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@@ -28,6 +28,9 @@ COPY src/market-analyst/ ./src/market-analyst/
 RUN uv sync --frozen --no-dev && \
     uv pip install -e . --system
 
+# Preload FinBERT model into standard cache directory
+RUN python -c "from transformers import pipeline; pipeline('sentiment-analysis', model='ProsusAI/finbert')"
+
 # Copy entry point
 COPY run.py .
 

README.md

@@ -33,7 +33,7 @@ A sophisticated **Multi-Agent System** that performs real-time technical and sen
 ```
 src/market-analyst/
 ├── app.py                  # Streamlit UI (Orchestrator)
-├── agents/                 # Agent Definitions
+├── aagents/                # Agent Definitions
 │   ├── market_analyst.py   # Technical Analysis Agent
 │   ├── sentiment_analyst.py# FinBERT Agent
 │   ├── strategy_advisor.py # Option Strategist

src/market-analyst/Dockerfile

@@ -28,6 +28,9 @@ COPY src/market-analyst/ ./src/market-analyst/
 RUN uv sync --frozen --no-dev && \
     uv pip install -e . --system
 
+# Preload FinBERT model into standard cache directory
+RUN python -c "from transformers import pipeline; pipeline('sentiment-analysis', model='ProsusAI/finbert')"
+
 # Copy entry point
 COPY run.py .
 

src/market-analyst/README.md

@@ -33,7 +33,7 @@ A sophisticated **Multi-Agent System** that performs real-time technical and sen
 ```
 src/market-analyst/
 ├── app.py                  # Streamlit UI (Orchestrator)
-├── agents/                 # Agent Definitions
+├── aagents/                # Agent Definitions
 │   ├── market_analyst.py   # Technical Analysis Agent
 │   ├── sentiment_analyst.py# FinBERT Agent
 │   ├── strategy_advisor.py # Option Strategist

src/market-analyst/aagents/market_analyst.py

@@ -0,0 +1,35 @@
+from autogen_agentchat.agents import AssistantAgent
+from autogen_core.tools import FunctionTool
+from tools.market_data import get_current_price, get_historical_volatility, get_option_chain_snapshot, get_technical_indicators
+
+def get_market_analyst(model_client):
+    
+    # Wrap tools
+    price_tool = FunctionTool(get_current_price, description="Get current price of a stock.")
+    vol_tool = FunctionTool(get_historical_volatility, description="Get historical volatility and VIX context.")
+    chain_tool = FunctionTool(get_option_chain_snapshot, description="Get option chain snapshot for near-term expiry.")
+    tech_tool = FunctionTool(get_technical_indicators, description="Calculate SMA (20/50/200) and RSI (14) technical indicators.")
+
+    return AssistantAgent(
+        name="MarketAnalyst",
+        model_client=model_client,
+        tools=[price_tool, vol_tool, chain_tool, tech_tool],
+        system_message="""
+        You are a Market Technician.
+        1. Fetch Price, Volatility, Option Chain, AND Technical Indicators (SMA, RSI) for the ticker.
+        2. Analyze the Trend (Bullish/Bearish/Neutral) based on price action and SMA alignment (Price vs SMA200).
+        3. Analyze the Volatility Regime (Low/Normal/High) using HV and VIX.
+        4. Analyze Momentum: Check RSI levels (Overbought > 70 / Oversold < 30).
+        5. Output a JSON similar to:
+           {
+             "ticker": "...",
+             "price": ...,
+             "trend": "...",
+             "volatility": "...",
+             "rsi_status": "Overbought/Neutral/Oversold",
+             "liquidity_check": "Pass/Fail based on option chain availability",
+             "notes": "..."
+           }
+        Do NOT recommend a trade yet. Just analyze the context.
+        """
+    )

src/market-analyst/aagents/risk_manager.py

@@ -0,0 +1,25 @@
+from autogen_agentchat.agents import AssistantAgent
+
+def get_risk_manager(model_client):
+    
+    return AssistantAgent(
+        name="RiskManager",
+        model_client=model_client,
+        system_message="""
+        You are the Chief Risk Officer.
+        1. Review the proposed strategy and confidence score.
+        2. STRICT RULE: If confidence < 70, reject the trade and recommend "WAIT".
+        3. Validate the score calculation against the Rubric (Start 50 + Trend/Vol/Sentiment addons).
+        4. Event Risk Check: If Earnings/CPI imminent, override and recommend "WAIT".
+        
+        Output final JSON:
+           {
+             "final_decision": "TRADE | WAIT",
+             "confidence": ..., // The final validated score
+             "actionable_recommendation": "Execute Bull Call Spread... / Stay in Cash",
+             "entry_signal": "Net Credit | Net Debit",
+             "entry_price": 1.50,
+             "risk_warning": "..."
+           }
+        """
+    )

