🧠 NeuralAditya - Trade Prediction Project

Trade Prediction Project is an advanced and modular trade prediction system that combines state-of-the-art machine learning and signal processing techniques to provide highly accurate stock trend forecasts.

Built with a powerful FastAPI backend and a modern Vite + React + TailwindCSS frontend, this full-stack application is optimized for real-time interaction and predictive insight delivery.

🔍 Core Features

• 🔗 FastAPI Backend: Lightweight and high-performance API for fast data processing and model predictions.
• ⚛️ React + Vite Frontend: Ultra-fast UI built with Vite, React, TailwindCSS, and ShadCN.
• 📈 ML Algorithms: Random Forest, ARIMA, and Markov Switching models for robust predictions.
• 🔧 Fourier Transform Analysis: Extracts frequency-domain features to capture cyclic trends in data.
• 🌊 Wavelet Transform: Multi-resolution analysis to uncover short-term vs long-term volatility patterns.
• 📡 Kalman Filter: Smooths noisy market signals and estimates hidden state trends.
• 📐 Topological Data Analysis (TDA): Captures shape and structure of time-series data using persistence diagrams.
• 🧮 Technical Indicators: Includes RSI, MACD, EMA, Bollinger Bands, and more.
• 🎯 Dimensionality Reduction: Uses PCA and t-SNE for compressing and visualizing high-dimensional features.
• 🖼️ Live Graphs: UI displays prediction results and historical performance in interactive charts.
• 🧾 CSV Upload & Visualization: Upload any stock OHLCV CSV and view results instantly.

⚡ Use Cases

• Short-term & long-term stock trend forecasting
• Backtesting and model evaluation
• Educational tool for data science and trading students
• Research into hybrid models and multi-signal strategies

📸 Frontend Screenshot

📁 Project Structure

Trade_Predictor_Project/
│
├── backend/
│   ├── api/
│   │   └── predict.py              # Main prediction endpoint logic
│   ├── models/
│   │   └── train_model.py          # (Optional) Re-train ML models
│   ├── utils/
│   │   └── helpers.py              # (Optional) Any helper functions
│   ├── __init__.py
│   ├── main.py                     # FastAPI entrypoint
│   └── requirements.txt           # Backend dependencies
│
├── frontend/
│   ├── src/
│   │   └── TradePredictApp.tsx     # UI for file upload and results
│   ├── public/
│   │   └── screenshot.png          # UI screenshot image
│   ├── package.json
│   ├── postcss.config.js
│   ├── tailwind.config.js
│   ├── vite.config.ts
│   └── tsconfig.json
│
├── docker-compose.yml
├── README.md
└── .gitignore

🚀 Getting Started

🧠 Backend (FastAPI)

cd backend
python -m venv venv
source venv/bin/activate         # On Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn main:app --reload

Backend will be running at: http://localhost:8000

💻 Frontend (Vite + React)

cd frontend
npm install
npm run dev

Frontend will be running at: http://localhost:5173

Ensure the backend is also running for full functionality.

📤 API Endpoint

POST /api/predict

Upload a .csv file with the following required columns:

Open, High, Low, Close, Volume

✅ Example Response

{
  "accuracy": 0.8123,
  "confusion_matrix": [[100, 20], [15, 80]]
}

🐳 Docker (Run Full Stack)

docker-compose up --build

Make sure Docker is installed and running before executing.

✅ Requirements

• Python 3.10+
• Node.js 18+
• Docker (optional)

📄 License

MIT © 2025 NeuralAditya