This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

EQ Equillium Inc. Common Stock

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

United States New York Stock Exchange: Index: Dow Jones US Oil Equipment Services & Distribution Index data was reported at 436.080 NA in Apr 2025. This records a decrease from the previous number of 495.270 NA for Mar 2025. United States New York Stock Exchange: Index: Dow Jones US Oil Equipment Services & Distribution Index data is updated monthly, averaging 449.025 NA from Jan 2012 (Median) to Apr 2025, with 160 observations. The data reached an all-time high of 918.710 NA in Jun 2014 and a record low of 150.500 NA in Mar 2020. United States New York Stock Exchange: Index: Dow Jones US Oil Equipment Services & Distribution 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: New York Stock Exchange: Dow Jones: Monthly.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

EQR EQ RESOURCES LIMITED

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The Dow Jones U.S. Select Medical Equipment index is expected to experience continued growth, driven by an aging global population, increasing demand for advanced medical technologies, and growing healthcare spending. However, risks remain, such as potential disruptions to supply chains, increased regulatory scrutiny, and price pressure from insurers.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dow Jones U.S. Select Medical Equipment: A Pulse on Healthcare Innovation?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

United States - Producer Price Index by Industry: Railroad Rolling Stock Manufacturing: Railway Maintenance of Way and All Other Railroad and Streetcar Equipment, Parts and Accessories was 195.80900 Index Jun 1984=100 in September of 2022, according to the United States Federal Reserve. Historically, United States - Producer Price Index by Industry: Railroad Rolling Stock Manufacturing: Railway Maintenance of Way and All Other Railroad and Streetcar Equipment, Parts and Accessories reached a record high of 195.80900 in September of 2022 and a record low of 100.00000 in July of 1984. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Producer Price Index by Industry: Railroad Rolling Stock Manufacturing: Railway Maintenance of Way and All Other Railroad and Streetcar Equipment, Parts and Accessories - last updated from the United States Federal Reserve on July of 2025.

Context

The National Stock Exchange of India Ltd. (NSE) is an Indian stock exchange located at Mumbai, Maharashtra, India. National Stock Exchange (NSE) was established in 1992 as a demutualized electronic exchange. It was promoted by leading financial institutions on request of the Government of India. It is India’s largest exchange by turnover. In 1994, it launched electronic screen-based trading. Thereafter, it went on to launch index futures and internet trading in 2000, which were the first of its kind in the country.

With the help of NSE, you can trade in the following segments:

• Equities

• Indices

• Mutual Funds

• Exchange Traded Funds

• Initial Public Offerings

• Security Lending and Borrowing Scheme

https://cdn6.newsnation.in/images/2019/06/24/Sharemarket-164616041_6.jpg" alt="Stock image">

Companies on successful IPOs gets their Stocks traded over different Stock Exchnage platforms. NSE is one important platofrm in India. There are thousands of companies trading their stocks in NSE. But, I have chosen two popular and high rated IT service companies of India; TCS and INFOSYS. and the third one is the benchmark for Indian IT companies , i.e. NIFTY_IT_INDEX .

Content

The dataset contains three csv files. Each resembling to INFOSYS, NIFTY_IT_INDEX, and TCS, respectively. One can easily identify that by the name of CSV files.

Timeline of Data recording : 1-1-2015 to 31-12-2015.

Source of Data : Official NSE website.

Method : We have used the NSEpy api to fetch the data from NSE site. I have also mentioned my approach in this Kernel - "**WebScraper to download data for NSE**". Please go though that to better understand the nature of this dataset.

Shape of Dataset:

INFOSYS - 248 x 15 || NIFTY_IT_INDEX - 248 x 7 || **TCS - 248 x 15

• Colum Descriptors:

• Date: date on which data is recorded

• Symbol: NSE symbol of the stock

• Series: Series of that stock | EQ - Equity

OTHER SERIES' ARE:

EQ: It stands for Equity. In this series intraday trading is possible in addition to delivery.

BE: It stands for Book Entry. Shares falling in the Trade-to-Trade or T-segment are traded in this series and no intraday is allowed. This means trades can only be settled by accepting or giving the delivery of shares.

