Dataset

This dataset was created by Aditya Rajuladevi

Released under CC0: Public Domain

Contents

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.

Analysis of ‘Time Series Forecasting with Yahoo Stock Price ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/arashnic/time-series-forecasting-with-yahoo-stock-price on 28 January 2022.

--- Dataset description provided by original source is as follows ---

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.

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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*

--- Original source retains full ownership of the source dataset ---

Full historical data for the S&P 500 (ticker ^GSPC), sourced from Yahoo Finance (https://finance.yahoo.com/).

Including Open, High, Low and Close prices in USD + daily volumes.

Info about S&P 500: https://en.wikipedia.org/wiki/S%26P_500

2023

What is the data?

Used data from Yahoo Finance to get daily data for Opening & Closing Price, Highest & Lowest Prices, Volume of the S&P 500 index.

How was the dataset compiled?

Code: Github Used the yfinance library (github) to import data from yahoo finance directly. Some processing of data was done.

Quality of data

All but a few open prices were missing between 1962-01-01 and 1982-04-10. For these, it was assumed that open price is equal to closing price of previous trading day.

Volume figures until 1949-12-13 are not available.

Some earlier years have less than expected calendar dates | Year with less than expected trading days| Number of Trading Days Recorded | | ---| --- | |1927| 1 | |1928| 195 | | 1929 | 199 | | 1930 | 155 | | 1931 | 183 | | 1932 | 169 | | 1933 | 136 | | 1934 | 91 | | 1935 | 83 | | 1936 | 107 | | 1937 | 83 | | 1938 | 57 | | 1939 | 27 | | 1940 | 8 | | 1941 | 6 | | 1942 | 16 | | 1943 | 7 | | 1944 | 6 | | 1945 | 42 | | 1946 | 48 | | 1947 | 18 | | 1948 | 16 | | 1949 | 1 | | 1968 | 226 |

Added columns for:

 1. percentage Gain/Loss (calculated by taking the percentage difference between closing prices of 2 consecutive trading days)
 2. price variation percentage: (High-Low)/Closing

The data files contain seven low-dimensional financial research data (in .txt format) and two high-dimensional daily stock prices data (in .csv format). The low-dimensional data sets are provided by Lorenzo Garlappi on his website, while the high-dimensional data sets are downloaded from Yahoo!Finance by the contributor's own effort. The description of the low-dimensional data sets can be found in DeMiguel et al. (2009, RFS). The two high-dimensional data sets contain daily adjusted close prices (from Jan 1, 2013 to Dec 31, 2014) of the stocks, which are in the index components list (as of Jan 7, 2015) of S&P 500 and Russell 2000 indices, respectively.

VIX Daily Price Data

Overview

Contains historical data of the VIX Volatility Index from 2000 - 2025. The data is obtained from the yfinance api created by yahoo finance and contains the daily price data for the VIX.

The dataset contains the daily Open, Close, High, and Low of the VIX.

Columns Open: Starting price level of VIX for the day Close: Final price level of VIX for the day High: Highest price level of VIX for the day Low: Lowest price level of VIX for the day

The VIX is an index that measures near term volatility expectations for the S&P 500 gathered from SPX options data. VIX was created and maintained by CBOE.

Uses

This data can be used to train models on predicting the market's volatility forecasts. The VIX can also be compared to the realized historical volatility over a period of time.

This report analyses the All Ordinaries index. The All Ordinaries index is a share price index, which comprises the 500 largest companies listed on the Australian Securities Exchange. Companies are ranked by market capitalisation, which is the only requirement for inclusion in the index. The All Ordinaries is a non-float adjusted, market capitalisation weighted, price index. The data for this report is sourced from Yahoo Finance and is represented by an average of the daily index points at close over each financial year.

