Dear Arun,

Thankyou for showing interest in Breeze API.

Kindly find attached Py_vollib code file to calculate iv values and Greek values.

[E29CF836] Thanks & Regards, Akash Sapkal, Team Breeze API, ICICI Securities

From: Vineet Verma @.> Sent: 21 August 2024 10:03 To: Idirect-Tech/Breeze-Python-SDK @.> Cc: Subscribed @.***> Subject: [Idirect-Tech/Breeze-Python-SDK] In option chain, iv and Greek values are not available (Issue #130)

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In option chain api, the iv values and Greek values are not available

how can we get these values

— Reply to this email directly, view it on GitHubhttps://github.com/Idirect-Tech/Breeze-Python-SDK/issues/130, or unsubscribehttps://github.com/notifications/unsubscribe-auth/A3TQZ46UOPDHK4KHNNRJDTTZSQJ75AVCNFSM6AAAAABM3FVFVSVHI2DSMVQWIX3LMV43ASLTON2WKOZSGQ3TOMBSHE4TQOI. You are receiving this because you are subscribed to this thread.Message ID: @.***>

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from breeze_connect import BreezeConnect import urllib import warnings warnings.filterwarnings('ignore') import pandas as pd from py_vollib.black_scholes import black_scholes from py_vollib.black_scholes.greeks.numerical import delta, gamma, theta, vega, rho from scipy.optimize import brentq import numpy as np from datetime import datetime

Connecting to Breeze

breeze = BreezeConnect(api_key=str(app_key))

print("https://api.icicidirect.com/apiuser/login?api_key="+urllib.parse.quote_plus(str(app_key)))

breeze.generate_session(api_secret=str(secret_key),session_token=str(session_key))

Finding price of an option

call

S = 24117 # Underlying asset price K_call = 24150 # Option strike price t = 7/365 # Time to expiration in years r = 0.10 # Risk-free interest rate sigma_call = 0.13 # Volatility flag_call = 'c' # 'c' for Call, 'p' for Put

Option price

option_price_call = black_scholes(flag_call, S, K_call, t, r, sigma_call) print("Option Price_Call:", option_price_call)

Put

S = 24117 # Underlying asset price K_put = 24150 # Option strike price t = 7/365 # Time to expiration in years r = 0.10 # Risk-free interest rate sigma_put = 0.17 # Volatility flag_put = 'p' # 'c' for Call, 'p' for Put

Option price

option_price_put = black_scholes(flag_put, S, K_put, t, r, sigma_put) print("Option Price_Put:", option_price_put)

Finding Greeks of an option

Put

Delta

delta_value_put = delta(flag_put, S, K_put, t, r, sigma_put) print("Delta_put:", delta_value_put)

Gamma

gamma_value_put = gamma(flag_put, S, K_put, t, r, sigma_put) print("Gamma_put:", gamma_value_put)

Theta

theta_value_put = theta(flag_put, S, K_put, t, r, sigma_put) print("Theta_put:", theta_value_put)

Vega

vega_value_put = vega(flag_put, S, K_put, t, r, sigma_put) print("Vega_put:", vega_value_put)

Rho

rho_value_put = rho(flag_put, S, K_put, t, r, sigma_put) print("Rho_put:", rho_value_put)

call

Delta

delta_value_call = delta(flag_call, S, K_call, t, r, sigma_call) print("Delta_call:", delta_value_call)

Gamma

gamma_value_call =gamma(flag_call, S, K_call, t, r, sigma_call) print("Gamma_call:", gamma_value_call)

Theta

theta_value_call = theta(flag_call, S, K_call, t, r, sigma_call) print("Theta_call:", theta_value_call)

Vega

vega_value_call = vega(flag_call, S, K_call, t, r, sigma_call) print("Vega_call:", vega_value_call)

Rho

rho_value_call = rho(flag_call, S, K_call, t, r, sigma_call) print("Rho_call:", rho_value_call)

Calculating Option Greeks in Option Chain

find ATM strike Price

Quotes = breeze.get_quotes(stock_code="NIFTY", exchange_code="NSE", expiry_date="", product_type="cash", right="", strike_price="")

Spot = Quotes["Success"][0]["ltp"] ATM = round((Quotes["Success"][0]["ltp"])/50)*50 print(ATM)

Simulate the data retrieval from breeze for Call

df = breeze.get_option_chain_quotes(stock_code="NIFTY", exchange_code="NFO", product_type="options", expiry_date="2024-08-14T06:00:00.000Z", right="call")["Success"]

Convert to DataFrame

df = pd.DataFrame(df)

Convert strike_price to float

df['strike_price'] = df['strike_price'].astype(float)

Define target strike and range parameters

target_strike = ATM strike_increment = 50 range_size = 5

Define the range for strike prices

lower_bound = target_strike - range_size strike_increment upper_bound = target_strike + range_size strike_increment

Filter the rows where the strike price is within the range and in increments of 50

filtered_df = df[(df['strike_price'] >= lower_bound) & (df['strike_price'] <= upper_bound) & (df['strike_price'] % strike_increment == 0)]

Print the filtered DataFrame to debug

print("Filtered DataFrame:", filtered_df[['strike_price']])

Check if any rows match the criteria

if filtered_df.empty: print("No strike prices found within the specified range.") else:

Sort by strike price

sorted_df = filtered_df.sort_values('strike_price').reset_index(drop=True)

# Check if the target_strike exists
if target_strike in sorted_df['strike_price'].values:
    target_index = sorted_df[sorted_df['strike_price'] == target_strike].index[0]

    # Slice the DataFrame to get 5 rows above and 5 rows below the target
    start_index = max(0, target_index - 5)
    end_index = min(len(sorted_df), target_index + 6)
    final_df = sorted_df.iloc[start_index:end_index]

    # Select only the required columns
    final_df = final_df[['stock_code', 'expiry_date', 'right', 'strike_price', 'ltp']]

    # Define constants for Greeks calculation
    risk_free_rate = 0.05  # Example risk-free rate (5%)

    # Add spot price column for the calculation
    final_df['spot_price'] = Spot  # Example spot price

    # Function to calculate implied volatility using LTP
    def calculate_implied_volatility(row):
        S = row['spot_price']  # Spot price
        K = row['strike_price']  # Strike price
        T = (pd.to_datetime(row['expiry_date']) - pd.Timestamp.now()).days / 365  # Time to expiration in years
        r = risk_free_rate  # Risk-free rate
        option_price = row['ltp']  # Last traded price
        right = row['right'].lower()  # 'call' or 'put'

        # Define the function to calculate implied volatility
        def objective_function(sigma):
            if right == 'call':
                option_type = 'c'
            elif right == 'put':
                option_type = 'p'
            else:
                raise ValueError("Invalid option type")

            return black_scholes(option_type, S, K, T, r, sigma) - option_price

        # Solve for implied volatility
        try:
            implied_vol = brentq(objective_function, 0.01, 5.0)
        except ValueError:
            implied_vol = np.nan
        return implied_vol

    # Calculate implied volatility and add to DataFrame
    final_df['implied_volatility'] = final_df.apply(calculate_implied_volatility, axis=1)

    # Function to calculate Greeks
    def calculate_greeks(row):
        S = row['spot_price']  # Spot price
        K = row['strike_price']  # Strike price
        T = (pd.to_datetime(row['expiry_date']) - pd.Timestamp.now()).days / 365  # Time to expiration in years
        r = risk_free_rate  # Risk-free rate
        sigma = row['implied_volatility']  # Implied volatility
        right = row['right'].lower()  # 'call' or 'put'

        if right == 'call':
            option_type = 'c'
        elif right == 'put':
            option_type = 'p'
        else:
            raise ValueError("Invalid option type")

        # Calculate Greeks using py_vollib
        delta_value = delta(option_type, S, K, T, r, sigma)
        gamma_value = gamma(option_type, S, K, T, r, sigma)
        theta_value = theta(option_type, S, K, T, r, sigma)
        vega_value = vega(option_type, S, K, T, r, sigma)
        rho_value = rho(option_type, S, K, T, r, sigma)

        return pd.Series([delta_value, gamma_value, theta_value, vega_value, rho_value], 
                         index=['Delta', 'Gamma', 'Theta', 'Vega', 'Rho'])

    # Calculate Greeks and add to DataFrame
    greeks_df = final_df.apply(calculate_greeks, axis=1)
    result_df = pd.concat([final_df, greeks_df], axis=1)

    # Round the values
    result_df['implied_volatility'] = result_df['implied_volatility'].round(2)
    result_df['Delta'] = result_df['Delta'].round(2)
    result_df['Theta'] = result_df['Theta'].round(2)
    result_df['Vega'] = result_df['Vega'].round(2)
    result_df['Rho'] = result_df['Rho'].round(2)

    # Print the resulting DataFrame with Greeks
    print("Final DataFrame with Greeks for Call:")
    print(result_df)
else:
    print(f"Strike price {target_strike} not found in the filtered data.")

Option greeks for Put

df = breeze.get_option_chain_quotes(stock_code="NIFTY", exchange_code="NFO", product_type="options", expiry_date="2024-08-14T06:00:00.000Z", right="put")["Success"]

Convert to DataFrame

df = pd.DataFrame(df)

Convert strike_price to float

df['strike_price'] = df['strike_price'].astype(float)

Define target strike and range parameters

target_strike = ATM strike_increment = 50 range_size = 5

Define the range for strike prices

lower_bound = target_strike - range_size strike_increment upper_bound = target_strike + range_size strike_increment

Filter the rows where the strike price is within the range and in increments of 50

filtered_df = df[(df['strike_price'] >= lower_bound) & (df['strike_price'] <= upper_bound) & (df['strike_price'] % strike_increment == 0)]

Print the filtered DataFrame to debug

print("Filtered DataFrame:", filtered_df[['strike_price']])

