Merge aafc3fbe31 into 13b826a31d
This commit is contained in:
godnight10061
GitHub
commit
e0c6ddb8f8
|
|
@ -0,0 +1,2 @@
|
|||
# Tests for TradingAgents
|
||||
|
||||
|
|
@ -0,0 +1,135 @@
|
|||
"""
|
||||
Tests for openai dataflow module to ensure compatibility with different LLM providers.
|
||||
This test reproduces issue #275 where Gemini and OpenRouter fail with openai vendor.
|
||||
"""
|
||||
import pytest
|
||||
from unittest.mock import Mock, patch, MagicMock
|
||||
from tradingagents.dataflows.openai import (
|
||||
get_stock_news_openai,
|
||||
get_global_news_openai,
|
||||
get_fundamentals_openai
|
||||
)
|
||||
|
||||
|
||||
class TestOpenAIDataflowCompatibility:
|
||||
"""Test that openai dataflow functions work with different LLM providers."""
|
||||
|
||||
@pytest.fixture
|
||||
def mock_config_openai(self):
|
||||
"""Mock config for OpenAI provider."""
|
||||
return {
|
||||
"backend_url": "https://api.openai.com/v1",
|
||||
"quick_think_llm": "gpt-4o-mini",
|
||||
"llm_provider": "openai"
|
||||
}
|
||||
|
||||
@pytest.fixture
|
||||
def mock_config_gemini(self):
|
||||
"""Mock config for Google Gemini provider."""
|
||||
return {
|
||||
"backend_url": "https://generativelanguage.googleapis.com/v1",
|
||||
"quick_think_llm": "gemini-2.0-flash",
|
||||
"llm_provider": "google"
|
||||
}
|
||||
|
||||
@pytest.fixture
|
||||
def mock_config_openrouter(self):
|
||||
"""Mock config for OpenRouter provider."""
|
||||
return {
|
||||
"backend_url": "https://openrouter.ai/api/v1",
|
||||
"quick_think_llm": "deepseek/deepseek-chat-v3-0324:free",
|
||||
"llm_provider": "openrouter"
|
||||
}
|
||||
|
||||
@patch('tradingagents.dataflows.openai.get_config')
|
||||
@patch('tradingagents.dataflows.openai.OpenAI')
|
||||
def test_get_global_news_with_openai(self, mock_openai_class, mock_get_config, mock_config_openai):
|
||||
"""Test get_global_news_openai works with OpenAI provider."""
|
||||
mock_get_config.return_value = mock_config_openai
|
||||
|
||||
# Mock the OpenAI client and response
|
||||
mock_client = Mock()
|
||||
mock_openai_class.return_value = mock_client
|
||||
|
||||
# Mock chat completion response (standard API)
|
||||
mock_response = Mock()
|
||||
mock_response.choices = [Mock()]
|
||||
mock_response.choices[0].message.content = "Test news content"
|
||||
mock_client.chat.completions.create.return_value = mock_response
|
||||
|
||||
# Call the function
|
||||
result = get_global_news_openai("2024-11-09", 7, 5)
|
||||
|
||||
# Verify it was called
|
||||
assert mock_client.chat.completions.create.called
|
||||
assert result == "Test news content"
|
||||
|
||||
@patch('tradingagents.dataflows.openai.get_config')
|
||||
@patch('tradingagents.dataflows.openai.OpenAI')
|
||||
def test_get_global_news_with_gemini(self, mock_openai_class, mock_get_config, mock_config_gemini):
|
||||
"""Test get_global_news_openai works with Gemini provider (via OpenAI-compatible API)."""
|
||||
mock_get_config.return_value = mock_config_gemini
|
||||
|
||||
# Mock the OpenAI client and response
|
||||
mock_client = Mock()
|
||||
mock_openai_class.return_value = mock_client
|
||||
|
||||
# Mock chat completion response
|
||||
mock_response = Mock()
|
||||
mock_response.choices = [Mock()]
|
||||
mock_response.choices[0].message.content = "Test Gemini news content"
|
||||
mock_client.chat.completions.create.return_value = mock_response
|
||||
|
||||
# Call the function - should not raise an error
|
||||
result = get_global_news_openai("2024-11-09", 7, 5)
|
||||
|
||||
# Verify it was called with standard chat completion API
|
||||
assert mock_client.chat.completions.create.called
|
||||
assert result == "Test Gemini news content"
|
||||
|
||||
@patch('tradingagents.dataflows.openai.get_config')
|
||||
@patch('tradingagents.dataflows.openai.OpenAI')
|
||||
def test_get_global_news_with_openrouter(self, mock_openai_class, mock_get_config, mock_config_openrouter):
|
||||
"""Test get_global_news_openai works with OpenRouter provider."""
