A Data Science project using Python in order to predict whether an equity in the IT sector has a positive growth or a negative growth. This project came into existence in order to gauge prevailing technical trading strategies on a large amount of historical data to determine the best performer. Performance is to be judged on the magnitude of return and consistency. The results can be used to build a statistically trained model that optimizes the chosen strategy's parameters for a selected trading period. The trading period can be defined as the time from entry (purchase of underlying security) to exit (sale of underlying security).

Structure of executing this project:

1. Create a folder called "data" in a location on your computer
2. Place the StockSymbols_All file in the data folder
3. Execute the Technical_indicators script
4. This script will take a while to complete execution
5. Notice that equities_IT_TS folder gets created which contains a preprocessed and a processed folder with the CSVs of each equity
6. Place the merge_All_CSV script in both the preprocessed and processed folders and execute them individually (Change the name of the Final CSV to whatever you desire. Currently it is main.csv)
7. Notice that a new csv with all the csv data is created
8. Use this CSV as your dataset
9. Execute the quantitative_finance script and make necessary changes

Note: Find the poster depicting the performance comparison between the various models fitted on the dataset