Dataset

This dataset was created by cse.Python

Contents

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Executive Summary Farmers' markets are an important part of building community, ethically sourcing food, and creating a culture around sustainable habits. In this project, I worked to source data for farmers' markets in North Carolina. Due to their impact on the community, I also joined this data with census data to obtain a better understanding of how they are distributed and what insights they can provide us socially and economically. This dataset can also be used with other census data as it has digestible location data and further research in social science fields.

Data The data includes farmers' market data, web scraped from the North Carolina Department of Agriculture and Food Services joined with census data from 2019, the most recent year I could find. The web scraping gathered the farmers' market name, address, and contact info, while the census data gave total population, median income, and the number of people from 18-30 based on zipcode. This data is unique in this field due to its recency. It is possible to find similar data through the Department of Agriculture, but that data is often outdated and can contain mistakes on a more granular level. This script I've constructed allows the most recent data to be pulled in North Carolina.

Power Analysis I conducted a power analysis with intention to find if the populations based on zipcodes with farmers' markets were significantly different than the average zipcode population of North Carolina, using a significance level of .05 and power of .8, resulting in a required sample of 127.52.

Exploratory Data Analysis You can find exploratory data analysis in the eda.py file to better acclimate yourself with the data. There were 247 farmers' markets collected, and three census variables were attached. Other distribution metrics are included with visualizations as well as general information on the data.

Link to Github https://github.com/tejasj02/Farmers-Market-Data-Curation

Ethics statement This dataset was curated on publicly available sources with intention to further research and information in this social science field. All scraping and data gathering was done ethically, not breaching any rules. Farmers' Market data was obtained from the North Carolina Department of Agriculture and Consumer Services while the census data was imported from the censusdata python library. Data is public and up to date as of 11/25/2024. Can be run with adjusted code to be updated. The dataset is open source and should adhere to normal ethical boundaries.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities of production on a monthly basis.

Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "All Other Uses" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

The Market Data Platform market is experiencing robust growth, driven by the increasing demand for real-time data analytics and the proliferation of sophisticated trading strategies across financial institutions. The market's expansion is fueled by several key factors: the rise of algorithmic trading, the need for faster and more accurate market information, the growing adoption of cloud-based solutions, and the increasing regulatory scrutiny demanding robust data management and compliance. The market is witnessing a shift towards integrated platforms offering a broader range of data sources, advanced analytics capabilities, and improved connectivity. This trend is being further accelerated by the increasing adoption of artificial intelligence (AI) and machine learning (ML) for enhanced data analysis and prediction. Companies like Bloomberg, Refinitiv, and TRDATA are major players, but the market is also witnessing increased competition from innovative technology providers offering specialized solutions and niche capabilities. The forecast period from 2025-2033 suggests substantial growth, driven by the continuous adoption of these solutions across various segments of the financial services industry. The regional distribution will likely favor North America and Europe initially, followed by a gradual increase in adoption rates across Asia-Pacific and other emerging markets. The competitive landscape is dynamic, with established players facing challenges from agile startups offering innovative solutions. The success of individual vendors depends on their ability to provide high-quality data, superior analytical capabilities, seamless integration with existing infrastructure, robust security features, and a commitment to regulatory compliance. While larger players dominate market share, smaller, specialized firms are capitalizing on the demand for specialized data sets and tailored analytical tools. The increasing focus on data security and privacy will impact vendors’ strategies, with enhanced security measures and data governance becoming crucial differentiating factors. Future growth will depend on the industry's continued embrace of technology and the further development of AI/ML-driven analytical applications within the Market Data Platform ecosystem. This growth will likely result in increased consolidation and strategic partnerships in the coming years, shaping the future competitive landscape significantly.

Comprehensive Airbnb dataset for Asuncion, Paraguay providing detailed vacation rental analytics including property listings, pricing trends, host information, review sentiment analysis, and occupancy rates for short-term rental market intelligence and investment research.

Use our Stock prices 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. The dataset includes all major data points: company name, company ID, summary, stock ticker, earnings date, closing price, previous close, opening price, and much more.

