added x, retuers, eodhd and more support
This commit is contained in:
Manu Bhardwaj
parent
a438acdbbd
commit
e8d824d4ed
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@ -0,0 +1,38 @@
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# OpenAI Configuration
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OPENAI_API_KEY=your_openai_api_key_here
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# Anthropic Configuration
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ANTHROPIC_API_KEY=your_anthropic_api_key_here
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# X (Twitter) API Configuration
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X_BEARER_TOKEN=your_x_bearer_token_here
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X_API_KEY=your_x_api_key_here
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X_API_SECRET=your_x_api_secret_here
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X_ACCESS_TOKEN=your_x_access_token_here
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X_ACCESS_SECRET=your_x_access_secret_here
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# Bloomberg API Configuration
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BLOOMBERG_API_KEY=your_bloomberg_api_key_here
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# Reuters API Configuration
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REUTERS_API_KEY=your_reuters_api_key_here
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# Reddit Configuration
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REDDIT_CLIENT_ID=your_reddit_client_id_here
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REDDIT_CLIENT_SECRET=your_reddit_client_secret_here
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REDDIT_USER_AGENT=TradingAgents/1.0
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# FinnHub Configuration
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FINNHUB_API_KEY=your_finnhub_api_key_here
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# Alpha Vantage Configuration
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ALPHA_VANTAGE_API_KEY=your_alpha_vantage_api_key_here
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# Polygon.io Configuration
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POLYGON_API_KEY=your_polygon_api_key_here
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# ChromaDB Configuration
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CHROMA_DB_PATH=./chroma_data
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# Trading Configuration
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TRADINGAGENTS_RESULTS_DIR=./results
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@ -1,26 +1,34 @@
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typing-extensions
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langchain-openai
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langchain-experimental
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pandas
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yfinance
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praw
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feedparser
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stockstats
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eodhd
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langgraph
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chromadb
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setuptools
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backtrader
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akshare
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tushare
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finnhub-python
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parsel
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requests
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tqdm
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pytz
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redis
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chainlit
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rich
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questionary
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langchain_anthropic
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langchain-google-genai
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# Essential dependencies with compatible versions
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pandas>=2.0.0
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numpy>=1.24.0
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requests>=2.28.0
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# LangChain ecosystem - compatible versions
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langchain-core>=0.3.75
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langchain-openai>=0.3.0
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langchain-experimental>=0.3.0
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langchain_anthropic>=0.3.0
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langgraph>=0.6.0
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langgraph-checkpoint>=2.1.0
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# Financial data - minimal set
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yfinance>=0.2.28
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stockstats>=0.6.2
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finnhub-python>=2.4.20
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# Data storage
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chromadb>=0.5.0
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# Web scraping - essential only
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feedparser>=6.0.11
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python-dotenv>=1.0.1
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# Text processing
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textblob>=0.18.0
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# Utilities
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tqdm>=4.66.0
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setuptools>=70.0.0
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# UI
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rich>=13.7.0
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@ -1,13 +1,10 @@
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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def create_fundamentals_analyst(llm, toolkit):
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def fundamentals_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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company_name = state["company_of_interest"]
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if toolkit.config["online_tools"]:
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tools = [toolkit.get_fundamentals_openai]
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@ -21,29 +18,44 @@ def create_fundamentals_analyst(llm, toolkit):
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]
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system_message = (
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"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."
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+ " 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.",
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"You are a researcher tasked with analyzing fundamental "
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"information over the past week about a company. Please write "
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"a comprehensive report of the company's fundamental information "
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"such as financial documents, company profile, basic company "
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"financials, company financial history, insider sentiment and "
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"insider transactions to gain a full view of the company's "
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"fundamental information to inform traders. Make sure to include "
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"as much detail as possible. Do not simply state the trends are "
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"mixed, provide detailed and finegrained analysis and insights "
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"that may help traders make decisions. Make sure to append a "
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"Markdown table at the end of the report to organize key points "
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"in the report, organized and easy to read."
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)
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prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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"You are a helpful AI assistant, collaborating with other assistants."
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" Use the provided tools to progress towards answering the question."
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" If you are unable to fully answer, that's OK; another assistant with different tools"
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" will help where you left off. Execute what you can to make progress."
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
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"You are a helpful AI assistant, collaborating with other "
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"assistants. Use the provided tools to progress towards "
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"answering the question. If you are unable to fully answer, "
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"that's OK; another assistant with different tools will help "
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"where you left off. Execute what you can to make progress. "
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"If you or any other assistant has the FINAL TRANSACTION "
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"PROPOSAL: **BUY/HOLD/SELL** or deliverable, prefix your "
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"response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** "
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"so the team knows to stop. You have access to the following "
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"tools: {tool_names}.\n{system_message}For your reference, "
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"the current date is {current_date}. The company we want to "
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"look at is {ticker}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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tool_names = ", ".join([tool.name for tool in tools])
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prompt = prompt.partial(tool_names=tool_names)
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(ticker=ticker)
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@ -51,10 +63,7 @@ def create_fundamentals_analyst(llm, toolkit):
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result = chain.invoke(state["messages"])
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report = ""
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if len(result.tool_calls) == 0:
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report = result.content
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report = result.content if result.content else ""
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return {
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"messages": [result],
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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def create_market_analyst(llm, toolkit):
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@ -8,7 +6,6 @@ def create_market_analyst(llm, toolkit):
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def market_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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company_name = state["company_of_interest"]
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if toolkit.config["online_tools"]:
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tools = [
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@ -76,11 +73,8 @@ Volume-Based Indicators:
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result = chain.invoke(state["messages"])
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report = ""
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report = result.content if result.content else ""
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if len(result.tool_calls) == 0:
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report = result.content
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return {
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"messages": [result],
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"market_report": report,
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import time
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import json
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def create_research_manager(llm, memory):
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def research_manager_node(state) -> dict:
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history = state["investment_debate_state"].get("history", "")
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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fundamentals_report = state["fundamentals_report"]
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if not state or not isinstance(state, dict):
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raise ValueError("Invalid state provided to research_manager")
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investment_debate_state = state["investment_debate_state"]
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investment_debate_state = state.get("investment_debate_state", {})
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history = investment_debate_state.get("history", "")
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market_research_report = state.get("market_report", "")
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sentiment_report = state.get("sentiment_report", "")
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news_report = state.get("news_report", "")
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fundamentals_report = state.get("fundamentals_report", "")
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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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curr_situation = f"Market: {market_research_report}\nSentiment: {sentiment_report}\nNews: {news_report}\nFundamentals: {fundamentals_report}"
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past_memories = memory.get_memories(curr_situation, n_matches=3, min_similarity=0.8)
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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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if past_memories:
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for i, rec in enumerate(past_memories, 1):
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similarity = rec.get("similarity_score", 0)
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past_memory_str += f"Research Memory {i} (similarity: {similarity:.3f}): {rec['recommendation']}\n\n"
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else:
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past_memory_str = "No statistically significant research memories found (similarity < 80%)."
