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TradingAgents
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gemini embedding, gemini search

This commit is contained in:
kimheesu 2025-07-01 10:05:06 +09:00
parent 4ff37a84da
commit 6a1f88da24
10 changed files with 263 additions and 123 deletions
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4
cli/utils.py
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@ -142,7 +142,7 @@ def select_shallow_thinking_agent(provider) -> str:
"google": [
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash"),
],
"openrouter": [
("Meta: Llama 4 Scout", "meta-llama/llama-4-scout:free"),
@ -205,7 +205,7 @@ def select_deep_thinking_agent(provider) -> str:
("Gemini 2.0 Flash-Lite - Cost efficiency and low latency", "gemini-2.0-flash-lite"),
("Gemini 2.0 Flash - Next generation features, speed, and thinking", "gemini-2.0-flash"),
("Gemini 2.5 Flash - Adaptive thinking, cost efficiency", "gemini-2.5-flash-preview-05-20"),
("Gemini 2.5 Pro", "gemini-2.5-pro-preview-06-05"),
("Gemini 2.5 Pro", "gemini-2.5-pro"),
],
"openrouter": [
("DeepSeek V3 - a 685B-parameter, mixture-of-experts model", "deepseek/deepseek-chat-v3-0324:free"),

4
main.py
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@ -5,8 +5,8 @@ from tradingagents.default_config import DEFAULT_CONFIG
config = DEFAULT_CONFIG.copy()
config["llm_provider"] = "google" # Use a different model
config["backend_url"] = "https://generativelanguage.googleapis.com/v1" # Use a different backend
config["deep_think_llm"] = "gemini-2.0-flash" # Use a different model
config["quick_think_llm"] = "gemini-2.0-flash" # Use a different model
config["deep_think_llm"] = "gemini-2.5-flash" # Use a different model
config["quick_think_llm"] = "gemini-2.5-flash" # Use a different model
config["max_debate_rounds"] = 1 # Increase debate rounds
config["online_tools"] = True # Increase debate rounds

1
requirements.txt
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@ -24,3 +24,4 @@ rich
questionary
langchain_anthropic
langchain-google-genai
google-genai

2
tradingagents/agents/analysts/fundamentals_analyst.py
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@ -10,7 +10,7 @@ def create_fundamentals_analyst(llm, toolkit):
company_name = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [toolkit.get_fundamentals_openai]
tools = [toolkit.get_fundamentals]
else:
tools = [
toolkit.get_finnhub_company_insider_sentiment,

2
tradingagents/agents/analysts/news_analyst.py
View File

@ -9,7 +9,7 @@ def create_news_analyst(llm, toolkit):
ticker = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [toolkit.get_global_news_openai, toolkit.get_google_news]
tools = [toolkit.get_global_news, toolkit.get_google_news]
else:
tools = [
toolkit.get_finnhub_news,

2
tradingagents/agents/analysts/social_media_analyst.py
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@ -10,7 +10,7 @@ def create_social_media_analyst(llm, toolkit):
company_name = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [toolkit.get_stock_news_openai]
tools = [toolkit.get_stock_news]
else:
tools = [
toolkit.get_reddit_stock_info,

24
tradingagents/agents/utils/agent_utils.py
View File

@ -363,12 +363,12 @@ class Toolkit:
@staticmethod
@tool
def get_stock_news_openai(
def get_stock_news(
ticker: Annotated[str, "the company's ticker"],
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
):
"""
Retrieve the latest news about a given stock by using OpenAI's news API.
Retrieve the latest news about a given stock by using LLM's web search capabilities.
Args:
ticker (str): Ticker of a company. e.g. AAPL, TSM
curr_date (str): Current date in yyyy-mm-dd format
@ -376,35 +376,35 @@ class Toolkit:
str: A formatted string containing the latest news about the company on the given date.
"""
openai_news_results = interface.get_stock_news_openai(ticker, curr_date)
results = interface.get_stock_news(ticker, curr_date)
return openai_news_results
return results
@staticmethod
@tool
def get_global_news_openai(
def get_global_news(
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
):
"""
Retrieve the latest macroeconomics news on a given date using OpenAI's macroeconomics news API.
Retrieve the latest macroeconomics news on a given date using LLM's web search capabilities.
Args:
curr_date (str): Current date in yyyy-mm-dd format
Returns:
str: A formatted string containing the latest macroeconomic news on the given date.
"""
openai_news_results = interface.get_global_news_openai(curr_date)
results = interface.get_global_news(curr_date)
return openai_news_results
return results
@staticmethod
@tool
def get_fundamentals_openai(
def get_fundamentals(
ticker: Annotated[str, "the company's ticker"],
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
):
"""
Retrieve the latest fundamental information about a given stock on a given date by using OpenAI's news API.
Retrieve the latest fundamental information about a given stock on a given date by using LLM's web search capabilities.
Args:
ticker (str): Ticker of a company. e.g. AAPL, TSM
curr_date (str): Current date in yyyy-mm-dd format
@ -412,8 +412,8 @@ class Toolkit:
str: A formatted string containing the latest fundamental information about the company on the given date.
"""
openai_fundamentals_results = interface.get_fundamentals_openai(
results = interface.get_fundamentals(
ticker, curr_date
)
return openai_fundamentals_results
return results

