We deliver via API access to Companies Financial statements, Insider transaction, Stock Ownership and all information relative to Stock Fundamental

Here is the extensive list of all the information that you can access via our API:

STOCK FUNDAMENTALS

Financial Statements Annual/Quarter Financial Statements As Reported International Filings Annual/Quarter Quarterly Earnings Reports Shares Float SEC RSS Feeds Real-time SEC Filings Rss feed 8K (Important Events)

STOCK FUNDAMENTALS ANALYSIS

Financial Ratios Annual/Quarter Enterprise Value Annual/Quarter Financial Statements Growth Annual Key Metrics Annual/Quarter Financial Growth Annual/Quarter Rating Daily DCF Real-time

STOCK CALENDARS

Earnings Calendar Popular IPO Calendar Stock Split Calendar Dividend Calendar Economic Calendar

COMPANY INFORMATION

Profile Minute Key Executives Market Capitalization Daily Company Outlook New Stock Peers

Finnhub is the ultimate stock api in the market, providing real-time and historical price for global stocks with Rest API and websocket. We also support a tons of other financial data like stock fundamentals, analyst estimates, fundamental data and more. Download the file to access balance sheet of Amazon.

Twelve Data is a technology-driven company that provides financial market data, financial tools, and dedicated solutions. Large audiences - from individuals to financial institutions - use our products to stay ahead of the competition and success.

At Twelve Data we feel responsible for where the markets are going and how people are able to explore them. Coming from different technological backgrounds, we see how the world is lacking the unique and simple place where financial data can be accessed by anyone, at any time. This is what distinguishes us from others, we do not only supply the financial data but instead, we want you to benefit from it, by using the convenient format, tools, and special solutions.

We believe that the human factor is still a very important aspect of our work and therefore our ethics guides us on how to treat people, with convenient and understandable resources. This includes world-class documentation, human support, and dedicated solutions.

This dataset contains financial fundamentals of Alphabet (Google Inc), which includes balance sheets, income statement and cashflow. The data in this dataset only contains 10 years of data. To get full 30+ years of historical fundamental data, check out our website Finnhub.

At CompanyData.com (BoldData), we specialize in delivering high-quality company data sourced directly from official trade registers. Our extensive dataset includes historical financial records for over 230 million companies worldwide, enabling deeper insight into business performance over time. Whether you're benchmarking companies, training AI models, or building risk profiles, our financial data equips you with the long-term perspective you need.

Our financial database includes multi-year balance sheets, profit and loss statements, and key performance indicators such as revenue, net income, assets, liabilities, and equity. We provide standardized and structured data—backed by rigorous validation processes—to ensure consistency and accuracy across jurisdictions. Each financial profile can be enriched with hierarchical data, firmographics, contact details, and industry classifications to support complex analyses.

This historical financial data supports a wide range of use cases including KYC and AML compliance, credit risk assessment, M&A research, financial modeling, competitive benchmarking, AI/ML training, and market segmentation. Whether you’re building a predictive scoring model or assessing long-term financial health, our data gives you the clarity and depth required for smarter decisions.

Delivery is flexible to suit your needs: access files in Excel or CSV, browse through our self-service platform, integrate via real-time API, or enhance your existing datasets through custom enrichment services. With access to 380 million verified companies across all industries and geographies, CompanyData.com (BoldData) provides the scale, precision, and historical context to power your next move—globally.

This dataset contains US stocks fundamental data, such as income statement, balance sheet and cash flows.

• 12,129 companies
• 8,526 unique indicators
• ~20 indicators comparable across most companies
• Five years of data, yearly

The data is provided by http://usfundamentals.com.

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.

LON:STG Stock: The Stock Market Bubble Is About to Burst

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

Global Stock Analysis Software market size 2021 was recorded $948.148 Million whereas by the end of 2025 it will reach $1518.75 Million. According to the author, by 2033 Stock Analysis Software market size will become $3896.78. Stock Analysis Software market will be growing at a CAGR of 12.5% during 2025 to 2033.

