Knuckles-Team PnL Attribution Workflow
Compose the named atomic skills without adding new attribution math here.
Inputs
Provide the multi-factor strategy definition, asset universe, and date range.
Steps
Step 0: quant-data-ingest [skill: quant-data-ingest]
Invoke $quant-data-ingest with the workflow inputs to ingest market
data for the universe and date range.
Expected: normalized_market_dataset
Step 1: qlib-backtester [skill: qlib-backtester] [depends_on: Step 0]
Invoke $qlib-backtester with normalized_market_dataset and the
multi-factor strategy definition to produce per-factor performance.
Expected: backtest_report
Step 2: document-converter [skill: document-converter] [depends_on: Step 1]
Invoke $document-converter with backtest_report to render the
per-factor breakdown as a formatted attribution document.
Expected: attribution_document
Output
Return backtest_report and attribution_document. Does not place trades.
Execution
- Run first: Step 0 —
$quant-data-ingest. - After level 0: Step 1 —
$qlib-backtester. - After level 1: Step 2 —
$document-converter.
Execution: If graph-os is reachable, offload the whole DAG via graph_orchestrate action=execute_workflow (or the kg-delegate skill) for true parallel/swarm execution. Otherwise execute the steps natively in dependency order: run steps with no unmet depends_on in parallel, then their dependents.