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Datasets:
gauss314
/
options-IV-SP500

Tasks:
Tabular Classification
Tabular Regression
Modalities:
Tabular
Text
Formats:
csv
Size:
1M - 10M
Tags:
NYSE
options
calls
puts
sp500
volatility
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
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metadata
license: apache-2.0
task_categories:
  - tabular-classification
  - tabular-regression
tags:
  - NYSE
  - options
  - calls
  - puts
  - sp500
  - volatility
  - implied volatility
  - vix
  - IV
pretty_name: USA options implied volatility features for machine learning
size_categories:
  - 1M<n<10M

Downloading the Options IV SP500 Dataset

This document will guide you through the steps to download the Options IV SP500 dataset from Hugging Face Datasets. This dataset includes data on the options of the S&P 500, including implied volatility.

To start, you'll need to install Hugging Face's datasets library if you haven't done so already. You can do this using the following pip command:

!pip install datasets

Here's the Python code to load the Options IV SP500 dataset from Hugging Face Datasets and convert it into a pandas DataFrame:

from datasets import load_dataset
import pandas as pd

id = "gauss314/options-IV-SP500"
data_iv = load_dataset(id)
df_iv = pd.DataFrame(data_iv['train'][:])

The dataset provided includes a variety of features and targets. In machine learning and predictive modeling, features are the input variables used to predict target variables, or the outcomes we're interested in predicting.

The features in this dataset encompass all of the data columns except for DITM_IV, ITM_IV, sITM_IV, ATM_IV, sOTM_IV, OTM_IV, and DOTM_IV. These features include data on traded contracts, open interest, the spread of strike prices, and the number of different expiration dates, among others. These features can be used to understand the characteristics of the security's options and their trading activity.

The target variables in this dataset are DITM_IV, ITM_IV, sITM_IV, ATM_IV, sOTM_IV, OTM_IV, and DOTM_IV. These represent implied volatilities for different categories of options, which are what we would be interested in predicting in a regression or classification model. Implied volatility is a key concept in options trading as it reflects the market's expectation of future volatility of the underlying security's price.

This dataset can also be used in dimensionality reduction machine learning models. These models aim to reduce the number of input variables in a dataset, while preserving as much of the relevant information as possible. Dimensionality reduction can be particularly useful when dealing with high-dimensional data, and can help improve model performance, speed up training times, and mitigate issues such as the curse of dimensionality.

This dataset has been shared specifically for the course "Applied Artificial Intelligence" at UCEMA. Students in this course can use this dataset to practice building and evaluating different types of predictive models, as well as working with real-world financial data. Remember, the application of AI and machine learning in finance is a complex field and should be approached with careful understanding of both the financial and the technical aspects.

Features

  • symbol: This represents the ticker symbol of the security, it is an unique series of letters representing a particular security listed on an exchange.

  • date: The date of the recorded data.

  • strikes_spread: The difference in strike prices for call and put options. Strike price is the set price at which an option contract can be bought or sold when it is exercised.

  • calls_contracts_traded: The total number of call option contracts that have been traded.

  • puts_contracts_traded: The total number of put option contracts that have been traded.

  • calls_open_interest: The number of outstanding call contracts that haven't been exercised or allowed to expire.

  • puts_open_interest: The number of outstanding put contracts that haven't been exercised or allowed to expire.

  • expirations_number: The number of different expiration dates for the options.

  • contracts_number: The total number of options contracts.

  • hv_20, hv_40, hv_60, hv_75, hv_90, hv_120, hv_180, hv_200: These represent historical volatility values over different periods of trading days (20, 40, 60, 75, 90, 120, 180, 200). Historical volatility measures the price changes of a security and is used to predict future price volatility.

Possible targets:

  • DITM_IV, ITM_IV, sITM_IV, ATM_IV, sOTM_IV, OTM_IV, DOTM_IV: These are implied volatilities (IV) for different categories of options: Deep-In-The-Money (DITM), In-The-Money (ITM), Slightly-In-The-Money (sITM), At-The-Money (ATM), Slightly-Out-Of-The-Money (sOTM), Out-Of-The-Money (OTM), Deep-Out-Of-The-Money (DOTM). Implied volatility is a metric that captures the market's view of the likelihood of changes in a given security's price.