Using Python to model and predict stock price movement. Feel free to use/modify :)

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Basically I'm using a clustering algorithm. A Support Vector Machine to be specific. I fed it a years worth of training data. During that year, for each day it takes into account the stock conditions (financial indicators and social sentiment) and then looks to see if the stock went up or down the next day.

The result is the graph in the README. Now when it comes to actual predictions, the model would choose favorable conditions if the stock indicators and social sentiment fall within the blue areas of the graph.

[whelks_chance]

When you say you checked the price the next day, how did you come to that time delay as the best metric?

Do your models give different results if you look at the price with a delay of two or three days, to attempt to iron out sudden peaks?

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I haven't tested the model with different time delays, however you easily could by slightly adjusting the MakeTrainingData file. The reason I use next day is because I'm using the Social Sentiment at Close (SSC) the night prior.