FXMachine learningQuant tradingRZorro
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
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Machine learningR
Machine learning for Trading:
Adventures in Feature SelectionUpdates: 2019: In this first Machine Learning for Trading post, we’ve added a section on feature selection using the Boruta
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BacktestingFXRTime series modellingTrading strategies
Time Series Analysis: Fitting ARIMA/GARCH predictions profitable for FX?
Recently, I wrote about using mean-reversion time series models to analyze financial data and build trading strategies based on their
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BacktestingCointegrationFXQuant tradingRTrading booksTrading strategiesZorro
Exploring Mean Reversion and Cointegration: Part 2
In the first Mean Reversion and Cointegration post, I explored mean reversion of individual financial time series using techniques such
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CointegrationFXQuant tradingRTrading booksZorro
Exploring mean reversion and cointegration with Zorro and R: part 1
This series of posts is inspired by several chapters from Ernie Chan’s highly recommended book Algorithmic Trading. The book follows Ernie’s
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BacktestingFXk-means clusteringMachine learningQuant tradingRTrading strategiesZorroA Robust Trading Framework Built with K-Means Clustering
Important preface: This post is in no way intended to showcase a particular trading strategy. It is purely to share
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BacktestingFXk-means clusteringMachine learningQuant tradingR
Unsupervised candlestick classification for fun and profit – part 2
In the last article, I described an application of the k-means clustering algorithm for classifying candlesticks based on the relative position
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FXk-means clusteringMachine learningQuant tradingR
Unsupervised candlestick classification for fun and profit – part 1
Candlestick patterns were used to trade the rice market in Japan back in the 1800’s. Steve Nison popularised the idea