Access real-time and historical US equity options data included as part of Databento's OPRA data feed. The NASDAQ Options Market offers immediate and automatic price improvement to orders. Orders designated to use the options routing feature are routed to other markets to ensure orders get the best price available. The NASDAQ Options Market also links to and complies with the obligations of the Options InterMarket Linkage.

Historical option data in 2014 to 2021, dataset files in CSV format.

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. Nasdaq MRX offers a customer priority, pro-rata allocation market and a price-time complex market. MRX provides similar features to ISE such as price improvement, routing strategies, and complex order book, with one main difference being an alternative pricing model.

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. Nasdaq BX utilizes a taker-maker pricing model and offers economic incentives for liquidity takers. Nasdaq BX features popular order types such as Mid-Point Peg and Post-Only orders, Order Modify functionality and Self Match Prevention. It also offers a Retail Price Improvement Program for retail investors.

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. Cboe EDGX is an all-electronic options exchange that holds a classic pro rata/customer priority/designated market maker (DMM) model, and was designed to complement Cboe BZX.

Option Care Health stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. Cboe was the first options exchange to launch in the United States. It currently operates a hybrid system offering electronic and floor-based trading, and holds a pro rata allocation model.

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. Nasdaq PHLX is a full service options trading platform offering both electronic and floor-based trading. PHLX runs a customer priority, pro rata allocation model focused on executions for Complex and Simple Orders. Features of PHLX include: price improvement on XL (PIXL), complex order systerm, flexible routing strategies, and more.

Option Care Health reported $409.73M in Trade Debtors for its fiscal quarter ending in December of 2024. Data for Option Care Health | BIOS - Trade Debtors including historical, tables and charts were last updated by Trading Economics this last March in 2025.

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. BOX is an electronic trading exchange that holds a price-time priority model, with the exception of certion options classes using a pro rata priority model. BOX is the first options market to offer a Price Improvement Period which allows investors the potential for price improvements through an electronic auction process.

Option Care Health reported $610.78M in Trade Creditors for its fiscal quarter ending in December of 2024. Data for Option Care Health | BIOS - Trade Creditors including historical, tables and charts were last updated by Trading Economics this last March in 2025.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-03-24 about VIX, volatility, stock market, and USA.

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. Nasdaq International Securities Exchange (ISE) was the first all-electronic options exchange to launch in the United States. ISE holds a modified maker-taker model and a pro-rata allocation model focusing on executions for Simple, Complex, and Crossing Orders. It aims to meet the needs of the entire trading industry, offering features such as: routing strategies, implied orders, complex order book, and more.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

LON:HSV Options & Futures Prediction

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Our proprietary Skew-Adjusted Gamma Exposure measurements make adjustments to Naive GEX calculations to more accurately reflect actual gamma positioning of Market Makers who employ delta-hedging strategies. When Market Makers carry substantial negative gamma a security will often "over-react" to fundamental news. Conversely, when MMs carry substantial positive gamma a security will often "under-react" to news. Our data includes a quantified segmentation of a security's gamma distribution across all option strikes as well as across relevant expiration dates. Our website provides numerical, graphical, and historical views of all gamma data in our database. Additionally, our API access allows for easy download of csv files or import into Excel for further analysis and custom applications.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

WPP Options & Futures Prediction

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Access real-time and historical US equity options data included as part of Databento's OPRA data feed. MIAX Emerald is an all-electronic options exchange that holds a maker-taker and pro-rata allocation model, encouraging market participants to quote in large bid and offer sizes. It was designed to act as a counterpart to both MIAX Options and MIAX Pearl, focusing on both Simple Orders/Quotes and Complex Orders/Quotes.

