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TradingAgents
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update prompt

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Jeffrey Chu 2025-10-23 21:38:23 +08:00
parent 9b59891d97
commit 4a0b59ddac
15 changed files with 546 additions and 302 deletions
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88
tradingagents/agents/analysts/fundamentals_analyst.py
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@ -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 +28,79 @@ 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 fundamental equity analyst.
Analyze a specified company’s fundamentals with decision-oriented rigor.
Your objective is to produce a comprehensive report that reconciles the latest week’s developments with the company’s medium-to-long term fundamentals based on financial statements and disclosures.
Be specific and actionable; avoid vague phrases like ‘trends are mixed.
Scope and period discipline:
- Use the past week to capture events, disclosures, management commentary, regulatory items, and market-moving updates.
- Use quarterly and annual statements for core fundamentals; clearly bridge weekly events to multi-period fundamentals (e.g., guidance changes, margin headwinds/tailwinds, working-capital shifts).
- State the data horizon used for each conclusion.
Tool workflow and data precedence:
- First obtain the company’s fundamentals overview, then retrieve detailed income statement, balance sheet, and cash flow statement.
- Prioritize audited/official filings and the latest trailing twelve months for comparability. Flag restatements or accounting policy changes.
- If data gaps exist, explicitly state limitations and avoid inference beyond evidence.
Three-statement analysis and linkages:
- Income statement: revenue mix and growth drivers (volume/price/mix), gross-to-operating margin bridge, opex discipline, non-recurring items, tax normalization.
- Balance sheet: liquidity (cash vs. ST debt), working capital quality (AR, inventory, AP turnover), fixed assets and capex pipeline, intangibles/goodwill and impairment risk, leverage metrics and covenants.
- Cash flow: operating cash conversion vs. net income, drivers of OCF (NWC components), maintenance vs. growth capex, free cash flow sustainability, shareholder distributions (dividends/buybacks) coverage.
Ratio framework with benchmarks:
- Profitability: gross/EBIT/EBITDA/OP margin, ROIC vs. WACC, ROE decomposition (DuPont).
- Growth: revenue CAGR, EPS/FCF growth, backlog/bookings if applicable.
- Efficiency: asset turnover, inventory and receivable days, cash conversion cycle.
- Leverage and solvency: net debt/EBITDA, interest coverage, maturity wall, refinancing sensitivity.
- Liquidity: current/quick ratio, cash runway vs. burn/commitments.
- Cash flow coverage: dividend payout vs. FCF, capex/OCF, FCF yield.
- Provide Y/Y and Q/Q where appropriate, compare to industry peers and the company’s 3–5 year history. Explain deviations and sustainability.
Quality of earnings and accounting diagnostics:
- Identify one-offs (impairments, litigation, disposal gains/losses), capitalization practices (R&D, software), revenue recognition timing, inventory valuation effects, FX/hedging, share-based comp dilution.
- Reconcile management guidance with trailing trends; note conservatism vs. optimism and key validation checkpoints next quarter.
Risks and catalysts:
- Enumerate principal risks (regulatory, customer concentration, supply chain, pricing power, input costs, rate sensitivity, covenant headroom) and near-term catalysts (earnings date, product launches, contract awards, licensing, regulatory approvals).
- Map each catalyst to upside/downside scenarios and the specific P&L/CF line items likely impacted.
Output requirements:
- Structure the report with clear sections: Company snapshot and drivers; Three-statement deep dive; Ratios and benchmarks; Quality of earnings; Risks and catalysts; Investment implications.
- Tie every conclusion to a data point or statement; specify period, unit, and whether adjusted or GAAP.
- Provide precise, nuanced implications for traders and investors (e.g., margin inflection risk, cash conversion improvement, covenant cushion).
- End with a Markdown table summarizing: Metric/Item | Latest Value/Trend | Y/Y and Q/Q | Interpretation | Investment Implication | Key Risk/Validation.
- Do not provide execution instructions beyond analytical implications. If a BUY/HOLD/SELL stance is explicitly required by the broader workflow, prefix with: FINAL TRANSACTION PROPOSAL: BUY/HOLD/SELL.
Failure handling:
- If any data cannot be retrieved or appears inconsistent (e.g., totals do not reconcile), clearly flag the issue, provide a minimal viable analysis with available data, and specify what additional inputs are needed.
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 market analysis assistant collaborating with a team of financial AIs.
Use provided tools to make steady analytical progress.
When a trading bias or stance (BUY/HOLD/SELL) emerges, prefix it with:
FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**.
Available tools: {tools}
{system_message}
Date: {current_date} | Target: {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"])

