Unsupervised candlestick classification for fun and profit – part 1

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 in the western world and claims that the technique, which is based on the premise that the appearance of certain patterns portend the future direction of the market, is applicable to modern financial markets. Today, […]

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The Financial Hacker’s Cold Blood Index

This post builds on work done by jcl over at his blog, The Financial Hacker. He proposes the Cold Blood Index as a means of objectively deciding whether to continue trading a system through a drawdown. I was recently looking for a solution like this and actually settled on a modification of jcl’s second example, where an allowance […]

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Benchmarking backtest results against random trading part 2

In the first part of this article, I described a procedure for empirically testing whether a trading strategy has predictive power by comparing its performance to the distribution of the performance of a large number of random strategies with similar trade distributions. In this post, I will present the results of the simple example described […]

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Benchmarking backtest results against random strategies

Picture this: A developer has coded up a brilliant strategy, taking great care not to over-optimize. There is no look-ahead bias and the developer has accounted for data-mining bias. The out of sample backtest looks great. Is it time to go live?    I would’ve said yes, until I read Ernie Chan’s Algorithmic Trading and realised […]

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