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https://github.com/TauricResearch/TradingAgents.git
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chore: release v0.2.4 — structured agents, checkpoint, memory log, providers
This release bundles substantial work since v0.2.3: - Structured-output Research Manager, Trader, and Portfolio Manager (canonical with_structured_output pattern, single LLM call per agent, rendered markdown preserves the existing report shape). - LangGraph checkpoint resume for crash recovery (--checkpoint flag). - Persistent decision log replacing the per-agent BM25 memory, with deferred reflection driven by yfinance returns + alpha vs SPY. - DeepSeek, Qwen, GLM, and Azure OpenAI provider support; dynamic OpenRouter model selection. - Docker support; cache and logs moved to ~/.tradingagents/ to fix Docker permission issues. - Windows UTF-8 encoding fix on every file I/O site. - 5-tier rating consistency (Buy / Overweight / Hold / Underweight / Sell) across Research Manager, Portfolio Manager, signal processor, memory log. Plus the small quality items in this commit: 1. Suppress noisy Pydantic serializer warnings from OpenAI Responses-API parse path by defaulting structured-output to method="function_calling" (root-cause fix, not a warnings filter — same typed result, no warnings). 2. Ship scripts/smoke_structured_output.py so contributors can verify their provider's structured-output path with one command. 3. Add opt-in memory_log_max_entries config — when set, oldest resolved memory log entries are pruned once the cap is exceeded; pending entries (unresolved) are never pruned. 4. backend_url default changed from the OpenAI URL to None so the per-provider client falls back to its native endpoint instead of leaking OpenAI's URL into Gemini / other clients. CHANGELOG.md added with the full v0.2.4 entry. 92 tests pass without API keys.
This commit is contained in:
Yijia-Xiao
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CHANGELOG.md
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266
CHANGELOG.md
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@@ -0,0 +1,266 @@
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# Changelog
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All notable changes to TradingAgents are documented here.
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The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/),
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and this project follows [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
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Breaking changes within the 0.x line are called out explicitly.
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## [0.2.4] — 2026-04-25
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### Added
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- **Structured-output decision agents.** Research Manager, Trader, and Portfolio
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Manager now use `llm.with_structured_output(Schema)` on their primary call
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and return typed Pydantic instances. Each provider's native structured-output
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mode is used (`json_schema` for OpenAI / xAI, `response_schema` for Gemini,
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tool-use for Anthropic, function-calling for OpenAI-compatible providers).
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Render helpers preserve the existing markdown shape so memory log, CLI
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display, and saved reports keep working unchanged. (#434)
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- **LangGraph checkpoint resume** — opt-in via `--checkpoint`. State is saved
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after each node so crashed or interrupted runs resume from the last
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successful step. Per-ticker SQLite databases under
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`~/.tradingagents/cache/checkpoints/`. `--clear-checkpoints` resets them. (#594)
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- **Persistent decision log** replacing the per-agent BM25 memory. Decisions
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are stored automatically at the end of `propagate()`; the next same-ticker
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run resolves prior pending entries with realised return, alpha vs SPY, and
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a one-paragraph reflection. Override path with `TRADINGAGENTS_MEMORY_LOG_PATH`.
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Optional `memory_log_max_entries` config caps resolved entries; pending
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entries are never pruned. (#578, #563, #564, #579)
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- **DeepSeek, Qwen (Alibaba DashScope), GLM (Zhipu), and Azure OpenAI**
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providers, plus dynamic OpenRouter model selection.
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- **Docker support** — multi-stage build with separate dev and runtime images.
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- **`scripts/smoke_structured_output.py`** — diagnostic that exercises the
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three structured-output agents against any provider so contributors can
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verify their setup with one command.
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- **5-tier rating scale** (Buy / Overweight / Hold / Underweight / Sell) used
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consistently by Research Manager, Portfolio Manager, signal processor, and
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the memory log; Trader keeps 3-tier (Buy / Hold / Sell) since transaction
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direction is naturally ternary.
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- **Pytest fixtures** — lazy LLM client imports plus placeholder API keys so
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the test suite runs cleanly without credentials. (#588)
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### Changed
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- **`backend_url` default is now `None`** rather than the OpenAI URL. Each
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provider client falls back to its native default. The previous default
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leaked the OpenAI URL into non-OpenAI clients (e.g. Gemini), producing
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malformed request URLs for Python users who switched providers without
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overriding `backend_url`. The CLI flow is unaffected.
