In dataflow API wrappers, generic `except Exception` handlers were swallowing
timeout exceptions (like `requests.exceptions.Timeout` and `ThirdPartyTimeoutError`),
preventing upstream retry logic and proper error handling from functioning correctly.
This patch:
- Adds explicit catching of `requests.exceptions.Timeout` to raise as `ThirdPartyTimeoutError` in `yfinance` wrapper modules (`y_finance.py`, `yfinance_news.py`, `yfinance_scanner.py`).
- Adds explicit catching and re-raising of `ThirdPartyTimeoutError` before generic exception handlers across the dataflows.
- Retains existing exception inheritance logic to avoid backward compatibility breaks.
- Updates `TestEnsureIndexesInInit` to use the explicit `ensure_indexes()` call per lazy loading changes.
Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
* chore: initial commit for feat/fast-reject-short-circuits branch
* test: fix fast-reject unit tests (25/25 passing)
- Fix test_normal_flow_without_abort: expected 'Bull Researcher' not
'Bear Researcher' — when current_response is empty the conditional
logic routes to Bull first (conditional_logic.py line 74)
- Analyst abort-instruction tests: replace closure-attribute hacks
(market_analyst.llm = ...) with patch() on prefetch_tools_parallel
and run_tool_loop so the tests run without network access
- Fix co_consts substring check: system_message is compiled into one
large string constant, so use any(...in str(c)...) instead of direct
tuple membership
- PM tests: create mock_llm before create_portfolio_manager so the
closure captures it and mock_llm.invoke.called assertions work; add
missing company_of_interest key to all state dicts
- Integration tests: merge analyst/PM node outputs back into state
({**state, **result}) instead of replacing it, preserving keys like
fundamentals_report and investment_debate_state; patch network calls;
fix normal-flow routing assertions to != 'Portfolio Manager' since
the exact next node depends on transient debate state
* feat: add macro scanner feedback loop and lessons memory
- Implements `LessonStore` to persist JSON screening lessons
- Adds `selection_reflector.py` to fetch performance and news, and generate LLM lessons
- Adds `memory_loader.py` to filter negative lessons into `FinancialSituationMemory`
- Integrates a rejection gate in `candidate_prioritizer.py` based on past negative lessons
- Adds `reflect` command to CLI
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
* feat: update macro scanner feedback loop for dual-lesson output (Trend DNA)
- Update `selection_reflector.py` to calculate exact terminal returns, `mfe_pct`, `mae_pct`, and `days_to_peak`.
- Update LLM prompt to generate distinct `screening_advice` and `exit_advice`.
- Update `lesson_store` tests to reflect new schema.
- Update `memory_loader.py` to use `screening_advice` for negative selection filtering.
- Update `micro_summary_agent.py` to inject `exit_advice` into PM context for current holdings.
- Update `cli/main.py` default horizons to `30,90` and print dual-advice columns.
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
* fix: resolve bugs in macro scanner feedback loop
- Address Key mismatch (stock_return_pct vs terminal_return_pct)
- Fix missing persistence of mfe_pct and mae_pct
- Use create_report_store() instead of raw ReportStore() in load_scan_candidates
- Clean up unused imports in fetch_news_summary
- Ensure default horizons match code in cli description
- Create isolated `_local_safe_pct` to remove cross-module dependency
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
* fix: address PR 124 feedback on macro scanner memory feedback loop
- Use `l.get('screening_advice')` gracefully in `memory_loader` to prevent KeyErrors.
- Properly instantiate `selection_memory` inside the graph in `portfolio_setup.py` and pass it to the prioriziation rejection gate.
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
---------
Co-authored-by: google-labs-jules[bot] <161369871+google-labs-jules[bot]@users.noreply.github.com>
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
Replace run_id with flow_id as the primary grouping concept (one flow =
one user analysis intent spanning scan + pipeline + portfolio). Reports
are now written as {timestamp}_{name}.json so load methods always return
the latest version by lexicographic sort, eliminating the latest.json
pointer pattern for new flows.
