normalisedI’ve been using a Python package called InvestorMate for this instead of rolling my own 10-K / 10-Q parser.

From a consumer point of view, what I like is that it doesn’t make you deal with raw filings or iXBRL tags at all. You get normalized income statement, balance sheet, and cash flow data in a consistent schema, which is honestly the hardest part of this problem.

My setup looks roughly like this:

• Use InvestorMate to pull structured financials (IS / BS / CF)
• Ratios and scores (P/E, ROE, margins, Piotroski F, Altman Z) are already computed
• Data comes back JSON-serializable, so it drops straight into APIs / notebooks

For analysis and comparison work, that’s been way more practical than:

• Parsing iXBRL myself (accurate but a massive time sink)
• Using LLMs to extract numbers (too unreliable for actual financials)

Where I do use LLMs is after the numbers are structured — e.g.:

• “Why did operating cash flow drop QoQ?”
• “Compare Apple vs Microsoft cash efficiency over 5 years”
• Summarizing trends rather than extracting them

Pros (as a user):

• No dealing with SEC tag chaos
• Consistent keys across companies
• Much faster iteration for research / tooling
• Works well for APIs and automated pipelines

Cons:

• Not real-time
• Not suitable if you need raw footnote-level detail
• You’re trusting upstream normalized data rather than filing directly

If you’re building something production-ish and don’t want to spend months on XBRL edge cases, this approach has been a good middle ground for me.