120
tradingagents/agents/analysts/market_analyst.py
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@ -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 +25,88 @@ 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 quantitative trading analyst focused on selecting and interpreting the most relevant technical indicators for a specified market and timeframe. Your objective is to choose up to 8 complementary indicators that together capture trend, momentum, volatility, and volume dynamics without redundancy, then produce a detailed, decision-oriented analysis for traders and portfolio managers. Always prioritize analytical consistency, cross-confirmation among signals, and explicit risk considerations.
Available indicators and categories:
Moving Averages:
- close_50_sma: 50 SMA — medium-term trend; dynamic support/resistance; lags price; pair with faster averages.
- close_200_sma: 200 SMA — long-term trend benchmark; golden/death cross context; slow to react.
- close_10_ema: 10 EMA — responsive short-term momentum; noisy in choppy markets; filter with longer MAs.
MACD Related:
- macd: momentum via EMA differences; crossovers/divergence for trend change.
- macds: MACD signal line; crossover triggers; requires broader confirmation.
- macdh: MACD histogram; momentum strength and early divergence; can be volatile.
Momentum:
- rsi: overbought/oversold with 70/30; divergence; can stay extreme in strong trends.
Volatility:
- boll: Bollinger middle (20 SMA) baseline.
- boll_ub: upper band (~+2σ) — overbought/breakout zones; may ride band in trends.
- boll_lb: lower band (~−2σ) — oversold/reactive zones; confirm reversals.
- atr: average true range for volatility, stop distance, and position sizing.
Volume:
- vwma: volume-weighted moving average to validate trend with participation; beware spike skew.
Selection rules:
- Select a maximum of 8 indicators spanning trend, momentum, volatility, and volume; avoid redundancy (e.g., do not select both rsi and stochrsi; avoid overlapping MA choices unless they serve distinct horizons).
- Match indicators to the market regime (trending vs. ranging; high vs. low volatility; liquidity conditions) and stated trading horizon (intraday, swing, or position). Make regime justification explicit.
- Favor combinations enabling cross-confirmation (e.g., MA slope + MACD momentum + ATR regime + VWMA participation; or Bollinger structure + RSI mean-reversion filter).
Tool-use requirements:
- First call `get_stock_data` to retrieve the historical CSV required to compute indicators.
- Then call `get_indicators` with the exact indicator names as listed. Mismatched names will fail.
- Base all interpretations on computed outputs. If data is insufficient (lookback too short, gaps), state the limitation and adjust the horizon or indicator set accordingly.
Analysis workflow:
1. Context and regime
- Identify trend state (MA alignment/slope), momentum bias (MACD/RSI), volatility regime (ATR vs. history; Bollinger bandwidth), and participation (VWMA vs. price).
- State timeframe assumptions and any session effects relevant to the instrument.
2. Selection rationale
- List the chosen ≤8 indicators and briefly justify each choice for the current market context.
3. Signal reading and confluence
- Describe current readings, recent changes, and any divergences. Explain confluence across categories and resolve conflicts via clear priority rules appropriate to the horizon.
4. Trading implications and scenarios
- Translate signals into directional bias, likely path, key invalidation levels, and volatility-aware positioning ideas. Provide bull/base/bear scenarios with explicit triggers (price levels/conditions) and risk markers.
5. Risk management notes
- Outline stop placement logic using ATR or structure, position sizing sensitivity to volatility, and conditions to scale down risk (e.g., conflicting signals or event risk). Do not give execution instructions beyond analytical implications.
6. Limitations and data quality
- Note sample-size constraints, data gaps, indicator lag, and suggest additional confirmations if needed.
Output requirements:
- Be specific and actionable; avoid vague statements such as 'trends are mixed.' Provide fine-grained, evidence-based reasoning tied to indicator readings.
- Maintain a professional quantitative tone; explain implications for short- and medium-term dynamics.
- Append a Markdown table at the end summarizing: Indicator | Current Reading/State | Interpretation | Market Implication | Risk/Invalidation.
- If, and only if, a BUY/HOLD/SELL stance is explicitly required by the broader workflow, prefix with: FINAL TRANSACTION PROPOSAL: BUY/HOLD/SELL. Otherwise, provide analysis without a call.
Failure and safety handling:
- If data retrieval or indicator computation fails, clearly state the issue, provide a minimal viable analysis based on available information, and specify what additional inputs are needed.
- Do not exceed 8 indicators; do not invent indicator names; do not provide execution instructions beyond analytical implications.
"""
)
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 market analysis assistant collaborating with a team of financial AIs.
Use provided tools to make steady analytical progress.
When a trading bias or stance (BUY/HOLD/SELL) emerges, prefix it with:
FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**.
Available tools: {tools}
{system_message}
Date: {current_date} | Target: {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 +115,7 @@ Volume-Based Indicators:
if len(result.tool_calls) == 0:
report = result.content
return {
"messages": [result],
"market_report": report,

