mirror of
https://github.com/farcasclaudiu/TradingAgents.git
synced 2026-06-29 11:01:40 +03:00
WIP
This commit is contained in:
luohy15
@@ -1,4 +1,4 @@
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from .utils.agent_utils import Toolkit, create_msg_delete
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from .utils.agent_utils import create_msg_delete
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from .utils.agent_states import AgentState, InvestDebateState, RiskDebateState
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from .utils.memory import FinancialSituationMemory
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@@ -21,7 +21,6 @@ from .trader.trader import create_trader
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__all__ = [
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"FinancialSituationMemory",
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"Toolkit",
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"AgentState",
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"create_msg_delete",
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"InvestDebateState",
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@@ -1,28 +1,27 @@
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_sentiment, get_insider_transactions
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from tradingagents.dataflows.config import get_config
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def create_fundamentals_analyst(llm, toolkit):
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def create_fundamentals_analyst(llm):
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def fundamentals_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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company_name = state["company_of_interest"]
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if toolkit.config["online_tools"]:
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tools = [toolkit.get_fundamentals_openai]
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else:
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tools = [
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toolkit.get_finnhub_company_insider_sentiment,
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toolkit.get_finnhub_company_insider_transactions,
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toolkit.get_simfin_balance_sheet,
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toolkit.get_simfin_cashflow,
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toolkit.get_simfin_income_stmt,
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]
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tools = [
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get_fundamentals,
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get_balance_sheet,
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get_cashflow,
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get_income_statement,
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]
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system_message = (
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"You are a researcher tasked with analyzing fundamental information over the past week about a company. Please write a comprehensive report of the company's fundamental information such as financial documents, company profile, basic company financials, company financial history, insider sentiment and insider transactions to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
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+ " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.",
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+ " Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."
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+ " Use the get_fundamental_data tool with different data_type parameters: 'full_fundamentals' for comprehensive analysis, 'balance_sheet', 'cashflow', 'income_statement' for specific financial statements, 'insider_sentiment' and 'insider_transactions' for insider information.",
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)
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prompt = ChatPromptTemplate.from_messages(
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@@ -1,25 +1,21 @@
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators
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from tradingagents.dataflows.config import get_config
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def create_market_analyst(llm, toolkit):
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def create_market_analyst(llm):
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def market_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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company_name = state["company_of_interest"]
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if toolkit.config["online_tools"]:
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tools = [
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toolkit.get_YFin_data_online,
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toolkit.get_stockstats_indicators_report_online,
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]
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else:
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tools = [
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toolkit.get_YFin_data,
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toolkit.get_stockstats_indicators_report,
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]
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tools = [
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get_stock_data,
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get_indicators,
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]
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system_message = (
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"""You are a trading assistant tasked with analyzing financial markets. Your role is to select the **most relevant indicators** for a given market condition or trading strategy from the following list. The goal is to choose up to **8 indicators** that provide complementary insights without redundancy. Categories and each category's indicators are:
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@@ -46,7 +42,7 @@ Volatility Indicators:
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Volume-Based Indicators:
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- vwma: VWMA: A moving average weighted by volume. Usage: Confirm trends by integrating price action with volume data. Tips: Watch for skewed results from volume spikes; use in combination with other volume analyses.
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- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_YFin_data first to retrieve the CSV that is needed to generate indicators. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."""
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- Select indicators that provide diverse and complementary information. Avoid redundancy (e.g., do not select both rsi and stochrsi). Also briefly explain why they are suitable for the given market context. When you tool call, please use the exact name of the indicators provided above as they are defined parameters, otherwise your call will fail. Please make sure to call get_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_technical_indicators with the specific indicator names. Write a very detailed and nuanced report of the trends you observe. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."""
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+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
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)
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@@ -1,25 +1,23 @@
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_news, get_global_news
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from tradingagents.dataflows.config import get_config
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def create_news_analyst(llm, toolkit):
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def create_news_analyst(llm):
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def news_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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if toolkit.config["online_tools"]:
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tools = [toolkit.get_global_news_openai, toolkit.get_google_news]
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else:
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tools = [
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toolkit.get_finnhub_news,
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toolkit.get_reddit_news,
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toolkit.get_google_news,
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]
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tools = [
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get_news,
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get_global_news,
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]
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system_message = (
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"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Look at news from EODHD, and finnhub to be comprehensive. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
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+ """ Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read."""
