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.

About:

This dataset encompasses the historical data of major stock indices from around the world, sourced directly from Yahoo Finance. With data reaching back to the early 1920s (where available), it serves as an invaluable repository for academic researchers, financial analysts, and market enthusiasts. Users can delve into trends across decades, evaluate historical market behaviors, or even design and validate predictive financial models.

Photo by Tötös Ádám on Unsplash

Info on CSVs:

1. all_indices_data.csv:

   • Description: A consolidated dataset merging all the stock indices from Yahoo Finance.
   • Columns:
     • date: The date of the data point (formatted as YYYY-MM-DD).
     • open: The opening value of the index on that date.
     • high: The highest value of the index during the trading session.
     • low: The lowest value of the index during the trading session.
     • close: The closing value of the index.
     • volume: The trading volume of the index on that date.
     • ticker: The ticker symbol of the stock index.

2. individual_indices_data/[SYMBOL]_data.csv:

   • Description: Individual datasets for each stock index, where [SYMBOL] denotes the ticker symbol of the respective stock index. Each dataset is curated from Yahoo Finance's historical data archives.
   • Columns:
     • date: The date of the data point (formatted as YYYY-MM-DD).
     • open: The opening value of the index on that date.
     • high: The highest value of the index during the trading session.
     • low: The lowest value of the index during the trading session.
     • close: The closing value of the index.
     • volume: The trading volume of the index on that date.

This is the name of the 38 global main stock indexes in the world. We collected from Yahoo! Finance. For the convenience of expression and computation later, we numbered it. For each item, the front is its serial number, followed by the corresponding stock index.

Explore the intricacies of wheat as a global commodity on Yahoo Finance, offering live price updates, historical data, and market insights. Discover how geopolitical events, weather conditions, and supply chain logistics influence wheat prices and affect various economic sectors. Stay informed with expert analyses and community discussions, providing comprehensive resources for both novice and seasoned investors in the agricultural markets.

20 years of Yahoo Finance Open, High, Low, Close, Adjusted Close, Volume data, plus generated technical features (RSI, SMA) on close to 5000 global equities. Various targets including 20 days raw returns, residual returns, etc. Use to create predictive models on Numerai Signals tournament to stake and earn/burn $NMR.

Explore how Yahoo Finance serves as a key resource for tracking soybeans, offering real-time analytics, historical insights, and expert commentary on the global soybean market's trends, supply chain dynamics, and economic impact.

This dataset contains CSV files of all tickers available via the Yahoo Finance API (stocks, currencies, cryptocurrencies, ETFs, etc.) and their associated name, performance, volume and market cap over the past 5/10 years. The 10_year_results.csv and 5_year_results.csv are filtered for assets with current market cap $1B+, decade-old volume $1K+, current volume $100K+, and sorted by top performance.

Explore wheat prices on Yahoo Finance and understand the various factors influencing market trends, from global demand and weather conditions to government policies and financial analysis tools. Discover real-time data and insightful charting on the commodity's performance.

Context

Stocks and financial instrument trading is a lucrative proposition. Stock markets across the world facilitate such trades and thus wealth exchanges hands. Stock prices move up and down all the time and having ability to predict its movement has immense potential to make one rich. Stock price prediction has kept people interested from a long time. There are hypothesis like the Efficient Market Hypothesis, which says that it is almost impossible to beat the market consistently and there are others which disagree with it.

There are a number of known approaches and new research going on to find the magic formula to make you rich. One of the traditional methods is the time series forecasting. Fundamental analysis is another method where numerous performance ratios are analyzed to assess a given stock. On the emerging front, there are neural networks, genetic algorithms, and ensembling techniques.

Another challenging problem in stock price prediction is Black Swan Event, unpredictable events that cause stock market turbulence. These are events that occur from time to time, are unpredictable and often come with little or no warning.

A black swan event is an event that is completely unexpected and cannot be predicted. Unexpected events are generally referred to as black swans when they have significant consequences, though an event with few consequences might also be a black swan event. It may or may not be possible to provide explanations for the occurrence after the fact – but not before. In complex systems, like economies, markets and weather systems, there are often several causes. After such an event, many of the explanations for its occurrence will be overly simplistic.

#
#

https://www.visualcapitalist.com/wp-content/uploads/2020/03/mm3_black_swan_events_shareable.jpg"> #
#
New bleeding age state-of-the-art deep learning models stock predictions is overcoming such obstacles e.g. "Transformer and Time Embeddings". An objectives are to apply these novel models to forecast stock price.

