Darktrace reported 4.04B in Market Capitalization this October of 2024, considering the latest stock price and the number of outstanding shares.Data for Darktrace | DARK - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Common-Stock-Shares-Outstanding Time Series for Dark Horse Beijing Technology Co Ltd. Dark Horse Technology Group Co., Ltd. provides enterprise services in China and internationally. It provides industry, artificial intelligence, and AI talent services. The company was formerly known as Dark Horse Venture (Beijing) Technology Co., Ltd. and changed its name to Dark Horse Technology Group Co., Ltd. in June 2019. Dark Horse Technology Group Co., Ltd. was founded in 2011 and is based in Beijing, China.

Black Hills stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

On March 9, 2020, also referred to as Black Monday 2020, a global stock market crash took place, stemming from the collapse of the OPEC deal and the economic impact of the coronavirus (COVID-19). As a result, Russian oil companies suffered the most significant falls in shares. Lukoil and Rosneft saw their shares plunge by **** and **** percent, respectively.

Stanley Black & Decker stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

A list of the top 50 Dark Forest Capital Management Lp holdings showing which stocks are owned by Dark Forest Capital Management Lp's hedge fund.

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Description

This Dataset contains the Details of Historical Daily Netflix Stock Price in USD in Nasdaq from 23-05-2002 to 25-02-2025.

Netflix, Inc. is an American media company founded in 1997 by Reed Hastings and Marc Randolph in Scotts Valley, California, and currently based in Los Gatos, California, with production offices and stages at the Los Angeles-based Hollywood studios (formerly Warner Brothers studios) and the Albuquerque Studios (formerly ABQ studios). It owns and operates an eponymous over-the-top subscription video on-demand service, which showcases acquired and original programming as well as third-party content licensed from other production companies and distributors. Netflix is also the first streaming media company to be a member of the Motion Picture Association.

Netflix's primary business is a streaming video on demand service now available in almost every country worldwide except China. Netflix delivers original and third-party digital video content to PCs, internet-connected TVs, and consumer electronic devices, including tablets, video game consoles, Apple TV, Roku, and Chromecast. In 2011, Netflix introduced DVD-only plans and separated the combined streaming and DVD plans, making it necessary for subscribers who want both to have separate plans.

Attribute Information

• Date: Date of trading
• Open: Open Price of the stock.
• High: High Price of the stock.
• Low: Low Price of the stock.
• Close: Close Price of the stock.
• Adj Close: Adjust Closing of the Stock
• Volume: Traded Volume of the Stock

Acknowledgements

https://finance.yahoo.com/

Photo by Thibault Penin on Unsplash

Photo by charlesdeluvio on Unsplash

A list of the top 50 Delphi Management holdings showing which stocks are owned by Scott Black's hedge fund.

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.

Sharp economic volatility, the continued effects of high interest rates and mixed sentiment among investors created an uneven landscape for stock and commodity exchanges. While trading volumes soared in 2020 due to the pandemic and favorable financial conditions, such as zero percent interest rates from the Federal Reserve, the continued effects of high inflation in 2022 and 2023 resulted in a hawkish pivot on interest rates, which curtailed ROIs across major equity markets. Geopolitical volatility amid the Ukraine-Russia and Israel-Hamas wars further exacerbated trade volatility, as many investors pivoted away from traditional equity markets into derivative markets, such as options and futures to better hedge on their investment. Nonetheless, the continued digitalization of trading markets bolstered exchanges, as they were able to facilitate improved client service and stronger market insights for interested investors. Revenue grew an annualized 0.1% to an estimated $20.9 billion over the past five years, including an estimated 1.9% boost in 2025. A core development for exchanges has been the growth of derivative trades, which has facilitated a significant market niche for investors. Heightened options trading and growing attraction to agricultural commodities strengthened service diversification among exchanges. Major companies, such as CME Group Inc., introduced new tradeable food commodities for investors in 2024, further diversifying how clients engage in trades. These trends, coupled with strengthened corporate profit growth, bolstered exchanges’ profit. Despite current uncertainty with interest rates and the pervasive fear over a future recession, the industry is expected to do well during the outlook period. Strong economic conditions will reduce investor uncertainty and increase corporate profit, uplifting investment into the stock market and boosting revenue. Greater levels of research and development will expand the scope of stocks offered because new companies will spring up via IPOs, benefiting exchange demand. Nonetheless, continued threat from substitutes such as electronic communication networks (ECNs) will curtail larger growth, as better technology will enable investors to start trading independently, but effective use of electronic platforms by incumbent exchange giants such as NASDAQ Inc. can help stem this decline by offering faster processing via electronic trade floors and prioritizing client support. Overall, revenue is expected to grow an annualized 3.5% to an estimated $24.8 billion through the end of 2031.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

