Automated Stock Filtering and Recommendation System Using Python and Azure Functions

• Efficient Data Pre-Processing: By retrieving a list of stock symbols using the finnhub API and filtering out non-relevant stocks (e.g., those that are not commodity stocks or do not provide options), the application creates a curated dataset. This pre-processing step ensures that only stocks meeting the initial criteria are considered for further computations, significantly reducing the volume of data to be processed.
• Top 10 Stock Selection: The final step involves sorting the stocks based on the percentage increase in trading volume and selecting the top 10 stocks. This streamlined and automated process ensures that users receive timely and relevant stock recommendations based on real-time trading data.
• Automated Email Dispatch: The application automatically compiles the list of the top 10 stocks and sends it to users via email each day. This automation eliminates the need for manual intervention and ensures consistent and timely delivery of stock recommendations.
• Up-to-date Information: Despite the potential delay caused by the execution time of the program, the application is designed to provide as up-to-date information as possible. By scheduling the execution to run later in the day, the application ensures that the data is current and reflects the latest trading activity.
• User-Friendly Format: The daily emails are structured in a user-friendly format, highlighting the top 10 stocks along with relevant metrics such as the percentage increase in trading volume. This concise and clear presentation makes it easy for users to quickly grasp the key insights and make informed decisions.
• Serverless Architecture: Azure Functions is a serverless compute service that automatically scales based on demand. This means that the application can handle varying workloads without requiring manual scaling or infrastructure management. Whether the application needs to process data for a few stocks or thousands, Azure Functions dynamically allocates resources to ensure optimal performance.
• Cost Efficiency: By leveraging a serverless architecture, the application incurs costs only for the actual computer resources used during execution. This pay-as-you-go model helps manage costs effectively, especially during periods of low usage.
• Reliability and Availability: Azure Functions provides high availability and reliability, ensuring that the application remains accessible and responsive even during peak times. The platform’s robust infrastructure minimizes downtime and ensures that users receive timely stock recommendations without interruptions.
• Integration with Azure Services: Azure Functions seamlessly integrate with other Azure services, such as Azure Storage and Azure Databases, enabling efficient data storage and retrieval. This integration supports the application’s data processing needs and enhances overall performance.

Let’s optimize your stock filtering process and provide you with real-time, accurate stock recommendations. Contact us today to learn how our solutions can drive your financial success and bring your trading to the next level.