Financial Portfolio Analytics Dashboard

Build a comprehensive investment portfolio analytics dashboard with risk metrics, performance tracking, and asset allocation visualizations.

## Scenario

You are a data analyst at a wealth management firm. The investment team needs a real-time dashboard to monitor client portfolios, track performance, and assess risk exposure.

## Objectives

Create an analytics dashboard that provides:

1. Portfolio Performance
- Returns over time (daily, weekly, monthly, YTD)
- Benchmark comparison (S&P 500, etc.)
- Risk-adjusted returns (Sharpe ratio, Sortino ratio)

2. Asset Allocation
- Current allocation by asset class
- Target vs actual allocation
- Rebalancing recommendations

3. Risk Metrics
- Value at Risk (VaR)
- Maximum drawdown
- Volatility (standard deviation)
- Beta vs benchmark

4. Holdings Analysis
- Top 10 holdings
- Sector exposure
- Geographic distribution

## Data Sources

- Daily stock prices (5 years historical)
- Portfolio holdings (current and historical)
- Benchmark indices (S&P 500, Bond indices)
- Risk-free rate (Treasury bills)
- Client information (risk tolerance, investment goals)

## Technical Requirements

### Data Processing
- Calculate daily returns
- Rolling window statistics (30-day, 90-day volatility)
- Correlation matrices
- Performance attribution

### Visualizations
- Time series charts (portfolio value, returns)
- Pie/donut charts (asset allocation)
- Heatmaps (correlation, sector performance)
- Scatter plots (risk vs return)
- Bar charts (top holdings, sector weights)

### Dashboard Features
- Client selector dropdown
- Date range picker
- Benchmark comparison toggle
- Export to PDF functionality

## Deliverables

1. Interactive Dashboard (Tableau/Power BI/Web)
2. Technical Documentation (methodology, formulas used)
3. User Guide (how to use the dashboard)
4. Sample Report (PDF export example)

## Evaluation

- Accuracy of calculations
- Dashboard usability
- Visualization quality
- Completeness of documentation
- Professional presentation

Data Provided:
- 5 years of daily stock prices (50+ stocks)
- Portfolio holdings for 10 model portfolios
- Benchmark indices (S&P 500, Aggregate Bond)
- Risk-free rate history
- Sector classifications

Data Format:
- CSV files for prices
- Excel for portfolio holdings
- JSON for configuration

Quality Notes:
- Adjusted for splits and dividends
- Missing dates (holidays) - handle appropriately
- Currency: All in USD
- Timezone: EST

Tools:
- Python (pandas, numpy, plotly, dash)
- Excel for initial analysis
- Tableau Public or Power BI
- Yahoo Finance API (for real-time data extension)

Analytics Framework:

1. Return Calculations
- Simple returns
- Log returns
- Cumulative returns
- Annualized returns

2. Risk Metrics
- Standard deviation (volatility)
- Value at Risk (VaR)
- Maximum Drawdown
- Sharpe Ratio
- Sortino Ratio
- Beta

3. Portfolio Metrics
- Weighted average returns
- Portfolio volatility
- Correlation matrix
- Efficient frontier (optional)

4. Visualization Strategy
- Line charts: Time series
- Pie charts: Allocation
- Heatmaps: Correlations
- Bar charts: Comparisons
- Scatter plots: Risk-return

Dashboard Layout:
- Top: KPI cards (AUM, YTD return, Sharpe)
- Middle: Performance chart + Benchmark
- Bottom Left: Allocation pie chart
- Bottom Right: Top holdings
- Sidebar: Filters (client, date, benchmark)

Formulas Reference:
- Sharpe Ratio = (Rp - Rf) / σp
- VaR = Portfolio Value × Z-score × σ
- Beta = Covariance(Rp, Rm) / Variance(Rm)