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# mean reversion Posted on Nov 24, 2020 by
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I'm a big fan of Ernie Chan's quant trading books: Quantitative Trading, Algorithmic Trading, and Machine Trading. There are some great insights in there, but the thing I like most is the simple but thorough treatment of various edges and the quant tools you might use to research and trade them. Ernie explicitly states that the examples in the books won't be tradable, but they've certainly provided fertile ground for ideas. In Machine Trading, there is an FX strategy based on an autoregressive model of intraday price data. It has a remarkably attractive pre-cost equity curve, and since I am attracted to shiny objects, I thought I'd take a closer look. Autoregressive Models 101 An autoregressive (AR) model is a time-series multiple regression where: the predictors are past values of the time series the target is the next realisation of the time series If we used a single prior value as the only predictor, the AR model would be called an $AR(1)$ and it would look like: $y_t = \beta_0 + \beta_1 y_{t-1} + \epsilon_t$ (the $\beta$'s are... Posted on Jan 03, 2017 by
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What if you had a tool that could help you decide when to apply mean reversion strategies and when to apply momentum to a particular time series? That's the promise of the Hurst exponent, which helps characterise a time series as mean reverting, trending, or a random walk. For a brief introduction to Hurst, including some Python code for its calculation, check out our previous post. Even if you have read this post previously, it is worth checking out again as we have updated our method for calculating Hurst and believe this new implementation is more accurate. It would be great if we could plug some historical time series data into the Hurst algorithm and know whether we expect the time series to mean revert or trend. But as is usually the case when we apply such tools to the financial domain, it isn't quite that straightforward. In the last post, we noted that Hurst gives different results depending on how it is calculated; this begs the question of how to choose a calculation method intelligently so that we avoid choosing... Posted on Oct 31, 2016 by
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This is the first post in a two-part series about the Hurst Exponent. Tom and I worked on this series together and I drew on some of his previously published work as well as other sources like Quantstart.com. UPDATE 03/01/16: Please note that the Python code below has been updated with a more accurate algorithm for calculating Hurst Exponent. Mean-reverting time series have long been a fruitful playground for quantitative traders. In fact, some of the biggest names in quant trading allegedly made their fortunes exploiting mean reversion of financial time series such as artificially constructed spreads, which are used in pairs trading. Identifying mean reversion is therefore of significant interest to algorithmic traders. This is not as simple as it sounds, in part due to the non-stationary nature of financial data. We both think that Ernie Chan's book “Algorithmic Trading: Winning Strategies and Their Rationale”, is one of the better introductions to mean reversion available in the public domain. In the book, Ernie talks about several tools that can be used when testing if a time series is mean reverting. One is the... Posted on Jan 02, 2016 by 