feat: factor in user's current position in decisions

cli/main.py

@@ -393,6 +393,11 @@ def update_display(layout, spinner_text=None):
     layout["footer"].update(Panel(stats_table, border_style="grey50"))
 
 
+def get_user_position():
+    """Get user's current position from user input."""
+    return typer.prompt("What is your current position on this ticker? (long, short, none)", default="none")
+
+
 def get_user_selections():
     """Get all user selections before starting the analysis display."""
     # Display ASCII art welcome message
@@ -435,21 +440,29 @@ def get_user_selections():
     )
     selected_ticker = get_ticker()
 
-    # Step 2: Analysis date
+    # Step 2: User position
+    console.print(
+        create_question_box(
+            "Step 2: User Position", "Enter your current position on this ticker", "none"
+        )
+    )
+    selected_position = get_user_position()
+
+    # Step 3: Analysis date
     default_date = datetime.datetime.now().strftime("%Y-%m-%d")
     console.print(
         create_question_box(
-            "Step 2: Analysis Date",
+            "Step 3: Analysis Date",
             "Enter the analysis date (YYYY-MM-DD)",
             default_date,
         )
     )
     analysis_date = get_analysis_date()
 
-    # Step 3: Select analysts
+    # Step 4: Select analysts
     console.print(
         create_question_box(
-            "Step 3: Analysts Team", "Select your LLM analyst agents for the analysis"
+            "Step 4: Analysts Team", "Select your LLM analyst agents for the analysis"
         )
     )
     selected_analysts = select_analysts()
@@ -457,26 +470,26 @@ def get_user_selections():
         f"[green]Selected analysts:[/green] {', '.join(analyst.value for analyst in selected_analysts)}"
     )
 
-    # Step 4: Research depth
+    # Step 5: Research depth
     console.print(
         create_question_box(
-            "Step 4: Research Depth", "Select your research depth level"
+            "Step 5: Research Depth", "Select your research depth level"
         )
     )
     selected_research_depth = select_research_depth()
 
-    # Step 5: LLM Provider
+    # Step 6: LLM Provider
     console.print(
         create_question_box(
-            "Step 5: LLM Provider", "Select which service to talk to"
+            "Step 6: LLM Provider", "Select which service to talk to"
         )
     )
     selected_llm_provider, backend_url = select_llm_provider()
     
-    # Step 6: Thinking agents
+    # Step 7: Thinking agents
     console.print(
         create_question_box(
-            "Step 6: Thinking Agents", "Select your thinking agents for analysis"
+            "Step 7: Thinking Agents", "Select your thinking agents for analysis"
         )
     )
     selected_shallow_thinker = select_shallow_thinking_agent(selected_llm_provider)
@@ -484,6 +497,7 @@ def get_user_selections():
 
     return {
         "ticker": selected_ticker,
+        "user_position": selected_position,
         "analysis_date": analysis_date,
         "analysts": selected_analysts,
         "research_depth": selected_research_depth,
@@ -522,6 +536,10 @@ def display_complete_report(final_state):
     """Display the complete analysis report with team-based panels."""
     console.print("\n[bold green]Complete Analysis Report[/bold green]\n")
 
+    # User Position
+    user_position = final_state.get("user_position", "none")
+    console.print(Panel(f"User's current position: [bold]{user_position}[/bold]", title="User Position", border_style="yellow", padding=(1, 2)))
+
     # I. Analyst Team Reports
     analyst_reports = []
 
@@ -745,6 +763,7 @@ def run_analysis():
     config["deep_think_llm"] = selections["deep_thinker"]
     config["backend_url"] = selections["backend_url"]
     config["llm_provider"] = selections["llm_provider"].lower()
+    config["user_position"] = selections["user_position"]
 
     # Initialize the graph
     graph = TradingAgentsGraph(
@@ -839,7 +858,7 @@ def run_analysis():
 
         # Initialize state and get graph args
         init_agent_state = graph.propagator.create_initial_state(
-            selections["ticker"], selections["analysis_date"]
+            selections["ticker"], selections["analysis_date"], selections["user_position"]
         )
         args = graph.propagator.get_graph_args()
 

tradingagents/agents/managers/research_manager.py

@@ -19,13 +19,19 @@ def create_research_manager(llm, memory):
         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.
+        user_position = state.get("user_position", "none")
 
-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.
+        prompt = f"""As the portfolio manager and debate facilitator, your role is to critically evaluate this round of debate and make a definitive decision. Your recommendation will depend on the user's current position on the ticker.
+
+- If the user has an open long position, your recommendation can be to maintain the long position, close the long position, or close the long position and open a short position.
+- If the user has an open short position, your recommendation can be to maintain the short position, close the short position, or close the short position and open a long position.
+- If the user has no open position, your recommendation can be to do nothing, open a long position, or open a short position.
+
+Summarize the key points from both sides concisely, focusing on the most compelling evidence or reasoning. Your recommendation must be clear and actionable. Avoid defaulting to a neutral stance simply because both sides have valid points; commit to a stance grounded in the debate's strongest arguments.
 
