Deploy Sentinel AI 2026-02-26_17:09:25

.env.example

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+# ============================================
+# Sentinel AI — API Keys Configuration
+# ============================================
+# Fill in your API keys below and rename this file to .env.
+
+# Google Gemini API Key (required)
+# Get yours at: https://aistudio.google.com/apikey
+GOOGLE_API_KEY=your_gemini_api_key_here
+
+# Optional: Groq API Key (Model Fallback)
+# Provides Llama-3 backup if Gemini hits rate limits
+# Get yours at: https://console.groq.com/keys
+GROQ_API_KEY=your_groq_api_key_here
+
+# Alpha Vantage API Key (required for live market data)
+# Get yours at: https://www.alphavantage.co/support/#api-key
+ALPHA_VANTAGE_API_KEY=your_alpha_vantage_api_key_here
+
+# Tavily API Key (required for web/news search)
+# Get yours at: https://tavily.com/
+TAVILY_API_KEY=your_tavily_api_key_here
+
+# ============================================
+# Optional: Email Delivery (Weekly Digest)
+# ============================================
+SMTP_USER=your_email@gmail.com
+SMTP_PASSWORD=your_app_specific_password
+SMTP_HOST=smtp.gmail.com
+SMTP_PORT=587
+
+# ============================================
+# Optional: FRED API (Macro Impact Analyzer)
+# ============================================
+# Get yours at: https://fred.stlouisfed.org/docs/api/api_key.html
+# FRED_API_KEY=your_fred_api_key_here

.gitattributes

@@ -1,35 +1,20 @@
+# .gitattributes for Hugging Face Spaces
+# This file tells git to use LFS for binary files
+
+# Additional binary formats from HF defaults
 *.7z filter=lfs diff=lfs merge=lfs -text
 *.arrow filter=lfs diff=lfs merge=lfs -text
 *.bin filter=lfs diff=lfs merge=lfs -text
 *.bz2 filter=lfs diff=lfs merge=lfs -text
 *.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
 *.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
 *.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
 *.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
 *.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
 *.pkl filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
 *.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
 *.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
 *.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+venv/
+.env
+.DS_Store
+
+# Logs
+logs/
+*.log
+
+# Database
+*.db
+
+# IDE
+.vscode/
+.idea/

.streamlit/config.toml

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+[theme]
+base="dark"
+primaryColor="#3b82f6"
+backgroundColor="#0b0e11"
+secondaryBackgroundColor="#15191e"
+textColor="#e2e8f0"
+font="sans serif"

.streamlit/secrets.toml.example

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+# Streamlit secrets (TOML format)
+# Add your API keys here
+
+TAVILY_API_KEY = "your-tavily-api-key-here"
+ALPHA_VANTAGE_API_KEY = "your-alpha-vantage-api-key-here"
+GOOGLE_API_KEY = "your-google-api-key-here"
+GROQ_API_KEY = "your-groq-api-key-here"

Dockerfile

@@ -1,20 +1,32 @@
-FROM python:3.13.5-slim
-
-WORKDIR /app
+FROM python:3.11-slim
 
+# Install system dependencies
 RUN apt-get update && apt-get install -y \
     build-essential \
     curl \
+    software-properties-common \
     git \
     && rm -rf /var/lib/apt/lists/*
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Create non-root user (required by HF Spaces)
+RUN useradd -m -u 1000 user
+ENV HOME=/home/user \
+    PATH="/home/user/.local/bin:$PATH"
+
+WORKDIR /app
+
+# Copy requirements first to leverage Docker cache
+COPY --chown=user requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
 
-RUN pip3 install -r requirements.txt
+# Copy the entire application
+COPY --chown=user . .
 
