The Indian Stock Market Dataset provides a comprehensive collection of stock market data sourced from secondary sources, primarily Google, offering insights into investment opportunities and trends within the Indian financial landscape. This dataset encompasses a wide array of information, with a primary focus on Return on Investment (ROI) metrics and the respective industry sectors in which investments are made.

With a reliability rating of 80%, this dataset offers valuable insights for investors, analysts, researchers, and enthusiasts seeking to understand and navigate the complexities of the Indian stock market. The dataset serves as a foundational resource for analyzing market performance, identifying lucrative investment opportunities, and making informed decisions in a dynamic financial environment.

Key features of the dataset include:

ROI Analysis: The dataset provides detailed ROI metrics, allowing stakeholders to assess the profitability of various investment avenues over specific timeframes. By analyzing ROI trends, investors can gauge the performance of individual stocks, portfolios, or entire industry sectors, facilitating strategic investment planning and risk management.

Industry Classification: Each investment entry in the dataset is categorized according to its respective industry sector. This classification enables users to explore investment opportunities within specific sectors such as technology, healthcare, finance, energy, consumer goods, and more. Understanding industry dynamics and market trends is essential for optimizing investment portfolios and diversifying risk exposure.

Historical Data: The dataset includes historical stock market data, offering insights into past performance trends and market behavior. By examining historical data, users can identify patterns, correlations, and anomalies that may impact future investment decisions. Historical analysis empowers investors to make informed predictions and adapt strategies in response to evolving market conditions.

Data Accuracy: While the dataset boasts an accuracy rate of 80%, users should exercise diligence and consider additional sources for validation and verification. While the majority of data points are reliable, occasional discrepancies or inaccuracies may exist, highlighting the importance of due diligence and comprehensive analysis in the investment process.

Accessibility: The Indian Stock Market Dataset is easily accessible and user-friendly, catering to a diverse audience ranging from seasoned investors to novices exploring the world of finance. The dataset can be utilized for various purposes, including academic research, financial modeling, algorithmic trading, and investment portfolio management.

In summary, the Indian Stock Market Dataset offers a valuable resource for analyzing ROI and industry trends within the Indian financial landscape. With a focus on accuracy, accessibility, and comprehensive data coverage, this dataset empowers stakeholders to make informed investment decisions, optimize portfolio performance, and navigate the complexities of the dynamic stock market environment. Whether you're a seasoned investor or a novice enthusiast, this dataset provides valuable insights for unlocking the potential of the Indian stock market.

Context

In this dataset you will find several characteristics on global companies listed on the stock exchange. These characteristics are analyzed by millions of investors before they invest their money.

Analyze the stock market performance of thousands of companies ! This is the objective of this dataset !

Content

Among thse charateristics you will find :

• The symbol : The stock symbol is a unique series of letters assigned to a security for trading purposes.
• The shortname : The name of the company
• The sector : The sector of the company (Technology, Financial services, consumer cyclical...)
• The country : The location of the head office.
• The market capitalisation : Market capitalization refers to the total dollar market value of a company's outstanding shares of stock. It is calculated by multiplying the total number of a company's outstanding shares by the current market price of one share.
• The current ratio : The current ratio is a liquidity ratio that measures a company’s ability to pay short-term obligations. A current ratio that is in line with the industry average or slightly higher is generally considered acceptable. A current ratio that is lower than the industry average may indicate a higher risk of distress or default.
• The beta : Beta is a measure of a stock's volatility in relation to the overall market. A beta greater than 1.0 suggests that the stock is more volatile than the broader market, and a beta less than 1.0 indicates a stock with lower volatility.
• The dividend rate : Represents the ratio of a company's annual dividend compared to its share price. (%)

All this data is public data, obtained from the annual financial reports of these companies. They have been retrieved from the Yahoo Finance API and have been checked beforehand.

Inspiration

This dataset has been designed so that it is possible to build a recommendation engine. For example, from an existing position in a portfolio, recommend an alternative with similar characteristics (sector, market capitalization, current ratio,...) but more in line with an investor's expectations (may be with less risk or with more dividends etc...)

If you have question about this dataset you can contact me

Overview

The Finance Trends Dataset investigates how people choose investments across a range of financial sectors. It includes comprehensive data about 12,000 individuals, including their demographics, investing choices, goals, and motivations. The purpose of this dataset is to assist scholars, analysts, and students in comprehending investing behavior, financial objectives, and new developments in the contemporary financial environment.