End-of-day prices refer to the closing prices of various financial instruments, such as equities (stocks), bonds, and indices, at the end of a trading session on a particular trading day. These prices are crucial pieces of market data used by investors, traders, and financial institutions to track the performance and value of these assets over time. The Techsalerator closing prices dataset is considered the most up-to-date, standardized valuation of a security trading commences again on the next trading day. This data is used for portfolio valuation, index calculation, technical analysis and benchmarking throughout the financial industry. The End-of-Day Pricing service covers equities, equity derivative bonds, and indices listed on 170 markets worldwide.

Huge US Stocks prices + 1292 columns extra data from Indicators. This Dataset provides historical Open, High, Low, Close, and Volume (OHLCV) prices of stocks traded in the United States financial markets AND calculated 1292 columns of indicators. You can use all this hyge data for stock price predictions.

Columns with Momentum Indicator values ADX - Average Directional Movement Index ADXR - Average Directional Movement Index Rating APO - Absolute Price Oscillator AROON - Aroon AROONOSC - Aroon Oscillator BOP - Balance Of Power CCI - Commodity Channel Index CMO - Chande Momentum Oscillator DX - Directional Movement Index MACD - Moving Average Convergence/Divergence MACDEXT - MACD with controllable MA type MACDFIX - Moving Average Convergence/Divergence Fix 12/26 MFI - Money Flow Index MINUS_DI - Minus Directional Indicator MINUS_DM - Minus Directional Movement MOM - Momentum PLUS_DI - Plus Directional Indicator PLUS_DM - Plus Directional Movement PPO - Percentage Price Oscillator ROC - Rate of change : ((price/prevPrice)-1)*100 ROCP - Rate of change Percentage: (price-prevPrice)/prevPrice ROCR - Rate of change ratio: (price/prevPrice) ROCR100 - Rate of change ratio 100 scale: (price/prevPrice)*100 RSI - Relative Strength Index STOCH - Stochastic STOCHF - Stochastic Fast STOCHRSI - Stochastic Relative Strength Index TRIX - 1-day Rate-Of-Change (ROC) of a Triple Smooth EMA ULTOSC - Ultimate Oscillator WILLR - Williams' %R

Columns with Volatility Indicator values ATR - Average True Range NATR - Normalized Average True Range TRANGE - True Range

Columns with Volume Indicator values AD - Chaikin A/D Line ADOSC - Chaikin A/D Oscillator OBV - On Balance Volume

Columns with Overlap Studies values BBANDS - Bollinger Bands DEMA - Double Exponential Moving Average EMA - Exponential Moving Average HT_TRENDLINE - Hilbert Transform - Instantaneous Trendline KAMA - Kaufman Adaptive Moving Average MA - Moving average MAMA - MESA Adaptive Moving Average MAVP - Moving average with variable period MIDPOINT - MidPoint over period MIDPRICE - Midpoint Price over period SAR - Parabolic SAR SAREXT - Parabolic SAR - Extended SMA - Simple Moving Average T3 - Triple Exponential Moving Average (T3) TEMA - Triple Exponential Moving Average TRIMA - Triangular Moving Average WMA - Weighted Moving Average

Columns with Cycle Indicator values HT_DCPERIOD - Hilbert Transform - Dominant Cycle Period HT_DCPHASE - Hilbert Transform - Dominant Cycle Phase HT_PHASOR - Hilbert Transform - Phasor Components HT_SINE - Hilbert Transform - SineWave HT_TRENDMODE - Hilbert Transform - Trend vs Cycle Mode

