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TradingAgents
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update prompt indent

This commit is contained in:
Jeffrey Chu 2025-10-23 22:32:37 +08:00
parent 28218b2db9
commit efe186cae1
14 changed files with 398 additions and 202 deletions
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49
tradingagents/agents/analysts/fundamentals_analyst.py
View File

@ -1,12 +1,12 @@
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, get_income_statement, get_insider_sentiment, get_insider_transactions
from tradingagents.dataflows.config import get_config
from tradingagents.agents.utils.agent_utils import get_fundamentals, get_balance_sheet, get_cashflow, \
get_income_statement
def create_fundamentals_analyst(llm, config):
"""Create the fundamentals analyst node with language support."""
def fundamentals_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
@ -28,35 +28,40 @@ def create_fundamentals_analyst(llm, config):
language_prompt = language_prompts.get(language, "")
system_message = (
"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."
+ " 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."
+ " Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements."
f"""
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.
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.
Use the available tools: `get_fundamentals` for comprehensive company analysis, `get_balance_sheet`, `get_cashflow`, and `get_income_statement` for specific financial statements.
"""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The company we want to look at is {ticker}"
"\n***{language_prompt}***",
f"""
You are a helpful AI assistant, collaborating with other assistants.
Use the provided tools to progress towards answering the question.
If you are unable to fully answer, that's OK; another assistant with different tools will help where you left off. Execute what you can to make progress.
If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable, prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop.
You have access to the following tools: {tools}.
{system_message}
For your reference, the current date is {current_date}.
The company we want to look at is {ticker}
Output language: ***{language_prompt}***
""",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
prompt = prompt.partial(language_prompt=language_prompt)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])

92
tradingagents/agents/analysts/market_analyst.py
View File

@ -1,12 +1,11 @@
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_stock_data, get_indicators
from tradingagents.dataflows.config import get_config
def create_market_analyst(llm, config):
"""Create the market analyst node with language support."""
def market_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
@ -17,7 +16,6 @@ def create_market_analyst(llm, config):
get_indicators,
]
language = config["output_language"]
language = config["output_language"]
language_prompts = {
"en": "",
@ -27,58 +25,60 @@ def create_market_analyst(llm, config):
language_prompt = language_prompts.get(language, "")
system_message = (
"""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:
Moving Averages:
- close_50_sma: 50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.
- close_200_sma: 200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.
- close_10_ema: 10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.
MACD Related:
- macd: MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.
- macds: MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.
- macdh: MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.
Momentum Indicators:
- rsi: RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.
Volatility Indicators:
- boll: Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.
- boll_ub: Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.
- boll_lb: Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.
- atr: ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.
Volume-Based Indicators:
- 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.
- 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."""
+ """ 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."""
f"""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:
Moving Averages:
- close_50_sma: 50 SMA: A medium-term trend indicator. Usage: Identify trend direction and serve as dynamic support/resistance. Tips: It lags price; combine with faster indicators for timely signals.
- close_200_sma: 200 SMA: A long-term trend benchmark. Usage: Confirm overall market trend and identify golden/death cross setups. Tips: It reacts slowly; best for strategic trend confirmation rather than frequent trading entries.
- close_10_ema: 10 EMA: A responsive short-term average. Usage: Capture quick shifts in momentum and potential entry points. Tips: Prone to noise in choppy markets; use alongside longer averages for filtering false signals.
MACD Related:
- macd: MACD: Computes momentum via differences of EMAs. Usage: Look for crossovers and divergence as signals of trend changes. Tips: Confirm with other indicators in low-volatility or sideways markets.
- macds: MACD Signal: An EMA smoothing of the MACD line. Usage: Use crossovers with the MACD line to trigger trades. Tips: Should be part of a broader strategy to avoid false positives.
- macdh: MACD Histogram: Shows the gap between the MACD line and its signal. Usage: Visualize momentum strength and spot divergence early. Tips: Can be volatile; complement with additional filters in fast-moving markets.
Momentum Indicators:
- rsi: RSI: Measures momentum to flag overbought/oversold conditions. Usage: Apply 70/30 thresholds and watch for divergence to signal reversals. Tips: In strong trends, RSI may remain extreme; always cross-check with trend analysis.
Volatility Indicators:
- boll: Bollinger Middle: A 20 SMA serving as the basis for Bollinger Bands. Usage: Acts as a dynamic benchmark for price movement. Tips: Combine with the upper and lower bands to effectively spot breakouts or reversals.
- boll_ub: Bollinger Upper Band: Typically 2 standard deviations above the middle line. Usage: Signals potential overbought conditions and breakout zones. Tips: Confirm signals with other tools; prices may ride the band in strong trends.
- boll_lb: Bollinger Lower Band: Typically 2 standard deviations below the middle line. Usage: Indicates potential oversold conditions. Tips: Use additional analysis to avoid false reversal signals.
- atr: ATR: Averages true range to measure volatility. Usage: Set stop-loss levels and adjust position sizes based on current market volatility. Tips: It's a reactive measure, so use it as part of a broader risk management strategy.
Volume-Based Indicators:
- 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.
- 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.
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.
"""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The company we want to look at is {ticker}"
"\n***{language_prompt}***",
f"""
You are a helpful AI assistant, collaborating with other assistants.
Use the provided tools to progress towards answering the question.
If you are unable to fully answer, that's OK; another assistant with different tools will help where you left off. Execute what you can to make progress.
If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable, prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop.
You have access to the following tools: {tools}.
{system_message}
For your reference, the current date is {current_date}.
The company we want to look at is {ticker}
Output language: ***{language_prompt}***
""",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
prompt = prompt.partial(language_prompt=language_prompt)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])
@ -87,7 +87,7 @@ Volume-Based Indicators:
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"market_report": report,