 Additionally, develop a detailed investment plan for the trader. This should include:
 
-Your Recommendation: A decisive stance supported by the most convincing arguments.
+Your Recommendation: A decisive stance supported by the most convincing arguments, tailored to the user's position of '{user_position}'.
 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. 

tradingagents/agents/managers/risk_manager.py

@@ -22,16 +22,24 @@ def create_risk_manager(llm, memory):
         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.
+        user_position = state.get("user_position", "none")
+
+        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 recommendation will depend on the user's current position on the ticker.
+
+- If the user has an open long position (user's position is '{user_position}'), your recommendation can be to maintain the long position, close the long position, or close the long position and open a short position.
+- If the user has an open short position (user's position is '{user_position}'), your recommendation can be to maintain the short position, close the short position, or close the short position and open a long position.
+- If the user has no open position (user's position is '{user_position}'), your recommendation can be to do nothing, open a long position, or open a short position.
+
+Your decision must result in a clear recommendation. Choose a neutral stance only if strongly justified by specific arguments, not as a fallback when all sides seem valid. Strive for clarity and decisiveness.
 
 Guidelines for Decision-Making:
 1. **Summarize Key Arguments**: Extract the strongest points from each analyst, focusing on relevance to the context.
 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.
+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 decision that loses money.
 
 Deliverables:
-- A clear and actionable recommendation: Buy, Sell, or Hold.
+- A clear and actionable recommendation.
 - Detailed reasoning anchored in the debate and past reflections.
 
 ---

tradingagents/agents/researchers/bear_researcher.py

@@ -22,7 +22,15 @@ def create_bear_researcher(llm, memory):
         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.
+        user_position = state.get("user_position", "none")
+
+        prompt = f"""You are a Bear Analyst making the case against investing in the stock. Your recommendation will depend on the user's current position on the ticker.
+
+- If the user has an open long position (user's position is '{user_position}'), your recommendation can be to maintain the long position, close the long position, or close the long position and open a short position.
+- If the user has an open short position (user's position is '{user_position}'), your recommendation can be to maintain the short position, close the short position, or close the short position and open a long position.
+- If the user has no open position (user's position is '{user_position}'), your recommendation can be to do nothing, open a long position, or open a short position.
+
+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:
 

tradingagents/agents/researchers/bull_researcher.py

@@ -22,7 +22,15 @@ def create_bull_researcher(llm, memory):
         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.
+        user_position = state.get("user_position", "none")
+
+        prompt = f"""You are a Bull Analyst advocating for investing in the stock. Your recommendation will depend on the user's current position on the ticker.
+
+- If the user has an open long position (user's position is '{user_position}'), your recommendation can be to maintain the long position, close the long position, or close the long position and open a short position.
+- If the user has an open short position (user's position is '{user_position}'), your recommendation can be to maintain the short position, close the short position, or close the short position and open a long position.
+- If the user has no open position (user's position is '{user_position}'), your recommendation can be to do nothing, open a long position, or open a short position.
+
+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.

tradingagents/agents/risk_mgmt/aggresive_debator.py

@@ -18,7 +18,15 @@ def create_risky_debator(llm):
 
         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:
+        user_position = state.get("user_position", "none")
+
+        prompt = f"""As the Risky Risk Analyst, your role is to actively champion high-reward, high-risk opportunities, emphasizing bold strategies and competitive advantages. Your recommendation will depend on the user's current position on the ticker.
+
+- If the user has an open long position (user's position is '{user_position}'), your recommendation can be to maintain the long position, close the long position, or close the long position and open a short position.
+- If the user has an open short position (user's position is '{user_position}'), your recommendation can be to maintain the short position, close the short position, or close the short position and open a long position.
+- If the user has no open position (user's position is '{user_position}'), your recommendation can be to do nothing, open a long position, or open a short position.
+
+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}
 

tradingagents/agents/risk_mgmt/conservative_debator.py

@@ -19,7 +19,15 @@ def create_safe_debator(llm):
 
