This project consists of predicting the prices of different stocks and based on those predictions getting the signals to buy/sell. The winning rate is around 50% with accuracy of 98%.

The models used are XGBBoost Regressor, LSTM, Linear Regression, Lasso and Ridge. Additionally, a model included sentiment analysis from data used from kaggle

Use the package manager pip to install all the packages from the requirements.txt file.

Run the main.py file in the gui folder All models are ran in the gui, main, we are predicting live.

cd Stocks_data_science/gui
main.py

.pkl files are some saved models but since we are running live data we have to train the model multiple times on the new data