Key changes:
- report_paths.py: add generate_flow_id(), ts_now() (ms precision),
flow_id kwarg on all path helpers; keep run_id / pointer helpers for
backward compatibility
- ReportStore: dual-mode save/load — flow_id uses timestamped layout,
run_id uses legacy runs/{id}/ layout with latest.json
- MongoReportStore: add flow_id field and index; run_id stays for compat
- DualReportStore: expose flow_id property
- store_factory: accept flow_id as primary param, run_id as alias
- runs.py / langgraph_engine.py: generate and thread flow_id through all
trigger endpoints and run methods
- Tests: add flow_id coverage for all layers; 905 tests pass
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: introduce flow_id with timestamp-based report versioning
Replace run_id with flow_id as the primary grouping concept (one flow =
one user analysis intent spanning scan + pipeline + portfolio). Reports
are now written as {timestamp}_{name}.json so load methods always return
the latest version by lexicographic sort, eliminating the latest.json
pointer pattern for new flows.
Key changes:
- report_paths.py: add generate_flow_id(), ts_now() (ms precision),
flow_id kwarg on all path helpers; keep run_id / pointer helpers for
backward compatibility
- ReportStore: dual-mode save/load — flow_id uses timestamped layout,
run_id uses legacy runs/{id}/ layout with latest.json
- MongoReportStore: add flow_id field and index; run_id stays for compat
- DualReportStore: expose flow_id property
- store_factory: accept flow_id as primary param, run_id as alias
- runs.py / langgraph_engine.py: generate and thread flow_id through all
trigger endpoints and run methods
- Tests: add flow_id coverage for all layers; 905 tests pass
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: PM brain upgrade — macro/micro summary agents, memory split, forensic dashboard
Replaces the PM's raw-JSON context (~6,800 tokens on deep_think) with a
MAP-REDUCE compression layer using two parallel mid_think summary agents,
achieving ~70% cost reduction at the PM tier.
Architecture:
- MacroMemory: new regime-level memory class (MongoDB/JSON, separate from
per-ticker reflexion memory) with record_macro_state/build_macro_context
- ReflexionMemory: extended with collection_name param to isolate
micro_reflexion from the pipeline reflexion collection (with distinct
local JSON fallback path to prevent file collision)
- Macro_Summary_Agent (mid_think): compresses scan_summary into a 1-page
regime brief with memory injection; sentinel guard prevents LLM call on
empty/error scan data ("NO DATA AVAILABLE - ABORT MACRO")
- Micro_Summary_Agent (mid_think): compresses holding_reviews + candidates
into a markdown table brief with per-ticker memory injection
- Portfolio graph: parallel fan-out (prioritize_candidates → macro_summary
‖ micro_summary → make_pm_decision) using _last_value reducers for safe
concurrent state writes (ADR-005 pattern)
- PM refactor: Pydantic PMDecisionSchema enforces Forensic Execution
Dashboard output (macro_regime, forensic_report, per-trade
macro_alignment/memory_note/position_sizing_logic); with_structured_output
as primary path, extract_json fallback for non-conforming providers
- PM sentinel handling: "NO DATA AVAILABLE" in macro_brief substituted
with actionable conservative guidance before LLM sees it
62 new unit tests across 4 test files covering all new components.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix: address code review — relaxed error guard, ticker_analyses, PM memory wiring
1. macro_summary_agent: relaxed error guard to only abort when scan_summary's
sole key is "error" (partial failures with real data are now processed)
2. micro_summary_agent: now reads ticker_analyses from state and enriches
the per-ticker table with trading graph analysis data
3. portfolio_graph: wires macro_memory and micro_memory to PM factory call
4. test_empty_state: updated test for new partial-failure behavior
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: introduce flow_id with timestamp-based report versioning
Replace run_id with flow_id as the primary grouping concept (one flow =
one user analysis intent spanning scan + pipeline + portfolio). Reports
are now written as {timestamp}_{name}.json so load methods always return
the latest version by lexicographic sort, eliminating the latest.json
pointer pattern for new flows.
