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TradingAgents
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feat: deprecate stock utilities

This commit is contained in:
Tomortec 2025-06-27 23:34:48 +08:00
parent e4a439ea92
commit 18b5808a60
15 changed files with 432 additions and 747 deletions
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3
README.md
View File

@ -178,7 +178,6 @@ config = DEFAULT_CONFIG.copy()
config["deep_think_llm"] = "gpt-4.1-nano" # Use a different model
config["quick_think_llm"] = "gpt-4.1-nano" # Use a different model
config["max_debate_rounds"] = 1 # Increase debate rounds
config["online_tools"] = True # Use online tools or cached data
# Initialize with custom config
ta = TradingAgentsGraph(debug=True, config=config)
@ -188,8 +187,6 @@ _, decision = ta.propagate("NVDA", "2024-05-10")
print(decision)
```
> For `online_tools`, we recommend enabling them for experimentation, as they provide access to real-time data. The agents' offline tools rely on cached data from our **Tauric TradingDB**, a curated dataset we use for backtesting. We're currently in the process of refining this dataset, and we plan to release it soon alongside our upcoming projects. Stay tuned!
You can view the full list of configurations in `tradingagents/default_config.py`.
## Contributing

1
main.py
View File

@ -8,7 +8,6 @@ config["backend_url"] = "https://generativelanguage.googleapis.com/v1" # Use a
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["max_debate_rounds"] = 1 # Increase debate rounds
config["online_tools"] = True # Increase debate rounds
# Initialize with custom config
ta = TradingAgentsGraph(debug=True, config=config)

13
tradingagents/agents/analysts/fundamentals_analyst.py
View File

@ -9,16 +9,9 @@ def create_fundamentals_analyst(llm, toolkit):
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [toolkit.get_fundamentals_openai]
else:
tools = [
toolkit.get_finnhub_company_insider_sentiment,
toolkit.get_finnhub_company_insider_transactions,
toolkit.get_simfin_balance_sheet,
toolkit.get_simfin_cashflow,
toolkit.get_simfin_income_stmt,
]
tools = [
toolkit.get_fundamentals_openai
]
system_message = (
get_prompts("analysts", "fundamentals_analyst", "system_message")

13
tradingagents/agents/analysts/market_analyst.py
View File

@ -10,16 +10,9 @@ def create_market_analyst(llm, toolkit):
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [
toolkit.get_binance_data,
toolkit.get_stockstats_indicators_report_online,
]
else:
tools = [
toolkit.get_YFin_data,
toolkit.get_stockstats_indicators_report,
]
tools = [
toolkit.get_binance_data
]
system_message = (
get_prompts("analysts", "market_analyst", "system_message")

20
tradingagents/agents/analysts/news_analyst.py
View File

@ -8,19 +8,13 @@ def create_news_analyst(llm, toolkit):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [
toolkit.get_global_news_openai,
toolkit.get_google_news,
toolkit.get_blockbeats_news,
toolkit.get_coindesk_news,
]
else:
tools = [
toolkit.get_finnhub_news,
toolkit.get_reddit_news,
toolkit.get_google_news,
]
tools = [
# toolkit.get_global_news_openai,
# toolkit.get_google_news,
# toolkit.get_reddit_news,
toolkit.get_blockbeats_news,
toolkit.get_coindesk_news,
]
system_message = (
get_prompts("analysts", "news_analyst", "system_message")

10
tradingagents/agents/analysts/social_media_analyst.py
View File

@ -9,12 +9,10 @@ def create_social_media_analyst(llm, toolkit):
ticker = state["company_of_interest"]
company_name = state["company_of_interest"]
if toolkit.config["online_tools"]:
tools = [toolkit.get_stock_news_openai]
else:
tools = [
toolkit.get_reddit_stock_info,
]
tools = [
toolkit.get_stock_news_openai,
toolkit.get_reddit_stock_info
]
system_message = (
get_prompts("analysts", "social_media_analyst", "system_message")

258
tradingagents/agents/utils/agent_utils.py
View File

@ -51,12 +51,12 @@ class Toolkit:
@staticmethod
@tool
def get_blockbeats_news(
count: Annotated[int, "Number of news articles to retrieve, no more than 50"] = 10,
count: Annotated[int, "Number of news articles to retrieve, no more than 30"] = 10,
) -> str:
"""
Retrieve the latest news from BlockBeats, especially useful for Cryptos.
Args:
count (int): Number of news articles to retrieve, no more than 50
count (int): Number of news articles to retrieve, no more than 30
Returns:
str: A formatted string containing the latest news from BlockBeats.
