In today's fast-paced stock market, investors and traders rely on accurate, timely predictions to make informed decisions. However, obtaining, cleaning, and processing stock price data for forecasting purposes often involves complex workflows and repetitive manual tasks. This project aims to build a Stock Price Prediction Analytics System that automates data extraction, storage, and forecasting using advanced data pipelines.
The system will:
• Use yfinance API to fetch historical stock price data.
• Store and process this data in a Snowflake data warehouse.
• Use machine learning models within Snowflake to forecast future stock prices.
• Automate the data extraction and forecasting processes using Apache Airflow.
A robust data pipeline is crucial for automating the ingestion, cleaning, and forecasting tasks. Without such automation, manual intervention would be needed every time stock data must be processed, which is both time-consuming and error-prone.