Merge branch 'TauricResearch:main' into save_results

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -22,7 +22,7 @@ def create_fundamentals_analyst(llm, toolkit):
 
         system_message = (
             "You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, company financial history, insider sentiment and insider transactions to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
-            + " Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read.",
+            + " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.",
         )
 
         prompt = ChatPromptTemplate.from_messages(
@@ -51,9 +51,14 @@ def create_fundamentals_analyst(llm, toolkit):
 
         result = chain.invoke(state["messages"])
 
+        report = ""
+
+        if len(result.tool_calls) == 0:
+            report = result.content
+
         return {
             "messages": [result],
-            "fundamentals_report": result.content,
+            "fundamentals_report": report,
         }
 
     return fundamentals_analyst_node

tradingagents/agents/analysts/market_analyst.py

@@ -76,9 +76,14 @@ Volume-Based Indicators:
 
         result = chain.invoke(state["messages"])
 
+        report = ""
+
+        if len(result.tool_calls) == 0:
+            report = result.content
+       
         return {
             "messages": [result],
-            "market_report": result.content,
+            "market_report": report,
         }
 
     return market_analyst_node

tradingagents/agents/analysts/news_analyst.py

@@ -47,9 +47,14 @@ def create_news_analyst(llm, toolkit):
         chain = prompt | llm.bind_tools(tools)
         result = chain.invoke(state["messages"])
 
+        report = ""
+
+        if len(result.tool_calls) == 0:
+            report = result.content
+
         return {
             "messages": [result],
-            "news_report": result.content,
+            "news_report": report,
         }
 
     return news_analyst_node

tradingagents/agents/analysts/social_media_analyst.py

@@ -47,9 +47,14 @@ def create_social_media_analyst(llm, toolkit):
 
         result = chain.invoke(state["messages"])
 
+        report = ""
+
+        if len(result.tool_calls) == 0:
+            report = result.content
+
         return {
             "messages": [result],
-            "sentiment_report": result.content,
+            "sentiment_report": report,
         }
 
     return social_media_analyst_node

tradingagents/agents/utils/agent_utils.py

@@ -12,14 +12,22 @@ from dateutil.relativedelta import relativedelta
 from langchain_openai import ChatOpenAI
 import tradingagents.dataflows.interface as interface
 from tradingagents.default_config import DEFAULT_CONFIG
+from langchain_core.messages import HumanMessage
 
 
 def create_msg_delete():
     def delete_messages(state):
-        """To prevent message history from overflowing, regularly clear message history after a stage of the pipeline is done"""
+        """Clear messages and add placeholder for Anthropic compatibility"""
         messages = state["messages"]
-        return {"messages": [RemoveMessage(id=m.id) for m in messages]}
-
+        
+        # Remove all messages
+        removal_operations = [RemoveMessage(id=m.id) for m in messages]
+        
+        # Add a minimal placeholder message
+        placeholder = HumanMessage(content="Continue")
+        
+        return {"messages": removal_operations + [placeholder]}
+    
     return delete_messages
 
 

tradingagents/agents/utils/memory.py

@@ -1,19 +1,23 @@
 import chromadb
 from chromadb.config import Settings
 from openai import OpenAI
-import numpy as np
 
 
 class FinancialSituationMemory:
-    def __init__(self, name):
-        self.client = OpenAI()
+    def __init__(self, name, config):
+        if config["backend_url"] == "http://localhost:11434/v1":
+            self.embedding = "nomic-embed-text"
+        else:
+            self.embedding = "text-embedding-3-small"
+            self.client = OpenAI()
         self.chroma_client = chromadb.Client(Settings(allow_reset=True))
         self.situation_collection = self.chroma_client.create_collection(name=name)
 
     def get_embedding(self, text):
         """Get OpenAI embedding for a text"""
+        
         response = self.client.embeddings.create(
-            model="text-embedding-ada-002", input=text
+            model=self.embedding, input=text
         )
         return response.data[0].embedding
 

