Free lesson · Prediction strategies

Evaluate the decision process, including failed fills

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A proposed edge must survive the full path from timestamped signal to filled position and final cash reconciliation.

Freeze event universe, resolution rules, forecast horizon and fees before the final chronological evaluation. Retain canceled, rejected and unmatched orders rather than selecting only attractive completed trades.

Use a scenario tree for completion and failure. Empirical probabilities need enough independent order episodes and must be conditioned on volatility and size. Report capacity and loss tails alongside mean profit.

Apply it in a strategy

• Write a reproducible strategy memo that can reject an attractive theoretical spread because of failed-leg losses.
• Record the input timestamp, executable quantity, currency and horizon. Reconcile the result with a cash-flow or state table.
• Stress this failure condition: Failure probability and loss severity often worsen together; averaging them independently can understate risk.

Research deliverable

Build and explain a evaluate the decision process, including failed fills worksheet. Write a reproducible strategy memo that can reject an attractive theoretical spread because of failed-leg losses.

Evidence boundary: Synthetic arithmetic and scenarios illustrate mechanics. They are not historical returns, a paper replication, or evidence of an executable edge. Research sources and their access limitations are recorded at the end of this module.

Primary research and operational references · reviewed 12 September 2026

Further reading: Saguillo et al. — Unravelling the Probabilistic Forest ↗

arXiv preprint v1, 5 August 2025 · Review: Abstract, data-section excerpts and full-text structure inspected 2026-09-12. Markets/data: Polymarket markets resolved 1 April 2024–1 April 2025; data-section endpoint dates checked in the primary manuscript. Interpretation: Distinguishes within-market complete sets from cross-market logical relations. Limits: Historical detected opportunities depend on contract classification, timestamps and execution assumptions; not a current opportunity list. No independent replication performed.

Further reading: Le — Decomposing Crowd Wisdom ↗

arXiv preprint v2, revised 4 August 2026 · Review: Abstract, data-section excerpts and full-text structure inspected 2026-09-12. Markets/data: Kalshi and Polymarket; the primary data tables identify a 31 December 2025 cutoff; inspect exact extraction rules before reproducing. Interpretation: Motivates calibration by domain/horizon and explicit sampling uncertainty. Limits: Descriptive calibration and in-sample decompositions do not establish net trading profits; clustered events matter. No independent replication performed.

Further reading: Polymarket — How positions work ↗

Living official product documentation · Review: Documentation inspected 2026-09-12. Markets/data: Conditional outcome-token mechanics; no empirical sample. Interpretation: Operational starting point for split, merge and redemption workflows. Limits: Record the exact market rules and collateral/version before applying a generic binary model; platform behavior may change. No independent replication performed.

Research sources, review dates and limitations

# Python 3.10+; standard library unless NumPy is imported below.
# Inputs and outputs use the units defined in this lesson. Synthetic teaching example.
def attempt_value(completion,gain,failure_loss,cost):
    if not 0<=completion<=1 or min(gain,failure_loss,cost)<0: raise ValueError("Invalid attempt inputs")
    return completion*gain-(1-completion)*failure_loss-cost

print(attempt_value(.9,5,20,1),attempt_value(.8,5,20,1))