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CryptoPredict 🔮

AI-powered cryptocurrency price forecasting with an active learning feedback loop.

CI Python FastAPI React TensorFlow License

✨ Features

Feature Description
🧠 LSTM Model Multi-layer LSTM trained on historical price data with RSI, Bollinger Bands, and MA indicators
📈 7/14-day Forecast Autoregressive multi-step price prediction with confidence scoring
💬 Active Learning User feedback loop that tracks model accuracy and triggers retraining
📊 Live Metrics MAE, RMSE, and accuracy dashboard updated in real time
🔄 Auto-Retrain Background retraining triggered after every 50 feedback entries
🌐 CoinGecko API Free-tier market data — no API key required
🐳 Docker Single-command full-stack deployment

📸 Screenshots

Add screenshots here after running the app locally.

🏗 Architecture

cryptopredict/
├── backend/
│   ├── main.py              # FastAPI application & REST endpoints
│   ├── train_model.py       # LSTM training pipeline (run once before API)
│   ├── requirements.txt
│   ├── Dockerfile
│   └── tests/
│       └── test_api.py
├── frontend/
│   ├── src/
│   │   ├── App.jsx          # Main React application
│   │   ├── utils/api.js     # Typed API client
│   │   ├── hooks/           # Custom React hooks
│   │   └── index.css        # Global styles
│   ├── Dockerfile
│   ├── nginx.conf
│   └── vite.config.js
├── docker-compose.yml
└── .github/workflows/ci.yml

🚀 Quick Start

Prerequisites

  • Python 3.11+
  • Node.js 20+
  • (Optional) Docker & Docker Compose

Option 1 — Docker (recommended)

git clone https://github.com/Sm3th/cryptopredict.git
cd cryptopredict

# 1. Train the model first (runs outside Docker to save the .keras file)
cd backend
pip install -r requirements.txt
python train_model.py --coin bitcoin --days 730

# 2. Spin up everything
cd ..
docker-compose up --build

Open http://localhost for the app, http://localhost:8000/docs for Swagger UI.

Option 2 — Local development

Backend

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

# Train the model (required before starting the API)
python train_model.py --coin bitcoin --days 730

# Start API
uvicorn main:app --reload --port 8000

Frontend

cd frontend
npm install
npm run dev
# → http://localhost:5173

🧠 Model Details

Architecture

Input (60 × 1)
   ↓
LSTM(128) → Dropout(0.2)
   ↓
LSTM(64)  → Dropout(0.2)
   ↓
LSTM(32)  → Dropout(0.2)
   ↓
Dense(16, relu)
   ↓
Dense(1)  ← price prediction

Features used

Feature Description
price Daily closing price (normalized 0–1)
MA_7 / MA_21 / MA_50 Moving averages
RSI Relative Strength Index (14-period)
BB_upper / BB_lower Bollinger Bands (20-period, ±2σ)
volatility 7-day rolling std
momentum price − price[−10]

Training configuration

Parameter Value
Lookback window 60 days
Train / Test split 80 / 20
Epochs 50 (early stopping, patience=10)
Batch size 32
Optimiser Adam
Loss MSE

🔌 API Reference

Method Endpoint Description
GET / Health check
GET /api/health Detailed system status
POST /api/predict Run price forecast
POST /api/feedback Submit prediction feedback
POST /api/retrain Trigger background retraining
GET /api/metrics Model performance metrics
GET /api/feedback/history All feedback records
GET /api/coins Supported cryptocurrencies

Full interactive documentation: http://localhost:8000/docs

Example — Predict

curl -X POST http://localhost:8000/api/predict \
  -H "Content-Type: application/json" \
  -d '{"coin_id": "bitcoin", "days": 7}'
{
  "coin_id": "bitcoin",
  "current_price": 67450.00,
  "predictions": [
    { "day": 1, "date": "2026-05-28", "price": 68120.50, "change_percentage": 0.99 },
    ...
  ],
  "confidence": 0.70,
  "sentiment": "Bullish",
  "sentiment_score": 72.4,
  "model_accuracy": 68.0
}

Example — Feedback

curl -X POST http://localhost:8000/api/feedback \
  -H "Content-Type: application/json" \
  -d '{
    "prediction_id": "bitcoin_1234567890",
    "actual_price": 68000.00,
    "predicted_price": 68120.50,
    "date": "2026-05-28",
    "is_accurate": true
  }'

🧪 Tests

cd backend
pip install pytest httpx anyio
pytest tests/ -v

⚙️ Environment Variables

Variable Default Description
ALLOWED_ORIGINS http://localhost:5173,... CORS allowed origins
MODEL_PATH crypto_lstm_model.keras Path to trained model
SCALER_PATH scaler.pkl Path to fitted scaler
VITE_API_URL /api API base URL (frontend)

🗺 Roadmap

  • Multi-coin comparison view
  • Sentiment analysis from Twitter/Reddit
  • Email alerts for large predicted swings
  • Model versioning & rollback
  • PostgreSQL for persistent feedback storage

⚠️ Disclaimer

This project is built for educational purposes only. Cryptocurrency markets are highly volatile. Do not make financial decisions based on these predictions.

📄 License

MIT © 2026 — İsmet Organ