added x, retuers, eodhd and more support

.env.example

@@ -0,0 +1,38 @@
+# OpenAI Configuration
+OPENAI_API_KEY=your_openai_api_key_here
+
+# Anthropic Configuration
+ANTHROPIC_API_KEY=your_anthropic_api_key_here
+
+# X (Twitter) API Configuration
+X_BEARER_TOKEN=your_x_bearer_token_here
+X_API_KEY=your_x_api_key_here
+X_API_SECRET=your_x_api_secret_here
+X_ACCESS_TOKEN=your_x_access_token_here
+X_ACCESS_SECRET=your_x_access_secret_here
+
+# Bloomberg API Configuration
+BLOOMBERG_API_KEY=your_bloomberg_api_key_here
+
+# Reuters API Configuration
+REUTERS_API_KEY=your_reuters_api_key_here
+
+# Reddit Configuration
+REDDIT_CLIENT_ID=your_reddit_client_id_here
+REDDIT_CLIENT_SECRET=your_reddit_client_secret_here
+REDDIT_USER_AGENT=TradingAgents/1.0
+
+# FinnHub Configuration
+FINNHUB_API_KEY=your_finnhub_api_key_here
+
+# Alpha Vantage Configuration
+ALPHA_VANTAGE_API_KEY=your_alpha_vantage_api_key_here
+
+# Polygon.io Configuration
+POLYGON_API_KEY=your_polygon_api_key_here
+
+# ChromaDB Configuration
+CHROMA_DB_PATH=./chroma_data
+
+# Trading Configuration
+TRADINGAGENTS_RESULTS_DIR=./results

requirements.txt

@@ -1,26 +1,34 @@
-typing-extensions
-langchain-openai
-langchain-experimental
-pandas
-yfinance
-praw
-feedparser
-stockstats
-eodhd
-langgraph
-chromadb
-setuptools
-backtrader
-akshare
-tushare
-finnhub-python
-parsel
-requests
-tqdm
-pytz
-redis
-chainlit
-rich
-questionary
-langchain_anthropic
-langchain-google-genai
+# Essential dependencies with compatible versions
+pandas>=2.0.0
+numpy>=1.24.0
+requests>=2.28.0
+
+# LangChain ecosystem - compatible versions
+langchain-core>=0.3.75
+langchain-openai>=0.3.0
+langchain-experimental>=0.3.0
+langchain_anthropic>=0.3.0
+langgraph>=0.6.0
+langgraph-checkpoint>=2.1.0
+
+# Financial data - minimal set
+yfinance>=0.2.28
+stockstats>=0.6.2
+finnhub-python>=2.4.20
+
+# Data storage
+chromadb>=0.5.0
+
+# Web scraping - essential only
+feedparser>=6.0.11
+python-dotenv>=1.0.1
+
+# Text processing
+textblob>=0.18.0
+
+# Utilities
+tqdm>=4.66.0
+setuptools>=70.0.0
+
+# UI
+rich>=13.7.0

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -1,13 +1,10 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 
 
 def create_fundamentals_analyst(llm, toolkit):
     def fundamentals_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["company_of_interest"]
-        company_name = state["company_of_interest"]
 
         if toolkit.config["online_tools"]:
             tools = [toolkit.get_fundamentals_openai]
@@ -21,40 +18,52 @@ def fundamentals_analyst_node(state):
             ]
 
         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 Markdown table at the end of the report to organize key points in the report, organized and easy to read.",
+            "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 "
+            "Markdown table at the end of the report to organize key points "
+            "in the report, organized and easy to read."
         )
 
         prompt = ChatPromptTemplate.from_messages(
             [
                 (
                     "system",
-                    "You are a helpful AI assistant, collaborating with other assistants."
-                    " Use the provided tools to progress towards answering the question."
-                    " If you are unable to fully answer, that's OK; another assistant with different tools"
-                    " will help where you left off. Execute what you can to make progress."
-                    " If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
-                    " prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
-                    " You have access to the following tools: {tool_names}.\n{system_message}"
-                    "For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
+                    "You are a helpful AI assistant, collaborating with other "
+                    "assistants. Use the provided tools to progress towards "
+                    "answering the question. If you are unable to fully answer, "
+                    "that's OK; another assistant with different tools will help "
+                    "where you left off. Execute what you can to make progress. "
+                    "If you or any other assistant has the FINAL TRANSACTION "
+                    "PROPOSAL: **BUY/HOLD/SELL** or deliverable, prefix your "
+                    "response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** "
+                    "so the team knows to stop. You have access to the following "
+                    "tools: {tool_names}.\n{system_message}For your reference, "
+                    "the current date is {current_date}. The company we want to "
+                    "look at is {ticker}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
         )
 