tradingagents/dataflows/interface.py

@@ -703,6 +703,7 @@ def get_YFin_data(
 
 
 def get_stock_news_openai(ticker, curr_date):
+    config = get_config()
     client = OpenAI()
 
     response = client.responses.create(
@@ -713,7 +714,7 @@ def get_stock_news_openai(ticker, curr_date):
                 "content": [
                     {
                         "type": "input_text",
-                        "text": f"Can you search Social Media for {ticker} on TSLA from 7 days before {curr_date} to {curr_date}? Make sure you only get the data posted during that period.",
+                        "text": f"Can you search Social Media for {ticker} from 7 days before {curr_date} to {curr_date}? Make sure you only get the data posted during that period.",
                     }
                 ],
             }
@@ -737,6 +738,7 @@ def get_stock_news_openai(ticker, curr_date):
 
 
 def get_global_news_openai(curr_date):
+    config = get_config()
     client = OpenAI()
 
     response = client.responses.create(
@@ -771,6 +773,7 @@ def get_global_news_openai(curr_date):
 
 
 def get_fundamentals_openai(ticker, curr_date):
+    config = get_config()
     client = OpenAI()
 
     response = client.responses.create(

tradingagents/default_config.py

@@ -9,8 +9,10 @@ DEFAULT_CONFIG = {
         "dataflows/data_cache",
     ),
     # LLM settings
+    "llm_provider": "openai",
     "deep_think_llm": "o4-mini",
     "quick_think_llm": "gpt-4o-mini",
+    "backend_url": "https://api.openai.com/v1",
     # Debate and discussion settings
     "max_debate_rounds": 1,
     "max_risk_discuss_rounds": 1,

tradingagents/graph/trading_graph.py

@@ -7,6 +7,9 @@ from datetime import date
 from typing import Dict, Any, Tuple, List, Optional
 
 from langchain_openai import ChatOpenAI
+from langchain_anthropic import ChatAnthropic
+from langchain_google_genai import ChatGoogleGenerativeAI
+
 from langgraph.prebuilt import ToolNode
 
 from tradingagents.agents import *
@@ -55,18 +58,26 @@ class TradingAgentsGraph:
         )
 
         # Initialize LLMs
-        self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"])
-        self.quick_thinking_llm = ChatOpenAI(
-            model=self.config["quick_think_llm"], temperature=0.1
-        )
+        if self.config["llm_provider"].lower() == "openai" or self.config["llm_provider"] == "ollama" or self.config["llm_provider"] == "openrouter":
+            self.deep_thinking_llm = ChatOpenAI(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
+            self.quick_thinking_llm = ChatOpenAI(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
+        elif self.config["llm_provider"].lower() == "anthropic":
+            self.deep_thinking_llm = ChatAnthropic(model=self.config["deep_think_llm"], base_url=self.config["backend_url"])
+            self.quick_thinking_llm = ChatAnthropic(model=self.config["quick_think_llm"], base_url=self.config["backend_url"])
+        elif self.config["llm_provider"].lower() == "google":
+            self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
+            self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
+        else:
+            raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")
+        
         self.toolkit = Toolkit(config=self.config)
 
         # Initialize memories
-        self.bull_memory = FinancialSituationMemory("bull_memory")
-        self.bear_memory = FinancialSituationMemory("bear_memory")
-        self.trader_memory = FinancialSituationMemory("trader_memory")
-        self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory")
-        self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory")
+        self.bull_memory = FinancialSituationMemory("bull_memory", self.config)
+        self.bear_memory = FinancialSituationMemory("bear_memory", self.config)
+        self.trader_memory = FinancialSituationMemory("trader_memory", self.config)
+        self.invest_judge_memory = FinancialSituationMemory("invest_judge_memory", self.config)
+        self.risk_manager_memory = FinancialSituationMemory("risk_manager_memory", self.config)
 
         # Create tool nodes
         self.tool_nodes = self._create_tool_nodes()