Skip to content

web_data_interface.md

Latest commit

 

History

History
49 lines (31 loc) · 2.63 KB

web_data_interface.md

File metadata and controls

49 lines (31 loc) · 2.63 KB
Raw

Web Data Interface

Overview

The WebDataInterface (src/interfaces/web_data.py) defines a standard contract for fetching and potentially parsing data from external web sources, such as financial news websites, economic calendars, or social media sentiment aggregators.

Purpose

  • Abstract the process of retrieving information from various web sources (e.g., scraping websites, calling specific news APIs).
  • Provide a consistent way for services (primarily the AIService) to access external information that might influence trading decisions (e.g., news headlines, sentiment scores, upcoming economic events).
  • Enable adding new web data sources by creating new implementations of the interface.
  • Facilitate testing by allowing mock implementations that return sample web data without making live HTTP requests.

Key Methods (Conceptual)

A WebDataInterface might define methods like:

  • fetch_news(query: str, max_results: int): Fetches recent news articles related to a specific query (e.g., a stock symbol, a market sector). Returns a list of news items (e.g., headlines, summaries, URLs, timestamps).
  • fetch_economic_events(date_range: Tuple[date, date]): Retrieves scheduled economic events (e.g., GDP releases, interest rate decisions) within a given date range.
  • fetch_sentiment(symbol: str): Attempts to fetch a sentiment score or related commentary for a specific trading symbol from a configured source.

The exact methods depend on the specific needs of the AIService and the data sources being integrated.

Implementations

Concrete implementations could include:

  • NewsAPIFetcher: Uses a commercial News API service.
  • WebScraper: Implements logic to scrape specific financial websites (requires careful handling of website terms of service and potential structure changes).
  • MockWebData: Returns predefined sample news or event data for testing.

The OrchestrationDaemon or AIService would select and instantiate the appropriate implementation.

Configuration

Implementations might require:

  • API keys for commercial data providers.
  • URLs for websites to be scraped.
  • Configuration for parsing logic.

These would typically be stored in the .env file.

Key Interactions

  • AIService: Likely the primary consumer, requesting external data to enrich the context provided to the LLM for analysis.
  • config.py: Reads API keys or URLs needed by the implementation.
  • External Libraries: May use libraries like requests, beautifulsoup4 (for scraping), or specific API client libraries.
  • logger.py: Logs attempts to fetch web data and their outcomes.