The United States recorded a trade deficit of 71.52 USD Billion in May of 2025. This dataset provides the latest reported value for - United States Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

US Stock Market Data (21st November 2023 – 2nd February 2024)

Overview

This dataset provides detailed historical data on the US stock market, covering the period from 21st November 2023 to 2nd February 2024. It includes daily performance metrics for major stocks and indices, enabling investors, analysts, and researchers to study short-term market trends, fluctuations, and patterns.

Dataset Contents

The dataset contains the following key attributes for each trading day:

Date: The trading date.

Ticker: Stock ticker symbol (e.g., AAPL for Apple, MSFT for Microsoft).

Open Price: The price at which the stock opened for trading.

Close Price: The price at which the stock closed for trading . High Price: The highest price reached during the trading session.

Low Price: The lowest price reached during the trading session.

Adjusted Close Price: The closing price adjusted for splits and dividend payouts.

Trading Volume: The total number of shares traded on that day.

Highlights

Time Period: Covers daily data for over two months of trading activity.

Market Scope: Includes data from a diverse set of stocks, industries, and sectors, reflecting the broader US market trends.

Indices and Major Stocks: Tracks key indices (e.g., S&P 500, NASDAQ) and major stocks across various sectors .

Potential Applications

Analyzing short-term market performance trends. Developing trading strategies or backtesting investment models. Exploring the impact of macroeconomic events on stock performance. Studying sector-wise performance in the US stock market.

Data Source

The data has been sourced from publicly available market records, ensuring reliability and accuracy. Each data point represents an official trading record from the respective exchange.

Usage Notes

The dataset is intended for educational, analytical, and research purposes only. Users should be mindful of potential market anomalies or external factors influencing data during this time frame.

Acknowledgments

Special thanks to the organizations and platforms that make financial market data accessible for analysis and research.

Brazil recorded a trade surplus of 5890 USD Million in June of 2025. This dataset provides the latest reported value for - Brazil Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In order to avoid missing representative features, we should select a lot of features as far as possible when using machine learning algorithms in stock trading. Meanwhile, these high dimensional features can lead to redundancy of information and reduce the efficiency, and accuracy of learning algorithms. It is worth noting that dimensionality reduction operation (DRO) is one of the main means to deal with stock high-dimensional data. However, there are few studies on whether DRO can significantly improve the trading performance of deep neural network (DNN) algorithms. Therefore, this paper selects large-scale stock datasets in the American market and in the Chinese market as the research objects. For each stock, we firstly apply four most widely used DRO, namely principal component analysis (PCA), least absolute shrinkage and selection operator (LASSO), classification and regression trees (CART), and autoencoder (AE) to deal with original features respectively, and then use the new features as inputs of the most six popular DNN algorithms such as Multilayer Perceptron (MLP), Deep Belief Network (DBN), Stacked Auto-Encoders(SAE), Recurrent Neural Network(RNN), Long Short-Term Memory(LSTM), Gated Recurrent Unit(GRU) to generate trading signals. Finally, we apply the trading signals to conduct a lot of daily trading back-testing and non-parameter statistical testing. The experiments show that LASSO can significantly improve the performance of RNN, LSTM, and GRU. In addition, any DRO mentioned in this paper do not significantly improve trading performance and the speed of generating trading signals of the other DNN algorithms.

Exports in the United States decreased to 279 USD Billion in May from 290.57 USD Billion in April of 2025. This dataset provides the latest reported value for - United States Exports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

South Korea recorded a trade surplus of 6940 USD Million in May of 2025. This dataset provides the latest reported value for - South Korea Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Algorithmic Trading Software Market Outlook

The algorithmic trading software market is witnessing significant growth, with a market size estimated to reach USD 19.5 billion by 2023 and projected to expand to USD 38.9 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 8.2% during the forecast period. This remarkable growth is largely attributed to the increasing adoption of automation in trading processes, which seeks to enhance efficiency, reduce human error, and capitalize on rapid market changes. The demand for algorithmic trading is primarily driven by the need for advanced data analytics and the growing influence of AI and machine learning technologies that enable rapid decision-making and enhance trading accuracy. These factors collectively underscore the burgeoning interest and investment in algorithmic trading software across various sectors.

