Kolmogorov-Smirnov test is a nonparametric test of the equality of continuous distribution that can be used to compare a sample with a reference probability distribution. KS-test is usually referred as goodness of fit test, but also it's test for "normality" (if our reference distribution is normal).

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Continuing SVM clustering I planned to run over all machine learning methods using same two indicator features, and possibly same data, maybe with slight variations. That wouldn't work in practice though as it would be overfit after seeing the out of sample results and using them to select best performers.

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OK, I'm continuing my dive into machine learning. I will make two indicators - RSI, which tries to represent (as believed), mean reversion (more acurrately probably - momentum) and momentum indicator in the form of relationship between price and moving average.

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Hidden Markov Models are generative, probabilistic models, in which a sequences of visible variables are generated by some unknown ("hidden") states. I've done it mainly to minimize the tendency of others to overcomplicate from the mere beginnings.

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Unlike event driven backtesting where we do calculations on each new arrived data element, we can do simple, fast, but very flexible backtest on the entire vector at once.

Let's try to tests simple thing - mean reversion strategy where we buy large down moves and sell large up moves in S&P500, based on last two bars, delayed by 1 day.

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