feat(028-strategy-signals-contrib): wire strategy signals into analyst prompts

- Add strategy_signals field to AgentState (list of StrategySignal dicts)
- Compute signals once in Propagator.create_initial_state() with graceful fallback
- Add strategy_utils.get_signal_section() helper for role-based formatting
- Inject signals into all 4 analyst nodes (market, fundamentals, news, social)
- Inject signals into bull/bear researchers and research manager
- Inject signals into all 3 risk debators (aggressive, conservative, neutral)
- Inject signals into portfolio manager and trader
- Log strategy_signals in state output JSON

tradingagents/agents/analysts/fundamentals_analyst.py

@@ -8,6 +8,7 @@ from tradingagents.agents.utils.agent_utils import (
     get_insider_transactions,
     get_language_instruction,
 )
+from tradingagents.agents.utils.strategy_utils import get_signal_section
 from tradingagents.dataflows.config import get_config
 
 
@@ -15,6 +16,7 @@ def create_fundamentals_analyst(llm):
     def fundamentals_analyst_node(state):
         current_date = state["trade_date"]
         instrument_context = build_instrument_context(state["company_of_interest"])
+        signal_section = get_signal_section(state, "fundamentals")
 
         tools = [
             get_fundamentals,
@@ -41,7 +43,7 @@ def create_fundamentals_analyst(llm):
                     " 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}. {instrument_context}",
+                    "For your reference, the current date is {current_date}. {instrument_context}{signal_section}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -51,6 +53,7 @@ def create_fundamentals_analyst(llm):
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(instrument_context=instrument_context)
+        prompt = prompt.partial(signal_section=signal_section)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/analysts/market_analyst.py

@@ -5,6 +5,7 @@ from tradingagents.agents.utils.agent_utils import (
     get_language_instruction,
     get_stock_data,
 )
+from tradingagents.agents.utils.strategy_utils import get_signal_section
 from tradingagents.dataflows.config import get_config
 
 
@@ -13,6 +14,7 @@ def create_market_analyst(llm):
     def market_analyst_node(state):
         current_date = state["trade_date"]
         instrument_context = build_instrument_context(state["company_of_interest"])
+        signal_section = get_signal_section(state, "market")
 
         tools = [
             get_stock_data,
@@ -60,7 +62,7 @@ Volume-Based Indicators:
                     " 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}. {instrument_context}",
+                    "For your reference, the current date is {current_date}. {instrument_context}{signal_section}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -70,6 +72,7 @@ Volume-Based Indicators:
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(instrument_context=instrument_context)
+        prompt = prompt.partial(signal_section=signal_section)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/analysts/news_analyst.py

@@ -5,6 +5,7 @@ from tradingagents.agents.utils.agent_utils import (
     get_language_instruction,
     get_news,
 )
+from tradingagents.agents.utils.strategy_utils import get_signal_section
 from tradingagents.dataflows.config import get_config
 
 
@@ -12,6 +13,7 @@ def create_news_analyst(llm):
     def news_analyst_node(state):
         current_date = state["trade_date"]
         instrument_context = build_instrument_context(state["company_of_interest"])
+        signal_section = get_signal_section(state, "news")
 
         tools = [
             get_news,
@@ -35,7 +37,7 @@ def create_news_analyst(llm):
                     " 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}. {instrument_context}",
+                    "For your reference, the current date is {current_date}. {instrument_context}{signal_section}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -45,6 +47,7 @@ def create_news_analyst(llm):
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(instrument_context=instrument_context)
+        prompt = prompt.partial(signal_section=signal_section)
 
         chain = prompt | llm.bind_tools(tools)
         result = chain.invoke(state["messages"])

tradingagents/agents/analysts/social_media_analyst.py

@@ -1,5 +1,6 @@
 from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction, get_news
+from tradingagents.agents.utils.strategy_utils import get_signal_section
 from tradingagents.dataflows.config import get_config
 
 
@@ -7,6 +8,7 @@ def create_social_media_analyst(llm):
     def social_media_analyst_node(state):
         current_date = state["trade_date"]
         instrument_context = build_instrument_context(state["company_of_interest"])
+        signal_section = get_signal_section(state, "social")
 
         tools = [
             get_news,
@@ -29,7 +31,7 @@ def create_social_media_analyst(llm):
                     " 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}. {instrument_context}",
+                    "For your reference, the current date is {current_date}. {instrument_context}{signal_section}",
                 ),
                 MessagesPlaceholder(variable_name="messages"),
             ]
@@ -39,6 +41,7 @@ def create_social_media_analyst(llm):
         prompt = prompt.partial(tool_names=", ".join([tool.name for tool in tools]))
         prompt = prompt.partial(current_date=current_date)
         prompt = prompt.partial(instrument_context=instrument_context)
+        prompt = prompt.partial(signal_section=signal_section)
 
         chain = prompt | llm.bind_tools(tools)
 

tradingagents/agents/managers/portfolio_manager.py

@@ -1,10 +1,12 @@
 from tradingagents.agents.utils.agent_utils import build_instrument_context, get_language_instruction
+from tradingagents.agents.utils.strategy_utils import get_signal_section
 
 
 def create_portfolio_manager(llm, memory):
     def portfolio_manager_node(state) -> dict:
 
         instrument_context = build_instrument_context(state["company_of_interest"])
+        signal_section = get_signal_section(state, "risk")
 
         history = state["risk_debate_state"]["history"]
         risk_debate_state = state["risk_debate_state"]
@@ -52,7 +54,8 @@ def create_portfolio_manager(llm, memory):
 
