u/ShowEuphoric

I Built a REST API that pulls S&P 500 earnings dates, EPS estimates, and company guidance from SEC filings. This clip shows Claude triggering a live calendar render using the API — 236 companies, colour-coded by sector.

The calendar is an HTML file with your API key and tickers baked in. Happy to share it if anyone wants to build their own version.

Website has more details, you can find it on my profile if interested. Would love to know what you guys would actually use something like this for. Trading bots, screeners, event-driven strategies? Open to feature suggestions.

I lost my mind calling useless APIs and buying NLP pipelines that dump raw filing text and let the user figure it out, so I built one. This is what I have so far and what I have learned:

The data source

EDGAR's full-text search API (efts.sec.gov) is completely free and has zero anti-bot friction. You can pull every 8-K filed by S&P 500 companies within seconds. The actual guidance language almost always lives in Exhibit 99.1 — the earnings press release attached to the 8-K — not the 8-K body itself.

Sentence splitting

Nothing fancy:

python

sentences = re.split(r'(?<=[.!?])\s+', exhibit_text)
sentences = [s.strip() for s in sentences if len(s.strip()) > 30]

The 30-character floor filters out headers, table fragments, and other noise that technically ends with a period.

Guidance sentence identification

Pure keyword matching against each sentence:

python

GUIDANCE_KEYWORDS = [
    "guidance", "outlook", "forecast", "expects", "anticipates",
    "revenue of", "earnings per share", "record revenue",
    "raised guidance", "lowered guidance", "dividend", "repurchase"
]

def extract_guidance_sentences(sentences, max_results=20):
    results = []
    for sentence in sentences:
        matched = [kw for kw in GUIDANCE_KEYWORDS if kw in sentence.lower()]
        if matched:
            results.append({"text": sentence, "keywords": matched})
        if len(results) >= max_results:
            break
    return results

No NLP, no transformer model, no dependency beyond the standard library. This runs fast enough that you can process hundreds of filings in a few minutes on a cheap VPS.

Event type classification

Same approach — keyword scan across the full filing text to tag what kind of event the 8-K is reporting:

python

EVENT_PATTERNS = {
    "earnings_release": ["earnings", "quarterly results", "financial results"],
    "guidance_update":  ["guidance", "outlook", "forecast"],
    "acquisition":      ["acqui", "merger", "transaction", "definitive agreement"],
    "executive_change": ["appointed", "resigned", "chief executive", "ceo", "cfo"],
    "dividend":         ["dividend", "repurchase", "buyback"],
    "restructuring":    ["restructuring", "workforce reduction", "layoff"],
}

def classify_events(text):
    text_lower = text.lower()
    return [
        event for event, patterns in EVENT_PATTERNS.items()
        if any(p in text_lower for p in patterns)
    ]

Multiple types can fire on the same filing — an earnings release that also announces a dividend increase gets both earnings_release and dividend tags, which is accurate.

What the output looks like

Real example from MetLife's 8-K filed June 29, 2026:

json

{
  "ticker": "MET",
  "filed_date": "2026-06-29",
  "event_types": ["earnings_release", "guidance_update"],
  "guidance_sentences": [
    {
      "text": "For the quarter ended June 30, 2026, the Company estimates that its variable investment income will be approximately $220 million to $270 million (pre-tax), which compares to full-year 2026 guidance of approximately $1.6 billion (pre-tax).",
      "keywords": ["guidance", "estimates"]
    }
  ]
}

Known limitations worth being upfront about

The keyword approach has obvious false positives — "earnings per share" fires on historical reported EPS just as readily as on forward estimates, and "dividend" fires on both declarations and boilerplate forward-looking disclaimers. There's no sentence-position awareness, no section detection (so you can't distinguish the MD&A from the safe-harbor boilerplate), and no semantic understanding of whether a matched sentence is actually forward-looking or backward-looking.

For algo trading use cases where you need high-precision guidance extraction, you'd want to layer in at least: section detection (identify the "Outlook" or "Guidance" section header and prioritize those sentences), tense detection (filter for future-tense verbs), or a small fine-tuned classifier. Happy to discuss any of those approaches in the comments.

I've been running this against S&P 500 8-Ks on a daily cron — it's cheap, fast, and surprisingly useful even with the limitations above. The structured JSON output is what makes it worth building: you can feed it directly into a model, a screener, or a signal pipeline without further parsing.

For anyone who enjoys watching chess content (or even anyone who doesn't), I highly reccomend Danya's Masterclass Speedrun series. Daniel Naroditsky was one of the greatest informative chess content creators I have ever come across, I have hundred of hours watching chess content and there is no higher quality than his.

Danya creates a brand new chess.com account and plays rapid games which discussing his thoughts throughout it, this is so great as if you are 500, 1500 or even 2000+ there will be a video of him playing and diccussing while playing games that are relative to yours.

I personally have learned countless principals from him such as 'the frozen pawn push' in an endgame, 'trading off minor pieces when the opponent has an isolated queens pawn' and just how important piece activation really is.

I reccomend starting the series at the rating you currently are and paying attention to his after game analysis. I first started watching at 1300 and am now sat comfortably at 1850 a handful of months later, a lot of which I attribute to his informative content.

Rest in peace mate, you have changed the chess world more than you know.

https://youtube.com/playlist?list=PLT1F2nOxLHOefj_z54LNBpnASnIROm43e&si=p4PL59ZoxTcYPD-C

For the record this is my own actor.

I have published an actor that scrapes structured earnings call transcripts and parsed SEC 8-K filings for any US public company.

What it returns:

• 8-K filings with event classification
• Guidance language extraction
• Earnings call transcripts
• Works for any US-listed ticker on demand

https://apify.com/filingapi/my-actor

https://filingapi.dev

What I need is feedback, and there is no better community than here. If you use it, let me know if it is tailored to your needs.