"""
@ -120,38 +120,6 @@ class Toolkit:
return global_news_result
@staticmethod
@tool
def get_finnhub_news(
ticker: Annotated[
str,
"Search query of a company, e.g. 'AAPL, TSM, etc.",
],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
):
"""
Retrieve the latest news about a given stock from Finnhub within a date range
Args:
ticker (str): Ticker of a company. e.g. AAPL, TSM
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns:
str: A formatted dataframe containing news about the company within the date range from start_date to end_date
"""
end_date_str = end_date
end_date = datetime.strptime(end_date, "%Y-%m-%d")
start_date = datetime.strptime(start_date, "%Y-%m-%d")
look_back_days = (end_date - start_date).days
finnhub_news_result = interface.get_finnhub_news(
ticker, end_date_str, look_back_days
)
return finnhub_news_result
@staticmethod
@tool
def get_reddit_stock_info(
@ -174,228 +142,6 @@ class Toolkit:
return stock_news_results
@staticmethod
@tool
def get_YFin_data(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "Start date in yyyy-mm-dd format"],
) -> str:
"""
Retrieve the stock price data for a given ticker symbol from Yahoo Finance.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns:
str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range.
"""
result_data = interface.get_YFin_data(symbol, start_date, end_date)
return result_data
@staticmethod
@tool
def get_YFin_data_online(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
end_date: Annotated[str, "Start date in yyyy-mm-dd format"],
) -> str:
"""
Retrieve the stock price data for a given ticker symbol from Yahoo Finance.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns:
str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range.
"""
result_data = interface.get_YFin_data_online(symbol, start_date, end_date)
return result_data
@staticmethod
@tool
def get_stockstats_indicators_report(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[
str, "technical indicator to get the analysis and report of"
],
curr_date: Annotated[
str, "The current trading date you are trading on, YYYY-mm-dd"
],
look_back_days: Annotated[int, "how many days to look back"] = 30,
) -> str:
"""
Retrieve stock stats indicators for a given ticker symbol and indicator.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
indicator (str): Technical indicator to get the analysis and report of
curr_date (str): The current trading date you are trading on, YYYY-mm-dd
look_back_days (int): How many days to look back, default is 30
Returns:
str: A formatted dataframe containing the stock stats indicators for the specified ticker symbol and indicator.
"""
result_stockstats = interface.get_stock_stats_indicators_window(
symbol, indicator, curr_date, look_back_days, False
)
return result_stockstats
@staticmethod
@tool
def get_stockstats_indicators_report_online(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[
str, "technical indicator to get the analysis and report of"
],
curr_date: Annotated[
str, "The current trading date you are trading on, YYYY-mm-dd"
],
look_back_days: Annotated[int, "how many days to look back"] = 30,
) -> str:
"""
Retrieve stock stats indicators for a given ticker symbol and indicator.
Args:
symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
indicator (str): Technical indicator to get the analysis and report of
curr_date (str): The current trading date you are trading on, YYYY-mm-dd
look_back_days (int): How many days to look back, default is 30
Returns:
str: A formatted dataframe containing the stock stats indicators for the specified ticker symbol and indicator.
"""
result_stockstats = interface.get_stock_stats_indicators_window(
symbol, indicator, curr_date, look_back_days, True
)
return result_stockstats
@staticmethod
@tool
def get_finnhub_company_insider_sentiment(
ticker: Annotated[str, "ticker symbol for the company"],
curr_date: Annotated[
str,
"current date of you are trading at, yyyy-mm-dd",
],
):
"""
Retrieve insider sentiment information about a company (retrieved from public SEC information) for the past 30 days
Args:
ticker (str): ticker symbol of the company
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the sentiment in the past 30 days starting at curr_date
"""
data_sentiment = interface.get_finnhub_company_insider_sentiment(
ticker, curr_date, 30
)
return data_sentiment
@staticmethod
@tool
def get_finnhub_company_insider_transactions(
ticker: Annotated[str, "ticker symbol"],
curr_date: Annotated[
str,
"current date you are trading at, yyyy-mm-dd",
],
):
"""
Retrieve insider transaction information about a company (retrieved from public SEC information) for the past 30 days
Args:
ticker (str): ticker symbol of the company
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's insider transactions/trading information in the past 30 days
"""
data_trans = interface.get_finnhub_company_insider_transactions(
ticker, curr_date, 30
)
return data_trans
@staticmethod
@tool
def get_simfin_balance_sheet(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual/quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
"""
Retrieve the most recent balance sheet of a company
Args:
ticker (str): ticker symbol of the company
freq (str): reporting frequency of the company's financial history: annual / quarterly
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's most recent balance sheet
"""
data_balance_sheet = interface.get_simfin_balance_sheet(ticker, freq, curr_date)
return data_balance_sheet
@staticmethod
@tool
def get_simfin_cashflow(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual/quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
"""
Retrieve the most recent cash flow statement of a company
Args:
ticker (str): ticker symbol of the company
freq (str): reporting frequency of the company's financial history: annual / quarterly