53
tradingagents/agents/utils/memory.py
View File

@ -1,25 +1,56 @@
import chromadb
from chromadb.config import Settings
from openai import OpenAI
import os
from google import genai
class FinancialSituationMemory:
def __init__(self, name, config):
if config["backend_url"] == "http://localhost:11434/v1":
self.embedding = "nomic-embed-text"
self.config = config
self.backend_url = config["backend_url"]
# Determine embedding configuration based on provider
if self.backend_url == "http://localhost:11434/v1":
# Ollama
self.embedding_model = "nomic-embed-text"
self.use_openai_api = True
elif "openai.com" in self.backend_url:
# OpenAI
self.embedding_model = "text-embedding-3-small"
self.use_openai_api = True
elif "generativelanguage.googleapis.com" in self.backend_url:
# Google Gemini API
self.embedding_model = "gemini-embedding-exp-03-07" # Use Google's embedding model
self.use_openai_api = False
else:
self.embedding = "text-embedding-3-small"
self.client = OpenAI(base_url=config["backend_url"])
# Default to OpenAI-compatible
self.embedding_model = "text-embedding-3-small"
self.use_openai_api = True
# Initialize clients
if self.use_openai_api:
self.client = OpenAI(base_url=self.backend_url)
else:
self.client = genai.Client()
self.chroma_client = chromadb.Client(Settings(allow_reset=True))
self.situation_collection = self.chroma_client.create_collection(name=name)
def get_embedding(self, text):
"""Get OpenAI embedding for a text"""
"""Get embedding for a text using the appropriate API"""
response = self.client.embeddings.create(
model=self.embedding, input=text
)
return response.data[0].embedding
if self.use_openai_api:
# Use OpenAI-compatible API
response = self.client.embeddings.create(
model=self.embedding_model, input=text
)
return response.data[0].embedding
else:
response = self.client.models.embed_content(
model=self.embedding_model,
contents=text
)
return response.embeddings[0].values
def add_situations(self, situations_and_advice):
"""Add financial situations and their corresponding advice. Parameter is a list of tuples (situation, rec)"""
@ -45,7 +76,7 @@ class FinancialSituationMemory:
)
def get_memories(self, current_situation, n_matches=1):
"""Find matching recommendations using OpenAI embeddings"""
"""Find matching recommendations using embeddings"""
query_embedding = self.get_embedding(current_situation)
results = self.situation_collection.query(

