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This dataset is a combination of four years of Apple ($AAPL) options end of day quotes ranging from 01-2016 to 03-2023. Each row represents the information associated with one contract's strike price and a given expiration date.

Dates quotes are given in in Unix and in "YYYY-MM-DD HH:MM" formats. Quote frequency is daily at 4:00 pm EST, which corresponds with end of day market closure.

REMEMBER: Apple stock split on August 28, 2020. This will be reflected in the data. Keep this in mind!

What is an option chain?

An option chain can be defined as the listing of all option contracts. It comes with two different sections: call and put. A call option means a contract that gives you the right but does not give you the obligation to buy an underlying asset at a particular price and within the option's expiration date. This means that in this dataset, there will be the entire option chain (all available option contracts for all expirations) for each business day between Q1 2016 and Q1 2023.

This dataset contains data for American options, which can be exercised on or before expiration date. This is unlike European options contracts, which can only be exercised on the expiration date.

I am also continuously working on the associated notebook to give a basic idea of how to load and explore the data. Stay tuned!

Similar Datasets: - $TSLA Option Chains - $SPY Option Chains - $NVDA Option Chains - $QQQ Option Chains

Free historical options data, dataset files in CSV format.

Title: Historical Options Data for BANKNIFTY Index

Description: This dataset provides historical options data for the BANKNIFTY index, which is the benchmark index for the banking sector in India. The dataset includes information on the ticker, date, time, open, high, low, close, volume, and open interest for various call options contracts.

The data is provided in CSV format and covers the time period from March 1, 2021 to the present day. Each row in the dataset corresponds to a single options contract, and includes information on the opening and closing prices, as well as the trading volume and open interest for that contract.

Columns:

Ticker: the ticker symbol for the options contract (string) Date: the date when the contract was traded (date) Time: the time when the contract was traded (time) Open: the opening price for the contract (float) High: the highest price for the contract during the trading session (float) Low: the lowest price for the contract during the trading session (float) Close: the closing price for the contract (float) Volume: the total number of contracts traded during the session (int) Open Interest: the number of outstanding contracts at the end of the session (int) Example entry:

Ticker Date Time Open High Low Close Volume Open Interest BANKNIFTY01APR2130600CE 03/01/2021 12/31/1899 14:39 5057.2 5065 5057.2 5065 50 48000

This dataset can be used to perform various types of analysis on options trading for the BANKNIFTY index, such as calculating the daily trading volume and open interest, identifying trends and patterns in the price movements of options contracts, and developing models to predict future price movements based on historical data.

Context

This Data is gathered from NSE website for the past three months I am posting this here so people can analyse this data and gather meaningful insights from this.

Example - Probability of Stock ending up at Max Pain with the help of Open Interest.

Content

The dataset contains stock symbol with which it is traded, Expiry Date. Strike Price and the Option pricing of the Symbol at that Strike price.

Acknowledgements

I thank the people working at NSE for publishing these reports everyday.

Inspiration

Whenever we want to initiate an Options trade we look at various parameters like OpenInterest, Change in OI, Technical Analysis Indicators before deciding to Buy/Sell the Option. Most times we need to browse to multiple websites to gather the data we need, This is an example to show how you can customise the data for our needs.

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.

Nifty50 Futures & Options (F&O) Filtered Historical Dataset

This dataset contains filtered F&O (Futures & Options) data for Nifty50 stocks. It includes detailed information such as open interest, volume, expiry dates, instrument types, and contract metadata — curated for options trading, F&O analytics, and quantitative strategy development.

Key Features

• Instrument types: FUTSTK, OPTSTK
• Contract metadata: expiry date, strike price, option type (CE/PE)
• Daily trading data: open, high, low, close, volume, open interest
• Filtered and cleaned for Nifty50 stocks only

Use Cases

• F&O analytics and strategy backtesting
• Options chain modeling and open interest trends
• Expiry-based contract behavior analysis
• Volatility and sentiment modeling using derivatives data

License

Released under CC0 1.0 Universal Public Domain. Free to use for personal, academic, or commercial purposes.

Keywords

nifty50, futures and options, derivatives, stock market, expiry data, open interest, strike price, options chain

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

Option Care Health reported $415.72M in Stock for its fiscal quarter ending in September of 2025. Data for Option Care Health | BIOS - Stock including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Option Care Health reported $22.06 in PE Price to Earnings for its fiscal quarter ending in September of 2025. Data for Option Care Health | BIOS - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Hong Kong Open Interest: Option: Stock Option data was reported at 9,856,996.000 Contract in Nov 2018. This records an increase from the previous number of 9,108,731.000 Contract for Oct 2018. Hong Kong Open Interest: Option: Stock Option data is updated monthly, averaging 3,098,809.000 Contract from Sep 1995 (Median) to Nov 2018, with 279 observations. The data reached an all-time high of 11,237,548.000 Contract in Jan 2018 and a record low of 35,570.000 Contract in Sep 1995. Hong Kong Open Interest: Option: Stock Option data remains active status in CEIC and is reported by Hong Kong Exchanges and Clearing Limited. The data is categorized under Global Database’s Hong Kong SAR – Table HK.Z011: Derivatives Market: Futures and Options: Open Interest.