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's most recent cash flow statement
"""
data_cashflow = interface.get_simfin_cashflow(ticker, freq, curr_date)
return data_cashflow
@staticmethod
@tool
def get_simfin_income_stmt(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual/quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
"""
Retrieve the most recent income statement of a company
Args:
ticker (str): ticker symbol of the company
freq (str): reporting frequency of the company's financial history: annual / quarterly
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's most recent income statement
"""
data_income_stmt = interface.get_simfin_income_statements(
ticker, freq, curr_date
)
return data_income_stmt
@staticmethod
@tool
def get_google_news(

26
tradingagents/dataflows/__init__.py
View File

@ -3,29 +3,19 @@ from .coindesk_utils import fetch_news_from_coindesk
from .googlenews_utils import getNewsData
from .binance_utils import *
from .reddit_utils import fetch_top_from_category
from .stockstats_utils import StockstatsUtils
from .yfin_utils import YFinanceUtils
from .interface import (
# News and sentiment functions
get_blockbeats_news,
get_coindesk_news,
get_finnhub_news,
get_finnhub_company_insider_sentiment,
get_finnhub_company_insider_transactions,
get_google_news,
get_reddit_global_news,
get_reddit_company_news,
# Financial statements functions
get_simfin_balance_sheet,
get_simfin_cashflow,
get_simfin_income_statements,
# TODO
# Technical analysis functions
get_stock_stats_indicators_window,
get_stockstats_indicator,
# TODO
# Market data functions
get_YFin_data_window,
get_YFin_data,
get_binance_data
)
@ -33,21 +23,13 @@ __all__ = [
# News and sentiment functions
"get_blockbeats_news",
"get_coindesk_news",
"get_finnhub_news",
"get_finnhub_company_insider_sentiment",
"get_finnhub_company_insider_transactions",
"get_google_news",
"get_reddit_global_news",
"get_reddit_company_news",
# Financial statements functions
"get_simfin_balance_sheet",
"get_simfin_cashflow",
"get_simfin_income_statements",
# TODO
# Technical analysis functions
"get_stock_stats_indicators_window",
"get_stockstats_indicator",
# TODO
# Market data functions
"get_YFin_data_window",
"get_YFin_data",
"get_binance_data"
]

3
tradingagents/dataflows/finnhub_utils.py
View File

@ -1,7 +1,8 @@
import json
import os
from warnings import deprecated
@deprecated("Utilities only for stocks are deprecated.")
def get_data_in_range(ticker, start_date, end_date, data_type, data_dir, period=None):
"""
Gets finnhub data saved and processed on disk.

793
tradingagents/dataflows/interface.py
View File

@ -17,17 +17,18 @@ from tqdm import tqdm
import yfinance as yf
from openai import OpenAI
from .config import get_config, set_config, DATA_DIR
from warnings import deprecated
def get_blockbeats_news(count: Annotated[int, "news' count, no more than 50"] = 10):
def get_blockbeats_news(count: Annotated[int, "news' count, no more than 30"] = 10):
"""
Retrieve the latest top Blockbeats news
Args:
count (int): number of news to retrieve, no more than 50
count (int): number of news to retrieve, no more than 30
Returns:
str: A formatted string containing the latest news articles and meta information
"""
if count > 50:
raise ValueError("Count should not be more than 50")
if count > 30:
raise ValueError("Count should not be more than 30")
news = fetch_news_from_blockbeats(count)
@ -65,272 +66,6 @@ def get_coindesk_news(
return f"## Coindesk News:\n\n{news_str}"
def get_finnhub_news(
ticker: Annotated[
str,
"Search query of a company's, e.g. 'AAPL, TSM, etc.",
],
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
look_back_days: Annotated[int, "how many days to look back"],
):
"""
Retrieve news about a company within a time frame
Args
ticker (str): ticker for the company you are interested in
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns
str: dataframe containing the news of the company in the time frame
"""
start_date = datetime.strptime(curr_date, "%Y-%m-%d")
before = start_date - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
result = get_data_in_range(ticker, before, curr_date, "news_data", DATA_DIR)
if len(result) == 0:
return ""
combined_result = ""
for day, data in result.items():
if len(data) == 0:
continue
for entry in data:
current_news = (
"### " + entry["headline"] + f" ({day})" + "\n" + entry["summary"]
)
combined_result += current_news + "\n\n"
return f"## {ticker} News, from {before} to {curr_date}:\n" + str(combined_result)
def get_finnhub_company_insider_sentiment(
ticker: Annotated[str, "ticker symbol for the company"],
curr_date: Annotated[
str,
"current date of you are trading at, yyyy-mm-dd",
],
look_back_days: Annotated[int, "number of days to look back"],
):
"""
Retrieve insider sentiment about a company (retrieved from public SEC information) for the past 15 days
Args:
ticker (str): ticker symbol of the company
curr_date (str): current date you are trading on, yyyy-mm-dd
Returns:
str: a report of the sentiment in the past 15 days starting at curr_date
"""
date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
before = date_obj - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
data = get_data_in_range(ticker, before, curr_date, "insider_senti", DATA_DIR)
if len(data) == 0:
return ""
result_str = ""
seen_dicts = []
for date, senti_list in data.items():
for entry in senti_list:
if entry not in seen_dicts:
result_str += f"### {entry['year']}-{entry['month']}:\nChange: {entry['change']}\nMonthly Share Purchase Ratio: {entry['mspr']}\n\n"
seen_dicts.append(entry)
return (
f"## {ticker} Insider Sentiment Data for {before} to {curr_date}:\n"
+ result_str
+ "The change field refers to the net buying/selling from all insiders' transactions. The mspr field refers to monthly share purchase ratio."