The key growth drivers of the algorithmic trading software market include technological advancements, increased market volatility, regulatory changes, and the rising importance of data-driven decision-making. Technological advancements in AI and machine learning have revolutionized financial markets by introducing strategies that can process vast amounts of data in real-time and execute trades at optimal prices. This technology enables traders to respond swiftly to market fluctuations, thereby optimizing gains and minimizing losses. Furthermore, the increased market volatility observed in recent years has amplified the demand for algorithmic trading, as these systems are better equipped to handle rapid price changes and capitalize on short-term trading opportunities. Additionally, regulatory changes aimed at increasing transparency and fairness in financial markets have encouraged the adoption of algorithmic systems, which provide accurate audit trails and compliance with legal standards.

Another significant growth factor is the growing emphasis on data-driven decision-making within the financial sector. As financial markets become more complex, the ability to analyze and interpret large datasets becomes crucial. Algorithmic trading software provides the necessary tools to process and analyze this data efficiently, offering traders a competitive advantage. Moreover, the proliferation of high-frequency trading (HFT), which relies heavily on algorithmic systems to execute a large number of trades in fractions of a second, further propels the market. The ability to swiftly process information and execute trades is increasingly being recognized as a critical component of successful trading strategies, making algorithmic trading software indispensable.

On a regional scale, North America remains a dominant player in the algorithmic trading software market, owing to the presence of major financial hubs and a tech-savvy trading community. The region's advanced technological infrastructure and regulatory environment conducive to trading activities have bolstered its market position. Europe follows closely, driven by increasing investments in fintech and the integration of AI in financial services. Meanwhile, the Asia Pacific region is witnessing rapid growth propelled by the burgeoning financial markets in countries like China and India. The adoption of algorithmic trading in these regions is bolstered by the increasing penetration of smartphones and internet connectivity, which facilitate broader access to trading platforms. This regional diversity highlights the global nature of the algorithmic trading phenomenon and its widespread adoption across various economic landscapes.

Component Analysis

The component segmentation of the algorithmic trading software market primarily divides it into software and services. The software segment commands the larger share of the market, encompassing various tools and platforms that facilitate automated trading. This includes advanced analytics software, execution management systems, and trading algorithms designed to optimize trade performance. The scalability and adaptability of trading software make it a crucial aspect for users ranging from individual traders to large financial institutions. As market complexity increases, the demand for sophisticated software solutions that can handle vast datasets and execute trades efficiently is anticipated to grow, solidifying the software segment's dominance in the market.

Within the software segment, there is a burgeoning demand for customizable software solutions that cater to the specific needs of different trading strategies. Traders seek software that can be tailored to integrate seamlessly with their existing systems and provide features such as real-time d

NYSE Integrated is a proprietary data feed that disseminates full order book updates from the New York Stock Exchange (XNYS). It delivers every quote and order at each price level, along with any event that updates the order book after an order is placed, such as trade executions, modifications, or cancellations.

NYSE is the leading venue for listing blue-chip companies and large-cap stocks. Powered by NYSE's Pillar platform, its hybrid market model of floor-based auction and electronic trading allows it to capture a significant portion of trading activity during the US equity market open and close. As of January 2025, the NYSE represented approximately 6.31% of the average daily volume (ADV) across all exchange-listed US securities, including those listed on Nasdaq, other NYSE venues, and Cboe exchanges.

NYSE is also the only exchange to offer Designated Market Maker (DMM) privileges, allowing the floor to send D-Quote Orders, short for Discretionary Orders, throughout the day. Most D-Quote Orders execute in the closing auction, where they're known as Closing D Orders and allow traders to access the NYSE closing auction after 3:50 PM. This creates significant price discovery during the NYSE Closing Auction, where interest represented via the floor contributes more than 40% of total volume.

NYSE is also unique for being the only exchange with a Parity/Priority Allocation model for matching. This resembles a mixed FIFO and pro-rata matching algorithm, where the participant who sets the best price is matched first, and then the remaining shares are allocated to other orders entered by floor brokers at that price (parity allocation). Floor brokers may utilize e-Quotes to to receive such parity allocation of incoming executions.

With L3 granularity, NYSE Integrated captures information beyond the L1, top-of-book data available through SIP feeds, enabling accurate modeling of the book imbalances, queue dynamics, and the auction process. This data includes explicit trade aggressor side, odd lots, and imbalances. Auction imbalances offer valuable insights into NYSE’s opening and closing auctions by providing details like imbalance quantity, paired quantity, imbalance reference price, and book clearing price.

Historical data is available for usage-based rates or with any Databento US Equities subscription. Visit our pricing page for more details or to upgrade your plan.

