Lesson 9.6: Information Extraction: Building a Financial Data Pipeline
We've learned the 'what' of NLP (NER, Topic Modeling). This lesson covers the 'how.' We will outline the practical data engineering pipeline required to build a system that can automatically ingest, parse, analyze, and store insights from a continuous stream of financial documents like news feeds and SEC filings.
Part 1: The Goal - From Raw Text to Actionable Data
The models from the previous lesson are powerful, but they are useless without a robust pipeline to feed them data and process their output. An **Information Extraction (IE)** pipeline is the software system that automates the entire process of turning a raw document (like a PDF of a 10-K filing) into a structured row in a database that a trading model can use.
The Core Analogy: The 'Assembly Line' for Text
Think of your IE pipeline as an assembly line in a factory:
- Raw Materials (Ingestion): Trucks deliver raw materials (PDFs, HTML from news websites, JSON from APIs).
- Cleaning & Preparation (Parsing): The raw materials are cleaned, sorted, and prepared. HTML tags are stripped, PDF text is extracted.
- The Assembly Line (NLP Models): The clean text moves down a line of specialized machines.
- Machine 1 (NER) tags all the company names.
- Machine 2 (Sentiment) scores each sentence.
- Machine 3 (Topic Modeler) assigns a topic profile to the document.
- Finished Goods (Database): The final, structured output is packaged and stored in a warehouse (a database table) with columns like `timestamp`, `ticker`, `sentiment`, `topic_profile`.
- Shipping (Trading Model): The trading algorithm can now easily query this warehouse for clean, ready-to-use features.
Part 2: The Stages of a Financial IE Pipeline
1. Ingestion
This stage is responsible for collecting the raw documents.
- SEC Filings: Use the SEC's EDGAR API to programmatically download all new 8-K, 10-K, and 10-Q filings.
- News Feeds: Subscribe to a real-time news API (like Bloomberg, Reuters, or specialized vendors) that delivers news articles as structured JSON.
- Web Scraping: Write a web scraper (e.g., using Python libraries like BeautifulSoup or Scrapy) to periodically crawl financial news websites or forums. This requires careful handling of HTML structures and ethical considerations (rate limiting, respecting `robots.txt`).
2. Parsing and Cleaning
This is often the most challenging engineering task. The goal is to get clean, plain text from messy source formats.
- HTML: Use libraries to parse the HTML and extract only the relevant content, stripping out ads, navigation bars, and boilerplate text.
- PDF: Use a PDF-to-text library. This can be difficult, as financial reports often have complex layouts with tables, images, and multiple columns that can confuse the text extraction process.
- Text Cleaning: Once you have plain text, perform standard preprocessing: convert to lowercase, remove punctuation, handle special characters, etc.
3. NLP Analysis
This is where the models from the previous lessons are applied to the clean text.
- The text is fed into an NER model to extract entities.
- Sentences or paragraphs are fed into a sentiment model.
- The whole document is fed into a topic model.
4. Storage and Feature Creation
The structured output from the NLP analysis is stored in a database for easy querying.
- Database: A time-series database (like InfluxDB or TimescaleDB) or a simple SQL database is used to store the structured events.
- Feature Aggregation: Before being used in a model, these raw events are often aggregated. For example, instead of using every single news sentiment score, you might create a feature like "24-hour rolling average sentiment for AAPL" or "Number of negative news stories this week for GOOG."
What's Next? Putting it All Together
We've learned how to turn text into numbers (Embeddings), how to extract specific insights from those numbers (NER, Sentiment, Topic Modeling), and now we've outlined the engineering pipeline to automate this process.
The final step is to understand how these newly created "text features" can be integrated into a real trading model alongside traditional price-based features.
In the next lesson, we will discuss the practicalities of **Integrating NLP Signals into a Trading Model**, setting the stage for our final capstone project.