China Options: Shanghai Stock Exchange: 50ETF: Open Interest: Call data was reported at 571.292 Contract th in 01 Dec 2025. This records a decrease from the previous number of 576.998 Contract th for 28 Nov 2025. China Options: Shanghai Stock Exchange: 50ETF: Open Interest: Call data is updated daily, averaging 1,012.312 Contract th from Feb 2015 (Median) to 01 Dec 2025, with 2626 observations. The data reached an all-time high of 2,763.982 Contract th in 22 Nov 2019 and a record low of 4.923 Contract th in 09 Feb 2015. China Options: Shanghai Stock Exchange: 50ETF: Open Interest: Call data remains active status in CEIC and is reported by Shanghai Stock Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZI: Shanghai Stock Exchange: Options: Open Interest: Daily.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-12-01 about VIX, volatility, stock market, and USA.

Hong Kong Open Interest: Option: Stock Option: Puts data was reported at 4,776,896.000 Contract in Jun 2018. This records a decrease from the previous number of 5,141,034.000 Contract for May 2018. Hong Kong Open Interest: Option: Stock Option: Puts data is updated monthly, averaging 1,562,540.000 Contract from Sep 1995 (Median) to Jun 2018, with 274 observations. The data reached an all-time high of 5,141,034.000 Contract in May 2018 and a record low of 11,290.000 Contract in Sep 1995. Hong Kong Open Interest: Option: Stock Option: Puts data remains active status in CEIC and is reported by Hong Kong Exchanges and Clearing Limited. The data is categorized under Global Database’s Hong Kong – Table HK.Z011: Derivatives Market: Futures and Options: Open Interest.

Hong Kong Turnover: Options: Stock Option: Calls data was reported at 5,280,109.000 Contract in Nov 2018. This records a decrease from the previous number of 5,434,621.000 Contract for Oct 2018. Hong Kong Turnover: Options: Stock Option: Calls data is updated monthly, averaging 1,305,830.000 Contract from Sep 1995 (Median) to Nov 2018, with 279 observations. The data reached an all-time high of 9,693,461.000 Contract in Jan 2018 and a record low of 36,077.000 Contract in Aug 1997. Hong Kong Turnover: Options: Stock Option: Calls data remains active status in CEIC and is reported by Hong Kong Exchanges and Clearing Limited. The data is categorized under Global Database’s Hong Kong SAR – Table HK.Z010: Derivatives Market: Futures and Options: Turnover.

CN: Options: Shanghai Stock Exchange: 500ETF: Turnover Volume data was reported at 1,221.655 Contract th in 13 May 2025. This records a decrease from the previous number of 1,246.038 Contract th for 12 May 2025. CN: Options: Shanghai Stock Exchange: 500ETF: Turnover Volume data is updated daily, averaging 836.183 Contract th from Sep 2022 (Median) to 13 May 2025, with 638 observations. The data reached an all-time high of 3,728.811 Contract th in 24 Sep 2024 and a record low of 224.725 Contract th in 20 Sep 2022. CN: Options: Shanghai Stock Exchange: 500ETF: Turnover Volume data remains active status in CEIC and is reported by Shanghai Stock Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZI: Shanghai Stock Exchange: Options: Turnover: Daily.

China Options: Shanghai Stock Exchange: 300ETF: Open Interest: Call data was reported at 845.199 Contract th in 08 May 2020. This records an increase from the previous number of 834.101 Contract th for 07 May 2020. China Options: Shanghai Stock Exchange: 300ETF: Open Interest: Call data is updated daily, averaging 875.530 Contract th from Dec 2019 (Median) to 08 May 2020, with 89 observations. The data reached an all-time high of 1,336.983 Contract th in 17 Mar 2020 and a record low of 107.629 Contract th in 23 Dec 2019. China Options: Shanghai Stock Exchange: 300ETF: Open Interest: Call data remains active status in CEIC and is reported by Shanghai Stock Exchange. The data is categorized under China Premium Database’s Financial Market – Table CN.ZI: Shanghai Stock Exchange: Options: Open Interest: Daily.

Option Care Health reported $1.18B in Cost of Sales for its fiscal quarter ending in September of 2025. Data for Option Care Health | BIOS - Cost Of Sales including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Index Time Series for FT Cboe Vest U.S. Equity Deep Buffer ETF - November. The frequency of the observation is daily. Moving average series are also typically included. Under normal market conditions, the fund will invest substantially all of its assets in FLexible EXchange® Options (FLEX Options) that reference the price performance of the SPDR® S&P 500® ETF Trust (the Underlying ETF). FLEX Options are customized equity or index option contracts that trade on an exchange, but provide investors with the ability to customize key contract terms like exercise prices, styles and expiration dates. It is non-diversified.

Index Time Series for FT Cboe Vest U.S. Equity Deep Buffer ETF - June. The frequency of the observation is daily. Moving average series are also typically included. Under normal market conditions, the fund will invest substantially all of its assets in FLexible EXchange® Options (FLEX Options) that reference the price performance of the SPDR® S&P 500® ETF Trust (the Underlying ETF). FLEX Options are customized equity or index option contracts that trade on an exchange, but provide investors with the ability to customize key contract terms like exercise prices, styles and expiration dates. It is non-diversified.

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.

Option Care Health: Is (OPCH) Poised for Growth?

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