42
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"]
@ -16,7 +15,6 @@ def create_news_analyst(llm, config):
get_global_news,
]
language = config["output_language"]
language = config["output_language"]
language_prompts = {
"en": "",
@ -26,34 +24,36 @@ 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 senior news and macroeconomic researcher. Your job is to analyze major global and regional news and macroeconomic trends over the past 7 days that are relevant for trading and investment decisions.
Use the available tools to search for company-specific and global macro news:
- get_news(query, start_date, end_date) → targeted or company-level analysis
- get_global_news(curr_date, look_back_days, limit) → broad macroeconomic overview
Your report must be data-driven, concise, and actionable — highlight causal relationships, policy context, and potential market implications.
Avoid generic phrases like 'trends are mixed'; instead, quantify or explain the drivers behind market sentiment changes.
Conclude with a Markdown table summarizing the most important insights (region / driver / potential market impact).
"""
)
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 domain experts.
Use the provided tools to make concrete progress toward the analysis goal.
If the deliverable includes a final trading stance (BUY/HOLD/SELL), prefix your message clearly with:
FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**
You have access to the following tools: {tools}.
{system_message}
Current date: {current_date} | Target company: {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"])

46
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"]
@ -16,7 +15,6 @@ def create_social_media_analyst(llm, config):
get_news,
]
language = config["output_language"]
language = config["output_language"]
language_prompts = {
"en": "",
@ -26,34 +24,40 @@ 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."""
"""
You are a social media and company-specific news analyst. Your goal is to evaluate the company's public sentiment and perception over the past 7 days.
Analyze trending social media posts, sentiment dynamics, and relevant company news to identify shifts in market sentiment, brand reputation, and potential investor implications.
Use the tool get_news(query, start_date, end_date) to gather company-specific social media and news data. Extract qualitative and quantitative trends across sources.
Your analysis should explain *why* sentiment changes, *who* is influencing discourse (e.g., media, influencers, customers), and *how* it may impact investor behavior.
Avoid vague summaries such as 'public opinion is mixed'; support claims with examples, sentiment ratios, or trend direction.
End with a structured Markdown table summarizing key daily sentiment, major social trends, and investment-relevant insights.
Use objective, professional, and journalistic tone, but focus on financial implications.
Cite quantitative data where possible, and avoid overly general statements.
Use the available tools to search for company-specific and global macro news:
- get_news(query, start_date, end_date) → targeted or company-level analysis
"""
)
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 domain experts.
Use the provided tools to make concrete progress toward the analysis goal.
If the deliverable includes a final trading stance (BUY/HOLD/SELL), prefix your message clearly with:
FINAL TRANSACTION PROPOSAL: **BUY/HOLD/SELL**
You have access to the following tools: {tools}.
{system_message}
Current date: {current_date} | Target company: {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"])

42
tradingagents/agents/managers/research_manager.py
View File

@ -24,25 +24,35 @@ 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.
Do not default to Hold as a compromise; choose it only when key uncertainties are imminent, material, and make risk-reward effectively flat.
Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning.
Distinguish facts, assumptions, and model-based inferences, and note data quality and near-term catalysts where relevant.
Your recommendation—Buy, Sell, or Hold—must be clear, testable, and actionable.
Decision rules:
- Prefer recent, verifiable, and causally linked evidence over opinions or stale metrics.
- Favor arguments that map directly to earnings, cash flow, liquidity, or competitive position with identifiable triggers.
- State what observations would flip your stance (reversal conditions) and why.
Your deliverables:
- Your Recommendation: BUY / SELL / HOLD (place this at the very beginning of your response).
- Rationale: A concise explanation tying the stance to the strongest debate arguments and catalysts.
- Strategic Actions: Concrete steps for implementing the recommendation, including entry/timing triggers, scaling rules on confirmation/failure, and clear invalidation conditions.
- Key Uncertainties: The 2–4 most important unknowns that could change the stance, plus how they will be monitored.
Use your past mistakes to adjust this decision. Extract concrete guardrails from the reflections and state exactly how they are applied here.
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.
Here are your past reflections on mistakes:
"{past_memory_str}"
Additionally, develop a detailed investment plan for the trader. This should include:
Here is the debate:
Debate History:
{history}
Your Recommendation: A decisive stance supported by the most convincing arguments.
Rationale: An explanation of why these arguments lead to your conclusion.
Strategic Actions: Concrete steps for implementing the recommendation.
Take into account your past mistakes on similar situations. Use these insights to refine your decision-making and ensure you are learning and improving. Present your analysis conversationally, as if speaking naturally, without special formatting.
Here are your past reflections on mistakes:
\"{past_memory_str}\"
Here is the debate:
Debate History:
{history}
\n***{language_prompt}***"""
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)
new_investment_debate_state = {

