Stock Market Prediction Model using Machine Learning in Python

Hello All, A Stock Market Prediction Model is to be created based on historical data which basically allow the investor to decide if the stock should be purchased or shorted/sold. The historical data needs to be used from a server which is being updated daily. Each day the file gets updated (file name eg: [login to view URL]) in Json format and contains the data like the (Open Price, Closing Price, High, Low, Volume, Adj_Close) for several stocks. Following is the format of the data: stock = { "2312": { "adj_close": "24.90", "close": "24.95", "high": "83.80", "low": "24.90", "open": "45.80", "volume": "24236200" }, "4456" : { "adj_close": "12.50", "close": "42.32", "high": "23.65", "low": "32.46", "open": "33.20", "volume": "92075" }, "2345" : { "adj_close": "54.30", "close": "54.50", "high": "54.90", "low": "54.50", "open": "54.90", "volume": "102150" } } the numbers: 2312, 4456, 2345 represents the stock reference number Also, a Decision File should be created and stored in Json format which will contain the data like which stock to but which stock needs to be sold and how long the stock be held if not selling based on the behavior of stocks predicted by the model.