fix: preserve exchange-qualified tickers across agent prompts
This commit is contained in:
CadeYu
parent
f362a160c3
commit
08bfe70a69
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@ -501,7 +501,9 @@ def get_user_selections():
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# Step 1: Ticker symbol
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console.print(
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create_question_box(
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"Step 1: Ticker Symbol", "Enter the ticker symbol to analyze", "SPY"
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"Step 1: Ticker Symbol",
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"Enter the exact ticker symbol to analyze, including exchange suffix when needed (examples: SPY, CNC.TO, 7203.T, 0700.HK)",
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"SPY",
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)
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)
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selected_ticker = get_ticker()
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@ -7,6 +7,8 @@ from cli.models import AnalystType
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console = Console()
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TICKER_INPUT_EXAMPLES = "Examples: SPY, CNC.TO, 7203.T, 0700.HK"
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ANALYST_ORDER = [
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("Market Analyst", AnalystType.MARKET),
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("Social Media Analyst", AnalystType.SOCIAL),
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@ -18,7 +20,7 @@ ANALYST_ORDER = [
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def get_ticker() -> str:
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"""Prompt the user to enter a ticker symbol."""
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ticker = questionary.text(
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"Enter the ticker symbol to analyze:",
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f"Enter the exact ticker symbol to analyze ({TICKER_INPUT_EXAMPLES}):",
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validate=lambda x: len(x.strip()) > 0 or "Please enter a valid ticker symbol.",
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style=questionary.Style(
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[
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@ -32,6 +34,11 @@ def get_ticker() -> str:
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console.print("\n[red]No ticker symbol provided. Exiting...[/red]")
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exit(1)
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return normalize_ticker_symbol(ticker)
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def normalize_ticker_symbol(ticker: str) -> str:
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"""Normalize ticker input while preserving exchange suffixes."""
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return ticker.strip().upper()
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@ -0,0 +1,18 @@
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import unittest
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from cli.utils import normalize_ticker_symbol
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from tradingagents.agents.utils.agent_utils import build_instrument_context
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class TickerSymbolHandlingTests(unittest.TestCase):
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def test_normalize_ticker_symbol_preserves_exchange_suffix(self):
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self.assertEqual(normalize_ticker_symbol(" cnc.to "), "CNC.TO")
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def test_build_instrument_context_mentions_exact_symbol(self):
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context = build_instrument_context("7203.T")
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self.assertIn("7203.T", context)
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self.assertIn("exchange suffix", context)
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if __name__ == "__main__":
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unittest.main()
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@ -1,7 +1,14 @@
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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_transactions
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from tradingagents.agents.utils.agent_utils import (
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build_instrument_context,
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get_balance_sheet,
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get_cashflow,
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get_fundamentals,
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get_income_statement,
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get_insider_transactions,
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)
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from tradingagents.dataflows.config import get_config
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@ -9,7 +16,7 @@ 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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instrument_context = build_instrument_context(ticker)
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tools = [
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get_fundamentals,
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@ -19,7 +26,7 @@ def create_fundamentals_analyst(llm):
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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, and company financial history 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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"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, and company financial history to gain a full view of the company's fundamental information to inform traders. Make sure to include as much detail as possible. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never merge fundamentals for similarly named companies from other exchanges. 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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+ " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements.",
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)
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@ -35,7 +42,7 @@ def create_fundamentals_analyst(llm):
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
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"For your reference, the current date is {current_date}. {instrument_context}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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@ -44,7 +51,7 @@ def create_fundamentals_analyst(llm):
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(ticker=ticker)
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prompt = prompt.partial(instrument_context=instrument_context)
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chain = prompt | llm.bind_tools(tools)
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@ -1,7 +1,11 @@
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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.agents.utils.agent_utils import (
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build_instrument_context,
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get_indicators,
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get_stock_data,
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)
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from tradingagents.dataflows.config import get_config
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@ -10,7 +14,7 @@ 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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instrument_context = build_instrument_context(ticker)
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tools = [
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get_stock_data,
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@ -42,7 +46,7 @@ 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_stock_data first to retrieve the CSV that is needed to generate indicators. Then use get_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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- 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_indicators with the specific indicator names. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never mix in similarly named companies from other exchanges. 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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@ -57,7 +61,7 @@ Volume-Based Indicators:
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. The company we want to look at is {ticker}",
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"For your reference, the current date is {current_date}. {instrument_context}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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@ -66,7 +70,7 @@ Volume-Based Indicators:
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(ticker=ticker)
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prompt = prompt.partial(instrument_context=instrument_context)
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chain = prompt | llm.bind_tools(tools)
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@ -1,7 +1,11 @@
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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.agents.utils.agent_utils import (
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build_instrument_context,
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get_global_news,
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get_news,
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)
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from tradingagents.dataflows.config import get_config
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@ -9,6 +13,7 @@ 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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instrument_context = build_instrument_context(ticker)
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tools = [
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get_news,
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@ -16,7 +21,7 @@ def create_news_analyst(llm):