Context

Both individual and societal development are significantly influenced by finance. To attain long-term objectives and financial security, people invest their resources in a variety of securities, including gold, government bonds, fixed deposits, equity markets, and mutual funds. This dataset covers the behavioral, motivational, and informational aspects that influence financial decisions, in addition to what people invest in. Patterns like how expectations, age, and gender affect investment preferences can be found by examining this dataset

Content

The Finance Trends Dataset explores how individuals invest their money and what factors influence their financial choices. Expected returns, preferred investment options, investor demographics, and the rationale behind the selection of various financial instruments are all covered. Analysis of investor behavior, financial incentives, and general market movements can all benefit from this dataset.

Conclusion

An excellent chance to investigate financial psychology and investment behavior at scale is provided by this dataset. Data scientists, finance students, and professionals with an interest in trend analysis, predictive modeling, and behavioral finance will find it excellent.

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.

The Investing in America (IIA) dataset provides details on federally funded programs and projects under the Bipartisan Infrastructure Law (BIL), Inflation Reduction Act (IRA), and CHIPS and Science Act (CHIPS). This dataset highlights a subset of what these laws will fund, illustrating their scope and the impact on American communities. It includes data derived from various public sources, such as press releases, funding announcements, and Treasury account information. While this dataset aims to offer a comprehensive view, it is not exhaustive and may contain errors. Data is preliminary and non-binding, with awards subject to meeting specific requirements. Project locations are approximate and generally represent the geographic center of the relevant city or county. Additional details and state fact sheets are available at invest.gov.

Disclaimer: Educational Purposes Only

The financial and International Securities Identification Number (ISIN) data listed on this platform is provided solely for educational purposes. The information is intended to serve as general guidance and does not constitute financial advice, an endorsement, or a recommendation for the purchase or sale of any securities.

While we strive to ensure the accuracy and timeliness of the information presented, we make no representations or warranties, express or implied, regarding the completeness, accuracy, reliability, suitability, or availability of the provided data. Users are encouraged to independently verify any information obtained from this platform before making any investment decisions.

This platform and its operators are not responsible for any errors, omissions, or inaccuracies in the provided data, nor for any actions taken in reliance on such information. Users are strongly advised to conduct thorough research and seek the advice of qualified financial professionals before making any investment decisions.

The use of International Securities Identification Numbers (ISINs) and other financial data is subject to various regulations and licensing agreements. Users are responsible for complying with all applicable laws and respecting any terms and conditions associated with the use of such data.

By accessing and using this platform, users acknowledge and agree that they are doing so at their own risk and discretion. This educational content is not a substitute for professional financial advice, and users should consult with qualified professionals for specific guidance tailored to their individual circumstances.

Reddit: /r/stocks

Analyzing User Engagement to Identify Market Trends

By Reddit [source]

About this dataset

This dataset provides a valuable opportunity for researchers to explore the fascinating world of stock exchange markets through the eyes of those participating in discussions on Reddit. We have compiled posts from the subredditstocks subreddit to provide researchers with an invaluable source of information on how stock market trends may be impacted by user sentiment. With detailed data columns such as post titles, scores, id's, URLs, comments counts and created times for each post we are offering a unique vantage point into understanding how stocks market discussions may inform our better understanding of these dynamics. By delving further into user sentiment and engagement with stock topics, investigators can put together meaningful pieces in assembling full-fledged investments picture that is based off sound evidence gained from real people’s experiences and opinion. Discovering new insights has never been made easier – let’s venture out on this journey together!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨! ### Research Ideas
• Using the score and comments data, researchers can determine which stocks are being discussed and tracked the most, indicating potential areas of interest in the stock market.
• Analyzing the body text of posts to identify common topics of conversation related to various stocks assists in providing a better understanding of users' feelings towards different stock investments.
• Through analyzing fluctuations in user engagement over time, researchers can observe which stocks have experienced an increase or decrease in user interest and reaction to new developments within different markets

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: stocks.csv | Column name | Description | |:--------------|:--------------------------------------------------------------------| | title | The title of the post. (String) | | score | The score of the post, based on the Reddit voting system. (Integer) | | url | The URL of the post. (String) | | comms_num | The number of comments on the post. (Integer) | | created | The date and time the post was created. (Timestamp) | | body | The body text of the post. (String) | | timestamp | The date and time the post was last updated. (Timestamp) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Reddit.

