The earlier Phase 1-4 recovery left one unique worker-1 slice unrecovered: provenance extraction logic was still duplicated in the runner and the full-state log path still dropped the structured research fields. This change centralizes provenance extraction in agent state helpers, reuses it from the LLM runner, and writes the same structured fields into TradingAgents full-state logs with focused regression tests.\n\nConstraint: Preserve the existing debate-string output shape while making provenance reuse consistent across runner and state-log surfaces\nRejected: Cherry-pick worker-1 auto-checkpoint wholesale | it mixed duplicate A/B files and uv.lock churn with the useful provenance helper changes\nConfidence: high\nScope-risk: narrow\nDirective: Keep research provenance extraction centralized; new consumers should call the helper instead of re-listing field names by hand\nTested: python -m pytest -q tradingagents/tests/test_research_guard.py orchestrator/tests/test_trading_graph_config.py orchestrator/tests/test_llm_runner.py orchestrator/tests/test_profile_stage_chain.py orchestrator/tests/test_profile_ab.py orchestrator/tests/test_contract_v1alpha1.py orchestrator/tests/test_live_mode.py\nTested: python -m compileall tradingagents/agents/utils/agent_states.py tradingagents/graph/trading_graph.py orchestrator/llm_runner.py orchestrator/tests/test_trading_graph_config.py tradingagents/tests/test_research_guard.py\nNot-tested: Live-provider end-to-end analysis run that emits a new full_states_log file
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 legacy path was already narrowed to market-only compact execution, but the research stage remained the slowest leg and the profiler lacked persistent raw event artifacts for comparison. This change further compresses the compact prompts for Bull Researcher, Bear Researcher, and Research Manager, adds durable raw event dumps to the graph profiler, and keeps profiling evidence out of the runtime contract itself.
Constraint: No new dependencies and no runtime-contract pollution for profiling-only data
Rejected: Add synthetic timing fields back into the subprocess protocol | those timings are not real graph-stage boundaries and would mislead diagnosis
Rejected: Skip raw event dump persistence and rely on console output | makes multi-run comparison and regression tracking fragile
Confidence: high
Scope-risk: narrow
Reversibility: clean
Directive: Keep profiling as an external diagnostic surface; if stage timing ever enters contracts again, it must come from real graph boundaries
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 -q
Tested: python -m compileall tradingagents/agents/researchers/bull_researcher.py tradingagents/agents/researchers/bear_researcher.py tradingagents/agents/managers/research_manager.py orchestrator/profile_stage_chain.py
Tested: real provider profiling via orchestrator/profile_stage_chain.py with market-only compact settings; dump persisted to orchestrator/profile_runs/600519.SS_2026-04-10_20260413T184742Z.json
Not-tested: browser/manual consumption of the persisted profiling dump
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
LLMs (especially smaller models) sometimes pass multiple indicator
names as a single comma-separated string instead of making separate
tool calls. Split and process each individually at the tool boundary.
- Replace FinnHub with Alpha Vantage API in README documentation
- Implement comprehensive Alpha Vantage modules:
- Stock data (daily OHLCV with date filtering)
- Technical indicators (SMA, EMA, MACD, RSI, Bollinger Bands, ATR)
- Fundamental data (overview, balance sheet, cashflow, income statement)
- News and sentiment data with insider transactions
- Update news analyst tools to use ticker-based news search
- Integrate Alpha Vantage vendor methods into interface routing
- Maintain backward compatibility with existing vendor system
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
- Added support for running CLI and Ollama server via Docker
- Introduced tests for local embeddings model and standalone Docker setup
- Enabled conditional Ollama server launch via LLM_PROVIDER
陈少杰
Yijia-Xiao
CadeYu
Ljx-007
luohy15
mirza-samad-ahmed-baig
Max Wong
Yijia Xiao
Geeta Chauhan
Edward Sun
Atharv Sabde
maxer137