Context:

The provided dataset is extracted from yahoo finance using pandas and yahoo finance library in python. This deals with stock market index of the world best economies. The code generated data from Jan 01, 2003 to Jun 30, 2023 that’s more than 20 years. There are 18 CSV files, dataset is generated for 16 different stock market indices comprising of 7 different countries. Below is the list of countries along with number of indices extracted through yahoo finance library, while two CSV files deals with annualized return and compound annual growth rate (CAGR) has been computed from the extracted data.

Number of Countries & Index:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F90ce8a986761636e3edbb49464b304d8%2FNumber%20of%20Index.JPG?generation=1688490342207096&alt=media" alt="">

Content:

Unit of analysis: Stock Market Index Analysis

This dataset is useful for research purposes, particularly for conducting comparative analyses involving capital market performance and could be used along with other economic indicators.

There are 18 distinct CSV files associated with this dataset. First 16 CSV files deals with number of indices and last two CSV file deals with annualized return of each year and CAGR of each index. If data in any column is blank, it portrays that index was launch in later years, for instance: Bse500 (India), this index launch in 2007, so earlier values are blank, similarly China_Top300 index launch in year 2021 so early fields are blank too.

The extraction process involves applying different criteria, like in 16 CSV files all columns are included, Adj Close is used to calculate annualized return. The algorithm extracts data based on index name (code given by the yahoo finance) according start and end date.

Annualized return and CAGR has been calculated and illustrated in below image along with machine readable file (CSV) attached to that.

To extract the data provided in the attachment, various criteria were applied:

1. Content Filtering: The data was filtered based on several attributes, including the index name, start and end date. This filtering process ensured that only relevant data meeting the specified criteria.

2. Collaborative Filtering: Another filtering technique used was collaborative filtering using yahoo finance, which relies on index similarity. This approach involves finding indices that are similar to other index or extended dataset scope to other countries or economies. By leveraging this method, the algorithm identifies and extracts data based on similarities between indices.

In the last two CSV files, one belongs to annualized return, that was calculated based on the Adj close column and new DataFrame created to store its outcome. Below is the image of annualized returns of all index (if unreadable, machine-readable or CSV format is attached with the dataset).

Annualized Return:

As far as annualised rate of return is concerned, most of the time India stock market indices leading, followed by USA, Canada and Japan stock market indices.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F37645bd90623ea79f3708a958013c098%2FAnnualized%20Return.JPG?generation=1688525901452892&alt=media" alt="">

Compound Annual Growth Rate (CAGR):

The best performing index based on compound growth is Sensex (India) that comprises of top 30 companies is 15.60%, followed by Nifty500 (India) that is 11.34% and Nasdaq (USA) all is 10.60%.

The worst performing index is China top300, however this is launch in 2021 (post pandemic), so would not possible to examine at that stage (due to less data availability). Furthermore, UK and Russia indices are also top 5 in the worst order.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F15657145%2F58ae33f60a8800749f802b46ec1e07e7%2FCAGR.JPG?generation=1688490409606631&alt=media" alt="">

Geography: Stock Market Index of the World Top Economies

Time period: Jan 01, 2003 – June 30, 2023

Variables: Stock Market Index Title, Open, High, Low, Close, Adj Close, Volume, Year, Month, Day, Yearly_Return and CAGR

File Type: CSV file

Inspiration:

• Time series prediction model
• Investment opportunities in world best economies
• Comparative Analysis of past data with other stock market indices or other indices

Disclaimer:

This is not a financial advice; due diligence is required in each investment decision.

Description:

Empowering Indian Investors: NSE's Historical Yearly Financial Ratios for 1800 Stocks with Predictive Modeling and Strategy Backtesting

Financial data is the lifeblood of investment analysis and decision-making, and for Indian investors navigating the dynamic National Stock Exchange (NSE), access to comprehensive and tailored datasets is crucial. The NSE 1800 Stocks Historical Yearly Financial Ratios dataset is a valuable resource designed to empower Indian investors, analysts, and financial professionals with the essential financial information needed for predictive modeling and strategy backtesting within the context of the Indian stock market.

Motivation:

The Indian stock market presents unique opportunities and challenges. The motivation behind this dataset is to provide Indian investors with a robust financial dataset that enables them to perform predictive modeling and strategy backtesting independently. It aims to streamline the analysis process, promote data-driven decision-making, and enhance the understanding of Indian stock market dynamics.

