Context

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Feb 2018 note: I have just updated the dataset to include data up to Feb 2018. I have also accounted for changes in the stocks on the S&P 500 index (RIP whole foods etc. etc.).

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the all_stocks_5yr.csv and corresponding folder).

The folder individual_stocks_5yr contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_5yr.csv contains the same data, presented in a merged .csv file. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - Highest price reached in the day

Low Close - Lowest price reached in the day

Volume - Number of shares traded

Name - the stock's ticker name

Acknowledgements

Due to volatility in google finance, for the newest version I have switched over to acquiring the data from The Investor's Exchange api, the simple script I use to do this is found here. Special thanks to Kaggle, Github, pandas_datareader and The Market.

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

Context

The script used to acquire all of the following data can be found in this GitHub repository. This repository also contains the modeling codes and will be updated continually, so welcome starring or watching!

Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here provided a dataset with historical stock prices (last 12 years) for 29 of 30 DJIA companies (excluding 'V' because it does not have the whole 12 years data).

   ['MMM', 'AXP', 'AAPL', 'BA', 'CAT', 'CVX', 'CSCO', 'KO', 'DIS', 'XOM', 'GE',

   'GS', 'HD', 'IBM', 'INTC', 'JNJ', 'JPM', 'MCD', 'MRK', 'MSFT', 'NKE', 'PFE',

   'PG', 'TRV', 'UTX', 'UNH', 'VZ', 'WMT', 'GOOGL', 'AMZN', 'AABA']

In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Content

The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 13 years of stock data (in the all_stocks_2006-01-01_to_2018-01-01.csv and corresponding folder) and a smaller version of the dataset (all_stocks_2017-01-01_to_2018-01-01.csv) with only the past year's stock data for those wishing to use something more manageable in size.

The folder individual_stocks_2006-01-01_to_2018-01-01 contains files of data for individual stocks, labelled by their stock ticker name. The all_stocks_2006-01-01_to_2018-01-01.csv and all_stocks_2017-01-01_to_2018-01-01.csv contain this same data, presented in merged .csv files. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - Highest price reached in the day

Low Close - Lowest price reached in the day

Volume - Number of shares traded

Name - the stock's ticker name

Inspiration

This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

Acknowledgement

This Data description is adapted from the dataset named 'S&P 500 Stock data'. This data is scrapped from Google finance using the python library 'pandas_datareader'. Special thanks to Kaggle, Github and the Market.

About Dataset Context Stock market data can be interesting to analyze and as a further incentive, strong predictive models can have large financial payoff. The amount of financial data on the web is seemingly endless. A large and well structured dataset on a wide array of companies can be hard to come by. Here I provide a dataset with historical stock prices (last 5 years) for all companies currently found on the S&P 500 index.

The script I used to acquire all of these .csv files can be found in this GitHub repository In the future if you wish for a more up to date dataset, this can be used to acquire new versions of the .csv files.

Feb 2018 note: I have just updated the dataset to include data up to Feb 2018. I have also accounted for changes in the stocks on the S&P 500 index (RIP whole foods etc. etc.).

Content The data is presented in a couple of formats to suit different individual's needs or computational limitations. I have included files containing 5 years of stock data (in the allstocks5yr.csv and corresponding folder).

The folder individualstocks5yr contains files of data for individual stocks, labelled by their stock ticker name. The allstocks5yr.csv contains the same data, presented in a merged .csv file. Depending on the intended use (graphing, modelling etc.) the user may prefer one of these given formats.

All the files have the following columns: Date - in format: yy-mm-dd

Open - price of the stock at market open (this is NYSE data so all in USD)

High - Highest price reached in the day

Low Close - Lowest price reached in the day

Volume - Number of shares traded

Name - the stock's ticker name

Acknowledgements Due to volatility in google finance, for the newest version I have switched over to acquiring the data from The Investor's Exchange api, the simple script I use to do this is found here. Special thanks to Kaggle, Github, pandas_datareader and The Market.

Inspiration This dataset lends itself to a some very interesting visualizations. One can look at simple things like how prices change over time, graph an compare multiple stocks at once, or generate and graph new metrics from the data provided. From these data informative stock stats such as volatility and moving averages can be easily calculated. The million dollar question is: can you develop a model that can beat the market and allow you to make statistically informed trades!

⚡ Energy Crisis and Stock Price Dataset: 2021-2024 📊

📋 About Dataset

This dataset provides a detailed view of how major energy companies' stock prices were influenced by the energy crises between 2021 and 2024. The data covers three prominent energy companies: ExxonMobil (XOM), Shell (SHEL), and BP (BP), with historical stock price information collected via the yfinance library. This dataset is particularly useful for those interested in financial analysis, market behavior, and the impact of global events on the energy sector. 🌍📉📈

📅 Date Range

• Start Date: January 1, 2021
• End Date: Present day (updated periodically)

🔍 Data Overview

The dataset contains the daily adjusted closing prices of the selected companies from January 2021 to the present. The data was gathered to analyze the impact of different energy crises, such as the fluctuations in oil and gas prices during 2021-2024, and to help provide insights into investor behavior during times of energy uncertainty.

