wip

.gitignore

@@ -217,3 +217,8 @@ __marimo__/
 
 # Cache
 **/data_cache/
+
+
+.vscode/
+
+**.json

tradingagents/default_config.py

@@ -8,14 +8,14 @@ DEFAULT_CONFIG = {
         "dataflows/data_cache",
     ),
     # LLM settings
-    "llm_provider": "openai",
-    "deep_think_llm": "gpt-5.2",
-    "quick_think_llm": "gpt-5-mini",
-    "backend_url": "https://api.openai.com/v1",
+    "llm_provider": "google",
+    "deep_think_llm": "gemini-3-pro-preview",
+    "quick_think_llm": "gemini-2.5-flash",
+    "backend_url": "https://generativelanguage.googleapis.com/v1beta",
     # Provider-specific thinking configuration
-    "google_thinking_level": None,      # "high", "minimal", etc.
-    "openai_reasoning_effort": None,    # "medium", "high", "low"
-    "anthropic_effort": None,           # "high", "medium", "low"
+    "google_thinking_level": "high",      # "high", "minimal", etc.
+    "openai_reasoning_effort": "high",    # "medium", "high", "low"
+    "anthropic_effort": "high",           # "high", "medium", "low"
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,

tradingagents/graph/trading_graph.py

@@ -7,6 +7,7 @@ from datetime import date
 from typing import Dict, Any, Tuple, List, Optional
 
 from langgraph.prebuilt import ToolNode
+from dotenv import load_dotenv
 
 from tradingagents.llm_clients import create_llm_client
 
@@ -290,3 +291,12 @@ class TradingAgentsGraph:
     def process_signal(self, full_signal):
         """Process a signal to extract the core decision."""
         return self.signal_processor.process_signal(full_signal)
+
+
+if __name__ == "__main__":
+    load_dotenv()
+    ta = TradingAgentsGraph(debug=True, config=DEFAULT_CONFIG.copy())
+
+    # forward propagate
+    _, decision = ta.propagate("NVDA", "2026-01-15")
+    print(decision)

tradingagents/llm_clients/factory.py

@@ -1,4 +1,8 @@
 from typing import Optional
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
 
 from .base_client import BaseLLMClient
 from .openai_client import OpenAIClient

tradingagents/llm_clients/google_client.py

@@ -1,3 +1,4 @@
+import os
 from typing import Any, Optional
 
 from langchain_google_genai import ChatGoogleGenerativeAI
@@ -31,6 +32,12 @@ class GoogleClient(BaseLLMClient):
             if key in self.kwargs:
                 llm_kwargs[key] = self.kwargs[key]
 
+        # Explicitly fallback to environment variable if google_api_key is missing
+        if "google_api_key" not in llm_kwargs:
+            env_key = os.getenv("GOOGLE_API_KEY")
+            if env_key:
+                llm_kwargs["google_api_key"] = env_key
+
         # Map thinking_level to appropriate API param based on model
         # Gemini 3 Pro: low, high
         # Gemini 3 Flash: minimal, low, medium, high
@@ -39,10 +46,9 @@ class GoogleClient(BaseLLMClient):
         if thinking_level:
             model_lower = self.model.lower()
             if "gemini-3" in model_lower:
-                # Gemini 3 Pro doesn't support "minimal", use "low" instead
-                if "pro" in model_lower and thinking_level == "minimal":
-                    thinking_level = "low"
-                llm_kwargs["thinking_level"] = thinking_level
+                # Use thinking_budget as Gemini 3 Pro/Flash SDK expects it
+                # Mapping: low/minimal -> small budget, high -> large budget
+                llm_kwargs["thinking_budget"] = 2000 if thinking_level == "high" else 1000
             else:
                 # Gemini 2.5: map to thinking_budget
                 llm_kwargs["thinking_budget"] = -1 if thinking_level == "high" else 0