Context:

Understanding the financial performance of Indian companies is fundamental for Indian investors, and this dataset offers a wealth of historical financial metrics and ratios for 1800 stocks listed on the NSE. It is distinguished by the ability to:

1 Perform Predictive Modeling: Users can leverage this dataset to build their predictive models tailored to the Indian market's unique characteristics. These models can assist investors in forecasting financial metrics, stock prices, and market trends specific to the Indian context.

2 Conduct Strategy Backtesting: Indian investors can independently test their investment strategies using historical data from the NSE. This dataset serves as the foundation for users to assess the performance of their strategies while considering factors such as Indian economic cycles, regulatory changes, and market dynamics.

3 Evaluate Financial Health: Users can assess the financial stability, profitability, and operational efficiency of Indian companies by utilizing a comprehensive collection of historical financial ratios and metrics.

4 Support Informed Decision-Making: By providing access to the historical financial data of 1800 stocks listed on the NSE, this dataset equips Indian investors with the information needed to make well-informed investment decisions, navigate the Indian stock market, and manage their portfolios effectively.

In summary, the NSE 1800 Stocks Historical Yearly Financial Ratios for Predictive Modeling and Strategy Backtesting (Tailored for Indian Investors) dataset is a robust resource that empowers Indian investors to independently perform predictive modeling and strategy backtesting. It serves as a foundational dataset to support data-driven investment decisions within the unique context of the Indian stock market. Whether you are an Indian investor, analyst, or financial professional, this dataset equips you with the financial data needed to enhance your investment strategies and decision-making.

Context

This dataset provides comprehensive historical data for the Nifty 50 Index, including daily open, high, low, close prices, and trade volumes. Spanning the period for Year 2024-2025, it captures market trends across India's leading stock index during a time of significant economic shifts, including the global pandemic and post-recovery phases.

The NIFTY 50 is a benchmark Indian stock market index that represents the weighted average of 50 of the largest Indian companies listed on the National Stock Exchange. It is one of the two main stock indices used in India, the other being the BSE SENSEX.

Nifty 50 is owned and managed by NSE Indices (previously known as India Index Services & Products Limited), which is a wholly-owned subsidiary of the NSE Strategic Investment Corporation Limited. NSE Indices had a marketing and licensing agreement with Standard & Poor's for co-branding equity indices until 2013. The Nifty 50 index was launched on 22 April 1996 and is one of the many stock indices of Nifty.

Data can be useful for trend analysis, volatility studies, and investment strategy development for both long-term and short-term market assessments.

The NIFTY 50 index is a free-float market capitalization weighted index. The index was initially calculated on a full market capitalization methodology. On 26 June 2009, the computation was changed to a free-float methodology. The base period for the NIFTY 50 index is 3 November 1995, which marked the completion of one year of operations of the National Stock Exchange Equity Market Segment. The base value of the index has been set at 1000 and a base capital of ₹ 2.06 trillion.

The All Indian Stocks listed in Nifty 500 dataset provides a comprehensive list of all Indian stocks listed in the Nifty 500 index. This dataset includes information on the company name, industry, symbol, series, and ISIN code for each stock. With this dataset, researchers, investors, and analysts can analyze and gain insights into the Indian stock market.

File Information:

Filename: All_Indian_Stocks_listed_in_nifty500.csv
File format: CSV (Comma Separated Values)
Number of records: 751
Columns: Company Name, Industry, Symbol, Series, ISIN Code
Size: 16 KB

Column description:

Company Name: The name of the company listed in the Nifty 500 index.
Industry: The industry sector to which the company belongs.
Symbol: The unique stock symbol of the company on the stock exchange.
Series: The trading series of the company's stock on the stock exchange.
ISIN Code: The International Securities Identification Number (ISIN) code, which is a unique identifier for the company's securities.

Use case: Stock analysis: The dataset can be used for analyzing and comparing the performance of various stocks listed in the Nifty 500 index. By looking at the company name, industry, and stock symbol, investors can identify trends and make informed decisions about where to invest their money.

Portfolio management: The dataset can be used to create and manage a diversified stock portfolio. Investors can use the industry information to ensure that they are investing in stocks from different sectors, which can help mitigate risk.

