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.

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.

What is the Seeking Alpha API? Seeking Alpha API from RapidAPI is an API that queries stock news, market-moving, price quotes, charts, indices, analysis, and many more from investors and experts on seeking alpha stock research platform. In addition, it has a comprehensive list of endpoints for different categories of data.

Currently, the API has three pricing plans and a free subscription. It supports various programming languages, including Python, PHP, Ruby, and Javascript. This article will dig deeper into its details and see how to use this API with multiple programming languages.

How does the Seeking Alpha API work? Seeking Alpha API works using simple API logic in which It sends a request to a specific endpoint and obtains the necessary output as the response. When sending a request, it includes x-RapidAPI-key and host as authentication parameters so that the server can identify it as a valid request. In addition, the API requests body contains the optional parameters to process the request. Once the API server has received the request, it will process the request using the back-end application. Finally, the server will send back the information requested by the client in JSON format.

Target Audience for the Seeking Alpha API Financial Application Developers Financial application developers can integrate this API to attract Seeking Alphas’ audience to their financial applications. Its comprehensive list of APIs enables providing the complete Seeking Alpha experience. This API has affordable pricing plans, each endpoint requires only a few lines of code, and integration to an application is pretty straightforward. Since it supports multiple programming languages, it has widespread usability.

Stock Market Investors and learners Investors, especially those who research financial companies and the stock market, can use this to get information straight from this API. In addition, it has a free plan, and its Pro plan only costs $10. Therefore, anyone who learns about the stock market can make use of it for a low cost.

How to connect to the Seeking Alpha API Tutorial – Step by Step Step 1 – Sign up and Get a RapidAPI Account. RapidAPI is the world’s largest API marketplace which is used by more than a million developers worldwide. You can use RapidAPI to search and connect to thousands of APIs using a single SDK, API key, and Dashboard.

To create a RapidAPI account, go to rapidapi.com and click on the Sign Up icon. You can use your Google, Github, or Facebook account for Single Sign-on (SSO) or create an account manually.

The "yahoo_finance_dataset(2018-2023)" dataset is a financial dataset containing daily stock market data for multiple assets such as equities, ETFs, and indexes. It spans from April 1, 2018 to March 31, 2023, and contains 1257 rows and 7 columns. The data was sourced from Yahoo Finance, and the purpose of the dataset is to provide researchers, analysts, and investors with a comprehensive dataset that they can use to analyze stock market trends, identify patterns, and develop investment strategies. The dataset can be used for various tasks, including stock price prediction, trend analysis, portfolio optimization, and risk management. The dataset is provided in XLSX format, which makes it easy to import into various data analysis tools, including Python, R, and Excel.

The dataset includes the following columns:

Date: The date on which the stock market data was recorded. Open: The opening price of the asset on the given date. High: The highest price of the asset on the given date. Low: The lowest price of the asset on the given date. Close*: The closing price of the asset on the given date. Note that this price does not take into account any after-hours trading that may have occurred after the market officially closed. Adj Close**: The adjusted closing price of the asset on the given date. This price takes into account any dividends, stock splits, or other corporate actions that may have occurred, which can affect the stock price. Volume: The total number of shares of the asset that were traded on the given date.

REST API access in JSON format for over 50,000 stocks, ETFs, funds and indices. Historical price data with up to 100 years history of stocks, funds, ETFs, crypto-currencies and bonds from over 50 exchanges (XETRA, Frankfurt Stock Exchange, London, New York) worldwide!

REST API access to fundamental data in JSON format for over 50,000 stocks und ETFs. 100,000 requests/da. Fundamental data, key figures and ISINs for stocks and components and ratings for ETFs from over 50 exchanges (XETRA, Frankfurt Stock Exchange, London, New York) worldwide. DAX 30, Nasdaq 100, EuroStoxx!

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Context

Stock time series are a favourite among data scientists because they are easily understood and widely available - in this extensive data set you will find long-time time-series with open/close/high/min/adjusted features, as well as data regarding stock splits, trading volume and dividends.