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*

Sale-Or-Purchase-of-Stock Time Series for Darktrace PLC. Darktrace plc, together with its subsidiaries, engages in the development and sale of cyber-threat defense technology solutions in the United Kingdom, the United States, Europe, and internationally. Its products include Darktrace PREVENT, an attack surface management that continuously monitors attack surface for risks, high-impact vulnerabilities, and external threats; and Darktrace DETECT, which analyzes thousands of metrics to reveal subtle deviations that may signal an evolving threat, including unknown techniques and novel malware, as well as installs in minutes, identifies threats, and avoids disruption. The company's products also include Darktrace RESPOND that works autonomously to disarm attacks whenever they occur and reacts to threats in seconds, as well as works 24/7 as it frees up security teams and resources; and Darktrace HEAL, which enables organizations to restore business affected by cyber-attacks to trusted operational states through AI assistance. The company was formerly known as Srenoog plc and changed its name to Darktrace plc in March 2021. Darktrace plc was founded in 2013 and is headquartered in Cambridge, the United Kingdom. As of October 1, 2024, Darktrace plc was taken private.

Indian Stock Market Dataset

This dataset contains various types of data related to the Bombay Stock Exchange (BSE), the oldest and largest stock exchange in India. Includes information about:

• Active companies listed on BSE, their industry, security ID, and status
• Stock quotes for each company, including open, high, low, close, volume, and other metrics
• Stock quote buy and sell data for each company
• Period trend data for each company for 1 month, 3 months, 6 months, and 12 months
• Historical and latest quarter financial statements for each company, including year-on-year results, quarter results, balance sheet, and cash flow
• Statement analysis for each company based on various indicators and ratios
• Peers comparisons for each company based on market capitalization, price-to-earnings ratio, dividend yield, and other metrics
• Corporate news for each company from various sources

The data was collected using Python libraries such as bsedata and bselib, which allow extracting real-time data from BSE website. The data was then cleaned, formatted, and organized into different CSV files for easy access and analysis.

The dataset can be used for various types of projects that require getting live quotes or historical data for a given stock or index, or building large data sets for data analysis and machine learning. Some possible applications are:

• Exploratory data analysis and visualization of the Indian stock market trends and patterns
• Fundamental analysis and valuation of individual companies based on their financial performance and ratios
• Technical analysis and trading strategies based on price movements and indicators
• Portfolio optimization and risk management based on diversification and correlation
• Sentiment analysis and natural language processing of corporate news and their impact on stock prices

The dataset is updated regularly with new data as it becomes available on BSE website. The dataset is also open-sourced and reproducible using Kaggle Notebooks, a cloud computational environment that enables interactive and collaborative analysis.

Preferred-Stock-and-Other-Adjustments Time Series for Jiangxi Black Cat Carbon Black. Jiangxi Black Cat Carbon Black Inc.,Ltd, together with its subsidiaries, manufactures and sells carbon black products in China. It operates through Carbon Black, Tar Refining, and Other divisions. The company offers carbon black for rubber, special carbon black, green carbon black, precipitated and fumed silica, and chemical products; and tar refining and white carbon black products. It is also involved in power generation, transmission, distribution, and supply; research and development of new material technology; energy-saving and environmental protection consultation, renovation, and installation; research, development, production, testing, and consulting on carbon composites and nanomaterials; and carbon black exhaust gas waste heat power generation. In addition, the company offers carbon black oil; chemical raw materials; hazardous chemicals; new material technology promotion services; information consulting services; graphite products; pigments; batteries; fiber, composite, and synthetic materials; and rubber products. It exports its products. Jiangxi Black Cat Carbon Black Inc.,Ltd was founded in 1994 and is headquartered in Jingdezhen, China.