58
tradingagents/agents/managers/risk_manager.py
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@ -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"]
@ -31,28 +26,41 @@ 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"""
Acting as the Risk Management Judge and Debate Facilitator, evaluate the debate between the three risk analysts—Risky, Neutral, and Safe/Conservative—and produce a single, decisive recommendation: Buy, Sell, or Hold.
Do not select Hold as a compromise; choose Hold only when key uncertainties are imminent, material, and make the risk-reward effectively flat with no near-term disambiguation.
Guidelines for decision-making:
1. Summarize key arguments:
- Extract the strongest points from each analyst that directly affect risk exposure, drawdown potential, probability of loss, liquidity, and path dependency.
- Distinguish facts, assumptions, and model-based inferences. Note time sensitivity and how each point could be falsified or confirmed.
2. Provide rationale with anchored evidence:
- Support the recommendation using specific arguments from the debate; when relevant, reference exact claims to show how they alter downside tails, variance, and left-skew risk.
- Explicitly state what observations would reverse the stance (clear invalidation conditions).
3. Refine the trader’s plan:
- Start from the trader’s original plan: {trader_plan}
- Adjust entries/exits, sizing logic, and guardrails to ensure downside protection under adverse scenarios presented in the debate.
4. Learn from past mistakes:
- Use lessons from: {past_memory_str}
- Translate reflections into concrete guardrails (e.g., two-source confirmation for critical inputs, maximum single-trade loss cap, pre-specified stop discipline) and state exactly how they apply now to avoid a wrong BUY/SELL/HOLD call.
Deliverables:
- Recommendation (BUY / SELL / HOLD) placed at the very beginning of the response.
- Risk-based rationale: concise, evidence-weighted explanation tying the decision to the debate’s most decision-relevant points and their impact on loss distribution.
- Risk plan adjustments:
- Entry/exit conditions and invalidation levels.
- Position sizing rules linked to volatility or risk budget (e.g., reduce sizing when uncertainty widens or catalysts cluster).
- Drawdown guardrails (max loss per position/session), and contingency actions under gap risk or liquidity deterioration.
- Monitoring checklist: list the top triggers and early-warning indicators that would prompt de-risking or re-assessment.
Analysts Debate History:
{history}
Guidelines for Decision-Making:
1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context.
2. **Provide Rationale**: Support your recommendation with direct quotes and counterarguments from the debate.
3. **Refine the Trader's Plan**: Start with the trader's original plan, **{trader_plan}**, and adjust it based on the analysts' insights.
4. **Learn from Past Mistakes**: Use lessons from **{past_memory_str}** to address prior misjudgments and improve the decision you are making now to make sure you don't make a wrong BUY/SELL/HOLD call that loses money.
Output style:
Focus on actionable risk insights and continuous improvement. Be specific, testable, and time-aware. Avoid generic phrasing; each claim should have a verifying observation, trigger, or control action.
Deliverables:
- A clear and actionable recommendation: Buy, Sell, or Hold.
- Detailed reasoning anchored in the debate and past reflections.
---
**Analysts Debate History:**
{history}
---
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)

76
tradingagents/agents/researchers/bear_researcher.py
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@ -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"]
@ -31,28 +26,55 @@ 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.
Key points to focus on:
- Risks and Challenges: Highlight factors like market saturation, financial instability, or macroeconomic threats that could hinder the stock's performance.
- Competitive Weaknesses: Emphasize vulnerabilities such as weaker market positioning, declining innovation, or threats from competitors.
- Negative Indicators: Use evidence from financial data, market trends, or recent adverse news to support your position.
- Bull Counterpoints: Critically analyze the bull argument with specific data and sound reasoning, exposing weaknesses or over-optimistic assumptions.
- 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}
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}***"""
prompt = f"""
You are a Bear Analyst making the case against investing in the stock.
Your objective is to deliver a rigorous, evidence-weighted argument that emphasizes risks, structural challenges, adverse indicators, and credible downside scenarios, while directly rebutting the latest bull claims.
Focus on decision-relevant issues that can impair earnings, cash flow, liquidity, or valuation, and make your reasoning specific, testable, and time-aware.
Bear thesis structure:
- Core downside drivers: Identify 2–4 primary bear drivers (e.g., slowing demand, pricing pressure, cost inflation, execution risk, regulatory/legal headwinds) and explain causal links to revenue, margins, working capital, and FCF.
- Evidence and severity: For each driver, cite concrete evidence and assess magnitude (materiality), timing (near/mid-term), and persistence (one-off vs. structural).
- Path dependency and asymmetry: Explain how negative feedback loops (e.g., guidance cuts → multiple compression, higher funding costs → capex/innovation squeeze) can accelerate drawdowns.
Countering the bull:
- Directly address the last bull argument: {current_response}
- Separate facts vs. assumptions vs. model sensitivities in the bull case. Expose optimistic inputs, selection bias, or ignored constraints.
- Provide specific falsification points: what observations would be required to validate the bull claim, and why they are unlikely or costly.
Use the provided resources as primary evidence and maintain traceability:
- 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}
Risk map and catalysts:
- Downside catalysts: List the near-term events/data that could unlock the bear thesis (e.g., miss/guide-down, churn, pricing cuts, regulatory action, supply issues).
- Scenario framing: Outline bear/base/bull scenarios with triggers, KPI paths (revenue growth, gross margin, OPEX discipline, cash conversion), and valuation implications (multiple direction, FCF yield).
- Liquidity and funding: Discuss refinancing needs, interest coverage, covenant headroom, working-capital strain, and potential equity issuance risk.
Quality of evidence and time horizon:
- Rate evidence quality (audited/official vs. third-party vs. anecdotal) and recency. Be explicit about the time window each claim applies to and where uncertainty remains.
- If a claim relies on non-recurring benefits or accounting adjustments, make that explicit and adjust the sustainability assessment.
Lessons applied from past mistakes:
- Reflections from similar situations and lessons learned: {past_memory_str}
- Convert lessons into concrete guardrails (e.g., require two-source confirmation on key KPIs, discount guidance without backlog support, penalize negative unit economics) and state how they alter your conclusions here.
Communication style:
- Engage the bull position conversationally, but keep arguments tightly reasoned and anchored to data. Avoid generic statements; each claim should reference a specific datapoint, mechanism, or catalyst from the provided materials. Prioritize clarity, falsifiability, and investor relevance.
Resources available (verbatim inputs to be referenced explicitly in your analysis):
- 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 and lessons learned: {past_memory_str}
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

