Proprietary quantitative market data
Proprietary datasets for overlooked market behavior.
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Dataset briefing
What it measures
See when restricted shares become eligible to join the market, and how large the locked block is relative to the shares sold in the offering. Track historical and upcoming IPO and follow-on lockups, with a separate row for each release tranche and the prospectus terms behind it.
A lockup expiration makes shares eligible for sale. It does not establish that holders will sell, how much they will sell, or which way the stock will move.
How traders use it
- Monitor upcoming releases and inspect the conditions behind the dates
- Compare release size with offering float across IPO and follow-on cohorts
- Anchor event studies to expiration or the first sellable session, with availability checks
date
Expiration date: contractual anchor plus lockup_days. Can be future-dated; date filters use this field.
shares_sellable_from
First NYSE session on or after expiration, rolling weekends and holidays forward. Does not confirm actual selling.
ticker
Listed symbol; can be missing for several days after a new IPO.
company_name
Issuer name as filed.
lockup_type
ipo or follow_on. Operating-company IPOs and offerings by already-reporting issuers, respectively.
locked_to_float_ratio
Total locked block divided by offering float. 3 means 3 times the float; apply tranche_pct to size an individual release. Null for missing inputs or ratios above 200.
tranche_pct
Percentage of the total locked block assigned to this tranche. 100 for a single release; null when the staged percentage is unavailable.
early_release_type
none, earnings, price, staggered, mixed, or other. For non-none values, treat date as the contractual outside date; earlier conditional dates are not computed.
status
upcoming (expiration today or later, ET) or expired, refreshed by the daily status sweep.
record_id
Stable row ID and pagination tiebreaker.
cik
SEC Central Index Key of the issuer.
exchange
Listing venue named on the prospectus cover.
accession_no
EDGAR accession of the final prospectus; shared by the offering's tranches.
offering_date
Printed prospectus (pricing) date; falls back to the filing date minus one business day.
first_trade_date
First observed session with volume for an IPO. Null on follow-ons or while unavailable.
shares_offered
Shares or ADSs sold in the offering, excluding the over-allotment option.
shares_outstanding_post_offering
Total shares outstanding immediately after the offering, as stated in the prospectus.
locked_shares
Total locked block for the offering, not this tranche alone. Prospectus-stated count, otherwise outstanding shares minus offered shares.
locked_pct_of_outstanding
100 times locked_shares divided by shares_outstanding_post_offering. 75 means 75%.
float_shares_at_offering
Offering shares plus over-allotment when its exercise is stated. An offering-float measure, not today's free float.
tranche_seq
One-based position in the offering's release schedule, ordered by release date.
tranche_count
Number of release tranches for the offering.
lockup_days
Length of this tranche's lockup in calendar days from its contractual anchor.
expiration_date
Alias of date, with the same YYYY-MM-DD value.
early_release_terms
Verbatim early-release or staged-release clause. Null for a plain fixed-period lockup.
market_cap_at_offering
First available market-cap observation on or after the offering, in USD. History begins in 2024 and fresh-ticker coverage can lag.
confidence
Quality score for the extracted terms from 0 to 1; not a probability of selling or of a price move.
first_seen_at
First observation of the prospectus. Pre-launch backfilled rows use a synthetic filing timestamp, not observed live availability.
published_at
When the row was first published into the served table.
last_updated
Last change to a served value; used by updated_since. Timestamp format: YYYY-MM-DD HH:MM:SS.
{
"count": 1,
"has_more": false,
"next_cursor": null,
"data": [
{
"record_id": 1001,
"ticker": "EXAMPLE",
"cik": 1234567,
"company_name": "Example Industries (illustrative)",
"exchange": "Nasdaq Global Market",
"accession_no": "0001234567-26-000001",
"lockup_type": "ipo",
"offering_date": "2026-04-23",
"first_trade_date": "2026-04-24",
"shares_offered": 10000000,
"shares_outstanding_post_offering": 40000000,
"locked_shares": 30000000,
"locked_pct_of_outstanding": 75,
"float_shares_at_offering": 10000000,
"locked_to_float_ratio": 3,
"tranche_seq": 1,
"tranche_count": 1,
"tranche_pct": 100,
"lockup_days": 180,
"date": "2026-10-20",
"expiration_date": "2026-10-20",
"shares_sellable_from": "2026-10-20",
"early_release_type": "none",
"early_release_terms": null,
"status": "upcoming",
"market_cap_at_offering": null,
"confidence": 0.9,
"first_seen_at": "2026-04-24 07:00:00",
"published_at": "2026-09-12 07:25:00",
"last_updated": "2026-09-12 07:25:00"
}
]
}Live rows are available with Pro.