44
tradingagents/agents/analysts/news_analyst.py
View File

@ -1,12 +1,11 @@
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_news, get_global_news
from tradingagents.dataflows.config import get_config
def create_news_analyst(llm, config):
"""Create the news analyst node with language support."""
def news_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
@ -26,34 +25,39 @@ def create_news_analyst(llm, config):
language_prompt = language_prompts.get(language, "")
system_message = (
"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."
+ """ 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."""
f"""
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.
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.
"""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. We are looking at the company {ticker}"
"\n***{language_prompt}***",
f"""
You are a helpful AI assistant, collaborating with other assistants.
Use the provided tools to progress towards answering the question.
If you are unable to fully answer, that's OK; another assistant with different tools will help where you left off. Execute what you can to make progress.
If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable, prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop.
You have access to the following tools: {tools}.
{system_message}
For your reference, the current date is {current_date}.
The company we want to look at is {ticker}
Output language: ***{language_prompt}***
""",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
prompt = prompt.partial(language_prompt=language_prompt)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])

44
tradingagents/agents/analysts/social_media_analyst.py
View File

@ -1,12 +1,11 @@
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.agents.utils.agent_utils import get_news
from tradingagents.dataflows.config import get_config
def create_social_media_analyst(llm, config):
"""Create the social media analyst node with language support."""
def social_media_analyst_node(state):
current_date = state["trade_date"]
ticker = state["company_of_interest"]
@ -26,34 +25,39 @@ def create_social_media_analyst(llm, config):
language_prompt = language_prompts.get(language, "")
system_message = (
"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."
+ """ 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."""
f"""
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.
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.
"""
)
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You are a helpful AI assistant, collaborating with other assistants."
" Use the provided tools to progress towards answering the question."
" If you are unable to fully answer, that's OK; another assistant with different tools"
" will help where you left off. Execute what you can to make progress."
" If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable,"
" prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop."
" You have access to the following tools: {tool_names}.\n{system_message}"
"For your reference, the current date is {current_date}. The current company we want to analyze is {ticker}"
"\n***{language_prompt}***",
f"""
You are a helpful AI assistant, collaborating with other assistants.
Use the provided tools to progress towards answering the question.
If you are unable to fully answer, that's OK; another assistant with different tools will help where you left off. Execute what you can to make progress.
If you or any other assistant has the FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** or deliverable, prefix your response with FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL** so the team knows to stop.
You have access to the following tools: {tools}.
{system_message}
For your reference, the current date is {current_date}.
The company we want to look at is {ticker}
Output language: ***{language_prompt}***
""",
),
MessagesPlaceholder(variable_name="messages"),
]
)
prompt = prompt.partial(system_message=system_message)
prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
prompt = prompt.partial(current_date=current_date)
prompt = prompt.partial(ticker=ticker)
prompt = prompt.partial(language_prompt=language_prompt)
chain = prompt | llm.bind_tools(tools)
result = chain.invoke(state["messages"])

