Free historical options data, dataset files in CSV format.

Dataset contains entire 2024 data pertaining to Nifty options. This dataset has all expiry day and its trading data. The dataset is arranged in month wise. Each month, you can see multiple files. The file has specify format. The format of the file is Nifty-{expiry day}-{trade day}.csv. Also there is one folder 2024Nifty, which contains Nifty's daily data. Nifty's daily data is crunched into single file for every month. Also, expiry.csv is available, which is overall expiries for the entire year 2024

Futures data can be ordered as full month ranges. To control costs it is possible to order Nearest to Expiry (NTE) data with overlap between expiring future and the next future in the month of expiry or with overlap over more than 1 month is needed. Of course you can also select all active expiries if required.

The data is available at tick level with millisecond resolution as well as at regular intervals of 1 Min, 5 Min and so on.

Data is priced separately for Trades (Tx) and Quotes (Qt).

Tick level Tx data consists of a millisecond timestamp and trade price Tx with an option to include the Volume field. Tick level Qt data consists of millisecond timestamp and quote Qt with a flag to indicate whether it is a Bid or an Ask and optionally the Qt size field can be added.

Regular interval data is usually supplied as one of these sets: CloseTx CloseBid, CloseAsk OpenTx, HighTx, LowTx, CloseTx OpenBid, HighBid, LowBid, CloseBid OpenAsk, HighAsk, LowAsk, CloseAsk

Additional Fields: IntervalTxVolume, CloseBidSize, CloseAskSize and some others are available if required.

Timestamps are by default in GMT but data can be in any Time Zone requested.

Pricing depends on frequency and number of fields.

100s of papers in finance and economics have been written since 1986 onwards using our data and several reputed banks and hedge funds use our data for back testing and risk management.

TraditionData’s FX Options Market Data service provides comprehensive information on FX options markets, leveraging the Volbroker platform for transparency and efficiency.

• Offers real-time volatility price transparency in ATM Straddles, Delta Risk Reversals, and Butterflies.
• Suitable for traders, risk managers, or portfolio managers managing currency risk and maximizing returns.

Visit FX Options Market Data for more information.

Explore Options Price Reporting Authority (OPRA) through LSEG. OPRA collects, consolidates and disseminates information for US Options.

This data package contains all the information related to the economy of a country including price index, commodities values and info about NASDAQ members.

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

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.

NSE LEMONTREE 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

We offer three easy-to-understand equity data packages to fit your business needs. Visit intrinio.com/pricing to compare packages.

Bronze

The Bronze package is ideal for developing your idea and prototyping your platform with high-quality EOD equity pricing data, standardized financial statement data, and supplementary fundamental datasets.

When you’re ready for launch, it’s a seamless transition to our Silver package for additional data sets, 15-minute delayed equity pricing data, expanded history, and more.

• Historical EOD equity prices & technicals (10 years history)
• Security reference data
• Standardized & as-reported financial statements (5 years history)
• 7 supplementary fundamental data sets

Bronze Benefits:

• Web API access
• 300 API calls/minute limit
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Silver

The Silver package is ideal for startups that are in development, testing, or in the beta launch phase. Hit the ground running with 15-minute delayed and historical intraday and EOD equity prices, plus our standardized and as-reported financial statement data with nine supplementary data sets, including insider transactions and institutional ownership.

When you’re ready to scale, easily move up to the Gold package for our full range of data sets and full history, real-time equity pricing data, premium support options, and much more.

• 15-minute delayed & historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (10 years history)
• 9 supplementary fundamental data sets

Silver Benefits:

• Web API access
• 2,000 API calls/minute limit
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support

Gold

The Gold package is ideal for funded companies that are in the growth or scaling stage, as well as institutions that are innovating within the fintech space. This full-service solution offers our complete collection of equity pricing data feeds, from real-time to historical EOD, plus standardized financial statement data and nine supplementary feeds.

You’ll also have access to our wide range of modern access methods, third-party data via Intrinio’s API with licensing assistance, support from our team of expert engineers, custom delivery architectures, and much more.

