selection bias

Posted on May 15, 2019 by Michael M

In an ideal trading universe (free from backtesting bias), we’d all have a big golden “causation magnifying glass”. Through the lens of this fictional tool, you’d zoom in and understand the fleeting, enigmatic nature of the financial markets, stripping bare all its causes and effects. Knowing exactly what causes exploitable inefficiencies would make predicting market behaviour and building profitable trading strategies a fairly cushy gig, right? If you’re an engineer or scientist reading this, you are probably nodding along, hoping I’ll say the financial markets show some kind of domino effect for capitalists. That you can model them with the kinds of analytical methods you’d throw at a construction project or the petri dish. But unfortunately, trying to shoehorn the markets into formulas is a futile exercise... like stuffing Robot Wealth’s frontman Kris into a suit. Since the markets aren’t strictly deterministic, this makes testing your new and exciting strategy ideas a bit tricky. We’d all love to know for sure whether our ideas will be profitable before we throw real money at them. But, since you can’t realistically apply...

Posted on Nov 24, 2015 by Kris Longmore

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 welcome. Backtest results presented are for illustrating the methodology and describing the outputs only. That done, on to the interesting stuff My last two posts (Part 1 here and Part 2 here) explored applying the k-means clustering algorithm for unsupervised discovery of candlestick patterns. The results were interesting enough (to me at least) to justify further research in this domain, but nothing presented thus far would be of much use in a standalone trading system. There are many possible directions in which this research could go. Some ideas that could be worth pursuing include: Providing the clustering algorithm with other data, such as trend or volatility information; Extending the search to include two- and three-day patterns; Varying the number of clusters; Searching across markets and asset...