If you want to download actual data - on today for example, then you can use python code from my github. tickers = ['CE.US', 'WELL.US', 'GRMN.US', 'IEX.US', 'CAG.US', 'BEN.US', 'ATO.US', 'WY.US', 'TSCO.US', 'COR.US', 'MOS.US', 'SWKS.US', 'ORCL.US', 'URI.US', 'INCY.US', 'MPC.US', 'HD.US', 'PPG.US', 'NUE.US', 'DDOG.US', 'HSIC.US', 'CAT.US', 'HSY.US', 'MKTX.US', 'CCEP.US', 'GWW.US', 'LEN.US', 'IFF.US', 'GL.US', 'MDB.US', 'SNPS.US', 'KR.US', 'DVN.US', 'SYY.US', 'USB.US', 'DRI.US', 'PARA.US', 'FMC.US', 'UBER.US', 'WRK.US', 'DLR.US', 'SO.US', 'AMGN.US', 'MA.US', 'STT.US', 'BWA.US', 'KVUE.US', 'GFS.US', 'BBY.US', 'BK.US', 'MRVL.US', 'VFC.US', 'EIX.US', 'ADSK.US', 'ZBH.US', 'MU.US', 'HUBB.US', 'PEAK.US', 'CVX.US', 'CPB.US', 'GILD.US', 'BXP.US', 'DD.US', 'MCD.US', 'KDP.US', 'GE.US', 'PKG.US', 'HST.US', 'WTW.US', 'XOM.US', 'ED.US', 'SPG.US', 'PFG.US', 'LVS.US', 'FAST.US', 'ROST.US', 'TTD.US', 'CNC.US', 'PGR.US', 'CMI.US', 'TEAM.US', 'MELI.US', 'BKR.US', 'EBAY.US', 'CPRT.US', 'MSFT.US', 'HOLX.US', 'ABBV.US', 'AMZN.US', 'FE.US', 'WYNN.US', 'KMI.US', 'APA.US', 'CRWD.US', 'DPZ.US', 'EQT.US', 'NOC.US', 'TAP.US', 'ETR.US', 'T.US', 'OMC.US', 'MTCH.US', 'TRMB.US', 'EXPE.US', 'DTE.US', 'PNR.US', 'LH.US', 'ALL.US', 'CTRA.US', 'VMC.US', 'XRAY.US', 'NWS.US', 'GOOGL.US', 'WEC.US', 'BIIB.US', 'LLY.US', 'BMY.US', 'STE.US', 'NI.US', 'MKC.US', 'AMT.US', 'CFG.US', 'LW.US', 'HIG.US', 'ETSY.US', 'AON.US', 'ULTA.US', 'DVA.US', 'LKQ.US', 'MPWR.US', 'TEL.US', 'FICO.US', 'CVS.US', 'CMA.US', 'NVDA.US', 'TDG.US', 'AWK.US', 'PSA.US', 'FOXA.US', 'ON.US', 'ODFL.US', 'NVR.US', 'ROP.US', 'TFX.US', 'HLT.US', 'EXPD.US', 'FOX.US', 'D.US', 'AMAT.US', 'AZO.US', 'DLTR.US', 'TT.US', 'SBUX.US', 'JNJ.US', 'HAS.US', 'DASH.US', 'NRG.US', 'JNPR.US', 'BIO.US', 'AMD.US', 'NFLX.US', 'VLTO.US', 'BRO.US', 'REGN.US', 'WRB.US', 'LRCX.US', 'SYK.US', 'MCO.US', 'CSGP.US', 'TROW.US', 'ETN.US', 'RTX.US', 'CRM.US', 'SIRI.US', 'UPS.US', 'HES.US', 'RSG.US', 'PEP.US', 'MET.US', 'HON.US', 'IQV.US', 'JPM.US', 'DG.US', 'CBRE.US', 'NDSN.US', 'DOW.US', 'SBAC.US', 'TSN.US', 'IT.US', 'WM.US', 'TPR.US', 'IBM.US', 'CHTR.US', 'HAL.US', 'ROL.US', 'FDS.US', 'SHW.US', 'EW.US', 'RJF.US', 'APH.US', 'AIZ.US', 'ZBRA.US', 'SRE.US', 'CTAS.US', 'PXD.US', 'MTD.US', 'NOW.US', 'MAS.US', 'FFIV.US', 'ELV.US', 'SYF.US', 'CSCO.US', 'APTV...