Dhaka Stock Exchange (DSE) Historical Stock Prices (2000-2025)

Dataset Overview:

This dataset provides a comprehensive historical record of stock prices from the Dhaka Stock Exchange (DSE), the primary stock exchange of Bangladesh. Spanning from January 1, 2000, to February 26, 2025, it offers a detailed look into the daily trading activity of 464 unique stocks.

Key Features:

• Date: The trading date (YYYY-MM-DD format).
• Script (Stock Name): The name or ticker symbol of the listed company.
• Open: The opening price of the stock on the given trading day.
• High: The highest price reached by the stock during the trading day.
• Low: The lowest price reached by the stock during the trading day.
• Close: The closing price of the stock on the given trading day.
• Volume: The total number of shares traded for the stock on the given trading day.

Data Characteristics:

• Time Span: January 1, 2000, to February 26, 2025.
• Number of Unique Stocks: 464
• Frequency: Daily
• Accuracy: Clean and accurate data, suitable for reliable analysis.

Potential Uses:

• Financial Analysis: Analyze stock trends, volatility, and performance over time.
• Machine Learning: Develop predictive models for stock price forecasting.
• Economic Research: Study the impact of economic events on the Bangladeshi stock market.
• Investment Strategies: Backtest trading strategies and identify potential investment opportunities.
• Educational Purposes: Learn about stock market dynamics and data analysis in finance.

Acknowledgements:

This dataset was meticulously compiled and cleaned to provide a valuable resource for researchers, analysts, and investors interested in the Dhaka Stock Exchange.

Note:

While efforts have been made to ensure the accuracy of the data, users are advised to conduct their own due diligence and validation before making any investment decisions based on this dataset.

This description highlights the key aspects of your dataset, its potential uses, and its reliability. Feel free to adjust it further based on any specific details or insights you want to emphasize!

Are hedge funds worth your money? Hedge funds have developed from investment funds that were designed to lower the risk of your portfolio to a multitude of different investment styles with different goals. Their heyday was probably during the 90s and early 2000s when several star hedge fund managers rose to prominence and their assets under management grew significantly. However, since then hedge funds have been under scrutiny as their investment returns have been lacking and their ability to function as a diversification to a traditional stock and bond portfolio was put into question. As hedge funds have their own set of leverage and investment rules it is no wonder they have been accused of being greedy, unsuccessful and secretive. However, with this dataset you can make your own analysis.

Content This dataset covers monthly hedge fund returns starting from 1997. The date column refers to the last day of the month - the end date of the return period, if I understand correctly. There are 12 different hedge fund strategies covered and the return index series are formed as an aggregate of other hedge fund index providers.

The strategy explanations are in EDHEC website:

Convertible Arbitrage - https://risk.edhec.edu/conv-arb/ CTA Global - https://risk.edhec.edu/cta-global/ Distressed Securities - https://risk.edhec.edu/dist-sec/ Emerging Markets - https://risk.edhec.edu/emg-mkts/ Equity Market Neutral - https://risk.edhec.edu/equity-market-neutral/ Event Driven - https://risk.edhec.edu/event-driven/ Fixed Income Arbitrage - https://risk.edhec.edu/fix-inc-arb/ Global Macro - https://risk.edhec.edu/global-macro/ Long/Short Equity - https://risk.edhec.edu/ls-equity/ Merger Arbitrage - https://risk.edhec.edu/merger-arb/ Relative Value - https://risk.edhec.edu/relative-value/ Short Selling - https://risk.edhec.edu/short-selling/ Funds of Funds - https://risk.edhec.edu/fof/ Acknowledgements All credit for the maintenance and upload of the data goes to EDHEC. You should check their website for additional resources:

https://risk.edhec.edu/all-downloads-hedge-funds-indices

Inspiration The EDHEC hedge fund data is the data used in examples/vignettes of PortfolioAnalytics - a package for optimizing, testing and analyzing portfolio returns. You should be easily able to expand the analysis from the vignettes just by using the larger dataset available here:

https://cran.r-project.org/web/packages/PortfolioAnalytics/index.html

Financial Access and Usage

Global Comparative Ratios Across 189 Jurisdictions

By International Monetary Fund [source]