BL: This series is for facilitating block deals. Block deal is a trade, with a minimum quantity of 5 lakh shares or minimum value of Rs. 5 crore, executed through a single transaction, on the special “Block Deal window”. The window is opened for only 35 minutes in the morning from 9:15 to 9:50AM.

BT: This series provides an exit route to small investors having shares in the physical form with a cap of maximum 500 shares.

GC: This series allows Government Securities and Treasury Bills to be traded under this category.

IL: This series allows only FIIs to trade among themselves. Permissible only in those securities where maximum permissible limit for FIIs is not breached.

• Prev Close: Last day close point

• Open: current day open point

• High: current day highest point

• Low: current day lowest point

• Last: the final quoted trading price for a particular stock, or stock-market index, during the most recent day of trading.

• Close: Closing point for the current day

• VWAP: volume-weighted average price is the ratio of the value traded to total volume traded over a particular time horizon

• Volume: the amount of a security that was traded during a given period of time. For every buyer, there is a seller, and each transaction contributes to the count of total volume.

• Turnover: Total Turnover of the stock till that day

• Trades: Number of buy or Sell of the stock.

• Deliverable: Volumethe quantity of shares which actually move from one set of people (who had those shares in their demat account before today and are selling today) to another set of people (who have purchased those shares and will get those shares by T+2 days in their demat account).

• %Deliverble: percentage deliverables of that stock

Acknowledgements

I woul dlike to acknowledge all my sincere thanks to the brains behind NSEpy api, and in particular SWAPNIL JARIWALA , who is also maintaining an amazing open source github repo for this api.

Inspiration

I have also built a starter kernel for this dataset. You can find that right here .

I am so excited to see your magical approaches for the same dataset.

THANKS!

Industrial Stock Index: PY=100: CA 2010: Mfg: Other Transport Equipment data was reported at 86.200 Prev Year=100 in May 2018. This records a decrease from the previous number of 100.300 Prev Year=100 for Apr 2018. Industrial Stock Index: PY=100: CA 2010: Mfg: Other Transport Equipment data is updated monthly, averaging 89.800 Prev Year=100 from Jan 2000 (Median) to May 2018, with 221 observations. The data reached an all-time high of 334.300 Prev Year=100 in Oct 2006 and a record low of 1.600 Prev Year=100 in Mar 2001. Industrial Stock Index: PY=100: CA 2010: Mfg: Other Transport Equipment data remains active status in CEIC and is reported by Statistical Office of the Republic of Serbia. The data is categorized under Global Database’s Serbia – Table RS.B008: Industrial Stock Index.

Dow Jones U.S. Select Oil Equipment & Services index is expected to experience a moderate increase due to rising demand for oil and gas services as global economies recover from the pandemic and energy consumption increases. However, uncertainty in the geopolitical landscape, supply chain disruptions, and macroeconomic factors could pose risks to this prediction.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Oil Equipment & Services Index Poised for Steady Growth Amidst Global Demand

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The Dow Jones U.S. Select Oil Equipment & Services index forecasts a positive trend. However, there are risks associated with this prediction, including a potential downturn in the oil and gas industry, supply chain disruptions, and geopolitical uncertainties.

H&E Equipment Services reported $3.45B in Market Capitalization this May of 2025, considering the latest stock price and the number of outstanding shares.Data for H&E Equipment Services | HEES - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last August in 2025.

United States New York Stock Exchange: Index: S&P Health Care Equipment Select Industry Index data was reported at 13,034.640 NA in Apr 2025. This records a decrease from the previous number of 13,578.020 NA for Mar 2025. United States New York Stock Exchange: Index: S&P Health Care Equipment Select Industry Index data is updated monthly, averaging 12,995.110 NA from Aug 2013 (Median) to Apr 2025, with 141 observations. The data reached an all-time high of 21,583.630 NA in Aug 2021 and a record low of 4,923.670 NA in Aug 2013. United States New York Stock Exchange: Index: S&P Health Care Equipment Select Industry 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: New York Stock Exchange: S&P: Monthly.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Dow Jones U.S. Select Medical Equipment: Easing Healthcare Costs?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

DJ US Medical Equipment: Market Strength in the Healthcare Equipment Sector?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for Producer Price Index by Industry: Railroad Rolling Stock Manufacturing: Railway Maintenance of Way Equipment and Parts, Parts for All Railcars, and Other Railway Vehicles (PCU33651033651054) from Jun 1984 to May 2025 about maintenance, railroad, stocks, parts, vehicles, equipment, manufacturing, PPI, industry, inflation, price index, indexes, price, and USA.

