don't interact (trade) directly with the market, but we will generate equity allocations that you could trade if you wanted. This simple algorithm can produce surprisingly good results. Or maybe Im just too stupid. An example of such a system can be found here. These advanced topics will be much easier to understand once you've mastered the core skills. So the samples are the model.
We now have k classes of samples, each in the neighborhood of one of the k points. In fact the most popular and surprisingly profitable data mining method works without any fancy neural networks or support vector machines. This will generate a new samples assignment, since some samples are now closer to another point. The preselection can be based on correlation between predictors, on significance, on information content, or simply on prediction success with a test set.
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Youre still looking at the open, high, low, and close of candles. It can be used for classification as well as for regression. Practice and theory go hand-in-hand. So there should be about as many winning as losing samples. However the number of patterns is quite limited when you only look at sequences of a few adjacent candles. Please take all those publications with a grain of salt. Alternate between practice and theory. Back to Table of Contents.
Understand how to assess a machine learning algorithm s performance for time series data (stock price data ).
Know how and why data mining ( machine learning ) techniques fail.
Construct a stock trading software system that uses current daily data.
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