)
def get_finnhub_company_insider_transactions(
ticker: Annotated[str, "ticker symbol"],
curr_date: Annotated[
str,
"current date you are trading at, yyyy-mm-dd",
],
look_back_days: Annotated[int, "how many days to look back"],
):
"""
Retrieve insider transcaction information about a company (retrieved from public SEC information) for the past 15 days
Args:
ticker (str): ticker symbol of the company
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's insider transaction/trading informtaion in the past 15 days
"""
date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
before = date_obj - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
data = get_data_in_range(ticker, before, curr_date, "insider_trans", DATA_DIR)
if len(data) == 0:
return ""
result_str = ""
seen_dicts = []
for date, senti_list in data.items():
for entry in senti_list:
if entry not in seen_dicts:
result_str += f"### Filing Date: {entry['filingDate']}, {entry['name']}:\nChange:{entry['change']}\nShares: {entry['share']}\nTransaction Price: {entry['transactionPrice']}\nTransaction Code: {entry['transactionCode']}\n\n"
seen_dicts.append(entry)
return (
f"## {ticker} insider transactions from {before} to {curr_date}:\n"
+ result_str
+ "The change field reflects the variation in share count—here a negative number indicates a reduction in holdings—while share specifies the total number of shares involved. The transactionPrice denotes the per-share price at which the trade was executed, and transactionDate marks when the transaction occurred. The name field identifies the insider making the trade, and transactionCode (e.g., S for sale) clarifies the nature of the transaction. FilingDate records when the transaction was officially reported, and the unique id links to the specific SEC filing, as indicated by the source. Additionally, the symbol ties the transaction to a particular company, isDerivative flags whether the trade involves derivative securities, and currency notes the currency context of the transaction."
)
def get_simfin_balance_sheet(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual / quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
data_path = os.path.join(
DATA_DIR,
"fundamental_data",
"simfin_data_all",
"balance_sheet",
"companies",
"us",
f"us-balance-{freq}.csv",
)
df = pd.read_csv(data_path, sep=";")
# Convert date strings to datetime objects and remove any time components
df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
# Convert the current date to datetime and normalize
curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
# Filter the DataFrame for the given ticker and for reports that were published on or before the current date
filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
# Check if there are any available reports; if not, return a notification
if filtered_df.empty:
print("No balance sheet available before the given current date.")
return ""
# Get the most recent balance sheet by selecting the row with the latest Publish Date
latest_balance_sheet = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
# drop the SimFinID column
latest_balance_sheet = latest_balance_sheet.drop("SimFinId")
return (
f"## {freq} balance sheet for {ticker} released on {str(latest_balance_sheet['Publish Date'])[0:10]}: \n"
+ str(latest_balance_sheet)
+ "\n\nThis includes metadata like reporting dates and currency, share details, and a breakdown of assets, liabilities, and equity. Assets are grouped as current (liquid items like cash and receivables) and noncurrent (long-term investments and property). Liabilities are split between short-term obligations and long-term debts, while equity reflects shareholder funds such as paid-in capital and retained earnings. Together, these components ensure that total assets equal the sum of liabilities and equity."
)
def get_simfin_cashflow(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual / quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
data_path = os.path.join(
DATA_DIR,
"fundamental_data",
"simfin_data_all",
"cash_flow",
"companies",
"us",
f"us-cashflow-{freq}.csv",
)
df = pd.read_csv(data_path, sep=";")
# Convert date strings to datetime objects and remove any time components
df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
# Convert the current date to datetime and normalize
curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
# Filter the DataFrame for the given ticker and for reports that were published on or before the current date
filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
# Check if there are any available reports; if not, return a notification
if filtered_df.empty:
print("No cash flow statement available before the given current date.")
return ""
# Get the most recent cash flow statement by selecting the row with the latest Publish Date
latest_cash_flow = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
# drop the SimFinID column
latest_cash_flow = latest_cash_flow.drop("SimFinId")
return (
f"## {freq} cash flow statement for {ticker} released on {str(latest_cash_flow['Publish Date'])[0:10]}: \n"
+ str(latest_cash_flow)
+ "\n\nThis includes metadata like reporting dates and currency, share details, and a breakdown of cash movements. Operating activities show cash generated from core business operations, including net income adjustments for non-cash items and working capital changes. Investing activities cover asset acquisitions/disposals and investments. Financing activities include debt transactions, equity issuances/repurchases, and dividend payments. The net change in cash represents the overall increase or decrease in the company's cash position during the reporting period."
)
def get_simfin_income_statements(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual / quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
data_path = os.path.join(
DATA_DIR,
"fundamental_data",
"simfin_data_all",
"income_statements",
"companies",
"us",
f"us-income-{freq}.csv",
)
df = pd.read_csv(data_path, sep=";")
# Convert date strings to datetime objects and remove any time components
df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
# Convert the current date to datetime and normalize
curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
# Filter the DataFrame for the given ticker and for reports that were published on or before the current date
filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
# Check if there are any available reports; if not, return a notification
if filtered_df.empty:
print("No income statement available before the given current date.")
return ""
# Get the most recent income statement by selecting the row with the latest Publish Date
latest_income = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
# drop the SimFinID column
latest_income = latest_income.drop("SimFinId")
return (
f"## {freq} income statement for {ticker} released on {str(latest_income['Publish Date'])[0:10]}: \n"
+ str(latest_income)
+ "\n\nThis includes metadata like reporting dates and currency, share details, and a comprehensive breakdown of the company's financial performance. Starting with Revenue, it shows Cost of Revenue and resulting Gross Profit. Operating Expenses are detailed, including SG&A, R&D, and Depreciation. The statement then shows Operating Income, followed by non-operating items and Interest Expense, leading to Pretax Income. After accounting for Income Tax and any Extraordinary items, it concludes with Net Income, representing the company's bottom-line profit or loss for the period."
