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Week 4: Options and Derivatives

Learning Material:

• Day 1: Introduction to Options

  • Objective: Understand the basics of options, call and put options, and option strategies.
  • Topics: Options terminology, types, and basic concepts.

• Day 2: Options Pricing Models

  • Objective: Learn about options pricing models like the Black-Scholes-Merton model.
  • Topics: Black-Scholes-Merton model, option pricing, and mathematical modeling.
• Day 3: Options Trading in Python
  • Objective: Implement options trading strategies in Python.
  • Topics: Strategies, options trading, and Python libraries for options trading.
  • Code Example: Implementing a basic covered call strategy in Python.
• pythonCopy code

  import yfinance as yf # Define the stock symbol and date range stock_symbol = "AAPL" start_date = "2020-01-01" end_date = "2022-12-31" # Download historical data data = yf.download(stock_symbol, start=start_date, end=end_date) # Define call option parameters strike_price = 150 expiration_date = "2023-01-20" # Simulate a covered call strategy call_option_premium = data.loc[expiration_date]["Close"] - data.loc[expiration_date]["Close"] * 0.02 # Example premium strategy_payoff = data["Close"] - call_option_premium # Visualize the covered call strategy import matplotlib.pyplot as plt plt.figure(figsize=(10, 6)) plt.plot(data.index, strategy_payoff) plt.xlabel("Date") plt.ylabel("Payoff") plt.title("Covered Call Strategy Payoff") plt.show()

  • Day 4: Introduction to Derivatives

    • Objective: Learn about financial derivatives and their use in risk management.
    • Topics: Derivatives overview, types, and applications.

  • Day 5: Exercise

    • Objective: Implement an options trading strategy in Python, analyze the results, and understand options trading principles.

  Note: Week 4 covers options and derivatives, including pricing models and basic trading strategies in Python.

• ← week 3 Finance with Python - access the market data
• week 5 Finance with Python →