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"You are a news researcher tasked with analyzing recent news and trends over the past week. Please write a comprehensive report of the current state of the world that is relevant for trading and macroeconomics. Use the get_news_data tool with different news_type parameters: 'global_news' for macroeconomic news, 'company_news' for company-specific news, 'reddit_global' for social sentiment, 'google' for general news searches. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
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+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read."""
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)
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prompt = ChatPromptTemplate.from_messages(
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@@ -1,24 +1,23 @@
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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import time
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import json
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from tradingagents.agents.utils.agent_utils import get_news
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from tradingagents.dataflows.config import get_config
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def create_social_media_analyst(llm, toolkit):
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def create_social_media_analyst(llm):
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def social_media_analyst_node(state):
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current_date = state["trade_date"]
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ticker = state["company_of_interest"]
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company_name = state["company_of_interest"]
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if toolkit.config["online_tools"]:
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tools = [toolkit.get_stock_news_openai]
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else:
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tools = [
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toolkit.get_reddit_stock_info,
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]
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tools = [
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get_news,
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]
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system_message = (
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"You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
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+ """ Make sure to append a Makrdown table at the end of the report to organize key points in the report, organized and easy to read.""",
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"You are a social media and company specific news researcher/analyst tasked with analyzing social media posts, recent company news, and public sentiment for a specific company over the past week. You will be given a company's name your objective is to write a comprehensive long report detailing your analysis, insights, and implications for traders and investors on this company's current state after looking at social media and what people are saying about that company, analyzing sentiment data of what people feel each day about the company, and looking at recent company news. Use get_news_data with news_type parameters: 'company_news' for company-specific news, 'reddit_stock' for Reddit discussions about the stock. Try to look at all sources possible from social media to sentiment to news. Do not simply state the trends are mixed, provide detailed and finegrained analysis and insights that may help traders make decisions."
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+ """ Make sure to append a Markdown table at the end of the report to organize key points in the report, organized and easy to read.""",
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)
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prompt = ChatPromptTemplate.from_messages(
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@@ -1,19 +1,24 @@
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from langchain_core.messages import BaseMessage, HumanMessage, ToolMessage, AIMessage
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from typing import List
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from typing import Annotated
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from langchain_core.messages import RemoveMessage
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from langchain_core.tools import tool
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from datetime import date, timedelta, datetime
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import functools
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import pandas as pd
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import os
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from dateutil.relativedelta import relativedelta
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from langchain_openai import ChatOpenAI
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import tradingagents.dataflows.interface as interface
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from tradingagents.default_config import DEFAULT_CONFIG
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from langchain_core.messages import HumanMessage
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from langchain_core.messages import HumanMessage, RemoveMessage
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# Import tools from separate utility files
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from tradingagents.agents.utils.core_stock_tools import (
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get_stock_data
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)
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from tradingagents.agents.utils.technical_indicators_tools import (
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get_indicators
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)
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from tradingagents.agents.utils.fundamental_data_tools import (
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get_fundamentals,
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get_balance_sheet,
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get_cashflow,
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get_income_statement
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)
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from tradingagents.agents.utils.news_data_tools import (
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get_news,
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get_insider_sentiment,
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get_insider_transactions,
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get_global_news
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)
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def create_msg_delete():
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def delete_messages(state):
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@@ -31,389 +36,4 @@ def create_msg_delete():
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return delete_messages
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class Toolkit:
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_config = DEFAULT_CONFIG.copy()
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@classmethod
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def update_config(cls, config):
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"""Update the class-level configuration."""
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cls._config.update(config)
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@property
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def config(self):
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"""Access the configuration."""
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return self._config
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def __init__(self, config=None):
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if config:
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self.update_config(config)
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@staticmethod
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@tool
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def get_reddit_news(
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curr_date: Annotated[str, "Date you want to get news for in yyyy-mm-dd format"],
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) -> str:
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"""
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Retrieve global news from Reddit within a specified time frame.
