How sanjeevsub21/portfolio-analytics works
This is a notebook-based portfolio analysis project with an interactive Power BI dashboard. The workflow gathers historical stock and benchmark prices, stores and queries them, computes investment metrics, optimizes allocations, validates weights on unseen data, and presents findings. The README documents these capabilities, but no notebook source was available to confirm internal call structure; stage-to-stage edges therefore represent documented data flow, not verified function calls.
Main entry points: Portfolio Analysis.ipynb Risk-Return Metrics · PowerBI_Dashboard.pbix Power BI Dashboard
Components 12
Market Data
- Historical Prices
Portfolio Analysis.ipynb - SQLite Store
Portfolio Analysis.ipynb
Portfolio Analysis
- Risk-Return Metrics
Portfolio Analysis.ipynb - Correlation Matrix
Portfolio Analysis.ipynb - Monte Carlo Optimization
Portfolio Analysis.ipynb - Training Period
Portfolio Analysis.ipynb - Unseen Test Period
Portfolio Analysis.ipynb - Backtest Results
Portfolio Analysis.ipynb
Results
- Analysis Findings
Portfolio Analysis.ipynb - Power BI Dashboard
PowerBI_Dashboard.pbix
Other components
- Analyst
- Price Data
Connections 15
- Analyst → Historical Prices: initiates
- Price Data → Historical Prices: provides prices
- Historical Prices → SQLite Store: stores prices
- SQLite Store → Risk-Return Metrics: supplies prices
- Risk-Return Metrics → Correlation Matrix: price returns
- Risk-Return Metrics → Monte Carlo Optimization: return inputs
- Risk-Return Metrics → Training Period: training data
- Training Period → Monte Carlo Optimization: fits weights
- Monte Carlo Optimization → Backtest Results: optimized weights
- Unseen Test Period → Backtest Results: evaluates returns
- Risk-Return Metrics → Analysis Findings: produces metrics
- Correlation Matrix → Analysis Findings: produces correlations
- Monte Carlo Optimization → Analysis Findings: produces allocation
- Backtest Results → Analysis Findings: produces comparison
- Analysis Findings → Power BI Dashboard: visualizes findings