StockViewers
A high-performance algorithmic and quantitative financial research platform designed to democratize institutional-grade market analytics.
🎯 The Mission
StockViewers was engineered to democratize institutional-grade financial analytics.
The platform bridges the gap between retail investors and Wall Street by providing everyday users, students, and professionals with the same quantitative tools, algorithmic backtesting, and AI-driven portfolio management techniques previously reserved for top-tier hedge funds. By stripping away complex jargon and replacing it with a sleek, intuitive interface, StockViewers empowers data-driven financial decision-making at scale.
Processed Capital
The platform has successfully processed, backtested, and simulated over $175 Million worth of portfolio assets, proving database and computational stability.
User base & Traction
A growing global user base actively leverages the platform to stress-test their portfolios, forecast risk, and optimize capital allocation.
Stochastic Forecasts (Monte Carlo)
Engineered a custom Monte Carlo engine projecting thousands of potential portfolio trajectories. By modeling asset paths using Geometric Brownian Motion (factoring in historical drift and volatility), the system visualizes the 5th (Pessimistic), 50th (Average), and 95th (Optimistic) percentile outcomes, showing long-term compounding risk.
"What-If" Portfolio Stress Testing
A dynamic simulation environment where users can apply theoretical macroeconomic "shocks" (e.g., a 20% market crash or specific asset devaluations) to custom portfolios. The quantitative engine instantly recalculates aggregate portfolio value, volatility metrics, and maximum drawdown under the stressed conditions.
Algorithmic Backtesting & ML
Users backtest technical trading strategies (like RSI signals or Moving Average Crossovers) against historical data to evaluate Sharpe ratios. Integrates Machine Learning models (Random Forest, XGBoost) using walk-forward validation to forecast short-term price movements and generate confidence scores.
Efficient Frontier Optimization
Utilizes SciPy optimization algorithms to calculate the Efficient Frontier. By analyzing the covariance matrix of selected assets, the engine determines the exact percentage weights required to maximize the Sharpe Ratio (highest return for the lowest risk) for any given basket of stocks.
Generative AI Wealth Advisory
Integrated with Google Gemini 1.5 Flash via a custom API wrapper. The AI engine acts as a low-latency "Wealth Manager," synthesizing user inputs (capital, risk appetite, time horizon, investment style) to generate strict JSON-formatted sector allocations and executive-level advisory reports.
Market Regime & Sector Mapping
Analyzes SPY moving averages against the VIX to classify the current market environment (Risk-On, Risk-Off, High Volatility). Features highly optimized, thread-safe concurrent fetching to analyze complex markets, including real-time constituents of the Indian Market (Nifty 50) without hitting rate limits.
Fundamental Radar & Insider Sentiment Tracking
Extracts complex fundamentals (P/E ratios, Revenue Growth, Profit Margins, Debt/Equity) and normalizes them into 0-100 scores for visual radar chart comparisons. It also aggregates recent corporate insider transactions (buys, sells, option exercises) to gauge executive sentiment.
🏗️ Decoupled System Architecture
StockViewers operates on a highly decoupled, high-performance architecture designed for speed and heavy data processing. By offloading complex mathematical modeling to a dedicated Python microservice, the client interface remains lightweight and responsive.
1. The Client
React & Tailwind CSS: A state-of-the-art frontend featuring deeply interactive financial charting (recharts, chart.js), dynamic Light/Dark mode synchronization, and an overarching "glassmorphism" UX designed to feel like a premium Bloomberg terminal.
2. The Gateway
Node.js & Express: A robust backend gateway handling CORS, API rate-limiting, request validation, and securely proxying REST API requests between the client and the Python quantitative engine.
3. The Quant Service
Python & FastAPI: A dedicated high-speed microservice built for heavy mathematical computation. Utilizes NumPy, Pandas, SciPy, and Scikit-Learn for algorithmic lifting and yfinance for data pipelines.