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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. Use the available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic 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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"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 available tools: get_news(query, start_date, end_date) for company-specific or targeted news searches, and get_global_news(curr_date, look_back_days, limit) for broader macroeconomic news. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never merge news for similarly named companies from other exchanges. 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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@ -31,7 +36,7 @@ def create_news_analyst(llm):
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. We are looking at the company {ticker}",
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"For your reference, the current date is {current_date}. {instrument_context}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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@ -40,7 +45,7 @@ def create_news_analyst(llm):
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(ticker=ticker)
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prompt = prompt.partial(instrument_context=instrument_context)
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chain = prompt | llm.bind_tools(tools)
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result = chain.invoke(state["messages"])
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@ -1,7 +1,7 @@
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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.agents.utils.agent_utils import build_instrument_context, get_news
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from tradingagents.dataflows.config import get_config
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@ -9,15 +9,15 @@ 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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instrument_context = build_instrument_context(ticker)
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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. Use the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. 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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"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 the get_news(query, start_date, end_date) tool to search for company-specific news and social media discussions. Try to look at all sources possible from social media to sentiment to news. Always preserve the exact ticker symbol provided by the user, including any exchange suffix, and never merge commentary for similarly named companies from other exchanges. 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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@ -31,7 +31,7 @@ def create_social_media_analyst(llm):
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" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
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" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
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" You have access to the following tools: {tool_names}.\n{system_message}"
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"For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}",
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"For your reference, the current date is {current_date}. {instrument_context}",
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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@ -40,7 +40,7 @@ def create_social_media_analyst(llm):
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prompt = prompt.partial(system_message=system_message)
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prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
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prompt = prompt.partial(current_date=current_date)
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prompt = prompt.partial(ticker=ticker)
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prompt = prompt.partial(instrument_context=instrument_context)
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chain = prompt | llm.bind_tools(tools)
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@ -1,9 +1,13 @@
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import time
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import json
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from tradingagents.agents.utils.agent_utils import build_instrument_context
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def create_research_manager(llm, memory):
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def research_manager_node(state) -> dict:
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ticker = state["company_of_interest"]
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instrument_context = build_instrument_context(ticker)
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history = state["investment_debate_state"].get("history", "")
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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@ -33,6 +37,8 @@ Take into account your past mistakes on similar situations. Use these insights t
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Here are your past reflections on mistakes:
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\"{past_memory_str}\"
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{instrument_context}
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Here is the debate:
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Debate History:
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{history}"""
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@ -1,11 +1,14 @@
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import time
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import json
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from tradingagents.agents.utils.agent_utils import build_instrument_context
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def create_risk_manager(llm, memory):
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def risk_manager_node(state) -> dict:
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company_name = state["company_of_interest"]
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instrument_context = build_instrument_context(company_name)
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history = state["risk_debate_state"]["history"]
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risk_debate_state = state["risk_debate_state"]
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@ -34,6 +37,8 @@ Deliverables:
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- A clear and actionable recommendation: Buy, Sell, or Hold.
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- Detailed reasoning anchored in the debate and past reflections.
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{instrument_context}
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---
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**Analysts Debate History:**
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@ -2,10 +2,13 @@ import functools
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import time
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import json
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from tradingagents.agents.utils.agent_utils import build_instrument_context
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def create_trader(llm, memory):
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def trader_node(state, name):
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company_name = state["company_of_interest"]
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instrument_context = build_instrument_context(company_name)
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investment_plan = state["investment_plan"]
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market_research_report = state["market_report"]
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sentiment_report = state["sentiment_report"]
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@ -24,7 +27,7 @@ def create_trader(llm, memory):
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context = {
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"role": "user",
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"content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
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"content": f"Based on a comprehensive analysis by a team of analysts, here is an investment plan tailored for {company_name}. {instrument_context} This plan incorporates insights from current technical market trends, macroeconomic indicators, and social media sentiment. Use this plan as a foundation for evaluating your next trading decision.\n\nProposed Investment Plan: {investment_plan}\n\nLeverage these insights to make an informed and strategic decision.",
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}
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messages = [
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@ -19,6 +19,16 @@ from tradingagents.agents.utils.news_data_tools import (
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get_global_news
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)
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def build_instrument_context(ticker: str) -> str:
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"""Describe the exact instrument so agents avoid cross-exchange symbol mixups."""
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return (
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f"The exact listed instrument to analyze is `{ticker}`. "
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"Use this exact ticker in every tool call, report, and recommendation. "
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"If it includes an exchange suffix such as `.TO`, `.L`, `.HK`, or `.T`, preserve that suffix and do not mix in companies from other exchanges that share the same root symbol. "
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"If it does not include a suffix, do not invent one."
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)
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def create_msg_delete():
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def delete_messages(state):
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"""Clear messages and add placeholder for Anthropic compatibility"""
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@ -35,4 +45,4 @@ def create_msg_delete():
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return delete_messages
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue