Top 10 Python Libraries For Stock Options Trading

pandas: for data manipulation and analysis
numpy: for numerical computation
scipy: for scientific and technical computing
matplotlib: for data visualization
yfinance: for retrieving stock data from Yahoo Finance
zipline: for backtesting algorithmic trading strategies
ib_insyc: for connecting to Interactive Brokers' Trader Workstation (TWS) API
pyfolio: for performance and risk analysis of financial portfolios
black: for option pricing using the Black-Scholes model
py_vollib: for option pricing and implied volatility calculations.

Examples of each Python Library Package

Pandas

pandas: You can use pandas to load and manipulate option chain data, such as reading in a CSV file of options data and manipulating the dataframe to extract specific information, such as strike prices and expiration dates.

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import pandas as pd

# Read in options data from a CSV file
options_data = pd.read_csv('options_data.csv')

# Extract specific information from the dataframe
strike_prices = options_data['strike_price']
expiration_dates = options_data['expiration_date']

Numpy

numpy: You can use numpy for numerical computations, such as calculating the implied volatility of an option using the Black-Scholes model.

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import numpy as np
from scipy.stats import norm

# Define the Black-Scholes function
def black_scholes(S, K, T, r, sigma):
    d1 = (np.log(S / K) + (r + 0.5 * sigma ** 2) * T) / (sigma * np.sqrt(T))
    d2 = d1 - sigma * np.sqrt(T)
    call = S * norm.cdf(d1) - K * np.exp(-r * T) * norm.cdf(d2)
    return call

# Calculate the implied volatility of an option
S = 100.0  # Stock price
K = 105.0  # Strike price
T = 1.0    # Time to expiration (in years)
r = 0.05   # Risk-free rate
call_price = 9.0  # Market price of the call option

implied_vol = optimize.newton(lambda x: black_scholes(S, K, T, r, x) - call_price, 0.2)

Scipy

scipy: You can use scipy for scientific and technical computing, such as using the scipy.optimize.minimize function to find the optimal values of parameters in an options trading strategy.

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from scipy.optimize import minimize

# Define the objective function for optimization
def objective(x):
    # x[0], x[1] are the parameters to optimize
    return (x[0] - 2)**2 + (x[1] - 3)**2

# Define the initial guess for the optimization
x0 = [0, 0]

# Perform the optimization
res = minimize(objective, x0)

# Print the optimal values of the parameters
print(res.x)

Matplotlib

matplotlib: You can use matplotlib to create visualizations of options data, such as creating a graph of the implied volatility surface for a specific stock.

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import matplotlib.pyplot as plt

# Create a scatter plot of implied volatilities
plt.scatter(strike_prices, implied_volatilities)

# Add labels to the axes
plt.xlabel('Strike Price')
plt.ylabel('Implied Volatility')

# Show the plot
plt.show()

yfinance

yfinance: You can use yfinance to retrieve stock data from Yahoo Finance, for example you can use it to download historical option prices for a specific stock and then use the data in your options trading strategy.

https://tradestie.com/apps/user/blog/how-to-use-yfinance-package-to-get-stock-data-free/

zipline

zipline: You can use zipline to backtest an options trading strategy, such as implementing a basic moving average strategy and testing it against historical options data.

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from zipline.api import order_target, record, symbol

# Define the moving average strategy
def moving_average_strategy(context

    # Access the 30-day moving average from the context
    ma = context.moving_average

    # Get the current stock price
    price = context.portfolio.positions[symbol('AAPL')].last_sale_price

    # Check if the current price is above or below the moving average
    if price > ma:
        # Buy the stock
        order_target(symbol('AAPL'), 1)
    else:
        # Sell the stock
        order_target(symbol('AAPL'), 0)

    # Record the current price and moving average
    record(price=price, ma=ma)

# Run the strategy
results = run_algorithm(start=datetime(2018, 1, 1),
                       end=datetime(2018, 12, 31),
                       initialize=initialize,
                       handle_data=moving_average_strategy)

ib_insync

ib_insyc: You can use ib_insyc to connect to Interactive Brokers' Trader Workstation (TWS) API and retrieve real-time options data, and also to execute trades based on your options trading strategy.

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from ib_insync import IB

# Connect to TWS
ib = IB()
ib.connect()

# Request option chain data for a specific stock
ib.reqSecDefOptParams(underlyingSymbol='AAPL', futFopExchange='SMART', underlyingSecType='STK')

# Execute an options trade
ib.placeOrder(contract, OrderSell('SELL', 1))

pyfolio

pyfolio: You can use pyfolio to analyze the performance and risk of a portfolio of options trades, such as calculating the Sharpe ratio and the maximum drawdown of the portfolio.

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import pyfolio as pf

# Load returns data from a CSV file
returns = pf.utils.get_symbol_returns('AAPL', start='2010-01-01', end='2020-01-01')

# Calculate the Sharpe ratio of the returns
sharpe_ratio = pf.timeseries.calc_sharpe_ratio(returns)

# Calculate the maximum drawdown of the returns
max_drawdown = pf.timeseries.gen_drawdown_table(returns)['drawdown'].min()

black

black: You can use black to price options using the Black-Scholes model, for example you can use it to calculate the theoretical value of a specific option.

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from black import implied_volatility

# Define the option contract
contract = {'strike': 105.0, 'expiration': 1.0, 'option_type': 'call'}

# Calculate the implied volatility of the option
implied_vol = implied_volatility(9.0, contract, underlying_price=100.0, rf_rate=0.05)

py_vollib

py_vollib: You can use py_vollib to calculate the implied volatility of an option using various volatility models, like Black-Scholes-Merton and implied volatility surface.

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from py_vollib.black_scholes import implied_volatility

# Calculate the implied volatility of an option
underlying_price = 100
strike = 105
rf_rate = 0.05
days_to_expiration = 30
call_price = 9.0

implied_vol = implied_volatility(flag='c', S=underlying_price, K=strike, t=days_to_expiration/365, r=rf_rate, sigma=0.2)