Stock Analysis Automation Project

In this project, I developed a comprehensive automated system for stock analysis and notification, leveraging multiple data sources to identify promising after-hours gainers. The system was built to extract, analyze, and filter stock information from Webull and Finviz, focusing on key financial metrics such as percentage change, trading volume, and shares float.

Automated Data Extraction: Implemented web scraping and API integration to collect financial data from Finviz and Webull.

Data Filtering: Created robust data filtering methods based on parameters like country, shares float, and volume to refine the dataset to the most promising tickers.

Automated Excel Report Generation: Designed a process to generate detailed Excel reports for each stock, which were saved locally and named with relevant information like percentage change for easy tracking.

Email Notifications: Built an email automation feature to notify users about the filtered stock analysis results. Each ticker is processed only once, ensuring emails are sent just once per ticker.

Change Percentage Highlight: Included the percentage change in the filenames, email subject, and body to provide a clear, quick reference for the performance of each stock.

Skills Used:

Python Programming: Built the entire automation system using Python.

Web Scraping & Data Extraction: Leveraged BeautifulSoup and requests to extract stock information from websites.

Data Analysis: Utilized Pandas for data manipulation, including filtering and aggregation of financial data.

Email Automation: Implemented SMTP-based automation to send customized notifications for each ticker.

Logging and Error Handling: Ensured robustness by incorporating extensive logging and error handling to track the process and manage failures.

Tools:

Python (Pandas, BeautifulSoup, Requests, smtplib)

Finviz and Webull APIs

Excel for report generation

Impact: This project streamlined the process of identifying promising after-hours gainers and notifying clients with detailed insights, saving significant time and ensuring precise analysis for stock trading decisions. The automation reduces the manual workload and improves decision-making efficiency by providing accurate and timely alerts.