         prompt = prompt.partial(system_message=system_message)
-        prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
+        tool_names = ", ".join([tool.name for tool in tools])
+        prompt = prompt.partial(tool_names=tool_names)
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(ticker=ticker)
 
         chain = prompt | llm.bind_tools(tools)
 
         result = chain.invoke(state["messages"])
 
-        report = ""
-
-        if len(result.tool_calls) == 0:
-            report = result.content
+        report = result.content if result.content else ""
 
         return {
             "messages": [result],

tradingagents/agents/analysts/market_analyst.py

@@ -1,14 +1,11 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
-import time
-import json
 
 
 def create_market_analyst(llm, toolkit):
 
     def market_analyst_node(state):
         current_date = state["trade_date"]
         ticker = state["company_of_interest"]
-        company_name = state["company_of_interest"]
 
         if toolkit.config["online_tools"]:
             tools = [
@@ -76,11 +73,8 @@ def market_analyst_node(state):
 
         result = chain.invoke(state["messages"])
 
-        report = ""
+        report = result.content if result.content else ""
 
-        if len(result.tool_calls) == 0:
-            report = result.content
-
         return {
             "messages": [result],
             "market_report": report,

tradingagents/agents/managers/research_manager.py

@@ -1,23 +1,25 @@
-import time
-import json
-
-
 def create_research_manager(llm, memory):
     def research_manager_node(state) -> dict:
-        history = state["investment_debate_state"].get("history", "")
-        market_research_report = state["market_report"]
-        sentiment_report = state["sentiment_report"]
-        news_report = state["news_report"]
-        fundamentals_report = state["fundamentals_report"]
+        if not state or not isinstance(state, dict):
+            raise ValueError("Invalid state provided to research_manager")
 
-        investment_debate_state = state["investment_debate_state"]
+        investment_debate_state = state.get("investment_debate_state", {})
+        history = investment_debate_state.get("history", "")
+        market_research_report = state.get("market_report", "")
+        sentiment_report = state.get("sentiment_report", "")
+        news_report = state.get("news_report", "")
+        fundamentals_report = state.get("fundamentals_report", "")
 
-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
+        curr_situation = f"Market: {market_research_report}\nSentiment: {sentiment_report}\nNews: {news_report}\nFundamentals: {fundamentals_report}"
+        past_memories = memory.get_memories(curr_situation, n_matches=3, min_similarity=0.8)
 
         past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
+        if past_memories:
+            for i, rec in enumerate(past_memories, 1):
+                similarity = rec.get("similarity_score", 0)
+                past_memory_str += f"Research Memory {i} (similarity: {similarity:.3f}): {rec['recommendation']}\n\n"
+        else:
+            past_memory_str = "No statistically significant research memories found (similarity < 80%)."
 
         prompt = f"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the bear analyst, the bull analyst, or choose Hold only if it is strongly justified based on the arguments presented.
 

tradingagents/agents/managers/risk_manager.py

@@ -1,5 +1,3 @@
-import time
-import json
 
 
 def create_risk_manager(llm, memory):
@@ -11,16 +9,20 @@ def risk_manager_node(state) -> dict:
         risk_debate_state = state["risk_debate_state"]
         market_research_report = state["market_report"]
         news_report = state["news_report"]
-        fundamentals_report = state["news_report"]
+        fundamentals_report = state["fundamentals_report"]
         sentiment_report = state["sentiment_report"]
         trader_plan = state["investment_plan"]
 