Stock market research: Researchers and analysts can use the dataset to conduct research on the Indian stock market. They can use the information to analyze trends in specific industries or to track the performance of individual companies over time.

Machine learning: The dataset can be used for developing machine learning algorithms that predict stock prices or identify potential investment opportunities. The information in the dataset can be used as input features for these models, allowing them to learn from historical data and make predictions about future stock performance.

Data visualization: The dataset can be visualized using various data visualization tools, such as charts and graphs, to help investors and researchers identify patterns and trends in the data. This can help to identify potential investment opportunities or to better understand the performance of individual stocks or sectors.

China's main stock market index, the SHANGHAI, fell to 3898 points on December 2, 2025, losing 0.42% from the previous session. Over the past month, the index has declined 1.98%, though it remains 15.36% higher than a year ago, 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 December of 2025.

The Nifty 50 Index data provides a comprehensive overview of the performance of the top 50 actively traded stocks listed on the National Stock Exchange of India (NSE). This dataset encompasses a wide range of industries, including finance, technology, healthcare, and consumer goods, offering insights into the overall health and direction of the Indian stock market.

Included in the data are key metrics such as daily opening and closing prices, high and low prices, trading volume, and percentage changes. These metrics allow analysts and investors to track trends, identify patterns, and make informed decisions regarding investment strategies.

Additionally, the dataset may incorporate historical data, enabling users to conduct thorough analyses over specific time periods and assess the long-term performance of individual stocks or the index as a whole. Whether used for research, financial modeling, or investment decision-making, the Nifty 50 Index data serves as a valuable resource for understanding and navigating the dynamic landscape of the Indian stock market.

This dataset provides a detailed view of mutual fund returns in India, organized across monthly, quarterly, and annual intervals. It aims to assist financial analysts, data scientists, and investors in understanding the performance trends of mutual funds over different time periods.

Description

This dataset consists of performance metrics for Indian mutual funds, with data categorized into three timeframes:

Monthly Returns (2024): Tracks the month-over-month performance for mutual funds.

Quarterly Returns: Provides a broader view of fund performance by summarizing quarterly data.

Annual Returns: Captures the year-long performance, offering insights into long-term growth trends.

Potential Use Cases

• Financial modeling and forecasting
• Investment portfolio optimization
• Mutual fund performance comparison
• Exploratory Data Analysis (EDA) for financial insights

Highlights

• Covers diverse time frames (monthly, quarterly, annual).
• Useful for both novice and professional investors.
• Data sourced from Social media , a reliable platform for mutual fund information.

Shark Tank India Data set.

Shark Tank India - Season 1 to season 4 information, with 80 fields/columns and 630+ records.

All seasons/episodes of 🦈 SHARKTANK INDIA 🇮🇳 were broadcasted on SonyLiv OTT/Sony TV.

Here is the data dictionary for (Indian) Shark Tank season's dataset.

