Updated to support news and fundamentals analysis for non-openai llms
This commit is contained in:
sdk451
parent
b256159d9e
commit
b600d59e31
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@ -771,7 +771,7 @@ def run_analysis():
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func(*args, **kwargs)
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timestamp, message_type, content = obj.messages[-1]
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content = content.replace("\n", " ") # Replace newlines with spaces
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with open(log_file, "a") as f:
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with open(log_file, "a", encoding="utf-8") as f:
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f.write(f"{timestamp} [{message_type}] {content}\n")
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return wrapper
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@ -782,7 +782,7 @@ def run_analysis():
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func(*args, **kwargs)
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timestamp, tool_name, args = obj.tool_calls[-1]
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args_str = ", ".join(f"{k}={v}" for k, v in args.items())
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with open(log_file, "a") as f:
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with open(log_file, "a", encoding="utf-8") as f:
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f.write(f"{timestamp} [Tool Call] {tool_name}({args_str})\n")
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return wrapper
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@ -795,7 +795,7 @@ def run_analysis():
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content = obj.report_sections[section_name]
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if content:
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file_name = f"{section_name}.md"
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with open(report_dir / file_name, "w") as f:
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with open(report_dir / file_name, "w", encoding="utf-8") as f:
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f.write(content)
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return wrapper
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@ -14,6 +14,8 @@ from tqdm import tqdm
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import yfinance as yf
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from openai import OpenAI
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from .config import get_config, set_config, DATA_DIR
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from langchain_google_genai import ChatGoogleGenerativeAI
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from langchain_anthropic import ChatAnthropic
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def get_finnhub_news(
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@ -704,104 +706,207 @@ def get_YFin_data(
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def get_stock_news_openai(ticker, curr_date):
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config = get_config()
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client = OpenAI(base_url=config["backend_url"])
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provider = config["llm_provider"].lower()
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if provider == "openai":
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client = OpenAI(base_url=config["backend_url"])
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response = client.responses.create(
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model=config["quick_think_llm"],
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input=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"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.",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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}
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],
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temperature=1,
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max_output_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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else:
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# Use Finnhub and Google News/Reddit for real news/social data
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news = get_finnhub_news(ticker, curr_date, 7)
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reddit = get_reddit_company_news(ticker, curr_date, 7, 5)
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google_news = get_google_news(ticker, curr_date, 7)
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context = f"""
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# Finnhub News
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{news}
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response = client.responses.create(
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model=config["quick_think_llm"],
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input=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"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.",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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}
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],
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temperature=1,
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max_output_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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# Reddit News
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{reddit}
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# Google News
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{google_news}
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"""
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prompt = (
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f"Given the following real company news and social data for {ticker} as of {curr_date}, "
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"write a detailed news and sentiment analysis. Summarize key events, sentiment, and any trends that would affect trading. "
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"Present the information in a markdown table at the end.\n\n"
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f"{context}"
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)
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if provider == "google":
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google_api_key = os.getenv("GOOGLE_API_KEY")
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llm = ChatGoogleGenerativeAI(model=config["quick_think_llm"], google_api_key=google_api_key)
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result = llm.invoke(prompt)
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return result.content
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elif provider == "anthropic":
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anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
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llm = ChatAnthropic(model_name=config["quick_think_llm"], api_key=anthropic_api_key)
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result = llm.invoke(prompt)
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return result.content
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else:
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return "Stock news retrieval is not supported for this LLM provider."
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def get_global_news_openai(curr_date):
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config = get_config()
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client = OpenAI(base_url=config["backend_url"])
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response = client.responses.create(
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model=config["quick_think_llm"],
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input=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"text": f"Can you search global or macroeconomics news from 7 days before {curr_date} to {curr_date} that would be informative for trading purposes? Make sure you only get the data posted during that period.",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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}
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],
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temperature=1,
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max_output_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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provider = config["llm_provider"].lower()
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if provider == "openai":
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client = OpenAI(base_url=config["backend_url"])
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response = client.responses.create(
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model=config["quick_think_llm"],
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input=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"text": f"Can you search global or macroeconomics news from 7 days before {curr_date} to {curr_date} that would be informative for trading purposes? Make sure you only get the data posted during that period.",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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}
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],
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temperature=1,
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max_output_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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else:
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reddit = get_reddit_global_news(curr_date, 7, 5)
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google_news = get_google_news("global macroeconomics", curr_date, 7)
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context = f"""
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# Reddit Global News
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{reddit}
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# Google News
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{google_news}
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"""
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prompt = (
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f"Given the following real global and macroeconomic news as of {curr_date}, "
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"write a detailed macroeconomic news analysis. Summarize key events, sentiment, and any trends that would affect trading. "
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"Present the information in a markdown table at the end.\n\n"
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f"{context}"
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)
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if provider == "google":
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google_api_key = os.getenv("GOOGLE_API_KEY")
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llm = ChatGoogleGenerativeAI(model=config["quick_think_llm"], google_api_key=google_api_key)
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result = llm.invoke(prompt)
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return result.content
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elif provider == "anthropic":
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anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
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llm = ChatAnthropic(model_name=config["quick_think_llm"], api_key=anthropic_api_key)
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result = llm.invoke(prompt)
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return result.content
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else:
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return "Global news retrieval is not supported for this LLM provider."