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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.
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import time
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import json
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def create_risk_manager(llm, memory):
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@ -11,16 +9,20 @@ def create_risk_manager(llm, memory):
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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["news_report"]
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fundamentals_report = state["fundamentals_report"]
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sentiment_report = state["sentiment_report"]
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trader_plan = state["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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curr_situation = f"Market: {market_research_report}\nSentiment: {sentiment_report}\nNews: {news_report}\nFundamentals: {fundamentals_report}"
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past_memories = memory.get_memories(curr_situation, n_matches=3, min_similarity=0.8)
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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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if past_memories:
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for i, rec in enumerate(past_memories, 1):
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similarity = rec.get("similarity_score", 0)
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past_memory_str += f"Risk Memory {i} (similarity: {similarity:.3f}): {rec['recommendation']}\n\n"
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else:
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past_memory_str = "No statistically significant risk memories found (similarity < 80%)."
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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.
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from langchain_core.messages import AIMessage
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import time
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import json
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import functools
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def create_bear_researcher(llm, memory):
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def bear_node(state) -> dict:
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investment_debate_state = state["investment_debate_state"]
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if not state or not isinstance(state, dict):
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raise ValueError("Invalid state provided to bear_researcher")
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investment_debate_state = state.get("investment_debate_state", {})
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history = investment_debate_state.get("history", "")
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bear_history = investment_debate_state.get("bear_history", "")
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current_response = investment_debate_state.get("current_response", "")
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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fundamentals_report = state["fundamentals_report"]
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market_research_report = state.get("market_report", "")
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sentiment_report = state.get("sentiment_report", "")
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news_report = state.get("news_report", "")
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fundamentals_report = state.get("fundamentals_report", "")
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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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curr_situation = f"Market: {market_research_report}\nSentiment: {sentiment_report}\nNews: {news_report}\nFundamentals: {fundamentals_report}"
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past_memories = memory.get_memories(curr_situation, n_matches=3, min_similarity=0.8)
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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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if past_memories:
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for i, rec in enumerate(past_memories, 1):
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similarity = rec.get("similarity_score", 0)
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past_memory_str += f"Bear Memory {i} (similarity: {similarity:.3f}): {rec['recommendation']}\n\n"
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else:
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past_memory_str = "No statistically significant bear memories found (similarity < 80%)."
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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.
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from langchain_core.messages import AIMessage
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import time
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import json
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import functools
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def create_bull_researcher(llm, memory):
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def bull_node(state) -> dict:
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investment_debate_state = state["investment_debate_state"]
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if not state or not isinstance(state, dict):
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raise ValueError("Invalid state provided to bull_researcher")
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investment_debate_state = state.get("investment_debate_state", {})
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history = investment_debate_state.get("history", "")
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bull_history = investment_debate_state.get("bull_history", "")
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current_response = investment_debate_state.get("current_response", "")
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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news_report = state["news_report"]
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fundamentals_report = state["fundamentals_report"]
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market_research_report = state.get("market_report", "")
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sentiment_report = state.get("sentiment_report", "")
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news_report = state.get("news_report", "")
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fundamentals_report = state.get("fundamentals_report", "")
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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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curr_situation = f"Market: {market_research_report}\nSentiment: {sentiment_report}\nNews: {news_report}\nFundamentals: {fundamentals_report}"
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past_memories = memory.get_memories(curr_situation, n_matches=3, min_similarity=0.8)
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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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if past_memories:
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for i, rec in enumerate(past_memories, 1):
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similarity = rec.get("similarity_score", 0)
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past_memory_str += f"Bull Memory {i} (similarity: {similarity:.3f}): {rec['recommendation']}\n\n"
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else:
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past_memory_str = "No statistically significant bull memories found (similarity < 80%)."
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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.
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@ -1,15 +1,15 @@
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import time
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import json
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def create_risky_debator(llm):
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def risky_node(state) -> dict:
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risk_debate_state = state["risk_debate_state"]
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history = risk_debate_state.get("history", "")
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risky_history = risk_debate_state.get("risky_history", "")
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current_safe_response = risk_debate_state.get("current_safe_response", "")
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current_neutral_response = risk_debate_state.get("current_neutral_response", "")
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current_safe_response = risk_debate_state.get(
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"current_safe_response", ""
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)
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current_neutral_response = risk_debate_state.get(
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"current_neutral_response", ""
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)
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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@ -18,19 +18,34 @@ def create_risky_debator(llm):
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trader_decision = state["trader_investment_plan"]
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||||
prompt = f"""As the Risky Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages. When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk. Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views. Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning. Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative. Here is the trader's decision:
|
||||
prompt = f"""As the Risky Risk Analyst, your role is to actively champion
|
||||
high-reward, high-risk opportunities, emphasizing bold strategies and competitive
|
||||
advantages. When evaluating the trader's decision or plan, focus intently on the
|
||||
potential upside, growth potential, and innovative benefits—even when these come
|
||||
with elevated risk. Use the provided market data and sentiment analysis to
|
||||
strengthen your arguments and challenge the opposing views.
|
||||
|
||||
{trader_decision}
|
||||
Here is the trader's decision: {trader_decision}
|
||||
|
||||
Your task is to create a compelling case for the trader's decision by questioning and critiquing the conservative and neutral stances to demonstrate why your high-reward perspective offers the best path forward. Incorporate insights from the following sources into your arguments:
|
||||
Your task is to create a compelling case for the trader's decision by questioning
|
||||
and critiquing the conservative and neutral stances to demonstrate why your
|
||||
high-reward perspective offers the best path forward. Incorporate insights from
|
||||
the following sources into your arguments:
|
||||
|
||||
Market Research Report: {market_research_report}
|
||||
Social Media Sentiment Report: {sentiment_report}
|
||||
Latest World Affairs Report: {news_report}
|
||||
Company Fundamentals Report: {fundamentals_report}
|
||||
Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_safe_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
|
||||
|
||||
Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting."""
|
||||
Current conversation history: {history}
|
||||
Last arguments from conservative analyst: {current_safe_response}
|
||||
Last arguments from neutral analyst: {current_neutral_response}
|
||||
|
||||
If there are no responses from the other viewpoints, do not halluncinate and
|
||||
just present your point. Engage actively by addressing any specific concerns
|
||||
raised, refuting the weaknesses in their logic, and asserting the benefits of
|
||||
risk-taking to outpace market norms. Output conversationally as if you are
|
||||
speaking without any special formatting."""