• Season Number - Season number
• Startup Name - Company name or product name
• Episode Number - Episode number within the season
• Pitch Number - Overall pitch number
• Season Start - Season first aired date
• Season End - Season last aired date
• Original Air Date - Episode original/first aired date, on OTT/TV
• Episode Title - Episode title in SonyLiv
• Anchor - Name of the episode presenter/host
• Industry - Industry name or type
• Business Description - Business Description
• Company Website - Company Website URL
• Started in - Year in which startup was started/incorporated
• Number of Presenters - Number of presenters
• Male Presenters - Number of male presenters
• Female Presenters - Number of female presenters
• Transgender Presenters - Number of transgender/LGBTQ presenters
• Couple Presenters - Are presenters wife/husband ? 1-yes, 0-no
• Pitchers Average Age - All pitchers average age, <30 young, 30-50 middle, >50 old
• Pitchers City - Presenter's town/city or place where company head office exists
• Pitchers State - Indian state pitcher hails from or state where company head office exists
• Yearly Revenue - Yearly revenue, in lakhs INR, -1 means negative revenue, 0 means pre-revenue
• Monthly Sales - Total monthly sales, in lakhs
• Gross Margin - Gross margin/profit of company, in percentages
• Net Margin - Net margin/profit of company, in percentages
• EBITDA - Earnings Before Interest, Taxes, Depreciation, and Amortization
• Cash Burn - In loss in current year; burning/paying money from their pocket (yes/no)
• SKUs - Stock Keeping Units or number of varieties, at the time of pitch
• Has Patents - Pitcher has Patents/Intellectual property (filed/granted), at the time of pitch
• Bootstrapped - Startup is bootstrapped or not (yes/no)
• Part of Match off - Competition between two similar brands, pitched at same time
• Original Ask Amount - Original Ask Amount, in lakhs INR
• Original Offered Equity - Original Offered Equity, in percentages
• Valuation Requested - Valuation Requested, in lakhs INR
• Received Offer - Received offer or not, 1-received, 0-not received
• Accepted Offer - Accepted offer or not, 1-accepted, 0-rejected
• Total Deal Amount - Total Deal Amount, in lakhs INR
• Total Deal Equity - Total Deal Equity, in percentages
• Total Deal Debt - Total Deal debt/loan amount, in lakhs INR
• Debt Interest - Debt interest rate, in percentages
• Deal Valuation - Deal Valuation, in lakhs INR
• Number of sharks in deal - Number of sharks involved in deal
• Deal has conditions - Deal has conditions or not? (yes or no)
• Royalty Percentage - Royalty percentage, if it's royalty deal
• Royalty Recouped Amount - Royalty recouped amount, if it's royalty deal, in lakhs
• Advisory Shares Equity - Deal with Advisory shares or equity, in percentages
• Namita Investment Amount - Namita Investment Amount, in lakhs INR
• Namita Investment Equity - Namita Investment Equity, in percentages
• Namita Debt Amount - Namita Debt Amount, in lakhs INR
• Vineeta Investment Amount - Vineeta Investment Amount, in lakhs INR
• Vineeta Investment Equity - Vineeta Investment Equity, in percentages
• Vineeta Debt Amount - Vineeta Debt Amount, in lakhs INR
• Anupam Investment Amount - Anupam Investment Amount, in lakhs INR
• Anupam Investment Equity - Anupam Investment Equity, in percentages
• Anupam Debt Amount - Anupam Debt Amount, in lakhs INR
• Aman Investment Amount - Aman Investment Amount, in lakhs INR
• Aman Investment Equity - Aman Investment Equity, in percentages
• Aman Debt Amount - Aman Debt Amount, in lakhs INR
• Peyush Investment Amount - Peyush Investment Amount, in lakhs INR
• Peyush Investment Equity - Peyush Investment Equity, in percentages
• Peyush Debt Amount - Peyush Debt Amount, in lakhs INR
• Ritesh Investment Amount - Ritesh Investment Amount, in lakhs INR
• Ritesh Investment Equity - Ritesh Investment Equity, in percentages
• Ritesh Debt Amount - Ritesh Debt Amount, in lakhs INR
• Amit Investment Amount - Amit Investment Amount, in lakhs INR
• Amit Investment Equity - Amit Investment Equity, in percentages
• Amit Debt Amount - Amit Debt Amount, in lakhs INR
• Guest Investment Amount - Guest Investment Amount, in lakhs INR
• Guest Investment Equity - Guest Investment Equity, in percentages
• Guest Debt Amount - Guest Debt Amount, in lakhs INR
• Invested Guest Name - Name of the guest(s) who invested in deal
• All Guest Names - Name of all guests, who are present in episode
• Namita Present - Whether Namita present in episode or not
• Vineeta Present - Whether Vineeta present in episode or not
• Anupam ...

API Overview The Indian Stock Exchange API provides detailed financial data for companies listed on the Bombay Stock Exchange (BSE) and National Stock Exchange (NSE), empowering users with comprehensive insights into the dynamic Indian stock market. This powerful API allows investors, financial analysts, and developers to access a wealth of information essential for making informed investment decisions and conducting thorough research.

Check out our Indian API Marketplace here: https://indianapi.in/

Unlock the potential of the Indian stock market with Indian Stock Exchange API's extensive features, including:

Company Profiles: Dive deep into the profiles of Indian companies, gaining valuable insights into their background, history, and industry presence. Stock Prices: Stay up-to-date with real-time stock prices for both BSE and NSE listings, ensuring you never miss a market movement. Technical Data: Access detailed technical analysis data for Indian stocks, enabling you to assess performance and trends with precision. Financials: Explore financial statements and data for Indian companies, including income statements, balance sheets, and cash flow statements. Key Metrics: Evaluate key financial ratios and metrics specific to the Indian stock market, such as profitability, liquidity, and solvency. Analyst Views: Stay informed with expert analyst views and recommendations tailored to Indian stocks, helping you understand market sentiment and investment opportunities. Shareholding Patterns: Gain insights into shareholding patterns of Indian companies, including institutional holdings, promoter holdings, and public shareholding structures. Corporate Actions: Track corporate actions such as dividends, stock splits, mergers, and acquisitions in the Indian market, staying informed about events that may impact stock prices. Recent News: Access the latest news articles related to Indian companies, industries, and market developments, ensuring you're always in the know. mail: contact@indianapi.in

We also offer custom endpoints and a dedicated server for your needs!

