Round 1: import upstream additions (TauricResearch/TradingAgents)
Pure file additions from upstream — zero conflict, zero risk to local customizations. Cherry-picked individually rather than via merge. New files: - tradingagents/llm_clients/model_catalog.py — centralized model registry - tradingagents/agents/managers/portfolio_manager.py — new PM (paired with upstream's risk_manager.py removal, deferred to a later round) - tradingagents/__init__.py — package init - tests/test_google_api_key.py - tests/test_model_validation.py - tests/test_ticker_symbol_handling.py - .env.enterprise.example — reference template Skipped (deferred to later rounds): - Round 2: bug-fix cherry-picks (yfinance retry, look-ahead bias fix, PM-reads-trader fix, indicator normalization, etc.) - Round 3: feat:add DeepSeek/Qwen/GLM provider support (b0f6058) — conflicts with local llm_clients/ customizations, needs careful merge - Round 4: portfolio manager restructure (b8b2825/318adda) — replaces risk_manager.py which has local customizations, needs hand-port Skipped entirely: - Docker support (anti-Docker stack) - Multi-language output (English-only arena) - GPT-5.4 default flip (would undoa913416glm-5.1 default) - azure_client.py (not in arena roster) - OpenRouter dynamic model selection (not in provider mix) Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
dtarkent2-sys
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# Azure OpenAI
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AZURE_OPENAI_API_KEY=
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AZURE_OPENAI_ENDPOINT=https://your-resource-name.openai.azure.com/
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AZURE_OPENAI_DEPLOYMENT_NAME=
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# OPENAI_API_VERSION=2024-10-21 # optional, required for non-v1 API
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import unittest
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from unittest.mock import patch
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from tradingagents.llm_clients.google_client import GoogleClient
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class TestGoogleApiKeyStandardization(unittest.TestCase):
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"""Verify GoogleClient accepts unified api_key parameter."""
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@patch("tradingagents.llm_clients.google_client.NormalizedChatGoogleGenerativeAI")
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def test_api_key_handling(self, mock_chat):
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test_cases = [
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("unified api_key is mapped", {"api_key": "test-key-123"}, "test-key-123"),
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("legacy google_api_key still works", {"google_api_key": "legacy-key-456"}, "legacy-key-456"),
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("unified api_key takes precedence", {"api_key": "unified", "google_api_key": "legacy"}, "unified"),
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]
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for msg, kwargs, expected_key in test_cases:
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with self.subTest(msg=msg):
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mock_chat.reset_mock()
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client = GoogleClient("gemini-2.5-flash", **kwargs)
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client.get_llm()
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call_kwargs = mock_chat.call_args[1]
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self.assertEqual(call_kwargs.get("google_api_key"), expected_key)
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if __name__ == "__main__":
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unittest.main()
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import unittest
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import warnings
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from tradingagents.llm_clients.base_client import BaseLLMClient
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from tradingagents.llm_clients.model_catalog import get_known_models
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from tradingagents.llm_clients.validators import validate_model
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class DummyLLMClient(BaseLLMClient):
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def __init__(self, provider: str, model: str):
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self.provider = provider
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super().__init__(model)
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def get_llm(self):
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self.warn_if_unknown_model()
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return object()
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def validate_model(self) -> bool:
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return validate_model(self.provider, self.model)
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class ModelValidationTests(unittest.TestCase):
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def test_cli_catalog_models_are_all_validator_approved(self):
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for provider, models in get_known_models().items():
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if provider in ("ollama", "openrouter"):
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continue
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for model in models:
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with self.subTest(provider=provider, model=model):
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self.assertTrue(validate_model(provider, model))
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def test_unknown_model_emits_warning_for_strict_provider(self):
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client = DummyLLMClient("openai", "not-a-real-openai-model")
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with warnings.catch_warnings(record=True) as caught:
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warnings.simplefilter("always")
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client.get_llm()
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self.assertEqual(len(caught), 1)
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self.assertIn("not-a-real-openai-model", str(caught[0].message))
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self.assertIn("openai", str(caught[0].message))
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def test_openrouter_and_ollama_accept_custom_models_without_warning(self):
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for provider in ("openrouter", "ollama"):
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client = DummyLLMClient(provider, "custom-model-name")
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with self.subTest(provider=provider):
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with warnings.catch_warnings(record=True) as caught:
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warnings.simplefilter("always")
