🤖 Tech Giants Quant Dashboard

A minimalist Quantitative Finance Dashboard built with Streamlit. It optimizes a portfolio of "Tech Giant" stocks using Hierarchical Risk Parity (HRP) and compares its performance against the S&P 500 (SPY).

🚀 Features

• Quantitative Engine: Uses PyPortfolioOpt to perform HRP optimization.
• Live Data: Fetches 2-year historical data using yfinance (with caching).
• Interactive Viz: Plotly charts for Asset Allocation, Cluster Dendrograms, and Cumulative Returns.
• Lightweight: Designed to run on minimal resources (e.g., free tier VPS with 1GB RAM).

🛠️ Stack

• Python 3.10+
• Streamlit: UI Framework.
• yfinance: Market Data.
• PyPortfolioOpt: Portfolio Optimization.
• Plotly: Visualization.

📦 Deployment Instructions

1. VPS Setup (Debian/Ubuntu)

Update your system and install Python/Pip.

sudo apt update && sudo apt upgrade -y
sudo apt install python3-pip python3-venv git -y

🤖 Tech Giants HRP Portfolio Dashboard

A full-stack quantitative finance dashboard that constructs a Hierarchical Risk Parity (HRP) portfolio of top Tech stocks. It features a FastAPI backend for robust calculation and a React (Vite) frontend for a modern, responsive UI.

🚀 Key Features

• HRP Optimization: Uses PyPortfolioOpt to build a risk-parity portfolio, robust to market noise.
• Modern Interactive UI: React + Recharts + TailwindCSS.
• Automated Updates: Dedicated scheduler service keeps market data fresh daily (Rolling Window).
• Persistence: SQLite database (data/portfolio.db) stores history and allocations.
• Dockerized: One-command deployment for the entire stack.

🛠️ Tech Stack

• Frontend: React, TypeScript, Vite, TailwindCSS, Recharts.
• Backend: Python, FastAPI, Pandas, PyPortfolioOpt, yfinance.
• Database: SQLite (persisted via Docker volumes).
• DevOps: Docker & Docker Compose.

📂 Project Structure

├── backend/            # FastAPI App & Worker
│   ├── main.py         # API Entrypoint
│   ├── scheduler.py    # Background Updater
│   ├── logic.py        # Core Quant Logic
│   └── database.py     # SQLite Handler
├── frontend/           # React App
│   ├── src/            # Components & Pages
│   └── Dockerfile      # Multi-stage Nginx build
├── data/               # Persistent Data Storage
├── compose.yml         # Full Stack Orchestration
└── analysis.ipynb      # Research Notebook (Backtesting)

🌍 Deployment

The application is designed to be deployed on a VPS using Docker.

1. Prerequisites

• Docker & Docker Compose installed.

2. Quick Start

Clone the repository and launch the stack:

# 1. Clone
git clone https://github.com/achillekrtf/HRP_Optimizer.git
cd HRP_Optimizer

# 2. Launch
docker compose up -d --build

3. Access

• Dashboard: http://<YOUR_VPS_IP> (Port 80)
• API: http://<YOUR_VPS_IP>:8000
• Scheduler: Runs automatically in the background (Updates daily at 22:00 UTC).

🔬 Research & Backtesting

For a deep dive into the algorithm and a rolling backtest of the strategy:

• Open analysis.ipynb (Jupyter Notebook).
• Includes Dendrograms, Correlation Matrices, and Equity Curves comparing HRP vs SPY.

🔄 Development

To run locally:

docker compose up

Or run services individually (requires python 3.11 and node 18+).

Rolling Window Logic

• Data Update: Fetches the last 2 years of data for all tickers daily.
• Rebalancing: Checks if the last allocation is older than 7 days. If yes, it re-runs HRP optimization and saves the new weights.

HRP_Optimizer