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- All file I/O passes explicit `encoding="utf-8"` so Windows users no longer
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hit `UnicodeEncodeError` with the cp1252 default. (#543, #550, #576)
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- Cache and log directories moved to `~/.tradingagents/` to resolve Docker
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permission issues. (#519)
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- `SignalProcessor` reads the rating from the Portfolio Manager's rendered
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markdown via a deterministic heuristic — no extra LLM call.
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- OpenAI structured-output calls default to `method="function_calling"` to
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avoid noisy `PydanticSerializationUnexpectedValue` warnings emitted by
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langchain-openai's Responses-API parse path. Same typed result, no warnings.
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### Fixed
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- Empty memory no longer triggers fabricated past-lessons in agent prompts;
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the memory-log redesign makes this structurally impossible since only the
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Portfolio Manager consults memory and only when entries exist. (#572)
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- Tool-call logging processes every chunk message, not just the last one, and
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memory score normalization handles empty score arrays. (#534, #531)
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### Removed
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- `FinancialSituationMemory` (the per-agent BM25 system) and the dead
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`reflect_and_remember()` plumbing; subsumed by the persistent decision log.
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- Hardcoded Google endpoint that caused 404 when `langchain-google-genai`
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changed its API path. (#493, #496)
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### Contributors
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Thanks to everyone who shaped this release through code, design, and reports:
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|
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- [@claytonbrown](https://github.com/claytonbrown) — checkpoint resume (#594), test fixtures (#588), design feedback on cost tracking (#582) and structured validation (#583)
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- [@Bcardo](https://github.com/Bcardo) — memory-log redesign (#579), empty-memory hallucination report (#572), encoding fix proposal (#570)
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- [@voidborne-d](https://github.com/voidborne-d) — memory persistence design (#564), portfolio manager state fix (#503)
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- [@mannubaveja007](https://github.com/mannubaveja007) — structured-output feature request (#434)
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- [@kelder66](https://github.com/kelder66) — RAM-only memory issue (#563)
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- [@Gujiassh](https://github.com/Gujiassh) — tool-call logging fix (#534), test stub PR (#533)
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- [@iuyup](https://github.com/iuyup) — memory score normalization fix (#531)
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- [@kaihg](https://github.com/kaihg) — Google base_url fix (#496)
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- [@32ryh98yfe](https://github.com/32ryh98yfe) — Gemini 404 report (#493)
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- [@uppb](https://github.com/uppb) — OpenRouter dynamic model selection (#482)
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- [@guoz14](https://github.com/guoz14) — OpenRouter limited-model report (#337)
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- [@samchenku](https://github.com/samchenku) — indicator name normalization (#490)
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- [@JasonOA888](https://github.com/JasonOA888) — y_finance pandas import fix (#488)
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- [@tiffanychum](https://github.com/tiffanychum) — stale import cleanup (#499)
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- [@zaizou](https://github.com/zaizou) — Docker permission issue (#519)
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- [@Stosman123](https://github.com/Stosman123), [@mauropuga](https://github.com/mauropuga), [@hotwind2015](https://github.com/hotwind2015) — Windows encoding bug reports (#543, #550, #576)
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- [@nnishad](https://github.com/nnishad), [@atharvajoshi01](https://github.com/atharvajoshi01) — encoding fix proposals (#568, #549)
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## [0.2.3] — 2026-03-29
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|
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### Added
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|
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- **Multi-language output** for analyst reports and final decisions, with a
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CLI selector. Internal agent debate stays in English for reasoning quality. (#472)
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- **GPT-5.4 family models** in the default catalog, with deep/quick model split.
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- **Unified model catalog** as a single source of truth for CLI options and
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provider validation.
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|
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### Changed
|
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|
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- `base_url` is forwarded to Google and Anthropic clients so corporate proxies
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work consistently across providers. (#427)
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- Standardised the Google `api_key` parameter to the unified `api_key` form.
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|
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### Fixed
|
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|
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- Backtesting fetchers no longer leak look-ahead data when `curr_date` is in
|
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the middle of a fetched window. (#475)
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- Invalid indicator names from the LLM are caught at the tool boundary instead
|
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of crashing the run. (#429)
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- yfinance news fetchers respect the same exponential-backoff retry as price
|
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fetchers. (#445)
|
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|
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### Contributors
|
||||
|
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- [@ahmedk20](https://github.com/ahmedk20) — multi-language output (#472)
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- [@CadeYu](https://github.com/CadeYu) — model catalog typing (#464)
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- [@javierdejesusda](https://github.com/javierdejesusda) — unified Google API key parameter (#453)
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- [@voidborne-d](https://github.com/voidborne-d) — yfinance news retry (#445)
|
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- [@kostakost2](https://github.com/kostakost2) — look-ahead bias report (#475)
|
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- [@lu-zhengda](https://github.com/lu-zhengda) — proxy/base_url support request (#427)
|
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- [@VamsiKrishna2021](https://github.com/VamsiKrishna2021) — invalid indicator crash report (#429)
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## [0.2.2] — 2026-03-22
|
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|
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### Added
|
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|
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- **Five-tier rating scale** (Buy / Overweight / Hold / Underweight / Sell)
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introduced for the Portfolio Manager.
|
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- **Anthropic effort level** support for Claude models.
|
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- **OpenAI Responses API** path for native OpenAI models.