8
tradingagents/agents/managers/research_manager.py
View File

@ -24,10 +24,9 @@ def create_research_manager(llm, memory, config):
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the bear analyst, the bull analyst, or choose Hold only if it is strongly justified based on the arguments presented.
prompt = f"""
As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision: align with the bear analyst, the bull analyst, or choose Hold only if it is strongly justified based on the arguments presented.
Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation—Buy, Sell, or Hold—must be clear and actionable. Avoid defaulting to Hold simply because both sides have valid points; commit to a stance grounded in the debate's strongest arguments.
Additionally, develop a detailed investment plan for the trader. This should include:
Your Recommendation: A decisive stance supported by the most convincing arguments.
@ -42,7 +41,8 @@ Here is the debate:
Debate History:
{history}
\n***{language_prompt}***"""
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)
new_investment_debate_state = {

11
tradingagents/agents/managers/risk_manager.py
View File

@ -1,7 +1,3 @@
import time
import json
def create_risk_manager(llm, memory, config):
"""Create the risk manager node with language support."""
language = config["output_language"]
@ -13,7 +9,6 @@ def create_risk_manager(llm, memory, config):
language_prompt = language_prompts.get(language, "")
def risk_manager_node(state) -> dict:
company_name = state["company_of_interest"]
history = state["risk_debate_state"]["history"]
@ -31,7 +26,8 @@ def create_risk_manager(llm, memory, config):
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Risky, Neutral, and Safe/Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
prompt = f"""
As the Risk Management Judge and Debate Facilitator, your goal is to evaluate the debate between three risk analysts—Risky, Neutral, and Safe/Conservative—and determine the best course of action for the trader. Your decision must result in a clear recommendation: Buy, Sell, or Hold. Choose Hold only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
Guidelines for Decision-Making:
1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context.
@ -52,7 +48,8 @@ Deliverables:
Focus on actionable insights and continuous improvement. Build on past lessons, critically evaluate all perspectives, and ensure each decision advances better outcomes.
\n***{language_prompt}***"""
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

57
tradingagents/agents/researchers/bear_researcher.py
View File

@ -1,8 +1,3 @@
from langchain_core.messages import AIMessage
import time
import json
def create_bear_researcher(llm, memory, config):
"""Create the bear researcher node with language support."""
language = config["output_language"]
@ -31,7 +26,10 @@ def create_bear_researcher(llm, memory, config):
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators. Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.
prompt = f"""
You are a Bear Analyst making the case against investing in the stock.
Your goal is to present a well-reasoned argument emphasizing risks, challenges, and negative indicators.
Leverage the provided research and data to highlight potential downsides and counter bullish arguments effectively.
Key points to focus on:
@ -42,17 +40,46 @@ Key points to focus on:
- Engagement: Present your argument in a conversational style, directly engaging with the bull analyst's points and debating effectively rather than simply listing facts.
Resources available:
Market research report:
{market_research_report}
Market research report: {market_research_report}
Social media sentiment report: {sentiment_report}
Latest world affairs news: {news_report}
Company fundamentals report: {fundamentals_report}
Conversation history of the debate: {history}
Last bull argument: {current_response}
Reflections from similar situations and lessons learned: {past_memory_str}
Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock. You must also address reflections and learn from lessons and mistakes you made in the past.
\n***{language_prompt}***"""
--------------------------------------
Social media sentiment report:
{sentiment_report}
--------------------------------------
Latest world affairs news:
{news_report}
--------------------------------------
Company fundamentals report:
{fundamentals_report}
--------------------------------------
Conversation history of the debate:
{history}
--------------------------------------
Last bull argument:
{current_response}
--------------------------------------
Reflections from similar situations and lessons learned:
{past_memory_str}
--------------------------------------
Use this information to deliver a compelling bear argument, refute the bull's claims, and engage in a dynamic debate that demonstrates the risks and weaknesses of investing in the stock.
You must also address reflections and learn from lessons and mistakes you made in the past.
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

