Framework on Automated Trade Systems using Time Series Forecasting Algorithms and Machine Learning Classifiers

Abstract

Main idea here is to implement a model which will predict the stock price(trend) for the future. Here data is gathered from the yahoo finance and data of stock is saved locally in csv format. Data then fed to the classifiers to predict the condition for Algorithmic Trading using classifiers and time series forecasting algorithms. Data is also fed to few time series forecasting algorithm to predict the trend and seasonality for the forecasting which will help the traders to invest wisely. Combined result of classifiers and time series algorithms is taken into consideration for the conditions buy/sell/hold of algorithmic trading. Data is first preprocessed so that only relevant data is present and rest of data can be dropped. Here we are only interested in finding the Close/AdjClose price of the MMM.