Content

This data set includes Dow Jones member stock prices (status 01.0.1.2021) with all their historic stock performances from 01.01.2020 to 31.12.2020.

• 30 Dow Jones stocks
• 21 years of data (depending on company age)
• 1 entry per day
• 150503 data points

Please also check the corresponding Jupyter Notebook to get some basic ideas how to use this data set: https://www.kaggle.com/deeplytics/dow-jones-historic-stock-data-2000-2020

Stock Names

In the data set, all companies use their stock ticker names. If you are unfamiliar with them, please check this overview: https://www.cnbc.com/dow-30/

Inspiration

Today's free APIs and coding libraries make it relatively easy for the average user to get an understanding of stock price movements. More advanced users may even be able to find patterns, that can be incorporated into investment decisions.

Acknowledgements

Photo by Dmitry Demidko on Unsplash: https://unsplash.com/photos/eBWzFKahEaU?utm_source=unsplash&utm_medium=referral&utm_content=creditShareLink

REST API access to corporate events including stock splits and IPO data. 100,000 requests/day. Historical and current corporate action data for stocks worldwide.

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.

API Group Soaring: (APG) Stock Forecast

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

REST API access to delayed live quotes for over 50,000 stocks, ETFs, funds and indices. 100,000 requests/day - €50/month. 15 minutes delayed quotes from over 50 exchanges (XETRA, Frankfurt Stock Exchange, London, New York, and many more) worldwide!

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.

(API) Abrdn Property Income: Riding the Wave of Real Estate Recovery

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

Our dynamic data offering is designed to provide a comprehensive view of over 108,000 publicly listed companies across the globe. This service is an essential tool for financial analysts, investors, corporate strategists, and market researchers, offering versatile data delivery options.

Key Features:

Rich Company Fundamentals: Access detailed profiles with financials, management information, operational metrics, and strategic insights. Historical Data Depth: Utilize our extensive historical data for trend analysis and benchmarking. Flexible Delivery Options: Bulk Data Access: Ideal for high-volume needs, get comprehensive data in bulk. Daily Updates: Stay current with daily data refreshes for timely and relevant insights. API Integration: Seamlessly integrate our data into your systems with our API, ensuring efficient data retrieval and analysis. Global News Integration: Get the latest news and updates, providing context and insights into market movements and company-specific events. Intuitive User Interface: Navigate our platform with ease for efficient data retrieval. Customizable Alerts and Reports: Stay informed with tailored alerts and custom reports. Expert Support: Rely on our dedicated support team for assistance and guidance. Benefits:

Enhance investment strategies with diverse and up-to-date data. Conduct in-depth market research and competitive analysis. Facilitate strategic planning and risk assessment with varied data access methods. Support academic research with a reliable data source. Ideal for:

Investment and Financial Firms Market Analysts and Economists Corporate Strategy and Business Development Teams Academic Researchers in Finance and Economics

API Crude Oil Stock Change in the United States increased to 13.40 BBL/1Million in February 6 from -11.10 BBL/1Million in the previous week. This dataset provides - United States API Crude Oil Stock Change- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

The Real-time Candlestick OHLC API provides current candlestick data that covers all major stock exchanges including NYSE, NASDAQ, LSE, Euronext to NSE of India, TSE, and a few more. Users can choose from candlestick data with 1 min, 2 min, 5 min, 15 min, 30 min, 1 hour, 4 hour, 1 day, 1 week, 1 month and 1 year interval. By using the real-time candlestick OHLC data, they can visualize data on candlestick charts and build financial products.

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.

How do you determine buy or sell? (LON:API Stock Forecast)

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

MealMe provides comprehensive grocery and retail SKU-level product data, including real-time pricing, from the top 100 retailers in the USA and Canada. Our proprietary technology ensures accurate and up-to-date insights, empowering businesses to excel in competitive intelligence, pricing strategies, and market analysis.

