Stock Market predictions are one of the most difficult problems to solve, and during the looming days of recession it’s extremely difficult and next to impossible to do. This is because there are numerous patterns in the stock prices trend throughout the day and every variation from the normal trend could mean something new, since stocks is ever expanding and hence new problems and pattern in the trends are visible which needs to be studied but these new trends are usually generated each and every day possible of the trade and to keep up with the change is a very lofty task to do especially for an individual who has a large or even semi large portfolio to maintain over a period of time. Stocks and bonds are immensely important for a country’s economy to boom and it’s collapse means the collapse of country’s economy and since these markets are linked with every possible sector that contribute to the economy, mostly organised sectors, it’s collapse would be seen on every sector linked in those markets by what the economists call as “Ripple effect” and this goes other way around as well that if a particular sector’s firm performs poorly, then that would be reflected in the other firms of that sector.
In financial research and analysis, it’s the most difficult obstacle that wears the analyst in order to know the stock price movements to predict the expected price and the more daunting task is to predict whether the stock is going to do better or worse in future and then bet on the stock, i.e to go short(simply sell and later buy it when price of share stoops down) or to go long(simply buy and hold it when it’s expected that price is going to steep upwards) The objective of this project along with the code is to forecast the price movements and on the basis of that and certain working rules we predict that prices would go up or down and then suggest the user to buy or sell the particular stock at a particular time. We used Genetic Algorithm, a tool of machine learning to predict the future movement of the stock.
Our prediction is based on the work done by Thomas Meyer and Norman Packard which is popularly known as Meyer Packard Genetic Algorithm on finding “regions of predictability” in time series generated by the Mackey-Glass equation. The possibility of utilizing Genetic Algorithm to forecast the momentum of stock price(kind of momentum trading strategy for Algorithmic trading) has been previously explored by many optimization models that have subsequently addressed much of the skepticism. We have added this machine learning concept of Meyer and Packard to see the trend and forecast and based on that predict which stocks to buy and sell and at what time. The coding and experiments are done in python programming and the results reveal that using this algorithm in stock trading could give hefty returns.
-To predict the stock price movement and in which stocks to bet on
-To determine the time when a stock needs to be longed or shorted