• Real-time equity prices
• Historical intraday equity prices
• Historical EOD equity prices & technicals (full history)
• Security reference data
• Standardized & as-reported financial statements (full history)
• 9 supplementary fundamental data sets

Gold Benefits:

• No exchange fees
• No user reporting or variable per-user exchange fees
• High liquidity (6%+)
• Web API & WebSocket access
• 2,000 API calls/minute limit
• Customizable access methods (Snowflake, FTP, etc.)
• Access to third-party datasets via Intrinio API (additional fees required)
• Unlimited internal users
• Unlimited internal & external display
• Built-in ticketing system
• Live chat & email support
• Access to engineering team
• Concierge customer success team
• Comarketing & promotional initiatives

Platinum

Don’t see a package that fits your needs? Our team can design premium custom packages for institutions.

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.

NSE MIDHANI 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

Dataset Description: 34-year Daily Stock Data (1990-2024)

Context and Inspiration

This dataset captures historical financial market data and macroeconomic indicators spanning over three decades, from 1990 onwards. It is designed for financial analysis, time series forecasting, and exploring relationships between market volatility, stock indices, and macroeconomic factors. This dataset is particularly relevant for researchers, data scientists, and enthusiasts interested in studying: - Volatility forecasting (VIX) - Stock market trends (S&P 500, DJIA, HSI) - Macroeconomic influences on markets (joblessness, interest rates, etc.) - The effect of geopolitical and economic uncertainty (EPU, GPRD)

Sources

The data has been aggregated from a mix of historical financial records and publicly available macroeconomic datasets: - VIX (Volatility Index): Chicago Board Options Exchange (CBOE). - Stock Indices (S&P 500, DJIA, HSI): Yahoo Finance and historical financial databases. - Volume Data: Extracted from official exchange reports. - Macroeconomic Indicators: Bureau of Economic Analysis (BEA), Federal Reserve, and other public records. - Uncertainty Metrics (EPU, GPRD): Economic Policy Uncertainty Index and Global Policy Uncertainty Database.

Columns

1. dt: Date of observation in YYYY-MM-DD format.
2. vix: VIX (Volatility Index), a measure of expected market volatility.
3. sp500: S&P 500 index value, a benchmark of the U.S. stock market.
4. sp500_volume: Daily trading volume for the S&P 500.
5. djia: Dow Jones Industrial Average (DJIA), another key U.S. market index.
6. djia_volume: Daily trading volume for the DJIA.
7. hsi: Hang Seng Index, representing the Hong Kong stock market.
8. ads: Aruoba-Diebold-Scotti (ADS) Business Conditions Index, reflecting U.S. economic activity.
9. us3m: U.S. Treasury 3-month bond yield, a short-term interest rate proxy.
10. joblessness: U.S. unemployment rate, reported as quartiles (1 represents lowest quartile and so on).
11. epu: Economic Policy Uncertainty Index, quantifying policy-related economic uncertainty.
12. GPRD: Geopolitical Risk Index (Daily), measuring geopolitical risk levels.
13. prev_day: Previous day’s S&P 500 closing value, added for lag-based time series analysis.

Key Features

• Cross-Market Analysis: Compare U.S. markets (S&P 500, DJIA) with international benchmarks like HSI.
• Macroeconomic Insights: Assess how external factors like joblessness, interest rates, and economic uncertainty affect markets.
• Temporal Scope: Longitudinal data facilitates trend analysis and machine learning model training.

Potential Use Cases

• Forecasting market indices using machine learning or statistical models.
• Building volatility trading strategies with VIX Futures.
• Economic research on relationships between policy uncertainty and market behavior.
• Educational material for financial data visualization and analysis tutorials.

Feel free to use this dataset for academic, research, or personal projects.

Finland - Total financial sector liabilities: Financial derivatives and employee stock options was 13.90 % of GDP in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Finland - Total financial sector liabilities: Financial derivatives and employee stock options - last updated from the EUROSTAT on September of 2025. Historically, Finland - Total financial sector liabilities: Financial derivatives and employee stock options reached a record high of 90.10 % of GDP in December of 2011 and a record low of -0.40 % of GDP in December of 1995.