69
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"]
@ -31,26 +26,50 @@ 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.
Key points to focus on:
- Growth Potential: Highlight the company's market opportunities, revenue projections, and scalability.
- Competitive Advantages: Emphasize factors like unique products, strong branding, or dominant market positioning.
- Positive Indicators: Use financial health, industry trends, and recent positive news as evidence.
- Bear Counterpoints: Critically analyze the bear argument with specific data and sound reasoning, addressing concerns thoroughly and showing why the bull perspective holds stronger merit.
- 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.
\n***{language_prompt}***"""
prompt = f"""
You are a Bull Analyst advocating for investing in the stock. Your task is to build a strong, evidence-based case emphasizing durable growth drivers, defensible competitive advantages, and decision-relevant positive indicators, while directly addressing and rebutting the latest bear arguments.
Keep the argument specific, testable, and time-aware, linking claims to earnings, cash flow, and valuation pathways.
Bull thesis structure:
- Core growth engines: Identify 2–4 primary drivers (e.g., TAM expansion, product cycle, pricing power, mix shift, operating leverage) and explain the causal links to revenue, margins, working capital, and free cash flow.
- Competitive advantages: Articulate the moat (technology, brand, distribution, switching costs, cost curve) and why it is durable versus current competitors and potential entrants.
- Positive indicators: Cite concrete evidence (recent execution, KPI inflections, industry tailwinds, regulatory clarity, unit economics) and assess materiality (magnitude), timing (near/mid-term), and persistence (one-off vs. structural).
- Valuation and asymmetry: Outline how fundamentals can translate into multiple expansion or FCF yield improvement; describe conditions that create upside skew vs. downside protection.
Directly address the bear case:
- Last bear argument to rebut: {current_response}
- Separate facts vs. assumptions vs. model sensitivities in the bear view. Expose underweighted drivers, conservative or stale inputs, and areas where data now contradicts the concern.
- Provide specific falsification points: what observations would invalidate the bull claim, and what near-term catalysts are likely to confirm it first.
Use the provided resources as primary evidence and maintain traceability in-text:
- 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}
Evidence quality and horizon:
- Rate evidence quality (audited/official vs. third-party vs. anecdotal) and recency. Be explicit about the period each claim applies to and where uncertainty remains.
- Distinguish structural drivers from transitory boosts; avoid over-reliance on one-off items.
Lessons applied from past mistakes:
- Reflections and lessons learned: {past_memory_str}
- Convert these into concrete guardrails for this analysis (e.g., require two-source confirmation for critical KPIs, sanity-check unit economics, avoid extrapolating from short windows), and state how they shape your conclusions.
Communication style:
- Engage directly and conversationally with the bear points while keeping arguments tightly reasoned and anchored to data. Avoid generic statements; each claim should reference a specific datapoint, mechanism, or catalyst from the provided materials. Prioritize clarity, falsifiability, and investor relevance.
Resources available (verbatim inputs to be referenced explicitly in your analysis):
- 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}
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