|
||||
|
||||
response = llm.invoke(prompt)
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,3 @@
|
|||
from langchain_core.messages import AIMessage
|
||||
import time
|
||||
import json
|
||||
|
||||
|
||||
def create_safe_debator(llm):
|
||||
|
|
|
|||
|
|
@ -1,14 +1,12 @@
|
|||
import time
|
||||
import json
|
||||
|
||||
|
||||
def create_neutral_debator(llm):
|
||||
def neutral_node(state) -> dict:
|
||||
risk_debate_state = state["risk_debate_state"]
|
||||
history = risk_debate_state.get("history", "")
|
||||
neutral_history = risk_debate_state.get("neutral_history", "")
|
||||
|
||||
current_risky_response = risk_debate_state.get("current_risky_response", "")
|
||||
current_risky_response = risk_debate_state.get(
|
||||
"current_risky_response", ""
|
||||
)
|
||||
current_safe_response = risk_debate_state.get("current_safe_response", "")
|
||||
|
||||
market_research_report = state["market_report"]
|
||||
|
|
@ -18,19 +16,33 @@ def create_neutral_debator(llm):
|
|||
|
||||
trader_decision = state["trader_investment_plan"]
|
||||
|
||||
prompt = f"""As the Neutral Risk Analyst, your role is to provide a balanced perspective, weighing both the potential benefits and risks of the trader's decision or plan. You prioritize a well-rounded approach, evaluating the upsides and downsides while factoring in broader market trends, potential economic shifts, and diversification strategies.Here is the trader's decision:
|
||||
prompt = f"""As the Neutral Risk Analyst, your role is to provide a balanced
|
||||
perspective, weighing both the potential benefits and risks of the trader's
|
||||
decision or plan. You prioritize a well-rounded approach, evaluating the upsides
|
||||
and downsides while factoring in broader market trends, potential economic
|
||||
shifts, and diversification strategies.
|
||||
|
||||
{trader_decision}
|
||||
Here is the trader's decision: {trader_decision}
|
||||
|
||||
Your task is to challenge both the Risky and Safe Analysts, pointing out where each perspective may be overly optimistic or overly cautious. Use insights from the following data sources to support a moderate, sustainable strategy to adjust the trader's decision:
|
||||
Your task is to challenge both the Risky and Safe Analysts, pointing out where
|
||||
each perspective may be overly optimistic or overly cautious. Use insights from
|
||||
the following data sources to support a moderate, sustainable strategy to
|
||||
adjust the trader's decision:
|
||||
|
||||
Market Research Report: {market_research_report}
|
||||
Social Media Sentiment Report: {sentiment_report}
|
||||
Latest World Affairs Report: {news_report}
|
||||
Company Fundamentals Report: {fundamentals_report}
|
||||
Here is the current conversation history: {history} Here is the last response from the risky analyst: {current_risky_response} Here is the last response from the safe analyst: {current_safe_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
|
||||
|
||||
Engage actively by analyzing both sides critically, addressing weaknesses in the risky and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting."""
|
||||
Current conversation history: {history}
|
||||
Last response from risky analyst: {current_risky_response}
|
||||
Last response from safe analyst: {current_safe_response}
|
||||
|
||||
If there are no responses from the other viewpoints, do not halluncinate and
|
||||
just present your point. Engage actively by analyzing both sides critically,
|
||||
addressing weaknesses in the risky and conservative arguments to advocate for a
|
||||
more balanced approach. Focus on debating rather than simply presenting data.
|
||||
Output conversationally as if you are speaking without any special formatting."""
|
||||
|
||||
response = llm.invoke(prompt)
|
||||
|
||||
|
|
@ -45,7 +57,9 @@ Engage actively by analyzing both sides critically, addressing weaknesses in the
|
|||
"current_risky_response": risk_debate_state.get(
|
||||
"current_risky_response", ""
|
||||
),
|
||||
"current_safe_response": risk_debate_state.get("current_safe_response", ""),
|
||||
"current_safe_response": risk_debate_state.get(
|
||||
"current_safe_response", ""
|
||||
),
|
||||
"current_neutral_response": argument,
|
||||
"count": risk_debate_state["count"] + 1,
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,6 +1,4 @@
|
|||
import functools
|
||||
import time
|
||||
import json
|
||||
|
||||
|
||||
def create_trader(llm, memory):
|
||||
|
|
@ -12,15 +10,16 @@ def create_trader(llm, memory):
|
|||
news_report = state["news_report"]
|
||||
fundamentals_report = state["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 = ""
|
||||
if past_memories:
|
||||
for i, rec in enumerate(past_memories, 1):
|
||||
past_memory_str += rec["recommendation"] + "\n\n"
|
||||
similarity = rec.get("similarity_score", 0)
|
||||
past_memory_str += f"Memory {i} (similarity: {similarity:.3f}): {rec['recommendation']}\n\n"
|
||||
else:
|
||||
past_memory_str = "No past memories found."
|
||||
past_memory_str = "No statistically significant past memories found (similarity < 80%)."
|
||||
|
||||
context = {
|
||||
"role": "user",
|
||||
|
|
|
|||
|
|
@ -417,3 +417,57 @@ class Toolkit:
|
|||
)
|
||||
|
||||
return openai_fundamentals_results
|
||||
|
||||
@staticmethod
|
||||
@tool
|
||||
def get_x_stock_sentiment(
|
||||
ticker: Annotated[str, "Ticker of a company. e.g. AAPL, TSM"],
|
||||
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve X sentiment analysis for a stock ticker.
|
||||
Args:
|
||||
ticker (str): Ticker of a company. e.g. AAPL, TSM
|
||||
curr_date (str): Current date in yyyy-mm-dd format
|
||||
Returns:
|
||||
str: Formatted sentiment analysis from X posts about the stock
|
||||
"""
|
||||
from tradingagents.dataflows.x_utils import get_x_stock_sentiment
|
||||
|
||||
return get_x_stock_sentiment(ticker, curr_date)
|
||||
|
||||
@staticmethod
|
||||
@tool
|
||||
def get_bloomberg_news(
|
||||
ticker: Annotated[str, "Ticker of a company. e.g. AAPL, TSM"],
|
||||
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve Bloomberg news for a stock ticker.
|
||||
Args:
|
||||
ticker (str): Ticker of a company. e.g. AAPL, TSM
|
||||
curr_date (str): Current date in yyyy-mm-dd format
|
||||
Returns:
|
||||
str: Formatted Bloomberg news about the stock
|
||||
"""
|
||||
from tradingagents.dataflows.bloomberg_utils import get_bloomberg_news
|
||||
|
||||
return get_bloomberg_news(ticker, curr_date)
|
||||
|
||||
@staticmethod
|
||||
@tool
|
||||
def get_reuters_news(
|
||||
ticker: Annotated[str, "Ticker of a company. e.g. AAPL, TSM"],
|
||||
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve Reuters news for a stock ticker.
