Need to show Stressed Data (how the account behaves in stress scenarios, complex calculation like worst flow, CVAR, redemption parameters, transaction level data, illiquid holdings, liquidity stress history, different checks like compliance, liquidity check, and vendor vs firm data) of multiple accounts in different visuals month over month.

• Handling of complex and large data sizes.
• Accurate data modelling in Power BI, as the data was being fetched from different data sources, some of them coming from on PREM databases, few of them were fetched over API calls.
• Dashboard performance tuning was extremely important. Business needs to export data daily so data export and dashboard load should be relatively quicker.

• The first step is to gather the data from different data sources into Power BI.
• CSV file upload (Data from Vendor) on Amazon S3 Bucket and then build python script and generate URL for reading this file. The URL (Rest API) is embedded in the m query and we fetch the data in Power BI.
• Vendor data uploaded in Oracle Database and fetch the data using Local query in Power BI.
• Reference Data from our firm already present the Oracle Data so we fetch using Local query as well.
• Prepare the Data Model. The data size is large and comes from different data tables, so we need to prepare the model most accurately and we need to take circular dependency and ambiguity concepts.
• Build the Visuals/Charts on top of that Data Model. We make Calculated Column and measure in such a way so that it would not impact on Dashboard Performance.

• Core Technologies: Python, Cash, Bash,
• Python Frameworks: Flask, Dash
• Configuration Management: SVN/Git, Quick build and Gitlab(CICD), JIRA
• Databases: Oracle SQL Developer, AWS S3 Data Store

It is difficult for the Risk Managers to perform these liquidity stress tests, or analyze risk data manually, given the data complexity, and data being received from different sources The Liquidity Stress Test Power BI report/dashboard assists the Risk managers to analyze scenario’s like (Portfolio Behavior in Stress Scenarios, % Worst Flow, CVAR, Redemption Parameters, Transaction Level Data, illiquid Holdings, Liquidity Stress History, Different checks like Compliance LIQUIDITY CHECK and Vendor vs Firm Data), and help to raise any alerts in a timely fashion.