The Indian Stock Exchange API provides detailed financial data for companies listed on the BSE and NSE. This API allows users to retrieve company profiles, stock prices, technical data, financials, key metrics, analyst views, shareholding patterns, corporate actions, and recent news.

Indian Stock Exchange API Documentation Welcome to the Indian Stock Exchange API! This API is built with FastAPI to provide real-time stock market data. Below, you will find detailed descriptions of the available endpoints, their methods, required parameters, and usage examples.

This dataset contains the FII and DII daily cash Buy/Sell activity in Indian stock market. FII is for foreign institutions and DII for domestic institutions. There daily Buy/Selling activity plays an important role in deciding the trend of market

TATA Motors

The TATA Motors Stock Price Dataset provides historical stock price and trading data for TATA Motors Limited, a prominent automotive company in India.

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This dataset spans from January 3, 2000, to September 2, 2023, offering insights into TATA Motors' stock performance over more than two decades.

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It includes daily records of open, high, low, close prices, adjusted close prices, and trading volumes. Investors, analysts, and researchers can use this dataset for various analyses, including trend identification, volatility assessment, and predictive modeling for stock price movements.

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Description:

The Open, High, Low and Close prices together form the price range for the stock on a given trading day. "Open" is the starting price, "High" is the highest price, "Low" is the lowest price, and Close is the final price at which the stock traded.

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The Adj Close price is particularly important for long-term analysis because it adjusts for events that can impact the stock's historical prices. This adjusted price allows you to assess the stock's true performance over time.

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The Volume column is essential for understanding the level of market activity on a specific day. High trading volumes can indicate increased market interest and potentially greater price volatility.

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By analyzing these columns and their historical trends, you can gain insights into how TATA Motors' stock has performed over time, identify patterns, and make informed investment decisions. Traders and investors often use this data to perform technical analysis, create trading strategies, and assess the stock's risk and potential for returns.

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The "Top Investments in Shark Tank India Dataset" offers a comprehensive compilation of the most notable and successful investment deals made on the popular Indian version of the television show "Shark Tank." This dataset provides a valuable resource for entrepreneurs, investors, researchers, and enthusiasts interested in studying the Indian startup ecosystem and its venture capital landscape.

Explore the Indian Mutual Fund Landscape 📊

This dataset offers an extensive look at over 16,000+ Indian mutual fund schemes, covering a wide array including old(closed), currently active, and new/recently launched funds. Featuring the latest Net Asset Value (NAV) and Assets Under Management (AUM) data, it's designed for easy analysis and comparison!

✳️ Access Mutual Fund Historic NAV Data

🔍 What's Inside

🆔 Fund Identification & Details

• Scheme_Code: Unique code assigned to a mutual fund scheme.
• AMC: The Asset Management Company that manages the mutual fund.
• Scheme_Name: Name of the mutual fund scheme.
• Scheme_NAV_Name: Detailed name of the scheme often indicating the specific plan(e.g., Growth, IDCW/Dividend).
• ISIN_Div_Payout/Growth: Unique International Securities Identification Number for dividend payout or growth option of the scheme.
• ISIN_Div_Reinvestment: Unique ISIN for dividend reinvestment option of the scheme.
• ISIN_Div_Payout/Growth/Div_Reinvestment: Comprehensive ISINs covering dividend payout, growth, or dividend reinvestment options, often a combination or primary identifier if others are not specific.
• Lauch_Date: Date when the mutual fund scheme was launched
• Closure_Date: Date when the mutual fund scheme was closed (if applicable)

🏷️ Classification

• Scheme_Type: How the fund is structured (e.g., Open Ended, Close Ended).
• Scheme_Category: Classification of the scheme based on its investment strategy(e.g., Equity Large Cap, Debt Liquid Fund).