)
def get_google_news(
query: Annotated[str, "Query to search with"],
curr_date: Annotated[str, "Curr date in yyyy-mm-dd format"],
@ -356,7 +91,6 @@ def get_google_news(
return f"## {query} Google News, from {before} to {curr_date}:\n\n{news_str}"
def get_reddit_global_news(
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
look_back_days: Annotated[int, "how many days to look back"],
@ -408,7 +142,6 @@ def get_reddit_global_news(
return f"## Global News Reddit, from {before} to {curr_date}:\n{news_str}"
def get_reddit_company_news(
ticker: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
@ -492,7 +225,7 @@ def get_binance_data(
symbol += "USDT" # Ensure the symbol ends with USDT for futures
klines_str = ""
klines = get_binance_klines(symbol, interval, klines_limit)
klines = fetch_klines_from_binance(symbol, interval, klines_limit)
if klines is not None and len(klines) != 0:
klines = list(map(lambda x: { "t": x[0], "o": x[1], "h": x[2], "l": x[3], "c": x[4], "v": x[5] }, klines))
klines_str = f"## {symbol} Futures **KLines Data** for {interval} interval:\n" + "\n".join(
@ -500,7 +233,7 @@ def get_binance_data(
) + "\n\n"
depth_str = ""
depth = get_binance_depth(symbol, depth_limit)
depth = fetch_depth_from_binance(symbol, depth_limit)
if depth is not None and isinstance(depth, dict):
bids = depth.get("bids", [-1, -1])
asks = depth.get("asks", [-1, -1])
@ -508,12 +241,12 @@ def get_binance_data(
depth_str = f"## {symbol} Futures **Current Depth Data**:\nBids: Price: {depth["bids"]["price"]}, Volume: {depth["bids"]["volume"]}\nAsks: Price: {depth["asks"]["price"]}, Volume: {depth["asks"]["volume"]}\n\n"
ticker_24hr_str = ""
ticker_24hr = get_binance_24hr_pricechange(symbol)
ticker_24hr = fetch_24hr_pricechange_from_binance(symbol)
if ticker_24hr is not None and isinstance(ticker_24hr, dict):
ticker_24hr_str = f"## {symbol} Futures **24-Hour Price Change**:\nPrice Change: {ticker_24hr.get("priceChange", "N/A")}, Price Change Percent: {ticker_24hr.get("priceChangePercent", "N/A")}, Weighted Avg Price: {ticker_24hr.get("weightedAvgPrice", "N/A")}\n\n"
top_longshort_position_ratio_str = ""
top_longshort_position_ratio = get_binance_toplongshort_position_ratio(symbol, interval, longshort_limit)
top_longshort_position_ratio = fetch_toplongshort_position_ratio_from_binance(symbol, interval, longshort_limit)
if top_longshort_position_ratio is not None and isinstance(top_longshort_position_ratio, list):
top_longshort_position_ratio = [
{ "t": entry["timestamp"], "longShortRatio": entry["longShortRatio"] }
@ -524,7 +257,7 @@ def get_binance_data(
) + "\n\n"
top_longshort_account_ratio_str = ""
top_longshort_account_ratio = get_binance_toplongshort_account_ratio(symbol, interval, longshort_limit)
top_longshort_account_ratio = fetch_toplongshort_account_ratio_from_binance(symbol, interval, longshort_limit)
if top_longshort_account_ratio is not None and isinstance(top_longshort_account_ratio, list):
top_longshort_account_ratio = [
{ "t": entry["timestamp"], "longShortRatio": entry["longShortRatio"] }
@ -535,7 +268,7 @@ def get_binance_data(
) + "\n\n"
global_longshort_account_ratio_str = ""
global_longshort_account_ratio = get_binance_global_longshort_account_ratio(symbol, interval, longshort_limit)
global_longshort_account_ratio = fetch_global_longshort_account_ratio_from_binance(symbol, interval, longshort_limit)
if global_longshort_account_ratio is not None and isinstance(global_longshort_account_ratio, list):
global_longshort_account_ratio = [
{ "t": entry["timestamp"], "longShortRatio": entry["longShortRatio"] }
@ -546,7 +279,7 @@ def get_binance_data(
) + "\n\n"
taker_longshort_ratio_str = ""
taker_longshort_ratio = get_binance_taker_longshort_ratio(symbol, interval, longshort_limit)
taker_longshort_ratio = fetch_taker_longshort_ratio_from_binance(symbol, interval, longshort_limit)
if taker_longshort_ratio is not None and isinstance(taker_longshort_ratio, list):
taker_longshort_ratio = [
{ "t": entry["timestamp"], "buySellRatio": entry["buySellRatio"], "buyVol": entry["buyVol"], "sellVol": entry["sellVol"] }
@ -567,6 +300,385 @@ def get_binance_data(
+ taker_longshort_ratio_str
)
def get_stock_news_openai(ticker, curr_date):
config = get_config()
client = OpenAI(
base_url=config["backend_url"],
api_key=os.getenv(config["api_key_env_name"])
)
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
def get_global_news_openai(curr_date):
config = get_config()
client = OpenAI(
base_url=config["backend_url"],
api_key=os.getenv(config["api_key_env_name"])
)
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
def get_fundamentals_openai(ticker, curr_date):
config = get_config()
client = OpenAI(
base_url=config["backend_url"],
api_key=os.getenv(config["api_key_env_name"])
)
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
#region Deprecated Stock Utilities
@deprecated("Utilities only for stocks are deprecated.")