In May 2024, over six million options were traded on the Australian Securities Exchange (ASX). This is above the monthly average of around 5.5 million recorded since January 2020, and an increase from the 5.18 million recorded in the previous month. However, The ASX options market is much lower than the volume of futures traded on the ASX. Options and futures are similar in that they are both financial derivatives that provide an investor the ability to buy (or sell) a financial asset for an agreed price at a certain point in time, but they differ in that futures require that the transaction take place, whereas options do not. Options and the coronavirus pandemic Coinciding with the global coronavirus (COVID-19) pandemic, the volume of options traded on the Australian Securities Exchange (ASX) spiked in March 2020. It is notable that the spike in terms of the value of options traded was much greater than in terms of volume. It is also notable that the majority of the spike in this month came from call options - which enable the option holder to purchase a financial instrument (like shares) for an agreed price at a date in the future. By contrast, put options enable holders to sell a financial instrument at an agreed value in the future. This suggests that the increased value for this month was driven by investors trying to capitalize on the pandemic by locking in lower prices for the future, with the (correct) assumption that prices would rise again in the following months. How is the value of derivatives calculated? Calculating the value of derivatives is different to an item like shares, in that derivatives contracts do not include the underlying asset price. Both options and futures are contracts which provide the ability to purchase a financial asset in the future for an agreed price – meaning the purchase of the contract does not include the purchasing of the asset itself. Generally, the ‘notional value’ is used to calculate the value of derivatives – which includes both the cost of the contract itself as well as the underlying asset. Note how options do not require the transaction take place, but yet the value of transaction is included. This one reason behind why, for example, banks in the U.S. and banks in the UK can hold derivates that are well above the national gross domestic product of their respective countries.

Deterministic and stochastic are two methods for modeling of crude oil and bottled water market. Forecasting the price of the market directly affected energy producer and water user.There are two software, Tableau and Python, which are utilized to model and visualize both markets for the aim of estimating possible price in the future.The role of those software is to provide an optimal alternative with different methods (deterministic versus stochastic). The base of predicted price in Tableau is deterministic—global optimization and time series. In contrast, Monte Carlo simulation as a stochastic method is modeled by Python software. The purpose of the project is, first, to predict the price of crude oil and bottled water with stochastic (Monte Carlo simulation) and deterministic (Tableau software),second, to compare the prices in a case study of Crude Oil Prices: West Texas Intermediate (WTI) and the U.S. bottled water. 1. Introduction Predicting stock and stock price index is challenging due to uncertainties involved. We can analyze with a different aspect; the investors perform before investing in a stock or the evaluation of stocks by means of studying statistics generated by market activity such as past prices and volumes. The data analysis attempt to identify stock patterns and trends that may predict the estimation price in the future. Initially, the classical regression (deterministic) methods were used to predict stock trends; furthermore, the uncertainty (stochastic) methods were used to forecast as same as deterministic. According to Deterministic versus stochastic volatility: implications for option pricing models (1997), Paul Brockman & Mustafa Chowdhury researched that the stock return volatility is deterministic or stochastic. They reported that “Results reported herein add support to the growing literature on preference-based stochastic volatility models and generally reject the notion of deterministic volatility” (Pag.499). For this argument, we need to research for modeling forecasting historical data with two software (Tableau and Python). In order to forecast analyze Tableau feature, the software automatically chooses the best of up to eight models which generates the highest quality forecast. According to the manual of Tableau , Tableau assesses forecast quality optimize the smoothing of each model. The optimization model is global. The main part of the model is a taxonomy of exponential smoothing that analyzes the best eight models with enough data. The real- world data generating process is a part of the forecast feature and to support deterministic method. Therefore, Tableau forecast feature is illustrated the best possible price in the future by deterministic (time – series and prices). Monte Carlo simulation (MCs) is modeled by Python, which is predicted the floating stock market index . Forecasting the stock market by Monte Carlo demonstrates in mathematics to solve various problems by generating suitable random numbers and observing that fraction of the numbers that obeys some property or properties. The method utilizes to obtain numerical solutions to problems too complicated to solve analytically. It randomly generates thousands of series representing potential outcomes for possible returns. Therefore, the variable price is the base of a random number between possible spot price between 2002-2016 that present a stochastic method.