|
||||
mock_get_config.return_value = mock_config_openrouter
|
||||
|
||||
# Mock the OpenAI client and response
|
||||
mock_client = Mock()
|
||||
mock_openai_class.return_value = mock_client
|
||||
|
||||
# Mock chat completion response
|
||||
mock_response = Mock()
|
||||
mock_response.choices = [Mock()]
|
||||
mock_response.choices[0].message.content = "Test OpenRouter news content"
|
||||
mock_client.chat.completions.create.return_value = mock_response
|
||||
|
||||
# Call the function - should not raise an error
|
||||
result = get_global_news_openai("2024-11-09", 7, 5)
|
||||
|
||||
# Verify it was called with standard chat completion API
|
||||
assert mock_client.chat.completions.create.called
|
||||
assert result == "Test OpenRouter news content"
|
||||
|
||||
@patch('tradingagents.dataflows.openai.get_config')
|
||||
@patch('tradingagents.dataflows.openai.OpenAI')
|
||||
def test_get_fundamentals_with_different_providers(self, mock_openai_class, mock_get_config, mock_config_gemini):
|
||||
"""Test get_fundamentals_openai works with different providers."""
|
||||
mock_get_config.return_value = mock_config_gemini
|
||||
|
||||
# Mock the OpenAI client and response
|
||||
mock_client = Mock()
|
||||
mock_openai_class.return_value = mock_client
|
||||
|
||||
# Mock chat completion response
|
||||
mock_response = Mock()
|
||||
mock_response.choices = [Mock()]
|
||||
mock_response.choices[0].message.content = "Test fundamentals data"
|
||||
mock_client.chat.completions.create.return_value = mock_response
|
||||
|
||||
# Call the function
|
||||
result = get_fundamentals_openai("AAPL", "2024-11-09")
|
||||
|
||||
# Verify it was called
|
||||
assert mock_client.chat.completions.create.called
|
||||
assert result == "Test fundamentals data"
|
||||
|
||||
|
|
@ -3,105 +3,110 @@ from .config import get_config
|
|||
|
||||
|
||||
def get_stock_news_openai(query, start_date, end_date):
|
||||
"""
|
||||
Retrieve stock news using LLM provider configured in backend_url.
|
||||
Compatible with OpenAI, Gemini (via OpenAI-compatible API), and OpenRouter.
|
||||
|
||||
Args:
|
||||
query: Stock ticker or search query
|
||||
start_date: Start date for news search
|
||||
end_date: End date for news search
|
||||
|
||||
Returns:
|
||||
str: News content as text
|
||||
"""
|
||||
config = get_config()
|
||||
client = OpenAI(base_url=config["backend_url"])
|
||||
|
||||
response = client.responses.create(
|
||||
# Use standard chat completions API for compatibility with all providers
|
||||
response = client.chat.completions.create(
|
||||
model=config["quick_think_llm"],
|
||||
input=[
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": [
|
||||
{
|
||||
"type": "input_text",
|
||||
"text": f"Can you search Social Media for {query} from {start_date} to {end_date}? Make sure you only get the data posted during that period.",
|
||||
}
|
||||
],
|
||||
}
|
||||
],
|
||||
text={"format": {"type": "text"}},
|
||||
reasoning={},
|
||||
tools=[
|
||||
"content": "You are a financial news analyst. Search and summarize relevant news from social media and news sources."
|
||||
},
|
||||
{
|
||||
"type": "web_search_preview",
|
||||
"user_location": {"type": "approximate"},
|
||||
"search_context_size": "low",
|
||||
"role": "user",
|
||||
"content": f"Can you search Social Media for {query} from {start_date} to {end_date}? Make sure you only get the data posted during that period."