def get_finnhub_news(
ticker: Annotated[
str,
"Search query of a company's, e.g. 'AAPL, TSM, etc.",
],
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
look_back_days: Annotated[int, "how many days to look back"],
):
"""
Retrieve news about a company within a time frame
Args
ticker (str): ticker for the company you are interested in
start_date (str): Start date in yyyy-mm-dd format
end_date (str): End date in yyyy-mm-dd format
Returns
str: dataframe containing the news of the company in the time frame
"""
start_date = datetime.strptime(curr_date, "%Y-%m-%d")
before = start_date - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
result = get_data_in_range(ticker, before, curr_date, "news_data", DATA_DIR)
if len(result) == 0:
return ""
combined_result = ""
for day, data in result.items():
if len(data) == 0:
continue
for entry in data:
current_news = (
"### " + entry["headline"] + f" ({day})" + "\n" + entry["summary"]
)
combined_result += current_news + "\n\n"
return f"## {ticker} News, from {before} to {curr_date}:\n" + str(combined_result)
@deprecated("Utilities only for stocks are deprecated.")
def get_finnhub_company_insider_sentiment(
ticker: Annotated[str, "ticker symbol for the company"],
curr_date: Annotated[
str,
"current date of you are trading at, yyyy-mm-dd",
],
look_back_days: Annotated[int, "number of days to look back"],
):
"""
Retrieve insider sentiment about a company (retrieved from public SEC information) for the past 15 days
Args:
ticker (str): ticker symbol of the company
curr_date (str): current date you are trading on, yyyy-mm-dd
Returns:
str: a report of the sentiment in the past 15 days starting at curr_date
"""
date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
before = date_obj - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
data = get_data_in_range(ticker, before, curr_date, "insider_senti", DATA_DIR)
if len(data) == 0:
return ""
result_str = ""
seen_dicts = []
for date, senti_list in data.items():
for entry in senti_list:
if entry not in seen_dicts:
result_str += f"### {entry['year']}-{entry['month']}:\nChange: {entry['change']}\nMonthly Share Purchase Ratio: {entry['mspr']}\n\n"
seen_dicts.append(entry)
return (
f"## {ticker} Insider Sentiment Data for {before} to {curr_date}:\n"
+ result_str
+ "The change field refers to the net buying/selling from all insiders' transactions. The mspr field refers to monthly share purchase ratio."
)
@deprecated("Utilities only for stocks are deprecated.")
def get_finnhub_company_insider_transactions(
ticker: Annotated[str, "ticker symbol"],
curr_date: Annotated[
str,
"current date you are trading at, yyyy-mm-dd",
],
look_back_days: Annotated[int, "how many days to look back"],
):
"""
Retrieve insider transcaction information about a company (retrieved from public SEC information) for the past 15 days
Args:
ticker (str): ticker symbol of the company
curr_date (str): current date you are trading at, yyyy-mm-dd
Returns:
str: a report of the company's insider transaction/trading informtaion in the past 15 days
"""
date_obj = datetime.strptime(curr_date, "%Y-%m-%d")
before = date_obj - relativedelta(days=look_back_days)
before = before.strftime("%Y-%m-%d")
data = get_data_in_range(ticker, before, curr_date, "insider_trans", DATA_DIR)
if len(data) == 0:
return ""
result_str = ""
seen_dicts = []
for date, senti_list in data.items():
for entry in senti_list:
if entry not in seen_dicts:
result_str += f"### Filing Date: {entry['filingDate']}, {entry['name']}:\nChange:{entry['change']}\nShares: {entry['share']}\nTransaction Price: {entry['transactionPrice']}\nTransaction Code: {entry['transactionCode']}\n\n"
seen_dicts.append(entry)
return (
f"## {ticker} insider transactions from {before} to {curr_date}:\n"
+ result_str
+ "The change field reflects the variation in share count—here a negative number indicates a reduction in holdings—while share specifies the total number of shares involved. The transactionPrice denotes the per-share price at which the trade was executed, and transactionDate marks when the transaction occurred. The name field identifies the insider making the trade, and transactionCode (e.g., S for sale) clarifies the nature of the transaction. FilingDate records when the transaction was officially reported, and the unique id links to the specific SEC filing, as indicated by the source. Additionally, the symbol ties the transaction to a particular company, isDerivative flags whether the trade involves derivative securities, and currency notes the currency context of the transaction."