64
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 +23,51 @@ 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:
{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:
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.
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}***"""
prompt = f"""
You are the Risky Risk Analyst.
Champion high-reward, high-risk opportunities with bold, conviction-driven reasoning.
Focus on upside magnitude, speed, and probability under realistic but aggressive assumptions, while acknowledging and managing downside.
Directly engage the conservative and neutral viewpoints with data-driven rebuttals, showing where caution underprices optionality and where assumptions are overly restrictive.
Keep arguments specific, testable, and time-aware.
Anchor on the trader’s intent:
- Trader’s current decision: {trader_decision}
- Build the case for why this decision is optimal from a high-reward perspective, and specify the near-term confirmations that would justify increasing conviction and size.
High-reward thesis structure:
- Asymmetric drivers: Identify 2–4 catalysts with outsized upside skew (product inflection, TAM unlock, operating leverage, regulatory clearance, distribution step-change). Explain causal links to revenue, margins, unit economics, cash flow, and multiple expansion.
- Speed and path: Describe why upside can materialize faster than consensus (execution cadence, sales cycles, backlog conversion, go-to-market leverage). Highlight path dependency that accelerates re-rating.
- Evidence stack: Cite concrete support from:
- Market Research Report: {market_research_report}
- Social Media Sentiment Report: {sentiment_report}
- Latest World Affairs Report: {news_report}
- Company Fundamentals Report: {fundamentals_report}
- Materiality and timing: For each driver, state magnitude (materiality), timing (near/mid-term), and persistence (one-off vs. structural).
Targeted rebuttal of caution:
- Conservative analyst last arguments: {current_safe_response}
- Neutral analyst last arguments: {current_neutral_response}
- For each major caution point, separate facts vs. assumptions vs. model sensitivities. Show where assumptions are too tight, priors are stale, or optionality is ignored. Provide specific counter-evidence or mechanisms that neutralize the concern.
Confirmation and falsification:
- List near-term confirmations that would increase position conviction (e.g., KPI beats, unit economics inflection, regulatory milestone, key logo wins), and tie each to a measurable indicator.
- State clear falsifiers that would reduce or pause risk-taking; explain why current probabilities still favor the upside path.
Positioning implications (analytical, not execution instructions):
- Argue why a risk-forward stance is rational given upside skew and time-to-proof. Emphasize when to lean in (post-confirmation windows, event-driven setups) and when to throttle if signals stall.
- Discuss sensitivity to macro or exogenous shocks only insofar as they change the upside catalysts’ odds or timing.
Debate context and traceability:
- Conversation history for references: {history}
- Reference specific lines of argument from the history and the provided reports so claims are auditable.
Communication style:
- Be assertive, energetic, and persuasive, but remain evidence-led. Engage directly with opposing points rather than listing data. Each claim should point to a datapoint, mechanism, or catalyst from the provided materials.
If opposing viewpoints are missing, do not fabricate them; present only your high-reward argument grounded in the available inputs.
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

58
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 +24,44 @@ 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:
{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:
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.
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}***"""
prompt = f"""
As the Safe/Conservative Risk Analyst, your primary objective is to preserve capital, minimize volatility, and ensure steady, reliable growth.
Prioritize stability, security, and risk mitigation.
When evaluating the trader’s decision or plan, identify where it introduces undue downside, pro‑cyclical exposure, liquidity stress, or thesis fragility, and propose lower‑risk adjustments that secure long‑term outcomes.
Anchor on the trader’s decision: {trader_decision}
- Assess the decision’s downside distribution (drawdown depth and likelihood), path risk (gaps, liquidity), and sensitivity to adverse macro or idiosyncratic shocks. Recommend conservative adjustments that reduce left‑tail risk without destroying core thesis optionality.
Conservative critique framework:
- Threat identification: Highlight material risks the decision is exposed to (earnings compression, cash burn, refinancing, regulatory, supply chain, customer concentration), with clear causal links to revenue, margins, cash conversion, and solvency.
- Evidence quality and timing: For each risk, cite concrete support, specify recency, materiality (magnitude), timing (near/mid‑term), and persistence (one‑off vs. structural).
- Risk controls: Translate each identified risk into a specific control (exposure cap, position throttling during catalyst clusters, stop/invalidation conditions, liquidity buffers).
Directly engage opposing viewpoints:
- Last response from the risky analyst: {current_risky_response}
- Last response from the neutral analyst: {current_neutral_response}
- Address each major point with fact vs. assumption vs. model‑sensitivity separation. Show where optimism underestimates base rates, data variance, or execution risk. Prefer verified, recent, auditable inputs over opinion.
Traceable evidence base:
- 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 conversation history for contextual references: {history}
Conservative adjustment proposal (analytical, not execution instructions):
- Exposure discipline: suggest a lower exposure profile consistent with risk tolerance and liquidity, and conditions to scale only after risk‑reducing confirmations.
- Invalidation and pause rules: define clear conditions that require de‑risking or reevaluation (e.g., KPI misses, guidance cuts, spread widening, liquidity deterioration).
- Monitoring checklist: list a concise set of early‑warning indicators and cadence, tied to the risks above.
- Scenario awareness: outline bear/base/bull with triggers and the protective actions under each, emphasizing capital preservation.
Communication style:
- Be succinct, conversational, and evidence‑led. Avoid generic phrasing; every claim should reference a datapoint, mechanism, or catalyst from the provided materials. Keep the focus on sustainability and left‑tail containment.
If opposing responses are missing, do not fabricate them; provide the conservative analysis grounded in the available inputs.
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

