Portfolio Management
NIFTY 50 portfolio construction with strategy views and future-ready market data architecture
Build, analyse and optimise portfolios from the NIFTY equity universe.
Overview
Build, analyse and optimise portfolios from the NIFTY equity universe. The current module uses mock NIFTY 50 data, simple covariance assumptions, and institutional-style portfolio cards so we can plug in real NSE equity and option chain data later without redesigning the product surface.
Universe
NIFTY 50
Strategies
4
Data Mode
Mock
NIFTY 50 Universe
RELIANCE
Reliance Industries | Energy
HDFCBANK
HDFC Bank | Financials
INFY
Infosys | Information Technology
ITC
ITC | Consumer Staples
LT
Larsen & Toubro | Industrials
Portfolio Inputs
NIFTY 50 universe
5 representative large-cap stocks from the benchmark universe
Expected returns
Mock annualised return estimates from 9.40% to 12.60%
Volatility
Mock annualised volatility estimates from 15.20% to 22.60%
Correlation matrix
5 x 5 cross-asset correlation set
Risk-free rate
6.80%
Investment amount
₹25,00,000
Strategy Metrics
Equal Weight
A clean baseline across the selected NIFTY 50 names with identical capital allocation.
Return
11.44%
Volatility
14.28%
Sharpe
0.32
Minimum Variance
Tilts toward lower-volatility and lower-correlation names to compress total portfolio risk.
Return
11.09%
Volatility
13.44%
Sharpe
0.32
Maximum Sharpe
Leans into stronger expected excess return while still respecting simple diversification assumptions.
Return
11.74%
Volatility
15.18%
Sharpe
0.33
Risk Parity
Balances exposure so each constituent contributes more evenly to overall risk.
Return
11.30%
Volatility
13.90%
Sharpe
0.32
Efficient Frontier
Mock frontier built from simple NIFTY covariance assumptions and four baseline optimisation strategies.
Portfolio Allocation
Mock maximum-Sharpe allocation across the selected NIFTY 50 basket, prepared for future live portfolio and option overlay views.
RELIANCE
Energy
24.00%
HDFCBANK
Financials
16.00%
INFY
Information Technology
23.00%
ITC
Consumer Staples
11.00%
LT
Industrials
26.00%
Correlation Matrix
| Stock | RELIANCE | HDFCBANK | INFY | ITC | LT |
|---|---|---|---|---|---|
| RELIANCE | 1.00 | 0.62 | 0.48 | 0.31 | 0.54 |
| HDFCBANK | 0.62 | 1.00 | 0.46 | 0.29 | 0.51 |
| INFY | 0.48 | 0.46 | 1.00 | 0.25 | 0.41 |
| ITC | 0.31 | 0.29 | 0.25 | 1.00 | 0.28 |
| LT | 0.54 | 0.51 | 0.41 | 0.28 | 1.00 |
Strategy Allocation Detail
Equal Weight
Drawdown 0.38%
₹25,00,000
Minimum Variance
Drawdown 0.10%
₹25,00,000
Maximum Sharpe
Drawdown 0.60%
₹25,00,000
Risk Parity
Drawdown 0.27%
₹25,00,000
Historical Backtest Placeholder
A simple NAV path from mock monthly returns. This section is intentionally structured to accept future historical NSE equity and derivative overlays.
Final NAV
1.054x
Max Drawdown
0.60%
Data Mode
Mock NIFTY
Future Data Architecture
The UI is deliberately separated from the current mock analytics so we can replace these assumptions with live NSE cash-market and option-chain feeds without changing the page structure or design language.
Market data adapters
Prepare typed ingestion adapters for NSE spot equity snapshots, NIFTY constituents, and option chain payloads.
Normalisation layer
Map vendor-specific symbol formats, timestamps, and contract metadata into a single internal schema.
Analytics engine
Swap mock expected returns, covariance estimates, and backtest series with live factor and market data inputs.
Execution surfaces
Expose portfolio construction, optimisation, and derivatives overlay views without coupling UI cards to a single API provider.
Next Step
Add typed repositories for constituent data, historical returns, realised volatility, and NSE option chain snapshots, then route those into the same portfolio cards and chart components.