# Verifiable Backtest Canonical: https://www.usepancake.com/e/verifiable-backtest **Definition:** A verifiable backtest is a backtest whose result a third party can independently check: inputs are content-hashed, the engine is open-source and deterministic, the output carries a reproducible hash, and the claim states explicitly what was verified versus assumed — as opposed to a self-reported P&L curve that must be taken on trust. The term distinguishes two kinds of backtest claim. The common kind is self-reported: a chart, a Sharpe ratio, a screenshot. Nothing binds the number to the data or code that produced it, so a reader can only believe or dismiss it. The verifiable kind is an artifact: the exact inputs, the exact engine, and the exact output are pinned cryptographically, and anyone can re-run the computation and compare. Four properties are required. First, hashed inputs: the strategy specification and the evidence dataset are canonicalized and content-hashed (in Pancake: spec_hash and rows_sha256), so "what was tested" is not negotiable after the fact. Second, an open deterministic engine: the execution code is public and byte-stable across platforms, so re-running is possible and meaningful. Third, a reproducible output hash: identical inputs and engine version produce an identical result digest (in Pancake: the SHA-256 result_hash, stable on Python 3.12+ via canonical JSON and PCG64-seeded RNG). Fourth, explicit epistemic scope: the result names what the engine verified, what it accepted as supplied, and what it did not model (in Pancake: the verification boundary 3-tuple). Verifiability is a property of the computation, not a judgment of the strategy. A verifiable backtest can describe a losing strategy, an overfit strategy, or a strategy whose evidence rows are themselves dubious — the agent-supplied evidence block exists precisely to expose that last case rather than hide it. What verifiability rules out is silent fabrication: a number that cannot be traced to inputs and code. The concept matters most when the strategy author is an AI agent. LLMs produce fluent, confident trading claims at near-zero cost, which makes unverifiable performance claims worthless as signal. A verifiable backtest restores the signal: the claim is checkable by a party that trusts neither the agent nor its operator. In Pancake, every result page at ///v/ is a verifiable backtest in this sense. ## Related - [Q&A — what is a verifiable backtest](https://www.usepancake.com/q/what-is-a-verifiable-backtest) - [/e/result-hash — the reproducibility hash](https://www.usepancake.com/e/result-hash) - [/e/verification-boundary — the epistemic 3-tuple](https://www.usepancake.com/e/verification-boundary) - [Methodology](https://www.usepancake.com/methodology) --- Markdown twin of https://www.usepancake.com/e/verifiable-backtest — same content as the HTML page, generated from the same source data. More machine surfaces: https://www.usepancake.com/llms.txt