Market Trends and Predictions

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Practical walkthroughs on machine learning, data exploration and finding insight. The getSymbols function downloaded daily data going all the way back to January The barChart function displays the data in a nice clean fashion following a theme-based parameter see the help file for more. Not bad for 2 lines of code!! For indexes and other esoteric symbols, refer to finance.

There are many ways to customize the display, for some examples check out the Quantmod Gallery. This will collate the data by time and fill in any missing data with NA s:. Be warned, that this does take a little time as quantmod will throttle the download. Each symbol is loaded in memory under the symbol name, therefore we have over new objects loaded in memory each with years of daily market data. As these are independent time series, we have to merge everything together and fill in missing data so everything fits nicely in a data frame.

Now that we have a handful of years of market data for every stock currently in the NASDAQ Index , we need to do something with it. The idea is to quantify stock moves as patterns by subtracting one day versus a previous one. What are we trying to predict? We will rely on this value for training and testing purposes. A value of 1 means the volume went up, and a 0 , that it went down:. Cast the date field to type date as it currently is of type character and sort by decreasing order:.

Here is the pattern maker function. This will take our raw market data and scale it so that we can compare any symbol with any other symbol. It then subtracts the different day ranges requested by the days parameter using the diff and lag calls and puts them all on the same row along with the outcome. To make things even more compatible, the roundByScaler parameter can round results.

Call the function with the following differences and scale it down to 2 decimal points this takes a little while to run:. You can get more details regarding parameter settings for this model at the xgboost wiki:. Conclusion Not bad, right? An AUC of 0. Hopefully this will pique your imagination with the many possibilities of quantmod and R. Full source code also on GitHub:. Home Linkedin Videos Feedback.

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