4 datasets found
  1. Beat US Stock market (2019 edition)

    • kaggle.com
    Updated Jan 13, 2020
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    Nicolas Carbone (2020). Beat US Stock market (2019 edition) [Dataset]. https://www.kaggle.com/datasets/cnic92/beat-us-stock-market-data
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 13, 2020
    Dataset provided by
    Kaggle
    Authors
    Nicolas Carbone
    Description

    Context

    The algorithmic trading space is buzzing with new strategies. Companies have spent billions in infrastructures and R&D to be able to jump ahead of the competition and beat the market. Still, it is well acknowledged that the buy & hold strategy is able to outperform many of the algorithmic strategies, especially in the long-run. However, finding value in stocks is an art that very few mastered, can a computer do that?

    Content

    This Data repo contains two datasets:

    1. Example_2019_price_var.csv. I built this dataset thanks to Financial Modeling Prep API and to pandas_datareader. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API, which is free and highly recommended). The column contains the percent price variation of each stock for the year 2019. In other words, it collects the percent price variation of each stock from the first trading day on Jan 2019 to the last trading day of Dec 2019. To compute this price variation I decided to consider the Adjusted Close Price.

    2. Example_DATASET.csv. I built this dataset thanks to Financial Modeling Prep API. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API). Each column is a financial indicator that can be found in the 2018 10-K filings of each company. There are no Nans or empty cells. Furthermore, the last column is the CLASS of each stock, where:

      1. class = 1 if the price of the stock increases during 2019
      2. class = 0 if the price of the stock decreases during 2019

    In other words, the last column is used to classify each stock in buy-worthy or not, and this relationship is what should allow a machine learning model to learn to recognize stocks that will increase their value from those that won't.

    NOTE: the number of stocks does not match between the two datasets because the API did not have all the required financial indicators for some stocks. It is possible to remove from Example_2019_price_var.csv those rows that do not appear in Example_DATASET.csv.

    Inspiration

    I built this dataset during the 2019 winter holidays period, because I wanted to answer a simple question: is it possible to have a machine learning model learn the differences between stocks that perform well and those that don't, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?

  2. United States NASDAQ: Index: NASDAQ 100 Technology Sector Index

    • ceicdata.com
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    CEICdata.com, United States NASDAQ: Index: NASDAQ 100 Technology Sector Index [Dataset]. https://www.ceicdata.com/en/united-states/nasdaq-monthly
    Explore at:
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Mar 1, 2024 - Feb 1, 2025
    Area covered
    United States
    Description

    NASDAQ: Index: NASDAQ 100 Technology Sector Index data was reported at 9,723.190 NA in Apr 2025. This records an increase from the previous number of 9,472.590 NA for Mar 2025. NASDAQ: Index: NASDAQ 100 Technology Sector Index data is updated monthly, averaging 4,219.390 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 10,862.950 NA in Jan 2025 and a record low of 1,306.370 NA in May 2012. NASDAQ: Index: NASDAQ 100 Technology Sector Index data remains active status in CEIC and is reported by Exchange Data International Limited. The data is categorized under Global Database’s United States – Table US.EDI.SE: NASDAQ: Monthly.

  3. T

    Japan Stock Market Index (JP225) Data

    • tradingeconomics.com
    • ko.tradingeconomics.com
    • +10more
    csv, excel, json, xml
    Updated Jun 15, 2025
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    TRADING ECONOMICS (2025). Japan Stock Market Index (JP225) Data [Dataset]. https://tradingeconomics.com/japan/stock-market
    Explore at:
    excel, csv, xml, jsonAvailable download formats
    Dataset updated
    Jun 15, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jan 5, 1965 - Jul 30, 2025
    Area covered
    Japan
    Description

    Japan's main stock market index, the JP225, rose to 40839 points on July 30, 2025, gaining 0.40% from the previous session. Over the past month, the index has climbed 2.13% and is up 4.44% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Japan. Japan Stock Market Index (JP225) - values, historical data, forecasts and news - updated on July of 2025.

  4. T

    Canada Stock Market Index (TSX) Data

    • tradingeconomics.com
    • de.tradingeconomics.com
    • +13more
    csv, excel, json, xml
    Updated Jul 31, 2025
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    TRADING ECONOMICS (2025). Canada Stock Market Index (TSX) Data [Dataset]. https://tradingeconomics.com/canada/stock-market
    Explore at:
    csv, xml, excel, jsonAvailable download formats
    Dataset updated
    Jul 31, 2025
    Dataset authored and provided by
    TRADING ECONOMICS
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Jun 29, 1979 - Jul 30, 2025
    Area covered
    Canada
    Description

    Canada's main stock market index, the TSX, fell to 27370 points on July 30, 2025, losing 0.62% from the previous session. Over the past month, the index has climbed 1.86% and is up 18.43% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from Canada. Canada Stock Market Index (TSX) - values, historical data, forecasts and news - updated on July of 2025.

  5. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

Share
FacebookFacebook
TwitterTwitter
Email
Click to copy link
Link copied
Close
Cite
Nicolas Carbone (2020). Beat US Stock market (2019 edition) [Dataset]. https://www.kaggle.com/datasets/cnic92/beat-us-stock-market-data
Organization logo

Beat US Stock market (2019 edition)

100+ financial indicators from 2018 to predict stocks performance in 2019

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Jan 13, 2020
Dataset provided by
Kaggle
Authors
Nicolas Carbone
Description

Context

The algorithmic trading space is buzzing with new strategies. Companies have spent billions in infrastructures and R&D to be able to jump ahead of the competition and beat the market. Still, it is well acknowledged that the buy & hold strategy is able to outperform many of the algorithmic strategies, especially in the long-run. However, finding value in stocks is an art that very few mastered, can a computer do that?

Content

This Data repo contains two datasets:

  1. Example_2019_price_var.csv. I built this dataset thanks to Financial Modeling Prep API and to pandas_datareader. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API, which is free and highly recommended). The column contains the percent price variation of each stock for the year 2019. In other words, it collects the percent price variation of each stock from the first trading day on Jan 2019 to the last trading day of Dec 2019. To compute this price variation I decided to consider the Adjusted Close Price.

  2. Example_DATASET.csv. I built this dataset thanks to Financial Modeling Prep API. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API). Each column is a financial indicator that can be found in the 2018 10-K filings of each company. There are no Nans or empty cells. Furthermore, the last column is the CLASS of each stock, where:

    1. class = 1 if the price of the stock increases during 2019
    2. class = 0 if the price of the stock decreases during 2019

In other words, the last column is used to classify each stock in buy-worthy or not, and this relationship is what should allow a machine learning model to learn to recognize stocks that will increase their value from those that won't.

NOTE: the number of stocks does not match between the two datasets because the API did not have all the required financial indicators for some stocks. It is possible to remove from Example_2019_price_var.csv those rows that do not appear in Example_DATASET.csv.

Inspiration

I built this dataset during the 2019 winter holidays period, because I wanted to answer a simple question: is it possible to have a machine learning model learn the differences between stocks that perform well and those that don't, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?

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