{"uid":"cap_YSoQl0_ncyogl-zBYCFJ9","slug":"cloudmaxi0x-trade-return-significance-validator-272c7c97","name":"CloudMaxi0x Trade Return Significance Validator","description":"Deterministic, verifiable crypto market data and honest backtest audits. Use GET /v1/data/ohlcv for exact OHLCV over a half-open range with a SHA-256 Merkle manifest - only closed candles are returned, exchange decimal strings are preserved byte for byte, so the same query always yields the same root. Use GET /v1/data/proof to verify one candle against a root you already hold. Use POST /v1/validate/trades to find out whether a list of trade returns can be distinguished from luck, and POST /v1/validate/filter to test a trade filter against a permutation null. GET /, /v1/schema and /v1/data/sources are free.","url":"https://cloudmaxi0x.com/v1/validate/trades","method":"GET","headers":{},"bodySchema":{"type":"object","properties":{"returns_r":{"type":"array","items":{"type":"number"},"title":"Returns R","minItems":30,"description":"Per-trade result in R (multiples of the risk taken on that trade). A trade stopped out at its initial stop is -1.0. Order must be chronological."},"trial_sharpes":{"anyOf":[{"type":"array","items":{"type":"number"}},{"type":"null"}],"title":"Trial Sharpes","default":null,"description":"Optional: the per-trade Sharpe of every variant you tried. Supplying these makes the multiple-testing correction exact instead of approximated."},"cost_per_trade_r":{"type":"number","title":"Cost Per Trade R","default":0,"minimum":0,"description":"Optional: additional round-trip cost in R to subtract from every trade before testing, if your backtest was run gross of fees and slippage."},"n_trials_declared":{"type":"integer","title":"N Trials Declared","default":1,"minimum":1,"description":"How many strategy variants you tried in total before settling on this one - every parameter set, symbol and timeframe, including the ones you discarded. This is the single most important number you can give us, and the one people understate."}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.05","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.05/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_ae72NaCF3dAzeQGHlyv7B","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.05","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Tests whether a list of per-trade R-multiple returns can be statistically distinguished from luck, applying multiple-testing correction based on the number of strategy variants tried","exampleAgentPrompt":"I have 120 trades from my BTC momentum strategy with R-multiples like [1.2, -1.0, 0.8, ...], and I tried about 40 different parameter combinations before settling on this one — can you test whether these results are statistically distinguishable from luck, with a cost of 0.05 R per trade for fees?","exampleUseCases":[{"title":"Overfitting check on crypto strategy","prompt":"I backtested a crypto mean-reversion strategy across 60 trades with these R-multiples: [0.9, -1.0, 1.5, 0.2, ...]. I tried 25 different parameter sets before picking this one. Can you tell me if the results are actually significant or if I just got lucky curve-fitting?"},{"title":"Validating a single-variant systematic strategy","prompt":"My algo strategy has exactly 1 version — no parameter tuning, pure signal — and it produced 85 trades with R-multiples I can share. Can you check if those returns are statistically real and not just noise?"},{"title":"Fee-adjusted edge test for live deployment","prompt":"Before I go live, I need to know if my backtest edge survives after accounting for 0.1 R per trade in slippage and commissions. I have 150 chronological trade R-multiples and tried 12 strategy variants. Can you run the significance test with that cost adjustment?"}],"resultDescription":"Returns a statistical verdict on whether the submitted trade returns can be distinguished from random luck, including a corrected p-value or significance score that accounts for the number of strategy variants tried (multiple-testing correction). If trial Sharpe values are supplied, the correction is exact rather than approximated.","failureModes":["Fewer than 30 trades supplied — minimum minItems constraint violated","n_trials_declared set to 1 when many variants were actually tried — leads to overconfident (uncorrected) result","cost_per_trade_r set to negative value — rejected by minimum:0 constraint","Non-chronological returns array — may produce misleading sequential statistics","Payment failure or insufficient USDC balance — 402 response, call not processed"],"whenToPreferThis":"Use this endpoint when you need to know whether a crypto trading strategy's backtest results are genuinely statistically significant or likely the product of overfitting and data-mining bias. It is specifically designed to apply honest multiple-testing correction based on how many variants were tried — making it superior to naive t-tests or Sharpe comparisons that ignore the strategy selection process. Prefer it over generic stats libraries when you want an auditable, fee-aware significance result that penalizes overfitting appropriately.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:41:36.489Z","isFirstParty":false}