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CloudMaxi0x Trade Filter Validator
x402
CloudMaxi0x Trade Filter Validator is a paid API for AI agents from cloudmaxi0x.com, paid per call via x402, $0.25/call, status unknown (last checked 2026-09-14).
Tests a trade filter against a permutation null hypothesis to determine whether its selection lift is statistically distinguishable from chance, with optional gross-return and claimed-lift gap reporting.
Price / call$0.25
Success—
StatusUnknown
How this service works
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.
Output
Returns a p-value indicating whether the filter's lift is distinguishable from chance, the measured lift in R over the unfiltered baseline, the permutation null distribution, and optionally the gap between claimed out-of-sample lift and audited lift; also reports any detected overlapping trade windows that would invalidate walk-forward testing.
Example use cases
Overfitting check for algo strategy
“I have a crypto trading filter I built on 18 months of data — 150 trades, each with 4 features I computed before entry, and their realized returns in R. Can you run a permutation test with 80 permutations to see if the filter's lift is statistically real or just noise?”
Auditing a vendor's claimed edge
“A signal provider is claiming their trade filter produces 0.6 R of lift over baseline. I have their 220 trades, 6 features, and returns. Can you run 100-permutation validation on it and tell me how big the gap is between their claimed 0.6 R lift and what actually survives a strict permutation audit?”
Walk-forward integrity check
“I want to make sure my trade filter isn't accidentally using overlapping trade windows that would invalidate my walk-forward test. I have entry and exit timestamps for 180 trades with 3 features each — can you run the permutation filter validation with 50 permutations and flag any overlapping trade periods?”
Technical details
Endpoint
GEThttps://cloudmaxi0x.com/v1/validate/filterWhen to prefer
Choose this endpoint when you need to statistically validate whether a trade filter's selection lift exceeds what random permutation of the same features would produce — specifically when you want a permutation null hypothesis test rather than a simple Sharpe or t-test. Prefer this over generic statistics APIs when the input is trade-level feature rows with R-denominated returns and you want an honest audit that accounts for baseline performance, overlap detection, and claimed-vs-actual lift gaps in a crypto trading context.
Side effects
- Fewer than 120 trades supplied — request rejected with validation error
- Features matrix row count does not match returns array length — schema mismatch error
- Features contain values not computable before trade entry (lookahead) — cannot be fully detected, audit proceeds with caveat
- Entry/exit timestamp arrays length mismatch — error returned
- Permutations below minimum of 20 — rejected
- p-value floor limited by 1/(permutations+1) — very low p-values unresolvable with few permutations
- Returns include post-fee costs but gross_returns_r omitted — audit proceeds but fee-snooping check skipped
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| exit_ms | — | Optional exit timestamps (epoch ms). |
| entry_ms | — | Optional entry timestamps (epoch ms). Supplying these lets us detect overlapping trades, which silently invalidate walk-forward testing. |
| features | array | One row per candidate trade, in chronological order. Every value must have been computable strictly BEFORE that trade's entry - this is the assumption the whole audit rests on, and the one we can only partly check. |
| returns_r | array | Outcome of each candidate trade in R, same order as `features`. Include the trades your filter rejected: without them there is no baseline to measure lift against, and lift is the entire question. |
| permutations | integer | Permutations used to build the chance distribution. The p-value cannot resolve below 1/(permutations+1). |
| feature_names | array | |
| claimed_lift_r | — | Optional: the out-of-sample edge your own backtest reported for the filtered subset, in R above the unfiltered baseline. We report the gap between your number and our strict replay. |
| gross_returns_r | — | Optional but strongly recommended: the same trades BEFORE fees and slippage. Any feature that is part of how `returns_r` is computed - a fee column, a slippage estimate, anything subtracted to get net - will correlate with it by arithmetic alone. Supplying gross lets us re-measure against a target the feature is not part of, which is the only way to tell a real effect from bookkeeping. |
| selection_threshold_r | number | Predicted-R cutoff above which a trade is selected. Higher means a more selective filter. |
Tags
x402
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