Graph and download economic data for NASDAQ 100 Index (NASDAQ100) from 1986-01-02 to 2025-09-03 about NASDAQ, stock market, indexes, and USA.

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.

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?

Index Time Series for Leverage Shares -3x Short US Tech 100 ETP Securities GBP. The frequency of the observation is daily. Moving average series are also typically included.

United States NASDAQ: Index: Total Return: NASDAQ 100 Technology Sector Index data was reported at 11,815.040 NA in Apr 2025. This records a decrease from the previous number of 12,175.450 NA for Mar 2025. United States NASDAQ: Index: Total Return: NASDAQ 100 Technology Sector Index data is updated monthly, averaging 4,805.300 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 13,180.480 NA in Jan 2025 and a record low of 1,366.100 NA in May 2012. United States NASDAQ: Index: Total Return: 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: Total Return: Monthly.

Dataset Card for technical-support-dataset

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/hk-gaianet/technical-support-dataset/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/harishkotra/technical-support-dataset.

We start by identifying U.S.-based software organizations in the computer programming and data processing industry (SIC 737), as a knowledge-intensive high-growth setting. We integrate two main data sources. First, to collect the knowledge-based measures, we use publicly available data provided by the U.S. Patent and Trademark Office (USPTO). Using the General Architecture for Text Engineering (GATE) software, we design queries that retrieve the complete class and subclass information for each patent, as well as citations, inventors, and total patents granted between 1998 and 2011 inclusive. We aggregate the data by organization-year observation at the class and subclass levels and use these aggregated measures to compute the knowledge-based predictors and covariates. To compute moving averages for some variables, we collect five years of additional USPTO data which makes our knowledge dataset span between 1993 and 2011. Second, we use Compustat to collect organization-level control variables such as assets, number of employees, market valuation, R&D expenditures, intangibles, solvency, and slack. The integration of the two datasets yields a final sample panel of 100 organizations with 3.2 years of observations on average per organization from 1998 to 2011.

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3....

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