)
@deprecated("Utilities only for stocks are deprecated.")
def get_simfin_balance_sheet(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual / quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
data_path = os.path.join(
DATA_DIR,
"fundamental_data",
"simfin_data_all",
"balance_sheet",
"companies",
"us",
f"us-balance-{freq}.csv",
)
df = pd.read_csv(data_path, sep=";")
# Convert date strings to datetime objects and remove any time components
df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
# Convert the current date to datetime and normalize
curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
# Filter the DataFrame for the given ticker and for reports that were published on or before the current date
filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
# Check if there are any available reports; if not, return a notification
if filtered_df.empty:
print("No balance sheet available before the given current date.")
return ""
# Get the most recent balance sheet by selecting the row with the latest Publish Date
latest_balance_sheet = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
# drop the SimFinID column
latest_balance_sheet = latest_balance_sheet.drop("SimFinId")
return (
f"## {freq} balance sheet for {ticker} released on {str(latest_balance_sheet['Publish Date'])[0:10]}: \n"
+ str(latest_balance_sheet)
+ "\n\nThis includes metadata like reporting dates and currency, share details, and a breakdown of assets, liabilities, and equity. Assets are grouped as current (liquid items like cash and receivables) and noncurrent (long-term investments and property). Liabilities are split between short-term obligations and long-term debts, while equity reflects shareholder funds such as paid-in capital and retained earnings. Together, these components ensure that total assets equal the sum of liabilities and equity."
)
@deprecated("Utilities only for stocks are deprecated.")
def get_simfin_cashflow(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual / quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
data_path = os.path.join(
DATA_DIR,
"fundamental_data",
"simfin_data_all",
"cash_flow",
"companies",
"us",
f"us-cashflow-{freq}.csv",
)
df = pd.read_csv(data_path, sep=";")
# Convert date strings to datetime objects and remove any time components
df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
# Convert the current date to datetime and normalize
curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
# Filter the DataFrame for the given ticker and for reports that were published on or before the current date
filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
# Check if there are any available reports; if not, return a notification
if filtered_df.empty:
print("No cash flow statement available before the given current date.")
return ""
# Get the most recent cash flow statement by selecting the row with the latest Publish Date
latest_cash_flow = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
# drop the SimFinID column
latest_cash_flow = latest_cash_flow.drop("SimFinId")
return (
f"## {freq} cash flow statement for {ticker} released on {str(latest_cash_flow['Publish Date'])[0:10]}: \n"
+ str(latest_cash_flow)
+ "\n\nThis includes metadata like reporting dates and currency, share details, and a breakdown of cash movements. Operating activities show cash generated from core business operations, including net income adjustments for non-cash items and working capital changes. Investing activities cover asset acquisitions/disposals and investments. Financing activities include debt transactions, equity issuances/repurchases, and dividend payments. The net change in cash represents the overall increase or decrease in the company's cash position during the reporting period."
)
@deprecated("Utilities only for stocks are deprecated.")
def get_simfin_income_statements(
ticker: Annotated[str, "ticker symbol"],
freq: Annotated[
str,
"reporting frequency of the company's financial history: annual / quarterly",
],
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
):
data_path = os.path.join(
DATA_DIR,
"fundamental_data",
"simfin_data_all",
"income_statements",
"companies",
"us",
f"us-income-{freq}.csv",
)
df = pd.read_csv(data_path, sep=";")
# Convert date strings to datetime objects and remove any time components
df["Report Date"] = pd.to_datetime(df["Report Date"], utc=True).dt.normalize()
df["Publish Date"] = pd.to_datetime(df["Publish Date"], utc=True).dt.normalize()
# Convert the current date to datetime and normalize
curr_date_dt = pd.to_datetime(curr_date, utc=True).normalize()
# Filter the DataFrame for the given ticker and for reports that were published on or before the current date
filtered_df = df[(df["Ticker"] == ticker) & (df["Publish Date"] <= curr_date_dt)]
# Check if there are any available reports; if not, return a notification
if filtered_df.empty:
print("No income statement available before the given current date.")
return ""
# Get the most recent income statement by selecting the row with the latest Publish Date
latest_income = filtered_df.loc[filtered_df["Publish Date"].idxmax()]
# drop the SimFinID column
latest_income = latest_income.drop("SimFinId")
return (
f"## {freq} income statement for {ticker} released on {str(latest_income['Publish Date'])[0:10]}: \n"
+ str(latest_income)
+ "\n\nThis includes metadata like reporting dates and currency, share details, and a comprehensive breakdown of the company's financial performance. Starting with Revenue, it shows Cost of Revenue and resulting Gross Profit. Operating Expenses are detailed, including SG&A, R&D, and Depreciation. The statement then shows Operating Income, followed by non-operating items and Interest Expense, leading to Pretax Income. After accounting for Income Tax and any Extraordinary items, it concludes with Net Income, representing the company's bottom-line profit or loss for the period."
