DataBridge Quant

Real-Time Crypto Risk Analytics • Pairs Trading • Backtesting • Market Intelligence

Built with Python, Streamlit, Plotly, Pandas, NumPy, Statsmodels, Binance API

Live Demo • 💻 GitHub Repo

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Overview

DataBridge Quant is a production-style quantitative analytics dashboard designed for cryptocurrency market monitoring, statistical arbitrage research, and portfolio risk analysis.

The platform provides real-time market intelligence, pair-trading opportunities, strategy backtesting, and advanced risk metrics through an elegant interactive dashboard.

It was built to simulate how modern hedge funds, fintech teams, and trading desks monitor markets and evaluate systematic strategies.

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Key Features

Live Market Dashboard

• BTC / ETH live or demo market feed
• Historical trend visualization
• Real-time pricing overview

Pair Trading Analytics

• BTC/ETH spread monitoring
• Z-score mean reversion signals
• Correlation analysis
• Trading opportunity detection

Risk Dashboard

• Annualized volatility
• 95% Daily Value at Risk (VaR)
• Maximum Drawdown
• Sharpe Ratio

Strategy Backtesting

• Mean reversion strategy engine
• Equity curve visualization
• Final return tracking
• Signal change count

Alerts Engine

• Volatility alerts
• Drawdown warnings
• Pair trading signal notifications
• Risk threshold monitoring

Custom Data Upload

• Upload your own CSV datasets
• Analyze custom BTC/ETH data instantly

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Product Screens

Dashboard Home

Add screenshot here

assets/dashboard-home.png

Risk Dashboard

Add screenshot here

assets/risk-dashboard.png

Pair Trading Signals

Add screenshot here

assets/pair-analysis.png

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🛠️ Tech Stack

Category	Tools
Frontend	Streamlit
Visualization	Plotly
Backend Logic	Python
Data Processing	Pandas, NumPy
Quant Models	Statsmodels, SciPy
Market Data	Binance Public API
Deployment	Streamlit Community Cloud

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Quant Models Used

Z-Score Mean Reversion

Measures how far current spread deviates from historical mean.

$$Z = \frac{X - \mu}{\sigma}$$

Used for pair trading signal generation.

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Sharpe Ratio

Measures risk-adjusted return.

$$Sharpe = \frac{R_p - R_f}{\sigma_p}$$

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Maximum Drawdown

Largest decline from equity peak.

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Value at Risk (VaR)

Estimated worst expected loss over a confidence level.

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Project Structure

DataBridge-Quant/
│── app.py
│── requirements.txt
│── README.md
│── .gitignore
│── data/
│   └── sample_crypto_data.csv
│── modules/
│   ├── __init__.py
│   ├── data_loader.py
│   ├── analytics.py
│   ├── risk.py
│   ├── backtesting.py
│   ├── alerts.py
│   ├── visualization.py
│   └── ui.py
│── .streamlit/
│   └── config.toml

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Run Locally

1 Clone Repo

git clone https://github.com/YOUR_USERNAME/DataBridge-Quant.git
cd DataBridge-Quant

2 Install Dependencies

pip install -r requirements.txt

3 Start App

streamlit run app.py

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Deployment

Hosted free on Streamlit Community Cloud.

https://your-streamlit-link.streamlit.app

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Example Use Cases

Recruiters / Hiring Managers

View production-ready Python dashboard skills.

Quant / Finance Roles

Showcase understanding of:

• Volatility
• Risk
• Drawdowns
• Strategy testing
• Statistical arbitrage

Data Analyst Roles

Demonstrates:

• Dashboarding
• Data storytelling
• KPI design
• Real-time data systems

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Why This Project Matters

Most student dashboards stop at charts.

DataBridge Quant goes further:

✅ Live API integration ✅ Finance domain knowledge ✅ Real quant metrics ✅ Strategy simulation ✅ Clean architecture ✅ Production deployment ✅ Strong UI/UX execution

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Future Enhancements

• Multi-asset portfolio optimizer
• LSTM price forecasting
• Options Greeks dashboard
• Real-time Telegram alerts
• TradingView integration
• AI-generated market commentary
• Multi-exchange data engine

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Author

Siyona B AI / Data / Quant Analytics Engineer

LinkedIn • GitHub

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If You Like This Project

Please star the repository and share feedback.

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