{"id":39537,"date":"2026-03-10T15:27:06","date_gmt":"2026-03-10T14:27:06","guid":{"rendered":"https:\/\/www.graviton.at\/letterswaplibrary\/structured-normalised-financial-data-financial-statements-insider-transactions-and-13-f-forms-straight-from-the-sec\/"},"modified":"2026-03-10T15:27:06","modified_gmt":"2026-03-10T14:27:06","slug":"structured-normalised-financial-data-financial-statements-insider-transactions-and-13-f-forms-straight-from-the-sec","status":"publish","type":"post","link":"https:\/\/www.graviton.at\/letterswaplibrary\/structured-normalised-financial-data-financial-statements-insider-transactions-and-13-f-forms-straight-from-the-sec\/","title":{"rendered":"Structured Normalised Financial Data (financial Statements, Insider Transactions And 13-F Forms) Straight From The SEC"},"content":{"rendered":"<p><!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>Hi everyone!<\/p>\n<p>I\u2019ve been working on a project to clean and normalize US equity fundamentals and filings as one thing that always frustrated me was how messy the raw filings from the SEC are.<\/p>\n<p>The underlying data (10-K, 10-Q, 13F, Form 4, etc.) is all publicly available through EDGAR, but the structure can be pretty inconsistent:<\/p>\n<ul>\n<li>company-specific XBRL tags<\/li>\n<li>missing or restated periods<\/li>\n<li>inconsistent naming across filings<\/li>\n<li>insider transaction data that\u2019s difficult to parse at scale<\/li>\n<li>13F holdings spread across XML tables with varying structures<\/li>\n<\/ul>\n<p>I ended up building a small pipeline to normalize some of this data into a consistent format. The dataset currently includes:<\/p>\n<ul>\n<li>normalized income statements, balance sheets and cashflow statements<\/li>\n<li>institutional holdings from <strong>13F filings<\/strong><\/li>\n<li><strong>insider transactions (Form 4)<\/strong><\/li>\n<\/ul>\n<p>All sourced from SEC filings but cleaned so that fields are consistent across companies and periods.<\/p>\n<p>The goal was to make it easier to pull structured data for feature engineering without spending a lot of time wrangling the raw filings.<\/p>\n<p>For example, querying profitability ratios across multiple years:<\/p>\n<pre><code>\/profitability-ratios?ticker=AAPL&amp;start=2020&amp;end=2025 <\/code><\/pre>\n<p>I wrapped it in a small API so it can be used directly in research pipelines or for quick exploration:<\/p>\n<p><a href=\"https:\/\/finqual.app\/\">https:\/\/finqual.app<\/a><\/p>\n<p>Hopefully people find this useful in their research and signal finding!<\/p>\n<p><strong>Disclaimer:<\/strong> This is a project I built. Sharing it here in case it\u2019s useful for others looking for financial data<\/p>\n<\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/myztaki\"> \/u\/myztaki <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1rpy8vp\/structured_normalised_financial_data_financial\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datasets\/comments\/1rpy8vp\/structured_normalised_financial_data_financial\/\">[comments]<\/a><\/span><\/p><div class='watch-action'><div class='watch-position align-right'><div class='action-like'><a class='lbg-style1 like-39537 jlk' href='javascript:void(0)' data-task='like' data-post_id='39537' data-nonce='65e0e39b87' rel='nofollow'><img class='wti-pixel' src='https:\/\/www.graviton.at\/letterswaplibrary\/wp-content\/plugins\/wti-like-post\/images\/pixel.gif' title='Like' \/><span class='lc-39537 lc'>0<\/span><\/a><\/div><\/div> <div class='status-39537 status align-right'><\/div><\/div><div class='wti-clear'><\/div>","protected":false},"excerpt":{"rendered":"<p>Hi everyone! I\u2019ve been working on a project to clean and normalize US equity fundamentals and filings&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[85],"tags":[],"class_list":["post-39537","post","type-post","status-publish","format-standard","hentry","category-datatards","wpcat-85-id"],"_links":{"self":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/39537","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/comments?post=39537"}],"version-history":[{"count":0,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/posts\/39537\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/media?parent=39537"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/categories?post=39537"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.graviton.at\/letterswaplibrary\/wp-json\/wp\/v2\/tags?post=39537"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}