Using Alphanume Short-Selling Research with TradeZero

The pre-market model answers one question: which candidates have a higher modeled chance of falling at least 5% from open to close? Other Alphanume datasets help explain a company's financing and potential share supply. They can add context to a research queue without becoming automatic instructions to short.

This guide shows how to keep those research inputs separate from execution. The downloadable starter continues to trade only its Pre-Market Drop Risk selection. It does not silently change the basket when you inspect another dataset.

Download the Python starter, Windows wrapper, and offline tests. Extract it before running commands. The ZIP includes setup instructions and the complete source.

Choose the question before choosing the dataset

• Dilution: inspect S-1 registration events, new share supply versus resale, and later effectiveness or withdrawal. A registration is a disclosure to investigate, not proof of an immediate stock sale.
• Shelf Registrations: examine registered issuance capacity, effectiveness and subsequent takedowns. Capacity to issue is different from shares already sold.
• Reverse Splits: check the consolidation ratio, execution date and financing context. A split changes units; compare prices and share counts on a consistent basis.
• Lockup Expirations: inspect when restricted shares become eligible for sale, the release size and early-release terms. Eligibility is not a statement that holders will sell.

These pages document their own fields and access requirements. Use your Alphanume API key with the appropriate dataset entitlement. TradeZero's borrow list cannot replace those historical event records, and an event record cannot tell you the current borrow price.

Read an event dataset with the shared client

For example, use the shelf endpoint to retrieve a dated research slice. Save this as a separate script beside client.py. The dates are illustrative historical inputs, not today's candidate list.

from client import alphanume_rows

events = alphanume_rows("capital/shelf-registrations", {
    "date_gte": "2026-09-01",
    "date_lte": "2026-09-25",
})
print("Shelf event rows:", len(events))
for event in events[:3]:
    print(event)

The helper sends the key in a header and follows the API's returned cursor until the slice is complete. Read the endpoint's filter and field documentation before changing the query. Keep account and strategy records separate from any material you plan to publish.

Turn rows into a review queue

1. Start with the dated Pre-Market Drop Risk candidates and save the selection time.
2. Look for relevant company events in the Alphanume datasets. Match the issuer and ticker carefully, especially around corporate actions.
3. Read the event's date, disclosure timing and source reference. Record which facts were available before your decision.
4. Write a short explanation of the potential share-supply issue and what remains unknown.
5. Only then check TradeZero borrow, locate cost and available routes. A research candidate may still be impossible or uneconomic to execute.

Keep later developments out of an earlier decision. For example, a filing's eventual withdrawal is useful for a retrospective study, but it was not necessarily known on filing day. A shelf's later takedown or a revised lockup release should be treated the same way. When you cannot establish when a field became available, do not assume it was usable at the earlier date.

A prompt for a research assistant

Use Alphanume's dated pre-market drop-risk candidates as the starting
list. For each company, review relevant dilution, shelf registration,
reverse split and lockup records available by the decision time.
Separate potential supply from completed issuance or actual selling.
List source references and unknowns. Produce a review queue only;
do not change the starter's ranks, invent a trade rule, pay locates,
or send TradeZero orders.

The output should be a small table or set of notes you can audit. It should not turn every event into a bearish signal. Two companies can share an event label while having very different financing needs, liquidity and ownership.

Test a new rule before calling it a strategy

If you want an event filter to change selection, specify the rule separately, freeze it, and evaluate it with information available at each decision time. Include unavailable borrow, locate costs and actual execution assumptions. This series has not tested the profitability of combining these event datasets with the pre-market model.

Common questions

Can a shelf registration tell me to short today?

It provides financing context. A shelf may never be used, may be used later, or may include securities other than common stock. Inspect the record and supporting filing before drawing a conclusion.

Does the starter already combine all five datasets?

No. Its automatic selection uses Pre-Market Drop Risk. The event examples are separate research reads, so adding context does not silently alter the published strategy.

Where does TradeZero fit into this research?

After candidate review, its account-specific borrow and routing checks help establish whether the proposed execution is feasible. They do not validate the research thesis.

Continue with borrow and locate checks, the AI setup walkthrough, or the series overview. Compare Alphanume dataset plans.

API documentation checked September 27, 2026. Route availability and account access can change. Examples describe the included starter; they do not establish trading performance.