Knuckles-Team Qlib Factor Backtest Workflow
Compose the named atomic skills without adding factor-discovery logic here.
Inputs
Provide the factor definition(s), 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
supplied factor definition(s).
Expected: backtest_report
Output
Return backtest_report. Does not discover new factors or place trades.
Execution
- Run first: Step 0 —
$quant-data-ingest. - After level 0: Step 1 —
$qlib-backtester.
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.