Key changes:
- report_paths.py: add generate_flow_id(), ts_now() (ms precision),
flow_id kwarg on all path helpers; keep run_id / pointer helpers for
backward compatibility
- ReportStore: dual-mode save/load — flow_id uses timestamped layout,
run_id uses legacy runs/{id}/ layout with latest.json
- MongoReportStore: add flow_id field and index; run_id stays for compat
- DualReportStore: expose flow_id property
- store_factory: accept flow_id as primary param, run_id as alias
- runs.py / langgraph_engine.py: generate and thread flow_id through all
trigger endpoints and run methods
- Tests: add flow_id coverage for all layers; 905 tests pass
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: finalise storage layout, run history loading & phase-level re-run
Storage / persistence
- flow_id (8-char hex) replaces run_id as the disk storage key; all
sub-phases of one auto run share the same flow_id directory
- Startup hydration: hydrate_runs_from_disk() rebuilds in-memory run
store from run_meta.json on server restart (events lazy-loaded)
WebSocket / run history fixes
- Lazy-load events from run_events.jsonl on first WS connect; fixes
blank terminal when clicking a historical run after restart
- Orphaned "running" runs (server restarted mid-run) auto-detected and
marked "failed" with partial events replayed correctly
Phase re-run fixes
- Analysts checkpoint: use any() instead of all() — Social Analyst is
optional; all() silently blocked checkpoint saves in typical runs
- Checkpoint lookup: pass original flow_id through rerun_params so
_date_root() resolves to the correct flow_id subdirectory
- Selective event filtering on re-run: preserves scan nodes and other
tickers; only removes stale events for the re-run phase+ticker
- Frontend graph now shows full auto-flow context during phase re-runs
Documentation
- ADR 018: canonical reference for storage layout, event schema,
WebSocket streaming flows, checkpoint structure, MongoDB vs local
- ADR 013 updated: reflects background-task + lazy-loading evolution
- ADR 015 marked superseded by ADR 018
- CLAUDE.md: AgentOS storage section + 4 new critical patterns
- CURRENT_STATE.md updated
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: introduce flow_id with timestamp-based report versioning
Replace run_id with flow_id as the primary grouping concept (one flow =
one user analysis intent spanning scan + pipeline + portfolio). Reports
are now written as {timestamp}_{name}.json so load methods always return
the latest version by lexicographic sort, eliminating the latest.json
pointer pattern for new flows.
Key changes:
- report_paths.py: add generate_flow_id(), ts_now() (ms precision),
flow_id kwarg on all path helpers; keep run_id / pointer helpers for
backward compatibility
- ReportStore: dual-mode save/load — flow_id uses timestamped layout,
run_id uses legacy runs/{id}/ layout with latest.json
- MongoReportStore: add flow_id field and index; run_id stays for compat
- DualReportStore: expose flow_id property
- store_factory: accept flow_id as primary param, run_id as alias
- runs.py / langgraph_engine.py: generate and thread flow_id through all
trigger endpoints and run methods
- Tests: add flow_id coverage for all layers; 905 tests pass
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* feat: load flow_id in FE to resume runs and fix max_tickers cap on continuation
- Add flow_id to RunParams interface and initial state
- loadRun() now restores flow_id + max_auto_tickers from history so the next
run continues in the same flow directory (Phase 1 scan skipped, already-done
tickers skipped via skip-if-exists logic)
- startRun() spreads flow_id into the request body when set, letting the backend
reuse the existing flow directory instead of generating a fresh flow_id
- After each run, params.flow_id is updated from the response so subsequent
runs automatically continue from the same flow
- max_auto_tickers restored from run.params.max_tickers ensures the ticker cap
matches the original run; scan_tickers[:max_t] on the backend then limits
the Phase 2 queue to the user's setting even when the existing scan has more
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(mongo): fast-fail timeout + lazy ensure_indexes to avoid 30s block on fallback
MongoClient previously used pymongo's 30-second serverSelectionTimeoutMS default,
causing store_factory to hang for 30s before falling back to the filesystem when
Atlas is unreachable. Also, ensure_indexes() was called eagerly in __init__,
making every store construction attempt block on a live network call.
- Set serverSelectionTimeoutMS=5_000 so fallback is triggered in ≤5s
- Move ensure_indexes() call out of __init__ — indexes are now created lazily
on the first _save() call via a guarded self._indexes_ensured flag
- ensure_indexes() is still idempotent and safe to call explicitly in tests
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(store): wrap all DualReportStore mongo calls in _try_mongo() for graceful degradation
Any MongoDB exception (SSL error, ServerSelectionTimeout, auth failure) was
propagating uncaught through DualReportStore and crashing the run. Reads
would return an error instead of falling back to local, and writes would
abort mid-run without saving anything.