About this dataset

This dataset provides an unprecedented opportunity to explore global financial access and usage trends from 2004-2016 from 189 of the world's reporting jurisdictions—which cover over 99 percent of the total adult population. With 152 time series and 47 indicator ratios, this Financial Access Survey gives insight into ways that access to and usage of financial services differ by households vs small/medium enterprises, life vs non-life insurance, deposits & microfinance institutions as well as credit unions & financial cooperatives. Utilizing this data, we can gain a better understanding of how policies or shifts in the global economy may influence or relate to access or utilization of services in certain regions while having comparable cross-economy comparisons. The IMF Monetary and Financial Statistics Manual Compilation Guide is utilized for all methodologies used in accumulating these datasets, while all data is available “as-is” with no guarantee provided either express or implied. Are you looking for ways to implement insightful macroeconomic analyses? Download FAS 2004–2016 now!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The Financial Access Survey provides global supply-side data on access to and usage of financial services by households and firms for 189 reporting jurisdictions, covering 99 percent of the world’s adult population. With a robust selection of time series in this dataset, users can make meaningful insights into trends over time or across countries concerning various indicators related to access and usage of financial services. To help users navigate this large dataset, the following guide explains how to use the data most effectively.

Understanding The Dataset Columns

The columns in the dataset provide information about each indicator such as country name, indicator name, code for that indicator, its attribute (i.e., rate/ratio), when data is available for that particular indicator. Once you have identified an interesting measure/indicator whether it be credit union density or life insurance penetration rate measure in a given country during a certain year period then you can look up those numbers from the rows provided in this dataset .

Understanding The Different Years Available & Comparing Numbers Over Time

It is useful for users to compare different indicators over time by looking at specific years within this dataset which will allow us to see if rates are increasing or decreasing worldwide patterns across these trends among different countries based on these various measures listed provided in this survey such as mortgage lending rate or ratio GDP per capita etc that have been collected . We can therefore make use of our knowledge off economic changes that have occurred over time within certain parts of world , no matter if they are longer term economic effects due increases certain capabilities within a geographical area or shorter term changes due taxation laws by governments etc driving some people either towards using or away from using certain kinds financial products .

#### Comparing Between Countries

This datasets allows us direct comparisons between different countries with regards how many people are currently making use particular types off finances services , we certainly be able analyse current international relationships between services providers as well customers where ever concerned about particular attributes mentioned above whether being deposit interest rates small business credits terms tenders so forth . Knowing more about related dynamics helps build better user experiences with providers who understand needs risks impacts generating larger customer bases globally which often beneficial both parties involved exchange relationship so not forget always keep cross border motif whenever eye process from afar !

Research Ideas

• Comparing the access to and usage of financial services in different countries to better inform research policy decisions.
• Analyzing trends in financial access and usage by jurisdiction over time, to identify areas needing improvement in order to promote financial inclusion and stability.
• Cross-referencing FAS data with macroeconomic indicators such as GDP information to measure the potential impact of changes in level of access on economic growth or other metrics specific to a country or region of interest

Acknowledgements

If you use this dataset in yo...

Amazon (AMZN) Stock Price Time-Series Dataset: May 2012 - November 2012

Dataset Overview

This dataset provides a detailed, intraday view of Amazon's stock (AMZN) price movements from May 21, 2012, to November 14, 2012. Meticulously compiled, it offers a granular perspective on market dynamics, enabling robust quantitative analysis and modeling.

Content

The dataset encompasses the following key financial metrics for each trading day:

• Date: The specific date of the trading session.
• Open: The initial price at the commencement of trading.
• High: The maximum price attained during the trading day.
• Low: The minimum price recorded during the trading day.
• Close: The final trading price at the market's close.
• Adj Close: The closing price adjusted for corporate actions like dividends and stock splits, providing a true return on investment.
• Volume: The number of shares exchanged throughout the trading day, indicating market activity and liquidity.

Intended Use Cases

This dataset is tailored for sophisticated financial analysis, model development, and academic research. Potential applications include:

• Algorithmic Trading Strategy Development: Design and back-test trading algorithms using historical price movements and volume data.
• Volatility Modeling: Analyze and forecast stock price volatility using time-series analysis techniques (e.g., GARCH models).
• Financial Forecasting: Implement machine learning models to predict future stock prices based on historical patterns.
• Event Study Analysis: Examine the impact of specific events or news announcements on Amazon's stock price.
• Risk Management: Evaluate potential risks associated with investing in Amazon stock during this period.
• Academic Research: Conduct studies on market efficiency, price discovery, and the impact of market microstructure on stock behavior.

