EdgeDepth Research

Measure how often a SINGLE stated condition is true over all eligible symbol-minute buckets in a window. base_rate_result.v1 reports predicate_true_buckets, predicate_false_buckets, eligible_buckets, absent_or_ineligible_buckets and true prevalence globally and per symbol. This is not a count of false-to-true occurrence episodes and computes no forward outcomes. Missing values are excluded from eligible, never coerced to false or zero. Deterministic and free, including reruns. field, operator and value must be grammar-valid (see list_features); expect the 422 contract code verbatim if not. The assembled one-clause document is echoed back so you can show the user exactly what ran.

Given N (symbol, time) moments, return the DETERMINISTIC intersection: per registry field a verdict of agree, partial, disagree, quiet or absent, plus the survivorship and multiple-comparisons honesty framing IN the output. Never similarity search. A shared feature is a description of the chosen moments, not a prediction: to test any AGREE chip, follow up with base_rate over the same window.

Omit hash8 to list published archive and record reports with count, revision and explicit integrity status. Provide an 8-hex canonical hash to fetch one report: structurally generated title, author, the definition that ran (re-submittable to run_scan) and the result pinned at publish time. Invalid or withdrawn reports carry correction and successor metadata and must not be presented as healthy.

Turn a plain-language market question into a PROPOSED research_query.v2 document. The result is a proposal, not a result: it is labeled proposal:true and is never executed here. Show the document (plus any unsupported[] stand-ins and clarify notices) to the user, then call run_scan with the document to execute. That second call is the confirm gate. Requires the research:interpret scope and is metered separately (each call spends provider money).

Return the closed registry for the pinned feature_version: every feature id with its dtype and range, the operators, window aggregates and durations, sequence rules, limits, sort fields, and the machine-readable validation and transport error-code lists. Call this FIRST: a document that only uses ids from here cannot invent field names. The registry is ETag-revalidated on every read, so additive feature ids become visible to long-running agents without inventing or hard-coding fields.

Return the authoritative feature-store universe derived from complete manifests. DEFAULT: a compact deterministic summary (coverage window, dataset revision, instrument counts by availability status) - the full body is ~360 KB and overflows agent contexts. Pass symbols: ["btcusdt", ...] for the complete canonical records of specific instruments (per-instrument coverage, present/excluded partition counts, provenance, replay entitlement). Pass full: true for the verbatim canonical bytes of the whole universe. Excluded symbol-days remain visible as universe membership. NVDAUSDT and QQQUSDT are Binance USDT-M perpetuals referencing TradFi assets, not direct Nasdaq data. This read is deterministic and free in every mode.

Continue a prior run_scan by re-sending the SAME document with the opaque cursor from the previous response. Never construct cursors. If X-Dataset-Revision changed since page 1, the data under THIS document changed (e.g. the nightly append) and the result set with it: restart from page 1. The revision is scoped to the document's symbols and window - never compare revisions across different documents. Every page has its own ETag and remains free, including a continuation cache miss. Complete-result counts and summaries remain independent of the page rows. Same counts_by_symbol projection as run_scan; full_counts: true for verbatim bytes.

Execute a research_query.v2 document as a predicate-complement comparison and return canonical cohort_result.v2 bytes: what followed the condition (the treatment side, byte-identical to the run_scan outcomes_summary for the same document) vs what followed every OTHER eligible bucket where the full predicate was FALSE (the baseline), each as the same 30m/1h/4h/24h return + MFE/MAE summary over ALL occurrences. WHERE-ONLY: a sequence document is refused with 422 COHORT_SEQUENCE_UNSUPPORTED (run it as an ordinary run_scan instead). The bytes carry NO ratios, lift, averages or significance - you compare the two distributions yourself. This is not covariate matching: treatment/baseline identities, different observation units, dedupe difference, and denominator arithmetic ship in the result with three mandatory caveats. Both sides honour the completeness rule: an occurrence closer to the end of recorded data than a horizon has that horizon ABSENT - quote present, not total, as the denominator. CONTRACT (relay to the user): Echo the exact document and the reproducibility key to the user with the result. Rates come from outcomes_summary over all occurrences; page rows are examples, never the denominator. Outcome fields cannot be filtered; expect OUTCOME_IN_PREDICATE if tried. A rerun of the same document is served from cache (X-Research-Cache: hit) in its own cohort namespace; reruns and 304 revalidations are free.

Execute a research_query.v2 document over the deterministic engine and return counts, denominators, outcomes_summary, the reproducibility key and page-1 rows as the engine's canonical bytes. outcomes_summary carries forward outcomes over FOUR horizons per occurrence: 30m return, 1h MFE/MAE, and 4h and 24h return + MFE/MAE (record_result.v3), with a closed threshold ladder to +/-20 pct - "how often did this setup reach +5% within 24h, with how much drawdown" is answered by the summary over ALL occurrences. Completeness caveat: an occurrence closer to the end of recorded data than a horizon has that horizon ABSENT (counted in the absent tally, never a truncated or implied-zero outcome) - quote present, not total_matching, as the denominator for any horizon rate. Document-only input: stated chips are the only input the scan path accepts (use interpret_prose first if you have prose, then re-submit its document). CONTRACT (relay to the user): Echo the exact document and the reproducibility key to the user with the result. Rates come from outcomes_summary over all occurrences; page rows are examples, never the denominator. Outcome fields cannot be filtered; expect OUTCOME_IN_PREDICATE if tried. sequence.within accepts 15m/30m/1h/4h/12h/24h and the matching ISO aliases; both normalize to canonical ISO before hashing. identity.symbol must be an exact lowercase Binance USDT-M perpetual symbol: case, whitespace and separators are rejected. You may rerun freely: a rerun of the same document is served from cache (X-Research-Cache: hit), and reruns and 304 revalidations are free. SIZE: page rows carry full 33-feature setup vectors (~2 KB each); page.limit 50 returns ~120 KB, which can overflow an agent context. Counts, outcomes_summary and representatives are complete-result regardless of page size - set page.limit 1-5 unless you need row-level evidence, then page with next_page. PROJECTION: counts_by_symbol lists only instruments with at least one match; the number of zero-co…

Read the engine registry AS-OF the grid bucket containing a moment: every feature value, the closed-list window aggregates, and the fired rulebook ids, as the engine's canonical bytes. A read, never a scan (not credit-metered). Use it to turn one observed moment into candidate clauses, then run_scan to find every other time it looked like that. Selection discipline: if you picked the moment because it moved, expect the follow-up scan to deflate over all occurrences. That is the product working.