📈 StockMarketPrediction

AI-Powered Indian Stock Market Forecasting Platform

[![Python](https://img.shields.io/badge/Python-3.10+-3776AB?style=for-the-badge&logo=python&logoColor=white)](https://python.org) [![Flask](https://img.shields.io/badge/Flask-2.x-000000?style=for-the-badge&logo=flask&logoColor=white)](https://flask.palletsprojects.com) [![SQLAlchemy](https://img.shields.io/badge/SQLAlchemy-ORM-red?style=for-the-badge)](https://www.sqlalchemy.org/) [![Gemini AI](https://img.shields.io/badge/Gemini-AI-4285F4?style=for-the-badge&logo=google&logoColor=white)](https://ai.google.dev/) [![License](https://img.shields.io/badge/License-MIT-22c55e?style=for-the-badge)](LICENSE) > A full-stack web application that predicts NSE/BSE stock prices using adaptive Machine Learning, > news sentiment analysis, and Gemini AI — with real-time alerts, user watchlists, and an admin dashboard.

--- ## ✨ Key Features | Feature | Description | |---|---| | 🤖 **Adaptive ML Engine** | Self-improving prediction models with accuracy tracking across algorithm versions | | 📰 **News Sentiment Analysis** | Scores news headlines as positive / negative / neutral to influence predictions | | 💎 **Gemini AI Integration** | Google Gemini AI powers advanced stock analysis and insights | | 🔔 **Smart Alerts System** | Real-time BUY / SELL / HIGH RISK / BULLISH alerts with email notifications | | 👤 **User Auth + OTP** | Secure registration, login, and password reset via email OTP | | 📋 **Watchlist** | Per-user watchlists with per-stock notification preferences | | 🛡️ **Admin Dashboard** | Manage stocks, users, email config, and model training from one panel | | 📊 **Technical Indicators** | SMA, EMA, RSI, MACD computed and stored for every stock | | 🇮🇳 **Indian Market Focus** | Pre-loaded with TCS, Reliance, HDFC Bank, Infosys, Wipro, SBI & more | --- ## 🏗️ Project Structure ``` StockMarketPrediction/ │ ├── app.py # Application factory — wires everything together ├── run.py # Entry point to start the server ├── run_adaptive_monitor.py # Background adaptive ML monitoring service ├── models.py # SQLAlchemy DB models (User, Stock, Prediction, Alert...) ├── config.py # App configuration (dev / prod) ├── configure_email.py # Email setup utility ├── requirements.txt # Python dependencies │ ├── routes/ # Flask Blueprints │ ├── auth.py # /auth — Login, register, OTP, password reset │ ├── user.py # /user — Home, watchlist, notifications │ ├── admin.py # /admin — Dashboard, stock & user management │ └── api.py # /api — JSON endpoints for charts & predictions │ ├── services/ # Business logic layer │ ├── ml_models/ │ └── adaptive/ # Adaptive self-improving ML models │ ├── utils/ │ ├── email_service.py # Flask-Mail wrapper │ ├── gemini_ai.py # Google Gemini AI client │ └── scheduler.py # Background job scheduler │ ├── templates/ # Jinja2 HTML templates │ └── errors/ # 404 / 500 error pages │ ├── static/ # CSS, JS, images ├── data/ # Raw & processed stock data ├── uploads/ # User-uploaded files └── .gitignore ``` --- ## 🗄️ Database Models The app uses **Flask-SQLAlchemy** with these core models: | Model | Purpose | |---|---| | `User` | Auth, roles (admin/user), email notifications | | `Stock` | NSE/BSE symbols, sectors, model version tracking | | `HistoricalData` | OHLCV + SMA, EMA, RSI, MACD per stock per day | | `Prediction` | ML predictions with confidence, trend, recommendation | | `NewsSentiment` | Headline sentiment scores linked to stocks | | `Watchlist` / `UserWatchlist` | Per-user stock tracking with notification toggle | | `StockAlert` | System-generated BUY/SELL/HIGH RISK alerts | | `Notification` | User notification history | | `ModelTraining` | Training logs — accuracy, MAE, RMSE per version | | `OTP` | Email OTP for registration and password reset | | `EmailConfig` | Admin-configurable SMTP settings | | `MarketStatus` | NSE market open/close calendar | --- ## ⚡ Getting Started ### Prerequisites - Python 3.10+ - pip - A Gmail (or SMTP) account for email features - Google Gemini API key _(optional but recommended)_ ### 1 · Clone the Repository ```bash git clone https://github.com/ganesha-raut/StockMarketPrediction.git cd StockMarketPrediction ``` ### 2 · Create Virtual Environment ```bash python -m venv venv # macOS / Linux source venv/bin/activate # Windows venv\Scripts\activate ``` ### 3 · Install Dependencies ```bash pip install -r requirements.txt ``` ### 4 · Configure the App Edit `config.py` and set: ```python SECRET_KEY = 'your-secret-key' GEMINI_API_KEY = 'your-gemini-api-key' # optional MAIL_USERNAME = 'your@gmail.com' MAIL_PASSWORD = 'your-app-password' ``` ### 5 · Run the Server ```bash python run.py ``` > 🟢 App live at **`http://127.0.0.1:5000`** > 🔑 Default admin: `admin@stockai.com` / `admin123` _(change immediately!)_ ### 6 · Start the Adaptive Monitor _(optional)_ ```bash python run_adaptive_monitor.py ``` Runs the background ML monitoring service that re-evaluates prediction accuracy and retrains models automatically. --- ## 🤖 Machine Learning Pipeline ``` Raw yfinance Data │ ▼ Feature Engineering (SMA, EMA, RSI, MACD, Volume, Sentiment Score) │ ▼ Adaptive ML Models ──► Accuracy Tracker (ml_models/adaptive/) │ │ Auto-retrain if ▼ accuracy drops Prediction + Confidence (trend: bullish / bearish / neutral) │ ▼ Recommendation: BUY / SELL / HOLD + Smart Alerts ``` **Pre-loaded Indian Stocks:** `TCS · RELIANCE · HDFCBANK · INFY · ICICIBANK · HINDUNILVR · ITC · SBIN · BHARTIARTL · WIPRO` --- ## 🛣️ API Endpoints | Method | Route | Description | |---|---|---| | `GET` | `/` | Landing page / redirect | | `GET` | `/health` | Health check | | `POST` | `/auth/login` | Login | | `POST` | `/auth/register` | Register with OTP | | `GET` | `/user/home` | User dashboard | | `GET` | `/admin/dashboard` | Admin panel | | `GET` | `/api/...` | JSON data for charts & predictions | --- ## 🔔 Alert Types | Alert | Severity | Trigger | |---|---|---| | `BULLISH_OPPORTUNITY` | 🟢 opportunity | Strong upward prediction | | `HIGH_RISK` | 🔴 critical | High predicted downside | | `TARGET_REACHED` | 🔵 info | User's target price hit | | `DROP_ALERT` | 🟡 warning | Price drop exceeds threshold | --- ## 🤝 Contributing 1. Fork the repo 2. Create your feature branch: `git checkout -b feature/amazing-feature` 3. Commit your changes: `git commit -m 'Add amazing feature'` 4. Push to the branch: `git push origin feature/amazing-feature` 5. Open a Pull Request --- ## 📄 License This project is open-source under the **[MIT License](LICENSE)**. ---

Made with ❤️ by [Ganesha Raut](https://github.com/ganesha-raut) ⭐ **Star this repo if you found it useful!** ⭐

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