Data Considerations

• Time Zone: Data is timestamped with Eastern Time (ET).
• Data Cleaning: The dataset has been verified for accuracy, but users are encouraged to perform their own data quality checks.

Contect info:

You can contect me for more data sets if you want any type of data to scrape

-E_mail

-Linkdin

-Kaggle

-X

-Github

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

Context

ETFs represent a cheap alternative to Mutual Funds and they are growing fast in the last years due to their passive approach (and the consequential lower fees). This dataset includes the financial information collected from Yahoo Finance and includes all U.S. Mutual Funds and along with their historical prices. Updated version relates to the November 2021 financial values.

Content

The file contains 23,783 Mutual Funds and 2,310 ETFs with: - General fund aspects (e.g. total_net_assets, fund family, inception date, etc.) - Portfolio indicators (e.g. cash, stocks, bonds, sectors, etc.) - Historical yearly and quarterly returns (e.g. year_to_date, 1-year, 3-years, etc.) - Financial ratios (price/earning, Treynor and Sharpe ratios, alpha, and beta) - ESG scores

Acknowledgements

Data has been scraped from the publicly available website https://finance.yahoo.com.

Inspiration

Datasets allow for multiple comparisons regarding portfolio decisions from investment managers in Mutual Funds and portfolio restrictions to the indexes in ETFs. The inspiration comes from the 2017 hype regarding ETFs, that convinced many investors to invest in Exchange Traded Funds rather than in Mutual Funds. Datasets will be updated every one or two semesters.

Unicorn Nest: Startup Fundraising Dataset

Our team has been active in fundraising for around 20 years. We’ve been thoroughly collecting all available data about deals on the market and we’ve aggregated all the essential data about funds: industries, average round sizes, exists, etc. in one of the most comprehensive datasets out there that consists of 500,000 cells.

In times of the COVID-19 crisis, we decided to share some elements of our dataset with the startup community for free. We suppose that file will save you dozens of hours that you’d previously spend on searching and collecting information about potential investors.

If you enjoy the dataset, please don't forget to upvote it at https://www.producthunt.com/posts/unicorn-nest-dataset

The dataset consists of two .csv files:

• "funds.csv" provides information about funds, where you can learn which funds invested before, after, or together with your selected fund, see the rankings based on the number of unicorns, exits, average round sizes, sort funds by country
• "key persons.csv" gives details about key persons at each of the funds.

This dataset includes over 500,000 cells with data about 26500 investment funds.

Please keep in mind that all the data in this dataset is relevant as of 4/1/2020. You can check the relevance of each of the indicators on our website.
Also, for ease of use, some indicators from UNICORN NEST database have been simplified, and some are missing in this dataset.
For your convenience, a link was placed on each of the indicators. By following it you can check the relevance of a particular indicator or see more detailed information about it.

Feel free to provide your feedback at info@unicorn-nest.com with the subject line ”UN dataset feedback”

By using this dataset you agree that the data in the file:

• is for startup and information use only
• is provided on “as is” basis with no warranties
• Shall not be used or allowed to be used by you for any science or academic research, in commerce, for analytical purpose, for or any mailout or information distribution purposes, as well as for any illegal purpose
• Is the property of Unicorn Nest, all rights reserved as may be applicable by law, hence you shall not perform or attempt to register the database or any its derivative object in your or any third person’s name.

You need to place attribution to Unicorn Nest if using the dataset for any type of paper.

The dataset consists of 1,500 individuals from Maharashtra, representing a diverse demographic in terms of age, gender, education, and income levels. The data provides valuable insights into behavioral finance and investment decision-making by capturing key socioeconomic factors influencing financial choices.

Age ranges from 20 to 60, reflecting varied investment preferences across different life stages. The gender distribution ensures inclusivity, while education levels—from high school to PhD—highlight varying degrees of financial literacy. Monthly incomes span from ₹15,000 to ₹70,000, shaping investment risk tolerance and asset preferences.

This dataset can help analyze how demographics impact financial decisions, risk behavior, and investment patterns among individuals in Maharashtra.

