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>
Replaces the O(N) database operations in the `TradeExecutor`'s
`execute_decisions` SELL loop with a single `batch_remove_holdings`
call to the repository. The new repository method calculates updates
in memory, resolves duplicate operations on the same ticker, and issues
the updates via newly implemented `psycopg2.extras.execute_batch`
routines on the `SupabaseClient`.
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
Optimize the _percentile calculation to use heapq.nsmallest or heapq.nlargest when requesting small extreme percentiles (like 5% VaR) from large lists, falling back to sorted() only when necessary. This avoids fully sorting the entire array.
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
- New tradingagents/api_usage.py: Pre-run estimation of API calls per vendor
for analyze, scan, and pipeline commands. Includes Alpha Vantage tier
assessment (free: 25/day vs premium: 75/min).
- New CLI command: `estimate-api [analyze|scan|pipeline|all]`
- Enhanced observability: RunLogger.summary() now includes vendor_methods
breakdown (vendor → method → call count)
- Enhanced CLI output: All 3 command summaries (analyze, scan, pipeline)
now show per-vendor breakdown and Alpha Vantage assessment after runs
- 32 new tests in tests/unit/test_api_usage.py
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
Agent-Logs-Url: https://github.com/aguzererler/TradingAgents/sessions/bb80e772-3e03-420e-bb0e-76cfdde14a04
- Create docs/FINANCIAL_TOOLS_ANALYSIS.md with comprehensive 4-point analysis:
1. Implementation accuracy review for all indicators and metrics
2. Library assessment (stockstats vs TA-Lib vs pandas-ta)
3. Alpha Vantage debate (local calc vs API-fetched)
4. Data flow & API mapping for every financial tool
- Fix off-by-one in ttm_analysis.py: YoY revenue used quarterly[-4]
(3 quarters back) instead of quarterly[-5] (4 quarters = 1 year back)
- Add test_revenue_yoy_is_four_quarters_back test to validate the fix
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
Agent-Logs-Url: https://github.com/aguzererler/TradingAgents/sessions/b594017b-ed84-4786-9b81-200a78eb5d76
- Added edge case test for `_find_col` in `tests/unit/test_ttm_analysis.py` (from PR #56).
- Enhanced `_clean_dataframe` in `tradingagents/dataflows/stockstats_utils.py` to parse dates, drop invalid rows, fill price gaps, and lowercase columns (combining PRs #58 and #60).
- Expanded the test suite in `tests/unit/test_stockstats_utils.py` to cover the new `_clean_dataframe` functionality.
Co-authored-by: aguzererler <6199053+aguzererler@users.noreply.github.com>
ahmet guzererler
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