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def get_fundamentals_openai(ticker, curr_date):
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config = get_config()
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client = OpenAI(base_url=config["backend_url"])
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provider = config["llm_provider"].lower()
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if provider == "openai":
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client = OpenAI(base_url=config["backend_url"])
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response = client.responses.create(
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model=config["quick_think_llm"],
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input=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"text": f"Can you search Fundamental for discussions on {ticker} during of the month before {curr_date} to the month of {curr_date}. Make sure you only get the data posted during that period. List as a table, with PE/PS/Cash flow/ etc",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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}
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],
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temperature=1,
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max_output_tokens=4096,
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top_p=1,
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store=True,
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)
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return response.output[1].content[0].text
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else:
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# Fetch fundamentals from Yahoo Finance
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import yfinance as yf
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ticker_obj = yf.Ticker(ticker)
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info = ticker_obj.info
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financials = ticker_obj.financials
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balance_sheet = ticker_obj.balance_sheet
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cashflow = ticker_obj.cashflow
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# Compose a context string
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context = f"""
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# Company Info
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Name: {info.get('shortName', 'N/A')}
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Industry: {info.get('industry', 'N/A')}
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Sector: {info.get('sector', 'N/A')}
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Country: {info.get('country', 'N/A')}
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Website: {info.get('website', 'N/A')}
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response = client.responses.create(
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model=config["quick_think_llm"],
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input=[
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{
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"role": "system",
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"content": [
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{
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"type": "input_text",
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"text": f"Can you search Fundamental for discussions on {ticker} during of the month before {curr_date} to the month of {curr_date}. Make sure you only get the data posted during that period. List as a table, with PE/PS/Cash flow/ etc",
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}
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],
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}
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],
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text={"format": {"type": "text"}},
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reasoning={},
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tools=[
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{
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"type": "web_search_preview",
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"user_location": {"type": "approximate"},
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"search_context_size": "low",
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}
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],
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temperature=1,
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max_output_tokens=4096,
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top_p=1,
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store=True,
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)
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# Financials (last available)
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{financials.to_string() if not financials.empty else 'N/A'}
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return response.output[1].content[0].text
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# Balance Sheet (last available)
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{balance_sheet.to_string() if not balance_sheet.empty else 'N/A'}
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# Cash Flow (last available)
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{cashflow.to_string() if not cashflow.empty else 'N/A'}
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"""
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prompt = (
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f"Given the following real company data for {ticker} as of {curr_date}, "
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"write a detailed fundamental analysis. Include company profile, financials, PE/PS/Cash flow, and any recent news or events that would affect fundamentals. "
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"Present the information in a markdown table at the end.\n\n"
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f"{context}"
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)
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if provider == "google":
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google_api_key = os.getenv("GOOGLE_API_KEY")
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llm = ChatGoogleGenerativeAI(model=config["quick_think_llm"], google_api_key=google_api_key)
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result = llm.invoke(prompt)
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return result.content
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elif provider == "anthropic":
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anthropic_api_key = os.getenv("ANTHROPIC_API_KEY")
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llm = ChatAnthropic(model_name=config["quick_think_llm"], api_key=anthropic_api_key)
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result = llm.invoke(prompt)
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return result.content
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else:
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return "Fundamental data retrieval is not supported for this LLM provider."
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@ -65,8 +65,9 @@ class TradingAgentsGraph:
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self.deep_thinking_llm = ChatAnthropic(model_name=self.config["deep_think_llm"], base_url=self.config["backend_url"], timeout=120, stop=None)
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self.quick_thinking_llm = ChatAnthropic(model_name=self.config["quick_think_llm"], base_url=self.config["backend_url"], timeout=120, stop=None)
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elif self.config["llm_provider"].lower() == "google":
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self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"])
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self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"])
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google_api_key = os.getenv("GOOGLE_API_KEY")
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self.deep_thinking_llm = ChatGoogleGenerativeAI(model=self.config["deep_think_llm"], google_api_key=google_api_key)
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self.quick_thinking_llm = ChatGoogleGenerativeAI(model=self.config["quick_think_llm"], google_api_key=google_api_key)
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else:
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raise ValueError(f"Unsupported LLM provider: {self.config['llm_provider']}")
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