💰 Financials & Investment Info

• NAV: Net Asset Value per unit of the fund scheme.
• Latest_NAV_Date: Date on which the latest NAV was declared.
• Scheme_Min_Amt: Minimum investment amount required to invest in the scheme.
• AAUM_Quarter: The quarter for which the average AUM is reported (e.g., January - March 2025)
• Average_AUM_Cr: Average assets under management in crores for the scheme.

⏱️ Update Frequency

Updated Daily (Typically reflects the previous trading day's NAV). Updates are run automatically via a scheduled Kaggle Notebook.

💡 Potential Uses

• ✅ Compare different mutual funds and AMCs.
• ✅ Analyze trends across fund categories and types.
• ✅ Get a quick overview of the Indian mutual fund market structure.
• ✅ Build dashboards or visualizations of the Indian MF landscape.
• ✅ Track NAV movements for specific schemes.

🙏 Acknowledgements

Data sourced from the Association of Mutual Funds in India (AMFI). This dataset is compiled for educational and analytical purposes.

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.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F61dae0b2c572fb594f9100e71124cd9b%2FRplot.png?generation=1706476233155458&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F1c202260ec85a27eb9c2fc00260d0edb%2Ffile20a834a35dcd.gif?generation=1706476881533958&alt=media" alt=""> This dataset provides comprehensive insights into the top 100 companies in India, encompassing a diverse range of industries such as finance, manufacturing, telecommunications, and more. It includes essential attributes such as company rankings, market capitalization, share prices, revenue, and additional categorical information. The dataset offers a snapshot of the Indian business landscape, shedding light on key players, their financial standing, and their contributions to the country's economic growth.

With the growing importance of India in the global landscape, this dataset becomes a valuable tool for market analysis, industry comparisons, and understanding the economic dynamics of the Indian corporate sector. Let's explore some of the key aspects of this dataset in detail.

First and foremost, company rankings provide an immediate insight into which companies are at the top of the Indian market. This can be useful for investors, analysts, and researchers looking to identify market leaders and emerging trends. Additionally, the market capitalization of companies is a key indicator of their size and influence in the market. This can be used to assess the financial stability of companies and their ability to withstand economic challenges.

Share prices are another crucial aspect of the dataset. They can be used to track the performance of companies over time and identify investment opportunities. Company revenue provides insights into their financial health and their growth over time, which is vital for investors and analysts looking to make informed decisions.

Furthermore, the dataset includes categorized information that allows for sector-wise analysis. This is critical for understanding how different sectors are contributing to the Indian economy and how they compare to each other. For example, one can investigate how technology companies are performing compared to manufacturing or energy companies.

The analysis of this dataset can have various practical applications. Investors can use it to identify investment opportunities, while researchers can use it to study economic trends and assess the impact of companies on the country's growth. Additionally, regulators and policymakers can use this data to make informed decisions about the business environment in India.

In summary, this dataset is a valuable source of information about the Indian corporate landscape. It offers a comprehensive view of the top 100 companies in the country, their financial figures, and their impact on the economy. By exploring and analyzing this data, valuable insights can be gained for making informed decisions in investments, market research, and economic development. It is a valuable tool for anyone interested in the Indian market and its ongoing growth.

Context

Recently, I saw a dataset based on Shark Tank USA. This dataset inspired me to create one for India as well and since season 1 recently ended, I thought this was the perfect time to look at some insights based on the deals.

Content

This dataset contains the following information - 1. episode - episode number 2. pitch_no - pitch number (unique) 3. company - company name 4. idea - company description 5. deal - final deal that was taken 6. ashneer - Did Ashneer invest? 7. namita - Did Ashneer invest? 8. anupam - Did Anupam invest? 9. vineeta - Did Vineeta invest? 10. aman - Did Aman invest? 11. peyush - Did - Did Peyush invest? 12. ghazal - Did Ghazal invest?

Acknowledgements

This data was scraped from Wikipedia.

Context

In the Indian Stock Market, the term "Nifty" was derived from "National" and "Fifty" as it comprised of 50 actively traded stocks in the National Stock Exchange. While initially one stock, the brand NIFTY grew to the point where it comprised of over 350 Indices as of February 28 2023, all of which serve as benchmarks for products traded on NSE.