Content

Stock price prediction is the task of forecasting the future value of a given stock. Given the historical daily close price for S&P 500 Index, prepare and compare forecasting solutions. S&P 500 or Standard and Poor's 500 index is an index comprising of 500 stocks from different sectors of US economy and is an indicator of US equities. Other such indices are the Dow 30, NIFTY 50, Nikkei 225, etc. For the purpose of understanding, we are utilizing S&P500 index, concepts, and knowledge can be applied to other stocks as well.

Dataset

The historical stock price information is also publicly available. For our current use case, we will utilize the pandas_datareader library to get the required S&P 500 index history using Yahoo Finance databases. We utilize the closing price information from the dataset available though other information such as opening price, adjusted closing price, etc., are also available. We prepare a utility function get_raw_data() to extract required information in a pandas dataframe. The function takes index ticker name as input. For S&P 500 index, the ticker name is ^GSPC. The following snippet uses the utility function to get the required data.(See Simple LSTM Regression)

Features and Terminology: In stock trading, the high and low refer to the maximum and minimum prices in a given time period. Open and close are the prices at which a stock began and ended trading in the same period. Volume is the total amount of trading activity. Adjusted values factor in corporate actions such as dividends, stock splits, and new share issuance.

Starter Kernel(s)

• Simple LSTM Regression

Acknowledgements

Mining and updating of this dateset will depend upon Yahoo Finance .

Inspiration

Sort of variation of sequence modeling and bleeding age e.g. attention can be applied for research and forecasting

Some Readings

• Applications of deep learning in stock market prediction: recent progress
• Stock predictions with state-of-the-art Transformer and Time Embeddings
• https://arxiv.org/pdf/1712.02136.pdf
• https://catanacapital.com/blog/unpredictable-black-swan-event-stock-market/
• https://par.nsf.gov/servlets/purl/10149715
• https://arxiv.org/pdf/1712.02136.pdf

*If you download and find the data useful your upvote is an explicit feedback for future works*

This statistic gives information on Yahoo!'s net income from 2004 to 2016. In the last reported year, the internet company's GAAP net loss was *** million US dollars, down from a net income of *** billion US dollars in 2014.

Yahoo has had its share of financial troubles, in part due to Google’s almost complete domination of market sectors where Yahoo used to be an important player, such as the search engine market. For example, as of April 2015, just under * percent of worldwide internet users search the web using Yahoo’s service, while more than ** percent use Google Search. But despite its ups and downs, the company has remained one of the most relevant multinational technology companies in the world. In 2014, Yahoo’s net income was a reported *** billion U.S. dollars, up from *** billion in the previous year. That same year, the company’s yearly revenue however was the second-lowest in the past decade – *** billion U.S. dollars. Especially the second quarter of 2014 displays lower than ever revenues for the company, as compared to previous years – just slightly over * billion U.S. dollars. According to the most recent report regarding Yahoo’s quarterly net income, the company generated a **** billion U.S. dollars profit in the third quarter of 2014, as a result the company's sale of Alibaba shares, but also a net loss of ***** million U.S. dollars in the second quarter of 2015. Yahoo was founded in the mid ***** in California, in the midst of the Silicon Valley technological boom. It is mostly known for its search engine, Yahoo Search, and the Yahoo web portal, featuring such services as Yahoo Finance, Yahoo News, Yahoo Answers and most notably Yahoo Mail. The company, which has made a lot of acquisitions since its modest beginnings, also provides advertising services, online mapping and video sharing. Since it acquired Tumblr in 2013, the company has also started to move into the social media sector. As of 2015, Yahoo is the second-most popular website in the United States, after Google, with more than *** million unique visitors per month on all of its properties combined.

Our project involves creating a model using Multiple Linear Regression to analyze and predict the stock prices of Pepsico. Multiple Linear Regression is a statistical technique that allows us to understand the relationship between multiple independent variables and a dependent variable, in this case, the stock price of Pepsico. By considering various factors such as historical stock prices, market trends, and financial indicators, we aim to develop a robust model that can provide valuable insights and predictions for investors and analysts. Through the implementation of this model, we hope to uncover meaningful patterns and correlations within the Pepsico share data, enabling more informed decision-making in the dynamic world of stock market investments.

