Multi-Strategy Quant Systems - Algorithmic Trading in Python
What you'll learn
- Implement Multi-Strategy Quantitative Portfolios with Python
- Build Modular Trading System such that components can be altered to trading preferences
- Implement code for data pipelines, alpha research, alpha strats, portfolio allocation and order submission
- Integrate with multiple brokerages with a single code base
- Handle multiple alpha signals to create unified signals, and learn industry standard risk management techniques
- Basic Financial/Quantitative Literacy is expected.
- Programming methodology (not necessarily in Python) is expected for coding in Python.
- Ability to self-learn and comprehend documentation and code is expected.
- Knowledge of tech stacks are NOT expected, basic software will be used.
Multi-Strategy Quant Systems in Python from Scratch - A First Course in Algorithmic Trading by HangukQuant.
As this is not a Python tutorial, we get right down to business and adopt a no-nonsense coding approach. It is advised that you slow down the pace of the course to your own needs. Even though only 7 hours of lectures have been distilled, the material within is fairly heavy. It is a walkthrough without the introductory explanations - since there are no explanations within the lecture walkthroughs, all students are expected to be hands-on and actively participate by asking questions; which we will compile in the Questions Section. Code is downloadable in Checkpoint Lectures.
Watch a (sped-up) live recording of a quant implementing a shoestring trading system for non-HFT in Python, from data pipelines to order management. Build robust and flexible modular systems + take home a professional setup to adapt and improve.
HangukQuant is the author of the 2nd highest rated quant trading blogs on Substack - Mathematics, Finance and Their Babies, with many years of experience in both quant research teams in both the industry and in academia.
Implement a Multi Strategy Quantitative System in Python from Scratch, while learning how to build robust frameworks for implementation of alpha signals and capital allocation. Learn industry standards in risk management, such as volatility targeting schemes and technical diversification. Code along an algorithmic trading system with modular approaches to integrate with multiple brokerages and switch between them in a matter of seconds, all with the same code.
Build diagnostic tools to analyse your trading system.
Integrate with multiple brokerages in a single code base.
Learn industry standards in risk management, such as volatility targeting schemes and technical diversification.
Take back with you the final product - Your Very Own, Robust Quant Systems to hone and develop!
Build quantitative strategies implementing factor premia and be given guidance for serious students of the market.
This course is not a beginner course; financial literacy, and programming methodology is expected. Students are expected to be able to understand code without being prompted, or at least learn how to comprehend medium-sized code systems of thousands of lines of code.
The course was first recorded and then sped up and voiced over chosen sections to meet Udemy course requirements. You may also choose to code along without the audio as the textual explanations should be primarily useful. As the course is an Advanced-Intermediate level, do note that you may face some difficulties along the way. It is expected that you would need to rewatch some of the lectures, and please do ask away in the Questions Section.
Those without programming experience are encouraged to first undergo courses in Python / Programming Methodology / Data Science for Finance courses.
Note that this course is not part of the Udemy Deals program, and no Udemy promotions are available. We believe that the material within is highly valuable and the cost price is cost de minimis in your quant journey.
Who this course is for:
- This course is for traders and programmers looking to implement multi-strategy quantitative portfolios for research or trading purposes in non-HFT spaces.
- For algorithmic traders who wish to have a more professional setup to integrate their strategies into more robust frameworks for their return harvesting.
Equity L/S PM. Published in academic journals in both Quantum Mechanics and Quant Finance. Bridging the gap in alpha research and trading to the public.
Author of the HangukQuant newsletter; Mathematics, Finance and Their Babies. Looking at financial markets through quantitative lenses. Many years of trading experience and being on alpha research teams in established quant funds.