{"id":27134632,"url":"https://github.com/neuraladitya/trade_predictor_project","last_synced_at":"2026-04-12T00:38:40.782Z","repository":{"id":286692022,"uuid":"962235661","full_name":"NeuralAditya/Trade_Predictor_Project","owner":"NeuralAditya","description":"An AI-powered trade prediction system using machine learning, technical analysis, and time series models. 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Trade Prediction Project\n\n[![Build Status](https://img.shields.io/badge/build-passing-brightgreen)](#)\n[![Python](https://img.shields.io/badge/python-3.10%2B-blue.svg)](https://www.python.org/downloads/)\n[![Node](https://img.shields.io/badge/node-18%2B-green.svg)](https://nodejs.org/)\n[![License](https://img.shields.io/badge/license-MIT-lightgrey)](LICENSE)\n\n**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.\n\nBuilt 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**.\n\n## 🔍 Core Features\n\n- 🔗 **FastAPI Backend**: Lightweight and high-performance API for fast data processing and model predictions.\n- ⚛️ **React + Vite Frontend**: Ultra-fast UI built with Vite, React, TailwindCSS, and ShadCN.\n- 📈 **ML Algorithms**: Random Forest, ARIMA, and Markov Switching models for robust predictions.\n- 🔧 **Fourier Transform Analysis**: Extracts frequency-domain features to capture cyclic trends in data.\n- 🌊 **Wavelet Transform**: Multi-resolution analysis to uncover short-term vs long-term volatility patterns.\n- 📡 **Kalman Filter**: Smooths noisy market signals and estimates hidden state trends.\n- 📐 **Topological Data Analysis (TDA)**: Captures shape and structure of time-series data using persistence diagrams.\n- 🧮 **Technical Indicators**: Includes RSI, MACD, EMA, Bollinger Bands, and more.\n- 🎯 **Dimensionality Reduction**: Uses PCA and t-SNE for compressing and visualizing high-dimensional features.\n- 🖼️ **Live Graphs**: UI displays prediction results and historical performance in interactive charts.\n- 🧾 **CSV Upload \u0026 Visualization**: Upload any stock OHLCV CSV and view results instantly.\n\n## ⚡ Use Cases\n\n- Short-term \u0026 long-term stock trend forecasting\n- Backtesting and model evaluation\n- Educational tool for data science and trading students\n- Research into hybrid models and multi-signal strategies\n---\n\n## 📸 Frontend Screenshot\n\n\u003cdiv align=\"center\"\u003e\n  \u003cimg src=\"frontend/public/screenshot.png\" alt=\"Trade Predictor UI\" width=\"700\"/\u003e\n\u003c/div\u003e\n\n---\n\n## 📁 Project Structure\n\n```bash\nTrade_Predictor_Project/\n│\n├── backend/\n│   ├── api/\n│   │   └── predict.py              # Main prediction endpoint logic\n│   ├── models/\n│   │   └── train_model.py          # (Optional) Re-train ML models\n│   ├── utils/\n│   │   └── helpers.py              # (Optional) Any helper functions\n│   ├── __init__.py\n│   ├── main.py                     # FastAPI entrypoint\n│   └── requirements.txt           # Backend dependencies\n│\n├── frontend/\n│   ├── src/\n│   │   └── TradePredictApp.tsx     # UI for file upload and results\n│   ├── public/\n│   │   └── screenshot.png          # UI screenshot image\n│   ├── package.json\n│   ├── postcss.config.js\n│   ├── tailwind.config.js\n│   ├── vite.config.ts\n│   └── tsconfig.json\n│\n├── docker-compose.yml\n├── README.md\n└── .gitignore\n```\n\n---\n\n## 🚀 Getting Started\n\n### 🧠 Backend (FastAPI)\n\n```bash\ncd backend\npython -m venv venv\nsource venv/bin/activate         # On Windows: venv\\Scripts\\activate\npip install -r requirements.txt\nuvicorn main:app --reload\n```\n\nBackend will be running at: [http://localhost:8000](http://localhost:8000)\n\n---\n\n### 💻 Frontend (Vite + React)\n\n```bash\ncd frontend\nnpm install\nnpm run dev\n```\n\nFrontend will be running at: [http://localhost:5173](http://localhost:5173)\n\nEnsure the backend is also running for full functionality.\n\n---\n\n## 📤 API Endpoint\n\n### `POST /api/predict`\n\nUpload a `.csv` file with the following required columns:\n\n```\nOpen, High, Low, Close, Volume\n```\n\n#### ✅ Example Response\n\n```json\n{\n  \"accuracy\": 0.8123,\n  \"confusion_matrix\": [[100, 20], [15, 80]]\n}\n```\n\n---\n\n## 🐳 Docker (Run Full Stack)\n\n```bash\ndocker-compose up --build\n```\n\n\u003e Make sure Docker is installed and running before executing.\n\n---\n\n## ✅ Requirements\n\n- Python 3.10+\n- Node.js 18+\n- Docker (optional)\n\n---\n\n## 📄 License\n\nMIT © 2025 NeuralAditya\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneuraladitya%2Ftrade_predictor_project","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fneuraladitya%2Ftrade_predictor_project","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fneuraladitya%2Ftrade_predictor_project/lists"}