Introduce a single _try_mongo(fn, default) helper that:
- Executes the Mongo callable
- Catches *any* exception, logs it as WARNING with type + message
- Returns the default value so the caller continues with local-only data
Pattern per method:
writes → try mongo (fire-and-forget); always return local result
reads → try mongo first; fall back to local on None or exception
lists → try mongo; fall back to local on empty/None
Runs now complete successfully even when Atlas is unreachable or returns SSL
errors. MongoDB sync resumes automatically once connectivity is restored.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix(observability): non-blocking MongoDB inserts + 5s timeout in RunLogger
Every LLM and tool callback called _append() which synchronously called
insert_one() against MongoDB. When Atlas was unreachable this blocked the
entire LangGraph run for pymongo's 30-second default timeout per event,
effectively serializing all agent work behind MongoDB retries.
Two fixes:
1. serverSelectionTimeoutMS=5_000 on the RunLogger's MongoClient — consistent
with the same fix applied to MongoReportStore.
2. MongoDB inserts are now fire-and-forget via daemon threads — _append() spawns
a Thread(target=_insert, daemon=True) and returns immediately. LLM callbacks
and tool events are never delayed by MongoDB connectivity issues.
Failures are still reported via WARNING log from the background thread.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* revert(observability): restore synchronous MongoDB inserts in RunLogger
Root cause was an IP whitelist issue on Atlas causing SSL failures, not
insert volume. The background-thread approach added unnecessary complexity.
The 5s serverSelectionTimeoutMS is retained as a defensive safeguard.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
---------
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
Feature 1 - Configurable max_auto_tickers:
- Add max_auto_tickers config key (default 10) with TRADINGAGENTS_MAX_AUTO_TICKERS env override
- Macro synthesis agent accepts max_scan_tickers param, injects exact count into LLM prompt
- ScannerGraph passes config value to create_macro_synthesis()
- Backend engine applies safety cap on scan candidates (portfolio holdings always included)
- Frontend adds Max Tickers number input in params panel, sends max_tickers in auto run body
Feature 2 - Run persistence + phase-level node re-run:
- 2A: ReportStore + MongoReportStore gain save/load_run_meta, save/load_run_events,
list_run_metas methods; runs.py persists to disk in finally block; startup hydration
restores historical runs; lazy event loading on GET /{run_id}
- 2B: Analysts + trader checkpoint save/load methods in both stores; engine saves
checkpoints after pipeline completion alongside complete_report.json
- 2C: GraphSetup gains build_debate_subgraph() and build_risk_subgraph() for partial
re-runs; TradingAgentsGraph exposes debate_graph/risk_graph as lazy properties;
NODE_TO_PHASE mapping + run_pipeline_from_phase() engine method;
POST /api/run/rerun-node endpoint with _append_and_store helper
- 2D: Frontend history popover (loads GET /api/run/, sorts by created_at, click to load);
triggerNodeRerun() calls rerun-node endpoint; handleNodeRerun uses phase-level
re-run when active run is loaded
All 890 existing tests pass (10 skipped).
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
* Initial plan
* feat: include portfolio holdings in auto mode pipeline analysis
In run_auto (both AgentOS and CLI), Phase 2 now loads current portfolio
holdings and merges their tickers with scan candidates before running
the per-ticker pipeline. This ensures the portfolio manager has fresh
analysis for both new opportunities and existing positions.
