Engines observed

Scan - 현금흐름

scan engine application skill for 현금흐름: OCF/ICF/FCF + 현금흐름 패턴 분류 (8종).

engines.scan.cashflow GitHub 원본

출력

기대 결과

  • public call
  • representative return shape
  • verification result

엔진 역할

scan engine application skill for 현금흐름: OCF/ICF/FCF + 현금흐름 패턴 분류 (8종).

공개 호출 방식

import dartlab
result = dartlab.scan("cashflow")

호출 동작

Reads financial universe prebuilt/provider data and computes candidates or rankings. Primitive axes may require a target argument, so check the guide example first.

대표 반환 형태

Returns a DataFrame. Core fields are stockCode/ticker, corpName/name, market/universe, latestAsOf/asOf, metric/value/score, rank, source/basis, and flags.

기본 실행 순서

  1. target, period, and source data are fixed first.
  2. Run the public call exactly as documented.
  3. Check latestAsOf/date, missing values, flags, and assumptions.
  4. Bind numeric claims to tableRef/valueRef/dateRef/executionRef.
  5. Hand off multi-axis narrative composition to story or the parent report skill.

기본 검증

This skill is a public execution document. If this axis call, representative return keys, error behavior, or runtime limits change, update this file in the same change.

런타임

실행 환경별 호환성

환경상태비고 / 제한
Local Python supported
Server supported
MCP supported
Web AI supported
Pyodide limited

실패 회피

흔한 실패 · 절대 금지

절대 금지
  • Do not list candidates without universe and datasetAsOf.
  • Do not answer with company names only; include a ranking/evidence table.
  • Do not present screening output as deep analysis.