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This dataset is a raw dump of the maximum recorded 1D price history of all 751 stocks listed under "Nifty Total Market", up until 2023-March-24. Sourced from Yahoo Finance. Original size of ~300 MB, compressed down to ~90 MB.
The motivation originated out of the necessity of having as complete and reliable a database as possible of the NSE's widest official equity coverage. As of the time of this writing, many alternative datasets that are similar in nature have either been abandoned, not updated recently, or have unadjusted prices that do not align with publicly viewable charts. This data was sourced using adjusted OHLC values, such that it's as close to charted prices (within a personally satisfiable margin). The primary reason for publicising the dataset is to make future searches and personal access easier than if it were private.
None of this data has been cleaned or has undergone deep verification. It has simply been pulled, randomly verified, archived, and uploaded (purely for personal convenience). This is directly related to the fact that the recommended contemporary Python equivalent for Yahoo Finance data retrieval removes rows that contain missing values. By extension, the Julia equivalent seeks to retain missing-values as NaN. As of the time of creation, Julia's package with the necessary change was undergoing pre-release testing and therefore unfortunately, this data had to be retrieved using Python's front. Whether this dataset will be updated in the near future or not remains to be decided.
symbol: the NSE ticker of the scrip the CSV belongs to. Is also the filename of the CSV (for example, "AXISBANK.NS"). This column repeats itself throughout the entirety of the CSV and hence, contains only one unique value across all ~5000 rows.date: the recorded date of the economic observation, formatted as YYYY-MM-DD (for example, "1998-11-27"). Each row within this column contains unique values throughout the entirety of the CSV (i.e. all ~5000 rows are unique).open: the opening transaction price of that scrip, for that day. Formatted as a float value (for example, 2.357349). Note that some scripts might have negative values due to adjustment artefacts.high: the highest recorded transaction price of that scrip, for that day. Formatted as a float value (for example, 2.357349). Note that some scripts might have negative values due to adjustment artefacts.low: the lowest recorded transaction price of that scrip, for that day. Formatted as a float value (for example, 1.799554). Note that some scripts might have negative values due to adjustment artefacts.volume: the volume of shares that exchanged hands for that day. Formatted as an integer value (for example, 21000).close: the last recorded transaction price of that scrip, for that day. Formatted as a float value (for example, 2.257743). Note that some scripts might have negative values due to adjustment artefacts.dividends: any dividend paid out on that day that affect prices. Formatted as a float value (for example, 2.5). This column is mostly filled with zeroes.splits: any stock splits that occurred on that day that affect prices. Formatted as an integer value (for example, 5). This column is mostly filled with zeroes.Appropriate recognition and appreciation goes to the National Stock Exchange of India and Yahoo Finance for their individual (and combined) efforts of economic and financial facilitation, proliferation, data collection, maintenance, management, and provision. Cover image credit.
All of this data is provided AS IS with no guarantee or warranty of any kind. The dataset is licensed under the GNU Affero General Public License.

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Context : 
NIFTY 50 is the flagship stock market index of the National Stock Exchange (NSE) in India which is one of the leading stock exchanges in India. 
NIFTY 50 represents the performance of 50 large-cap companies across various sectors of the Indian economy.
Similarly NIFTY 100 represents the performance of the top 100 companies listed on the NSE based on market capitalization. NIFTY 100 is also part of several other indices, such as NIFTY 200, NIFTY 500, and NIFTY 100 Equal Weight Index.
In the National Stock Exchange (NSE) of India, there are three market segments based on the market capitalization of the listed companies. They are: - Large-cap: This segment includes the top 100 companies listed on the NSE based on market capitalization. - Mid-cap: This segment includes companies that rank between 101 and 250 based on market capitalization. - Small-cap: This segment includes companies that rank below the top 250 companies based on market capitalization. Market capitalization is calculated by multiplying a company's total outstanding shares by its current market price per share. The NSE's NIFTY Mid-cap 100 and NIFTY Small-cap 250 indices track the performance of companies in the mid-cap and small-cap segments of the market, respectively.
The NIFTY500 Multicap 50:25:25 index is a variant of the NIFTY 500 index, which represents the top 500 companies listed on India's National Stock Exchange (NSE). The Multicap 50:25:25 variant is a modified version of the NIFTY500 index that divides stocks into three categories based on market capitalization. The top 50 companies by market capitalization are classified as large-cap companies under this variant, while the next 150 companies are classified as mid-cap companies. The remaining 300 businesses are classified as small-cap.
Content : This Dataset contains records for all NIFTY-50 , NIFTY 200, NIFTY Midcap 100, NIFTY Smallcap 250, NIFTY500 Multicap 50:25:25 stocks, as on 1st March, 2023 - Open - open value of the index on that day - High - highest value of the index on that day - Low - lowest value of the index on that day - PREV. CLOSE - Previous Close Value - LTP - Last Traded Price - CHNG - Change in the price - %CHNG - Percentage change - Volume - volume of transaction - Value - Turn over in lakhs - 52W H - 52 Week High price - 52W L - 52 Week Lowest price - 365 D % CHNG - Past 365 Days Change Percentage - 30 D % CHNG - Past 30 Days Change Percentage
Note : - %CHNG: % change is calculated with respect to adjusted price on ex-date for Corporate Actions like: Dividend, Bonus, Rights & Face Value Split and also adjusted for Past 365 days & 30 days. - 52 W H/L: 52 week High & Low prices are adjusted for Bonus, Split & Rights Corporate actions.
Acknowledgements : The data is obtained from NSE website This is just daily level data provided here, you will get vast and detailed real-time & historical data from the official website.
Image Credit : https://gettyimages.com