• Data from Dec 1989 to Dec 2020.
• Includes Income Statement, Balance Sheet, and Cashflow statement.
• Adjusted for restatements.
• Includes valuation metrics such as enterprise valuation and market capitalization.
• Over 30 ratios such as p/e ratio, EBITDA/sales, gross margin etc..
• Standardized categories for comparison between companies.

Company fundamentals data provides the user with a company's current financial health and when combined historically, the financial 'life-story' of the company.

The dataset contains the file required for training and testing and split accordingly.

There are two groups of features that you can use for prediction:

1. Fundamentals and ratios: Values collected form statements and balance sheets for each ticker
2. Technical indicators and strategy flags: Technical indicators calculated on close value of each day and buy and sell signals generated using some commonly used trading strategies.

Files found in Fundamentals folder is a processed format of the files found in raw folder. Ratios and other values are stretched to match the length of the closing price column such that the value in the pe_ratio column for example is the PE ratio from the most recent quarter and this applies for every column.

Technical indicators are calculated with the default parameters used in Pandas_TA package.

Data is collected form finance.yahoo.com and macrotrends.net Timeframe for the given data is different from one ticker to another because of unavailability of some stocks for a given time frame on either of the websites.

All code required to collect the data and perform preprocessing and feature engineering to get the data in the given format can be found in the following notebooks:

1. https://www.kaggle.com/code/mohammedobeidat/us-stocks-data-collection
2. https://www.kaggle.com/code/mohammedobeidat/us-stocks-technicals-feature-engineering-and-eda
3. https://www.kaggle.com/code/mohammedobeidat/us-stocks-fundamentals-preprocessing-and-eda

Files

• {<>_ticker_train}.csv - the training set
• {<>_ticker_train}.csv - the test set

Columns

Columns names are supposed to be self-explanatory assuming you are familiar with the stock market. Some acronyms you may encounter:

1. tmm is short for Trailing Twelve Months
2. pe is short for Price to Earnings
3. pb is short for Price to Book Value
4. ps is short for Price to Sales
5. fcf is short for Free Cash Flow
6. eps is short for Earnings per Share

This dataset includes information about company stock fundamentals in 2021 and the stock price increase percentage in a four-years period (i.e. in 2025). This dataset was automatically obtained through Yahoo Finance and some basic algorithms. For now, the fundamentals include Price to Earning Ratio (PER) (also known as P/E ratio) and net margin(%). For now, we have considered separately the companies from NASDAQ-100 and SP500 indexes.

It is not so often that one can find fundamental data of companies on which it would be possible to accurately assess the value of a company.

So I decided to use yahoo_fin api to collect some fundamentals of 48 companies from the S&P 500 index.

The content of indicators in each table: - total assets. - cash. - stockholder equity. - profit. - revenue. - return on equity, return on assets, profit margin. - trailing P/E, P/S, P/B, PEG, forward P/E.

In addition, the dataset has prices for all stocks for four years.

Access historical and point-in-time financial statements, ratios, multiples, and press releases, with LSEG's S&P Compustat Database.

Fundamental Global common stock net from 2013 to 2025. Common stock net can be defined as the value of common equity ownership.