)
@deprecated("Utilities only for stocks are deprecated.")
def get_stock_stats_indicators_window(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to get the analysis and report of"],
@ -702,7 +814,7 @@ def get_stock_stats_indicators_window(
return result_str
@deprecated("Utilities only for stocks are deprecated.")
def get_stockstats_indicator(
symbol: Annotated[str, "ticker symbol of the company"],
indicator: Annotated[str, "technical indicator to get the analysis and report of"],
@ -731,7 +843,7 @@ def get_stockstats_indicator(
return str(indicator_value)
@deprecated("Utilities only for stocks are deprecated.")
def get_YFin_data_window(
symbol: Annotated[str, "ticker symbol of the company"],
curr_date: Annotated[str, "Start date in yyyy-mm-dd format"],
@ -772,7 +884,7 @@ def get_YFin_data_window(
+ df_string
)
@deprecated("Utilities only for stocks are deprecated.")
def get_YFin_data_online(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
@ -814,7 +926,7 @@ def get_YFin_data_online(
return header + csv_string
@deprecated("Utilities only for stocks are deprecated.")
def get_YFin_data(
symbol: Annotated[str, "ticker symbol of the company"],
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
@ -848,117 +960,4 @@ def get_YFin_data(
filtered_data = filtered_data.reset_index(drop=True)
return filtered_data
def get_stock_news_openai(ticker, curr_date):
config = get_config()
client = OpenAI(
base_url=config["backend_url"],
api_key=os.getenv(config["api_key_env_name"])
)
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
def get_global_news_openai(curr_date):
config = get_config()
client = OpenAI(
base_url=config["backend_url"],
api_key=os.getenv(config["api_key_env_name"])
)
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
def get_fundamentals_openai(ticker, curr_date):
config = get_config()
client = OpenAI(
base_url=config["backend_url"],
api_key=os.getenv(config["api_key_env_name"])
)
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
#endregion

3
tradingagents/dataflows/stockstats_utils.py
View File

@ -5,7 +5,8 @@ from typing import Annotated
import os
from .config import get_config
from warnings import deprecated
@deprecated("Utilities only for stocks are deprecated.")
class StockstatsUtils:
@staticmethod
def get_stock_stats(

4
tradingagents/dataflows/yfin_utils.py
View File

@ -1,5 +1,4 @@
# gets data/stats
import yfinance as yf
from typing import Annotated, Callable, Any, Optional
from pandas import DataFrame
@ -19,7 +18,8 @@ def init_ticker(func: Callable) -> Callable:
return wrapper
from warnings import deprecated
@deprecated("Utilities only for stocks are deprecated.")
@decorate_all_methods(init_ticker)
class YFinanceUtils:

3
tradingagents/default_config.py
View File

@ -18,12 +18,11 @@ DEFAULT_CONFIG = {
"quick_think_llm": "qwen-turbo",
"backend_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
"api_key_env_name": "DASHSCOPE_API_KEY",
"max_tokens": 1000, # Maximum tokens for LLM responses
# Debate and discussion settings
"max_debate_rounds": 1,
"max_risk_discuss_rounds": 1,
"max_recur_limit": 200,
# Tool settings
"online_tools": True,
# Language settings
"language": "zh", # 支持 'zh' 或 'en'
}

2
tradingagents/graph/propagation.py
View File

@ -11,7 +11,7 @@ from tradingagents.agents.utils.agent_states import (
class Propagator:
"""Handles state initialization and propagation through the graph."""
def __init__(self, max_recur_limit=100):
def __init__(self, max_recur_limit=200):
"""Initialize with configuration parameters."""
self.max_recur_limit = max_recur_limit

27
tradingagents/graph/trading_graph.py
View File

@ -117,44 +117,27 @@ class TradingAgentsGraph:
return {
"market": ToolNode(
[
# online tools
self.toolkit.get_binance_data,
self.toolkit.get_stockstats_indicators_report_online,
# offline tools
self.toolkit.get_YFin_data,
self.toolkit.get_stockstats_indicators_report,
self.toolkit.get_binance_data
]
),
"social": ToolNode(
[
# online tools
self.toolkit.get_stock_news_openai,
# offline tools
self.toolkit.get_reddit_stock_info,
]
),
"news": ToolNode(
[
# online tools
self.toolkit.get_global_news_openai,
self.toolkit.get_google_news,
# self.toolkit.get_global_news_openai,
# self.toolkit.get_google_news,
self.toolkit.get_blockbeats_news,
self.toolkit.get_coindesk_news,
# offline tools
self.toolkit.get_finnhub_news,
self.toolkit.get_reddit_news,
# self.toolkit.get_reddit_news,
]
),
"fundamentals": ToolNode(
[
# online tools
self.toolkit.get_fundamentals_openai,
# offline tools
self.toolkit.get_finnhub_company_insider_sentiment,
self.toolkit.get_finnhub_company_insider_transactions,
self.toolkit.get_simfin_balance_sheet,
self.toolkit.get_simfin_cashflow,
self.toolkit.get_simfin_income_stmt,
self.toolkit.get_fundamentals_openai
]
),
}