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In September 2025, among all the indices listed on the National Stock Exchange (NSE) of India, Nifty 100 had the highest dividend yield. This was closely followed by Nifty 200. What are broad market indices? Broad market indices, also called market indices, are utilized to monitor the performance of a collection of stocks that closely mirror the overall stock market. They generally consist of large, liquid stocks listed on the stock exchange. They serve as a benchmark for measuring the performance of the stock market or portfolios such as mutual fund investments. In many broad-based indexes, companies are weighted based on their market value. This means that larger companies carry more weight in determining the index price compared to smaller ones. For instance, in the Nifty-50 index, Cipla, a major pharmaceutical company, has a significant impact, while smaller companies like Natco Pharma have less influence due to their lower market capitalization. What is Nifty 50? Nifty-50 is the flagship index of NSE. It tracks the movement of the portfolio of the 50 largest blue-chip companies and most liquid securities in the Indian market. It is extensively used by domestic and foreign investors as the barometer of the Indian capital market. Annual returns of Nifty-50 were around 20 percent in fiscal year 2023, indicating strong market performance.

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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Key information about India Sensitive 30 (Sensex)

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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Israel's main stock market index, the TA-125, fell to 3310 points on October 27, 2025, losing 0.02% from the previous session. Over the past month, the index has climbed 4.71% and is up 52.34% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Israel. Israel Stock Market (TA-125) - values, historical data, forecasts and news - updated on October of 2025.

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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Japan's main stock market index, the JP225, rose to 50413 points on October 27, 2025, gaining 2.26% from the previous session. Over the past month, the index has climbed 11.92% and is up 30.58% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on October of 2025.

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https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
Latest broker research reports with buy, sell, hold recommendations along with forecast share price targets and upside. Browse thousands of reports and search by company or the broker.
This dataset contains the data of quarterly results of NIFTY 500 companies. Nifty 500 is an index maintained by the National Stock Exchange of India (NSE). The Nifty 500 is designed to represent the performance of the 500 largest publicly traded companies listed on the NSE, spanning various sectors of the Indian economy. Nifty 500 includes stocks from large, mid, and small-cap segments, providing a comprehensive view of the overall market.
The dataset contains 17 columns covering aspects of the companies and their quarterly results. These columns are described below: Name: The name of the company.
NSE Code: The National Stock Exchange code assigned to the company for trading on the NSE.
BSE Code: The Bombay Stock Exchange code assigned to the company for trading on the BSE.
Sector: The sector to which the company belongs. Sectors classify companies based on the nature of their business activities.
Industry: The industry in which the company operates, providing more detailed information about its specific line of business.
Revenue: The total income generated by the company during a specific quarter, reflecting its sales or service revenue.
Operating Expenses: The total expenses incurred by the company in its day-to-day operations, excluding financial and tax-related expenses.
Operating Profit: The profit obtained after deducting operating expenses from revenue, indicating the profitability of the core business operations.
Operating Profit Margin: The percentage representing the proportion of revenue that translates into operating profit, indicating operational efficiency.
Depreciation: The decrease in the value of the company's assets over time, representing the allocated cost of tangible assets.
Interest: The cost of borrowing for the company, reflecting interest payments on loans and other financial obligations.
Profit Before Tax: The company's profit before deducting income tax, including operating profit and other non-operating income or expenses.
Tax: The income tax expense incurred by the company during the quarter.
Net Profit: The profit remaining after deducting all expenses, including operating expenses, depreciation, interest, and taxes.
Earnings Per Share (EPS): The portion of a company's profit allocated to each outstanding share of common stock, providing a measure of profitability on a per-share basis.
Net Profit TTM (Trailing 12 Months): The sum of net profits over the most recent 12-month period, giving a broader perspective on the company's performance.
EPS TTM (Trailing 12 Months): The earnings per share calculated over the trailing 12-month period, providing a longer-term view of earnings on a per-share basis.

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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Key information about India Market Capitalization

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After some rigorous SQL queries and coding on python. I made this dataset. In this dataset, all stocks of the Indian Stock Market are present a total of 2435 stocks. The data is of 1-year rows represent stock name and column represent date and I have filled the table with closing price. Enjoy and do some stock price predictions.

