SSNC
SS&C Technologies Inc
stock NASDAQ

Do you recall what your average purchase price was?
I just added to CRM and 472 in SSNC, 866 IN SCHW.

They're Schwab's portfolio margin risk metrics. EPR (expected price range) is the *theoretical* maximum amount a security could move up or down in a single day. PNR (point of no return) is the percentage move an underlying would have to make before the entire brokerage account goes to zero. Basically the more contracts you pile on, the more leveraged the position becomes and the PNR gets lower and lower since it would take a smaller move in the stock to wipe you out. So if the stock has an EPR of 25% (the lowest value Schwab gives to individual stocks), I'm going to make sure my PNR doesn't get below 75% (3x of 25%). This is where the share price comes in since a stock trading at $500 carries a lot more notional risk (when levered up with options) than a stock trading at $50, etc.
I target a range of stocks that meet my risk requirements, everything listed above, but basically they're all more or less equally (un)likely to move 30% within 2 weeks. I filter it by earnings so stocks come and go from the list as they move through earnings reports.
Here's what I traded the last weekly and the last monthly expirations. There's a few that are greater than $350/share and those were mostly 0dte.
BA, BROS, BURL, CHTR, COF, CVX, DE, DHR, DLR, DLTR, FIS, GDDY, HD, HON, HSY, IBM, JNJ, KMB, LOW, MCD, MMM, PEP, PGR, PM, SPGI, TXRH, UNP, UPS, WDAY
  ADI, AEE, AEP, AER, AFL, AGCO, ALLE, ALSN, AME, AMT, APD, ATO, ATR, AWK, BCPC, BR, CBOE, CBRE, CHRW, CNI, DE, DOV, DPZ, DTE, DUK, ECL, ED, EFX, ESS, ETN, EXR, FLS, FRT, FSS, GPC, GRMN, IEX, IFF, ISRG, ITT, ITW, J, JCI, JLL, JNJ, LDOS, MA, MAA, MCD, MHK, MIDD, MLI, NSC, OC, OPEN, ORLY, PCAR, PEG, PKG, PNC, PNW, PSA, RPM, RSG, SPG, SPGI, SSNC, SUI, SWK, TMO, TRI, UNP, VRSK, WAB, WCN, WEC, WM, WMS, XYL, YUM

Background: I've spent twenty years doing data ontology work professionally — building the semantic structures that turn raw, ungoverned data into something usable, most recently at SurveyMonkey. On the side I've built a personal screener pulling 16 years of SEC XBRL data across roughly 1,700 tickers, normalizing inconsistent tags so true FCF (operating cash flow minus CapEx minus SBC) is comparable across companies. I'm posting this here specifically because I think the methodology question is more interesting than the stock picks, and this sub seems like the right place to have that argued with rather than just agreed with.
**The consensus trade and why I think it's incomplete**
Everyone agrees the AI infrastructure trade is the data platform layer — Snowflake, Databricks, Amplitude. Raw data storage, query, and governance tooling. The market has priced this consensus in fully; these names carry premium multiples on the "picks and shovels" thesis.
My argument: raw data infrastructure is closer to a commodity than people are pricing it as. SQL servers, data warehouses, analytics capture platforms — this category has been re-invented every decade with marginal differentiation, and the switching costs, while real, are mostly operational (migration pain) rather than epistemic (the new platform can do everything the old one could, eventually). What's scarce isn't the pipe. It's validated, structured, domain-specific content moving through the pipe.
**The taxonomy I'm using**
I split AI-relevant data companies into four categories:
Foundational language data — Reddit (RDDT) is the only name here. Granular subreddit classification plus upvote-based quality signal is genuinely unique training corpus for natural, idiomatic language. I don't own it — FCF yield too low for my framework, still in a cash-consuming growth phase — but the data moat argument is real.
Industry-specific contextual data — FactSet (FDS), Veeva (VEEV), Roper (ROP), S&P Global (SPGI). These companies have spent decades organizing messy, heavily regulated domain data into clean, structured ontologies: financial workflows, FDA-validated clinical trial records, county tax administration, credit ratings methodology. None of this is scrapeable. A general model trained on public web data has zero exposure to what a structured clinical trial submission or a properly normalized financial model actually looks like internally.
Workflow/usage data — Adobe (ADBE), Salesforce (CRM), SS&C (SSNC). The moat here is encoded human process rather than raw content. A Salesforce lead-to-contact-to-opportunity data model isn't bad design — it's encoding a specific sales workflow that took years to standardize across millions of companies. Replacing it means replicating not just the data but the process logic embedded in how that data gets created and transformed.
Data foundation platforms — Amplitude (AMPL), Snowflake (SNOW). The commodity layer described above.
**The valuation argument**
The names in categories 2 and 3 are trading at meaningfully better true FCF yields than the consensus infrastructure plays, despite (in my view) deeper and more durable moats — partly because the SaaSpocalypse selloff has lumped them in indiscriminately with software companies that genuinely do have weak, scrapeable moats. I think the market is pricing the wrong layer of the stack.
**The honest open question I'd actually like pushback on**
Is "irreplaceable context" really a durable moat, or just a temporary information asymmetry that AI labs close over time as they get better at synthetic data generation, data partnerships, or simply paying for licensing access to exactly this kind of structured content? If OpenAI or Anthropic can license FactSet's data outright, or if regulatory data eventually becomes more standardized and shareable industry-wide (think FDA pushing toward common data standards), does the moat compress faster than the multiple suggests it will? I think the moat holds longer than the market is currently pricing, but I'm genuinely less certain about the 10-year case than the 3-year case, and would like to hear from anyone closer to enterprise AI procurement or regulatory data standards on how real this risk is.
Full piece with the four-category breakdown and a true FCF yield comparison table is here, for anyone who wants the data: [https://cavemanscreener.substack.com/p/context-is-50-iq-points-part-ii-data](https://cavemanscreener.substack.com/p/context-is-50-iq-points-part-ii-data)
Disclosure: I own FDS and ADBE.