Multi Strategy Allocation

Multi-Strategy Allocation Workflow

Compose the named atomic skills without adding execution logic here.

Inputs

Provide the candidate strategy definitions, 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 each candidate strategy definition.

Expected: strategy_backtest_reports

Step 2: trading-debate [skill: trading-debate] [depends_on: Step 1]

Invoke $trading-debate with strategy_backtest_reports to vet the relative evidence via the TradingAgents swarm debate.

Expected: allocation_evidence_verdict

Output

Return strategy_backtest_reports and allocation_evidence_verdict. Does not execute the allocation or place trades.

Execution

• Run first: Step 0 — $quant-data-ingest.
• After level 0: Step 1 — $qlib-backtester.
• After level 1: Step 2 — $trading-debate.

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.