Proprietary quantitative market data
Proprietary datasets for overlooked market behavior.
Alphanume turns difficult-to-reconstruct signals, events, and point-in-time market states into research-ready datasets. Explore the rows first, understand the trading thesis, then decide what belongs in your process.
- Dataset catalog
- 25
- Construction
- Point-in-time
- Delivery
- Dashboard · REST · MCP
- Public preview
- No sign-in required
Dataset briefing
What it measures
See which actively trading US microcaps the model flags for a drop of at least 5% from the open to the close. Each morning's candidates are ranked by probability using only price and return information available by the 09:00 ET cutoff.
The model predicts an open-to-close decline, not whether a short can be located or traded profitably. Borrow and locate costs are absent, especially where sub_dollar equals 1.
How traders use it
- Pull max_rank=5 for a compact daily review queue
- Compare hit rate and realized return across probability bands
- Run separate tests for sub-$1 names and names priced at $1 or above
date
Date
rank_for_date
Daily rank, with 1 assigned to the highest prob_drop
ticker
Ticker
prob_drop
Modeled probability that the open-to-close return is -5% or worse
px_at_trading
Last unadjusted pre-market price at or before 09:00 ET
sub_dollar
1 when px_at_trading is below $1
premarket_return_at_cutoff
Cumulative return from the prior adjusted close through the last print at or before 09:00 ET
intraday_return_pct
Actual unadjusted open-to-close return, filled after the session
return_lead_5d
Adjusted return after five trading sessions, null until mature
t_0...t_9
Cumulative pre-market returns at 30-minute clock marks from 04:30 through 09:00 ET
market_cap
Point-in-time market capitalization for the signal date
return_lead_1d
Adjusted return after one trading session, null until mature
return_lead_30d
Adjusted return after 20 trading sessions, null until mature
{
"count": 1,
"has_more": false,
"next_cursor": null,
"data": [
{
"date": "2026-08-28",
"ticker": "XYZ",
"rank_for_date": 1,
"prob_drop": 0.781432,
"px_at_trading": 0.83,
"sub_dollar": 1,
"premarket_return_at_cutoff": 18.57,
"t_0": 0.0,
"t_1": 0.0,
"t_2": 4.29,
"t_3": 7.14,
"t_4": 9.43,
"t_5": 11.86,
"t_6": 13.57,
"t_7": 16.14,
"t_8": 17.43,
"t_9": 18.57,
"return_lag_1y": -63.21,
"return_lag_1m": -1.84,
"return_lag_5d": 22.06,
"market_cap": 17640000.0,
"intraday_return_pct": -8.43,
"return_lead_1d": -6.18,
"return_lead_5d": -14.72,
"return_lead_30d": null
}
]
}Live rows are available with Pro.
Unlock the latest observations, complete history, REST access, and higher request limits.
Unlock with Pro →We test the data against what happened next.
Every study uses Alphanume observations as they were available at the time. Strong results, weak links, proxy limitations, and gross-versus-net caveats are reported on the same page.
- Datasets tested
- 12
- Confirmed cleanly
- 10
- Reported with caveats
- 2
- Charts published
- 36
Different datasets. Measurable effects.
- Stock Dilution
Median -14.9% at one month.
Across the reported study, 71% of dilutive filings were lower one month later (z = 8.9), with a stronger effect in smaller companies.
- Earnings Implied vs Realized
The implied move exceeded the realized move 64% of the time.
The published study reports a gross edge of 1.1% of spot per event (t = 10.4). Costs, slippage, and path risk still matter.
- IV/HV Premium
A 14.5-vol-point spread across reported quintiles.
The seller edge sorted monotonically from -9.4 to +5.1 vol points as implied volatility became richer versus realized volatility.
These studies establish historical market effects under the stated methodology. They are not expected returns, personalized advice, or a guarantee that an effect survives costs or future regimes.
Data you can defend in a research review.
- 01
Point-in-time by default
Observations are stamped as they became available. Fixed history prevents a silent revision or today's universe from leaking into yesterday's 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
Each tested dataset links to the sample, period, test, result, and caveats. Nuanced findings remain visible instead of disappearing from the catalog.
- 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.
Pro for your research. Enterprise for your team.
Pro
$99/moor $900/year, save 24%
Full history, live data, and internal live-trading rights for one researcher.
Enterprise
CustomSeats, SLA, custom delivery
Deploy across a team or product, with rights by contract.
Looking for the course? Alphanume Learn is separate.
$499 once for permanent course access, or included while subscribed to Pro. The five-lesson introduction is free with no account required. A Learn purchase does not include Pro or general-purpose API access.
Explore Alphanume Learn →The answers a buyer actually needs.
- Can I explore Alphanume before subscribing?
- Yes. Public dashboard previews, basic pricing calculators, documentation, and Proof studies remain available without a subscription. Preview availability and limits vary by dataset. Pro provides full data access, dashboard exports, and advanced calculators; Enterprise supports organizational requirements.
- What is happening to the Free data plan?
- New Free signups are closed. Existing Free API and MCP access ends September 6, 2026. Existing accounts retain their current limits until retirement. Existing users can upgrade through their account. Learn's five-lesson introduction remains available without an account.
- 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 and point-in-time contract. Tested datasets also link to a public market-effect study. The Proof page reports strong and nuanced findings alike rather than labeling every result a success.
- 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.
Explore the public dashboard preview, then access the full catalog with Pro. Every dataset ships under the same key, schema conventions, and point-in-time contract, with new releases included.