Quanta AI

Bridging the gap between retail trading and institutional-grade quantitative analysis

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Quanta AI: Engineering a Quantitative Trading Terminal Inspiration Many people lose money in the stock market because they enter risky positions without a solid plan. Messing up a portfolio like that is the fastest way to go broke before even graduating. Our team, consisting of Siddharth L, Krishna A, Atharv R, and Shreyas G was inspired by our own personal experiences with financial loss and witnessing the impact of market volatility on the savings of our families and peers. Market traders and students often rely on guessing their stocks or scattered tools. We built Quanta AI to help investors navigate high-risk assets more effectively by providing access to institutional-style trading analytics in a simple, data-driven platform through lessons, paper trading, AI, and more! What It Does Quanta is a quantitative trading terminal that combines real-time market data with advanced strategy analysis. Predictive Engine: We use a system powered by Google Gemini AI to process millions of data points from yfinance, including historical prices and market trends. Historical Recreation: This feature acts as a time machine, allowing users to see exactly how a stock performed during specific past events, such as the 2020 crash. Quantitative Metrics: The platform calculates risk-adjusted metrics like the Sharpe Ratio to measure performance accuracy. How We Built It We engineered a high-density analytics dashboard using a modern technical stack: Backend: Built on a Python engine using FastAPI to integrate the Gemini AI model and yfinance for live data. Frontend: We utilized React.js and Tailwind CSS to create a responsive, terminal-style interface. Data & Auth: Firebase provides our database and secure authentication. Logic: We implemented a 60-second data cache for Yahoo Finance responses to avoid redundant fetches and ensure high speeds. Challenges The biggest challenge was synchronizing real-time market data with historical backtests so strategy performance remained accurate. We also had to implement institutional metrics while keeping the interface simple enough for beginners. Though we faced many challenges and considered giving up, we persevered to create a completely operational website.

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