Check if any rows match the criteria

if filtered_df.empty: print("No strike prices found within the specified range.") else:

Sort by strike price

sorted_df = filtered_df.sort_values('strike_price').reset_index(drop=True)

# Check if the target_strike exists
if target_strike in sorted_df['strike_price'].values:
    target_index = sorted_df[sorted_df['strike_price'] == target_strike].index[0]

    # Slice the DataFrame to get 5 rows above and 5 rows below the target
    start_index = max(0, target_index - 5)
    end_index = min(len(sorted_df), target_index + 6)
    final_df = sorted_df.iloc[start_index:end_index]

    # Select only the required columns
    final_df = final_df[['stock_code', 'expiry_date', 'right', 'strike_price', 'ltp']]

    # Define constants for Greeks calculation
    risk_free_rate = 0.05  # Example risk-free rate (5%)

    # Add spot price column for the calculation
    final_df['spot_price'] = Spot  # Example spot price

    # Function to calculate implied volatility using LTP
    def calculate_implied_volatility(row):
        S = row['spot_price']  # Spot price
        K = row['strike_price']  # Strike price
        T = (pd.to_datetime(row['expiry_date']) - pd.Timestamp.now()).days / 365  # Time to expiration in years
        r = risk_free_rate  # Risk-free rate
        option_price = row['ltp']  # Last traded price
        right = row['right'].lower()  # 'call' or 'put'

        # Define the function to calculate implied volatility
        def objective_function(sigma):
            if right == 'call':
                option_type = 'c'
            elif right == 'put':
                option_type = 'p'
            else:
                raise ValueError("Invalid option type")

            return black_scholes(option_type, S, K, T, r, sigma) - option_price

        # Solve for implied volatility
        try:
            implied_vol = brentq(objective_function, 0.01, 5.0)
        except ValueError:
            implied_vol = np.nan
        return implied_vol

    # Calculate implied volatility and add to DataFrame
    final_df['implied_volatility'] = final_df.apply(calculate_implied_volatility, axis=1)

    # Function to calculate Greeks
    def calculate_greeks(row):
        S = row['spot_price']  # Spot price
        K = row['strike_price']  # Strike price
        T = (pd.to_datetime(row['expiry_date']) - pd.Timestamp.now()).days / 365  # Time to expiration in years
        r = risk_free_rate  # Risk-free rate
        sigma = row['implied_volatility']  # Implied volatility
        right = row['right'].lower()  # 'call' or 'put'

        if right == 'call':
            option_type = 'c'
        elif right == 'put':
            option_type = 'p'
        else:
            raise ValueError("Invalid option type")

        # Calculate Greeks using py_vollib
        delta_value = delta(option_type, S, K, T, r, sigma)
        gamma_value = gamma(option_type, S, K, T, r, sigma)
        theta_value = theta(option_type, S, K, T, r, sigma)
        vega_value = vega(option_type, S, K, T, r, sigma)
        rho_value = rho(option_type, S, K, T, r, sigma)

        return pd.Series([delta_value, gamma_value, theta_value, vega_value, rho_value], 
                         index=['Delta', 'Gamma', 'Theta', 'Vega', 'Rho'])

    # Calculate Greeks and add to DataFrame
    greeks_df = final_df.apply(calculate_greeks, axis=1)
    result_df = pd.concat([final_df, greeks_df], axis=1)

    # Round the values
    result_df['implied_volatility'] = result_df['implied_volatility'].round(2)
    result_df['Delta'] = result_df['Delta'].round(2)
    result_df['Theta'] = result_df['Theta'].round(2)
    result_df['Vega'] = result_df['Vega'].round(2)
    result_df['Rho'] = result_df['Rho'].round(2)

    # Print the resulting DataFrame with Greeks
    print("Final DataFrame with Greeks for Put:")
    print(result_df)
else:
    print(f"Strike price {target_strike} not found in the filtered data.")

Idirect-Tech / Breeze-Python-SDK

In option chain, iv and Greek values are not available #130

Connecting to Breeze

Finding price of an option

call

Option price

Put

Option price

Finding Greeks of an option

Put

Delta

Gamma

Theta

Vega

Rho

call

Delta

Gamma

Theta

Vega

Rho

Calculating Option Greeks in Option Chain

find ATM strike Price

Simulate the data retrieval from breeze for Call

Convert to DataFrame

Convert strike_price to float

Define target strike and range parameters

Define the range for strike prices

Filter the rows where the strike price is within the range and in increments of 50

Print the filtered DataFrame to debug

print("Filtered DataFrame:", filtered_df[['strike_price']])

Check if any rows match the criteria

Sort by strike price

Option greeks for Put

Convert to DataFrame

Convert strike_price to float

Define target strike and range parameters

Define the range for strike prices

Filter the rows where the strike price is within the range and in increments of 50

Print the filtered DataFrame to debug

print("Filtered DataFrame:", filtered_df[['strike_price']])

Check if any rows match the criteria

Sort by strike price