57
tradingagents/agents/researchers/bull_researcher.py
View File

@ -1,8 +1,3 @@
from langchain_core.messages import AIMessage
import time
import json
def create_bull_researcher(llm, memory, config):
"""Create the bull researcher node with language support."""
language = config["output_language"]
@ -31,7 +26,10 @@ def create_bull_researcher(llm, memory, config):
for i, rec in enumerate(past_memories, 1):
past_memory_str += rec["recommendation"] + "\n\n"
prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators. Leverage the provided research and data to address concerns and counter bearish arguments effectively.
prompt = f"""
You are a Bull Analyst advocating for investing in the stock.
Your task is to build a strong, evidence-based case emphasizing growth potential, competitive advantages, and positive market indicators.
Leverage the provided research and data to address concerns and counter bearish arguments effectively.
Key points to focus on:
- Growth Potential: Highlight the company's market opportunities, revenue projections, and scalability.
@ -41,16 +39,45 @@ Key points to focus on:
- Engagement: Present your argument in a conversational style, engaging directly with the bear analyst's points and debating effectively rather than just listing data.
Resources available:
Market research report: {market_research_report}
Social media sentiment report: {sentiment_report}
Latest world affairs news: {news_report}
Company fundamentals report: {fundamentals_report}
Conversation history of the debate: {history}
Last bear argument: {current_response}
Reflections from similar situations and lessons learned: {past_memory_str}
Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position. You must also address reflections and learn from lessons and mistakes you made in the past.
Market research report:
{market_research_report}
\n***{language_prompt}***"""
--------------------------------------
Social media sentiment report:
{sentiment_report}
--------------------------------------
Latest world affairs news:
{news_report}
--------------------------------------
Company fundamentals report:
{fundamentals_report}
--------------------------------------
Conversation history of the debate:
{history}
--------------------------------------
Last bear argument:
{current_response}
--------------------------------------
Reflections from similar situations and lessons learned:
{past_memory_str}
--------------------------------------
Use this information to deliver a compelling bull argument, refute the bear's concerns, and engage in a dynamic debate that demonstrates the strengths of the bull position.
You must also address reflections and learn from lessons and mistakes you made in the past.
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

65
tradingagents/agents/risk_mgmt/aggresive_debator.py
View File

@ -1,7 +1,3 @@
import time
import json
def create_risky_debator(llm, config):
"""Create the risky debator node with language support."""
language = config["output_language"]
@ -27,21 +23,62 @@ def create_risky_debator(llm, config):
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Risky Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages. When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk. Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views. Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning. Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative. Here is the trader's decision:
prompt = f"""
As the Risky Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages.
When evaluating the trader's decision or plan, focus intently on the potential upside, growth potential, and innovative benefits—even when these come with elevated risk.
Use the provided market data and sentiment analysis to strengthen your arguments and challenge the opposing views.
Specifically, respond directly to each point made by the conservative and neutral analysts, countering with data-driven rebuttals and persuasive reasoning.
Highlight where their caution might miss critical opportunities or where their assumptions may be overly conservative.
Here is the trader's decision:
{trader_decision}
Your task is to create a compelling case for the trader's decision by questioning and critiquing the conservative and neutral stances to demonstrate why your high-reward perspective offers the best path forward. Incorporate insights from the following sources into your arguments:
Your task is to create a compelling case for the trader's decision by questioning and critiquing the conservative and neutral stances to demonstrate why your high-reward perspective offers the best path forward.
Incorporate insights from the following sources into your arguments:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here are the last arguments from the conservative analyst: {current_safe_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Market Research Report:
{market_research_report}
Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms. Maintain a focus on debating and persuading, not just presenting data. Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting.
\n***{language_prompt}***"""
--------------------------------------
Social Media Sentiment Report:
{sentiment_report}
--------------------------------------
Latest World Affairs Report:
{news_report}
--------------------------------------
Company Fundamentals Report:
{fundamentals_report}
--------------------------------------
Here is the current conversation history:
{history}
--------------------------------------
Here are the last arguments from the conservative analyst:
{current_safe_response}
--------------------------------------
Here are the last arguments from the neutral analyst:
{current_neutral_response}
--------------------------------------
If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Engage actively by addressing any specific concerns raised, refuting the weaknesses in their logic, and asserting the benefits of risk-taking to outpace market norms.
Maintain a focus on debating and persuading, not just presenting data.
Challenge each counterpoint to underscore why a high-risk approach is optimal. Output conversationally as if you are speaking without any special formatting.
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