The dataset reports a collection of earnings call transcripts, the related stock prices, and the sector index In terms of volume, there is a total of 188 transcripts, 11970 stock prices, and 1196 sector index values. Furthermore, all of these data originated in the period 2016-2020 and are related to the NASDAQ stock market. Furthermore, the data collection was made possible by Yahoo Finance and Thomson Reuters Eikon. Specifically, Yahoo Finance enabled the search for stock values and Thomson Reuters Eikon provided the earnings call transcripts. Lastly, the dataset can be used as a benchmark for the evaluation of several NLP techniques to understand their potential for financial applications. Moreover, it is also possible to expand the dataset by extending the period in which the data originated following a similar procedure. Contact at Tilburg University: Francesco Lelli

This report analyses movements in the Chicago Board Options Exchange (CBOE) Volatility Index. Known by its ticker symbol VIX, the CBOE Volatility Index is a real-time market index that indicates the stock market's expectation of volatility and is derived from the price inputs of the S&P 500 Index options - the S&P 500 is a US stock market index based on the market capitalisation of 500 large companies having common stock listed on the New York Stock Exchange (NYSE), the Nasdaq Stock Market (NASDAQ), or the Cboe BZX Exchange. Effectively, the VIX measures the degree of variation in S&P 500 stocks' trading price observed over a period of time. The data is sourced from Yahoo Finance, which ultimately derives from the CBOE, in addition to estimates by IBISWorld. The figures represent the average daily unadjusted close value of the index over the UK financial year (i.e. April through March).

This report analyses the movements of the Financial Times Stock Exchange (FTSE) 100 Index. The FTSE 100 is a share index of the 100 companies listed on the London Stock Exchange (LSE) with the highest market capitalisation (i.e. the market value of a publicly-traded company's outstanding shares). Constituents listed in the FTSE 100 are subject to change, whereby a publicly-traded entity can be demoted or promoted to or from the FTSE 250 index - this consists of the 101st to the 350th largest companies listed on the LSE by market capitalisation - when a quarterly reshuffle occurs in March, June, September and December of each calendar year. Movements in the FTSE 100 index are responsive to the weighted average movements of the constituents' stocks, which are ranked according to market capitalisation value. The data is sourced from Yahoo Finance, which ultimately derives from the LSE, and represents the closing price of the FTSE 100 index on the last day of each financial year (i.e. the close price on 31 March).

The data set is collected for a quantile regression analysis testing the predictive ability of investor sentiment on bitcoin return and volatility. The data is obtained from several online sources including Google trends, Wikipedia, Twitter, News headlines, Bitcointalk.org, and market indexes available at yahoo finance. the dataset includes daily values from mid-2015 to the end of 2020.

Cryptocurrency historical datasets from January 2012 (if available) to October 2021 were obtained and integrated from various sources and Application Programming Interfaces (APIs) including Yahoo Finance, Cryptodownload, CoinMarketCap, various Kaggle datasets, and multiple APIs. While these datasets used various formats of time (e.g., minutes, hours, days), in order to integrate the datasets days format was used for in this research study. The integrated cryptocurrency historical datasets for 80 cryptocurrencies including but not limited to Bitcoin (BTC), Ethereum (ETH), Binance Coin (BNB), Cardano (ADA), Tether (USDT), Ripple (XRP), Solana (SOL), Polkadot (DOT), USD Coin (USDC), Dogecoin (DOGE), Tron (TRX), Bitcoin Cash (BCH), Litecoin (LTC), EOS (EOS), Cosmos (ATOM), Stellar (XLM), Wrapped Bitcoin (WBTC), Uniswap (UNI), Terra (LUNA), SHIBA INU (SHIB), and 60 more cryptocurrencies were uploaded in this online Mendeley data repository. Although the primary attribute of including the mentioned cryptocurrencies was the Market Capitalization, a subject matter expert i.e., a professional trader has also guided the initial selection of the cryptocurrencies by analyzing various indicators such as Relative Strength Index (RSI), Moving Average Convergence/Divergence (MACD), MYC Signals, Bollinger Bands, Fibonacci Retracement, Stochastic Oscillator and Ichimoku Cloud. The primary features of this dataset that were used as the decision-making criteria of the CLUS-MCDA II approach are Timestamps, Open, High, Low, Closed, Volume (Currency), % Change (7 days and 24 hours), Market Cap and Weighted Price values. The available excel and CSV files in this data set are just part of the integrated data and other databases, datasets and API References that was used in this study are as follows: [1] https://finance.yahoo.com/ [2] https://coinmarketcap.com/historical/ [3] https://cryptodatadownload.com/ [4] https://kaggle.com/philmohun/cryptocurrency-financial-data [5] https://kaggle.com/deepshah16/meme-cryptocurrency-historical-data [6] https://kaggle.com/sudalairajkumar/cryptocurrencypricehistory [7] https://min-api.cryptocompare.com/data/price?fsym=BTC&tsyms=USD [8] https://min-api.cryptocompare.com/ [9] https://p.nomics.com/cryptocurrency-bitcoin-api [10] https://www.coinapi.io/ [11] https://www.coingecko.com/en/api [12] https://cryptowat.ch/ [13] https://www.alphavantage.co/ This dataset is part of the CLUS-MCDA (Cluster analysis for improving Multiple Criteria Decision Analysis) and CLUS-MCDAII Project: https://aimaghsoodi.github.io/CLUSMCDA-R-Package/ https://github.com/Aimaghsoodi/CLUS-MCDA-II https://github.com/azadkavian/CLUS-MCDA