Graph and download economic data for CBOE Volatility Index: VIX (VIXCLS) from 1990-01-02 to 2025-09-29 about VIX, volatility, stock market, and USA.

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.

TER 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

Corn fell to 414.53 USd/BU on October 1, 2025, down 0.23% from the previous day. Over the past month, Corn's price has risen 2.86%, but it is still 4.16% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on October of 2025.

Japan OSE: Turnover: Value: Nikkei 225 Call and Put Options data was reported at 516.962 JPY bn in Nov 2018. This records a decrease from the previous number of 749.344 JPY bn for Oct 2018. Japan OSE: Turnover: Value: Nikkei 225 Call and Put Options data is updated monthly, averaging 228.702 JPY bn from Jun 1989 (Median) to Nov 2018, with 354 observations. The data reached an all-time high of 1,544.252 JPY bn in May 2013 and a record low of 44.521 JPY bn in Jul 2005. Japan OSE: Turnover: Value: Nikkei 225 Call and Put Options data remains active status in CEIC and is reported by Japan Exchange Group. The data is categorized under Global Database’s Japan – Table JP.Z016: Osaka Exchange Inc: Futures and Options.

Dataset Overview:

The dataset contains information about 6,717 financial instruments listed on the National Stock Exchange (NSE) of India. The data includes a variety of instruments such as equity indices, stocks, and other financial products. Here’s a breakdown of the columns:

• instrument_token: A unique identifier for each instrument.
• exchange_token: Another identifier specific to the exchange.
• tradingsymbol: The trading symbol of the instrument.
• name: The full name of the instrument (some entries might be missing this information).
• last_price: The last traded price of the instrument.
• expiry: Expiry date for derivative instruments (mostly null for this dataset).
• strike: Strike price for options (mostly zero or not applicable here).
• tick_size: The minimum price movement for the instrument.
• lot_size: The lot size for trading the instrument.
• instrument_type: The type of instrument (e.g., equity, index).
• segment: The market segment to which the instrument belongs (e.g., INDICES, EQUITIES).
• exchange: The exchange where the instrument is listed (in this case, all are from NSE).

Sample Data:

Here’s a quick look at the first few entries:

Dataset Description for Kaggle:

Instruments NSE Dataset

Description:

This dataset contains detailed information on 6,717 financial instruments listed on the National Stock Exchange (NSE) of India. It includes a range of instruments such as equity indices, stocks, and derivatives. This dataset can be used for financial analysis, trading strategy development, and backtesting.

Columns:

• instrument_token: Unique identifier for each instrument.
• exchange_token: NSE-specific identifier.
• tradingsymbol: The trading symbol used to identify the instrument on the NSE.
• name: Full name of the instrument (where available).
• last_price: The most recent traded price of the instrument.
• expiry: Expiry date for derivatives (where applicable).
• strike: Strike price for options (mostly irrelevant for this dataset).
• tick_size: The smallest price movement allowed in trading this instrument.
• lot_size: The lot size, indicating the number of units per trade.
• instrument_type: Indicates the type of instrument, such as equity or index.
• segment: The market segment, such as indices or equities.
• exchange: The exchange where the instrument is listed, which in this dataset is the NSE.

Usage:

This dataset is ideal for: - Market Analysis: Understanding the structure and constituents of the NSE. - Trading Strategies: Developing and backtesting trading strategies using historical data. - Educational Purposes: Learning about financial markets and instruments.

Acknowledgments:

The data has been sourced from the National Stock Exchange of India (NSE).

You can use this description as the text when you upload the dataset to Kaggle. It covers all the essential details, making it easy for users to understand the contents and potential applications of the dataset.

Index Time Series for ETC 6 Meridian Hedged Equity-Index Option Strategy ETF. The frequency of the observation is daily. Moving average series are also typically included. Under normal circumstances, the fund invests at least 80% of its net assets (plus the amount of any borrowings for investment purposes) in equity securities. The equity securities in which it invests are mainly common stocks. The fund may invest in equity securities of companies of any capitalization. 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.

PPL 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

Option Care Health reported $1.33B in Operating Expenses for its fiscal quarter ending in June of 2025. Data for Option Care Health | BIOS - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last October in 2025.