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Args:
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curr_date (str): Date you want to get news for in yyyy-mm-dd format
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Returns:
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str: A formatted dataframe containing the latest global news from Reddit in the specified time frame.
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"""
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global_news_result = interface.get_reddit_global_news(curr_date, 7, 5)
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return global_news_result
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@staticmethod
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@tool
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def get_finnhub_news(
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ticker: Annotated[
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str,
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"Search query of a company, e.g. 'AAPL, TSM, etc.",
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],
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start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
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end_date: Annotated[str, "End date in yyyy-mm-dd format"],
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):
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"""
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Retrieve the latest news about a given stock from Finnhub within a date range
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Args:
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ticker (str): Ticker of a company. e.g. AAPL, TSM
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start_date (str): Start date in yyyy-mm-dd format
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end_date (str): End date in yyyy-mm-dd format
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Returns:
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str: A formatted dataframe containing news about the company within the date range from start_date to end_date
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"""
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end_date_str = end_date
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end_date = datetime.strptime(end_date, "%Y-%m-%d")
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start_date = datetime.strptime(start_date, "%Y-%m-%d")
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look_back_days = (end_date - start_date).days
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finnhub_news_result = interface.get_finnhub_news(
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ticker, end_date_str, look_back_days
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)
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return finnhub_news_result
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@staticmethod
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@tool
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def get_reddit_stock_info(
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ticker: Annotated[
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str,
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"Ticker of a company. e.g. AAPL, TSM",
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],
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curr_date: Annotated[str, "Current date you want to get news for"],
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) -> str:
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"""
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Retrieve the latest news about a given stock from Reddit, given the current date.
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Args:
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ticker (str): Ticker of a company. e.g. AAPL, TSM
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curr_date (str): current date in yyyy-mm-dd format to get news for
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Returns:
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str: A formatted dataframe containing the latest news about the company on the given date
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"""
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stock_news_results = interface.get_reddit_company_news(ticker, curr_date, 7, 5)
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return stock_news_results
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@staticmethod
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@tool
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def get_YFin_data(
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symbol: Annotated[str, "ticker symbol of the company"],
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start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
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end_date: Annotated[str, "End date in yyyy-mm-dd format"],
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) -> str:
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"""
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Retrieve the stock price data for a given ticker symbol from Yahoo Finance.
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Args:
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symbol (str): Ticker symbol of the company, e.g. AAPL, TSM
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start_date (str): Start date in yyyy-mm-dd format
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end_date (str): End date in yyyy-mm-dd format
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Returns:
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str: A formatted dataframe containing the stock price data for the specified ticker symbol in the specified date range.
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"""
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result_data = interface.get_YFin_data(symbol, start_date, end_date)
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return result_data
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@staticmethod
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@tool
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def get_YFin_data_online(
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symbol: Annotated[str, "ticker symbol of the company"],
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start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
|
||||
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
|
||||
) -> str:
|
||||
"""
|
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Retrieve the stock price data for a given ticker symbol from Yahoo Finance.
|
||||
Args:
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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.
|
||||
"""
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result_data = interface.get_YFin_data_online(symbol, start_date, end_date)
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||||
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||||
return result_data
|
||||
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||||
@staticmethod
|
||||
@tool
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||||
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
|
||||
)
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||||
|
||||
return result_stockstats
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||||
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||||
@staticmethod
|
||||
@tool
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||||
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(
|
||||
query: Annotated[str, "Query to search with"],
|
||||
curr_date: Annotated[str, "Curr date in yyyy-mm-dd format"],
|
||||
):
|
||||
"""
|
||||
Retrieve the latest news from Google News based on a query and date range.
|
||||
Args:
|
||||
query (str): Query to search with
|
||||
curr_date (str): Current date in yyyy-mm-dd format
|
||||
look_back_days (int): How many days to look back
|
||||
Returns:
|
||||
str: A formatted string containing the latest news from Google News based on the query and date range.