The global stock analysis software market is experiencing robust growth, driven by increasing adoption of algorithmic trading, rising retail investor participation, and the expanding use of advanced analytical tools. The market, currently valued at approximately $2.5 billion in 2025 (estimated based on typical market sizes for similar software segments and a logical extrapolation considering the provided CAGR), is projected to witness a Compound Annual Growth Rate (CAGR) of 12% over the forecast period (2025-2033). Key segments driving this expansion include the banking, financial services, and insurance (BFSI) sector, alongside the rapidly growing healthcare, telecom, and IT industries. The preference for sophisticated fundamental and technical analysis tools is fueling demand, with evolutionary analysis gaining traction as a promising emerging segment. Regional dominance is currently held by North America, attributable to a mature financial market and high technology adoption. However, Asia Pacific is anticipated to exhibit the highest growth rate, fueled by increasing market awareness and expanding internet penetration. The market's expansion is further propelled by the rising availability of user-friendly, cloud-based stock analysis platforms. However, challenges remain. These include the high initial investment costs for advanced software and the potential for complexities in data interpretation for less experienced users. Nonetheless, innovative features such as AI-powered predictive analytics and integration with brokerage accounts are expected to mitigate these barriers and enhance market adoption. The competitive landscape is marked by both established players and emerging startups, leading to innovation and further driving market growth. Competitive differentiation is achieved through advanced features, user experience, and robust customer support. The consistent need for accurate, timely, and actionable insights ensures the continued importance of this sector in navigating global financial markets.

Data collected from Datastream, a proprietary commercial database containing financial data, published by Thomson Reuters. The dataset consists of fundamental stock data; return, price, unadjusted price, in two frequencies: annual and daily. Daily set contains price index, return index, unadjusted price, the annual set contains stock fundamentals, time series data and static data such as geographical location and others. The data is used for research purposes, but also for teaching in the school of economics and finance and for staff training

Global Shares Data Reference data on more than 80K stocks worldwide. Historical data from 2000 onwards. Pay only for the parameters you need. Flexible in customizing our product to the customer's needs. Free test access as long as you need for integration. Reliable sources: issues documents, disclosure website, global depositories data and other open sources. The cost depends on the amount of required parameters and re-distribution right.

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.

Inflation: Friend or Foe to the Stock Market?

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

Don't forget to upvote in case the provided data was helpful.

Context

45 financial metrics and ratios of every company included in the Nasdaq-100 stock market index (as of 09/2021) for the last five fiscal years. Some metrics or ratios might not be calculated, depending on the company's profitability [...].

Inspiration

The dataset offers a vast variety of possibilities for data exploration, data preparation and visualization, classification or clustering of the different companies, and the prediction of future developments of certain metrics and ratios.

Covered Metrics and Ratios

Besides the stock symbol, the company name and the respective GICS sector and GICS subsector classification, the datasets comprises information about (1) Asset Turnover, (2) Buyback Yield, (3) CAPEX to Revenue, (4) Cash Ratio, (5) Cash to Debt, (6) COGS to Revenue, (7) Beneish M-Score, (8) Altman Z-Score, (9) Current Ratio, (10) Days Inventory, (11) Debt to Equity, (12) Debt to Assets, (13) Debt to EBITDA, (14) Debt to Revenue, (15) E10 (by Prof. Robert Shiller), (16) Effective Interest Rate, (17) Equity to Assets, (18) Enterprise Value to EBIT, (19) Enterprise Value to EBITDA, (20) Enterprise Value to Revenue, (21) Financial Distress, (22) Financial Strength, (23) Joel Greenblatt Earnings Yield (by Joel Greenblatt), (24) Free Float Percentage, (25) Piotroski F-Score, (26) Goodwill to Assets, (27) Gross Profit to Assets, (28) Interest Coverage, (29) Inventory Turnover, (30) Inventory to Revenue, (31) Liabilities to Assets, (32) Long-term Debt to Assets, (33) Price-to-Book-Ratio, (34) Price-to-Earnings-Ratio, (35) Price-to-Earnings-Ratio (Non-Recurring Items), (36) Price-Earnings-Growth-Ratio, (37) Price-to-Free-Cashflow, (38) Price-to-Operating-Cashflow, (39) Predictability, (40) Profitability, (41) Rate of Return, (42) Scaled Net Operating Assets, (43) Year-over-Year EBITDA Growth, (44) Year-over-Year EPS Growth, (45) Year-over-Year Revenue Growth

Note, that the dates defining a fiscal year may vary from company to company.

Acknowledgements

The contents are provided by wikipedia.de and gurufocus.com from where the data was scraped.