-EXPOSE 8501
+# Switch to non-root user
+USER user
 
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+# Expose the port Hugging Face expects
+EXPOSE 7860
 
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# Run the orchestration script
+CMD ["python", "main.py"]

README.md

@@ -1,20 +1,48 @@
 ---
-title: Sentinel V2
-emoji: 🚀
-colorFrom: red
-colorTo: red
+title: Sentinel AI Financial Intelligence
+emoji: 🛡️
+colorFrom: gray
+colorTo: indigo
 sdk: docker
-app_port: 8501
-tags:
-- streamlit
 pinned: false
-short_description: Streamlit template space
 license: mit
 ---
 
-# Welcome to Streamlit!
+# Sentinel AI - Financial Intelligence Platform
 
-Edit `/src/streamlit_app.py` to customize this app to your heart's desire. :heart:
+Transform raw market data into actionable business insights with the power of AI. Analyze stocks, news, and portfolios automatically using intelligent agents.
 
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
+## Features
+
+- 🧠 **Intelligent Analysis**: AI automatically understands market structures and generates insights
+- 📊 **Smart Visualizations**: Creates appropriate charts and graphs with interactive visualizations
+- 🎯 **Actionable Recommendations**: Get specific, measurable recommendations based on data-driven insights
+- 🚨 **Live Wire**: Real-time market alerts and trending information
+
+## Technology Stack
+
+- **Frontend**: Streamlit
+- **AI/ML**: Google Gemini, LangGraph
+- **Data Sources**: Alpha Vantage, Tavily Search
+- **Architecture**: Multi-agent system with orchestrated workflows
+
+## Configuration
+
+Before running, you need to set up the following secrets in Hugging Face Spaces settings:
+
+```toml
+GOOGLE_API_KEY = "your-google-api-key"
+ALPHA_VANTAGE_API_KEY = "your-alpha-vantage-key"
+TAVILY_API_KEY = "your-tavily-api-key"
+```
+
+## Local Development
+
+```bash
+pip install -r requirements.txt
+streamlit run app.py
+```
+
+## License
+
+MIT License - See LICENSE file for details

