This is the main text corpus used to train the QuantOxide Reasoning Agent. It contains clean, semantic text chunks extracted from the 10-K filings of the top 50 S&P 500 companies.

The Parsing Logic

Parsing SEC filings is unbelievably difficult due to inconsistent HTML, broken table tags, and "incorporation by reference." This dataset was created using a unique parsing technique:

Instead of relying on broken regex headers, the parser scores chunks based on "Financial Density" (presence of metrics, reasoning keywords, dates, or lack of boilerplate language). Additionally, all HTML tables are parsed and converted into Markdown to preserve column alignment for LLM tokenization.

Dataset Contents

Years: 2022, 2023, 2024
Sections:
- Item 1A: Risk Factors (Predictive signals)
- Item 7: Management's Discussion & Analysis (Causal signals)
- Item 7A: Quantitative Market Risk (Exposure signals)

Usage

This dataset is ideal for:

Pre-training financial Language Models (Continued Pre-training).
Testing RAG (Retrieval Augmented Generation) chunking strategies.
Financial NLP research (Sentiment Analysis on Risk Factors).

from datasets import load_dataset

dataset = load_dataset("YourUsername/quantoxide-raw-10k")

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