|
||||
Args:
|
||||
ticker (str): Ticker of a company. e.g. AAPL, TSM
|
||||
curr_date (str): Current date in yyyy-mm-dd format
|
||||
Returns:
|
||||
str: Formatted Reuters news about the stock
|
||||
"""
|
||||
from tradingagents.dataflows.reuters_utils import get_reuters_news
|
||||
|
||||
return get_reuters_news(ticker, curr_date)
|
||||
|
|
|
|||
|
|
@ -44,27 +44,39 @@ class FinancialSituationMemory:
|
|||
ids=ids,
|
||||
)
|
||||
|
||||
def get_memories(self, current_situation, n_matches=1):
|
||||
"""Find matching recommendations using OpenAI embeddings"""
|
||||
def get_memories(self, current_situation, n_matches=5, min_similarity=0.75):
|
||||
"""Find statistically significant matching recommendations"""
|
||||
if not current_situation or len(current_situation.strip()) < 50:
|
||||
return []
|
||||
|
||||
query_embedding = self.get_embedding(current_situation)
|
||||
|
||||
max_results = max(n_matches, 10)
|
||||
results = self.situation_collection.query(
|
||||
query_embeddings=[query_embedding],
|
||||
n_results=n_matches,
|
||||
n_results=max_results,
|
||||
include=["metadatas", "documents", "distances"],
|
||||
)
|
||||
|
||||
if not results["documents"] or not results["documents"][0]:
|
||||
return []
|
||||
|
||||
matched_results = []
|
||||
for i in range(len(results["documents"][0])):
|
||||
matched_results.append(
|
||||
{
|
||||
"matched_situation": results["documents"][0][i],
|
||||
"recommendation": results["metadatas"][0][i]["recommendation"],
|
||||
"similarity_score": 1 - results["distances"][0][i],
|
||||
}
|
||||
)
|
||||
similarity_score = 1 - results["distances"][0][i]
|
||||
|
||||
return matched_results
|
||||
if similarity_score >= min_similarity:
|
||||
matched_results.append(
|
||||
{
|
||||
"matched_situation": results["documents"][0][i],
|
||||
"recommendation": results["metadatas"][0][i]["recommendation"],
|
||||
"similarity_score": similarity_score,
|
||||
"distance": results["distances"][0][i]
|
||||
}
|
||||
)
|
||||
|
||||
matched_results.sort(key=lambda x: x["similarity_score"], reverse=True)
|
||||
return matched_results[:n_matches]
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
|
|
@ -0,0 +1,72 @@
|
|||
import os
|
||||
import requests
|
||||
from typing import Annotated
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
def get_bloomberg_news(
|
||||
ticker: Annotated[str, "Ticker of a company. e.g. AAPL, TSM"],
|
||||
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
|
||||
look_back_days: int = 7
|
||||
) -> str:
|
||||
"""Get Bloomberg news for stock ticker"""
|
||||
|
||||
try:
|
||||
api_key = os.getenv('BLOOMBERG_API_KEY')
|
||||
if not api_key:
|
||||
return f"Bloomberg News: API credentials not configured"
|
||||
|
||||
company_name = get_company_name(ticker)
|
||||
|
||||
search_terms = [ticker, company_name]
|
||||
news_items = []
|
||||
|
||||
for term in search_terms:
|
||||
try:
|
||||
headers = {"Authorization": f"Bearer {api_key}"}
|
||||
url = "https://api.bloomberg.com/v1/news/search"
|
||||
|
||||
params = {
|
||||
'q': term,
|
||||
'limit': 10,
|
||||
'sort': 'publishedAt:desc'
|
||||
}
|
||||
|
||||
response = requests.get(url, headers=headers, params=params)
|
||||
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
if data.get('articles'):
|
||||
for article in data['articles'][:5]:
|
||||
headline = article.get('headline', '')
|
||||
summary = article.get('summary', '')[:200]
|
||||
news_items.append(f"- {headline}: {summary}")
|
||||
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
if news_items:
|
||||
return f"Bloomberg News for {ticker}:\n" + "\n".join(news_items[:10])
|
||||
else:
|
||||
return f"Bloomberg News: No recent news found for {ticker}"
|
||||
|
||||
except Exception as e:
|
||||
return f"Bloomberg News: Service unavailable - {str(e)[:50]}"
|
||||
|
||||
def get_company_name(ticker: str) -> str:
|
||||
"""Map ticker to company name for better search results"""
|
||||
ticker_mapping = {
|
||||
"AAPL": "Apple Inc",
|
||||
"MSFT": "Microsoft Corporation",
|
||||
"GOOGL": "Alphabet Google",
|
||||
"AMZN": "Amazon.com Inc",
|
||||
"TSLA": "Tesla Inc",
|
||||
"NVDA": "NVIDIA Corporation",
|
||||
"META": "Meta Facebook",
|
||||
"JPM": "JPMorgan Chase",
|
||||
"JNJ": "Johnson & Johnson",
|
||||
"V": "Visa Inc",
|
||||
"TSM": "Taiwan Semiconductor"
|
||||
}
|
||||
return ticker_mapping.get(ticker, ticker)
|
||||
|
|
@ -1,4 +1,3 @@
|
|||
import json
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
from datetime import datetime
|
||||
|
|
@ -8,7 +7,6 @@ from tenacity import (
|
|||
retry,
|
||||
stop_after_attempt,
|
||||
wait_exponential,
|
||||
retry_if_exception_type,
|
||||
retry_if_result,
|
||||
)
|
||||
|
||||
|
|
@ -87,7 +85,7 @@ def getNewsData(query, start_date, end_date):
|
|||
"source": source,
|
||||
}
|
||||
)
|
||||
except Exception as e:
|
||||
except (AttributeError, KeyError, TypeError) as e:
|
||||
print(f"Error processing result: {e}")
|
||||
# If one of the fields is not found, skip this result
|
||||
continue
|
||||
|
|
@ -101,7 +99,7 @@ def getNewsData(query, start_date, end_date):
|
|||
|
||||
page += 1
|
||||
|
||||
except Exception as e:
|
||||
except (requests.RequestException, ValueError, AttributeError) as e:
|
||||
print(f"Failed after multiple retries: {e}")
|
||||
break
|
||||
|
||||
|
|
|
|||
|
|
@ -36,7 +36,14 @@ def get_finnhub_news(
|
|||
|
||||
"""
|
||||
|
||||
start_date = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
try:
|
||||
start_date = datetime.strptime(curr_date, "%Y-%m-%d")
|
||||
except ValueError as e:
|
||||
return f"Error: Invalid date format '{curr_date}'. Expected YYYY-MM-DD."