agents/data_analysis_agent.py

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+import pandas as pd
+import numpy as np
+import plotly.express as px
+import json
+import logging
+import re
+from typing import TypedDict, Dict, Any, List
+
+from langgraph.graph import StateGraph, END
+from features.utils import call_gemini
+
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+class AnalysisState(TypedDict):
+    dataframe: pd.DataFrame
+    dataset_info: Dict[str, Any]
+    insights: str
+    visualizations: List[Dict[str, Any]]
+    charts: List[Any]
+
+class DataAnalysisAgent:
+    def __init__(self):
+        self.workflow = self._create_workflow()
+
+    def _create_workflow(self):
+        """Creates the graph workflow for the data analysis sub-agent."""
+        workflow = StateGraph(AnalysisState)
+        workflow.add_node("data_profiler", self._profile_dataset)
+        # This new node will generate insights AND plan visualizations in one LLM call
+        workflow.add_node("insight_and_viz_planner", self._generate_insights_and_plan_visualizations)
+        workflow.add_node("chart_creator", self._create_charts)
+        
+        workflow.add_edge("data_profiler", "insight_and_viz_planner")
+        workflow.add_edge("insight_and_viz_planner", "chart_creator")
+        workflow.add_edge("chart_creator", END)
+        
+        workflow.set_entry_point("data_profiler")
+        return workflow.compile()
+
+    def _profile_dataset(self, state: AnalysisState):
+        """Profiles the dataset to understand its structure for the LLM."""
+        logger.info("--- 📊 (Sub-Agent) Profiling Data ---")
+        df_for_profiling = state["dataframe"].copy().reset_index()
+        
+        profile = {
+            "shape": df_for_profiling.shape,
+            "columns": list(df_for_profiling.columns),
+            "dtypes": {col: str(dtype) for col, dtype in df_for_profiling.dtypes.to_dict().items()},
+            "numeric_columns": df_for_profiling.select_dtypes(include=[np.number]).columns.tolist(),
+            "datetime_columns": df_for_profiling.select_dtypes(include=['datetime64']).columns.tolist()
+        }
+        logger.info("   Data profile created.")
+        return {"dataset_info": profile}
+
+    def _generate_insights_and_plan_visualizations(self, state: AnalysisState):
+        """Generates key insights and plans visualizations in a single LLM call."""
+        logger.info("--- 🧠 (Sub-Agent) Generating Insights & Visualization Plan ---")
+        info = state["dataset_info"]
+        datetime_col = info.get("datetime_columns", [None])[0] or info.get("columns", ["index"])[0]
+
+        prompt = f"""
+        You are an expert financial data scientist. Based on the following data profile from a time-series stock dataset,
+        generate key insights and plan effective visualizations.
+
+        Data Profile: {json.dumps(info, indent=2)}
+
+        Instructions:
+        Your response MUST be ONLY a single valid JSON object. Do not include any other text or markdown.
+        The JSON object must have two keys: "insights" and "visualizations".
+        - "insights": A list of 3-5 concise, bullet-point style strings focusing on trends, correlations, and anomalies.
+        - "visualizations": A list of 3 JSON objects, each planning a chart.
+            - Plan a line chart for the 'close' price over time using the '{datetime_col}' column.
+            - Plan a histogram for the 'volume' column.
+            - Plan one other relevant chart (e.g., scatter plot, bar chart).
+
+        Example Response:
+        {{
+            "insights": [
+                "The closing price shows a significant upward trend over the period.",
+                "Trading volume spiked on dates corresponding to major news events.",
+                "There is a strong positive correlation between opening and closing prices."
+            ],
+            "visualizations": [
+                {{"type": "line", "columns": ["{datetime_col}", "close"], "title": "Closing Price Over Time"}},
+                {{"type": "histogram", "columns": ["volume"], "title": "Trading Volume Distribution"}},
+                {{"type": "scatter", "columns": ["open", "close"], "title": "Opening vs. Closing Price"}}
+            ]
+        }}
+        """
+        response_str = call_gemini(prompt)
+        logger.info(f"   LLM raw output for insights & viz plan:\n{response_str}")
+
+        try:
+            json_match = re.search(r'\{.*\}', response_str, re.DOTALL)
+            if not json_match:
+                raise ValueError("No JSON object found in the LLM response.")
+            
+            clean_json_str = json_match.group(0)
+            response_json = json.loads(clean_json_str)
+            
+            insights_list = response_json.get("insights", [])
+            insights_str = "\n".join(f"* {insight}" for insight in insights_list)
+            viz_plan = response_json.get("visualizations", [])
+            
+            logger.info("   Successfully parsed insights and viz plan.")
+            return {"insights": insights_str, "visualizations": viz_plan}
+
+        except (json.JSONDecodeError, ValueError) as e:
+            logger.error(f"Failed to parse insights and visualization plan from LLM. Error: {e}")
+            logger.info("   Using a default visualization plan as a fallback.")
+            default_plan = [
+                {"type": "line", "columns": [datetime_col, "close"], "title": "Closing Price Over Time (Default)"},
+                {"type": "histogram", "columns": ["volume"], "title": "Trading Volume (Default)"}
+            ]
+            return {"insights": "Analysis generated, but detailed insights could not be parsed.", "visualizations": default_plan}
+
+    def _create_charts(self, state: AnalysisState):
+        """Creates Plotly charts - HARDCODED for reliability."""
+        logger.info("--- 🎨 (Sub-Agent) Creating Charts ---")
+        
+        # 1. Prepare DataFrame
