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feat: add multi-provider LLM support with thinking configurations

Browse Source 
Models added:
- OpenAI: GPT-5.2, GPT-5.1, GPT-5, GPT-5 Mini, GPT-5 Nano, GPT-4.1
- Anthropic: Claude Opus 4.5/4.1, Claude Sonnet 4.5/4, Claude Haiku 4.5
- Google: Gemini 3 Pro/Flash, Gemini 2.5 Flash/Flash Lite
- xAI: Grok 4, Grok 4.1 Fast (Reasoning/Non-Reasoning)

Configs updated:
- Add unified thinking_level for Gemini (maps to thinking_level for Gemini 3,
  thinking_budget for Gemini 2.5; handles Pro's lack of "minimal" support)
- Add OpenAI reasoning_effort configuration
- Add NormalizedChatGoogleGenerativeAI for consistent response handling

Fixes:
- Fix Bull/Bear researcher display truncation
- Replace ChromaDB with BM25 for memory retrieval
This commit is contained in:
Yijia Xiao
2026-01-26 16:48:28 +00:00
parent 79051580b8
commit d4dadb82fc
17 changed files with 639 additions and 958 deletions
Download Patch File Download Diff File Expand all files Collapse all files
tradingagents/dataflows/config.py
+2 -5
View File
@@ -3,24 +3,21 @@ from typing import Dict, Optional
# Use default config but allow it to be overridden
_config: Optional[Dict] = None
DATA_DIR: Optional[str] = None
def initialize_config():
"""Initialize the configuration with default values."""
global _config, DATA_DIR
global _config
if _config is None:
_config = default_config.DEFAULT_CONFIG.copy()
DATA_DIR = _config["data_dir"]
def set_config(config: Dict):
"""Update the configuration with custom values."""
global _config, DATA_DIR
global _config
if _config is None:
_config = default_config.DEFAULT_CONFIG.copy()
_config.update(config)
DATA_DIR = _config["data_dir"]
def get_config() -> Dict:
tradingagents/dataflows/interface.py
+14 -25
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@@ -1,10 +1,15 @@
from typing import Annotated
# Import from vendor-specific modules
from .local import get_YFin_data, get_finnhub_news, get_finnhub_company_insider_sentiment, get_finnhub_company_insider_transactions, get_simfin_balance_sheet, get_simfin_cashflow, get_simfin_income_statements, get_reddit_global_news, get_reddit_company_news
from .y_finance import get_YFin_data_online, get_stock_stats_indicators_window, get_balance_sheet as get_yfinance_balance_sheet, get_cashflow as get_yfinance_cashflow, get_income_statement as get_yfinance_income_statement, get_insider_transactions as get_yfinance_insider_transactions
from .google import get_google_news
from .openai import get_stock_news_openai, get_global_news_openai, get_fundamentals_openai
from .y_finance import (
get_YFin_data_online,
get_stock_stats_indicators_window,
get_balance_sheet as get_yfinance_balance_sheet,
get_cashflow as get_yfinance_cashflow,
get_income_statement as get_yfinance_income_statement,
get_insider_transactions as get_yfinance_insider_transactions,
)
from .yfinance_news import get_news_yfinance, get_global_news_yfinance
from .alpha_vantage import (
get_stock as get_alpha_vantage_stock,
get_indicator as get_alpha_vantage_indicator,
@@ -13,7 +18,7 @@ from .alpha_vantage import (
get_cashflow as get_alpha_vantage_cashflow,
get_income_statement as get_alpha_vantage_income_statement,
get_insider_transactions as get_alpha_vantage_insider_transactions,
get_news as get_alpha_vantage_news
get_news as get_alpha_vantage_news,
)
from .alpha_vantage_common import AlphaVantageRateLimitError
@@ -44,21 +49,18 @@ TOOLS_CATEGORIES = {
]
},
"news_data": {
"description": "News (public/insiders, original/processed)",
"description": "News and insider data",
"tools": [
"get_news",
"get_global_news",
"get_insider_sentiment",
"get_insider_transactions",
]
}
}
VENDOR_LIST = [
"local",
"yfinance",
"openai",
"google"
"alpha_vantage",
]
# Mapping of methods to their vendor-specific implementations
@@ -67,52 +69,39 @@ VENDOR_METHODS = {
"get_stock_data": {
"alpha_vantage": get_alpha_vantage_stock,
"yfinance": get_YFin_data_online,
"local": get_YFin_data,
},
# technical_indicators
"get_indicators": {
"alpha_vantage": get_alpha_vantage_indicator,
"yfinance": get_stock_stats_indicators_window,
"local": get_stock_stats_indicators_window
},
# fundamental_data
"get_fundamentals": {
"alpha_vantage": get_alpha_vantage_fundamentals,
"openai": get_fundamentals_openai,