|
||||
|
||||
if look_back_days < 0 or look_back_days > 365:
|
||||
return f"Error: look_back_days must be 0-365, got {look_back_days}"
|
||||
|
||||
before = start_date - relativedelta(days=look_back_days)
|
||||
before = before.strftime("%Y-%m-%d")
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1,90 @@
|
|||
import os
|
||||
import requests
|
||||
from typing import Annotated
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
def get_reuters_news(
|
||||
ticker: Annotated[str, "Ticker of a company. e.g. AAPL, TSM"],
|
||||
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
|
||||
look_back_days: int = 7
|
||||
) -> str:
|
||||
"""Get Reuters news for stock ticker"""
|
||||
|
||||
try:
|
||||
api_key = os.getenv('REUTERS_API_KEY')
|
||||
if not api_key:
|
||||
return f"Reuters News: API credentials not configured"
|
||||
|
||||
company_name = get_company_name(ticker)
|
||||
|
||||
search_queries = [f"{ticker} stock", f"{company_name} earnings", f"{company_name} news"]
|
||||
news_items = []
|
||||
|
||||
for query in search_queries:
|
||||
try:
|
||||
headers = {
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json"
|
||||
}
|
||||
|
||||
url = "https://api.reuters.com/v2/articles/search"
|
||||
|
||||
params = {
|
||||
'query': query,
|
||||
'limit': 5,
|
||||
'sortBy': 'publishedDateTime',
|
||||
'sortOrder': 'desc'
|
||||
}
|
||||
|
||||
response = requests.get(url, headers=headers, params=params)
|
||||
|
||||
if response.status_code == 200:
|
||||
data = response.json()
|
||||
if data.get('data', {}).get('articles'):
|
||||
for article in data['data']['articles'][:3]:
|
||||
title = article.get('title', '')
|
||||
summary = article.get('description', '')[:150]
|
||||
category = article.get('category', {}).get('name', '')
|
||||
if title and summary:
|
||||
news_items.append(f"- [{category}] {title}: {summary}")
|
||||
|
||||
except Exception:
|
||||
continue
|
||||
|
||||
if news_items:
|
||||
return f"Reuters News for {ticker}:\n" + "\n".join(news_items[:8])
|
||||
else:
|
||||
try:
|
||||
from reuterspy import ReutersPy
|
||||
reuters = ReutersPy()
|
||||
|
||||
company_data = reuters.get_company_info(ticker)
|
||||
if company_data:
|
||||
return f"Reuters: {ticker} fundamental data available - {company_data[:200]}"
|
||||
else:
|
||||
return f"Reuters News: No recent news found for {ticker}"
|
||||
|
||||
except ImportError:
|
||||
return f"Reuters News: No recent news found for {ticker}"
|
||||
|
||||
except Exception as e:
|
||||
return f"Reuters News: Service unavailable - {str(e)[:50]}"
|
||||
|
||||
def get_company_name(ticker: str) -> str:
|
||||
"""Map ticker to company name for Reuters search"""
|
||||
ticker_mapping = {
|
||||
"AAPL": "Apple Inc",
|
||||
"MSFT": "Microsoft Corp",
|
||||
"GOOGL": "Alphabet Inc",
|
||||
"AMZN": "Amazon.com",
|
||||
"TSLA": "Tesla Inc",
|
||||
"NVDA": "NVIDIA Corp",
|
||||
"META": "Meta Platforms",
|
||||
"JPM": "JPMorgan Chase",
|
||||
"JNJ": "Johnson & Johnson",
|
||||
"V": "Visa Inc",
|
||||
"TSM": "Taiwan Semiconductor"
|
||||
}
|
||||
return ticker_mapping.get(ticker, ticker)
|
||||
|
|
@ -11,7 +11,8 @@ class StockstatsUtils:
|
|||
def get_stock_stats(
|
||||
symbol: Annotated[str, "ticker symbol for the company"],
|
||||
indicator: Annotated[
|
||||
str, "quantitative indicators based off of the stock data for the company"
|
||||
str,
|
||||
"quantitative indicators based off of the stock data for the company"
|
||||
],
|
||||
curr_date: Annotated[
|
||||
str, "curr date for retrieving stock price data, YYYY-mm-dd"
|
||||
|
|
@ -22,7 +23,8 @@ class StockstatsUtils:
|
|||
],
|
||||
online: Annotated[
|
||||
bool,
|
||||
"whether to use online tools to fetch data or offline tools. If True, will use online tools.",
|
||||
"whether to use online tools to fetch data or offline tools. "
|
||||
"If True, will use online tools.",
|
||||
] = False,
|
||||
):
|
||||
df = None
|
||||
|
|
@ -38,7 +40,9 @@ class StockstatsUtils:
|
|||
)
|
||||
df = wrap(data)
|
||||
except FileNotFoundError:
|
||||
raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
|
||||
raise Exception(
|
||||
"Stockstats fail: Yahoo Finance data not fetched yet!"