+        df = state["dataframe"].copy()
+        if df.index.name in ['timestamp', 'date', 'datetime', 'index']:
+            df = df.reset_index()
+        
+        # Normalize column names to lowercase
+        df.columns = [str(c).lower() for c in df.columns]
+        
+        charts = []
+        
+        # Find X-axis column (timestamp)
+        x_col = None
+        for candidate in ['timestamp', 'date', 'datetime', 'index']:
+            if candidate in df.columns:
+                x_col = candidate
+                break
+        
+        if not x_col:
+            logger.warning("   No timestamp column found. Skipping charts.")
+            return {"charts": []}
+        
+        # --- CHART 1: Price History (Line) ---
+        if 'close' in df.columns:
+            try:
+                logger.info(f"   Generating Price Chart (x={x_col}, y=close)")
+                fig = px.line(df, x=x_col, y='close', 
+                             title="📈 Price History", 
+                             template="plotly_dark",
+                             labels={'close': 'Price ($)', x_col: 'Time'})
+                fig.update_traces(line_color='#00ff41')
+                charts.append(fig)
+            except Exception as e:
+                logger.error(f"   Failed to generate price chart: {e}")
+        
+        # --- CHART 2: Volume (Bar) ---
+        if 'volume' in df.columns:
+            try:
+                logger.info(f"   Generating Volume Chart (x={x_col}, y=volume)")
+                fig = px.bar(df, x=x_col, y='volume',
+                            title="📊 Trading Volume",
+                            template="plotly_dark",
+                            labels={'volume': 'Volume', x_col: 'Time'})
+                fig.update_traces(marker_color='#ff6b35')
+                charts.append(fig)
+            except Exception as e:
+                logger.error(f"   Failed to generate volume chart: {e}")
+        
+        # --- CHART 3: Price vs Volume (Scatter) ---
+        if 'close' in df.columns and 'volume' in df.columns:
+            try:
+                logger.info("   Generating Price vs Volume Scatter Plot")
+                fig = px.scatter(df, x='volume', y='close',
+                                title="🔍 Price vs Volume Correlation",
+                                template="plotly_dark",
+                                labels={'volume': 'Trading Volume', 'close': 'Price ($)'},
+                                trendline="ols",  # Add regression line
+                                opacity=0.6)
+                fig.update_traces(marker=dict(size=8, color='#4ecdc4'))
+                charts.append(fig)
+            except Exception as e:
+                logger.error(f"   Failed to generate scatter plot: {e}")
+        
+        # --- CHART 4: Daily Returns Histogram ---
+        if 'close' in df.columns and len(df) > 1:
+            try:
+                logger.info("   Generating Daily Returns Histogram")
+                # Calculate returns
+                df['returns'] = df['close'].pct_change() * 100
+                df_returns = df.dropna(subset=['returns'])
+                
+                if not df_returns.empty:
+                    fig = px.histogram(df_returns, x='returns',
+                                      nbins=30,
+                                      title="📊 Daily Returns Distribution",
+                                      template="plotly_dark",
+                                      labels={'returns': 'Daily Return (%)'},
+                                      color_discrete_sequence=['#9b59b6'])
+                    fig.add_vline(x=0, line_dash="dash", line_color="white", 
+                                 annotation_text="Zero Return", annotation_position="top")
+                    charts.append(fig)
+            except Exception as e:
+                logger.error(f"   Failed to generate histogram: {e}")
+        
+        # --- CHART 5: Box Plot (Price Distribution) ---
+        if 'close' in df.columns:
+            try:
+                logger.info("   Generating Box Plot")
+                fig = px.box(df, y='close',
+                            title="📦 Price Distribution (Box Plot)",
+                            template="plotly_dark",
+                            labels={'close': 'Price ($)'},
+                            color_discrete_sequence=['#a29bfe'])
+                charts.append(fig)
+            except Exception as e:
+                logger.error(f"   Failed to generate box plot: {e}")
+        
+        # --- CHART 6: Violin Plot (Volume Distribution) ---
+        if 'volume' in df.columns:
+            try:
+                logger.info("   Generating Violin Plot")
+                fig = px.violin(df, y='volume',
+                               title="🎻 Volume Distribution (Violin Plot)",
+                               template="plotly_dark",
+                               labels={'volume': 'Trading Volume'},
+                               color_discrete_sequence=['#74b9ff'],
+                               box=True,  # Show box plot inside violin
+                               points='all')  # Show all data points
+                charts.append(fig)
+            except Exception as e:
+                logger.error(f"   Failed to generate violin plot: {e}")
+        
+        logger.info(f"   Successfully created {len(charts)} charts.")
+        return {"charts": charts}
+
+    def run_analysis(self, dataframe: pd.DataFrame):
+        """Runs the full analysis workflow on the given DataFrame."""
+        if dataframe.empty:
+            logger.warning("Input DataFrame is empty. Skipping analysis.")
+            return {"insights": "No data available for analysis.", "charts": []}
+        initial_state = {"dataframe": dataframe}
+        # The final state will now contain insights and charts after the workflow runs
+        final_state = self.workflow.invoke(initial_state)
+        return final_state

