Last Updated: 2026-10-27
An LLM can help organize a market's rules, evidence and unanswered questions. A coherent explanation is not a calibrated forecast, and a forecast is not an executable trade. This is a design for a read-only research experiment, not a working bot or a claim that Claude outperforms a market. No account funding, API purchase or live order is required for the examples.
Give the research assistant a bounded job
Start with a small, declared set of contracts and save why each was included. Preserve the exact question, outcome definitions, resolution source, deadline and timezone, plus the time you captured them. A headline is not the contract: Polymarket's resolution documentation distinguishes the title from the rules that determine the outcome.
For each evidence item, retain its URL or document identifier, publication time, retrieval time and the relevant passage. Separate an official release from commentary about it. Keep contradictions, corrections and missing documents; do not ask the model to fill gaps from memory. An inaccessible source should remain inaccessible in the record.
The useful output is a short dossier: what the rules require, what the supplied evidence supports, what contradicts it and what is still unknown. If an experimental probability is requested, label it uncalibrated until evaluated. A token-generation probability or a "high confidence" phrase is not an event probability.
Keep source text outside the authority boundary
News, social posts and retrieved pages are untrusted inputs. Instructions inside them must not grant tool access, change the research rules or request secrets. Separate source passages from trusted instructions, restrict capabilities and validate outputs independently. This experiment needs no wallet key or order-placement tool. Anthropic's prompt-injection guidance describes this indirect-input risk; supplying citations does not eliminate it.
Freeze the model identifier, prompt version, evidence cutoff and generation settings in each record. Compare models only using the same held-out tasks and cost accounting. Do not infer that a larger context window or a more expensive model forecasts better. Check current supported features and prices when budgeting rather than retaining an undated model table.
Make abstention a valid result
The following object is invented and inert. It describes missing evidence, not a command to trade:
{
"market_id": "synthetic-example",
"as_of_utc": "2026-10-27T12:00:00Z",
"status": "abstain",
"probability_yes": null,
"source_ids": [
"rule-1"
],
"reason": "The stated resolution source is missing."
}status can be abstain or estimate. Require a null probability for abstention; for an estimate require a finite number from zero to one, the matching market identifier and source IDs present in the supplied dossier. Reject unknown fields that try to introduce an action. Reject a mismatched market, malformed timestamp, future timestamp or evidence older than the experiment's declared validity window. That window depends on the question; one hour is not a universal rule.
Anthropic's structured-output documentation addresses output format. A valid schema does not establish that a source is true, a citation supports the sentence or a probability is accurate. Refusal, timeout and truncated output need visible failure states, not a default probability of 0.5.
Evaluate forecasts before imagining profits
Save predictions before the outcome and score them only against the exact resolved contract. Freeze a development set and a later evaluation set. Repeated predictions of one event and several contracts tied to the same outcome are correlated observations, not independent trials. Record abstentions and unresolved markets rather than deleting inconvenient rows.
For binary outcomes, the Brier score is the average squared difference between forecast probability and outcome (0 or 1); lower is better. This standard-library example uses four invented observations and no network calls:
predictions = [0.70, 0.20, 0.60, 0.80]
outcomes = [1, 0, 0, 1]
market = [0.60, 0.30, 0.55, 0.70]
def brier(probabilities, outcomes):
assert len(probabilities) == len(outcomes) and outcomes
assert all(type(p) in (int, float) and 0 <= p <= 1 for p in probabilities)
assert all(type(y) is int and y in (0, 1) for y in outcomes)
return sum((p - y) ** 2 for p, y in zip(probabilities, outcomes)) / len(outcomes)
assert abs(brier(predictions, outcomes) - 0.1325) < 1e-12
assert abs(brier(market, outcomes) - 0.160625) < 1e-12
assert brier([0.5] * 4, outcomes) == 0.25
print("Synthetic scores only; four observations do not establish an edge.")The model-shaped numbers beat both comparison series in this tiny example. That does not establish calibration, persistent advantage or profit. A useful larger evaluation also examines reliability by probability range, missingness, correlated events and performance on a genuinely later period. A high win fraction alone can be achieved by selecting expensive likely outcomes; it does not measure value relative to price.
Budget against possible net value, not stake size
Synthetic arithmetic: spending $50 on 100 shares at $0.50, with an assumed true win probability of 0.52 and a $1 winning payout, gives $2 expected gross profit. That probability is an assumption, not something this workflow has proven. Spending $2.50 on research is only 5% of the stake but already exceeds the modeled gross edge, before execution costs. A plausible probability error can erase it entirely.
Likewise, an assumed annual return of 8–12% on $200 is $16–$24. A $30–$300 monthly infrastructure bill is $360–$3,600 annually. These are examples, not current provider prices or return forecasts; they show why "pays for itself" needs a full cost ledger.
Keep research quality, paper execution and live operations as separate evaluations. Paper fills need explicit price, size, spread, fees, delay and unfilled-order assumptions. A quote is not a fill. A profitable simulation does not establish eligibility, signing safety, duplicate-order handling or recovery after uncertain submission. This article supplies none of those live components and gives no automatic progression to them.
Educational research design, not financial advice. No personal trade record, measured win rate or expected return is claimed. Check the actual platform/entity terms; do not evade access restrictions. The site may earn referral commissions elsewhere, but no signup is needed here.