objects, as the Interactive Brokers API heavily relies on these concepts.
Browse courses on Object-Oriented Programming
Show steps
  • Study OOP principles and their implementation in Python.
  • Write Python classes with inheritance and polymorphism.
  • Work through OOP exercises on platforms like HackerRank or LeetCode.
Read 'Python for Finance' by Yves Hilpisch
Gain a deeper understanding of Python's application in finance, including data analysis and algorithmic trading, to complement the course material.
View Python for Finance on Amazon
Show steps
  • Read the chapters related to data analysis and algorithmic trading.
  • Implement some of the examples provided in the book.
  • Relate the concepts to the Interactive Brokers API.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Develop a Simple Trading Strategy Backtester
Apply your knowledge by building a backtesting tool for trading strategies, reinforcing your understanding of data extraction, signal generation, and performance evaluation.
Show steps
  • Choose a simple trading strategy (e.g., moving average crossover).
  • Extract historical data using the Interactive Brokers API.
  • Implement the trading strategy and calculate returns.
  • Evaluate the performance of the strategy using key performance indicators (KPIs).
Write a Blog Post on a Specific IB API Function
Solidify your understanding of the Interactive Brokers API by explaining a specific function or feature in detail, helping others and reinforcing your own knowledge.
Show steps
  • Select an IB API function (e.g., placing an order, getting historical data).
  • Research the function and its parameters thoroughly.
  • Write a clear and concise blog post explaining the function with examples.
  • Share your blog post on relevant online forums and communities.
Read 'Mastering Algorithmic Trading' by Michael Halls-Moore
Explore advanced algorithmic trading strategies and techniques to further enhance your skills and knowledge beyond the course curriculum.
View Putting Knowledge to Work on Amazon
Show steps
  • Read the chapters related to advanced strategies and risk management.
  • Implement some of the strategies discussed in the book.
  • Compare the strategies with those covered in the course.
Contribute to an Open-Source Algorithmic Trading Project
Deepen your understanding and contribute to the community by participating in an open-source project related to algorithmic trading, gaining practical experience and collaborating with other developers.
Show steps
  • Find an open-source project related to algorithmic trading on platforms like GitHub.
  • Understand the project's codebase and contribution guidelines.
  • Identify a bug or feature to work on.
  • Submit a pull request with your changes.