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|
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### Changed
|
||||
|
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- `risk_manager` renamed to `portfolio_manager` to match the role description
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shown in the CLI display.
|
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- Exchange-qualified tickers (e.g. `7203.T`, `BRK.B`) preserved across all
|
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agent prompts and tool calls.
|
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- Process-level UTF-8 default attempted for cross-platform consistency
|
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(note: this approach did not actually take effect; replaced in v0.2.4 with
|
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explicit per-call `encoding="utf-8"` arguments).
|
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|
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### Fixed
|
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|
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- yfinance rate-limit errors are retried with exponential backoff. (#426)
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- HTTP client SSL customisation is supported for environments that need
|
||||
custom certificate bundles. (#379)
|
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- Report-section writes handle list-of-string content gracefully.
|
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|
||||
### Contributors
|
||||
|
||||
- [@CadeYu](https://github.com/CadeYu) — exchange-qualified ticker preservation (#413)
|
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- [@yang1002378395-cmyk](https://github.com/yang1002378395-cmyk) — HTTP client SSL customisation (#379)
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|
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## [0.2.1] — 2026-03-15
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### Security
|
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|
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- Patched `langchain-core` vulnerability (LangGrinch). (#335)
|
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- Removed `chainlit` dependency affected by CVE-2026-22218.
|
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|
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### Added
|
||||
|
||||
- `pyproject.toml` build-system configuration; the project now installs via
|
||||
modern packaging tooling.
|
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|
||||
### Removed
|
||||
|
||||
- `setup.py` — dependencies consolidated to `pyproject.toml`.
|
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|
||||
### Fixed
|
||||
|
||||
- Risk manager reads the correct fundamental report source. (#341)
|
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- All `open()` calls receive an explicit UTF-8 encoding (initial pass).
|
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- `get_indicators` tool handles comma-separated indicator names from the LLM. (#368)
|
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- `Propagation` initialises every debate-state field so risk debaters never
|
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see missing keys.
|
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- Stock data parsing tolerates malformed CSVs and NaN values.
|
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- Conditional debate logic respects the configured round count. (#361)
|
||||
|
||||
### Contributors
|
||||
|
||||
- [@RinZ27](https://github.com/RinZ27) — `langchain-core` security patch (#335)
|
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- [@Ljx-007](https://github.com/Ljx-007) — risk manager fundamental-report fix (#341)
|
||||
- [@makk9](https://github.com/makk9) — debate-rounds config issue (#361)
|
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|
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## [0.2.0] — 2026-02-04
|
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|
||||
This is the largest release since the initial public version. The framework
|
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moved from single-provider to a multi-provider architecture and grew several
|
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production-ready surfaces.
|
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|
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### Added
|
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|
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- **Multi-provider LLM support** (OpenAI, Google, Anthropic, xAI, OpenRouter,
|
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Ollama) via a factory pattern, with provider-specific thinking configurations.
|
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- **Alpha Vantage** integration as a configurable primary data provider, with
|
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yfinance as a community-stability fallback.
|
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- **Footer statistics** in the CLI: real-time tracking of LLM calls, tool
|
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calls, and token usage via LangChain callbacks.
|
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- **Post-analysis report saving** — the framework writes per-section markdown
|
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files (analyst reports, debate transcripts, final decision) when a run
|
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completes.
|
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- **Announcements panel** — fetches updates from `api.tauric.ai/v1/announcements`
|
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for the CLI welcome screen.
|
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- **Tool fallbacks** so a single vendor outage does not stop the pipeline.
|
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|
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### Changed
|
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|
||||
- Risky / Safe risk debaters renamed to **Aggressive / Conservative** for
|
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consistency with the displayed agent labels.
|
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- Default data vendor switched to balance reliability and quota across
|
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community deployments.
|
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- Ollama and OpenRouter model lists updated; default endpoints clarified.
|
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|
||||
### Fixed
|
||||
|
||||
- Analyst status tracking and message deduplication in the live display.