The key columns available in each CSV file are:

💡 Potential Use Cases

This dataset can be used for various purposes including, but not limited to:

• Financial Time Series Analysis 📈: Explore trends and volatility in the stock market, particularly in the energy sector.
• Predictive Modeling 🤖: Develop models to predict future stock prices based on historical data.
• Energy Crisis Impact Studies ⚡: Assess the effect of energy crises on global markets, specifically the energy sector.
• Portfolio Analysis 💼: Evaluate the stability and performance of energy companies during different crisis periods.

📊 Data Files

Each CSV file includes the daily stock prices from January 1, 2021, to the present, with columns for open, high, low, close, adjusted close, and volume.

📂 Dataset Structure

• Directory: data/raw/
  • XOM_data.csv
  • SHEL_data.csv
  • BP_data.csv

🚀 Data Collection Process

The data for this dataset was collected using the yfinance Python library, which provides access to historical market data from Yahoo Finance. The collection script (data_collection.py) automates the download of stock data for the selected companies, saving each company's data in CSV format within the data/raw/ directory.

🔧 Tools Used

• Python 🐍: For scripting and data processing.
• yfinance 📈: To download historical stock data.
• pandas 🐼: For data manipulation and cleaning.

📜 License

The dataset is provided under the MIT License. You are free to use, modify, and distribute this dataset, provided that proper attribution is given.

🙌 Contributions

Contributions are welcome! If you have any suggestions or improvements, feel free to fork the repository and make a pull request. Let's make this dataset even more comprehensive and insightful together. 💪🌟

📧 Contact

For any questions or further information, feel free to reach out:

I hope this dataset helps you uncover new insights about the relationship between energy crises and stock prices! If you find it helpful, don't forget to give it a ⭐️ on Kaggle! 😊✨

Context

The Dataset here is the CSV (Comma Separated Value) formatted data of 1000+ Indian companies' historical stock data which are listed on NSE web scrapped using python. This data helps the community to dive into algorithmic trading using the ML techniques and can be used for any task. Hope this will be of great use for everyone.

Content

This dataset(.zip) is a collection of numerous CSV formatted files that are in format of ['Date','Open','high','low','close','adj close','volume']. I've acquired this data using the yahoo finance v7 server using the python requests and a bit of pre-processing.

• Maruti_data.csv is the sample data of Maruti stock data from 2003-07 to till data (updated on 18-Feb-2018) .
• Companies_dict.d is the python pickle dictionary variable to get company name from the SYMBOL or name if the csv file. You can load this using the pickle library and get the actual company SYMBOL to Legal Name. ###### e.g.Python Code ###### Symbol2Name = pickle.load(open('company_symbol_name_dict.d','rb')) ###### print(Symbol2Name['MARUTI']) #Will give you Maruti_Suzuki_India_Ltd

Acknowledgements

I would like to thank this githubrepo for making the python file this script of mine is based on.

Inspiration

I would love to see many people like me to get their hands dirty with this data and use it effectively to correlate the inter relationships among the companies.

Context

This dataset contains historical data of stocks listed on IHSG with time ranges per minutes, hourly, and daily. The source of the dataset is taken from Yahoo Finance's public data and the IDX website which is listed in the metadata tab. This dataset was created with the intention of academic research purposes and not to be commercialized. If you have questions about the dataset, please ask in the discussion tab. Code snippet: https://github.com/muamkh/IHSGstockscraper

Content

Stock minutes data is taken from 1 November 2021 until 6 January 2023. Stock hourly data is taken from 16 April 2020 until 6 January 2023. Stock daily data is taken from 16 April 2001 until 6 January 2023. All of the data is using CSV format. Stock data isnt adjusted with dividend, stock split, and other corporate action.

Stocklist Structure

• Code = Stock code
• Name = Company name
• ListingDate = Listing date of stock on Indonesia Stock Exchange
• Shares = Amount of shares
• ListingBoard = Board category (Main Board, Development Board or Acceleration). More info: https://www.idx.co.id/en-us/products/stocks/
• Sector = Sector Category based on IDX-IC. More info: https://www.idx.co.id/en-us/products/stocks/
• LastPrice = Last stock price
• MarketCap = Market Capitalization.
• MinutesFirstAdded = Date the data first retrieved in minute range
• MinutesLastAdded = Date the data last retrieved in minute range
• HourlyFirstAdded = Date the data first retrieved in hourly range
• HourlyLastAdded = Date the data last retrieved in hourly range
• DailyFirstAdded = Date the data first retrieved in daily range
• DailyLastAdded = Date the data last retrieved in daily range

Struktur Data Saham

• timestamp = Date and time of stock transaction
• open = opening price
• low = lowest price in the timespan
• high = highest price in the timespan
• close = closing price
• volume = Total volume traded in the timespan