Problem Statement

The client lacked a predictive modeling framework to analyze long-term trends in Options and Futures. Decision-makers were forced to manually extract and analyze years of historical data, a time-consuming and error-prone process. The Tidal scheduling system in use was incompatible with modern cloud tools, preventing seamless automation and real-time forecasting.

Key Challenges

• No Predictive Modeling Framework

  The client lacked machine learning models to analyze historical trends and forecast future Options and Futures activity.

• Manual Data Analysis at Scale

  Trading teams had to manually scan large datasets to derive insights, increasing workload and reducing decision speed.

• Disconnected Scheduling Infrastructure

  Limited interoperability between the on-prem Tidal scheduler and cloud-based services hampered seamless pipeline execution.

• Poor Schema Consistency & Delivery Automation

  Data pipelines faced schema management issues and lacked automated output delivery for consumption.

Solution Implemented

• Built Databricks Notebooks for Forecasting Outputs: Developed Azure Databricks notebooks to analyze historical data, generate CSV-based trend forecasts, and store results in Azure Blob Storage.

• Applied ML Algorithms for Derivative Analytics: Used integrated ML libraries in Databricks to evaluate key factors like moneyness, volatility, and strike distribution across Options and Futures.

• Automated Pipelines via Azure Data Factory : Designed and deployed ADF pipelines to automate daily processing tasks, fully integrated with legacy Tidal job execution.

• Modular & Governed Code Framework : Established reusable, modular code in Databricks with strong schema governance to ensure long-term maintainability.

• Real-Time Monitoring & Support Framework : Provided on-call support for job failures in ADF and Databricks, ensuring business continuity and reliability.

Business Impact

• Predictive Insight into Market Trends

  Enabled the client to make proactive trading decisions using machine learning-based forecasts on Options & Futures.

• Strengthened Financial Forecasting

  Empowered teams with deeper historical trend analysis, improving both strategic planning and risk management.

• Reliable and Scalable Pipelines

  Reduced manual intervention and increased system reliability with robust monitoring and integration into Tidal.

• Cloud-Native Modernization

  Connected legacy systems with scalable Azure services, positioning the client for future data platform expansion.