Asset class: Equities

Origin: Directly captured at Equinix NY4 (Secaucus, NJ) with an FPGA-based network card and hardware timestamping. Synchronized to UTC with PTP.

Supported data encodings: DBN, CSV, JSON (Learn more)

Supported market data schemas: MBO, MBP-1, MBP-10, TBBO, Trades, BBO-1s, BBO-1m, OHLCV-1s, OHLCV-1m, OHLCV-1h, OHLCV-1d, Definition, Imbalance, Statistics, Status (Learn more)

Resolution: Immediate publication, nanosecond-resolution timestamps

India recorded a trade deficit of 18.78 USD Billion in June of 2025. This dataset provides the latest reported value for - India Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Yahoo Finance Business Information dataset to access comprehensive details on companies, including financial data and business profiles. Popular use cases include market analysis, investment research, and competitive benchmarking.

Use our Yahoo Finance Business Information dataset to access comprehensive financial and corporate data, including company profiles, stock prices, market capitalization, revenue, and key performance metrics. This dataset is tailored for financial analysts, investors, and researchers to analyze market trends and evaluate company performance.

Popular use cases include investment research, competitor benchmarking, and trend forecasting. Leverage this dataset to make informed financial decisions, identify growth opportunities, and gain a deeper understanding of the business landscape.

Dataset Features

• name: Represents the company name.
• company_id: Unique identifier assigned to each company.
• entity_type: Denotes the type/category of the business entity.
• summary: A brief description or summary of the company.
• stock_ticker: The ticker symbol used for trading on stock exchanges.
• currency: The currency in which financial values are expressed.
• earnings_date: The date for the reported earnings.
• exchange: The stock exchange on which the company is listed.
• closing_price: The final stock price at the end of the trading day.
• previous_close: The stock price at the close of the previous trading day.
• open: The price at which the stock opened for the trading day.
• bid: The current highest price that a buyer is willing to pay for the stock.
• ask: The current lowest price that a seller is willing to accept.
• day_range: The range between the lowest and highest prices during the trading day.
• week_range: A broader price range over the past week.
• volume: Number of shares that traded in the session.
• avg_volume: Average daily share volume over a specific period.
• market_cap: Total market capitalization of the company.
• beta: A measure of the stock's volatility in comparison to the market.
• pe_ratio: Price-to-earnings ratio for valuation.
• eps: Earnings per share.
• dividend_yield: Dividend yield percentage.
• ex_dividend_date: The date on which the stock trades without the right to the declared dividend.
• target_est: The analyst's target price estimate.
• url: The URL to more detailed company information.
• people_also_watch: Companies frequently watched alongside this company.
• similar: Other companies with similar profiles.
• risk_score: A quantified risk score.
• risk_score_text: A textual interpretation of the risk score.
• risk_score_percentile: The risk score expressed in percentile terms.
• recommendation_rating: Analyst recommendation ratings.
• analyst_price_target: Analyst provided stock price target.
• company_profile_address: Company address from the profile.
• company_profile_website: URL for the company’s website.
• company_profile_phone: Contact phone number.
• company_profile_sector: The sector in which the company operates.
• company_profile_industry: Industry classification of the company.
• company_profile_employees: Number of employees in the company.
• company_profile_description: A detailed profile description of the company.
• valuation_measures: Contains key valuation ratios and metrics such as enterprise value, price-to-book, and price-to-sales ratios.
• Financial_highlights: Offers summary financial statistics including EPS, profit margin, revenue, and cash flow indicators.
• financials: This column appears to provide financial statement data.
• financials_quarterly: Similar to the previous field but intended to capture quarterly financial figures.
• earnings_estimate: Contains consensus earnings estimates including average, high, and low estimates along with the number of analysts involved.
• revenue_estimate: Provides revenue estimates with details such as average estimate, high and low values, and sales growth factors.
• earnings_history: This field tracks historical earnings and surprises by comparing actual EPS with estimates.
• eps_trend: Contains information on how the EPS has trended over various recent time intervals.
• eps_revisions: Captures recent changes in EPS forecasts.
• growth_estimates: Offers projections related to growth prospects over different time horizons.
• top_analysts: Intended to list the top analysts covering the company.
• upgrades_and_downgrades: This field shows recent analyst upgrades or downgrades.
• recent_news: Meant to contain recent news articles related to the company.
• fanacials_currency: Appears to indicate the currency used for financial reporting or valuation in the dataset.
• **company_profile_he

Disclaimer

All information presented here is for display purpose only, and may not be complete nor accurate. This information does not constitute a financial advice, and should not be used to make any investment decisions or financial transactions. This author rejects any claims for liabilities resulting from the use, misuse, or abuse of this information. Use at your own risk.