Baltic Dry rose to 2,018 Index Points on August 1, 2025, up 0.75% from the previous day. Over the past month, Baltic Dry's price has risen 39.85%, and is up 20.48% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Baltic Exchange Dry Index - values, historical data, forecasts and news - updated on August of 2025.

The data sets used in this research are divided into two parts:

Data from fractional Brownian motion (fBm) simulations: These data are generated using the Wood-Chan algorithm for fractional Gaussian noise. Additionally, to verify the value of the Hurst exponent of each fBm sample, the Multifractal Detrended Fluctuation Analysis (MF-DFA) method implemented in Python by Gorjao et. al. (https://doi.org/10.1016/j.cpc.2021.108254) Is used. Financial time series data: This data is generated by an API designed in Python to directly download financial time series data from Yahoo Finance with the ticker name. From these, the time series of returns, log returns (log-returns), absolute log returns and volatilities of log returns are generated.

Context

Stock Market Supplementary Data: Company Names and Ticker Symbols

Content

Company Names and Ticker Symbols

nasdaq.csv

nyse.csv

sp500.csv

forbes2000.csv

yahootickers.xlsx

• About
• Currency
• ETF
• Future
• Index
• Mutual Fund
• Stock

Acknowledgements

Data from - https://datahub.io/core/nasdaq-listings (License) - https://datahub.io/core/s-and-p-500-companies (License) - https://datahub.io/core/nyse-other-listings (License) - https://investexcel.net/all-yahoo-finance-stock-tickers (open data) - https://www.kaggle.com/ash316/forbes-top-2000-companies (open data)

Banner Photo: https://unsplash.com/photos/VP4WmibxvcY

This repository contains the supplementary materials for a deep learning study on stock price forecasting and trading strategy enhancement using volatility indicators.

The provided dataset and code support a CNN-GRU hybrid model designed to predict stock prices and evaluate trading strategies, with a focus on the Volatility Index (VIX) as an additional feature.

Included are two versions of the feature datasets (with and without VIX), preprocessed technical indicators (SMA, EMA, MACD, RSI, etc.), and the full implementation code in a Jupyter Notebook. The code enables reproduction of the experimental results, including model training, forecasting, and trading performance analysis.

These materials are shared to support research transparency, reproducibility, and reuse by other researchers in the fields of financial forecasting and applied deep learning.

Please refer to the included `README.txt` and `requirements.txt` for usage instructions and software dependencies.

**Data sources**:
- Historical stock prices: Yahoo Finance
- VIX data: Chicago Board Options Exchange (CBOE)

Ukraine's main stock market index, the PFTS, closed flat at 464 points on August 1, 2025. Over the past month, the index has declined 5.81% and is down 8.46% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Ukraine. Ukraine Stock Market (PFTS) - values, historical data, forecasts and news - updated on August of 2025.

Comparison of evaluation metrics for different models.