Mohammed Rashad
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SQLDashboardsFinanceJoins & CTEsViewsRisk Metrics

Investment Portfolio Analytics & SQL Data Pipeline

problem · Consolidate fragmented high-net-worth asset data into a single source of truth for real-time risk monitoring.

repo · Rashad-Mohammed02/InvestmentPortfolioSQLPipeline ↗updated · 2 months agosize · 0 B

what shipped

  • Engineered a relational pipeline using Joins, CTEs, and Views to aggregate disparate asset classes.
  • Computed Sigma (volatility) and risk-adjusted return to benchmark asset performance.
  • Shipped a dynamic dashboard (12M / 24M returns) and a risk-bubble chart to isolate underperformers for rebalancing.

repository readme

fetched from github · rebuilt daily

Investment Portfolio Analytics & SQL Data Pipeline

Executive Summary

This project involved engineering a relational database pipeline to analyze the financial assets of a high-net-worth client (Paul Bistre). Using Advanced SQL, I aggregated disparate data sources (asset classifications, historical pricing, and account details) to construct a unified view of the client's portfolio. The analysis culminated in a dynamic performance dashboard that identified key asset allocation imbalances and risk exposure.

Tools Used

Language: SQL (Joins, CTEs, Views, Window Functions) [cite: 38] Visualization: Excel Dashboard (Risk Bubble Charts, Price Trends) Database: Relational Schema (Invest Schema)

Key Technical Steps

  1. Data Modeling: Established relationships between Customer, Account, and Holdings tables to map assets to their owners.
  2. View Creation: Developed a robust SQL View (rashad_mohammed) to automate the extraction of real-time portfolio value, major/minor asset classes, and price types.
  3. Risk Engineering: Calculated Sigma (Volatility) and Risk-Adjusted Returns for 12M, 18M, and 24M intervals to quantify asset stability.

Key Findings

  • Performance: The portfolio showed significant variance in returns across different time horizons, with specific securities driving the bulk of the 12-month gains.
  • Risk Analysis: The "Risk Bubble Chart" revealed outliers with high volatility but low risk-adjusted returns, prompting a recommendation for immediate rebalancing.
  • Asset Allocation: Identified a heavy concentration in specific major asset classes, suggesting a need for diversification to mitigate sector-specific downturns.