This dataset contains the historical stock price data for Amazon.com, Inc. (AMZN), one of the largest and most influential technology companies in the world. The data has been sourced directly from Yahoo Finance, a widely trusted provider of financial market data. It spans a significant time range, enabling users to analyze Amazon’s market performance over the years, observe long-term trends, and identify key events in the company’s history.

Data Source

• Source: Yahoo Finance (Amazon AMZN Historical Data)
• Stock Ticker: AMZN (Amazon.com, Inc.)

Dataset Features

The dataset is structured as a CSV file, with each row representing a single trading day. The following columns are included:

• Date: The date of the trading session (in YYYY-MM-DD format).
• Open: The price at which Amazon stock opened for trading on that day.
• High: The highest price reached during the trading session.
• Low: The lowest price reached during the day.
• Close: The closing price of Amazon stock for that day.
• Adj Close: The adjusted closing price, which accounts for any corporate actions (e.g., dividends, stock splits) that might have affected the price.
• Volume: The total number of shares traded on that day.

Coverage and Frequency

• Frequency: Daily (including all trading days)
• Time Period: The dataset covers all available historical data for Amazon stock as provided by Yahoo Finance. For specific start and end dates, please check the dataset page or CSV file.

Potential Uses

This dataset is suitable for a wide range of financial, academic, and data science projects, such as:

• Time Series Analysis: Analyze trends and patterns in Amazon’s stock price movements over time.
• Forecasting: Build predictive models to forecast future stock prices using statistical or machine learning techniques.
• Technical Analysis: Apply traditional financial indicators and strategies (e.g., moving averages, RSI, MACD) to study price action.
• Event Impact Studies: Examine the effect of external events (earnings releases, product launches, macroeconomic news) on Amazon’s stock price.
• Portfolio Simulation: Use historical data to backtest trading strategies or simulate investment portfolios.
• Educational Purposes: Teach or learn about financial markets, data manipulation, and data visualization.

Data Quality and Notes

• The data is directly downloaded from Yahoo Finance and is believed to be accurate. However, users should verify specific data points if using for investment or trading decisions.
• Missing or non-trading days (weekends, holidays) are omitted from the dataset.
• Adjusted Close is the most accurate reflection of the stock’s value over time, especially when analyzing performance across long periods.

Acknowledgments

• Yahoo Finance for providing free and accessible financial data.
• Please cite Yahoo Finance as the original data source if you use this dataset in your work.

License

• This dataset is provided for educational and research purposes only. Please review Yahoo Finance’s terms of service before using this data for commercial purposes.

Content

This dataset provides a comprehensive, consolidated collection of daily historical stock data for all companies included in the S&P 100 index. It is designed to be a clean and reliable resource for financial analysis, machine learning, and academic research.

Key Features

Consolidated Data: All data is combined into a single, easy-to-use CSV file, simplifying cross-company analysis.

Top U.S. Companies: Contains data for the 100 largest and most influential non-financial companies in the S&P 500.

Daily Updates: The dataset is updated daily.

Comprehensive Metrics: Each entry includes key OHLCV (Open, High, Low, Close, Volume) data points.

Data Dictionary

Date: The date of the trading session in YYYY-MM-DD format.

ticker: The standard ticker symbol for the company on Yahoo Finance.

name: The full name of the company.

Open: The opening price of the stock in USD at market open.

High: The highest price the stock reached during the trading day in USD.

Low: The lowest price the stock reached during the trading day in USD.

Close: The final price of the stock at market close in USD.

Volume: The total volume of shares traded during the day.

Data Collection

The data for this dataset is sourced from the Yahoo Finance API using the yfinance Python library. The list of S&P 100 companies is sourced from a reliable financial resource to ensure accuracy and relevance.

Potential Use Cases

Financial Analysis: Analyze market trends, performance correlations, and historical volatility.

Machine Learning: Train models to predict stock prices, identify trading patterns, or classify market regimes.

Time Series Modeling: Forecast future stock movements using historical price and volume data.

Educational Projects: Use as a practical, real-world dataset for learning data science and finance.

Content

Daily price data for World indices stock exchanges from all over the world (United States, China, Canada, Germany, Japan, and more). The data was all collected from Yahoo Finance, which had several decades of data available for most exchanges. Prices are quoted in terms of the USD currency of where each exchange is located.

Acknowledgement

Data collected from Yahoo Finance.