},
"get_balance_sheet": {
"alpha_vantage": get_alpha_vantage_balance_sheet,
"yfinance": get_yfinance_balance_sheet,
"local": get_simfin_balance_sheet,
},
"get_cashflow": {
"alpha_vantage": get_alpha_vantage_cashflow,
"yfinance": get_yfinance_cashflow,
"local": get_simfin_cashflow,
},
"get_income_statement": {
"alpha_vantage": get_alpha_vantage_income_statement,
"yfinance": get_yfinance_income_statement,
"local": get_simfin_income_statements,
},
# news_data
"get_news": {
"alpha_vantage": get_alpha_vantage_news,
"openai": get_stock_news_openai,
"google": get_google_news,
"local": [get_finnhub_news, get_reddit_company_news, get_google_news],
"yfinance": get_news_yfinance,
},
"get_global_news": {
"openai": get_global_news_openai,
"local": get_reddit_global_news
},
"get_insider_sentiment": {
"local": get_finnhub_company_insider_sentiment
"yfinance": get_global_news_yfinance,
},
"get_insider_transactions": {
"alpha_vantage": get_alpha_vantage_insider_transactions,
"yfinance": get_yfinance_insider_transactions,
"local": get_finnhub_company_insider_transactions,
},
}
tradingagents/dataflows/stockstats_utils.py
+32 -50
View File
@@ -3,7 +3,7 @@ import yfinance as yf
from stockstats import wrap
from typing import Annotated
import os
from .config import get_config, DATA_DIR
from .config import get_config
class StockstatsUtils:
@@ -17,63 +17,45 @@ class StockstatsUtils:
str, "curr date for retrieving stock price data, YYYY-mm-dd"
],
):
# Get config and set up data directory path
config = get_config()
online = config["data_vendors"]["technical_indicators"] != "local"
df = None
data = None
today_date = pd.Timestamp.today()
curr_date_dt = pd.to_datetime(curr_date)
if not online:
try:
data = pd.read_csv(
os.path.join(
DATA_DIR,
f"{symbol}-YFin-data-2015-01-01-2025-03-25.csv",
)
)
df = wrap(data)
except FileNotFoundError:
raise Exception("Stockstats fail: Yahoo Finance data not fetched yet!")
end_date = today_date
start_date = today_date - pd.DateOffset(years=15)
start_date_str = start_date.strftime("%Y-%m-%d")
end_date_str = end_date.strftime("%Y-%m-%d")
# Ensure cache directory exists
os.makedirs(config["data_cache_dir"], exist_ok=True)
data_file = os.path.join(
config["data_cache_dir"],
f"{symbol}-YFin-data-{start_date_str}-{end_date_str}.csv",
)
if os.path.exists(data_file):
data = pd.read_csv(data_file)
data["Date"] = pd.to_datetime(data["Date"])
else:
# Get today's date as YYYY-mm-dd to add to cache
today_date = pd.Timestamp.today()
curr_date = pd.to_datetime(curr_date)
end_date = today_date
start_date = today_date - pd.DateOffset(years=15)
start_date = start_date.strftime("%Y-%m-%d")
end_date = end_date.strftime("%Y-%m-%d")
# Get config and ensure cache directory exists
os.makedirs(config["data_cache_dir"], exist_ok=True)
data_file = os.path.join(
config["data_cache_dir"],
f"{symbol}-YFin-data-{start_date}-{end_date}.csv",
data = yf.download(
symbol,
start=start_date_str,
end=end_date_str,
multi_level_index=False,
progress=False,
auto_adjust=True,
)
data = data.reset_index()
data.to_csv(data_file, index=False)
if os.path.exists(data_file):
data = pd.read_csv(data_file)
data["Date"] = pd.to_datetime(data["Date"])
else:
data = yf.download(
symbol,
start=start_date,
end=end_date,
multi_level_index=False,
progress=False,
auto_adjust=True,
)
data = data.reset_index()
data.to_csv(data_file, index=False)
df = wrap(data)
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
curr_date = curr_date.strftime("%Y-%m-%d")
df = wrap(data)
df["Date"] = df["Date"].dt.strftime("%Y-%m-%d")
curr_date_str = curr_date_dt.strftime("%Y-%m-%d")
df[indicator] # trigger stockstats to calculate the indicator
matching_rows = df[df["Date"].str.startswith(curr_date)]
matching_rows = df[df["Date"].str.startswith(curr_date_str)]
if not matching_rows.empty:
indicator_value = matching_rows[indicator].values[0]
tradingagents/dataflows/yfinance_news.py
+190
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@@ -0,0 +1,190 @@
"""yfinance-based news data fetching functions."""
import yfinance as yf
from datetime import datetime
from dateutil.relativedelta import relativedelta
def _extract_article_data(article: dict) -> dict:
"""Extract article data from yfinance news format (handles nested 'content' structure)."""