Retailers Covered: MealMe’s database includes detailed SKU-level data and pricing from leading grocery and retail chains such as Walmart, Target, Costco, Kroger, Safeway, Publix, Whole Foods, Aldi, ShopRite, BJ’s Wholesale Club, Sprouts Farmers Market, Albertsons, Ralphs, Pavilions, Gelson’s, Vons, Shaw’s, Metro, and many more. Our coverage spans the most influential retailers across North America, ensuring businesses have the insights needed to stay competitive in dynamic markets.

Key Features: SKU-Level Granularity: Access detailed product-level data, including product descriptions, categories, brands, and variations. Real-Time Pricing: Monitor current pricing trends across major retailers for comprehensive market comparisons. Regional Insights: Analyze geographic price variations and inventory availability to identify trends and opportunities. Customizable Solutions: Tailored data delivery options to meet the specific needs of your business or industry. Use Cases: Competitive Intelligence: Gain visibility into pricing, product availability, and assortment strategies of top retailers like Walmart, Costco, and Target. Pricing Optimization: Use real-time data to create dynamic pricing models that respond to market conditions. Market Research: Identify trends, gaps, and consumer preferences by analyzing SKU-level data across leading retailers. Inventory Management: Streamline operations with accurate, real-time inventory availability. Retail Execution: Ensure on-shelf product availability and compliance with merchandising strategies. Industries Benefiting from Our Data CPG (Consumer Packaged Goods): Optimize product positioning, pricing, and distribution strategies. E-commerce Platforms: Enhance online catalogs with precise pricing and inventory information. Market Research Firms: Conduct detailed analyses to uncover industry trends and opportunities. Retailers: Benchmark against competitors like Kroger and Aldi to refine assortments and pricing. AI & Analytics Companies: Fuel predictive models and business intelligence with reliable SKU-level data. Data Delivery and Integration MealMe offers flexible integration options, including APIs and custom data exports, for seamless access to real-time data. Whether you need large-scale analysis or continuous updates, our solutions scale with your business needs.

Why Choose MealMe? Comprehensive Coverage: Data from the top 100 grocery and retail chains in North America, including Walmart, Target, and Costco. Real-Time Accuracy: Up-to-date pricing and product information ensures competitive edge. Customizable Insights: Tailored datasets align with your specific business objectives. Proven Expertise: Trusted by diverse industries for delivering actionable insights. MealMe empowers businesses to unlock their full potential with real-time, high-quality grocery and retail data. For more information or to schedule a demo, contact us today!

Context

According to Investopedia:

FAANG is an acronym referring to the stocks of the five most popular and best-performing American technology companies: Facebook, Amazon, Apple, Netflix and Alphabet (formerly known as Google). In addition to being widely known among consumers, the five FAANG stocks are among the largest companies in the world, with a combined market capitalization of over $4.1 trillion as of January 2020. Some have raised concerns that the FAANG stocks may be in the midst of a bubble, whereas others argue that their growth is justified by the stellar financial and operational performance they have shown in recent years.

Regardless of the myriad of accolades, comments, and even controversies surrounding the FAANG stocks, they are nevertheless a data science/mining treasure and the bellwether of the NASDAQ index, if not the entire US technology sector.

This Kaggle dataset contains over 20 years of daily historical data for the five FAANG constituents, as retrieved from this free stock API. It is a public-domain dataset that gives the data science practitioners (a.k.a., you!) the full flexibility to derive second-order insights and investment heuristics from it.

Content

Over 20 years of daily historical data (2000-01-01 to 2020-10-01) for the five FAANG stocks: Facebook, Amazon, Apple, Netflix, and Alphabet/Google. For completeness, both raw and adjusted prices are included, along with historical split events and dividend payouts (check out here for how stock market API providers perform price adjustments).

Acknowledgements

Data source: https://www.alphavantage.co/

REST API access to intraday quotes for over 50,000 stocks, ETFs, funds, cryptos and indices. 100,000 requests/day - €50/month. Months of historical data at 5-minute intervals from over 50 exchanges (XETRA, Frankfurt Stock Exchange, London, New York, and more) worldwide!