Context

This dataset consists of the daily stock prices and volume of 14 different tech companies, including Apple (AAPL), Amazon (AMZN), Alphabet (GOOGL), and Meta Platforms (META), Adobe (ADBE), Cisco Systems (CSCO), IBM, Intel Corporation (INTC), Netflix (NFLX), Tesla (TSLA), NVIDIA (NVDA), and more!

Note: All stock_symbols have 3271 prices, except META (2688) and TSLA (3148) because they were not publicly traded for part of the period examined.

Content

Geography: Worldwide

Time period: Jan 2010- Jan 2023

Unit of analysis: Big Tech Giants Stock Price Data

Variables

Acknowledgements

Datasource: Yahoo Finance Credit: Evan Gower

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2F77ed318834f67e5ec3dea9fa961efe50%2Fpic1.png?generation=1718970886706508&alt=media" alt="">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F18335022%2F68b2014347f4b9e388025f9f4c31248e%2Fpic2.png?generation=1718970898986658&alt=media" alt="">

Context

Time series modelling for the prediction of stocks prices is a challenging task. Political events, market expectations and economic factors are just a few known factors that can impact financial market behaviour. The financial market is a complex, noisy, evolutionary and chaotic field of study that attracts many enthusiasts and researches — the first, usually driven by the economic benefit of it, the latter, inspired by the challenge of handling such complex data.

This project aims to predict Facebook (FB) next day stock price direction with machine learning algorithms. Technical indicators and global market indexes are used, and their influence on the forecast accuracy is analysed.

Content

Daily values were retrieved (volume, open, close, low and high prices) from Yahoo! Finance website. For Facebook (FB), July 2012 was the earliest data available. The date range is July 2012 to November 2018.

The closing price of current day C(t) and closing price from the previous day C(t-1) are compared to build the initial dataset. The objective is to define if the price trend is going up or down by analysing these two values. For each instance, a comparison was made and recorded. If the price is going up, C(t) > C(t-1), class “1” is assigned. Class “0” is assigned for the opposite case.

• ID: Sample ID
• Close: Closing value of previous day
• Low: Lowest value of previous day
• High: Highest value of previous day
• Volume: Volume value of previous day

Research was initiated to understand which features could help the model to forecast the stock direction. Three main routes were found: Lag features, Technical Indicators and Global Market Indexes. Below is an explanation of each group of features.

Lag features are features that contain the closing price and direction of previous days and it is a common strategy for Time Series models. The following features were added:

• C(t-5): Closing price of 5 days before
• C(t-4): Closing price of 4 days before
• C(t-3): Closing price of 3 days before
• C(t-2): Closing price of 2 days before
• C_up_4: Output 1 if closing price went up 4 days ago
• C_up_3: Output 1 if closing price went up 3 days ago
• C_up_2: Output 1 if closing price went up 2 days ago
• C_up_1: Output 1 if closing price went up 1 day ago

Technical indicators are used by researches and financial market analysts to support stock market trend forecasting. Common indicators retrieved from the literature were selected and calculated for Facebook stock. Techical Indicators added:

• MA-10: Moving Average considering previous 10 days
• MA-5: Moving Average considering previous 5 days
• WMA-10: Weighted Moving Average considering previous 10 days
• SO: Stochastic Oscillator
• M: Momentum as the difference in closing price in a 10 days interval
• SSO: Slow Stochastic Oscillator
• EMA: Exponential Moving Average for a 10 day period
• MACD_Sline_9: MACD Signal Line for a 9 day period
• RSI: Relative Strength Index
• CCI: Commodity Channel Index
• ADO: Accumulation Distribution Oscillator

Technical indicators provide a suggestion of the stock price movement. Additional features were created for each technical indicator by analysing its daily value and assigning a class according to their meaning. Class “1” is given if the indicator numerical value suggests upper trend, class “0” for a downtrend. In other words, financial market analysis is performed at a simplistic level, in the attempt to translate what the continuous value means.

• MA-10>C: If MA-10 is higher than Closing price output 1
• MA-5>C: If MA-5 is higher than Closing price output 1
• WMA-10>C: If WMA-10 is higher than Closing price output 1
• SO>SOt-1: Output is 1 if SO current value is higher than previous day
• M>0: A positive momentum outputs 1
• SSO>SSOt-1: SSO current value is higher than previous day
• EMA>C: If EMA is higher than Closing price output 1
• MACD>MACDt-1: If MACD current value is higher than previous day output 1
• RSI70-30: If RSI is above 70, output 0. Values below 30 output is one. For values within this range it compares to previous day and outputs 1 if value has increased
• CCI200-200: Similar to RSI, but if threshold set for 200 and -200.
• ADO>ADOt-1: Output is 1 if ADO current value is higher than previous day

For a given country or region, the stock market index characterises the performance of its financial market and the overall local economy. For this reason, the same day performance of these markets could contribute to the machine learning model predictions. Six global indexes were added as features, with their closing direction as up or down, class “1” or “0”, respectively. Data for these indexes (Nikkei, Hang Seng, All Ordinaries, Euronext 100, SSE and DAX) were also retrieved from Yahoo! Finance.