|
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- Infinite-loop guard in the agent loop; reflection and logging hardened.
|
||||
- Various data-vendor implementation bugs and tool-signature mismatches.
|
||||
|
||||
### Contributors
|
||||
|
||||
This release is the first with substantial outside contributions; many community
|
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PRs from late 2025 also landed here.
|
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|
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- [@luohy15](https://github.com/luohy15) — Alpha Vantage data-vendor integration (#235)
|
||||
- [@EdwardoSunny](https://github.com/EdwardoSunny) — yfinance fetching optimisations (#245)
|
||||
- [@Mirza-Samad-Ahmed-Baig](https://github.com/Mirza-Samad-Ahmed-Baig) — infinite-loop guard, reflection, and logging fixes (#89)
|
||||
- [@ZeroAct](https://github.com/ZeroAct) — saved results path support (#29)
|
||||
- [@Zhongyi-Lu](https://github.com/Zhongyi-Lu) — `.env` gitignore (#49)
|
||||
- [@csoboy](https://github.com/csoboy) — local Ollama setup (#53)
|
||||
- [@chauhang](https://github.com/chauhang) — initial Docker support attempt (#47, later reverted; the merged Docker support shipped in v0.2.4)
|
||||
|
||||
## [0.1.1] — 2025-06-07
|
||||
|
||||
### Removed
|
||||
|
||||
- Static site assets that had been bundled with v0.1.0; the public site now
|
||||
lives separately.
|
||||
|
||||
## [0.1.0] — 2025-06-05
|
||||
|
||||
### Added
|
||||
|
||||
- **Initial public release** of the TradingAgents multi-agent trading
|
||||
framework: market / sentiment / news / fundamentals analysts; bull and bear
|
||||
researchers; trader; aggressive, conservative, and neutral risk debaters;
|
||||
portfolio manager. LangGraph orchestration, yfinance data, per-agent
|
||||
BM25 memory, single-provider OpenAI integration, interactive CLI.
|
||||
|
||||
[0.2.4]: https://github.com/TauricResearch/TradingAgents/compare/v0.2.3...v0.2.4
|
||||
[0.2.3]: https://github.com/TauricResearch/TradingAgents/compare/v0.2.2...v0.2.3
|
||||
[0.2.2]: https://github.com/TauricResearch/TradingAgents/compare/v0.2.1...v0.2.2
|
||||
[0.2.1]: https://github.com/TauricResearch/TradingAgents/compare/v0.2.0...v0.2.1
|
||||
[0.2.0]: https://github.com/TauricResearch/TradingAgents/compare/v0.1.1...v0.2.0
|
||||
[0.1.1]: https://github.com/TauricResearch/TradingAgents/compare/v0.1.0...v0.1.1
|
||||
[0.1.0]: https://github.com/TauricResearch/TradingAgents/releases/tag/v0.1.0
|
||||
@@ -28,6 +28,7 @@
|
||||
# TradingAgents: Multi-Agents LLM Financial Trading Framework
|
||||
|
||||
## News
|
||||
- [2026-04] **TradingAgents v0.2.4** released with structured-output agents (Research Manager, Trader, Portfolio Manager), LangGraph checkpoint resume, persistent decision log, DeepSeek/Qwen/GLM/Azure provider support, Docker, and a Windows UTF-8 encoding fix. See [CHANGELOG.md](CHANGELOG.md) for the full list.
|
||||
- [2026-03] **TradingAgents v0.2.3** released with multi-language support, GPT-5.4 family models, unified model catalog, backtesting date fidelity, and proxy support.
|
||||
- [2026-03] **TradingAgents v0.2.2** released with GPT-5.4/Gemini 3.1/Claude 4.6 model coverage, five-tier rating scale, OpenAI Responses API, Anthropic effort control, and cross-platform stability.
|
||||
- [2026-02] **TradingAgents v0.2.0** released with multi-provider LLM support (GPT-5.x, Gemini 3.x, Claude 4.x, Grok 4.x) and improved system architecture.
|
||||
@@ -251,6 +252,8 @@ _, decision = ta.propagate("NVDA", "2026-01-15")
|
||||
|
||||
We welcome contributions from the community! Whether it's fixing a bug, improving documentation, or suggesting a new feature, your input helps make this project better. If you are interested in this line of research, please consider joining our open-source financial AI research community [Tauric Research](https://tauric.ai/).
|
||||
|
||||
Past contributions, including code, design feedback, and bug reports, are credited per release in [`CHANGELOG.md`](CHANGELOG.md).
