Problem Statement
Algorithmic trading has revolutionised the stock market and its surrounding industry. Over 70% of all trades happening all over the world right now are being handled by bots. Gone are the days of the packed stock exchange with suited people waving sheets of paper shouting into telephones.The problem we are targeting here is to predict the future vale of S&P500 given the past values. Needless to say this would be tremendously helpful for investors. However the key is to predict the value faster with increased accuracy in comparison to classical techniques. Classical technics would involve a team of quantitative analyst, weeks or months of labours pouring over the past data only to come up with a model with not so great accuracy.
Our Approach Here we used a techniques in deep learning called Long-Short Term Memory(LSTM) that is used to learning the pattern occuring in a temporal data. This learning is then used to predict the future movement of the data.
Benefits of our approach over traditional approach The benifits of our approach is as follows
Its faster to predict the future value Its more accurate compared to performance of traditional approach Its adaptable i.e as more data pours in the system learns better and adapt its modelling scheme. It can pour over data from over 100 years in minutes and can learn for any pattern occuring thats unnoticeable by human analysts.