64
tradingagents/agents/risk_mgmt/conservative_debator.py
View File

@ -1,8 +1,3 @@
from langchain_core.messages import AIMessage
import time
import json
def create_safe_debator(llm, config):
"""Create the safe debator node with language support."""
language = config["output_language"]
@ -29,21 +24,60 @@ def create_safe_debator(llm, config):
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Safe/Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth. You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility. When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains. Here is the trader's decision:
prompt = f"""
As the Safe/Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth.
You prioritize stability, security, and risk mitigation, carefully assessing potential losses, economic downturns, and market volatility.
When evaluating the trader's decision or plan, critically examine high-risk elements, pointing out where the decision may expose the firm to undue risk and where more cautious alternatives could secure long-term gains.
Here is the trader's decision:
{trader_decision}
Your task is to actively counter the arguments of the Risky and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability. Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision:
Your task is to actively counter the arguments of the Risky and Neutral Analysts, highlighting where their views may overlook potential threats or fail to prioritize sustainability.
Respond directly to their points, drawing from the following data sources to build a convincing case for a low-risk approach adjustment to the trader's decision:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here is the last response from the risky analyst: {current_risky_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Market Research Report:
{market_research_report}
Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked. Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets. Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches. Output conversationally as if you are speaking without any special formatting.
\n***{language_prompt}***"""
--------------------------------------
Social Media Sentiment Report:
{sentiment_report}
--------------------------------------
Latest World Affairs Report:
{news_report}
--------------------------------------
Company Fundamentals Report:
{fundamentals_report}
--------------------------------------
Here is the current conversation history:
{history}
--------------------------------------
Here is the last response from the risky analyst:
{current_risky_response}
--------------------------------------
Here is the last response from the neutral analyst:
{current_neutral_response}
--------------------------------------
If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Engage by questioning their optimism and emphasizing the potential downsides they may have overlooked.
Address each of their counterpoints to showcase why a conservative stance is ultimately the safest path for the firm's assets.
Focus on debating and critiquing their arguments to demonstrate the strength of a low-risk strategy over their approaches.
Output conversationally as if you are speaking without any special formatting.
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