Turkey Cement: Stock: Black Sea data was reported at 109,890.000 Ton in Aug 2018. This records a decrease from the previous number of 111,746.000 Ton for Jul 2018. Turkey Cement: Stock: Black Sea data is updated monthly, averaging 81,689.000 Ton from Jan 1999 (Median) to Aug 2018, with 236 observations. The data reached an all-time high of 234,699.000 Ton in Sep 2017 and a record low of 27,827.000 Ton in May 2006. Turkey Cement: Stock: Black Sea data remains active status in CEIC and is reported by Turkish Cement Manufacturers Association. The data is categorized under Global Database’s Turkey – Table TR.EA026: Cement and Clinker Production, Stocks and Sales.

Option pricing is crucial in enabling investors to hedge against risks. The Black–Scholes option pricing model is widely used for this purpose. This paper investigates whether the Black–Scholes model is a good indicator of option pricing in the United States stock market. We examine the relevance of the Black–Scholes model to certain stocks using paired sample t-test and Corrado and Miller’s approximation for the implied volatility. Empirical tests are applied to determine the significance of the relationship between the actual market values and the Black–Scholes model values. Paired sample t-tests are applied to 582 call options and 579 put options. The empirical test results show that there is no significant difference between the actual market premium value and the Black–Scholes model premium value for seven out of nine stocks considered for call options, and four out of nine stocks considered for put options. Thus, we conclude that the Black–Scholes option pricing model can be used to price call options but is not suitable for pricing put options in the United States stock market.

Black Hills reported $173M in Stock for its fiscal quarter ending in September of 2025. Data for Black Hills | BKH - Stock including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The Mediterranean Sea is classified as a “data-poor” region in fisheries due to its low number of assessed stocks given its biodiversity and number of exploited species. In this study, the CMSY method was applied to assess the status and exploitation levels of 54 commercial fish and invertebrate stocks belonging to 34 species fished by Turkish fleets in the Eastern Mediterranean (Levantine) and Black Seas, by using catch data and resilience indices. Most of these marine taxa currently lack formal stock assessments. The CMSY method uses a surplus production model (SPM), based on official catch statistics and an abundance index derived from scientific surveys. The SPM estimates maximum sustainable yield (MSY), fishing mortality (F), biomass (B), fishing mortality to achieve sustainable catches (Fmsy), and the biomass to support sustainable catches (Bmsy). Our results show the estimated biomass values for 94% of the stocks were lower than the required amount to support sustainable fisheries (Bmsy). Of the 54 stocks, 85% of them can be deemed as overfished; two stocks were not subject to overfishing (Sardina pilchardus and Trachurus mediterraneus in the Marmara Sea) while only one stock (Sprattus sprattus in the Black Sea) is healthy and capable of producing MSY. Annual values of the stock status indicators, F/Fmsy and B/Bmsy, had opposing trends in all regions, suggesting higher stock biomasses could only be achieved if fishing mortality is drastically reduced. Recovery times and levels were then explored under four varying F/Fmsy scenarios. Under the best-case scenario (i.e., F = 0.5Fmsy), over 60% of the stocks could be rebuilt by 2032. By contrast, if normal fishing practices continue as usual, all stocks will soon be depleted if not already, whose recover may be impossible at later dates. The results of this study are supported by previous regional assessments confirming the overexploitation of Turkish fisheries is driving the near-total collapse of these marine wild fisheries. Hence, the need to urgently rebuild Turkey’s marine fisheries ought to be prioritized to ensure their future viability.

Black Diamond reported CAD4.26M in Stock for its fiscal quarter ending in December of 2024. Data for Black Diamond | BDI - Stock including historical, tables and charts were last updated by Trading Economics this last December in 2025.