A comprehensive full-stack web application for real-time stock price analysis and prediction using 7 machine learning algorithms, supporting both US and Indian markets with live data fetching, historical analysis, and interactive visualization.
- 7 ML Algorithms: Linear Regression, Decision Tree, Random Forest, SVM (basic models) + KNN, ARIMA, Autoencoder (advanced models)
- Real-time Data: US stocks via Finnhub API, Indian stocks via Upstox API with permanent storage fallback
- Modern Dashboard: React 18 + TypeScript + Tailwind CSS with interactive Recharts for 5-year historical analysis
- 1000+ Stocks: 1,001 total stocks available (501 US + 500 Indian), 936 used for training after filtering insufficient data
- Currency Support: Real-time USD/INR conversion via forex-python
- Smart Training: Percentage-based predictions with proper price conversion and confidence scoring
- Standalone Trainers: Independent training scripts for each model with progress tracking
Automatic USD/INR conversion for all stocks:
- Sources: Live forex APIs → Cached rates → Static rate (83.5 USD/INR)
- Display: Original currency + converted price shown for all stocks
Backend: Flask 2.3.3, Python 3.8+, TensorFlow 2.20, scikit-learn 1.5.2, statsmodels 0.14.4, pandas, numpy
Frontend: React 18.3.1, TypeScript, Vite 6.4.0, Tailwind CSS, Radix UI, Recharts 2.15.2
APIs: Finnhub (US stocks), Upstox (Indian stocks), yfinance (historical data)
The system works completely offline without any API keys! This is the recommended way to get started.
- ✅ Stock Information: 500 Indian + 501 US stocks (1,001 total) from permanent directory
- ✅ Historical Charts: Complete 5-year OHLCV data (2020-2024)
- ✅ ML Predictions: All trained models work with offline data (trained on 936 stocks with sufficient data)
- ✅ Search: Full-text search across 1,001 stocks
- ✅ Technical Indicators: 38 indicators calculated from historical data
- ❌ Live Prices: Requires API keys (Finnhub for US, Upstox for India)
- Python: 3.8 or higher
- Node.js: 16 or higher
- Git: For cloning repository
- RAM: 8GB minimum, 16GB recommended
- Storage: 15GB free space
# Using winget (Windows Package Manager)
winget install Python.Python.3.11
# Or download from python.org
# Verify installation
python --version# Using winget
winget install OpenJS.NodeJS
# Verify installation
node --version
npm --version# Clone repository
git clone https://github.com/ankit02327/stock-price.git
cd ml
# Backend setup
cd backend
python -m venv venv
venv\Scripts\activate
pip install -r requirements.txt
# Create empty .env file (REQUIRED)
type nul > .env
# Frontend setup (new terminal)
cd ../frontend
npm install/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"# Install Python and Node.js
brew install python@3.11 node
# Verify installations
python3 --version
node --version
npm --version# Clone repository
git clone https://github.com/ankit02327/stock-price.git
cd ml
# Backend setup
cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# Create empty .env file (REQUIRED)
touch .env
# Frontend setup (new terminal)
cd ../frontend
npm installsudo apt update# Install Python and pip
sudo apt install python3.11 python3.11-pip python3.11-venv
# Install Node.js
curl -fsSL https://deb.nodesource.com/setup_18.x | sudo -E bash -
sudo apt-get install -y nodejs
# Verify installations
python3 --version
node --version
npm --version# Clone repository
git clone https://github.com/ankit02327/stock-price.git
cd ml
# Backend setup
cd backend
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
# Create empty .env file (REQUIRED)
touch .env
# Frontend setup (new terminal)
cd ../frontend
npm installcd backend
venv\Scripts\activate # Windows
source venv/bin/activate # macOS/Linux
python main.pycd frontend
npm run devAccess: http://localhost:5173 (Backend: http://localhost:5000)
Indian Stocks (500): Use ISIN codes (INExxxxxxxx) at permanent/ind_stocks/
US Stocks (501): Use ticker symbols (AAPL, MSFT, etc.) at permanent/us_stocks/
Status: 4/7 trained (Linear Regression ✅, Decision Tree ✅, Random Forest ✅, SVM ✅)
# Train: python backend/training/basic_models/{model}/trainer.py
# Status: python status.py
# Details: See [Training Guide](documentation/TRAINING.md)Add API keys to backend/.env for real-time data (Finnhub for US, Upstox for India).
Guides: Offline Mode | Training Guide | Backend API | Upstox Integration
Check: python status.py | Test API: curl http://localhost:5000/health
Working: Data fetching, 5-year historical charts, search (1000+ stocks), currency conversion, interactive dashboard, 38 indicators, ML predictions (4/7 models trained)
| Issue | Solution |
|---|---|
| Backend won't start | Verify permanent/ directory exists and create empty .env file |
| Frontend build errors | Run rm -rf node_modules package-lock.json && npm install |
| Python import errors | Activate venv: venv\Scripts\activate (Windows) or source venv/bin/activate (macOS/Linux) |
| Out of memory | Close other apps, ensure 8GB+ RAM, train one model at a time |
- Educational Use: This application is for learning and research purposes only
- Investment Disclaimer: Stock predictions are inherently uncertain and should not be used as sole investment advice
- API Limits: Finnhub (60 calls/min free), Upstox tokens expire daily
- Offline First: System designed to work without internet or API keys
python status.py # Check status
python main.py # Start backend
npm run dev # Start frontend