|
||||
"""
|
||||
|
||||
google_news_results = interface.get_google_news(query, curr_date, 7)
|
||||
|
||||
return google_news_results
|
||||
|
||||
@staticmethod
|
||||
@tool
|
||||
def get_stock_news_openai(
|
||||
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.
|
||||
Args:
|
||||
ticker (str): Ticker of a company. e.g. AAPL, TSM
|
||||
curr_date (str): Current date in yyyy-mm-dd format
|
||||
Returns:
|
||||
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)
|
||||
|
||||
return openai_news_results
|
||||
|
||||
@staticmethod
|
||||
@tool
|
||||
def get_global_news_openai(
|
||||
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.
|
||||
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)
|
||||
|
||||
return openai_news_results
|
||||
|
||||
@staticmethod
|
||||
@tool
|
||||
def get_fundamentals_openai(
|
||||
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.
|
||||
Args:
|
||||
ticker (str): Ticker of a company. e.g. AAPL, TSM
|
||||
curr_date (str): Current date in yyyy-mm-dd format
|
||||
Returns:
|
||||
str: A formatted string containing the latest fundamental information about the company on the given date.
|
||||
"""
|
||||
|
||||
openai_fundamentals_results = interface.get_fundamentals_openai(
|
||||
ticker, curr_date
|
||||
)
|
||||
|
||||
return openai_fundamentals_results
|
||||
|
||||
@@ -0,0 +1,22 @@
|
||||
from langchain_core.tools import tool
|
||||
from typing import Annotated
|
||||
from tradingagents.dataflows.interface import route_to_vender
|
||||
|
||||
|
||||
@tool
|
||||
def get_stock_data(
|
||||
symbol: Annotated[str, "ticker symbol of the company"],
|
||||
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
|
||||
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve stock price data (OHLCV) for a given ticker symbol.
|
||||
Uses the configured core_stock_apis vendor.
|
||||
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.
|
||||
"""
|
||||
return route_to_vender("get_stock_data", symbol, start_date, end_date)
|
||||
@@ -0,0 +1,77 @@
|
||||
from langchain_core.tools import tool
|
||||
from typing import Annotated
|
||||
from tradingagents.dataflows.interface import route_to_vender
|
||||
|
||||
|
||||
@tool
|
||||
def get_fundamentals(
|
||||
ticker: Annotated[str, "ticker symbol"],
|
||||
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve comprehensive fundamental data for a given ticker symbol.
|
||||
Uses the configured fundamental_data vendor.
|
||||
Args:
|
||||
ticker (str): Ticker symbol of the company
|
||||
curr_date (str): Current date you are trading at, yyyy-mm-dd
|
||||
Returns:
|
||||
str: A formatted report containing comprehensive fundamental data
|
||||
"""
|
||||
return route_to_vender("get_fundamentals", ticker, curr_date)
|
||||
|
||||
|
||||
@tool
|
||||
def get_balance_sheet(
|
||||
ticker: Annotated[str, "ticker symbol"],
|
||||
freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly",
|
||||
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve balance sheet data for a given ticker symbol.
|
||||
Uses the configured fundamental_data vendor.
|
||||
Args:
|
||||
ticker (str): Ticker symbol of the company
|
||||
freq (str): Reporting frequency: annual/quarterly (default quarterly)
|
||||
curr_date (str): Current date you are trading at, yyyy-mm-dd
|
||||
Returns:
|
||||
str: A formatted report containing balance sheet data
|
||||
"""
|
||||
return route_to_vender("get_balance_sheet", ticker, freq, curr_date)
|
||||
|
||||
|
||||
@tool
|
||||
def get_cashflow(
|
||||
ticker: Annotated[str, "ticker symbol"],
|
||||
freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly",
|
||||
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve cash flow statement data for a given ticker symbol.
|
||||
Uses the configured fundamental_data vendor.
|
||||
Args:
|
||||
ticker (str): Ticker symbol of the company
|
||||
freq (str): Reporting frequency: annual/quarterly (default quarterly)
|
||||
curr_date (str): Current date you are trading at, yyyy-mm-dd
|
||||
Returns:
|
||||
str: A formatted report containing cash flow statement data
|
||||
"""
|
||||
return route_to_vender("get_cashflow", ticker, freq, curr_date)
|
||||
|
||||
|
||||
@tool
|
||||
def get_income_statement(
|
||||
ticker: Annotated[str, "ticker symbol"],
|
||||
freq: Annotated[str, "reporting frequency: annual/quarterly"] = "quarterly",
|
||||
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve income statement data for a given ticker symbol.
