NSDQ

• Interacts with the NASDAQ API to retrieve asset-related data (quotes, historical data, option chains, etc.).

• Retrieves real-time trade data and both raw and processed option chain data.

• Calculates implied volatility and option Greeks using pricing models (e.g., Black-Scholes-Merton).

• Provides dedicated functions to plot 2D & 3D representations of Greeks, option prices & implied volatilities over strike and time to expiry.

• Guide is available here.

Requirements & Installation :

• Requirements: requests, datetime, pandas, numpy, scipy, py_vollib_vectorized, matplotlib, re.
• Install using: pip install NSDQ (https://pypi.org/project/NSDQ/)

Example:

import NSDQ

# Retrieve asset information (e.g., for AAPL)
Apple = NSDQ.Asset("AAPL", "stocks")
print(Apple.Informations())
# >>> {'Symbol': 'AAPL', 'Exchange': 'NASDAQ', 'Sector': 'Technology', ...}

# Get the asset's historical data as a DataFrame
historical_data = Apple.HistoricalData()
print(historical_data.head())

# Retrieve the most recent real-time trades (e.g., the last 5 trades)
RealtimeTrades = Apple.RealTime(NumberOfTrades=5)
print(RealtimeTrades)

# Retrieve the processed option chain data (for both calls and puts)
Options = Apple.Options(
    DataType="processed", 
    ExchangeCode="oprac", 
    Strategy="callput", 
    RiskFreeRate=0.04375,
)
print(OptionsChain.Data.head())

# Create and display 2D plots of Greeks vs Strike & Time to Expiry:
Options.Plot2D(Type='call')

# Create and display 3D plots of Greeks vs Strike & Time to Expiry:
Options.Plot3D(Type='call', ViewAngle=(15,45))

Note:

• Implied Volatilities are calculated using the `py_vollib` library.
• Outliers are identified using Median Absolute deviation (MAD).
• An Implied Volatility Value is considered an outlier if:

\frac{X_i - \tilde{X}}{\mathrm{MAD}} > 3, \quad \text{where } \mathrm{MAD} = \mathrm{median}\left(|X_i - \tilde{X}|\right)

• Greeks are then calculated for remaining options.

When plotting:

• "Years Until Expiry" is converted into months and then rounded to the nearest multiple of 3.
• "Contract Strike" is rounded down to the nearest multiple of 10.
• The data is then grouped by these two new columns, and the mean of the specified column is computed for each group.
• Important: This is a basic visualization; therefore, the results should be interpreted with caution.