Kalman Filter Example:
Pairs Trading in R

This Kalman Filter Example post is the first in a series where we deploy the Kalman Filter in pairs trading. Be sure to follow our progress in Part 2: Pairs Trading in Zorro, and Part 3: Putting It All Together. Anyone who’s tried pairs trading will tell you that real financial series don’t exhibit truly …

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Practical Pairs Trading

Some price series are mean reverting some of the time, but it is also possible to create portfolios which are specifically constructed to have mean-reverting properties. Series that can be combined to create stationary portfolios are called cointegrating, and there are a bunch of statistical tests for this property. We’ll return to these shortly. While …

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ETF Rotation Strategies in Zorro

At Robot Wealth we get more questions than even the most sleep-deprived trader can handle. So whilst we develop the algo equivalent of Siri and brag about how we managed to get 6 hours downtime last night, we thought we’d start a new format of blog posts — answering your most burning questions. Lately our …

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Dual Momentum Investing: A Quant’s Review

I recently read Gary Antonacci’s book Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, and it was clear to me that this was an important book to share with the Robot Wealth community. It is important not only because it describes a simple approach to exploiting the “premier anomaly” (Fama and French, …

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Machine learning for Trading: Part 2

Introduction My first post on using machine learning for financial prediction took an in-depth look at various feature selection methods as a data pre-processing step in the quest to mine financial data for profitable patterns. I looked at various methods to identify predictive features including Maximal Information Coefficient (MIC), Recursive Feature Elimination (RFE), algorithms with …

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Time Series Analysis: Fitting ARIMA/GARCH predictions profitable for FX?

Recently, I wrote about fitting mean-reversion time series analysis models to financial data and using the models’ predictions as the basis of a trading strategy. Continuing our exploration of time series modelling, let’s research the autoregressive and conditionally heteroskedastic family of time series models. In particular, we want to understand the autoregressive integrated moving average …

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A framework for rapid and robust system development based on k-means clustering

Important preface: This post is in no way intended to showcase a particular trading strategy. It is purely to share and demonstrate the use of the framework I’ve put together to speed the research and development process for a particular type of trading strategy. Comments and critiques regarding the framework and the methodology used are most …

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