|
||||
|
||||
## Citation
|
||||
|
||||
Please reference our work if you find *TradingAgents* provides you with some help :)
|
||||
|
||||
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
|
||||
|
||||
[project]
|
||||
name = "tradingagents"
|
||||
version = "0.2.3"
|
||||
version = "0.2.4"
|
||||
description = "TradingAgents: Multi-Agents LLM Financial Trading Framework"
|
||||
readme = "README.md"
|
||||
requires-python = ">=3.10"
|
||||
|
||||
176
scripts/smoke_structured_output.py
Normal file
176
scripts/smoke_structured_output.py
Normal file
@@ -0,0 +1,176 @@
|
||||
"""End-to-end smoke for structured-output agents against a real LLM provider.
|
||||
|
||||
Runs the three decision-making agents (Research Manager, Trader, Portfolio
|
||||
Manager) directly with their structured-output bindings and prints the
|
||||
typed Pydantic instance + the rendered markdown for each. Use this to
|
||||
verify a provider's native structured-output mode (json_schema for
|
||||
OpenAI / xAI / DeepSeek / Qwen / GLM, response_schema for Gemini, tool-use
|
||||
for Anthropic) returns clean instances on the schemas we ship.
|
||||
|
||||
Usage:
|
||||
OPENAI_API_KEY=... python scripts/smoke_structured_output.py openai
|
||||
GOOGLE_API_KEY=... python scripts/smoke_structured_output.py google
|
||||
ANTHROPIC_API_KEY=... python scripts/smoke_structured_output.py anthropic
|
||||
DEEPSEEK_API_KEY=... python scripts/smoke_structured_output.py deepseek
|
||||
|
||||
The script does NOT call propagate(), to keep the surface tight and the
|
||||
cost low — it exercises only the three structured-output calls we just
|
||||
added, plus the heuristic SignalProcessor.
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
|
||||
from tradingagents.agents.managers.portfolio_manager import create_portfolio_manager
|
||||
from tradingagents.agents.managers.research_manager import create_research_manager
|
||||
from tradingagents.agents.trader.trader import create_trader
|
||||
from tradingagents.graph.signal_processing import SignalProcessor
|
||||
from tradingagents.llm_clients import create_llm_client
|
||||
|
||||
|
||||
PROVIDER_DEFAULTS = {
|
||||
"openai": ("gpt-5.4-mini", None),
|
||||
"google": ("gemini-2.5-flash", None),
|
||||
"anthropic": ("claude-sonnet-4-6", None),
|
||||
"deepseek": ("deepseek-chat", None),
|
||||
"qwen": ("qwen-plus", None),
|
||||
"glm": ("glm-5", None),
|
||||
"xai": ("grok-4", None),
|
||||
}
|
||||
|
||||
|
||||
# Minimal but realistic state for the three agents.
|
||||
DEBATE_HISTORY = """
|
||||
Bull Analyst: NVDA's data-center revenue grew 60% YoY last quarter, driven by
|
||||
Blackwell ramp; sovereign AI deals with multiple governments add a $40B+
|
||||
multi-year tailwind. Margins remain above peer average.
|
||||
|
||||
Bear Analyst: Concentration risk is real — top three customers are >40% of
|
||||
revenue. Any pause in hyperscaler capex would compress the multiple. China
|
||||
export restrictions still cap a meaningful portion of demand.
