Max Drawdown Recovery

Max Drawdown Recovery Workflow

Compose the named atomic skills without adding execution logic here.

Inputs

Provide the strategy definition, asset universe, date range, and the candidate recovery hypothesis to vet.

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 strategy definition to surface drawdown episodes.

Expected: backtest_report

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

Invoke $trading-debate with backtest_report and the candidate recovery hypothesis to run the TradingAgents swarm debate.

Expected: recovery_hypothesis_verdict

Output

Return backtest_report and recovery_hypothesis_verdict. Does not place trades or guarantee recovery.

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.