You can obtain the data from the Analyst Ratings through our API and databases. It is available through JSON REST API or downloadable as a csv or excel file. We obtain the data from analysts and financial experts in all sectors and industries which we constantly up date as analysts release new reports and statements. We then compile this data in an intuitively API that is easily used by developers to build systems for your platforms and applications.

Filtering Parameters: - Stock Ticker Symbol - ISIN identifier - Analyst name - Analyst firm - Analyst success rate - Analyst return rate - date ranges from when you want output

Output example:

},
"results": [
  {
  "basics": {
    "name": "Apple Inc",
    "stock_ticker_symbol": "AAPL"
    "isin_identifier": "US0378331005"
    "exchange": "nasdaq"
    },
  "output": {
    "averageRecommendation": {
      "current": "1.33",
      "one_month_ago": "1.29",
      "two_months_ago": "1.35",
      "three_months_ago": "1.35"
    },
    "strongBuy": {
      "current": "18",
      "one_month_ago": "19",
      "two_months_ago": "20",
      "three_months_ago": "20"
    },
    "hold": {
      "current": "2",
      "one_month_ago": "2",
      "two_months_ago": "3",
      "three_months_ago": "3"
    },
    "strongSell": {
      "current": "0",
      "one_month_ago": "0",
      "two_months_ago": "0",
      "three_months_ago": "0"
    }
  }

You can find the detailed documentation on finnworlds.com/documentation. Be sure to let us know you found us through Datarade, which helps this platform in return.

The global Stock Market API market is experiencing robust growth, driven by the increasing demand for real-time and historical financial data across various sectors. The proliferation of algorithmic trading, quantitative analysis, and the development of sophisticated financial applications are key factors fueling this expansion. The market is segmented by deployment (cloud-based and on-premises) and user type (SMEs and large enterprises), with cloud-based solutions gaining significant traction due to their scalability, cost-effectiveness, and accessibility. Large enterprises, with their extensive data processing needs and investment in advanced analytics, currently dominate the market share, but the SME segment is exhibiting impressive growth potential as access to affordable and user-friendly APIs becomes increasingly widespread. Geographic expansion is also a significant driver, with North America and Europe holding substantial market shares, while Asia-Pacific is emerging as a rapidly growing region fueled by increasing technological adoption and economic expansion. While competitive pressures from numerous providers and data security concerns present some restraints, the overall market outlook remains highly positive, projected to maintain a strong Compound Annual Growth Rate (CAGR) over the forecast period (2025-2033). The competitive landscape is characterized by a diverse range of established players and emerging startups. Established players like Refinitiv and Bloomberg offer comprehensive data solutions, while smaller companies like Alpha Vantage and Marketstack provide specialized APIs focusing on specific data sets or user needs. This competitive environment fosters innovation, driving the development of new features and capabilities within Stock Market APIs. The increasing demand for integrated data solutions—combining market data with alternative data sources—is another key trend shaping the market. Future growth will likely be fueled by the expansion of fintech, the rise of robo-advisors, and increasing adoption of APIs in academic research and financial education. The market's continued evolution necessitates ongoing adaptation and innovation from both established players and new entrants to cater to the evolving needs of a dynamic and technology-driven financial ecosystem. This ongoing innovation and increasing demand will drive the market to significant growth over the next decade.

Context

The algorithmic trading space is buzzing with new strategies. Companies have spent billions in infrastructures and R&D to be able to jump ahead of the competition and beat the market. Still, it is well acknowledged that the buy & hold strategy is able to outperform many of the algorithmic strategies, especially in the long-run. However, finding value in stocks is an art that very few mastered, can a computer do that?

Content

This Data repo contains two datasets:

1. Example_2019_price_var.csv. I built this dataset thanks to Financial Modeling Prep API and to pandas_datareader. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API, which is free and highly recommended). The column contains the percent price variation of each stock for the year 2019. In other words, it collects the percent price variation of each stock from the first trading day on Jan 2019 to the last trading day of Dec 2019. To compute this price variation I decided to consider the Adjusted Close Price.

2. Example_DATASET.csv. I built this dataset thanks to Financial Modeling Prep API. Each row is a stock from the technology sector of the US stock market (that is available from the aforementioned API). Each column is a financial indicator that can be found in the 2018 10-K filings of each company. There are no Nans or empty cells. Furthermore, the last column is the CLASS of each stock, where:

   1. class = 1 if the price of the stock increases during 2019
   2. class = 0 if the price of the stock decreases during 2019

In other words, the last column is used to classify each stock in buy-worthy or not, and this relationship is what should allow a machine learning model to learn to recognize stocks that will increase their value from those that won't.

NOTE: the number of stocks does not match between the two datasets because the API did not have all the required financial indicators for some stocks. It is possible to remove from Example_2019_price_var.csv those rows that do not appear in Example_DATASET.csv.

Inspiration

I built this dataset during the 2019 winter holidays period, because I wanted to answer a simple question: is it possible to have a machine learning model learn the differences between stocks that perform well and those that don't, and then leverage this knowledge in order to predict which stock will be worth buying? Moreover, is it possible to achieve this simply by looking at financial indicators found in the 10-K filings?

The stock market serves as the backbone of modern economies, facilitating the buying and selling of shares in publicly traded companies. This dynamic marketplace allows investors to own a piece of a company and share in its success, providing essential liquidity and capital for businesses. As a pivotal element in th

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.

Table of INEBase Overall and by activity sector. Annual. National. Stock and Supply Short-term Survey

Who can use this app: Any website or portal that would like to provide more information about a stock from Islamic finance perspective, or any portal willing to expand to new customer segment interested in Islamic investment and halal stock trading. The list here is not exhaustive, other users may want to use this service and check the halalness of a stock.

How a stock is considered as Halal? To be considered as halal or to qualify as compliant to Islamic finance and Islamic investment rules, the stock should pass 2 sets of tests: activity test AND some other quantitative tests. Under activity-based test, business related to some sectors are excluded such as: Alcohol, Tobacco, Pork, Adult Entertainment, Conventional Financials, Gambling / Casinos, Weapons, etc. Under quantitative tests we examine ratio such as liquidity, receivables, and debt.

Is the test result reliable? Finispia provides results based upon five mainstream Islamic investment methodologies including: DowJones, Standard & Poors, FTSE, MSCI and AAOIFI. Those standards are endorsed by reputable and globally renowned scholars. However, chance of error is possible, and we do our best to minimize it. Please check Finispia term of use of more details.

What is Stock Screening? Stock screening is the process of filtering the investment universe (the list of available stocks) to a small list of equities that passes specific characteristics that investors are looking for.

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

This API provides data on fossil fuel stocks for electricity generation. Data organized by fuel type, i.e., coal, bituminous coal, subbituminous coal, lignite coal, petroleum liquids, and petroleum coke. Also by sector, i.e., electric power, electric utility, and independent power producers. Annual, quarterly, and monthly data available. Based on Form EIA-423 and Form EIA-923 data. Users of the EIA API are required to obtain an API Key via this registration form: http://www.eia.gov/beta/api/register.cfm

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Analysis of ‘General index and by activity sector. Monthly series. ECSE (API identifier: 25934)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/urn-ine-es-tabla-t3-450-25934 on 07 January 2022.

--- Dataset description provided by original source is as follows ---

Table of INEBase General index and by activity sector. Monthly series. National. Stock and Supply Short-term Survey

--- Original source retains full ownership of the source dataset ---

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries

Eximpedia Export import trade data lets you search trade data and active Exporters, Importers, Buyers, Suppliers, manufacturers exporters from over 209 countries