A Comprehensive Guide for Automating IV Analysis in Python

				
					import plotly.graph_objects as go  # Import Plotly for graph objects
from plotly.subplots import make_subplots  # Import Plotly for creating subplots

def plot_volatility_smile(options_data, recent_price, ticker):
    if options_data is not None:  # Check if options data is available
        calls_data = options_data[options_data["type"] == "call"]  # Filter for call options
        puts_data = options_data[options_data["type"] == "put"]  # Filter for put options

        expirations = options_data['expiration'].unique()  # Get unique expiration dates
        color_map_2d = px.colors.qualitative.Prism  # Define a color map for plotting

        fig = make_subplots(rows=1, cols=2, subplot_titles=["Calls", "Puts"], shared_yaxes=True)  # Create subplots for calls and puts

        for exp, color in zip(expirations, color_map_2d):  # Loop through expiration dates and colors
            exp_calls = calls_data[calls_data["expiration"] == exp]  # Filter call options by expiration date
            exp_puts = puts_data[puts_data["expiration"] == exp]  # Filter put options by expiration date

            fig.add_trace(go.Scatter(x=exp_calls["strike"], y=exp_calls["implied_volatility"], mode='markers',
                                     marker=dict(color=color), name=exp), row=1, col=1)  # Plot call options
            fig.add_trace(go.Scatter(x=exp_puts["strike"], y=exp_puts["implied_volatility"], mode='markers',
                                     marker=dict(color=color), name=exp, showlegend=False), row=1, col=2)  # Plot put options

        avg_iv_by_strike_calls = calls_data.groupby("strike")["implied_volatility"].mean()  # Calculate average IV by strike for calls
        avg_iv_by_strike_puts = puts_data.groupby("strike")["implied_volatility"].mean()  # Calculate average IV by strike for puts

        fig.add_trace(go.Scatter(x=avg_iv_by_strike_calls.index, y=avg_iv_by_strike_calls.values, mode='lines',
                                 line=dict(color='black', dash='dash'), name='Avg. IV by Strike (Calls)'), row=1, col=1)  # Plot average IV for calls
        fig.add_trace(go.Scatter(x=avg_iv_by_strike_puts.index, y=avg_iv_by_strike_puts.values, mode='lines',
                                 line=dict(color='black', dash='dash'), name='Avg. IV by Strike (Puts)', showlegend=False), row=1, col=2)  # Plot average IV for puts

        overall_avg_iv_calls = calls_data["implied_volatility"].mean()  # Calculate overall average IV for calls
        overall_avg_iv_puts = puts_data["implied_volatility"].mean()  # Calculate overall average IV for puts

        fig.add_hline(y=overall_avg_iv_calls, line=dict(color='gray', dash='dash'), 
                      annotation_text=f"Overall Avg. IV (Calls): {overall_avg_iv_calls:.2f}%",
                      row=1, col=1)  # Add horizontal line for overall average IV of calls
        fig.add_hline(y=overall_avg_iv_puts, line=dict(color='gray', dash='dash'), 
                      annotation_text=f"Overall Avg. IV (Puts): {overall_avg_iv_puts:.2f}%",
                      row=1, col=2)  # Add horizontal line for overall average IV of puts

        fig.update_layout(title=f"{ticker} Volatility Smile - Current Price: {recent_price:.2f}", showlegend=True, legend_title_text='Expiration Date')  # Update layout
        fig.update_xaxes(title_text="Strike Price")  # Set x-axis title
        fig.update_yaxes(title_text="Implied Volatility (%)")  # Set y-axis title

        fig.show()  # Display the plot

        # Return necessary data for interpretation
        return overall_avg_iv_calls, overall_avg_iv_puts, avg_iv_by_strike_calls, avg_iv_by_strike_puts, expirations

# Assuming options_data and recent_price are already defined
overall_avg_iv_calls, overall_avg_iv_puts, avg_iv_by_strike_calls, avg_iv_by_strike_puts, expirations = plot_volatility_smile(options_data, recent_price, ticker)


def interpret_volatility_smile(ticker, overall_avg_iv_calls, overall_avg_iv_puts, avg_iv_by_strike_calls, avg_iv_by_strike_puts):
    # Interpretation based on data
    interpretation = f"**Interpretation of {ticker} Volatility Smile:**\n"

    # Average IV comparison
    interpretation += f"- The average implied volatility for call options is {overall_avg_iv_calls:.2f}%.\n"
    interpretation += f"- The average implied volatility for put options is {overall_avg_iv_puts:.2f}%.\n"

    # Volatility smile presence
    if avg_iv_by_strike_calls.var() > 0.1:  # Check if variance of IV by strike for calls is significant
        interpretation += "- The call options exhibit a noticeable 'volatility smile,' indicating varying implied volatility across different strike prices.\n"
    else:
        interpretation += "- The call options do not show a significant 'volatility smile,' suggesting more stable implied volatility across strike prices.\n"

    if avg_iv_by_strike_puts.var() > 0.1:  # Check if variance of IV by strike for puts is significant
        interpretation += "- The put options exhibit a noticeable 'volatility smile,' indicating varying implied volatility across different strike prices.\n"
    else:
        interpretation += "- The put options do not show a significant 'volatility smile,' suggesting more stable implied volatility across strike prices.\n"

    # Market sentiment based on IV
    market_sentiment = "bullish" if overall_avg_iv_calls > overall_avg_iv_puts else "bearish"
    interpretation += f"- The overall market sentiment is {market_sentiment}, inferred from the average implied volatility of calls and puts.\n"

    # Conclusion based on data trends
    if overall_avg_iv_calls > overall_avg_iv_puts:
        interpretation += "- The higher implied volatility in call options suggests that traders expect upward price movements or higher uncertainty in the stock price.\n"
    else:
        interpretation += "- The higher implied volatility in put options suggests that traders expect downward price movements or higher uncertainty in the stock price.\n"

    print(interpretation)  # Print the interpretation

# Generate interpretation based on the results from the plot function
interpret_volatility_smile(ticker, overall_avg_iv_calls, overall_avg_iv_puts, avg_iv_by_strike_calls, avg_iv_by_strike_puts)