Career center

Learners who complete Algorithmic Trading using Interactive Broker's Python API will develop knowledge and skills that may be useful to these careers:
Algorithmic Trader
An algorithmic trader designs, develops, and deploys automated trading systems. This course is essentially tailored for anyone who wants to be an algorithmic trader. The course helps you gain practical skills in using Interactive Broker's API to automate various trading processes, from data extraction to trade execution. Furthermore, the comprehensive syllabus covers vital aspects like backtesting strategies, risk management, and incorporating technical indicators, which are all essential for success as an algorithmic trader. The course helps you thoroughly prepare for real-world trading scenarios.
See salaries and explore the career path for Algorithmic Trader
Quantitative Analyst
The role of a quantitative analyst involves developing and implementing mathematical models for financial markets. A course focusing on algorithmic trading with Interactive Broker's Python API directly helps a prospective quantitative analyst by providing hands-on experience in automating trading strategies. You learn how to extract data, perform analysis, generate signals, and manage risk, all crucial components of a quantitative analyst's toolkit. The course's emphasis on advanced Python concepts, such as object oriented programming and multithreading, further strengthens the analytical skillset needed for this role.
See salaries and explore the career path for Quantitative Analyst
Quantitative Developer
Quantitative developers build and maintain software tools used in quantitative finance. This course is highly relevant for a quantitative developer because it provides hands-on experience in using Interactive Broker's Python API to develop trading systems. The course covers essential topics such as extracting data, automating order management, and handling asynchronous calls. It helps you with the practical skills and knowledge needed to develop and deploy robust quantitative trading applications.
See salaries and explore the career path for Quantitative Developer
Financial Data Scientist
A financial data scientist applies data analysis techniques to extract insights and improve decision-making in the financial industry. This course is especially relevant for a financial data scientist because it provides practical experience in extracting, storing, and analyzing financial data using Python. The course covers topics like extracting historical and fundamental data, handling streaming market data, and managing SQLite databases. These are all essential skills for a financial data scientist working in trading or investment.
See salaries and explore the career path for Financial Data Scientist
Trading System Developer
A trading system developer specializes in building and maintaining the software infrastructure that supports automated trading. This course helps someone become a trading system developer by providing extensive knowledge of Interactive Broker's Python API. The course covers essential areas such as extracting historical and fundamental data, implementing technical indicators, managing orders, and handling real-time market data streams. All of these are critical components in building robust trading systems. Gaining proficiency in these areas through this course helps you greatly in developing and maintaining trading systems.
See salaries and explore the career path for Trading System Developer
Proprietary Trader
A proprietary trader trades financial instruments with a firm's own capital to generate profits. This course is well-suited for someone who wants to be a proprietary trader because it offers a practical guide to algorithmic trading using Interactive Broker's Python API. The course covers topics like strategy design, backtesting, and risk management. Gaining proficiency in these areas enables proprietary traders to develop and deploy profitable trading strategies. The section on backtesting sample strategies and KPI calculations is directly pertinent.
See salaries and explore the career path for Proprietary Trader
Hedge Fund Analyst
Hedge fund analysts support investment decisions by conducting in-depth research and analysis. This course helps a someone become a hedge fund analyst as it provides a strong foundation in algorithmic trading and quantitative analysis. The course covers topics like extracting and analyzing financial data, implementing technical indicators, and backtesting trading strategies. These skills enable hedge fund analysts to develop and evaluate sophisticated trading models. Specifically, the section on streaming tick-level data and converting ticks to candles demonstrates techniques critical to some hedge fund strategies.
See salaries and explore the career path for Hedge Fund Analyst
Data Engineer
A data engineer designs, builds, and maintains data pipelines and infrastructure. This course may be useful for data engineers interested in the finance sector because it provides hands-on experience in extracting and storing financial data using Python and SQLite databases. The course covers topics like handling streaming market data, parsing XML data from fundamental data APIs, and storing tick data efficiently. These are practical skills that enable data engineers to build robust data solutions for algorithmic trading and quantitative analysis.
See salaries and explore the career path for Data Engineer
Financial Engineer
Financial engineers use mathematical and computational tools to solve complex financial problems. This course may be useful for financial engineers because it offers a practical approach to algorithmic trading using Python and Interactive Broker's API. The course helps build a foundation in automating trading strategies, handling market data, and implementing technical indicators. The focus on advanced Python concepts and database management further enhances the ability to develop and deploy complex financial models, which is a crucial skill for a financial engineer.
See salaries and explore the career path for Financial Engineer
Risk Manager
Risk managers identify, assess, and mitigate financial risks within an organization. This course may be useful to risk managers as it covers essential topics such as backtesting strategies and understanding key performance indicators (KPIs) for trading strategies. The course provides insights into managing risk exposure that enables risk managers to better assess and mitigate financial risks. Specifically, modules on maximum drawdown implementation and volatility analysis using Interactive Brokers' API are applicable here.
See salaries and explore the career path for Risk Manager
Investment Analyst
An investment analyst evaluates investment opportunities and provides recommendations. This course may be useful for an investment analyst as it helps build skills in algorithmic trading and data analysis using Python. The course explores how to extract and analyze both historical and fundamental data, incorporate technical indicators, and backtest trading strategies. These skills enable investment analysts to enhance their research capabilities and make more informed investment decisions. Specifically, learning to parse corporate events data would directly translate to better, data-driven investment analyses.
See salaries and explore the career path for Investment Analyst
Portfolio Manager
Portfolio managers oversee investment portfolios to achieve specific financial goals. This course may be useful for a portfolio manager as it helps them automate trading strategies and manage risk using Python and Interactive Broker's API. The course explains topics like backtesting strategies, handling streaming market data, and implementing technical indicators. These are directly applicable to optimizing portfolio performance and managing risk exposure. The course also covers essential skills in account management, relevant for overseeing overall portfolio health.
See salaries and explore the career path for Portfolio Manager
Securities Trader
A securities trader buys and sells securities on behalf of clients or a financial institution. This course may be useful for aspiring securities traders who wish to automate their trading strategies. The course covers topics like placing and modifying orders, managing positions, and using technical indicators. Learning to automate these processes using Interactive Brokers' API can enhance a trader's efficiency and decision-making. The comprehensive coverage of order types and API calls is particularly relevant.
See salaries and explore the career path for Securities Trader
Market Maker
Market makers provide liquidity in financial markets by quoting bid and ask prices for securities. This course may be useful for market makers as it offers insights into algorithmic trading strategies and tools. The course's coverage of streaming market data, order management, and technical indicators can help market makers refine their pricing and risk management strategies. Specifically, sections on streaming tick level data and converting those ticks to candles is applicable in a market making context.
See salaries and explore the career path for Market Maker
Financial Consultant
Financial consultants provide advice on financial planning, investments, and risk management. This course may be useful for a financial consultant as it helps build a solid understanding of algorithmic trading and quantitative analysis. The course covers topics like extracting and analyzing financial data, implementing technical indicators, and backtesting trading strategies. These skills enable financial consultants to better advise their clients on investment opportunities and risk management, particularly relating to automated strategies.
See salaries and explore the career path for Financial Consultant

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Algorithmic Trading using Interactive Broker's Python API.
Cover image
Python for Finance
Save
Provides a comprehensive overview of using Python for financial analysis. It covers topics such as data analysis, visualization, and algorithmic trading. It valuable resource for understanding how Python can be applied to the specific challenges of algorithmic trading. This book is useful as additional reading to expand on the concepts taught in the course.
Python for Finance
Paperback
Check
Python for Finance
Kindle Edition
Check
Cover image
Putting Knowledge to Work
Save
Delves into advanced algorithmic trading strategies using Python. It covers topics such as statistical analysis, machine learning, and risk management. It valuable resource for those looking to take their algorithmic trading skills to the next level. This book is useful as additional reading to expand on the concepts taught in the course.
Putting Knowledge to Work
Paperback
Check
Putting Knowledge to Work
Kindle Edition
Check

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Effort
13 total hours
Via
Udemy
Instructors
Mayank Rasu
RASUQUANT LTD
Language
English
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