|
||||
"""
|
||||
|
||||
|
||||
def _make_rm_state():
|
||||
return {
|
||||
"company_of_interest": "NVDA",
|
||||
"investment_debate_state": {
|
||||
"history": DEBATE_HISTORY,
|
||||
"bull_history": "Bull Analyst: NVDA's data-center revenue grew 60% YoY...",
|
||||
"bear_history": "Bear Analyst: Concentration risk is real...",
|
||||
"current_response": "",
|
||||
"judge_decision": "",
|
||||
"count": 1,
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
def _make_trader_state(investment_plan: str):
|
||||
return {
|
||||
"company_of_interest": "NVDA",
|
||||
"investment_plan": investment_plan,
|
||||
}
|
||||
|
||||
|
||||
def _make_pm_state(investment_plan: str, trader_plan: str):
|
||||
return {
|
||||
"company_of_interest": "NVDA",
|
||||
"past_context": "",
|
||||
"risk_debate_state": {
|
||||
"history": "Aggressive: lean in. Conservative: trim. Neutral: balanced sizing.",
|
||||
"aggressive_history": "Aggressive: ...",
|
||||
"conservative_history": "Conservative: ...",
|
||||
"neutral_history": "Neutral: ...",
|
||||
"judge_decision": "",
|
||||
"current_aggressive_response": "",
|
||||
"current_conservative_response": "",
|
||||
"current_neutral_response": "",
|
||||
"count": 1,
|
||||
},
|
||||
"market_report": "Market report.",
|
||||
"sentiment_report": "Sentiment report.",
|
||||
"news_report": "News report.",
|
||||
"fundamentals_report": "Fundamentals report.",
|
||||
"investment_plan": investment_plan,
|
||||
"trader_investment_plan": trader_plan,
|
||||
}
|
||||
|
||||
|
||||
def _print_section(title: str, content: str) -> None:
|
||||
bar = "=" * 70
|
||||
print(f"\n{bar}\n{title}\n{bar}\n{content}")
|
||||
|
||||
|
||||
def main() -> int:
|
||||
parser = argparse.ArgumentParser(description=__doc__)
|
||||
parser.add_argument("provider", choices=list(PROVIDER_DEFAULTS.keys()))
|
||||
parser.add_argument("--deep-model", default=None, help="Override deep_think_llm")
|
||||
parser.add_argument("--quick-model", default=None, help="Override quick_think_llm")
|
||||
args = parser.parse_args()
|
||||
|
||||
default_model, _ = PROVIDER_DEFAULTS[args.provider]
|
||||
deep_model = args.deep_model or default_model
|
||||
quick_model = args.quick_model or default_model
|
||||
|
||||
print(f"Provider: {args.provider}")
|
||||
print(f"Deep model: {deep_model}")
|
||||
print(f"Quick model: {quick_model}")
|
||||
|
||||
# Build the LLM clients via the framework's factory.
|
||||
deep_client = create_llm_client(provider=args.provider, model=deep_model)
|
||||
quick_client = create_llm_client(provider=args.provider, model=quick_model)
|
||||
deep_llm = deep_client.get_llm()
|
||||
quick_llm = quick_client.get_llm()
|
||||
|
||||
# 1) Research Manager
|
||||
rm = create_research_manager(deep_llm)
|
||||
rm_result = rm(_make_rm_state())
|
||||
investment_plan = rm_result["investment_plan"]
|
||||
_print_section("[1] Research Manager — investment_plan", investment_plan)
|
||||
|
||||
# 2) Trader (consumes RM's plan)
|
||||
trader = create_trader(quick_llm)
|
||||
trader_result = trader(_make_trader_state(investment_plan))
|
||||
trader_plan = trader_result["trader_investment_plan"]
|
||||
_print_section("[2] Trader — trader_investment_plan", trader_plan)
|
||||
|
||||
# 3) Portfolio Manager (consumes both)
|
||||
pm = create_portfolio_manager(deep_llm)
|
||||
pm_result = pm(_make_pm_state(investment_plan, trader_plan))
|
||||
final_decision = pm_result["final_trade_decision"]
|
||||
_print_section("[3] Portfolio Manager — final_trade_decision", final_decision)
|
||||
|
||||
# 4) SignalProcessor extracts the rating with zero LLM calls.
|
||||
sp = SignalProcessor()
|
||||
rating = sp.process_signal(final_decision)
|
||||
_print_section("[4] SignalProcessor → rating", rating)
|
||||
|
||||
# 5) Lightweight checks: each rendered output should carry the expected
|
||||
# section headers so downstream consumers (memory log, CLI display,
|
||||
# saved reports) keep working.
|
||||
checks = [
|
||||
("Research Manager", investment_plan, ["**Recommendation**:"]),
|
||||
("Trader", trader_plan, ["**Action**:", "FINAL TRANSACTION PROPOSAL:"]),
|
||||
("Portfolio Manager", final_decision, ["**Rating**:", "**Executive Summary**:", "**Investment Thesis**:"]),
|
||||
]
|
||||
print("\n" + "=" * 70 + "\nStructure checks\n" + "=" * 70)
|
||||
failures = 0
|
||||
for name, text, required in checks:
|
||||
for marker in required:
|
||||
ok = marker in text
|
||||
print(f" {'PASS' if ok else 'FAIL'} {name}: contains {marker!r}")
|
||||
failures += int(not ok)
|
||||
|
||||
print()
|
||||
if failures:
|
||||
print(f"Smoke FAILED: {failures} structure check(s) missing.")
|
||||
return 1
|
||||
print("Smoke PASSED: structured output → rendered markdown chain works for", args.provider)
|
||||
return 0
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
@@ -291,6 +291,59 @@ class TestTradingMemoryLogCore:
|
||||
assert log.load_entries() == []
|
||||
assert log.get_past_context("NVDA") == ""
|
||||
|
||||
# Rotation: opt-in cap on resolved entries
|
||||
|
||||
def test_rotation_disabled_by_default(self, tmp_path):
|
||||
"""Without max_entries, all resolved entries are kept."""