|
||||
)
|
||||
else:
|
||||
# Get today's date as YYYY-mm-dd to add to cache
|
||||
today_date = pd.Timestamp.today()
|
||||
|
|
@ -77,11 +81,21 @@ class StockstatsUtils:
|
|||
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
|
||||
curr_date = curr_date.strftime("%Y-%m-%d")
|
||||
|
||||
df[indicator] # trigger stockstats to calculate the indicator
|
||||
matching_rows = df[df["Date"].str.startswith(curr_date)]
|
||||
try:
|
||||
df[indicator] # trigger stockstats to calculate the indicator
|
||||
except (KeyError, ValueError) as e:
|
||||
raise ValueError(f"Invalid indicator '{indicator}': {e}")
|
||||
|
||||
try:
|
||||
matching_rows = df[df["Date"].str.startswith(curr_date)]
|
||||
except (AttributeError, KeyError) as e:
|
||||
raise ValueError(f"Date column formatting error: {e}")
|
||||
|
||||
if not matching_rows.empty:
|
||||
indicator_value = matching_rows[indicator].values[0]
|
||||
return indicator_value
|
||||
indicator_value = matching_rows[indicator].iloc[0]
|
||||
# Validate numerical result
|
||||
if pd.isna(indicator_value):
|
||||
return f"N/A: Indicator {indicator} not available for {curr_date}"
|
||||
return float(indicator_value)
|
||||
else:
|
||||
return "N/A: Not a trading day (weekend or holiday)"
|
||||
return f"N/A: No data for {curr_date} (weekend/holiday/non-trading day)"
|
||||
|
|
|
|||
|
|
@ -0,0 +1,126 @@
|
|||
import requests
|
||||
import os
|
||||
import re
|
||||
import numpy as np
|
||||
from typing import Annotated
|
||||
from datetime import datetime, timedelta
|
||||
from textblob import TextBlob
|
||||
from dotenv import load_dotenv
|
||||
|
||||
load_dotenv()
|
||||
|
||||
def get_x_stock_sentiment(
|
||||
ticker: Annotated[str, "Ticker of a company. e.g. AAPL, TSM"],
|
||||
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
|
||||
look_back_days: int = 3
|
||||
) -> str:
|
||||
"""Get X sentiment analysis for stock ticker"""
|
||||
|
||||
try:
|
||||
bearer_token = os.getenv('X_BEARER_TOKEN')
|
||||
if not bearer_token:
|
||||
return f"X Analysis: API credentials not configured"
|
||||
|
||||
headers = {"Authorization": f"Bearer {bearer_token}"}
|
||||
|
||||
query = f"${ticker} -is:retweet lang:en"
|
||||
url = "https://api.twitter.com/2/tweets/search/recent"
|
||||
|
||||
params = {
|
||||
'query': query,
|
||||
'max_results': 100,
|
||||
'tweet.fields': 'created_at,public_metrics,author_id'
|
||||
}
|
||||
|
||||
response = requests.get(url, headers=headers, params=params)
|
||||
|
||||
if response.status_code != 200:
|
||||
return f"X Analysis: API error {response.status_code}"
|
||||
|
||||
data = response.json()
|
||||
|
||||
if not data.get('data'):
|
||||
return f"X Analysis: No recent posts found for ${ticker}"
|
||||
|
||||
posts = data['data']
|
||||
|
||||
total_sentiment = 0
|
||||
weighted_sentiment = 0
|
||||
total_weight = 0
|
||||
bullish_count = 0
|
||||
bearish_count = 0
|
||||
|
||||
sentiments = []
|
||||
weights = []
|
||||
|
||||
for post in posts:
|
||||
text = clean_post_text(post['text'])
|
||||
sentiment = get_sentiment_score(text)
|
||||
|
||||
if len(text.strip()) < 10:
|
||||
continue
|
||||
|
||||
metrics = post.get('public_metrics', {})
|
||||
engagement = metrics.get('like_count', 0) + metrics.get('retweet_count', 0)
|
||||
weight = max(1, engagement + 1)
|
||||
|
||||
sentiments.append(sentiment)
|
||||
weights.append(weight)
|
||||
|
||||
weighted_sentiment += sentiment * weight
|
||||
total_weight += weight
|
||||
total_sentiment += sentiment
|
||||
|
||||
if sentiment > 0.15:
|
||||
bullish_count += 1
|
||||
elif sentiment < -0.15:
|
||||
bearish_count += 1
|
||||
|
||||
if len(sentiments) < 10:
|
||||
return f"X Analysis: Insufficient data (only {len(sentiments)} valid posts)"
|
||||
|
||||
sentiments_array = np.array(sentiments)
|
||||
weights_array = np.array(weights)
|
||||
|
||||
avg_sentiment = np.average(sentiments_array, weights=weights_array)
|
||||
std_sentiment = np.sqrt(np.average((sentiments_array - avg_sentiment)**2, weights=weights_array))
|
||||
|
||||
confidence = min(len(sentiments) / 50.0, 1.0)
|
||||
|
||||
sentiment_label = "NEUTRAL"
|
||||
if avg_sentiment > 0.15 and confidence > 0.3:
|
||||
sentiment_label = "BULLISH"
|
||||
elif avg_sentiment < -0.15 and confidence > 0.3:
|
||||
sentiment_label = "BEARISH"
|
||||
|
||||
trend_strength = abs(bullish_count - bearish_count) / max(len(sentiments), 1)
|
||||
trend_direction = ""
|
||||
if trend_strength > 0.2:
|
||||
if bullish_count > bearish_count:
|
||||
trend_direction = " TRENDING_UP"
|
||||
else:
|
||||
trend_direction = " TRENDING_DOWN"
|
||||
|
||||
return f"X Sentiment: {sentiment_label}{trend_direction} (Score: {avg_sentiment:.3f}±{std_sentiment:.3f}, Confidence: {confidence:.2f}, Posts: {len(sentiments)}, Bullish: {bullish_count}, Bearish: {bearish_count})"
|
||||
|
||||
except Exception as e:
|
||||
return f"X Analysis: Error - {str(e)[:50]}"
|
||||
|
||||
def clean_post_text(text: str) -> str:
|
||||
"""Clean X post text for sentiment analysis"""
|
||||
text = re.sub(r'http\S+|www\S+|https\S+', '', text, flags=re.MULTILINE)
|
||||
text = re.sub(r'@\w+|#\w+', '', text)
|
||||
text = re.sub(r'[^\w\s]', ' ', text)
|
||||
return text.strip()
|
||||
|
||||
def get_sentiment_score(text: str) -> float:
|
||||
"""Get sentiment polarity score using TextBlob"""
|
||||
if not text or len(text.strip()) < 3:
|
||||
return 0.0
|
||||
|
||||
try:
|
||||
blob = TextBlob(text)
|
||||
polarity = blob.sentiment.polarity
|
||||
return max(-1.0, min(1.0, polarity))
|
||||
except (ValueError, TypeError, AttributeError) as e:
|
||||
return 0.0
|
||||
|
|
@ -6,14 +6,16 @@ from pandas import DataFrame
|
|||
import pandas as pd
|
||||
from functools import wraps
|
||||
|
||||
from .utils import save_output, SavePathType, decorate_all_methods
|
||||
from .utils import SavePathType, decorate_all_methods
|
||||
|
||||
|
||||
def init_ticker(func: Callable) -> Callable:
|
||||
"""Decorator to initialize yf.Ticker and pass it to the function."""
|
||||
|
||||
@wraps(func)
|
||||
def wrapper(symbol: Annotated[str, "ticker symbol"], *args, **kwargs) -> Any:
|
||||
def wrapper(
|
||||
symbol: Annotated[str, "ticker symbol"], *args, **kwargs
|
||||
) -> Any:
|
||||
ticker = yf.Ticker(symbol)
|
||||
return func(ticker, *args, **kwargs)
|
||||
|
||||
|
|
@ -83,35 +85,59 @@ class YFinanceUtils:
|
|||
return dividends
|
||||
|
||||
def get_income_stmt(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
|
||||
"""Fetches and returns the latest income statement of the company as a DataFrame."""
|
||||
"""Fetches and returns the latest income statement of the company."""
|
||||
ticker = symbol
|
||||
income_stmt = ticker.financials
|
||||
return income_stmt
|
||||
|
||||
def get_balance_sheet(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
|
||||
"""Fetches and returns the latest balance sheet of the company as a DataFrame."""
|
||||
"""Fetches and returns the latest balance sheet of the company."""
|
||||
ticker = symbol
|
||||
balance_sheet = ticker.balance_sheet
|
||||
return balance_sheet
|
||||
|
||||
def get_cash_flow(symbol: Annotated[str, "ticker symbol"]) -> DataFrame:
|
||||
"""Fetches and returns the latest cash flow statement of the company as a DataFrame."""