 ---
 
-Be decisive and ground every conclusion in specific evidence from the analysts.{get_language_instruction()}"""
+Be decisive and ground every conclusion in specific evidence from the analysts.{get_language_instruction()}
+{signal_section}"""
 
         response = llm.invoke(prompt)
 

tradingagents/agents/managers/research_manager.py

@@ -1,10 +1,12 @@
 
 from tradingagents.agents.utils.agent_utils import build_instrument_context
+from tradingagents.agents.utils.strategy_utils import get_signal_section
 
 
 def create_research_manager(llm, memory):
     def research_manager_node(state) -> dict:
         instrument_context = build_instrument_context(state["company_of_interest"])
+        signal_section = get_signal_section(state, "researcher")
         history = state["investment_debate_state"].get("history", "")
         market_research_report = state["market_report"]
         sentiment_report = state["sentiment_report"]
@@ -38,7 +40,8 @@ Here are your past reflections on mistakes:
 
 Here is the debate:
 Debate History:
-{history}"""
+{history}
+{signal_section}"""
         response = llm.invoke(prompt)
 
         new_investment_debate_state = {

tradingagents/agents/researchers/bear_researcher.py

@@ -1,5 +1,8 @@
 
 
+from tradingagents.agents.utils.strategy_utils import get_signal_section
+
+
 def create_bear_researcher(llm, memory):
     def bear_node(state) -> dict:
         investment_debate_state = state["investment_debate_state"]
@@ -11,6 +14,7 @@ def create_bear_researcher(llm, memory):
         sentiment_report = state["sentiment_report"]
         news_report = state["news_report"]
         fundamentals_report = state["fundamentals_report"]
+        signal_section = get_signal_section(state, "researcher")
 
         curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
         past_memories = memory.get_memories(curr_situation, n_matches=2)
@@ -39,7 +43,7 @@ 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.
-"""
+{signal_section}"""
 
         response = llm.invoke(prompt)
 

tradingagents/agents/researchers/bull_researcher.py

@@ -1,5 +1,8 @@
 
 
+from tradingagents.agents.utils.strategy_utils import get_signal_section
+
+
 def create_bull_researcher(llm, memory):
     def bull_node(state) -> dict:
         investment_debate_state = state["investment_debate_state"]
@@ -11,6 +14,7 @@ def create_bull_researcher(llm, memory):
         sentiment_report = state["sentiment_report"]
         news_report = state["news_report"]
         fundamentals_report = state["fundamentals_report"]
+        signal_section = get_signal_section(state, "researcher")
 
         curr_situation = f"{market_research_report}\n\n{sentiment_report}\n\n{news_report}\n\n{fundamentals_report}"
         past_memories = memory.get_memories(curr_situation, n_matches=2)
@@ -37,7 +41,7 @@ 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.
-"""
+{signal_section}"""
 
         response = llm.invoke(prompt)
 

tradingagents/agents/risk_mgmt/aggressive_debator.py

@@ -1,5 +1,8 @@
 
 
+from tradingagents.agents.utils.strategy_utils import get_signal_section
+
+
 def create_aggressive_debator(llm):
     def aggressive_node(state) -> dict:
         risk_debate_state = state["risk_debate_state"]
@@ -13,6 +16,7 @@ def create_aggressive_debator(llm):
         sentiment_report = state["sentiment_report"]
         news_report = state["news_report"]
         fundamentals_report = state["fundamentals_report"]
+        signal_section = get_signal_section(state, "risk")
 
         trader_decision = state["trader_investment_plan"]
 
@@ -28,7 +32,8 @@ 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_conservative_response} Here are the last arguments from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
 
-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."""
+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.
+{signal_section}"""
 
         response = llm.invoke(prompt)
 

tradingagents/agents/risk_mgmt/conservative_debator.py

@@ -1,5 +1,8 @@
 
 
+from tradingagents.agents.utils.strategy_utils import get_signal_section
+
+
 def create_conservative_debator(llm):
     def conservative_node(state) -> dict:
         risk_debate_state = state["risk_debate_state"]
@@ -13,6 +16,7 @@ def create_conservative_debator(llm):
         sentiment_report = state["sentiment_report"]
         news_report = state["news_report"]
         fundamentals_report = state["fundamentals_report"]
+        signal_section = get_signal_section(state, "risk")
 
         trader_decision = state["trader_investment_plan"]
 
@@ -28,7 +32,8 @@ 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 aggressive analyst: {current_aggressive_response} Here is the last response from the neutral analyst: {current_neutral_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
 
