Trading Mean Reversion with Kalman Filters

🚀 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* Any time we are trying to make an informed decision in the face of uncertainty, we are making assumptions about model specification and parameterization. Model selection is rarely the issue, but in the real world, price processes often do not follow theoretical models like the Ornstein-Uhlenbeck process, and a strategy may work for a period, stop, and then start again. Model parameterization behaves similarly; because the mean level and other parameters are subject to change, a "stale" parameterization can destroy a beautiful equity curve. We’d like to move to a more dynamic modeling space, and the Kalman Filter is one such way to help solve the problem of non-stationarity and shifting means. Effectively, we are combining a model representation (the "knowledge" or laws of physics) with data that we actually observe ("experience"), allowing the mean to adapt in real time. We are then able to pull different levers to dictate how much we trust the observed market data (which may be noisy) relative to our original model assumptions. There are a variety of techniques to inform these decisions, such as calibration windows and backtests, though there is no asymptotic guarantee in reality like there is in the classroom. Moreover, there is no "free lunch"—a more adaptive model is more sensitive to noise, and successful trading requires a combination of quantitative skills and a grasp of the overall market climate to outperform systematic strategies. I hope you enjoyed, and I hope you learned something! Roman ___________________________________________ 📖 Chapters: 00:00 - Trading Mean Reversion and Kalman Filters 01:11 - What Trading Mean Reversion Should Look Like 03:55 - The Reality of Trading Mean Reversion 05:43 - Trading with Models and Assumptions 07:31 - Kalman Filters and Non-Stationarity 08:59 - Double Edged Sword of Adaptive Models 10:15 - Playing Your Hand of Poker with Models ___________________________________________ 🗣️ 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   ___________________________________________