Week 5: Lists and Arrays in Python

Learn how to work with lists and arrays to handle financial time series data, stock prices, and portfolio holdings.

Learning Objectives:

✓ Understand lists and arrays in Python
✓ Learn list operations and methods
✓ Introduction to NumPy arrays for financial data
✓ Work with time series data

1. Python Lists

Understanding basic list operations for storing financial data.

# Creating a list of stock prices
prices = [105.25, 106.00, 104.50, 107.25, 108.00]
                
# Calculating daily returns
daily_returns = [(prices[i] - prices[i-1])/prices[i-1] * 100 
                 for i in range(1, len(prices))]

2. NumPy Arrays

Using NumPy arrays for efficient financial calculations.

import numpy as np

# Converting stock prices to numpy array
prices_array = np.array([105.25, 106.00, 104.50, 107.25, 108.00])

# Calculating returns using numpy
returns = np.diff(prices_array) / prices_array[:-1] * 100

# Calculate volatility (standard deviation of returns)
volatility = np.std(returns)

3. Lists and Arrays for Financial Data

Understanding how to structure financial data using lists and arrays.

# Stock portfolio using lists
symbols = ['AAPL', 'MSFT', 'GOOGL']
quantities = [100, 50, 75]
prices = [190.50, 375.00, 140.50]

# Calculate portfolio value
portfolio_value = sum([q * p for q, p in zip(quantities, prices)])

# Historical prices as numpy array
import numpy as np
historical_prices = np.array([
    [100.0, 200.0, 150.0],  # Day 1 prices
    [101.0, 202.0, 151.0],  # Day 2 prices
    [99.0, 198.0, 149.0]    # Day 3 prices
])

# Calculate daily returns for all stocks
daily_returns = np.diff(historical_prices, axis=0) / historical_prices[:-1] * 100

4. Time Series Data

Working with financial time series using lists and arrays.

from datetime import datetime, timedelta

# Creating date range for stock data
dates = [(datetime.now() - timedelta(days=x)).strftime('%Y-%m-%d') 
         for x in range(5)]
         
# Combining dates with prices
stock_data = list(zip(dates, prices))

Exercises:

Create a basic portfolio tracker:
- Store stock symbols and quantities in lists
- Create a list of prices and calculate total value
- Use list comprehension to calculate position values
Implement a simple moving average calculator:
- Create a list of historical prices
- Calculate 5-day and 20-day moving averages
- Generate signals when short MA crosses long MA
Create a bond calculator:
- Store bond details (face value, coupon, maturity) in nested lists
- Calculate yield to maturity
- Compare multiple bonds using list operations
Build a price analyzer:
- Create a NumPy array of daily prices
- Calculate daily, weekly, and monthly returns
- Find highest/lowest prices and their positions
Implement a simple asset allocation tool:
- Store current and target allocations in arrays
- Calculate allocation differences
- Determine rebalancing needs
Create a basic risk calculator:
- Store historical returns in NumPy arrays
- Calculate correlation between assets
- Compute portfolio volatility

💡 ChatGPT Learning Tips

Use these prompts to enhance your learning:

"Show me how to use list comprehension for financial calculations"
"Explain the difference between lists and NumPy arrays for financial data"
"Help me understand how to structure portfolio data using lists"
"What are efficient ways to calculate returns using NumPy arrays?"