We use a stochastic model to treat stocks as semi-random agents, bounded by drift and influenced by volatility, in order to make a prediction that uses a random, noise generating function to make future predictions as to how stocks will behave. Consequently, we graph the vectorized data from the mathematical model to display the predictions; in the future we hope to use a realtime API, but for the time being it uses an included CSV file for 2013 Hewlett-Packard data to predict HP stock data for 2014.
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Stock prediction using a stochastic model
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