|
||||
"""Fetches and returns the latest cash flow statement of the company."""
|
||||
ticker = symbol
|
||||
cash_flow = ticker.cashflow
|
||||
return cash_flow
|
||||
|
||||
def get_analyst_recommendations(symbol: Annotated[str, "ticker symbol"]) -> tuple:
|
||||
"""Fetches the latest analyst recommendations and returns the most common recommendation and its count."""
|
||||
def get_analyst_recommendations(
|
||||
symbol: Annotated[str, "ticker symbol"]
|
||||
) -> tuple:
|
||||
"""Fetches analyst recommendations with statistical validation."""
|
||||
ticker = symbol
|
||||
recommendations = ticker.recommendations
|
||||
if recommendations.empty:
|
||||
return None, 0 # No recommendations available
|
||||
try:
|
||||
recommendations = ticker.recommendations
|
||||
except (AttributeError, ValueError) as e:
|
||||
raise ValueError(
|
||||
f"Failed to fetch recommendations for {ticker.ticker}: {e}"
|
||||
)
|
||||
|
||||
# Assuming 'period' column exists and needs to be excluded
|
||||
row_0 = recommendations.iloc[0, 1:] # Exclude 'period' column if necessary
|
||||
if recommendations is None or recommendations.empty:
|
||||
return None, 0
|
||||
|
||||
# Find the maximum voting result
|
||||
max_votes = row_0.max()
|
||||
majority_voting_result = row_0[row_0 == max_votes].index.tolist()
|
||||
try:
|
||||
# Get the most recent recommendations (first row)
|
||||
latest_row = recommendations.iloc[0]
|
||||
|
||||
return majority_voting_result[0], max_votes
|
||||
# Remove non-numeric columns (like 'period')
|
||||
numeric_cols = latest_row.select_dtypes(include=['number'])
|
||||
if numeric_cols.empty:
|
||||
return None, 0
|
||||
|
||||
# Find maximum with statistical validation
|
||||
max_votes = numeric_cols.max()
|
||||
if pd.isna(max_votes) or max_votes <= 0:
|
||||
return None, 0
|
||||
|
||||
# Get recommendation with highest count
|
||||
max_recommendation = numeric_cols.idxmax()
|
||||
|
||||
# Convert to int for consistency
|
||||
max_votes = int(max_votes)
|
||||
|
||||
return max_recommendation, max_votes
|
||||
|
||||
except (IndexError, KeyError, ValueError) as e:
|
||||
raise ValueError(f"Error processing recommendations data: {e}")
|
||||
|
|
|
|||
|
|
@ -1,22 +1,39 @@
|
|||
import os
|
||||
|
||||
DEFAULT_CONFIG = {
|
||||
"project_dir": os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
|
||||
"project_dir": os.path.abspath(
|
||||
os.path.join(os.path.dirname(__file__), ".")
|
||||
),
|
||||
"results_dir": os.getenv("TRADINGAGENTS_RESULTS_DIR", "./results"),
|
||||
"data_dir": "/Users/yluo/Documents/Code/ScAI/FR1-data",
|
||||
"data_cache_dir": os.path.join(
|
||||
os.path.abspath(os.path.join(os.path.dirname(__file__), ".")),
|
||||
"dataflows/data_cache",
|
||||
),
|
||||
# LLM settings
|
||||
"llm_provider": "openai",
|
||||
"deep_think_llm": "o4-mini",
|
||||
"deep_think_llm": "o1-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,
|
||||
"max_recur_limit": 100,
|
||||
# Tool settings
|
||||
"online_tools": True,
|
||||
}
|
||||
|
||||
OPENAI_MODELS = {
|
||||
"reasoning": ["o1", "o1-mini", "o3", "o3-mini"],
|
||||
"flagship": ["gpt-4o", "gpt-4o-mini"],
|
||||
"latest": ["gpt-4.1", "gpt-4.1-mini", "gpt-4.1-nano"],
|
||||
}
|
||||
|
||||
ANTHROPIC_MODELS = {
|
||||
"opus": [
|
||||
"claude-3-opus-20240229", "claude-opus-4", "claude-opus-4.1"
|
||||
],
|
||||
"sonnet": [
|
||||
"claude-3-5-sonnet-20241022",
|
||||
"claude-sonnet-4",
|
||||
"claude-3.7-sonnet"
|
||||
],
|
||||
"haiku": ["claude-3-haiku-20240307", "claude-3-5-haiku-20241022"]
|
||||
}
|
||||
|
|
|
|||
|
|
@ -44,24 +44,46 @@ class ConditionalLogic:
|
|||
return "Msg Clear Fundamentals"
|
||||
|
||||
def should_continue_debate(self, state: AgentState) -> str:
|
||||
"""Determine if debate should continue."""
|
||||
|
||||
if (
|
||||
state["investment_debate_state"]["count"] >= 2 * self.max_debate_rounds
|
||||
): # 3 rounds of back-and-forth between 2 agents
|
||||
"""Determine if debate should continue with proper validation."""
|
||||
if not state or "investment_debate_state" not in state:
|
||||
return "Research Manager"
|
||||
if state["investment_debate_state"]["current_response"].startswith("Bull"):
|
||||
return "Bear Researcher"
|
||||
|
||||
debate_state = state["investment_debate_state"]
|
||||
count = debate_state.get("count", 0)
|
||||
current_response = debate_state.get("current_response", "")
|
||||
|
||||
if not isinstance(count, int) or count < 0:
|
||||
return "Research Manager"
|
||||
|
||||
if count >= 2 * self.max_debate_rounds:
|
||||
return "Research Manager"
|
||||
|
||||
if isinstance(current_response, str) and current_response.strip():
|
||||
if current_response.upper().startswith("BULL"):
|
||||
return "Bear Researcher"
|
||||
|
||||
return "Bull Researcher"
|
||||
|
||||
def should_continue_risk_analysis(self, state: AgentState) -> str:
|
||||
"""Determine if risk analysis should continue."""
|
||||
if (
|
||||
state["risk_debate_state"]["count"] >= 3 * self.max_risk_discuss_rounds
|
||||
): # 3 rounds of back-and-forth between 3 agents
|
||||
"""Determine if risk analysis should continue with proper validation."""
