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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.

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Cryptocurrencies

Finage offers you more than 1700+ cryptocurrency data in real time.

With Finage, you can react to the cryptocurrency data in Real-Time via WebSocket or unlimited API calls. Also, we offer you a 7-year historical data API.

You can view the full Cryptocurrency market coverage with the link given below. https://finage.s3.eu-west-2.amazonaws.com/Finage_Crypto_Coverage.pdf

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!

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.

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

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!

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!

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Forex Symbols

Finage offers you more than 1300+ forex data as real-time.

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Commodities Bonds Metals Forex You can view the full FX market coverage with the link given below. https://finage.s3.eu-west-2.amazonaws.com/Finage_FX_Symbol_List.pdf

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API Crude Oil Stock Change in the United States decreased to -2.48 BBL/1Million in November 28 from -1.90 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.

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

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.

Introduction

Since Investing.com does not have an API, I decided to develop this Python package in order to retrieve historical data from the companies that integrate the Continuous Spanish Stock Market. So on, I decided to generate, via investpy, the datasets for every company so that any Data Scientist or Data Enthusiastic can handle it and abstract their own conclusions and research.

The main purpose of developing investpy, the package from which these datasets have been retrieved, was to use it as the Data Extraction tool for its namesake section, for my Final Degree Project at the University of Salamanca titled "*Machine Learning for stock investment recommendation systems*". The package end up being so consistent, reliable and usable that it is going to be used as the main Data Extraction tool by another students in their Final Degree Projects named "*Recommender system of banking products*" and "*Robo-Advisor Application*".

License

MIT License

Additional Information

investpy, the Python package from which datasets were generated is currently in a development beta version, so please, if needed open an issue to solve all the possible problems the package may be causing or any dataset error. Also, any new ideas or proposals are welcome, and will be gladly implemented in the package if the are positive and useful.

For further information or any question feel free to contact me via email at alvarob96@usal.es

You can also check my Medium Publication, where I upload weekly posts related to Data Science and mainly on Data Extraction techniques via Web Scraping. In this case, you can read "investpy — a Python package for historical data extraction from the Spanish stock market" where I explain the basics on investpy development and some insights on Web Scraping with Python.

Disclaimer

This Python Package has been made for research purposes in order to fit a needs that Investing.com does not cover, so this package works like an Application Programming Interface (API) of Investing.com developed in an altruistic way. Conclude that this package is not related in any way with Investing.com or any dependant company, the only requirement for developing this package was to mention the source where data is retrieved.

There is only one base interest rate per currency, corresponding to the bank’s unsecured lending/borrowing rate (such as LIBOR). The interest rate used to discount cashflows may include a credit spread above or below the base rate.

The risk-free discount factor is exp (-rT) where r is the interest rate and T is the maturity.

The risky discount factor is exp[-(r+s)T] where s is the credit spread.

Credit spread can be derived by either structural model or reduced-form (intensity) model. The structural approach regards default as an endogenous event by focusing on the capital structure of the firm. Whereas the reduced-form approach does not explain the event of default endogenously, but characterizes it exogenously by a jump process.

Structural models are derived from theory and often contain some unobservable assumptions, while reduced-form models use only market observable information. Therefore, many practitioners in the credit trading arena have tended to gravitate toward the reduced-from models given their mathematical tractability and market compatibility.

Many researchers group similar credits. These groupings are loosely referred to as rating categories. Regardless of how the rating categories are constructed and of how many categories there are, it is necessary to specify the default likelihood for each category and provide a credit spread to correspond to each category.

FinPricing offer forward credit spread curves for various sectors and ratings. These curves are derived/bootstrapped through a compilation of market prices of credit-bearing instruments provided by major dealers. We review the contributed information on a daily basis to ensure accuracy and consistency.

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

We couldn’t find a data source for the historical prices of the Swiss Stock Exchange, also known as SIX. www.alphavantage.co is a free of charge API which we used to extract the data (daily figures) from finance.yahoo.com We set the start date as max (to go in the past as far as possible), some shares go back to the year 1990, and the youngest one is 2001. We got the data of the biggest 30 equities in the SIX, including the SMI. For more information: https://en.wikipedia.org/wiki/Swiss_Market_Index https://www.six-group.com/en/products-services/the-swiss-stock-exchange/market-data/shares/share-explorer.html