288
tradingagents/dataflows/interface.py
View File

@ -702,106 +702,214 @@ def get_YFin_data(
return filtered_data
def get_stock_news_openai(ticker, curr_date):
def get_stock_news(ticker, curr_date):
config = get_config()
client = OpenAI(base_url=config["backend_url"])
# Check if using Google API - implement grounding with Google Search
if "generativelanguage.googleapis.com" in config["backend_url"]:
try:
from google import genai
from google.genai.types import Tool, GenerateContentConfig, GoogleSearch
client = genai.Client()
# Create Google Search grounding tool
google_search_tool = Tool(
google_search=GoogleSearch()
)
# Generate content with grounding
response = client.models.generate_content(
model=config["quick_think_llm"],
contents=f"Can you search for recent social media and news about {ticker} stock from 7 days before {curr_date} to {curr_date}? Focus on sentiment, price movements, and any significant developments that could impact trading decisions.",
config=GenerateContentConfig(
tools=[google_search_tool],
response_modalities=["TEXT"]
)
)
# Extract text from response
result_text = ""
for part in response.candidates[0].content.parts:
if hasattr(part, 'text'):
result_text += part.text
return result_text
except Exception as e:
return f"Error retrieving stock news for {ticker}: {str(e)}"
else:
# For OpenAI and other APIs, use original implementation
client = OpenAI(base_url=config["backend_url"])
response = client.responses.create(
model=config["quick_think_llm"],
input=[
{
"role": "system",
"content": [
{
"type": "input_text",
"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.",
}
],
}
],
text={"format": {"type": "text"}},
reasoning={},
tools=[
{
"type": "web_search_preview",
"user_location": {"type": "approximate"},
"search_context_size": "low",
}
],
temperature=1,
max_output_tokens=4096,
top_p=1,
store=True,
)
response = client.responses.create(
model=config["quick_think_llm"],
input=[
{
"role": "system",
"content": [
{
"type": "input_text",
"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.",
}
],
}
],
text={"format": {"type": "text"}},
reasoning={},
tools=[
{
"type": "web_search_preview",
"user_location": {"type": "approximate"},
"search_context_size": "low",
}
],
temperature=1,
max_output_tokens=4096,
top_p=1,
store=True,
)
return response.output[1].content[0].text
return response.output[1].content[0].text
def get_global_news_openai(curr_date):
def get_global_news(curr_date):
config = get_config()
client = OpenAI(base_url=config["backend_url"])
# Check if using Google API - implement grounding with Google Search
if "generativelanguage.googleapis.com" in config["backend_url"]:
try:
from google import genai
from google.genai.types import Tool, GenerateContentConfig, GoogleSearch
client = genai.Client()
# Create Google Search grounding tool
google_search_tool = Tool(
google_search=GoogleSearch()
)
# Generate content with grounding
response = client.models.generate_content(
model=config["quick_think_llm"],
contents=f"Search for global macroeconomic news and financial market updates from 7 days before {curr_date} to {curr_date}. Focus on central bank decisions, economic indicators, geopolitical events, and market-moving news that would be important for trading decisions.",
config=GenerateContentConfig(
tools=[google_search_tool],
response_modalities=["TEXT"]
)
)
# Extract text from response
result_text = ""
for part in response.candidates[0].content.parts:
if hasattr(part, 'text'):
result_text += part.text
return result_text
except Exception as e:
return f"Error retrieving global news: {str(e)}"
else:
# For OpenAI and other APIs, use original implementation
client = OpenAI(base_url=config["backend_url"])
response = client.responses.create(
model=config["quick_think_llm"],
input=[
{
"role": "system",
"content": [
{
"type": "input_text",
"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.",
}
],
}
],
text={"format": {"type": "text"}},
reasoning={},
tools=[
{
"type": "web_search_preview",
"user_location": {"type": "approximate"},
"search_context_size": "low",
}
],
temperature=1,
max_output_tokens=4096,
top_p=1,
store=True,
)
response = client.responses.create(
model=config["quick_think_llm"],
input=[
{
"role": "system",
"content": [
{
"type": "input_text",
"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.",
}
],
}
],
text={"format": {"type": "text"}},
reasoning={},
tools=[
{
"type": "web_search_preview",
"user_location": {"type": "approximate"},
"search_context_size": "low",
}
],
temperature=1,
max_output_tokens=4096,
top_p=1,
store=True,
)
return response.output[1].content[0].text
return response.output[1].content[0].text
def get_fundamentals_openai(ticker, curr_date):
def get_fundamentals(ticker, curr_date):
config = get_config()
client = OpenAI(base_url=config["backend_url"])
# Check if using Google API - implement grounding with Google Search
if "generativelanguage.googleapis.com" in config["backend_url"]:
try:
from google import genai
from google.genai.types import Tool, GenerateContentConfig, GoogleSearch
client = genai.Client()
# Create Google Search grounding tool
google_search_tool = Tool(
google_search=GoogleSearch()
)
# Generate content with grounding
response = client.models.generate_content(
model=config["quick_think_llm"],
contents=f"Search for fundamental analysis data and financial metrics for {ticker} stock from the month before {curr_date} to the month of {curr_date}. Look for earnings reports, financial ratios like PE, PS, cash flow, revenue growth, analyst ratings, and any fundamental analysis discussions. Please present key metrics in a structured format.",
config=GenerateContentConfig(
tools=[google_search_tool],
response_modalities=["TEXT"]
)
)
# Extract text from response
result_text = ""
for part in response.candidates[0].content.parts:
if hasattr(part, 'text'):
result_text += part.text
return result_text
except Exception as e:
return f"Error retrieving fundamentals for {ticker}: {str(e)}"
else:
# For OpenAI and other APIs, use original implementation
client = OpenAI(base_url=config["backend_url"])
response = client.responses.create(
model=config["quick_think_llm"],
input=[
{
"role": "system",
"content": [
{
"type": "input_text",
"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",
}
],
}
],
text={"format": {"type": "text"}},
reasoning={},
tools=[
{
"type": "web_search_preview",
"user_location": {"type": "approximate"},
"search_context_size": "low",
}
],
temperature=1,
max_output_tokens=4096,
top_p=1,
store=True,
)
response = client.responses.create(
model=config["quick_think_llm"],
input=[
{
"role": "system",
"content": [
{
"type": "input_text",
"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",
}
],
}
],
text={"format": {"type": "text"}},
reasoning={},
tools=[
{
"type": "web_search_preview",
"user_location": {"type": "approximate"},
"search_context_size": "low",
}
],
temperature=1,
max_output_tokens=4096,
top_p=1,
store=True,
)
return response.output[1].content[0].text
return response.output[1].content[0].text

6
tradingagents/graph/trading_graph.py
View File

@ -125,7 +125,7 @@ class TradingAgentsGraph:
"social": ToolNode(
[
# online tools
self.toolkit.get_stock_news_openai,
self.toolkit.get_stock_news,
# offline tools
self.toolkit.get_reddit_stock_info,
]
@ -133,7 +133,7 @@ class TradingAgentsGraph:
"news": ToolNode(
[
# online tools
self.toolkit.get_global_news_openai,
self.toolkit.get_global_news,
self.toolkit.get_google_news,
# offline tools
self.toolkit.get_finnhub_news,
@ -143,7 +143,7 @@ class TradingAgentsGraph:
"fundamentals": ToolNode(
[
# online tools
self.toolkit.get_fundamentals_openai,
self.toolkit.get_fundamentals,
# offline tools
self.toolkit.get_finnhub_company_insider_sentiment,
self.toolkit.get_finnhub_company_insider_transactions,