A modular stock prediction framework combining time series models and modern ML techniques. Supports custom pipelines for feature engineering (lag features, technical indicators), model training (ARIMA, XGBoost, LSTM), and walk-forward backtesting with performance metrics like Sharpe ratio, MSE, and cumulative return. Currently under development.

An interactive Jupyter Notebook-based trading simulator that models and projects trading profits through data-driven scenario analysis. Enables traders and investors to simulate various trading strategies, analyze potential profit outcomes, and visualize performance metrics in an accessible, collaborative environment.