A full-stack quant finance platform with 30+ financial models, real-time market data, paper trading, and interactive visualizations — built for learning and exploration.
Live Ticker Tape — Scrolling real-time prices for 80+ global instruments across NYSE (USA), LSE (UK), NSE/BSE (India), HKEX (Hong Kong), XETRA (Germany), TSE (Japan), ASX (Australia). Covers stocks, ETFs, crypto, forex, and commodity futures with multi-currency display and timezone-aware market hours.
Model Catalog — 30+ financial models organized by category. Each page has parameter inputs, mathematical formulas, interactive Chart.js visualizations, educational tooltips, and methodology explanations.
Paper Trading — Trade stocks at live prices with a virtual $100,000. Tracks positions, P&L, and trade history with 0.1% commission. State persists in localStorage.
Offline Mode — When the backend is unavailable, the frontend uses client-side GBM simulation (Box-Muller transform) to keep prices animated. Health checks retry every 30 seconds.
python run.pyStarts the backend on http://localhost:8000 and frontend on http://localhost:5173.
Or run separately:
# Backend
cd backend && pip install -r requirements.txt && python start.py
# Frontend
cd frontend && npm install && npm run dev| Model | Description |
|---|---|
| Monte Carlo Simulation | Simulates thousands of GBM price paths to produce return distributions and probability estimates. |
| Geometric Brownian Motion | Exact log-normal price path simulation with drift analysis and terminal distribution. |
| Heston Stochastic Volatility | Two-factor model with correlated price and variance processes; generates the volatility smile. |
| Ornstein-Uhlenbeck Process | Continuous mean-reversion model used for interest rates and pairs trading spread dynamics. |
| Ito's Lemma | Educational demo comparing corrected GBM vs naive simulation to illustrate the drift correction term. |
| Girsanov's Theorem | Shows measure change from real-world to risk-neutral probability via likelihood ratio paths. |
| Feynman-Kac Theorem | Links PDEs to stochastic expectations; solves the heat equation via Monte Carlo path averaging. |
| Model | Description |
|---|---|
| Black-Scholes | Closed-form European call/put pricing with all five Greeks and implied volatility surface. |
| CRR Binomial Tree | Discrete-time lattice pricing with visualized tree structure and early exercise premium for American options. |
| Finite Difference Methods | Solves the Black-Scholes PDE numerically via explicit, implicit, and Crank-Nicolson schemes. |
| Risk-Neutral Valuation | Monte Carlo option pricing under the Q-measure with convergence analysis and put-call parity verification. |
| Model | Description |
|---|---|
| Vasicek Model | Mean-reverting short rate model with analytical bond pricing and yield curve generation. |
| Short Rate Models | Side-by-side comparison of Vasicek and CIR models with calibrated parameters and simulated rate paths. |
| Model | Description |
|---|---|
| CAPM | Estimates beta and alpha relative to a benchmark, plots the Security Market Line and rolling beta. |
| Markowitz MVO | Generates the efficient frontier with minimum-variance and maximum-Sharpe portfolios highlighted. |
| Model | Description |
|---|---|
| Value at Risk (VaR) | Computes VaR and CVaR via historical, parametric, and Monte Carlo methods for tail risk analysis. |
| Risk Management Suite | Full risk dashboard: VaR, CVaR, Sharpe, Sortino, max drawdown, beta, alpha, and stress scenarios. |
| Copula Models | Models joint tail dependence between assets using Gaussian and Student-t copulas. |
| Model | Description |
|---|---|
| Volatility Suite | Historical, Parkinson, and GARCH(1,1) volatility with regime detection and volatility cone charts. |
| Model | Description |
|---|---|
| DCF Valuation | Projects free cash flows and terminal value to estimate fair value per share with BUY/HOLD/SELL signal. |
All regression models use 13 engineered features (SMA, EMA, MACD, RSI, Bollinger Bands, momentum, volatility, lagged returns) with an 80/20 train/test split.
| Model | Description |
|---|---|
| Linear Regression | Standard OLS baseline for price prediction with residual diagnostics. |
| Ridge Regression | L2-regularized regression to reduce overfitting on correlated technical features. |
| Lasso Regression | L1-regularized regression that performs feature selection by zeroing out weak predictors. |
| Elastic Net | Combines L1 and L2 penalties for a balance between feature selection and coefficient shrinkage. |
| Polynomial Regression | Captures non-linear price relationships by expanding features to higher-degree terms. |
| Random Forest | Ensemble of decision trees with feature importance ranking and out-of-bag error estimate. |
| Gradient Boosting | Sequential boosting that fits each tree to the residuals of the previous, improving accuracy iteratively. |
| SVR (Support Vector Regression) | Kernel-based regression that finds a tube minimizing prediction error with margin tolerance. |
| XGBoost | Optimized gradient boosting with regularization, pruning, and fast training on tabular data. |
Predicts next-day price direction (Bullish / Bearish) using the same 13 engineered features with an 80/20 train/test split. Each classifier has its own dedicated page with model-specific parameters, full metrics (accuracy, precision, recall, F1, ROC-AUC), confusion matrix visualization, per-day signal chart with up-probability, and feature importance. A combined comparison page lets you switch between all four in one view.
| Model | Description |
|---|---|
| Logistic Regression Classifier | L2-regularized binary classifier (tunable C) that learns a linear decision boundary. Feature weights directly show which indicators (RSI, MACD, momentum) drive the bullish/bearish signal — the most interpretable classifier. |
| Random Forest Classifier | Ensemble of 200 decision trees with balanced class weights and max_depth=8. Aggregates votes for calibrated probabilities; outputs Gini-based feature importance ranking all 13 technical indicators. |
| SVM Classifier | RBF kernel SVM that finds the maximum-margin hyperplane in a high-dimensional kernel space. Reports support vector count and Platt-calibrated class probabilities for each test day. |
| XGBoost Classifier | Gradient-boosted trees with histogram splits, column subsampling, and automatic class-imbalance correction via scale_pos_weight. Outputs gain-based feature importance alongside full classification metrics. |
| ML Direction Classifier (Combined) | Unified comparison page — switch between all four classifiers on the same ticker and period to compare accuracy, ROC-AUC, confusion matrices, and signal charts side-by-side. |
| Model | Description |
|---|---|
| ARIMA | AutoRegressive Integrated Moving Average with automatic order selection for price forecasting. |
| SARIMA | Extends ARIMA with seasonal components to capture periodic patterns in price data. |
| Model | Description |
|---|---|
| Markov Chains | Models market regimes (Bear / Neutral / Bull) as a discrete Markov process with transition probabilities and steady-state distribution. |
| Pairs Trading | Identifies cointegrated stock pairs and generates mean-reversion entry/exit signals using the spread z-score. |
Backend: Python, FastAPI, NumPy, SciPy, pandas, scikit-learn, statsmodels, yfinance
Frontend: React 19, Vite, Tailwind CSS 4, Chart.js, Framer Motion, React Router