Research provenance now rides with the debate state, cache metadata, live payloads, and trace dumps so degraded research no longer masquerades as a normal sample. Bull/Bear/Manager nodes also return explicit guarded fallbacks on timeout or exception, which gives the graph a real node budget boundary without rewriting the bull/bear output shape or removing debate.\n\nConstraint: Must preserve bull/bear debate structure and output shape while adding provenance and node guards\nRejected: Skip bull/bear debate in compact mode | would trade away analysis quality before A/B evidence exists\nConfidence: high\nScope-risk: moderate\nReversibility: clean\nDirective: Treat research_status and data_quality as rollout gates; do not collapse degraded research back into normal success samples\nTested: python -m pytest tradingagents/tests/test_research_guard.py orchestrator/tests/test_llm_runner.py orchestrator/tests/test_live_mode.py web_dashboard/backend/tests/test_executors.py web_dashboard/backend/tests/test_services_migration.py web_dashboard/backend/tests/test_api_smoke.py -q; python -m compileall tradingagents/graph/setup.py tradingagents/agents/utils/agent_states.py tradingagents/graph/propagation.py orchestrator/llm_runner.py orchestrator/live_mode.py orchestrator/profile_stage_chain.py; python orchestrator/profile_stage_chain.py --ticker 600519.SS --date 2026-04-10 --provider anthropic --model MiniMax-M2.7-highspeed --base-url https://api.minimaxi.com/anthropic --selected-analysts market --analysis-prompt-style compact --timeout 45 --max-retries 0 --overall-timeout 120 --dump-raw-on-failure\nNot-tested: Full successful live-provider completion through Portfolio Manager after the post-research connection failure
The rollout-ready branch still conflated dashboard auth with provider credentials, discarded diagnostics when both signal lanes degraded, and treated RESULT_META as optional even though downstream contracts now depend on it. This change separates provider runtime settings from request auth, preserves source diagnostics/data quality in full-failure contracts, requires RESULT_META in the subprocess protocol, and moves A-share holidays into an updateable calendar data source.
Constraint: No external market-calendar dependency is available in env312 and dependency policy forbids adding one casually
Rejected: Keep reading provider keys from request headers | couples dashboard auth to execution and breaks non-anthropic providers
Rejected: Leave both-signals-unavailable as a bare ValueError | loses diagnostics before live/backend contracts can serialize them
Rejected: Keep A-share holidays embedded in Python constants | requires code edits every year and preserves the stopgap design
Confidence: high
Scope-risk: moderate
Reversibility: clean
Directive: Keep subprocess protocol fields explicit and fail closed when RESULT_META is missing; do not route provider credentials through dashboard auth again
Tested: python -m pytest web_dashboard/backend/tests/test_executors.py web_dashboard/backend/tests/test_services_migration.py web_dashboard/backend/tests/test_api_smoke.py orchestrator/tests/test_market_calendar.py orchestrator/tests/test_live_mode.py orchestrator/tests/test_application_service.py orchestrator/tests/test_quant_runner.py orchestrator/tests/test_llm_runner.py -q
Tested: python -m compileall orchestrator web_dashboard/backend
Not-tested: real provider-backed execution across openai/google providers
Not-tested: browser/manual verification beyond existing frontend contract consumers
Phase 3 adds concrete data-quality states to the contract surface so weekend runs, stale market data, partial payloads, and provider/config mismatches stop collapsing into generic success or failure. The backend now carries those diagnostics from quant/llm runners through the legacy executor contract, while the frontend reads decision/confidence fields from result or compat instead of assuming legacy top-level payloads.
Constraint: existing recommendation/task files and current dashboard routes must remain readable during migration
Rejected: infer data quality only in the service layer | loses source-specific evidence and violates the executor/orchestrator boundary
Rejected: leave frontend on top-level decision fields | breaks as soon as contract-first payloads become the default
Confidence: high
Scope-risk: moderate
Reversibility: clean
Directive: keep new data-quality states explicit in contract metadata and route all UI reads through result/compat helpers
Tested: python -m pytest orchestrator/tests/test_quant_runner.py orchestrator/tests/test_llm_runner.py orchestrator/tests/test_signals.py orchestrator/tests/test_application_service.py orchestrator/tests/test_trading_graph_config.py web_dashboard/backend/tests/test_executors.py web_dashboard/backend/tests/test_services_migration.py web_dashboard/backend/tests/test_api_smoke.py web_dashboard/backend/tests/test_main_api.py web_dashboard/backend/tests/test_portfolio_api.py -q
Tested: python -m compileall orchestrator tradingagents web_dashboard/backend
Tested: npm run build (web_dashboard/frontend)
Not-tested: real exchange holiday calendars beyond weekend detection
Not-tested: real provider-backed end-to-end runs for provider_mismatch and stale-data scenarios
This change set introduces a versioned result contract, shared config schema/loading, provider/data adapter seams, and a no-strategy application-service skeleton so the current research graph, orchestrator layer, and dashboard backend stop drifting further apart. It also keeps the earlier MiniMax compatibility and compact-prompt work aligned with the new contract shape and extends regression coverage so degradation, fallback, and service migration remain testable during the next phases.
Constraint: Must preserve existing FastAPI entrypoints and fallback behavior while introducing an application-service seam
Constraint: Must not turn application service into a new strategy or learning layer
Rejected: Full backend rewrite to service-only execution now | too risky before contract and fallback paths stabilize
Rejected: Leave provider/data/config logic distributed across scripts and endpoints | continues boundary drift and weakens verification
Confidence: high
Scope-risk: broad
Directive: Keep future application-service changes orchestration-only; move any scoring, signal fusion, or learning logic to orchestrator or tradingagents instead
Tested: python -m compileall orchestrator tradingagents web_dashboard/backend
Tested: python -m pytest orchestrator/tests/test_signals.py orchestrator/tests/test_llm_runner.py orchestrator/tests/test_quant_runner.py orchestrator/tests/test_contract_v1alpha1.py orchestrator/tests/test_application_service.py orchestrator/tests/test_provider_adapter.py web_dashboard/backend/tests/test_main_api.py web_dashboard/backend/tests/test_portfolio_api.py web_dashboard/backend/tests/test_api_smoke.py web_dashboard/backend/tests/test_services_migration.py -q
Not-tested: live MiniMax/provider execution against external services
Not-tested: full dashboard/manual websocket flow against a running frontend
Not-tested: omx team runtime end-to-end in the primary workspace
陈少杰