|
||||
}
|
||||
],
|
||||
temperature=1,
|
||||
max_output_tokens=4096,
|
||||
max_tokens=4096,
|
||||
top_p=1,
|
||||
store=True,
|
||||
)
|
||||
|
||||
return response.output[1].content[0].text
|
||||
return response.choices[0].message.content
|
||||
|
||||
|
||||
def get_global_news_openai(curr_date, look_back_days=7, limit=5):
|
||||
"""
|
||||
Retrieve global news using LLM provider configured in backend_url.
|
||||
Compatible with OpenAI, Gemini (via OpenAI-compatible API), and OpenRouter.
|
||||
|
||||
Args:
|
||||
curr_date: Current date in yyyy-mm-dd format
|
||||
look_back_days: Number of days to look back (default 7)
|
||||
limit: Maximum number of articles to return (default 5)
|
||||
|
||||
Returns:
|
||||
str: Global news content as text
|
||||
"""
|
||||
config = get_config()
|
||||
client = OpenAI(base_url=config["backend_url"])
|
||||
|
||||
response = client.responses.create(
|
||||
# Use standard chat completions API for compatibility with all providers
|
||||
response = client.chat.completions.create(
|
||||
model=config["quick_think_llm"],
|
||||
input=[
|
||||
messages=[
|
||||
{
|
||||
"role": "system",
|
||||
"content": [
|
||||
{
|
||||
"type": "input_text",
|
||||
"text": f"Can you search global or macroeconomics news from {look_back_days} 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. Limit the results to {limit} articles.",
|
||||
}
|
||||
],
|
||||
}
|
||||
],
|
||||
text={"format": {"type": "text"}},
|
||||
reasoning={},
|
||||
tools=[
|
||||
"content": "You are a financial news analyst. Search and summarize relevant global and macroeconomic news for trading purposes."
|
||||
},
|
||||
{
|
||||
"type": "web_search_preview",
|
||||
"user_location": {"type": "approximate"},
|
||||
"search_context_size": "low",
|
||||
"role": "user",
|
||||
"content": f"Can you search global or macroeconomics news from {look_back_days} 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. Limit the results to {limit} articles."
|
||||
}
|
||||
],
|
||||
temperature=1,
|
||||
max_output_tokens=4096,
|
||||
max_tokens=4096,
|
||||
top_p=1,
|
||||
store=True,
|
||||
)
|
||||
|
||||
return response.output[1].content[0].text
|
||||
return response.choices[0].message.content
|
||||
|
||||
|
||||
def get_fundamentals_openai(ticker, curr_date):
|
||||
"""
|
||||
Retrieve fundamental data using LLM provider configured in backend_url.
|
||||
Compatible with OpenAI, Gemini (via OpenAI-compatible API), and OpenRouter.
|
||||
|
||||
Args:
|
||||
ticker: Stock ticker symbol
|
||||
curr_date: Current date in yyyy-mm-dd format
|
||||
|
||||
Returns:
|
||||
str: Fundamental data as text (table format with PE/PS/Cash flow etc)
|
||||
"""
|
||||
config = get_config()
|
||||
client = OpenAI(base_url=config["backend_url"])
|
||||
|
||||
response = client.responses.create(
|
||||
# Use standard chat completions API for compatibility with all providers
|
||||
response = client.chat.completions.create(
|
||||
model=config["quick_think_llm"],
|
||||
input=[
|
||||
messages=[
|
||||
{
|
||||
"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=[
|
||||
"content": "You are a financial analyst. Search and provide fundamental data for stocks in a structured table format."
|
||||
},
|
||||
{
|
||||
"type": "web_search_preview",
|
||||
"user_location": {"type": "approximate"},
|
||||
"search_context_size": "low",
|
||||
"role": "user",
|
||||
"content": 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"
|
||||
}
|
||||
],
|
||||
temperature=1,
|
||||
max_output_tokens=4096,
|
||||
max_tokens=4096,
|
||||
top_p=1,
|
||||
store=True,
|
||||
)
|
||||
|
||||
return response.output[1].content[0].text
|
||||
return response.choices[0].message.content
|
||||
Loading…
Reference in New Issue