The global data marketplace market is booming, projected to reach $15 billion in 2025 and grow at a CAGR of 25% to 2033. Discover key trends, drivers, restraints, and leading companies shaping this dynamic industry. Explore regional market shares and future growth potential in this in-depth analysis.

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Sweetener Market Data (SMD) report - beet and cane processors and cane refiners in the U.S. are required by the FAIR Act of 1996, as amended, to report data on physical quantities delivered by use for "Retail Grocers and Chain Stores" on a monthly basis. Quantities are reported by region. Regions include: "New England", "Mid Atlantic", "North Central", "South", "West" and "Puerto Rico".

Market Data and Analytics Market Outlook

According to our latest research, the global market size for the Market Data and Analytics market reached USD 41.3 billion in 2024, reflecting robust demand across industries. The market is expected to grow at a CAGR of 13.7% from 2025 to 2033, reaching a forecasted value of USD 127.2 billion by 2033. This exceptional growth is being driven by the increasing adoption of advanced analytics solutions, the proliferation of big data, and the critical need for real-time decision-making across sectors.

A primary growth factor for the Market Data and Analytics market is the exponential increase in data generation from both structured and unstructured sources. Enterprises are leveraging sophisticated analytics platforms to extract actionable insights from massive volumes of data, which is crucial for gaining competitive advantages. The proliferation of Internet of Things (IoT) devices, social media platforms, and connected ecosystems has resulted in unprecedented data flows, necessitating advanced analytical tools and services. Organizations are investing heavily in data infrastructure and analytics capabilities to enhance operational efficiency, optimize business processes, and drive innovation. These investments are further propelled by the growing realization that data-driven decision-making is pivotal for long-term business sustainability and growth.

Another significant catalyst for market expansion is the rapid integration of artificial intelligence (AI) and machine learning (ML) technologies into analytics platforms. AI-powered analytics solutions enable predictive modeling, anomaly detection, and automated data processing, providing enterprises with real-time, actionable intelligence. The convergence of AI, ML, and big data analytics is transforming industries such as financial services, healthcare, and retail by enabling personalized customer experiences, fraud detection, and efficient supply chain management. Moreover, the democratization of data analytics through user-friendly interfaces and self-service analytics tools is empowering a broader range of business users to harness the power of data, thereby accelerating market growth.

Cloud adoption is also a pivotal driver in the Market Data and Analytics market. The shift toward cloud-based analytics solutions is enabling organizations to scale their data processing capabilities efficiently and cost-effectively. Cloud platforms offer flexibility, accessibility, and seamless integration with other enterprise applications, making them an attractive choice for businesses of all sizes. Small and medium enterprises (SMEs), in particular, are leveraging cloud-based analytics to access cutting-edge capabilities without the need for significant upfront investments in hardware or IT infrastructure. This trend is expected to intensify as more organizations embrace digital transformation initiatives, further fueling market expansion.

From a regional perspective, North America continues to dominate the Market Data and Analytics market, accounting for the largest share in 2024, followed closely by Europe and the Asia Pacific. The strong presence of leading technology companies, a mature digital ecosystem, and high levels of investment in advanced analytics solutions underpin North America's leadership. Europe is witnessing substantial growth driven by stringent data regulations and increasing adoption of analytics in sectors such as manufacturing and healthcare. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by rapid digitalization, expanding internet penetration, and government initiatives to foster innovation and smart city development. The Middle East & Africa and Latin America are also experiencing steady growth, albeit from a smaller base, as enterprises in these regions increasingly recognize the value of data-driven insights.

Component Analysis

The Market Data and Analytics market is segmented by component into software, hardware, and services, each playing a distinct role in the data analytics ecosystem. The software segment commands the largest share, driven by the widespread adoption of analytics platforms, business intelligence tools, and data visualization solutions. These software offerings enable organizations to process, analyze, and visualize vast datasets, empowering stakeholders to make informed decisions swiftly. Advances in AI and ML algorithms have further enhance

Utilize chip analysis and institutional buy-sell daily data to track the movement of funds in the stock market.

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The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.