This dataset consists of five CSV files that provide detailed data on a stock portfolio and related market performance over the last 5 years. It includes portfolio positions, stock prices, and major U.S. market indices (NASDAQ, S&P 500, and Dow Jones). The data is essential for conducting portfolio analysis, financial modeling, and performance tracking.

1. Portfolio

This file contains the portfolio composition with details about individual stock positions, including the quantity of shares, sector, and their respective weights in the portfolio. The data also includes the stock's closing price.

• Columns:
  • Ticker: The stock symbol (e.g., AAPL, TSLA)
  • Quantity: The number of shares in the portfolio
  • Sector: The sector the stock belongs to (e.g., Technology, Healthcare)
  • Close: The closing price of the stock
  • Weight: The weight of the stock in the portfolio (as a percentage of total portfolio)

2. Portfolio Prices

This file contains historical pricing data for the stocks in the portfolio. It includes daily open, high, low, close prices, adjusted close prices, returns, and volume of traded stocks.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol
  • Open: The opening price of the stock on that day
  • High: The highest price reached on that day
  • Low: The lowest price reached on that day
  • Close: The closing price of the stock
  • Adjusted: The adjusted closing price after stock splits and dividends
  • Returns: Daily percentage return based on close prices
  • Volume: The volume of shares traded that day

3. NASDAQ

This file contains historical pricing data for the NASDAQ Composite index, providing similar data as in the Portfolio Prices file, but for the NASDAQ market index.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for NASDAQ index, this will be "IXIC")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

4. S&P 500

This file contains similar historical pricing data, but for the S&P 500 index, providing insights into the performance of the top 500 U.S. companies.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for S&P 500 index, this will be "SPX")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

5. Dow Jones

This file contains similar historical pricing data for the Dow Jones Industrial Average, providing insights into one of the most widely followed stock market indices in the world.

• Columns:
  • Date: The date of the data point
  • Ticker: The stock symbol (for Dow Jones index, this will be "DJI")
  • Open: The opening price of the index
  • High: The highest value reached on that day
  • Low: The lowest value reached on that day
  • Close: The closing value of the index
  • Adjusted: The adjusted closing value after any corporate actions
  • Returns: Daily percentage return based on close values
  • Volume: The volume of shares traded

Personal Portfolio Data

This data is received using a custom framework that fetches real-time and historical stock data from Yahoo Finance. It provides the portfolio’s data based on user-specific stock holdings and performance, allowing for personalized analysis. The personal framework ensures the portfolio data is automatically retrieved and updated with the latest stock prices, returns, and performance metrics.

This part of the dataset would typically involve data specific to a particular user’s stock positions, weights, and performance, which can be integrated with the other files for portfolio performance analysis.

Overview This dataset provides daily snapshots of cryptocurrency, stock market, and forex data.

Sources Yahoo Finance (via yfinance)

Features Automated daily updates Covers major global indices and top cryptocurrencies Includes sentiment analysis for financial news

Use Cases Financial market analysis Machine learning for price prediction Trading strategy research

License Data compiled from public APIs for educational and analytical use.

CONTEXT

"This dataset contains historical stock market data for Tata Consultancy Services (TCS), an Indian multinational information technology services and consulting company." The dataset includes daily stock prices, trading volume, and other financial metrics for TCS from April 29, 2013, to April 28, 2023. The information was gathered from publicly available sources such as Yahoo Finance and NSE India.

CONTENT

Tata Consultancy Services (TCS) is a global provider of IT services and consulting. TCS's stock price is closely tracked by investors, traders, and financial experts all over the world, considering it is a prominent player in the global technology business. This dataset includes 2,769 rows and 9 columns, including Date, Open Price, High Price, Low Price, Close Price, Adj. Close, Volume, Dividends, and Stock Splits.

ACKNOWLEDGEMENT

The data was scraped from finance.yahoo.com