Inspired by many specialized ETFs (An investment vehicle that pools a group of securities into a fund. It can be traded like an individual stock on an exchange), creating a basket of stocks with weights for specific investment strategies or weighting criteria beyond traditional market-capitalization indexes and treating them all as a unit.

Portfolio Analysis with Python

Portfolio Analysis, Risk Management & EfficientFrontier of NSE stocks

Data Extraction and Preprocessing

Utilized yfinance for data retrieval of NSE stocks, obtaining historical price data for a specified date range

Value at risk | Historical (VaR) | Expected Shortfall (CVaR)

'df' is the DataFrame that contains daily prices of stocks.
'weights' is the array that contains portfolio weights.
Finding var95 and cvar95:

• returns = df.pct_change()
returns.dropna(inplace = True)
• returns_pf = returns.dot(weights)
• var = np.percentile(returns_pf, 5)
• cvar = returns_pf [returns_pf <= var].mean()

Annualized return

• pf_AUM = df.dot(weights)
• total_return = (pf_AUM[-1] - pf_AUM[0]) / pf_AUM[0]
• annualized_return = ((1 + total_return) * * (12 / months)) - 1

Risk-adjusted return | Sharpe Ratio | Efficiency of risk taking

• pf_returns = pf_AUM.pct_change()
• pf_vol = pf_returns.std()
• pf_vol = pf_vol * np.sqrt(250)
• sharpe_ratio = ((annualized_return - rfr) / pf_vol)

Portfolio Optimization | Efficient Frontier

Portfolio Variance

Optimal weights

Efficient Frontier

Python libraries:

pandas, numpy, yfinance, matplotlib, PyPortfolioOpt (including EfficientFrontier for portfolio optimization)