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client.get_llm()
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self.assertEqual(caught, [])
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import unittest
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from cli.utils import normalize_ticker_symbol
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from tradingagents.agents.utils.agent_utils import build_instrument_context
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class TickerSymbolHandlingTests(unittest.TestCase):
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def test_normalize_ticker_symbol_preserves_exchange_suffix(self):
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self.assertEqual(normalize_ticker_symbol(" cnc.to "), "CNC.TO")
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def test_build_instrument_context_mentions_exact_symbol(self):
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context = build_instrument_context("7203.T")
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self.assertIn("7203.T", context)
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self.assertIn("exchange suffix", context)
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if __name__ == "__main__":
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unittest.main()
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import os
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os.environ.setdefault("PYTHONUTF8", "1")
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from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction
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def create_portfolio_manager(llm, memory):
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def portfolio_manager_node(state) -> dict:
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instrument_context = build_instrument_context(state["company_of_interest"])
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history = state["risk_debate_state"]["history"]
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risk_debate_state = state["risk_debate_state"]
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market_research_report = state["market_report"]
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news_report = state["news_report"]
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fundamentals_report = state["fundamentals_report"]
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sentiment_report = state["sentiment_report"]
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research_plan = state["investment_plan"]
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trader_plan = state["trader_investment_plan"]
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curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
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past_memories = memory.get_memories(curr_situation, n_matches=2)
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past_memory_str = ""
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for i, rec in enumerate(past_memories, 1):
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past_memory_str += rec["recommendation"] + "\n\n"
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prompt = f"""As the Portfolio Manager, synthesize the risk analysts' debate and deliver the final trading decision.
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{instrument_context}
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---
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**Rating Scale** (use exactly one):
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- **Buy**: Strong conviction to enter or add to position
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- **Overweight**: Favorable outlook, gradually increase exposure
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- **Hold**: Maintain current position, no action needed
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- **Underweight**: Reduce exposure, take partial profits
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- **Sell**: Exit position or avoid entry
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**Context:**
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- Research Manager's investment plan: **{research_plan}**
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- Trader's transaction proposal: **{trader_plan}**
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- Lessons from past decisions: **{past_memory_str}**
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**Required Output Structure:**
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1. **Rating**: State one of Buy / Overweight / Hold / Underweight / Sell.
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2. **Executive Summary**: A concise action plan covering entry strategy, position sizing, key risk levels, and time horizon.
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3. **Investment Thesis**: Detailed reasoning anchored in the analysts' debate and past reflections.
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---
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**Risk Analysts Debate History:**
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{history}
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---
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Be decisive and ground every conclusion in specific evidence from the analysts.{get_language_instruction()}"""
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response = llm.invoke(prompt)
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new_risk_debate_state = {
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"judge_decision": response.content,
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"history": risk_debate_state["history"],
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"aggressive_history": risk_debate_state["aggressive_history"],
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"conservative_history": risk_debate_state["conservative_history"],
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"neutral_history": risk_debate_state["neutral_history"],
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"latest_speaker": "Judge",
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"current_aggressive_response": risk_debate_state["current_aggressive_response"],
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"current_conservative_response": risk_debate_state["current_conservative_response"],
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"current_neutral_response": risk_debate_state["current_neutral_response"],
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"count": risk_debate_state["count"],
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}
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return {
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"risk_debate_state": new_risk_debate_state,
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"final_trade_decision": response.content,
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}
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return portfolio_manager_node
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"""Shared model catalog for CLI selections and validation."""