src/market-analyst/aagents/sentiment_analyst.py

@@ -0,0 +1,28 @@
+from autogen_agentchat.agents import AssistantAgent
+from autogen_core.tools import FunctionTool
+from tools.news_data import search_news
+
+def get_sentiment_analyst(model_client):
+    
+    news_tool = FunctionTool(search_news, description="Search for recent news about the ticker.")
+
+    return AssistantAgent(
+        name="SentimentAnalyst",
+        model_client=model_client,
+        tools=[news_tool],
+        system_message="""
+        You are a Sentiment Analyst.
+        1. Search for recent news. The results now include **FinBERT Sentiment Scores** (e.g. [FinBERT: positive (0.95)]).
+        2. Aggregate these FinBERT scores to determine the overall sentiment (Bullish/Bearish/Neutral).
+        3. Assign a 'Sentiment Confidence' score based on the FinBERT probability scores.
+           - If multiple articles have >0.90 positive/negative, confidence is HIGH.
+           - If signals are mixed or low probability, confidence is LOW/MEDIUM.
+        4. Output JSON:
+           {
+             "sentiment": "...",
+             "confidence": "...",
+             "key_events": ["..."],
+             "risk_factors": ["..."]
+           }
+        """
+    )

src/market-analyst/aagents/strategy_advisor.py

@@ -0,0 +1,40 @@
+from autogen_agentchat.agents import AssistantAgent
+
+def get_strategy_advisor(model_client):
+    
+    return AssistantAgent(
+        name="StrategyAdvisor",
+        model_client=model_client,
+        system_message="""
+        You are an Option Strategist.
+        1. Review the Market Analysis (Trend, Volatility, Option Chain) and Sentiment Analysis.
+        2. Select a specific strategy with EXPLICIT STRIKES and EXPIRY from the provided option chain.
+        
+        RULES:
+        - HIGH Volatility + Range Bound -> Iron Condor (Credit)
+        - HIGH Volatility + Directional -> Credit Spread (Bull Put / Bear Call)
+        - LOW Volatility + Directional -> Debit Spread (Bull Call / Bear Put)
+        - LOW Volatility + Range Bound -> Calendar Spread (or WAIT)
+        
+        CONFIDENCE SCORE RUBRIC (Start at 50):
+        1. Technical Trend Aligns with Strategy (Price vs SMA200): +20
+        2. Volatility Regime Aligns (e.g. High Vol/VIX > 20 for Credit): +10
+        3. Sentiment Analysis is Confirming (Same direction): +10
+        4. Technical Momentum Confluence (RSI isn't fighting the trade): +10
+        5. Option Liquidity is Sufficient: +10
+        6. Conflicting Signals (Trend vs Sentiment mismatch): -20
+        
+        CALCULATE the score explicitly based on this rubric.
+        - Target > 70% confidence for a trade recommendation.
+        
+        Output JSON:
+           {
+             "strategy": "Bull Call Spread | Iron Condor | WAIT",
+             "confidence_score": 95, // Integer 0-100
+             "reasoning": "...",
+             "proposed_legs": "Buy 100 Call, Sell 105 Call (Exp: 2024-XX-XX)", // MUST use actual strikes from chain
+             "entry_signal": "Net Credit | Net Debit",
+             "estimated_entry_price": 1.50 // Midpoint estimate of the spread
+           }
+        """
+    )

src/market-analyst/teams/team.py

@@ -16,16 +16,23 @@ from autogen_agentchat.conditions import MaxMessageTermination, TextMentionTermi
 # Import agents
 # Adjust imports to work whether called from here or app.py
 try:
-    from ..agents.market_analyst import get_market_analyst
-    from ..agents.sentiment_analyst import get_sentiment_analyst
-    from ..agents.strategy_advisor import get_strategy_advisor
-    from ..agents.risk_manager import get_risk_manager
+    from ..aagents.market_analyst import get_market_analyst
+    from ..aagents.sentiment_analyst import get_sentiment_analyst
+    from ..aagents.strategy_advisor import get_strategy_advisor
+    from ..aagents.risk_manager import get_risk_manager
 except ImportError:
     # Fallback if running from proper package context
-    from agents.market_analyst import get_market_analyst
-    from agents.sentiment_analyst import get_sentiment_analyst
-    from agents.strategy_advisor import get_strategy_advisor
-    from agents.risk_manager import get_risk_manager
+    try:
+        from aagents.market_analyst import get_market_analyst
+        from aagents.sentiment_analyst import get_sentiment_analyst
+        from aagents.strategy_advisor import get_strategy_advisor
+        from aagents.risk_manager import get_risk_manager
+    except ImportError:
+         # Try absolute (if market-analyst is in path but not as package)
+         from src.market_analyst.aagents.market_analyst import get_market_analyst
+         from src.market_analyst.aagents.sentiment_analyst import get_sentiment_analyst
+         from src.market_analyst.aagents.strategy_advisor import get_strategy_advisor
+         from src.market_analyst.aagents.risk_manager import get_risk_manager
 
 def get_trading_team(model_client):
     """