# Handle nested content structure
if "content" in article:
content = article["content"]
title = content.get("title", "No title")
summary = content.get("summary", "")
provider = content.get("provider", {})
publisher = provider.get("displayName", "Unknown")
# Get URL from canonicalUrl or clickThroughUrl
url_obj = content.get("canonicalUrl") or content.get("clickThroughUrl") or {}
link = url_obj.get("url", "")
# Get publish date
pub_date_str = content.get("pubDate", "")
pub_date = None
if pub_date_str:
try:
pub_date = datetime.fromisoformat(pub_date_str.replace("Z", "+00:00"))
except (ValueError, AttributeError):
pass
return {
"title": title,
"summary": summary,
"publisher": publisher,
"link": link,
"pub_date": pub_date,
}
else:
# Fallback for flat structure
return {
"title": article.get("title", "No title"),
"summary": article.get("summary", ""),
"publisher": article.get("publisher", "Unknown"),
"link": article.get("link", ""),
"pub_date": None,
}
def get_news_yfinance(
ticker: str,
start_date: str,
end_date: str,
) -> str:
"""
Retrieve news for a specific stock ticker using yfinance.
Args:
ticker: Stock ticker symbol (e.g., "AAPL")
start_date: Start date in yyyy-mm-dd format
end_date: End date in yyyy-mm-dd format
Returns:
Formatted string containing news articles
"""
try:
stock = yf.Ticker(ticker)
news = stock.get_news(count=20, tab="news")
if not news:
return f"No news found for {ticker}"
# Parse date range for filtering
start_dt = datetime.strptime(start_date, "%Y-%m-%d")
end_dt = datetime.strptime(end_date, "%Y-%m-%d")
news_str = ""
filtered_count = 0
for article in news:
data = _extract_article_data(article)
# Filter by date if publish time is available
if data["pub_date"]:
pub_date_naive = data["pub_date"].replace(tzinfo=None)
if not (start_dt <= pub_date_naive <= end_dt + relativedelta(days=1)):
continue
news_str += f"### {data['title']} (source: {data['publisher']})\n"
if data["summary"]:
news_str += f"{data['summary']}\n"
if data["link"]:
news_str += f"Link: {data['link']}\n"
news_str += "\n"
filtered_count += 1
if filtered_count == 0:
return f"No news found for {ticker} between {start_date} and {end_date}"
return f"## {ticker} News, from {start_date} to {end_date}:\n\n{news_str}"
except Exception as e:
return f"Error fetching news for {ticker}: {str(e)}"
def get_global_news_yfinance(
curr_date: str,
look_back_days: int = 7,
limit: int = 10,
) -> str:
"""
Retrieve global/macro economic news using yfinance Search.
Args:
curr_date: Current date in yyyy-mm-dd format
look_back_days: Number of days to look back
limit: Maximum number of articles to return
Returns:
Formatted string containing global news articles
"""
# Search queries for macro/global news
search_queries = [
"stock market economy",
"Federal Reserve interest rates",
"inflation economic outlook",
"global markets trading",
]
all_news = []
seen_titles = set()
try:
for query in search_queries:
search = yf.Search(
query=query,
news_count=limit,
enable_fuzzy_query=True,
)
if search.news:
for article in search.news:
# Handle both flat and nested structures
if "content" in article:
data = _extract_article_data(article)
title = data["title"]
else:
title = article.get("title", "")
# Deduplicate by title
if title and title not in seen_titles:
seen_titles.add(title)
all_news.append(article)
if len(all_news) >= limit:
break
if not all_news:
return f"No global news found for {curr_date}"
# Calculate date range
curr_dt = datetime.strptime(curr_date, "%Y-%m-%d")
start_dt = curr_dt - relativedelta(days=look_back_days)
start_date = start_dt.strftime("%Y-%m-%d")
news_str = ""
for article in all_news[:limit]:
# Handle both flat and nested structures
if "content" in article:
data = _extract_article_data(article)
title = data["title"]
publisher = data["publisher"]
link = data["link"]
summary = data["summary"]
else:
title = article.get("title", "No title")
publisher = article.get("publisher", "Unknown")
link = article.get("link", "")
summary = ""
news_str += f"### {title} (source: {publisher})\n"
if summary:
news_str += f"{summary}\n"
if link:
news_str += f"Link: {link}\n"
news_str += "\n"
return f"## Global Market News, from {start_date} to {curr_date}:\n\n{news_str}"
except Exception as e:
return f"Error fetching global news: {str(e)}"