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from __future__ import annotations
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from typing import Dict, List, Tuple
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ModelOption = Tuple[str, str]
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ProviderModeOptions = Dict[str, Dict[str, List[ModelOption]]]
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MODEL_OPTIONS: ProviderModeOptions = {
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"openai": {
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"quick": [
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("GPT-5.4 Mini - Fast, strong coding and tool use", "gpt-5.4-mini"),
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("GPT-5.4 Nano - Cheapest, high-volume tasks", "gpt-5.4-nano"),
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("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
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("GPT-4.1 - Smartest non-reasoning model", "gpt-4.1"),
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],
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"deep": [
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("GPT-5.4 - Latest frontier, 1M context", "gpt-5.4"),
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("GPT-5.2 - Strong reasoning, cost-effective", "gpt-5.2"),
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("GPT-5.4 Mini - Fast, strong coding and tool use", "gpt-5.4-mini"),
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("GPT-5.4 Pro - Most capable, expensive ($30/$180 per 1M tokens)", "gpt-5.4-pro"),
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],
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},
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"anthropic": {
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"quick": [
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("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
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("Claude Haiku 4.5 - Fast, near-instant responses", "claude-haiku-4-5"),
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("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
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],
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"deep": [
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("Claude Opus 4.6 - Most intelligent, agents and coding", "claude-opus-4-6"),
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("Claude Opus 4.5 - Premium, max intelligence", "claude-opus-4-5"),
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("Claude Sonnet 4.6 - Best speed and intelligence balance", "claude-sonnet-4-6"),
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("Claude Sonnet 4.5 - Agents and coding", "claude-sonnet-4-5"),
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],
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},
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"google": {
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"quick": [
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("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
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("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
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("Gemini 3.1 Flash Lite - Most cost-efficient", "gemini-3.1-flash-lite-preview"),
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("Gemini 2.5 Flash Lite - Fast, low-cost", "gemini-2.5-flash-lite"),
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],
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"deep": [
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("Gemini 3.1 Pro - Reasoning-first, complex workflows", "gemini-3.1-pro-preview"),
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("Gemini 3 Flash - Next-gen fast", "gemini-3-flash-preview"),
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("Gemini 2.5 Pro - Stable pro model", "gemini-2.5-pro"),
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("Gemini 2.5 Flash - Balanced, stable", "gemini-2.5-flash"),
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],
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},
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"xai": {
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"quick": [
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("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
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("Grok 4 Fast (Non-Reasoning) - Speed optimized", "grok-4-fast-non-reasoning"),
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("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
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],
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"deep": [
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("Grok 4 - Flagship model", "grok-4-0709"),
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("Grok 4.1 Fast (Reasoning) - High-performance, 2M ctx", "grok-4-1-fast-reasoning"),
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("Grok 4 Fast (Reasoning) - High-performance", "grok-4-fast-reasoning"),
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("Grok 4.1 Fast (Non-Reasoning) - Speed optimized, 2M ctx", "grok-4-1-fast-non-reasoning"),
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],
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},
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"deepseek": {
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"quick": [
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("DeepSeek V3.2", "deepseek-chat"),
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("Custom model ID", "custom"),
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],
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"deep": [
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("DeepSeek V3.2 (thinking)", "deepseek-reasoner"),
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("DeepSeek V3.2", "deepseek-chat"),
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("Custom model ID", "custom"),
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],
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},
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"qwen": {
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"quick": [
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("Qwen 3.5 Flash", "qwen3.5-flash"),
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("Qwen Plus", "qwen-plus"),
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("Custom model ID", "custom"),
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],
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"deep": [
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("Qwen 3.6 Plus", "qwen3.6-plus"),
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("Qwen 3.5 Plus", "qwen3.5-plus"),
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("Qwen 3 Max", "qwen3-max"),
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("Custom model ID", "custom"),
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],
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},
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"glm": {
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"quick": [
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("GLM-4.7", "glm-4.7"),
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("GLM-5", "glm-5"),
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("Custom model ID", "custom"),
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],
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"deep": [
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("GLM-5.1", "glm-5.1"),
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("GLM-5", "glm-5"),
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("Custom model ID", "custom"),
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],
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},
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# OpenRouter: fetched dynamically. Azure: any deployed model name.
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"ollama": {
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"quick": [
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("Qwen3:latest (8B, local)", "qwen3:latest"),
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("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
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("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
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],
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"deep": [
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("GLM-4.7-Flash:latest (30B, local)", "glm-4.7-flash:latest"),
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("GPT-OSS:latest (20B, local)", "gpt-oss:latest"),
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("Qwen3:latest (8B, local)", "qwen3:latest"),
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],
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},
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}
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def get_model_options(provider: str, mode: str) -> List[ModelOption]:
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"""Return shared model options for a provider and selection mode."""
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return MODEL_OPTIONS[provider.lower()][mode]
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def get_known_models() -> Dict[str, List[str]]:
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"""Build known model names from the shared CLI catalog."""
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return {
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provider: sorted(
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{
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value
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for options in mode_options.values()
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for _, value in options
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}
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)
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for provider, mode_options in MODEL_OPTIONS.items()
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}
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