Key changes:
- macro_bridge.py: add candidates_from_holdings() factory
- langgraph_engine.py run_auto: merge holding tickers with scan tickers
- cli/main.py auto: load holdings, create StockCandidates, pass to run_pipeline
- cli/main.py run_pipeline: accept optional holdings_candidates parameter
- 9 new unit tests covering holdings inclusion, dedup, and graceful fallback
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
Agent-Logs-Url: https://github.com/aguzererler/TradingAgents/sessions/53065a07-d9f8-47be-9956-0eb4ee8c87da
* fix: normalize ticker case in dedup and clarify count display
Address code review feedback:
- Use .upper() for case-insensitive ticker comparison in run_pipeline
- Display accurate filtered scan count instead of raw candidate count
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
Agent-Logs-Url: https://github.com/aguzererler/TradingAgents/sessions/53065a07-d9f8-47be-9956-0eb4ee8c87da
---------
Co-authored-by: copilot-swe-agent[bot] <198982749+Copilot@users.noreply.github.com>
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
## Summary
- Adds `smart_money_scanner` as a new Phase 1b node that runs sequentially after `sector_scanner`, surfacing institutional footprints via Finviz screeners
- Introduces the **Golden Overlap** strategy in `macro_synthesis`: stocks confirmed by both top-down macro themes and bottom-up Finviz signals are labelled high-conviction
- Fixes model-name badge overflow in AgentGraph (long model IDs like OpenRouter paths were visually spilling into adjacent nodes)
- Completes all documentation: ADR-014, dataflow, architecture, components, glossary, current-state
## Key Decisions (see ADR-014)
- 3 zero-parameter tools (`get_insider_buying_stocks`, `get_unusual_volume_stocks`, `get_breakout_accumulation_stocks`) instead of 1 parameterised tool — prevents LLM hallucinations on string args
- Sequential after `sector_scanner` (not parallel fan-out) — gives access to `sector_performance_report` context and avoids `MAX_TOOL_ROUNDS=5` truncation in market_movers_scanner
- Graceful fallback: `_run_finviz_screen()` catches all exceptions and returns an error string — pipeline never hard-fails on web-scraper failure
- `breakout_accumulation` (52-wk high + 2x vol = O'Neil CAN SLIM institutional signal) replaces `oversold_bounces` (RSI<30 = retail contrarian, not smart money)
## Test Plan
- [x] 6 new mocked tests in `tests/unit/test_scanner_mocked.py` (happy path, empty DF, exception, sort order)
- [x] Fixed `tests/unit/test_scanner_graph.py` — added `smart_money_scanner` mock to compilation test
- [x] 2 pre-existing test failures excluded (verified baseline before changes)
- [x] AgentGraph badge: visually verified truncation with long OpenRouter model identifiers
🤖 Generated with [Claude Code](https://claude.com/claude-code)
- ReportStore.clear_portfolio_stage(date, portfolio_id): deletes pm_decision
(.json + .md) and execution_result files for a given date/portfolio
- DELETE /api/run/portfolio-stage endpoint: calls clear_portfolio_stage
and returns list of deleted files
- Dashboard: 'Reset Decision' button calls the endpoint, then user can
run Auto to re-run Phase 3 from scratch while skipping Phase 1 & 2
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
TRADINGAGENTS_REPORTS_DIR now controls where all reports land (scans,
analysis, portfolio artifacts). Both report_paths.REPORTS_ROOT and
ReportStore.data_dir read from the same env var so the entire
reports/daily/{date}/... tree is rooted at one configurable location.
PORTFOLIO_DATA_DIR still works as a portfolio-specific override.
Falls back to "reports" (relative to CWD) when neither is set.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
- Extend run_portfolio to save Holding Reviews, Risk Metrics, PM Decision,
and Execution Result from final state
- Add run_trade_execution method for resuming trade execution from a saved
PM decision without re-running the full portfolio graph
- Update run_auto skip logic to check for execution result (not just decision)
and resume from saved decision when available
- Gitignore uv.lock and untrack it from version control
Co-Authored-By: Oz <oz-agent@warp.dev>
Added comprehensive unit tests for `fundamentals_analyst`, `market_analyst`,
`social_media_analyst`, and `news_analyst` to verify that they correctly
handle recursive tool calling via `run_tool_loop`. A MockLLM was created
to simulate a two-turn conversation (tool call request followed by a final
report generation) to ensure the `.invoke()` bug does not regress. Added
missing `build_instrument_context` imports to those agents to prevent
NameErrors.
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
This commit updates the `fundamentals_analyst`, `market_analyst`,
`social_media_analyst`, and `news_analyst` files to use `run_tool_loop`
instead of `.invoke()`. Using `.invoke()` resulted in the LLM execution
stopping immediately upon a tool call request without executing the tool,
returning an empty report or raw JSON. The `run_tool_loop` function
ensures tools are executed recursively and the final text content is
returned.
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
Add effort parameter (high/medium/low) for Claude 4.5+ and 4.6 models,
consistent with OpenAI reasoning_effort and Google thinking_level.
Also add content normalization for Anthropic responses.
