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.

Gain access to LSEG's National Stock Exchange of India data, India's largest stock exchange with more than 180,000 terminals across 600 districts.

The National Stock Exchange of India cemented its place as the largest derivatives exchange in the world in 2023. Mumbai-based NSE traded nearly ** billion derivatives contracts in 2023, followed by the Brazilian exchange, B3, with *** billion contracts. What is a derivative? A derivative is a financial instrument that is based on an underlying asset, such as an equity, commodity, or currency. It can be traded over-the-counter or on an exchange. The most common types of derivatives are futures, options, forwards and swaps. How large is the derivative market? There are billions of derivatives traded globally every year. The largest markets for derivatives trading are Asia Pacific and North America. Currency options and futures alone contribute hundreds of millions of dollars in volume to the largest exchanges. Much of this volume is due to large corporations trying to hedge risk. For example, an international corporation may invest in a currency derivative to ensure that it can buy a particular currency at or below a certain price at some point in the future, protecting against an unfavorable shift in the exchange rate.