🚀 WarpCrypto-Trader v2.0

🎯 Overview

WarpCrypto-Trader is an advanced AI-powered cryptocurrency analysis platform that provides real-time predictions, technical analysis, and trading strategy comparisons for Bitcoin, Ethereum, and Solana. Perfect for creating engaging content for social media platforms like X.com (Twitter)!

✨ Features

🤖 AI-Powered Predictions

• LSTM/GRU Hybrid Models - Advanced deep learning architecture for accurate price predictions
• Multi-Horizon Forecasting - 1-day, 3-day, and 7-day price predictions
• AI Confidence Score - Measures prediction consistency and reliability
• Per-Crypto Models - Separately trained models for BTC, ETH, and SOL

📊 Technical Analysis

• Real-time Data - Live cryptocurrency data from Yahoo Finance
• 25+ Technical Indicators - RSI, MACD, Bollinger Bands, Moving Averages, and more
• Interactive RSI Gauge - Visual representation of market conditions
• Dynamic Price Charts - Candlestick charts with volume analysis

🏆 Strategy Leaderboard

• Multiple Trading Strategies - RSI, MACD, Bollinger Bands, and combined strategies
• Backtesting Engine - Historical performance analysis
• Win Rate & Returns - Comprehensive strategy metrics
• Real-time Comparison - Find the best strategy for current market conditions

🎨 Modern UI/UX

• Glassmorphism Design - Beautiful, transparent card layouts
• Dynamic Color Themes - Each cryptocurrency has its unique color scheme
• Responsive Layout - Works perfectly on all screen sizes
• Dark Mode - Eye-friendly interface for extended use

🛠️ Installation

Prerequisites

• Python 3.10 or higher
• pip package manager
• Git

Step 1: Clone the Repository

git clone https://github.com/turtir-ai/WarpCrypto-Trader.git
cd WarpCrypto-Trader

Step 2: Install Dependencies

pip install -r requirements.txt

Step 3: Train the Models (Optional)

Train models for all cryptocurrencies:

Windows PowerShell:

.\train_all_models.ps1

Linux/Mac:

chmod +x train_all_models.sh
./train_all_models.sh

Or train individually:

python train_model_fixed.py --ticker BTC-USD
python train_model_fixed.py --ticker ETH-USD
python train_model_fixed.py --ticker SOL-USD

Step 4: Launch the Application

streamlit run app_v2.py

The application will open in your browser at http://localhost:8501

📁 Project Structure

WarpCrypto-Trader/
│
├── app_v2.py                  # Main Streamlit application (English version)
├── data_processor.py          # Data fetching and processing module
├── train_model_fixed.py       # LSTM/GRU model training script
├── strategy_backtester.py     # Trading strategy backtesting engine
│
├── train_all_models.ps1       # Windows batch training script
├── train_all_models.sh        # Linux/Mac batch training script
│
├── models/                    # Trained models directory (auto-created)
│   ├── btc_usd_predictor_improved.keras
│   ├── eth_usd_predictor_improved.keras
│   └── sol_usd_predictor_improved.keras
│
├── requirements.txt           # Python dependencies
├── LICENSE                    # MIT License
└── README.md                  # This file

🎮 Usage Guide

1. Select Cryptocurrency

Use the sidebar dropdown to choose between Bitcoin, Ethereum, or Solana.

2. Train Models (First Time)

Click the "Train Model" button in the sidebar if the model hasn't been trained yet.

3. Customize Display

• RSI Gauge Indicator - Toggle the visual RSI gauge
• Strategy Leaderboard - Show/hide strategy comparison table
• AI Confidence Score - Display prediction reliability metric

4. Analyze Results

• Check current price and trend
• Review AI predictions for different time horizons
• Compare trading strategy performances
• Examine technical indicators

📹 Content Creation Tips

Perfect for creating engaging content on X.com (Twitter):

Video Ideas

1. "AI Predicts Crypto Prices!" - Show AI predictions with confidence scores
2. "Best Trading Strategy Revealed" - Highlight the strategy leaderboard
3. "Bitcoin vs Ethereum vs Solana" - Quick comparison between cryptos
4. "RSI Alert!" - Focus on the RSI gauge when it's in extreme zones

Recording Tips

• Use dark theme for better visuals
• Slow transitions between sections
• Highlight AI confidence scores
• Show strategy performance comparisons
• Zoom in on key metrics

🔧 Configuration

Modify Cryptocurrencies

Edit the CRYPTO_OPTIONS dictionary in app_v2.py:

CRYPTO_OPTIONS = {
    'Bitcoin (BTC)': 'BTC-USD',
    'Ethereum (ETH)': 'ETH-USD',
    'Solana (SOL)': 'SOL-USD',
    # Add more here
}

Adjust Model Parameters

In train_model_fixed.py, modify:

predictor = ImprovedCryptoPredictor(
    ticker=ticker,
    sequence_length=30,  # Days of historical data
    prediction_horizons=[1, 3, 7]  # Prediction days
)

Change Analysis Period

Default periods can be modified in the sidebar options.

📊 Technical Indicators

The platform calculates and displays:

• Price Metrics: Open, High, Low, Close, Volume
• Moving Averages: SMA (10, 30, 50, 200), EMA (12, 26)
• Momentum: RSI, MACD, Stochastic Oscillator
• Volatility: Bollinger Bands, ATR
• Volume: OBV, Volume Ratio
• Custom: Support/Resistance Levels, Trend Direction

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Yahoo Finance - Real-time cryptocurrency data
• Streamlit - Amazing web app framework
• TensorFlow - Deep learning models
• Plotly - Interactive visualizations
• TA-Lib - Technical analysis indicators

⚡ Powered by Turtir-AI

This project is powered by Turtir-AI - Advanced AI Solutions for Trading and Analysis.

📧 Contact

For questions, suggestions, or collaborations:

• Twitter: @turtirai
• GitHub: Turtir-AI

---

Made with ❤️ for the crypto community
⭐ Star this repo if you find it helpful!