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Unlock with Pro →We test the data against what happened next.
See where Alphanume data maps to forward returns, changes in volatility, and larger price moves. Real prices, clear comparisons, and the numbers behind each finding.
- Studies featured
- 11
- Datasets represented
- 11
- Charts published
- 25
- Prices through
- 2026-09-18
Different datasets. Measurable effects.
- Toxic Financing
Variable-price financing announcements: average 1-month return of -8.4% (t = -2.4). 70% of stocks finished lower.
The deal terms help distinguish financing with persistent downside from the broader placement feed.
- Earnings Implied vs Realized
The implied move exceeded the realized move by 1.17% of spot on average (t = 6.8); it was larger 64% of the time.
Use the event history to compare how much movement options priced with how much the stock delivered.
- IV/HV Premium
The implied-minus-forward-realized gap averaged +5.7 volatility points in the highest fifth versus -8.7 in the lowest (difference t = 14.3).
Use the ratio to screen where implied volatility has been rich or cheap relative to the movement that followed.
Historical findings, with sample dates, entry rules, and statistics available in each study.
Data you can defend in a research review.
- 01
Point-in-time by default
Dataset documentation distinguishes fixed observations from evolving deal records and filing histories. Check filing dates and availability timestamps so later information does not leak into an earlier test.
- 02
Constructed from primary sources
Alphanume turns raw filings, market observations, and model inputs into research-ready event histories and signals. Dataset pages state what came from the source and what Alphanume created.
- 03
Evidence attached
Featured studies show the sample, dates, entry rules, and measured result. Explore where the data maps to forward returns, volatility, and price movement.
- 04
One consistent interface
Use the same key across the dashboard, REST API, and hosted MCP server. Add a new signal without a new vendor integration.
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The answers a buyer actually needs.
- What does Pro include?
- Pro includes the full dataset catalog, REST API, hosted MCP, dashboard exports, and advanced calculators. Start with a 3-day free trial, then $349/month or $1,999/year.
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- Can I use Pro in a live trading workflow?
- Yes. Pro is the self-serve plan for one named user and may be used for that user's research, backtests, automation, and internal live trading. Choose Enterprise when you need multiple users or keys, redistribution or client-facing rights, custom delivery, an SLA, or organization-specific contract terms.
- What makes the datasets proprietary?
- Alphanume constructs datasets from primary sources and its own models rather than simply reselling a standard market feed. Each dataset page identifies the source inputs, the fields or labels Alphanume created, the observation-time policy, update cadence, and historical coverage.
- Does every dataset have proof?
- Every dataset has a documented methodology. The Proof page features selected historical findings for forward returns, volatility, and price movement. Each study states its qualifying conditions and sample dates; other datasets support screening, research, and portfolio analysis.
- How does agent access work?
- Add https://mcp.alphanume.com/mcp as a custom connector in Claude or Codex and sign in with your Alphanume account. There is no key to copy and no separate MCP subscription: sign-in resolves the key already on your account, so your tier and limits match the REST API exactly. Cursor, scripts, and CI use a key endpoint instead. The agent chooses the tools and parameters; Alphanume returns the same deterministic dataset rows available through REST.
- Are new datasets included in Pro?
- New standard Alphanume datasets are included in Pro as they launch. Separately licensed third-party data, custom datasets, bulk delivery, and organization-specific rights may require Enterprise.
- Are these trade recommendations?
- No. Alphanume provides data, models, and historical research for quantitative workflows. The published studies describe historical effects under stated assumptions; they are not personalized recommendations or guarantees of future performance.
Give your next strategy a differentiated input.
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