|
||||
log = make_log(tmp_path)
|
||||
for i in range(7):
|
||||
_resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Lesson {i}.")
|
||||
assert len(log.load_entries()) == 7
|
||||
|
||||
def test_rotation_prunes_oldest_resolved(self, tmp_path):
|
||||
"""When max_entries is set and exceeded, oldest resolved entries are pruned."""
|
||||
log = TradingMemoryLog({
|
||||
"memory_log_path": str(tmp_path / "trading_memory.md"),
|
||||
"memory_log_max_entries": 3,
|
||||
})
|
||||
# Resolve 5 entries; rotation should keep only the 3 most recent.
|
||||
for i in range(5):
|
||||
_resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Lesson {i}.")
|
||||
entries = log.load_entries()
|
||||
assert len(entries) == 3
|
||||
# Confirm the OLDEST were dropped, not the newest.
|
||||
dates = [e["date"] for e in entries]
|
||||
assert dates == ["2026-01-03", "2026-01-04", "2026-01-05"]
|
||||
|
||||
def test_rotation_never_prunes_pending(self, tmp_path):
|
||||
"""Pending entries (unresolved) are kept regardless of the cap."""
|
||||
log = TradingMemoryLog({
|
||||
"memory_log_path": str(tmp_path / "trading_memory.md"),
|
||||
"memory_log_max_entries": 2,
|
||||
})
|
||||
# 3 resolved + 2 pending. With cap=2, only 2 resolved survive; both pending stay.
|
||||
for i in range(3):
|
||||
_resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Resolved {i}.")
|
||||
log.store_decision("NVDA", "2026-02-01", DECISION_BUY)
|
||||
log.store_decision("NVDA", "2026-02-02", DECISION_OVERWEIGHT)
|
||||
# Trigger rotation by resolving one more entry — pending entries must stay.
|
||||
_resolve_entry(log, "NVDA", "2026-01-04", DECISION_BUY, "Resolved 3.")
|
||||
entries = log.load_entries()
|
||||
pending = [e for e in entries if e["pending"]]
|
||||
resolved = [e for e in entries if not e["pending"]]
|
||||
assert len(pending) == 2, "pending entries must never be pruned"
|
||||
assert len(resolved) == 2, f"expected 2 resolved after rotation, got {len(resolved)}"
|
||||
|
||||
def test_rotation_under_cap_is_noop(self, tmp_path):
|
||||
"""No rotation when resolved count <= max_entries."""
|
||||
log = TradingMemoryLog({
|
||||
"memory_log_path": str(tmp_path / "trading_memory.md"),
|
||||
"memory_log_max_entries": 10,
|
||||
})
|
||||
for i in range(3):
|
||||
_resolve_entry(log, "NVDA", f"2026-01-{i+1:02d}", DECISION_BUY, f"Lesson {i}.")
|
||||
assert len(log.load_entries()) == 3
|
||||
|
||||
# Rating parsing: markdown bold and numbered list formats
|
||||
|
||||
def test_rating_parsed_from_bold_markdown(self, tmp_path):
|
||||
|
||||
@@ -17,11 +17,14 @@ class TradingMemoryLog:
|
||||
_REFLECTION_RE = re.compile(r"REFLECTION:\n(.*?)$", re.DOTALL)
|
||||
|
||||
def __init__(self, config: dict = None):
|
||||
cfg = config or {}
|
||||
self._log_path = None
|
||||
path = (config or {}).get("memory_log_path")
|
||||
path = cfg.get("memory_log_path")
|
||||
if path:
|
||||
self._log_path = Path(path).expanduser()
|
||||
self._log_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
# Optional cap on resolved entries. None disables rotation.
|
||||
self._max_entries = cfg.get("memory_log_max_entries")
|
||||
|
||||
# --- Write path (Phase A) ---
|
||||
|
||||
@@ -153,6 +156,7 @@ class TradingMemoryLog:
|
||||
if not updated:
|
||||
return
|
||||
|
||||
new_blocks = self._apply_rotation(new_blocks)
|
||||
new_text = self._SEPARATOR.join(new_blocks)
|
||||
tmp_path = self._log_path.with_suffix(".tmp")
|
||||
tmp_path.write_text(new_text, encoding="utf-8")
|
||||
@@ -206,6 +210,7 @@ class TradingMemoryLog:
|
||||
if not matched:
|
||||
new_blocks.append(block)
|
||||
|
||||
new_blocks = self._apply_rotation(new_blocks)
|
||||
new_text = self._SEPARATOR.join(new_blocks)
|
||||
tmp_path = self._log_path.with_suffix(".tmp")
|
||||
tmp_path.write_text(new_text, encoding="utf-8")
|
||||
@@ -213,6 +218,43 @@ class TradingMemoryLog:
|
||||
|
||||
# --- Helpers ---
|
||||
|
||||
def _apply_rotation(self, blocks: List[str]) -> List[str]:
|
||||
"""Drop oldest resolved blocks when their count exceeds max_entries.