-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."""
+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.
+{signal_section}"""
 
         response = llm.invoke(prompt)
 

tradingagents/agents/risk_mgmt/neutral_debator.py

@@ -1,5 +1,8 @@
 
 
+from tradingagents.agents.utils.strategy_utils import get_signal_section
+
+
 def create_neutral_debator(llm):
     def neutral_node(state) -> dict:
         risk_debate_state = state["risk_debate_state"]
@@ -13,6 +16,7 @@ def create_neutral_debator(llm):
         sentiment_report = state["sentiment_report"]
         news_report = state["news_report"]
         fundamentals_report = state["fundamentals_report"]
+        signal_section = get_signal_section(state, "risk")
 
         trader_decision = state["trader_investment_plan"]
 
@@ -28,7 +32,8 @@ 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 aggressive analyst: {current_aggressive_response} Here is the last response from the conservative analyst: {current_conservative_response}. If there are no responses from the other viewpoints yet, present your own argument based on the available data.
 
-Engage actively by analyzing both sides critically, addressing weaknesses in the aggressive 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."""
+Engage actively by analyzing both sides critically, addressing weaknesses in the aggressive 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.
+{signal_section}"""
 
         response = llm.invoke(prompt)
 

tradingagents/agents/trader/trader.py

@@ -1,12 +1,14 @@
 import functools
 
 from tradingagents.agents.utils.agent_utils import build_instrument_context
+from tradingagents.agents.utils.strategy_utils import get_signal_section
 
 
 def create_trader(llm, memory):
     def trader_node(state, name):
         company_name = state["company_of_interest"]
         instrument_context = build_instrument_context(company_name)
+        signal_section = get_signal_section(state, "researcher")
         investment_plan = state["investment_plan"]
         market_research_report = state["market_report"]
         sentiment_report = state["sentiment_report"]
@@ -31,7 +33,8 @@ def create_trader(llm, memory):
         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. Apply lessons from past decisions to strengthen your analysis. Here are 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. Apply lessons from past decisions to strengthen your analysis. Here are reflections from similar situations you traded in and the lessons learned: {past_memory_str}
+{signal_section}""",
             },
             context,
         ]

tradingagents/agents/utils/agent_states.py

@@ -1,4 +1,4 @@
-from typing import Annotated
+from typing import Annotated, Any
 from typing_extensions import TypedDict
 from langgraph.graph import MessagesState
 
@@ -70,3 +70,6 @@ class AgentState(MessagesState):
         RiskDebateState, "Current state of the debate on evaluating risk"
     ]
     final_trade_decision: Annotated[str, "Final decision made by the Risk Analysts"]
+
+    # Quantitative strategy signals (computed once, consumed by all nodes)
+    strategy_signals: Annotated[list[dict[str, Any]], "Deterministic strategy signals from the strategies framework"]

tradingagents/agents/utils/strategy_utils.py

@@ -0,0 +1,18 @@
+"""Utility to extract formatted strategy signals from agent state."""
+
+from __future__ import annotations
+
+from typing import Any
+
+
+def get_signal_section(state: dict[str, Any], role: str) -> str:
+    """Return a formatted strategy signals section for *role*, or empty string."""
+    signals = state.get("strategy_signals")
+    if not signals:
+        return ""
+    try:
+        from tradingagents.strategies import format_signals_for_role
+        section = format_signals_for_role(signals, role)
+        return f"\n\n{section}" if section else ""
+    except Exception:
+        return ""

tradingagents/graph/propagation.py

@@ -1,5 +1,6 @@
 # TradingAgents/graph/propagation.py
 
+import logging
 from typing import Dict, Any, List, Optional
 from tradingagents.agents.utils.agent_states import (
     AgentState,
@@ -7,6 +8,8 @@ from tradingagents.agents.utils.agent_states import (
     RiskDebateState,
 )
 
+logger = logging.getLogger(__name__)
+
 
 class Propagator:
     """Handles state initialization and propagation through the graph."""
@@ -19,6 +22,14 @@ class Propagator:
         self, company_name: str, trade_date: str
     ) -> Dict[str, Any]:
         """Create the initial state for the agent graph."""
+        # Compute strategy signals once up-front
+        strategy_signals: list[dict[str, Any]] = []
+        try:
+            from tradingagents.strategies import compute_signals
+            strategy_signals = compute_signals(company_name, str(trade_date))
+        except Exception:
+            logger.warning("Strategy signal computation failed; continuing without signals", exc_info=True)
+
         return {
             "messages": [("human", company_name)],
             "company_of_interest": company_name,
@@ -51,6 +62,7 @@ class Propagator:
             "fundamentals_report": "",
             "sentiment_report": "",
             "news_report": "",
+            "strategy_signals": strategy_signals,
         }
 
     def get_graph_args(self, callbacks: Optional[List] = None) -> Dict[str, Any]:

tradingagents/graph/trading_graph.py

@@ -254,6 +254,7 @@ class TradingAgentsGraph:
             },
             "investment_plan": final_state["investment_plan"],
             "final_trade_decision": final_state["final_trade_decision"],
+            "strategy_signals": final_state.get("strategy_signals", []),
         }
 
         # Save to file