Скачать или смотреть How to Preprocess FED Meeting Transcripts for Sentiment-Based Trading Strategies (With Python)

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📄 Want to turn raw FED meeting transcripts into actionable signals?
In this tutorial, we walk you through the full data pre-processing pipeline for sentiment-based trading strategies—from text extraction to minute-level segmentation, and finally price data retrieval for backtesting.

Using Python, you'll learn how to:

Extract relevant text from FOMC meeting PDFs using the PyPDF library

Filter for just the FED chairman’s core speech, excluding headers, footers, and Q&A

Identify consistent speech patterns (e.g., starting with "Good afternoon" and ending with "Thank you")

Segment the transcript into 1-minute intervals based on word count

Use this segmentation to analyze sentiment trends minute-by-minute

Fetch price data via the Alpaca API to align text data with market movement

Prepare a clean dataset for backtesting sentiment-driven strategies

Whether you’re a data science beginner or a quant looking to integrate textual data into your models, this video gives you practical, code-first insight into converting unstructured financial documents into trading-ready datasets.

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