Motivation

Due to time zone differences between Australia and most of the rest of the world, Australians have the advantage of knowing what happened at markets elsewhere in the world, before the Australian market (ASX) is open in the morning, Sydney time.

This prior knowledge provides an excellent opportunity for arbitrage. In the hands of a savvy day-trader, or a shrewd long-term investor, this information gives you the advantage of predicting the ASX, and achieve potentially significant financial gains.

Method

For the ten years period from 1/7/2010 to 30/6/2020, the daily closing prices for 41 global market indicators are collected from various reliable public-domain sources. We checked the data for error or omissions and normalised all tabulated records in a format that facilitates further analysis and visulaisation.

Those 41 market indicators are what we consider significant measures of various external factors that may affect the performance of the Australian Stock Market, as represented by the ASX200. Those indicators are:

• Nine other major stock market indices from the USA, Europe, and Asia.

• The exchange rate of the $AU against 10 world currencies that are most relevant to Australia's international trade.

• Official interest rates by the RBA and the US Feds, as indicators of affinity of foreign funds to Australia.

• Yield rates for governments-issued bonds by 10 countries from Western and Asian economies, as measures of relative availability of credit and cross-border investment. Bonds are grouped into "Short-term" (one year maturity) and "Long-term" (10 to 30 years maturity).

• Since Australia's economy is mainly an exporter of raw materials, we include prices for commodities that are most traded by Australia, as indicators for potential profitability for various relevant sectors of the ASX.

We feed relevant data to a machine learning model, which uses this data to extract heuristic parameters that are used to predict the ASX200 on daily basis, before market opens, and validates predictions at market close, with favourable results.

For more information, please visit the Tableau viz at: https://public.tableau.com/app/profile/yasser.ali.phd/viz/PredictingAustralianStockMarket/Story

Imports in the United States decreased to 350.52 USD Billion in May from 350.83 USD Billion in April of 2025. This dataset provides the latest reported value for - United States Imports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Leading Economic Index Kazakhstan increased 5 percent in September of 2021 over the same month in the previous year. This dataset provides - Kazakhstan Leading Economic Index- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Economic developments in the Middle East, North Africa, Afghanistan, and Pakistan (MENAP) continue to reflect the diversity of conditions prevailing across the region. Most high-income oil exporters, primarily in the GCC, continue to record steady growth and solid economic and financial fundamentals, albeit with medium-term challenges that need to be addressed. In contrast, other countries --Iraq, Libya, Syria -- mired in conflicts with not just humanitarian but also economic consequences. And yet other countries, mostly oil importers, are making continued but uneven progress in advancing their economic agenda, often in tandem with political transitions and amidst difficult social conditions. In most of these countries, without extensive economic and structural reforms, economic prospects for the medium term remain insufficient to reduce high unemployment and improve living standards. Economic activity in the Caucasus and Central Asia (CCA) region is weakening, mainly because of the near-term slowdown and rising regional tensions affecting Russia, a key trading partner and sources of remittance and investment inflows, as well as weaker domestic demand in a number of CCA countries. Near-term risks are to the downside and tied to the fortunes of large trading partners. Policies need to focus on bolstering economic stability and, where needed, short-term support to ailing economic growth. In addition, a new model for high, sustained, diversified, and inclusive growth is needed to set the direction for economic policies for the next decade.

Mexico recorded a trade surplus of 1029 USD Million in May of 2025. This dataset provides the latest reported value for - Mexico Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Exports in Mexico decreased to 54295.70 USD Million in April from 55527.33 USD Million in March of 2025. This dataset provides the latest reported value for - Mexico Exports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Exports in Turkey decreased to 20500 USD Million in June from 24816.80 USD Million in May of 2025. This dataset provides the latest reported value for - Turkey Exports - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Switzerland recorded a trade surplus of 1981.64 CHF Million in May of 2025. This dataset provides the latest reported value for - Switzerland Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

France recorded a trade deficit of 7766.10 EUR Million in May of 2025. This dataset provides the latest reported value for - France Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Georgia recorded a trade deficit of 762.10 USD Million in May of 2025. This dataset provides the latest reported value for - Georgia Balance of Trade - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.