- Point requirements.txt to pyproject.toml as single source of truth
- Resolve welcome.txt path relative to module for CLI portability
- Include cli/static files in package build
- Extract shared normalize_content for OpenAI Responses API and
Gemini 3 list-format responses into base_client.py
- Update README install and CLI usage instructions
Enable use_responses_api for native OpenAI provider, which supports
reasoning_effort with function tools across all model families.
Removes the UnifiedChatOpenAI subclass workaround.
Closes#403
- y_finance.py: replace print() with logger.warning() in bulk-stats fallback
- macro_bridge.py: add elapsed_seconds field to TickerResult, populate in
run_ticker_analysis (success + error paths)
- cli/main.py: move inline 'import time as _time' and rich.progress imports
to module level; use result.elapsed_seconds for accurate per-ticker timing
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
Agent-Logs-Url: https://github.com/aguzererler/TradingAgents/sessions/68fcf34c-8d55-4436-b743-f79fff68713f
Before this change, the pipeline showed a generic 'Analyzing...' spinner
for the entire multi-ticker run with no way to know which ticker was
processing or whether anything was actually working.
Changes:
- macro_bridge.py:
- run_ticker_analysis: logs '▶ Starting', '✓ complete in Xs', '✗ FAILED'
with elapsed time per ticker using logger.info/logger.error
- run_all_tickers: replaced asyncio.gather (swallows all progress) with
asyncio.as_completed + optional on_ticker_done(result, done, total)
callback; uses asyncio.Semaphore for max_concurrent control
- Added time and Callable imports
- cli/main.py run_pipeline:
- Replaced Live(Spinner) with Rich Progress bar (spinner + bar + counter
+ elapsed time)
- Prints '▷ Queued: TICKER' before analysis starts for each ticker
- on_ticker_done callback prints '✓ TICKER (N/M, Xs elapsed) → decision'
or '✗ TICKER failed ...' immediately as each ticker finishes
- Prints total elapsed time when all tickers complete
## Problem (Incident Post-mortem)
The pipeline was emitting hundreds of errors:
'Invalid number of return arguments after parsing column name: Date'
Root cause: after _clean_dataframe() lowercases all columns, stockstats.wrap()
promotes 'date' to the DataFrame index. Subsequent df['Date'] access caused
stockstats to try parsing 'Date' as a technical indicator name.
## Fixes
### 1. Fix df['Date'] stockstats bug (already shipped in prior commit)
- stockstats_utils.py + y_finance.py: use df.index.strftime() instead of
df['Date'] after wrap()
### 2. Extract _load_or_fetch_ohlcv() — single OHLCV authority
- Eliminates duplicated 30-line download+cache boilerplate in two places
- Cache filename is always derived from today's date — hardcoded stale date
'2015-01-01-2025-03-25' in local mode is gone
- Corruption/truncation detection: files <50 rows or unparseable are deleted
and re-fetched rather than silently returning bad data
- Drops on_bad_lines='skip' — malformed CSVs now raise instead of silently
dropping rows that would distort indicator calculations
### 3. YFinanceError typed exception
- Defined in stockstats_utils.py; raised instead of print()+return ''
- get_stockstats_indicator now raises YFinanceError on failure so errors
surface to callers rather than delivering empty strings to LLM agents
- interface.py route_to_vendor now catches YFinanceError alongside
AlphaVantageError and FinnhubError — failures appear in observability
telemetry and can trigger vendor fallback
### 4. Explicit date column discovery in alpha_vantage_common
- _filter_csv_by_date_range: replaced df.columns[0] positional assumption
with explicit search for 'time'/'timestamp'/'date' column
- ValueError re-raised (not swallowed) so bad API response shape is visible
### 5. Structured logging
- Replaced all print() calls in changed files with logging.getLogger()
- Added logging import + logger to alpha_vantage_common
## Tests
- tests/unit/test_incident_fixes.py: 12 new unit tests covering all fixes
(dynamic cache filename, corruption re-fetch, YFinanceError propagation,
explicit column lookup, empty download raises)
- tests/integration/test_stockstats_live.py: 11 live tests against real
yfinance API (all major indicators, weekend N/A, regression guard)
- All 70 tests pass (59 unit + 11 live integration)
- Removed unused `import time` from `tradingagents/agents/analysts/news_analyst.py`
- Verified file syntax with `py_compile`
- Confirmed that the import was successfully removed
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
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