week 2 Finance with Python
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Week 2: Portfolio Management and Risk Analysis
Learning Material:
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Day 1: Portfolio Theory
- Objective: Understand portfolio theory, diversification, and risk-return trade-offs.
- Topics: Portfolio construction and risk management.
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Day 2: Building Portfolios in Python
- Objective: Learn how to create diversified portfolios in Python.
- Topics: Portfolio optimization and Python libraries for financial data analysis.
- Code Example: Building a diversified portfolio in Python.
python
import pandas as pd # Sample stock data (stock prices in a DataFrame) stock_data = pd.read_csv("stock_data.csv") # Define portfolio weights weights = [0.4, 0.3, 0.2, 0.1] # Calculate portfolio returns and risk portfolio_returns = (stock_data.pct_change() * weights).sum(axis=1) portfolio_risk = portfolio_returns.std() -
Day 3: Risk Measures
- Objective: Explore different risk measures and metrics in finance.
- Topics: Standard deviation, beta, and other risk metrics.
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Day 4: Value at Risk (VaR)
- Objective: Learn about VaR and its calculation in Python.
- Topics: Value at Risk calculation and Python libraries for financial risk analysis.
- Code Example: Calculating VaR in Python.
python
import numpy as np from scipy.stats import norm # Define portfolio returns (daily returns) returns = np.array([0.02, 0.01, -0.03, 0.015, -0.02, 0.01, 0.015, -0.015]) # Calculate VaR at a specific confidence level (e.g., 95%) confidence_level = 0.95 z_score = norm.ppf(confidence_level) var = -np.percentile(returns, z_score * 100) print(f"Value at Risk (VaR) at {confidence_level*100}%: {var}") - Day 5: Exercise
- Objective: Apply risk analysis concepts to real data and calculate risk measures and VaR using Python.
Note: Week 2 focuses on portfolio management and risk analysis, introducing concepts and their practical application with Python.
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