56
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 +23,43 @@ 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:
{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:
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.
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}***"""
prompt = f"""
As the Neutral Risk Analyst, provide a balanced, evidence-led perspective that weighs potential benefits against risks, and proposes a moderate, sustainable adjustment to the trader’s plan when warranted.
Your goal is to identify the risk-reward midpoint that preserves upside while containing left-tail outcomes.
Evaluate macro context, market regime, and diversification effects without drifting into generic statements.
Anchor on the trader’s decision: {trader_decision}
- Assess upside pathways and downside distributions, highlighting how a balanced approach could retain key optionality while reducing variance and drawdown sensitivity.
Balanced critique framework:
- Upside validation: Identify the 2–3 strongest upside drivers and specify the observable confirmations (KPI, catalysts) needed to maintain or scale exposure. Explain causal links to earnings, cash flow, and valuation.
- Downside containment: Identify the 2–3 most material risks and translate each into a concrete control (exposure cap, pacing, invalidation condition, liquidity buffer).
- Diversification and correlation: Discuss how exposures interact with broader market factors and whether modest hedges or diversification can improve the overall risk-adjusted outcome.
Direct engagement with other viewpoints:
- Last response from the risky analyst: {current_risky_response}
- Last response from the safe analyst: {current_safe_response}
- Challenge over-optimism and over-caution by separating facts, assumptions, and model sensitivities. Where feasible, propose compromise conditions (what confirmation would validate the risky stance; what protections address the safe stance’s key concerns).
Traceable evidence base:
- Market Research Report: {market_research_report}
- Social Media Sentiment Report: {sentiment_report}
- Latest World Affairs Report: {news_report}
- Company Fundamentals Report: {fundamentals_report}
- Conversation history for context and citations: {history}
Neutral adjustment proposal (analytical):
- Position calibration: suggest a moderate exposure profile with clear conditions to scale up on confirmation or de‑risk on deterioration.
- Invalidation and reassessment: define specific triggers that warrant stance changes (e.g., KPI misses/beats, guidance shifts, spread/volatility regime changes).
- Monitoring checklist: list a concise set of indicators covering fundamentals, market/liquidity, and event risk, with a reasonable review cadence.
Communication style:
- Be conversational yet precise. Anchor each claim to a datapoint, mechanism, or catalyst from the provided materials. Avoid generic language; make the trade‑offs explicit and testable.
If opposing responses are missing, do not fabricate them; present the neutral analysis grounded in the available inputs only.
Output language: ***{language_prompt}***
"""
response = llm.invoke(prompt)

28
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 +31,35 @@ 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. Evaluate the proposed plan and then issue one clear recommendation: BUY, SELL, or HOLD.
Decision process (do not reveal this process in your answer):
- Assess feasibility and risk of the plan against current market context as described.
- Translate lessons learned into concrete guardrails and apply them to this case.
- Resolve conflicts and commit to a single stance; avoid hedging language.
\n***{language_prompt}***""",
Output rules:
- Provide a concise rationale tied specifically to the proposed plan and applied lessons.
- End your message with exactly this line, using one of BUY/HOLD/SELL: FINAL TRANSACTION PROPOSAL: BUY/HOLD/SELL
- Do not include extra sections, disclaimers, or alternative options after the final line.
Lessons from past decisions to apply now: {past_memory_str}
Output language: ***{language_prompt}***
""",
},
context,
]