The Nifty 50 Index consists of 50 companies spread across 13 sectors and is the largest single financial product of India. However, there are many other Indices which represent different segments and industries within the stock market and many investors use these indices to track the performance of specific sectors or market segments, and to gain exposure to different areas of the Indian economy.

Financial Investments need to be distributed across multiple investment vehicles to reduce risk. Methods to reduce risk involve diversifying one's portfolio across * various asset classes(Equity, Bonds, etc.) * sectors(IT, Pharma, etc.) * sub-groups based on parameters like Size (Large Cap, Mid Cap, Small Cap) and Geography(International Funds).

Analysis on the data of different investment vehicles could provide insights which could help an investor make informed decisions while investing and reduce risks.

Content

This dataset is divided into 3 folders. All files contain data before 7th April 2023.

Bonds and ETFs

Historical Data about two ETFs and one bond

BHARATBOND_2030: An investment option facilitated by Edelweiss Mutual Fund. Invests in bonds issues by Indian Public Sector companies.

GoldBeEs: ETF that invests in physical gold and aims to provide returns that closely correspond to the returns provided by the domestic price of gold.

SilverBeEs: ETF that invests in physical silver and aims to provide returns that closely correspond to the returns provided by the domestic price of silver.

Investment Factor Indices

Historical Data about four Indices tracking the performance of top companies based on specific parameters.

NIFTY50 Value 20: Tracks the 20 stocks which are Top ranked in Value among all the Nifty 50 stocks.

NIFTY200 Momentum 30: Tracks the 30 stocks with the highest Momentum among the Top-200 stocks.

NIFTY200 Quality 30: Tracks the 30 stocks with highest quality rating among Top-200 stocks

NIFTY Alpha Low Volatility 30: Tracks the 30 stocks with high Alpha and Low Volatility from among the Top 150 stocks

NIFTY_Indices

Contains the daily open, high, low and close of 18 NIFTY Indices across different sectors and sizes from their launch date till April 6 2023. The file CLOSE-INDICES.csv consists of the daily close prices of all 18 indices from 29 December 2006 till April 6 2023.

Acknowledgements

This data is sourced from NSE and Yahoo Finance.

The dataset was created by web scraping all active mutual funds available for retail investors in India through Groww.
The notebook used for scraping can be found here.
The dataset can be used for Data Analysis.
Disclaimer: Mutual Funds are subjected to market risks, please read policy documents carefully before investment. The dataset is in no way intended to be used as investment advice.

Mutual Fund Historic NAV Data ✨

This dataset boasts 20M+ Historical Net Asset Value (NAV) records. Perfect for analysts, investors & data scientists to spot trends, test strategies & grasp market dynamics.

⚡ 6,000+ New NAV Records Added Daily for 6,000+ unique funds means you get fresh, comprehensive data!

✴️ Mutual Fund Scheme Data

📊 Core Data Inside

• Scheme_Code: 🔑 Unique fund ID.
• Date: 📅 NAV reporting date (YYYY-MM-DD).
• NAV: 💰 Net Asset Value per unit on that Date.

📈 What You Can Calculate & Analyze

• Performance & Returns 🚀:
  • Absolute & Annualized (CAGR) Returns
  • Rolling & Point-to-Point Returns
  • Daily/Log Returns
• Risk & Volatility 📉:
  • Standard Deviation
  • Sharpe & Sortino Ratios (needs risk-free rate)
  • Max Drawdown
  • Beta & Alpha (needs benchmark data)
• Trends & Momentum 📊:
  • Moving Averages (SMA, EMA)
  • Rate of Change (ROC)
• Comparisons & Market View 🧐:
  • Fund Performance Rankings
  • Correlations Between Funds
• Basic Stats 🔢:
  • Highest/Lowest NAV
  • Average/Median NAV

⏱️ Freshness & Updates

Massive History of 20 Million+ NAV Records! with 6,000+ New NAVs Added Daily(previous trading day's values). Updates run via a scheduled Kaggle Notebook and New Indian funds added as they launch!

💡 Top Uses

• ✅ Develop & Backtest Investment Strategies
• ✅ Build Financial Dashboards & Visualizations
• ✅ Quantitative Financial Modeling
• ✅ Academic & Market Research
• ✅ Personal Portfolio Tracking
• ✅ Compare Funds & Categories
• ✅ Spot Market Anomalies & Opportunities

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.