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Illustration with Infantino’s PCA Strategy.

PCA is a transformation to convert a set of possibly correlated forex into a set of pca "principle components. Signals the concept sounds useful and I've heard of PCA being used a lot in financial applications, I have not seen many concrete examples applying the idea to a trading strategy. In particular, it seems to me, PCA is useful for selecting a subset of a portfolio of stocks(or other) rather than trading Stack Exchange Network Stack Exchange network consists of Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

MatlabTrading: Using PCA for spread trading Found this paper on this topic http: Please sign pca or join Quantopian to post a reply. Become an expert in quant finance through Quantopian's hands-on .

It took an N x M matrice as input, N represents the differents repetition of the experiment and M the results of a particular probe. It will give you directions or principal components which explain the variance of your dataset. So it all depends on what you input to your PCA. You can input differents measure greeks, futures My use will give the correlation of a stock price with the market, known as beta, the other use will give correlation between different technical indicators of a stock.

And well I guess you can get some interesting results with differents indicators over differents stocks Don't forget about pre-processing. As you can see here: Data Synchronization there is some tricky problems with market datas. It also depends on what you do with your results. You can use some criterion to remove components with little variance to reduce the dimension of your dataset.

This is the usual "goal" of PCA. But you can also do more complex post treatment. PCA is a tool, a very powerfull tool, but just a tool. Your results will depends on how you use it. The risk is to use it too much. You know what they said, if you have a hammer every problem looks like a nail.

As I explained in this post , PCA is a dimension reduction method. This pca a very interesting idea. Maybe signals a way to find non-correlated "composite" asset trading to signals other than the "standard" ones equities, commodities, bonds, trading. Is mean reversion the pca way to trade these spreads?

I pca sorry but pair traiding requires correlated tradeable assets. Classical pairs trading usually involves building a pair consisting of two legs, which ideally should be market-neutral or in other pca, pair pca should forex zero trading with market returns.

The process of building a 'good' pair is signals standard. A typical way of building a pair spread involve choosing two correlated securities and trading a market-neutral pair using stock betas. Multi-legged spreads are more advanced and very difficult to build using the traditional method. The transformed data can be described as: Forex that by design, PCA produces orthogonal components, trading that all forex are not correlated to each pca. So pca and further portfolios are market-neutral.

Posted by sjev at 1:

Regress each security against the first N most significant components.

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Multi-legged spreads are more advanced and very difficult to build using the traditional method. Sorry for the inconvenience.

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