📈 QuantPair

A dashboard for pair trading strategy system 📈📊 — visualize, analyze, and manage statistically paired assets.

A complete end-to-end dashboard built with a frontend UI and backend logic to support pairs trading strategies in financial markets.

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🧠 About

QuantPair is an interactive Pairs Trading Dashboard that helps you:

✔ Explore statistical relationships between asset pairs
✔ Generate trading signals using classic quantitative finance techniques
✔ Track performance and visualize entry/exit opportunities

A perfect tool for traders, quants, and data-driven investors!

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📦 Features

Feature	Description
📊 Interactive Dashboard	Frontend UI to visualize price spreads and signals
📈 Pairs Strategy Logic	Compute co-integration and trading signals
🔄 Backend API	Serve data and analytics securely
🧪 Strategy Testing	Test historic data to validate signals
📁 Modular Architecture	Separate frontend and backend projects

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🛠 Tech Stack

Layer	Technology
Frontend	JavaScript / React
Backend	Python (FastAPI / Flask / Pandas / Numpy / Yfinance)
Visualization	Chart.js
Deployment	Vercel / Render/ Cloud Hosting

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📁 Project Structure

QuantPair/
├── backend/ 
      ├── api.py
      ├── backtest.py
      ├── data_acquisition.py
      ├── hedge_ratio.py
      ├── pair_selection.py
      ├── risk_management.py
      ├── run.py
      ├── spread_signal.py
      ├── trading_rules.py
      ├── visualization_data.py
      ├── visualization.py
      └── requirements.txt             
├── frontend/quantpair/
        ├── public/
        ├── src/
            ├── pages/
            ├── api/
            └── components/
        └── index.html            
└── README.md               

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⚙️ Installation & Setup

📥 Clone the Repository

git clone https://github.com/Arijit2175/QuantPair.git
cd QuantPair

🧠 Backend Setup

cd backend
pip install -r requirements.txt

🎨 Frontend Setup

cd frontend/quantpair
npm install

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👀 Preview

A quick look at the QuantPair Dashboard and its features 📊🚀

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🖥️ Dashboard View

Feature	Preview
📈 Pair Price Chart
📊 Spread & Z-Score

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🧪 Strategy Signal Example

Asset A	Asset B	Z-Score	Signal
KO	PEP	1.57	📉 Sell KO / 📈 Buy PEP

📌 Interpretation

• 🥤 KO (Coca-Cola) is overpriced relative to PEP
• 🥤 PEP (Pepsi) is underpriced relative to KO
• 📊 Z-score of 1.57 indicates a divergence from the mean
• ⚡ Strategy suggests:
  • Short (Sell) KO
  • Long (Buy) PEP

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📊 Metrics Evaluated

QuantPair evaluates multiple statistical and trading metrics to identify strong asset pairs and generate reliable trading signals.

📈 1. Price Spread

What it is:
The difference between the prices of two assets.

How it is calculated: Spread = Price(A) − Price(B)

Purpose:
Helps measure how far apart two related assets have moved from each other.

📊 2. Z-Score of Spread

What it is:
A normalized value indicating how far the current spread deviates from its historical mean.

How it is calculated: Z-score = (Spread − Mean Spread) / Standard Deviation

Purpose:

• Identifies overvaluation and undervaluation
• Used to generate buy/sell signals
• Higher absolute value ⇒ stronger divergence

🔗 3. Cointegration Score

What it is:
A statistical test that checks if two assets move together in the long run.

How it is evaluated:

• Uses historical price series
• Applies cointegration test (e.g., Engle-Granger)

Purpose:
Ensures only statistically related pairs are traded
Reduces false signals

📉 4. Correlation

What it is:
Measures short-term linear relationship between two assets.

How it is calculated: Correlation = cov(A, B) / (std(A) × std(B))

Purpose:

• Filters weakly related asset pairs
• Confirms similarity in price movement

⚡ 5. Trading Signal

What it is:
Final action recommendation based on Z-score thresholds.

How it is generated:

Z-Score Range	Signal
Z > +Threshold	📉 Sell Asset A / 📈 Buy Asset B
Z < -Threshold	📈 Buy Asset A / 📉 Sell Asset B
Between limits	⏸️ Hold

🧪 6. Strategy Performance Metrics

Metric	Purpose
📈 Total Return	Measures profit from strategy
📉 Max Drawdown	Measures worst loss
📊 Sharpe Ratio	Risk-adjusted return
🔁 Win Rate	Percentage of profitable trades

🎯 Why These Metrics?

These metrics help ensure:

• 📉 Mean reversion behavior
• 📊 Statistical validity
• ⚡ Actionable trading signals
• 🧠 Data-driven decisions

All calculations are based on historical price data and updated dynamically within the dashboard.

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🌐 Deployment

QuantPair is already deployed and accessible online 🎉

🔗 Live Application:
👉 https://quant-pair.vercel.app/

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📚 References

1. J. Sen, “Designing Efficient Pair-Trading Strategies Using Cointegration for the Indian Stock Market,” IEEE International Conference ASIANCON'22, Pune, India, 2022.
2. X. Law, W. Li & P. Yu, “A single-stage approach for cointegration-based pairs trading,” Finance Research Letters, vol. 26, pp. 177–184, 2018.
3. Y. Ti, T. Dai, K. Wang, et al., “Improving Cointegration-Based Pairs Trading Strategy with Asymptotic Analyses and Convergence Rate Filters,” Computational Economics, vol. 64, pp. 2717–2745, Jan. 2024.
4. M. Clegg & C. Krauss, “Pairs trading with partial cointegration,” Quantitative Finance, vol. 18(1), pp. 121–138, 2017.
5. H. Xing, “A singular stochastic control approach for optimal pairs trading with proportional transaction costs,” arXiv, Nov. 2019.
6. G. Zhang, “Pairs Trading with Nonlinear and Non-Gaussian State Space Models,” arXiv, May 2020.
7. Masood Tadi & Irina Kortchemski, “Evaluation of dynamic cointegration-based pairs trading strategy in the cryptocurrency market,” Studies in Economics and Finance, vol. 38, no. 5, 2021.
8. “Cointegration-based pairs trading: identifying and exploiting similar exchange-traded funds,” Journal of Asset Management, Springer, 2025.
9. C. Ungever, “Pairs Trading to the Commodities Futures Market Using Cointegration Method,” International Journal of Commerce and Finance, vol. 1, no. 1, 2015.

👨‍💻 Developed by – @Arijit2175