Trying to predict the stock market is an enticing prospect to data scientists motivated not so much as a desire for material gain, but for the challenge.We see the daily up and downs of the market and imagine there must be patterns we, or our models, can learn in order to beat all those day traders with business degrees. Naturally, when I started using additive models for time series prediction, I had to test the method in the proving ground of the stock market with simulated funds. Inevitably, I joined the many others who have tried to beat the market on a day-to-day basis and failed. However, in the process, I learned a ton of Python including object-oriented programming, data manipulation, modeling, and visualization. I also found out why we should avoid playing the daily stock market without losing a single dollar .
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- Build a model that will predict whether a stock price will increase or decrease the next day - We will be basing our prediction on the amount of volume exchanged by the end of a certain day - The price of a stock at the end of the day is different than at the beginning of the next day. We want to retrieve this difference and figure out if sto…
Monsieurvishal/Stock-prediction
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- Build a model that will predict whether a stock price will increase or decrease the next day - We will be basing our prediction on the amount of volume exchanged by the end of a certain day - The price of a stock at the end of the day is different than at the beginning of the next day. We want to retrieve this difference and figure out if sto…
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