InvestingCom Class

InvestingCom Class#

This class implements the MarketInterface and provides methods to interact with the Investing.com API for retrieving market data such as historical data, quotes, and symbols.

Attributes#

  • scrapper (cloudscraper): A scraper object for making HTTP requests.

  • url (str): The base URL for API requests.

  • headers (dict): HTTP headers for requests to the API.

  • history_schema (HistorySchema): Schema defining the structure of historical data.

Methods#

__init__(self)#

Initializes the InvestingCom object by setting up the scrapper for HTTP requests.

url#

url(self) → str#

Returns the URL for API requests. The URL is dynamically generated using a UUID.

headers#

headers(self) → dict#

Returns the headers required for the API requests, including a User-Agent and Content-Type.

get#

get(self, url: str, params: Dict[str, Any], headers: Dict[str, str]) → requests.Response#

Makes a GET request using the scrapper to the provided URL with the given parameters and headers.

_request#

_request(self, endpoint: Literal['history.py', 'search', 'quotes', 'symbols'], params: Dict[str, Any]) → Dict[str, Any] | List[Dict[str, Any]]#

Sends a request to the Investing.com API for a given endpoint (e.g., “history.py”, “search”, “quotes”, “symbols”). Returns the response as a JSON object or raises a ConnectionError if the request fails.

history_schema#

history_schema(self)#

Returns the schema used to structure historical market data. It defines index columns (date, security) and data columns (close, open, high, low, volume).

_format_history#

_format_history(self, params: Dict[str, Any], response: Dict[str, Any]) → History#

Formats the response from the API into a History object using the history_schema.

_historical#

_historical(self, params: Dict[str, Any]) → History#

Retrieves historical market data for a symbol or a list of symbols. The method supports retrieving data for multiple symbols and combines the results into a History object.

historical#

historical(self, symbols: List[str] | str, start: datetime.datetime, end: datetime.datetime, interval: Literal[1, 5, 15, 30, 60, 'D', 'W', 'M'] = 'D')#

Retrieves historical market data for the specified symbols and date range. The data resolution can be specified (e.g., ‘D’ for daily, ‘M’ for monthly).

quotes#

quotes(self, params: Dict[str, Any]) → Dict[str, Any]#

Retrieves the latest quotes for a specified symbol.

symbols#

symbols(self, params: Dict[str, Any]) → List[Dict[str, Any]]#

Retrieves symbols based on the provided parameters (e.g., symbols).

listen#

listen(self, symbols: List[str], interval: float = 60) → AsyncGenerator#

Asynchronously listens to market data by periodically fetching the latest quotes for the specified symbols. The data is returned as an async generator.

Example Usage#

The following example demonstrates how to use the InvestingCom class to fetch historical data and search for a symbol.

>>> from dxlib.interfaces.external import investing_com as ext
>>> import datetime

>>> api = ext.InvestingCom()
>>> history = api.historical(
>>>     symbols="AAPL",
>>>     start=datetime.datetime(2021, 1, 1),
>>>     end=datetime.datetime(2021, 12, 31)
>>> )

>>> history.head()
                              close        high  ...        open       volume
date       security                          ...
2021-01-04 AAPL      129.410004  133.610001  ...  133.520004  143301888.0
2021-01-05 AAPL      131.009995  131.740005  ...  128.889999   97664896.0
2021-01-06 AAPL      126.599998  131.050003  ...  127.720001  155087968.0
2021-01-07 AAPL      130.919998  131.630005  ...  128.360001  109578160.0
2021-01-08 AAPL      132.050003  132.630005  ...  132.429993  105158248.0

[5 rows x 5 columns]q
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