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CHIMAVMV/Stock-Market-Volatility-Forecasting_SA

Domaine:

socioeconomic

Type de record:

softwaremodel
Créateur:
CHI
Hôte:
This project builds a volatility forecasting system for the South African stock market using financial time series modeling and a production-ready backend architecture. It combines: 📊 Financial modeling (GARCH) 🗄️ Database integration (SQLite) 🌐 API development (FastAPI) 📡 External data ingestion (Alpha Vantage) ... # 📈 Volatility Forecasting in South Africa Stock Market ## 🧠 Project Overview This project builds a **volatility forecasting system** for the South African stock market using financial time series modeling and a production-ready backend architecture. It combines: - 📊 Financial modeling (GARCH) - 🗄️ Database integration (SQLite) - 🌐 API development (FastAPI) - 📡 External data ingestion (Alpha Vantage) The goal is to simulate how real-world financial systems **forecast risk and serve predictions via an API**. --- ## 🎯 Key Features - 📥 Fetches stock market data using Alpha Vantage API - 🧮 Computes returns and volatility - 📉 Implements **GARCH models** for volatility forecasting - 🗄️ Stores data using a **SQLite database** - 🧱 Uses a **Repository Pattern (SQLRepository)** for clean architecture - ⚡ Exposes predictions via a **FastAPI backend** --- ## 🏗️ Project Architecture ├── .env # Environment variables (API keys, DB config) ├── config.py # Application configuration ├── data.py # Data ingestion & repository logic ├── garch.py # Volatility modeling (GARCH) ├── model.py # Model logic & utilities ├── main.py # FastAPI application entry point ├── README.md # Project documentation ## ⚙️ Tech Stack - **Python** - **FastAPI** (API layer) - **SQLite** (database) - **Pandas / NumPy** (data processing) - **ARCH / GARCH models** (volatility forecasting) - **Alpha Vantage API** (financial data) --- ## 🧩 Methodology ### 1. Data Ingestion - Stock data is fetched from Alpha Vantage API - Data is cleaned and stored in SQLite ### 2. Data Processing - Log returns are computed - Time series prepared for modeling ### 3. Volatility Modeling - GARCH models are used to capture: - Volatility clustering - Time-varying risk ### 4. Backend System - FastAPI serves predictions through endpoints - Repository pattern ensures modular database access --- ## 🚀 Running the Project ### 1. Clone Repository ``` git clone github.com