🏹 AetherQuant AI: Advanced Crypto Predictive Engine

A professional, machine learning-powered predictive dashboard built with Python and Streamlit. This tool utilizes an XGBoost Classifier and 24 technical indicators to predict Bitcoin (BTC-USD) price movements with high-precision temporal awareness.

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🚀 Live Demo

Click here to try the Live App

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📺 Demo Preview

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✨ Features

• AI-Powered Signals: Uses an XGBoost pipeline to classify market trends into "BUY" or "SELL" signals.
• Feature Engineering: Real-time calculation of 24 indicators including RSI, MACD, Bollinger Bands, and VWAP.
• Temporal Awareness: Incorporates time-series features (Hour, Day of Week) to capture cyclical market patterns.
• Interactive Visualizations: High-fidelity price charts and indicator overlays powered by Plotly.
• Live Market Data: Direct integration with the Yahoo Finance API (yfinance) for up-to-the-minute accuracy.

🛠️ Tech Stack

• Language: Python 3.13
• Framework: Streamlit (Web UI)
• Machine Learning: Scikit-learn & XGBoost
• Data Handling: Pandas & NumPy
• Visualization: Plotly Graph Objects
• Deployment: Streamlit Community Cloud

🚀 Installation & Local Setup

1. Clone the repository:

   git clone [https://github.com/ali-faraz-py/AetherQuant](https://github.com/ali-faraz-py/AetherQuant)
   cd AetherQuant
   

2. Install dependencies:

    pip install -r requirements.txt
   

3. Run the application:

    streamlit run app.py
   

📂 Project Structure

AetherQuant/
├── app.py              # Streamlit Web Application and UI logic
├── engine.py           # Technical indicator and data processing engine
├── train_model.py      # Model training, feature engineering, and validation
├── aether_model.pkl    # Pre-trained XGBoost Pipeline (24 features)
├── requirements.txt    # Project dependencies
├── .gitignore          # Prevents tracking of temporary files
└── .gitattributes      # LFS tracking for the model file

🧠 Model Insights

The model is trained on 730 days of hourly data and currently achieves a 66.81% accuracy rate on unseen test sets.

The engine analyzes 24 unique dimensions including Trend, Volatility, Momentum, and Volume-Weighted indicators to minimize false signals in volatile crypto markets.

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👤 Author

Syed Ali Faraz - GitHub Profile

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