|
||||
|
||||
Pending blocks are always kept (they represent unprocessed work).
|
||||
Returns ``blocks`` unchanged when rotation is disabled or under cap.
|
||||
"""
|
||||
if not self._max_entries or self._max_entries <= 0:
|
||||
return blocks
|
||||
|
||||
# Tag each block with (kept, is_resolved) by parsing tag-line markers.
|
||||
decisions = []
|
||||
for block in blocks:
|
||||
stripped = block.strip()
|
||||
if not stripped:
|
||||
decisions.append((block, False))
|
||||
continue
|
||||
tag_line = stripped.splitlines()[0].strip()
|
||||
is_resolved = (
|
||||
tag_line.startswith("[")
|
||||
and tag_line.endswith("]")
|
||||
and not tag_line.endswith("| pending]")
|
||||
)
|
||||
decisions.append((block, is_resolved))
|
||||
|
||||
resolved_count = sum(1 for _, r in decisions if r)
|
||||
if resolved_count <= self._max_entries:
|
||||
return blocks
|
||||
|
||||
to_drop = resolved_count - self._max_entries
|
||||
kept: List[str] = []
|
||||
for block, is_resolved in decisions:
|
||||
if is_resolved and to_drop > 0:
|
||||
to_drop -= 1
|
||||
continue
|
||||
kept.append(block)
|
||||
return kept
|
||||
|
||||
def _parse_entry(self, raw: str) -> Optional[dict]:
|
||||
lines = raw.strip().splitlines()
|
||||
if not lines:
|
||||
|
||||
@@ -7,6 +7,10 @@ DEFAULT_CONFIG = {
|
||||
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", os.path.join(_TRADINGAGENTS_HOME, "logs")),
|
||||
"data_cache_dir": os.getenv("TRADINGAGENTS_CACHE_DIR", os.path.join(_TRADINGAGENTS_HOME, "cache")),
|
||||
"memory_log_path": os.getenv("TRADINGAGENTS_MEMORY_LOG_PATH", os.path.join(_TRADINGAGENTS_HOME, "memory", "trading_memory.md")),
|
||||
# Optional cap on the number of resolved memory log entries. When set,
|
||||
# the oldest resolved entries are pruned once this limit is exceeded.
|
||||
# Pending entries are never pruned. None disables rotation entirely.
|
||||
"memory_log_max_entries": None,
|
||||
# LLM settings
|
||||
"llm_provider": "openai",
|
||||
"deep_think_llm": "gpt-5.4",
|
||||
|
||||
@@ -18,6 +18,22 @@ class NormalizedChatOpenAI(ChatOpenAI):
|
||||
def invoke(self, input, config=None, **kwargs):
|
||||
return normalize_content(super().invoke(input, config, **kwargs))
|
||||
|
||||
def with_structured_output(self, schema, *, method=None, **kwargs):
|
||||
"""Wrap with structured output, defaulting to function_calling for OpenAI.
|
||||
|
||||
langchain-openai's Responses-API-parse path (the default for json_schema
|
||||
when use_responses_api=True) calls response.model_dump(...) on the OpenAI
|
||||
SDK's union-typed parsed response, which makes Pydantic emit ~20
|
||||
PydanticSerializationUnexpectedValue warnings per call. The function-calling
|
||||
path returns a plain tool-call shape that does not trigger that
|
||||
serialization, so it is the cleaner choice for our combination of
|
||||
use_responses_api=True + with_structured_output. Both paths use OpenAI's
|
||||
strict mode and produce the same typed Pydantic instance.
|
||||
"""
|
||||
if method is None:
|
||||
method = "function_calling"
|
||||
return super().with_structured_output(schema, method=method, **kwargs)
|
||||
|
||||
# Kwargs forwarded from user config to ChatOpenAI
|
||||
_PASSTHROUGH_KWARGS = (
|
||||
"timeout", "max_retries", "reasoning_effort",
|
||||
|
||||
Reference in New Issue
Block a user