|
||||
Uses the configured fundamental_data vendor.
|
||||
Args:
|
||||
ticker (str): Ticker symbol of the company
|
||||
freq (str): Reporting frequency: annual/quarterly (default quarterly)
|
||||
curr_date (str): Current date you are trading at, yyyy-mm-dd
|
||||
Returns:
|
||||
str: A formatted report containing income statement data
|
||||
"""
|
||||
return route_to_vender("get_income_statement", ticker, freq, curr_date)
|
||||
@@ -0,0 +1,71 @@
|
||||
from langchain_core.tools import tool
|
||||
from typing import Annotated
|
||||
from tradingagents.dataflows.interface import route_to_vender
|
||||
|
||||
@tool
|
||||
def get_news(
|
||||
query: Annotated[str, "Search query or ticker symbol"],
|
||||
start_date: Annotated[str, "Start date in yyyy-mm-dd format"],
|
||||
end_date: Annotated[str, "End date in yyyy-mm-dd format"],
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve news data for a given query or ticker symbol.
|
||||
Uses the configured news_data vendor.
|
||||
Args:
|
||||
query (str): Search query or ticker symbol
|
||||
start_date (str): Start date in yyyy-mm-dd format
|
||||
end_date (str): End date in yyyy-mm-dd format
|
||||
Returns:
|
||||
str: A formatted string containing news data
|
||||
"""
|
||||
return route_to_vender("get_news", query, start_date, end_date)
|
||||
|
||||
@tool
|
||||
def get_global_news(
|
||||
curr_date: Annotated[str, "Current date in yyyy-mm-dd format"],
|
||||
look_back_days: Annotated[int, "Number of days to look back"] = 7,
|
||||
limit: Annotated[int, "Maximum number of articles to return"] = 5,
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve global news data.
|
||||
Uses the configured news_data vendor.
|
||||
Args:
|
||||
curr_date (str): Current date in yyyy-mm-dd format
|
||||
look_back_days (int): Number of days to look back (default 7)
|
||||
limit (int): Maximum number of articles to return (default 5)
|
||||
Returns:
|
||||
str: A formatted string containing global news data
|
||||
"""
|
||||
return route_to_vender("get_global_news", curr_date, look_back_days, limit)
|
||||
|
||||
@tool
|
||||
def get_insider_sentiment(
|
||||
ticker: Annotated[str, "ticker symbol for the company"],
|
||||
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve insider sentiment information about a company.
|
||||
Uses the configured news_data vendor.
|
||||
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 insider sentiment data
|
||||
"""
|
||||
return route_to_vender("get_insider_sentiment", ticker, curr_date)
|
||||
|
||||
@tool
|
||||
def get_insider_transactions(
|
||||
ticker: Annotated[str, "ticker symbol"],
|
||||
curr_date: Annotated[str, "current date you are trading at, yyyy-mm-dd"],
|
||||
) -> str:
|
||||
"""
|
||||
Retrieve insider transaction information about a company.
|
||||
Uses the configured news_data vendor.
|
||||
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 insider transaction data
|
||||
"""
|
||||
return route_to_vender("get_insider_transactions", ticker, curr_date)
|
||||
@@ -0,0 +1,23 @@
|
||||
from langchain_core.tools import tool
|
||||
from typing import Annotated
|
||||
from tradingagents.dataflows.interface import route_to_vender
|
||||
|
||||
@tool
|
||||
def get_indicators(
|
||||
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 technical indicators for a given ticker symbol.
|
||||
Uses the configured technical_indicators vendor.
|
||||
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 technical indicators for the specified ticker symbol and indicator.
|
||||
"""
|
||||
return route_to_vender("get_indicators", symbol, indicator, curr_date, look_back_days)
|
||||
Reference in New Issue
Block a user