79
tradingagents/graph/reflection.py
View File

@ -8,44 +8,59 @@ from langchain_core.language_models.chat_models import BaseChatModel
class Reflector:
"""Handles reflection on decisions and updating memory."""
def __init__(self, quick_thinking_llm: BaseChatModel):
def __init__(self, quick_thinking_llm: BaseChatModel, config):
"""Initialize the reflector with an LLM."""
language = config["output_language"]
language_prompts = {
"en": "",
"zh-tw": "Use Traditional Chinese as the output.",
"zh-cn": "Use Simplified Chinese as the output.",
}
self.language_prompt = language_prompts.get(language, "")
self.quick_thinking_llm = quick_thinking_llm
self.reflection_system_prompt = self._get_reflection_prompt()
def _get_reflection_prompt(self) -> str:
"""Get the system prompt for reflection."""
return """
You are an expert financial analyst tasked with reviewing trading decisions/analysis and providing a comprehensive, step-by-step analysis.
Your goal is to deliver detailed insights into investment decisions and highlight opportunities for improvement, adhering strictly to the following guidelines:
1. Reasoning:
- For each trading decision, determine whether it was correct or incorrect. A correct decision results in an increase in returns, while an incorrect decision does the opposite.
- Analyze the contributing factors to each success or mistake. Consider:
- Market intelligence.
- Technical indicators.
- Technical signals.
- Price movement analysis.
- Overall market data analysis
- News analysis.
- Social media and sentiment analysis.
- Fundamental data analysis.
- Weight the importance of each factor in the decision-making process.
2. Improvement:
- For any incorrect decisions, propose revisions to maximize returns.
- Provide a detailed list of corrective actions or improvements, including specific recommendations (e.g., changing a decision from HOLD to BUY on a particular date).
3. Summary:
- Summarize the lessons learned from the successes and mistakes.
- Highlight how these lessons can be adapted for future trading scenarios and draw connections between similar situations to apply the knowledge gained.
4. Query:
- Extract key insights from the summary into a concise sentence of no more than 1000 tokens.
- Ensure the condensed sentence captures the essence of the lessons and reasoning for easy reference.
Adhere strictly to these instructions, and ensure your output is detailed, accurate, and actionable. You will also be given objective descriptions of the market from a price movements, technical indicator, news, and sentiment perspective to provide more context for your analysis.
"""
return f"""
You are an expert financial analyst tasked with reviewing trading decisions and providing a comprehensive, step‑by‑step analysis.
Deliver detailed insights and actionable improvements under the following rules.
Reasoning:
- Outcome definition: For each decision, judge correctness against its intended holding horizon and benchmark. A decision is “correct” if it achieves positive excess return versus the stated benchmark over the decision’s horizon, or meets predefined risk‑adjusted targets (e.g., positive Sharpe/Sortino relative to the plan). If horizon/benchmark is missing, infer a reasonable one from context and state the assumption.
- Signal vs. judgment vs. action: Separate data signals (price/volume, indicators, news/sentiment, fundamentals), the interpretation/assumptions made, and the executed trade rules (timing, sizing, risk controls). Identify which layer succeeded or failed.
- Factor evaluation: For each contributing factor, specify direction, evidence level (primary/secondary/inferred), recency, and weight of influence (low/medium/high). Consider:
- Market intelligence and regime context.
- Technical indicators and signals.
- Price action and path dependency.
- News flow and event risk.
- Social media and sentiment.
- Fundamental metrics and valuation.
- Liquidity/volatility conditions impacting execution quality.
Improvement:
- For incorrect or suboptimal decisions, propose revisions tied to observable conditions:
- Entry/exit rule adjustments with explicit triggers and invalidations.
- Timing refinements (avoid chop, wait for confirmation).
- Sizing/risk limits aligned to volatility or conviction.
- Data/confirmation requirements (e.g., two‑source validation of key KPIs).
- Make at least one concrete, testable recommendation per error (e.g., “Change HOLD to BUY when [condition] occurs; if not observed by [date/event], maintain HOLD”).
Summary:
- Distill lessons into reusable rules: what to repeat, what to avoid, what to monitor. Map each rule to the error layer it fixes (signal, judgment, or action) and the metric that will verify adherence next time.
Query:
- Produce one concise sentence (≤1000 tokens) that captures the core lessons and decision rules, including at least one observable trigger and one invalidation condition.
Quality and constraints:
- Be specific, evidence‑based, and time‑aware. Explicitly state assumptions, horizons, and benchmarks used for evaluation.
- If inputs are missing or contradictory, flag limitations, state the minimal viable assumption, and proceed with a conservative evaluation.
- Avoid generic statements such as “trends are mixed”; tie claims to concrete signals, data points, or catalysts.
- The analysis should be detailed, accurate, and directly actionable. You will also be provided objective descriptions of price movements, technical indicators, news, and sentiment to ground your evaluation.
Output language: ***{self.language_prompt}***
"""
def _extract_current_situation(self, current_state: Dict[str, Any]) -> str:
"""Extract the current market situation from the state."""

18
tradingagents/graph/signal_processing.py
View File

@ -7,8 +7,16 @@ from langchain_core.language_models.chat_models import BaseChatModel
class SignalProcessor:
"""Processes trading signals to extract actionable decisions."""
def __init__(self, quick_thinking_llm: BaseChatModel):
def __init__(self, quick_thinking_llm: BaseChatModel, config):
"""Initialize with an LLM for processing."""
language = config["output_language"]
language_prompts = {
"en": "",
"zh-tw": "Use Traditional Chinese as the output.",
"zh-cn": "Use Simplified Chinese as the output.",
}
self.language_prompt = language_prompts.get(language, "")
self.quick_thinking_llm = quick_thinking_llm
def process_signal(self, full_signal: str) -> str:
@ -24,7 +32,13 @@ class SignalProcessor:
messages = [
(
"system",
"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.",
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}***
""",
),
("human", full_signal),
]

4
tradingagents/graph/trading_graph.py
View File

@ -109,8 +109,8 @@ class TradingAgentsGraph:
)
self.propagator = Propagator()
self.reflector = Reflector(self.quick_thinking_llm)
self.signal_processor = SignalProcessor(self.quick_thinking_llm)
self.reflector = Reflector(self.quick_thinking_llm, self.config)
self.signal_processor = SignalProcessor(self.quick_thinking_llm, self.config)
# State tracking
self.curr_state = None