-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
+        curr_situation = f"Market: {market_research_report}\nSentiment: {sentiment_report}\nNews: {news_report}\nFundamentals: {fundamentals_report}"
+        past_memories = memory.get_memories(curr_situation, n_matches=3, min_similarity=0.8)
 
         past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
+        if past_memories:
+            for i, rec in enumerate(past_memories, 1):
+                similarity = rec.get("similarity_score", 0)
+                past_memory_str += f"Risk Memory {i} (similarity: {similarity:.3f}): {rec['recommendation']}\n\n"
+        else:
+            past_memory_str = "No statistically significant risk memories found (similarity < 80%)."
 
         prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Risky, Neutral, and Safe/Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
 

tradingagents/agents/researchers/bear_researcher.py

@@ -1,26 +1,31 @@
-from langchain_core.messages import AIMessage
-import time
-import json
+import functools
 
 
 def create_bear_researcher(llm, memory):
     def bear_node(state) -> dict:
-        investment_debate_state = state["investment_debate_state"]
+        if not state or not isinstance(state, dict):
+            raise ValueError("Invalid state provided to bear_researcher")
+
+        investment_debate_state = state.get("investment_debate_state", {})
         history = investment_debate_state.get("history", "")
         bear_history = investment_debate_state.get("bear_history", "")
 
         current_response = investment_debate_state.get("current_response", "")
-        market_research_report = state["market_report"]
-        sentiment_report = state["sentiment_report"]
-        news_report = state["news_report"]
-        fundamentals_report = state["fundamentals_report"]
+        market_research_report = state.get("market_report", "")
+        sentiment_report = state.get("sentiment_report", "")
+        news_report = state.get("news_report", "")
+        fundamentals_report = state.get("fundamentals_report", "")
 
-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
+        curr_situation = f"Market: {market_research_report}\nSentiment: {sentiment_report}\nNews: {news_report}\nFundamentals: {fundamentals_report}"
+        past_memories = memory.get_memories(curr_situation, n_matches=3, min_similarity=0.8)
 
         past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
+        if past_memories:
+            for i, rec in enumerate(past_memories, 1):
+                similarity = rec.get("similarity_score", 0)
+                past_memory_str += f"Bear Memory {i} (similarity: {similarity:.3f}): {rec['recommendation']}\n\n"
+        else:
+            past_memory_str = "No statistically significant bear memories found (similarity < 80%)."
 
         prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.
 

tradingagents/agents/researchers/bull_researcher.py

@@ -1,26 +1,31 @@
-from langchain_core.messages import AIMessage
-import time
-import json
+import functools
 
 
 def create_bull_researcher(llm, memory):
     def bull_node(state) -> dict:
-        investment_debate_state = state["investment_debate_state"]
+        if not state or not isinstance(state, dict):
+            raise ValueError("Invalid state provided to bull_researcher")
+
+        investment_debate_state = state.get("investment_debate_state", {})
         history = investment_debate_state.get("history", "")
         bull_history = investment_debate_state.get("bull_history", "")
 
         current_response = investment_debate_state.get("current_response", "")
-        market_research_report = state["market_report"]
-        sentiment_report = state["sentiment_report"]
-        news_report = state["news_report"]
-        fundamentals_report = state["fundamentals_report"]
+        market_research_report = state.get("market_report", "")
+        sentiment_report = state.get("sentiment_report", "")
+        news_report = state.get("news_report", "")
+        fundamentals_report = state.get("fundamentals_report", "")
 
-        curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
-        past_memories = memory.get_memories(curr_situation, n_matches=2)
+        curr_situation = f"Market: {market_research_report}\nSentiment: {sentiment_report}\nNews: {news_report}\nFundamentals: {fundamentals_report}"
+        past_memories = memory.get_memories(curr_situation, n_matches=3, min_similarity=0.8)
 
         past_memory_str = ""
-        for i, rec in enumerate(past_memories, 1):
-            past_memory_str += rec["recommendation"] + "\n\n"
+        if past_memories:
+            for i, rec in enumerate(past_memories, 1):
+                similarity = rec.get("similarity_score", 0)
+                past_memory_str += f"Bull Memory {i} (similarity: {similarity:.3f}): {rec['recommendation']}\n\n"
+        else:
+            past_memory_str = "No statistically significant bull memories found (similarity < 80%)."
 
         prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.