         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:
+        user_position = state.get("user_position", "none")
+
+        prompt = f"""As the Safe/Conservative Risk Analyst, your primary objective is to protect assets, minimize volatility, and ensure steady, reliable growth. Your recommendation will depend on the user's current position on the ticker.
+
+- If the user has an open long position (user's position is '{user_position}'), your recommendation can be to maintain the long position, close the long position, or close the long position and open a short position.
+- If the user has an open short position (user's position is '{user_position}'), your recommendation can be to maintain the short position, close the short position, or close the short position and open a long position.
+- If the user has no open position (user's position is '{user_position}'), your recommendation can be to do nothing, open a long position, or open a short position.
+
+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}
 

tradingagents/agents/risk_mgmt/neutral_debator.py

@@ -18,7 +18,15 @@ def create_neutral_debator(llm):
 
         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:
+        user_position = state.get("user_position", "none")
+
+        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. Your recommendation will depend on the user's current position on the ticker.
+
+- If the user has an open long position (user's position is '{user_position}'), your recommendation can be to maintain the long position, close the long position, or close the long position and open a short position.
+- If the user has an open short position (user's position is '{user_position}'), your recommendation can be to maintain the short position, close the short position, or close the short position and open a long position.
+- If the user has no open position (user's position is '{user_position}'), your recommendation can be to do nothing, open a long position, or open a short position.
+
+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}
 

tradingagents/agents/trader/trader.py

@@ -22,6 +22,8 @@ def create_trader(llm, memory):
         else:
             past_memory_str = "No past memories found."
 
+        user_position = state.get("user_position", "none")
+
         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.",
@@ -30,7 +32,13 @@ 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. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str}""",
+                "content": f"""You are a trading agent analyzing market data to make investment decisions. Your recommendation will depend on the user's current position on the ticker.
+
+- If the user has an open long position (user's position is '{user_position}'), your recommendation can be to maintain the long position, close the long position, or close the long position and open a short position.
+- If the user has an open short position (user's position is '{user_position}'), your recommendation can be to maintain the short position, close the short position, or close the short position and open a long position.
+- If the user has no open position (user's position is '{user_position}'), your recommendation can be to do nothing, open a long position, or open a short position.
+
+Based on your analysis, provide a specific recommendation. End with a firm decision and always conclude your response with 'FINAL TRANSACTION PROPOSAL: **YOUR_RECOMMENDATION**' to confirm your recommendation. Do not forget to utilize lessons from past decisions to learn from your mistakes. Here is some reflections from similar situatiosn you traded in and the lessons learned: {past_memory_str}""",
             },
             context,
         ]

tradingagents/agents/utils/agent_states.py

@@ -48,6 +48,7 @@ class RiskDebateState(TypedDict):
 
 
 class AgentState(MessagesState):
+    user_position: Annotated[str, "User's current position on the ticker. Can be long, short or none"]
     company_of_interest: Annotated[str, "Company that we are interested in trading"]
     trade_date: Annotated[str, "What date we are trading at"]
 

tradingagents/default_config.py

@@ -24,4 +24,5 @@ DEFAULT_CONFIG = {
     "max_recur_limit": 100,
     # Tool settings
     "online_tools": True,
+    "user_position": "none",
 }

tradingagents/graph/propagation.py

@@ -16,13 +16,14 @@ class Propagator:
         self.max_recur_limit = max_recur_limit
 
     def create_initial_state(
-        self, company_name: str, trade_date: str
+        self, company_name: str, trade_date: str, user_position: str = "none"
     ) -> Dict[str, Any]:
         """Create the initial state for the agent graph."""
         return {
             "messages": [("human", company_name)],
             "company_of_interest": company_name,
             "trade_date": str(trade_date),
+            "user_position": user_position,
             "investment_debate_state": InvestDebateState(
                 {"history": "", "current_response": "", "count": 0}
             ),

tradingagents/graph/trading_graph.py

@@ -182,14 +182,14 @@ class TradingAgentsGraph:
             ),
         }
 
-    def propagate(self, company_name, trade_date):
+    def propagate(self, company_name, trade_date, user_position="none"):
         """Run the trading agents graph for a company on a specific date."""
 
         self.ticker = company_name
 
         # Initialize state
         init_agent_state = self.propagator.create_initial_state(
-            company_name, trade_date
+            company_name, trade_date, user_position
         )
         args = self.propagator.get_graph_args()