Dataset Description:

Title: Sales Order Dataset

Description: This dataset contains sales order information from an e-commerce platform for a specific period. The dataset includes the following columns:

Order Number: A unique identifier for each order. Order Date: The date when the order was placed. SKU ID: Stock Keeping Unit (SKU) identifier for the product. Warehouse ID: Identifier for the warehouse from which the product was shipped. Customer Type: Type of customer (e.g., individual, business). Order Quantity: The quantity of the product ordered. Unit Sale Price: The price per unit of the product. Revenue: The total revenue generated by the order. Purpose: This dataset is suitable for exploring sales patterns, analyzing customer behavior, and predicting future sales trends. It can be used by data analysts, data scientists, and business analysts to gain insights into sales performance, identify potential areas for improvement, and make data-driven business decisions.

Potential Use Cases:

Analyzing sales trends over time. Identifying best-selling products and customer segments. Predicting future sales based on historical data. Evaluating the effectiveness of marketing campaigns and promotions. Optimizing inventory management and supply chain operations. Data Source: The dataset was collected from an e-commerce platform and has been anonymized to protect sensitive information. It represents a subset of sales order data for analysis and research purposes.

Acknowledgements: We acknowledge the contribution of the e-commerce platform for providing the sales order data used in this dataset.

License: This dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0). You are free to use, share, and adapt the data, provided you give appropriate credit to the original source.

Dataset

This dataset was created by Jason

Released under Data files © Original Authors

Contents

Context

The STOCK Act (Stop Trading on Congressional Knowledge) was signed into law by President Barack Obama in 2012. This law prevents insider trading by members of Congress and other government employees that may have access to non-public information. As a result, every Senator is required to publicly file any transaction of stock, bond, commodities futures, and other securities within 45 days.

Disclosures can be found here: https://efdsearch.senate.gov/

Data was collected using a Python script with the Selenium and Pandas modules. The program accessed the site and scrolled through all electronically submitted disclosures, adding each to a data frame.

Content

The data set contains 9 columns: 1) Name - Which Senator the transaction is 'related' to 2) Transaction Date - Date of transaction 3) Owner - Who completed the transaction 4) Ticker - Stock/ Security Ticker (if applicable) 5) Asset Name - Name of asset 6) Asset Type - Category of asset 7) Type - Type of transaction 8) Amount - USD Amount of trade broken up into 5 categories. 9) Comment

Important Data Disclosures

It's important to note that I wasn't able to scrape data that was physically submitted, meaning some Senators submit handwritten documents to disclose trades. I arranged my code to skip over these documents because I simply lack the knowledge to scrape handwriting with Python.

Inspiration

I'd love to see how Senators' investment returns compare to those of 'normal' people. I plan on running my script again to scrape additional government employee investments to see how they compare!

This dataset includes a sample of 1000 mutual fund investors and their attitudes towards various characteristics of why those chose to invest in mutual funds, rated on a scale of (0-7). Each variable is explained as follows - Investor_ID - Serial Number, as all other identification details have been masked and coded Longevity - the number of years they have been a client Female - coded as 0 for male and 1 for female Age - grouped into 3 age groups young (typically 20 to 35), middle aged (35 to 50) and older (50-65) Income - coded as 4 groups, 1 being the lowest income group and 4 being the highest ProfManage - how much the investor values Professional Management of their funds as a reason to invest in Mutual Funds (on a scale of 0 to 7) Diversification - how much the investor values Diversification of their funds as a reason to invest in Mutual Funds (on a scale of 0 to 7) Affordability - how much the investor values Mutual Funds being Affordable (low entry barrier) as a reason to invest in Mutual Funds (on a scale of 0 to 7) Liquidity - how much the investor values Mutual Funds having Liquidity as a reason to invest in Mutual Funds (on a scale of 0 to 7) Growth - how much the investor values Growth of their funds as a reason to invest in Mutual Funds (on a scale of 0 to 7) Trustworthiness - how much the investor values Trustworthiness of the advice given by the advisor as a reason to invest in Mutual Funds (on a scale of 0 to 7) Technology - how much the investor values Digital access (such as mobile app) & ease of investing online as a reason to invest in Mutual Funds (on a scale of 0 to 7) Integrity - how much the investor values Professional Integrity of their advisor as a reason to invest in Mutual Funds (on a scale of 0 to 7) BrandValue - how much the investor regards Brand Value of the distributor they are investing with as a reason to invest in Mutual Funds (on a scale of 0 to 7) AUM - the total Assets they have Under Management (their Portfolio amount)

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.