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India's main stock market index, the SENSEX, rose to 84779 points on October 27, 2025, gaining 0.67% from the previous session. Over the past month, the index has climbed 5.49% and is up 5.97% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

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Attribution-ShareAlike 4.0 (CC BY-SA 4.0)https://creativecommons.org/licenses/by-sa/4.0/
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Starting something in FinTech is the most difficult thing. You have no open data. These days I'm trying to do some algo-trading. Maybe not in true sense, because it's not high frequency scalping. But anyway that's that.
The data gives almost-Realtime data for half of the Nifty 50 stocks for last week of May and first 2 Weeks of July.
Now here is the obvious question. The dataset does not have timestamp. That's because it is collected via Web-Socket streaming as it happens. Sometimes once in a couple of seconds, sometimes 10-15 times in the same span. So there is no point to timestamp IMHO. Anyway it'll be client-side timestamp, so not a true timestamp.
P.S:
**All the data points are not tick-by-tick update. Rather it is mostly an update after 600 ms, provided a trade happened **

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China's main stock market index, the SHANGHAI, rose to 3950 points on October 24, 2025, gaining 0.71% from the previous session. Over the past month, the index has climbed 2.52% and is up 19.72% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from China. China Shanghai Composite Stock Market Index - values, historical data, forecasts and news - updated on October of 2025.

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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
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Key information about India Market Capitalization: % of GDP

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Canada's main stock market index, the TSX, rose to 30353 points on October 24, 2025, gaining 0.55% from the previous session. Over the past month, the index has climbed 2.09% and is up 24.07% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on October of 2025.

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Key information about India P/E ratio

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Pakistan's main stock market index, the KSE 100, fell to 162172 points on October 27, 2025, losing 0.84% from the previous session. Over the past month, the index has declined 1.02%, though it remains 79.80% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from Pakistan. Pakistan Stock Market (KSE100) - values, historical data, forecasts and news - updated on October of 2025.
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http://www.gnu.org/licenses/agpl-3.0.htmlhttp://www.gnu.org/licenses/agpl-3.0.html
This dataset is a raw dump of the maximum recorded 1D price history of all 751 stocks listed under "Nifty Total Market", up until 2023-March-24. Sourced from Yahoo Finance. Original size of ~300 MB, compressed down to ~90 MB.
The motivation originated out of the necessity of having as complete and reliable a database as possible of the NSE's widest official equity coverage. As of the time of this writing, many alternative datasets that are similar in nature have either been abandoned, not updated recently, or have unadjusted prices that do not align with publicly viewable charts. This data was sourced using adjusted OHLC values, such that it's as close to charted prices (within a personally satisfiable margin). The primary reason for publicising the dataset is to make future searches and personal access easier than if it were private.
None of this data has been cleaned or has undergone deep verification. It has simply been pulled, randomly verified, archived, and uploaded (purely for personal convenience). This is directly related to the fact that the recommended contemporary Python equivalent for Yahoo Finance data retrieval removes rows that contain missing values. By extension, the Julia equivalent seeks to retain missing-values as NaN. As of the time of creation, Julia's package with the necessary change was undergoing pre-release testing and therefore unfortunately, this data had to be retrieved using Python's front. Whether this dataset will be updated in the near future or not remains to be decided.
symbol: the NSE ticker of the scrip the CSV belongs to. Is also the filename of the CSV (for example, "AXISBANK.NS"). This column repeats itself throughout the entirety of the CSV and hence, contains only one unique value across all ~5000 rows.date: the recorded date of the economic observation, formatted as YYYY-MM-DD (for example, "1998-11-27"). Each row within this column contains unique values throughout the entirety of the CSV (i.e. all ~5000 rows are unique).open: the opening transaction price of that scrip, for that day. Formatted as a float value (for example, 2.357349). Note that some scripts might have negative values due to adjustment artefacts.high: the highest recorded transaction price of that scrip, for that day. Formatted as a float value (for example, 2.357349). Note that some scripts might have negative values due to adjustment artefacts.low: the lowest recorded transaction price of that scrip, for that day. Formatted as a float value (for example, 1.799554). Note that some scripts might have negative values due to adjustment artefacts.volume: the volume of shares that exchanged hands for that day. Formatted as an integer value (for example, 21000).close: the last recorded transaction price of that scrip, for that day. Formatted as a float value (for example, 2.257743). Note that some scripts might have negative values due to adjustment artefacts.dividends: any dividend paid out on that day that affect prices. Formatted as a float value (for example, 2.5). This column is mostly filled with zeroes.splits: any stock splits that occurred on that day that affect prices. Formatted as an integer value (for example, 5). This column is mostly filled with zeroes.Appropriate recognition and appreciation goes to the National Stock Exchange of India and Yahoo Finance for their individual (and combined) efforts of economic and financial facilitation, proliferation, data collection, maintenance, management, and provision. Cover image credit.
All of this data is provided AS IS with no guarantee or warranty of any kind. The dataset is licensed under the GNU Affero General Public License.