Schoeller-Bleckmann Oilfield Equipment reported EUR464.9M in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for Schoeller-Bleckmann Oilfield Equipment | SBO - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last August in 2025.

Taiwan TWSE: Equity Market Index: Computer & Peripheral Equipment data was reported at 83.890 29Jun2007=100 in Nov 2018. This records an increase from the previous number of 82.180 29Jun2007=100 for Oct 2018. Taiwan TWSE: Equity Market Index: Computer & Peripheral Equipment data is updated monthly, averaging 91.540 29Jun2007=100 from Jul 2007 (Median) to Nov 2018, with 137 observations. The data reached an all-time high of 114.680 29Jun2007=100 in Oct 2007 and a record low of 45.250 29Jun2007=100 in Jan 2009. Taiwan TWSE: Equity Market Index: Computer & Peripheral Equipment data remains active status in CEIC and is reported by Taiwan Stock Exchange Corporation. The data is categorized under Global Database’s Taiwan – Table TW.Z001: Taiwan Stock Exchange (TWSE): Indices.

Content

Company: Ticker Major index membership: Index Market capitalization: Market Cap Income (ttm): Income Revenue (ttm): Sales Book value per share (mrq): Book/sh Cash per share (mrq): Cash/sh Dividend (annual): Dividend Dividend yield (annual): Dividend % Full time employees: Employees Stock has options trading on a market exchange: Optionable Stock available to sell short: Shortable Analysts' mean recommendation (1=Buy 5=Sell): Recom Price-to-Earnings (ttm): P/E Forward Price-to-Earnings (next fiscal year): Forward P/E Price-to-Earnings-to-Growth: PEG Price-to-Sales (ttm): P/S Price-to-Book (mrq): P/B Price to cash per share (mrq): P/C Price to Free Cash Flow (ttm): P/FCF Quick Ratio (mrq): Quick Ratio Current Ratio (mrq): Current Ratio Total Debt to Equity (mrq): Debt/Eq Long Term Debt to Equity (mrq): LT Debt/Eq Distance from 20-Day Simple Moving Average: SMA20 Diluted EPS (ttm): EPS (ttm) EPS estimate for next year: EPS next Y EPS estimate for next quarter: EPS next Q EPS growth this year: EPS this Y EPS growth next year: EPS next Y Long term annual growth estimate (5 years): EPS next 5Y Annual EPS growth past 5 years: EPS past 5Y Annual sales growth past 5 years: Sales past 5Y Quarterly revenue growth (yoy): Sales Q/Q Quarterly earnings growth (yoy): EPS Q/Q Earnings date

BMO = Before Market Open
AMC = After Market Close: Earnings Distance from 50-Day Simple Moving Average: SMA50 Insider ownership: Insider Own Insider transactions (6-Month change in Insider Ownership): Insider Trans Institutional ownership: Inst Own Institutional transactions (3-Month change in Institutional Ownership): Inst Trans Return on Assets (ttm): ROA Return on Equity (ttm): ROE Return on Investment (ttm): ROI Gross Margin (ttm): Gross Margin Operating Margin (ttm): Oper. Margin Net Profit Margin (ttm): Profit Margin Dividend Payout Ratio (ttm): Payout Distance from 200-Day Simple Moving Average: SMA200 Shares outstanding: Shs Outstand Shares float: Shs Float Short interest share: Short Float Short interest ratio: Short Ratio Analysts' mean target price: Target Price 52-Week trading range: 52W Range Distance from 52-Week High: 52W High Distance from 52-Week Low: 52W Low Relative Strength Index: RSI (14) Relative volume: Rel Volume Average volume (3 month): Avg Volume Volume: Volume Performance (Week): Perf Week Performance (Month): Perf Month Performance (Quarter): Perf Quarter Performance (Half Year): Perf Half Y Performance (Year): Perf Year Performance (Year To Date): Perf YTD Beta: Beta Average True Range (14): ATR Volatility (Week, Month): Volatility Previous close: Prev Close Current stock price: Price Performance (today): Change