Led by Niv Roychowdhury and Ruthwik Pabbu
A Streamlit-based algorithmic trading dashboard that lets you build, test, and analyze quantitative trading strategies through an interactive web interface. The project provides a full-stack environment for strategy development — you configure trading strategies through a visual Strategy Builder, run backtests, and evaluate results via equity curves, drawdown charts, and trade history. A built-in SQL Reports module lets you dig deeper with custom queries (JOINs, aggregations, filtering) against a local SQLite database. The frontend is built with Streamlit and Plotly for rapid, interactive data visualization, while the backend (currently in development) uses Flask, SQLAlchemy, and SQLite to handle strategy persistence and backtesting logic. Data processing runs on Pandas and NumPy. Current functionality includes a performance dashboard, a strategy library for saving and managing multiple configurations, and backtest result visualizations. On the roadmap: a complete backtesting engine, live market data via Yahoo Finance, additional strategy implementations, and user authentication.