Xorbix Technologies
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Project Description

Xorbix Technologies Inc. developed an application designed to filter stocks based on specific trading conditions and return the top 10 matches. Built with Python and the yfinance library, the solution retrieves real-time market data and performs precise computations to deliver accurate results. As part of our custom software solution development and digital transformation services, this project streamlines the stock filtering process, enabling users to access timely and reliable stock recommendations.

Challenge

Problem

1. Manual stock filtering was time-consuming and prone to delays.

2. Existing systems lacked automation to handle real-time trading data efficiently.

3. Frequent manual interventions increased operational overhead and reduced productivity.

4. Identifying top-performing stocks under changing market conditions required complex calculations.

5. Delayed updates resulted in missed trading opportunities.

6. The absence of a centralized system caused inconsistencies in filtering and reporting results.

Project Goals

Application Goals

  • Develop an automated stock filtering system to process real-time data.
  • Eliminate the need for manual input during stock analysis.
  • Deliver consistent and accurate filtering results with minimal latency.

Performance Goals

  • Ensure the solution provides near real-time updates for top-performing stocks.
  • Optimize data processing to handle large datasets efficiently.
  • Minimize execution time to meet fast-paced trading environment requirements.

Operational Goals

  • Automate data retrieval, computation, and reporting processes.
  • Enable seamless integration with existing trading tools or APIs.
  • Ensure system scalability and reliability for future growth.

Solution

To streamline stock analysis and automate filtering, Xorbix developed a Python-based application that integrates real-time stock data retrieval, computation, and automated reporting. The system efficiently processes large datasets, identifies top-performing stocks, and delivers actionable insights to users through daily updates, all without manual intervention.

Data Retrieval & Pre-Processing

  • Integrated Finnhub API and YFinance for real-time stock data collection.
  • Filtered and curated raw data to meet initial trading conditions.
  • Ensured accuracy by validating stock symbols and removing anomalies.

Computation & Analysis

  • Calculated three-month average trading volume for each stock.
  • Compared current trading volumes against historical averages.
  • Computed percentage increase in volume to rank high-performing stocks.

Ranking & Selection

  • Automatically identified and sorted stocks by performance metrics.
  • Selected the top 10 stocks meeting predefined trading criteria.
  • Ensured consistent accuracy and timely updates in rankings.

Notification & Reporting

  • Automated daily email dispatch of top 10 stock recommendations.
  • Delivered concise and actionable insights directly to decision-makers.
  • Reduced manual analysis time, improving response speed in trading operations.

High Level Architecture

Innovations

  • Automated Data Retrieval: Utilized the Finnhub API and yfinance library to automate and streamline stock data collection.
  • Optimized Data Processing: Pre-processed stock information to create a curated dataset, reducing execution time during core computations.
  • Cloud-Powered Scalability: Deployed the application on Azure Functions, applying Xorbix’s cloud migration expertise to offload processing and ensure seamless scalability.

Security

  • Encrypted Data Handling: Ensured all stock data transfers and communications between modules were encrypted using HTTPS and TLS protocols.
  • Credential Protection: Secured API keys and sensitive credentials using Azure Key Vault to prevent unauthorized access.
  • Cloud-Level Access Control: Implemented Azure role-based access control (RBAC) to safeguard cloud functions and application components.
  • Real-Time Monitoring: Enabled automated alerts and performance tracking via Azure Monitor to detect and respond to anomalies promptly.

Core Technologies

  • Python: Served as the core programming language for application development.
  • Azure Functions: Provided a serverless platform to run the application with high scalability and flexibility.
  • Microsoft SQL Server: Used as the database to securely store and manage the curated stock dataset.

Process

Development Challenges

    • Execution Time Optimization: Reduced delays by implementing efficient data pre-processing and streamlined computational workflows.
    • Data Accuracy Assurance: Achieved reliability through real-time data retrieval, ensuring that users always receive up-to-date results.
    • Scalability Considerations: Optimized deployment on Azure Functions to handle varying data loads without performance degradation.

Testing & Quality Assurance

    • Component-Level Testing: Each module—data retrieval, computation, and notification—was individually tested for functionality and accuracy.
    • Integration Testing: Verified smooth communication between modules to ensure seamless end-to-end operation.
    • Performance Testing: Evaluated system speed and responsiveness to confirm the application meets defined efficiency benchmarks.

Optimization & Continuous Improvement

    • Code Refinement: Improved algorithms for faster data computation and reduced API call frequency.
    • Monitoring Feedback: Implemented Azure monitoring tools to detect anomalies and refine the solution based on real-time usage data.
    • Maintenance Planning: Established a periodic review process to update APIs, dependencies, and optimize resource consumption.

Results

  • Optimized Data Processing: Curated only relevant stock symbols, reducing data volume and improving computation efficiency.
  • Real-Time Insights: Automated ranking and selection of top-performing stocks based on live trading conditions.
  • Automated Notifications: Daily email reports delivered timely stock recommendations without manual input.
  • Up-to-Date Accuracy: Scheduled execution ensured results reflected the latest trading activity each day.
  • Scalable Architecture: Azure Functions enabled seamless scalability, efficiently managing fluctuating workloads.
  • Cost-Effective Operations: Serverless deployment minimized operational costs through a pay-as-you-go model.
  • High Reliability: Azure’s resilient infrastructure ensured consistent performance and minimal downtime.
  • User-Friendly Experience: Concise daily reports provided actionable insights for informed investment decisions.

Optimize your stock filtering process with intelligent, cloud-powered automation.

Contact Xorbix

A trusted custom software development and cloud solutions company, to enhance your financial decision-making with real-time, data-driven insights.

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Address

802 N. Pinyon Ct,
Hartland, WI 53029

Billing Inquiries


(866) 568-8615

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info@xorbix.com

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