60
tradingagents/agents/risk_mgmt/neutral_debator.py
View File

@ -1,7 +1,3 @@
import time
import json
def create_neutral_debator(llm, config):
"""Create the neutral debator node with language support."""
language = config["output_language"]
@ -27,21 +23,59 @@ def create_neutral_debator(llm, config):
trader_decision = state["trader_investment_plan"]
prompt = f"""As the Neutral Risk Analyst, your role is to provide a balanced perspective, weighing both the potential benefits and risks of the trader's decision or plan. You prioritize a well-rounded approach, evaluating the upsides and downsides while factoring in broader market trends, potential economic shifts, and diversification strategies.Here is the trader's decision:
prompt = f"""
As the Neutral Risk Analyst, your role is to provide a balanced perspective, weighing both the potential benefits and risks of the trader's decision or plan.
You prioritize a well-rounded approach, evaluating the upsides and downsides while factoring in broader market trends, potential economic shifts, and diversification strategies.
Here is the trader's decision:
{trader_decision}
Your task is to challenge both the Risky and Safe Analysts, pointing out where each perspective may be overly optimistic or overly cautious. Use insights from the following data sources to support a moderate, sustainable strategy to adjust the trader's decision:
Your task is to challenge both the Risky and Safe Analysts, pointing out where each perspective may be overly optimistic or overly cautious.
Use insights from the following data sources to support a moderate, sustainable strategy to adjust the trader's decision:
Market Research Report: {market_research_report}
Social Media Sentiment Report: {sentiment_report}
Latest World Affairs Report: {news_report}
Company Fundamentals Report: {fundamentals_report}
Here is the current conversation history: {history} Here is the last response from the risky analyst: {current_risky_response} Here is the last response from the safe analyst: {current_safe_response}. If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Market Research Report:
{market_research_report}
Engage actively by analyzing both sides critically, addressing weaknesses in the risky and conservative arguments to advocate for a more balanced approach. Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility. Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes. Output conversationally as if you are speaking without any special formatting.
\n***{language_prompt}***"""
--------------------------------------
Social Media Sentiment Report:
{sentiment_report}
--------------------------------------
Latest World Affairs Report:
{news_report}
--------------------------------------
Company Fundamentals Report:
{fundamentals_report}
--------------------------------------
Here is the current conversation history:
{history}
--------------------------------------
Here is the last response from the risky analyst:
{current_risky_response}
--------------------------------------
Here is the last response from the safe analyst:
{current_safe_response}
--------------------------------------
If there are no responses from the other viewpoints, do not halluncinate and just present your point.
Engage actively by analyzing both sides critically, addressing weaknesses in the risky and conservative arguments to advocate for a more balanced approach.
Challenge each of their points to illustrate why a moderate risk strategy might offer the best of both worlds, providing growth potential while safeguarding against extreme volatility.
Focus on debating rather than simply presenting data, aiming to show that a balanced view can lead to the most reliable outcomes.
Output conversationally as if you are speaking without any special formatting.
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

29
tradingagents/agents/trader/trader.py
View File

@ -1,6 +1,4 @@
import functools
import time
import json
def create_trader(llm, memory, config):
@ -33,15 +31,36 @@ def create_trader(llm, memory, config):
context = {
"role": "user",
"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.",
"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.
Proposed Investment Plan:
{investment_plan}
--------------------------------------
Leverage these insights to make an informed and strategic decision.
""",
}
messages = [
{
"role": "system",
"content": f"""You are a trading agent analyzing market data to make investment decisions. Based on your analysis, provide a specific recommendation to buy, sell, or hold. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situations you traded in and the lessons learned: {past_memory_str}
"content": f"""
You are a trading agent analyzing market data to make investment decisions.
Based on your analysis, provide a specific recommendation to buy, sell, or hold.
End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**' to confirm your recommendation.
Do not forget to utilize lessons from past decisions to learn from your mistakes.
\n***{language_prompt}***""",
Here is some reflections from similar situations you traded in and the lessons learned:
{past_memory_str}
--------------------------------------
Output language: ***{language_prompt}***
""",
},
context,
]

10
tradingagents/graph/reflection.py
View File

@ -74,7 +74,15 @@ Output language: ***{self.language_prompt}***
("system", self.reflection_system_prompt),
(
"human",
f"Returns: {returns_losses}\n\nAnalysis/Decision: {report}\n\nObjective Market Reports for Reference: {situation}",
f"""
Returns: {returns_losses}
Analysis/Decision: {report}
Objective Market Reports for Reference: {situation}
""",
),
]

10
tradingagents/graph/signal_processing.py
View File

@ -33,11 +33,11 @@ class SignalProcessor:
(
"system",
f"""
You are an efficient assistant designed to analyze paragraphs or financial reports provided by a group of analysts.
Your task is to extract the investment decision: SELL, BUY, or HOLD.
Provide only the extracted decision (SELL, BUY, or HOLD) as your output, without adding any additional text or information.
Output language: ***{self.language_prompt}***
You are an efficient assistant designed to analyze paragraphs or financial reports provided by a group of analysts.
Your task is to extract the investment decision: SELL, BUY, or HOLD.
Provide only the extracted decision (SELL, BUY, or HOLD) as your output, without adding any additional text or information.
Output language: ***{self.language_prompt}***
""",
),
("human", full_signal),