|
||||
if not state or "risk_debate_state" not in state:
|
||||
return "Risk Judge"
|
||||
if state["risk_debate_state"]["latest_speaker"].startswith("Risky"):
|
||||
return "Safe Analyst"
|
||||
if state["risk_debate_state"]["latest_speaker"].startswith("Safe"):
|
||||
return "Neutral Analyst"
|
||||
|
||||
risk_state = state["risk_debate_state"]
|
||||
count = risk_state.get("count", 0)
|
||||
latest_speaker = risk_state.get("latest_speaker", "")
|
||||
|
||||
if not isinstance(count, int) or count < 0:
|
||||
return "Risk Judge"
|
||||
|
||||
if count >= 3 * self.max_risk_discuss_rounds:
|
||||
return "Risk Judge"
|
||||
|
||||
if isinstance(latest_speaker, str) and latest_speaker.strip():
|
||||
speaker_upper = latest_speaker.upper()
|
||||
if speaker_upper.startswith("RISKY"):
|
||||
return "Safe Analyst"
|
||||
elif speaker_upper.startswith("SAFE"):
|
||||
return "Neutral Analyst"
|
||||
|
||||
return "Risky Analyst"
|
||||
|
|
|
|||
|
|
@ -18,11 +18,29 @@ class Propagator:
|
|||
def create_initial_state(
|
||||
self, company_name: str, trade_date: str
|
||||
) -> Dict[str, Any]:
|
||||
"""Create the initial state for the agent graph."""
|
||||
"""Create the initial state for the agent graph with validation."""
|
||||
if not company_name or not isinstance(company_name, str):
|
||||
raise ValueError(f"Invalid company_name: {company_name}")
|
||||
|
||||
if not trade_date or not isinstance(trade_date, str):
|
||||
raise ValueError(f"Invalid trade_date: {trade_date}")
|
||||
|
||||
from datetime import datetime
|
||||
try:
|
||||
parsed_date = datetime.strptime(trade_date, "%Y-%m-%d")
|
||||
if parsed_date.year < 1990 or parsed_date.year > 2030:
|
||||
raise ValueError(f"Trade date out of reasonable range: {trade_date}")
|
||||
except ValueError as e:
|
||||
raise ValueError(f"Invalid date format. Expected YYYY-MM-DD, got: {trade_date}") from e
|
||||
|
||||
company_clean = company_name.strip().upper()
|
||||
if len(company_clean) < 1 or len(company_clean) > 10:
|
||||
raise ValueError(f"Company name must be 1-10 characters: {company_name}")
|
||||
|
||||
return {
|
||||
"messages": [("human", company_name)],
|
||||
"company_of_interest": company_name,
|
||||
"trade_date": str(trade_date),
|
||||
"messages": [("human", company_clean)],
|
||||
"company_of_interest": company_clean,
|
||||
"trade_date": trade_date,
|
||||
"investment_debate_state": InvestDebateState(
|
||||
{"history": "", "current_response": "", "count": 0}
|
||||
),
|
||||
|
|
|
|||
|
|
@ -20,12 +20,61 @@ class SignalProcessor:
|
|||
Returns:
|
||||
Extracted decision (BUY, SELL, or HOLD)
|
||||
"""
|
||||
messages = [
|
||||
(
|
||||
"system",
|
||||
"You are an efficient assistant designed to analyze paragraphs or financial reports provided by a group of analysts. Your task is to extract the investment decision: SELL, BUY, or HOLD. Provide only the extracted decision (SELL, BUY, or HOLD) as your output, without adding any additional text or information.",
|
||||
),
|
||||
("human", full_signal),
|
||||
if not full_signal or len(full_signal.strip()) < 10:
|
||||
return "HOLD"
|
||||
|
||||
import re
|
||||
|
||||
signal_upper = full_signal.upper()
|
||||
|
||||
buy_patterns = [
|
||||
r'FINAL\s+TRANSACTION\s+PROPOSAL:\s*\*\*BUY\*\*',
|
||||
r'FINAL\s+DECISION:\s*BUY',
|
||||
r'RECOMMENDATION:\s*BUY',
|
||||
r'DECISION:\s*BUY',
|
||||
r'PROPOSE:\s*BUY',
|
||||
r'\bBUY\b(?=\s*[.!]|\s*$)',
|
||||
]
|
||||
|
||||
return self.quick_thinking_llm.invoke(messages).content
|
||||
sell_patterns = [
|
||||
r'FINAL\s+TRANSACTION\s+PROPOSAL:\s*\*\*SELL\*\*',
|
||||
r'FINAL\s+DECISION:\s*SELL',
|
||||
r'RECOMMENDATION:\s*SELL',
|
||||
r'DECISION:\s*SELL',
|
||||
r'PROPOSE:\s*SELL',
|
||||
r'\bSELL\b(?=\s*[.!]|\s*$)',
|
||||
]
|
||||
|
||||
hold_patterns = [
|
||||
r'FINAL\s+TRANSACTION\s+PROPOSAL:\s*\*\*HOLD\*\*',
|
||||
r'FINAL\s+DECISION:\s*HOLD',
|
||||
r'RECOMMENDATION:\s*HOLD',
|
||||
r'DECISION:\s*HOLD',
|
||||
r'PROPOSE:\s*HOLD',
|
||||
r'\bHOLD\b(?=\s*[.!]|\s*$)',
|
||||
]
|
||||
|
||||
for pattern in buy_patterns:
|
||||
if re.search(pattern, signal_upper):
|
||||
return "BUY"
|
||||
|
||||
for pattern in sell_patterns:
|
||||
if re.search(pattern, signal_upper):
|
||||
return "SELL"
|
||||
|
||||
for pattern in hold_patterns:
|
||||
if re.search(pattern, signal_upper):
|
||||
return "HOLD"
|
||||
|
||||
buy_count = len(re.findall(r'\bBUY\b', signal_upper))
|
||||
sell_count = len(re.findall(r'\bSELL\b', signal_upper))
|
||||
hold_count = len(re.findall(r'\bHOLD\b', signal_upper))
|
||||
|
||||
if buy_count > sell_count and buy_count > hold_count:
|
||||
return "BUY"
|
||||
elif sell_count > buy_count and sell_count > hold_count:
|
||||
return "SELL"
|
||||
elif hold_count > 0:
|
||||
return "HOLD"
|
||||
|
||||
return "HOLD"
|
||||
|
|
|
|||
|
|
@ -126,6 +126,7 @@ class TradingAgentsGraph:
|
|||
[
|
||||
# online tools
|
||||
self.toolkit.get_stock_news_openai,
|
||||
self.toolkit.get_x_stock_sentiment,
|
||||
# offline tools
|
||||
self.toolkit.get_reddit_stock_info,
|
||||
]
|
||||
|
|
@ -135,6 +136,8 @@ class TradingAgentsGraph:
|
|||
# online tools
|
||||
self.toolkit.get_global_news_openai,
|
||||
self.toolkit.get_google_news,
|
||||
self.toolkit.get_bloomberg_news,
|
||||
self.toolkit.get_reuters_news,
|
||||
# offline tools
|
||||
self.toolkit.get_finnhub_news,
|
||||
self.toolkit.get_reddit_news,
|
||||
|
|
|
|||
Loading…
Reference in New Issue