agents/orchestrator_v3.py

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+import os
+import sys
+import pandas as pd
+import ast
+from dotenv import load_dotenv
+from typing import TypedDict, List, Dict, Any
+
+from langgraph.graph import StateGraph, END
+
+sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from agents.tool_calling_agents import WebResearchAgent, MarketDataAgent, InternalPortfolioAgent
+from agents.data_analysis_agent import DataAnalysisAgent
+from features.utils import call_gemini
+
+# --- Configuration ---
+load_dotenv()
+
+# --- Initialize workers (Stateless) ---
+web_agent = WebResearchAgent()
+market_agent = MarketDataAgent()
+portfolio_agent = InternalPortfolioAgent()
+
+# --- Define the Enhanced State ---
+class AgentState(TypedDict):
+    task: str
+    symbol: str
+    web_research_results: str
+    market_data_results: str
+    portfolio_data_results: str
+    scan_intent: str # "DOWNWARD", "UPWARD", "ALL", or None
+    # --- NEW FIELDS FOR ANALYSIS ---
+    analysis_dataframe: pd.DataFrame
+    analysis_results: Dict[str, Any]
+    final_report: str
+    # Debug fields
+    debug_market_data_raw: Any
+    debug_dataframe_head: Any
+    debug_analysis_results_full: Any
+
+def get_orchestrator(llm_provider="gemini", api_key=None):
+    """
+    Factory function to create the orchestrator graph with a specific LLM.
+    """
+    
+    # 2. Initialize Data Analyzer (Now uses global call_gemini fallback)
+    data_analyzer = DataAnalysisAgent()
+
+    # 3. Define Nodes
+
+    # 3. Define Nodes (Closure captures 'llm' and 'data_analyzer')
+
+    def extract_symbol_step(state: AgentState):
+        print("--- 🔬 Symbol & Time Range Extraction ---")
+        prompt = f"""
+        Analyze the user's request: "{state['task']}"
+        
+        Extract TWO things:
+        1. Stock symbol or scan intent
+        2. Time range (if mentioned)
+        
+        RULES:
+        - If request mentions a SPECIFIC company → Extract symbol
+        - If request mentions time period → Extract time range
+        - ONLY set scan_intent for "top gainers", "losers", "scan market"
+        
+        Response Format: JSON ONLY.
+        {{
+            "symbol": "TICKER" or null,
+            "scan_intent": "DOWNWARD" | "UPWARD" | "ALL" or null,
+            "time_range": "INTRADAY" | "1D" | "3D" | "1W" | "1M" | "3M" | "1Y" or null
+        }}
+        
+        Time Range Examples:
+        - "today", "now", "current", "recent" → "INTRADAY"
+        - "yesterday", "1 day back" → "1D"
+        - "3 days back", "last 3 days" → "3D"
+        - "last week", "1 week", "7 days" → "1W"
+        - "last month", "1 month", "30 days" → "1M"
+        - "3 months", "quarter" → "3M"
+        - "1 year", "12 months" → "1Y"
+        
+        Full Examples:
+        - "Analyze Tesla" → {{"symbol": "TSLA", "scan_intent": null, "time_range": null}}
+        - "3 days back stocks of Tesla" → {{"symbol": "TSLA", "scan_intent": null, "time_range": "3D"}}
+        - "Last week AAPL performance" → {{"symbol": "AAPL", "scan_intent": null, "time_range": "1W"}}
+        - "1 month trend for NVDA" → {{"symbol": "NVDA", "scan_intent": null, "time_range": "1M"}}
+        - "Recent analysis of Tesla" → {{"symbol": "TSLA", "scan_intent": null, "time_range": "INTRADAY"}}
+        - "Show me top gainers" → {{"symbol": null, "scan_intent": "UPWARD", "time_range": null}}
+        
+        CRITICAL: Default to null for time_range if not explicitly mentioned!
+        """
+        raw_response = call_gemini(prompt).strip()
+        
+        symbol = None
+        scan_intent = None
+        time_range = None
+        
+        try:
+            import json
+            import re
+            # Find JSON in response
+            json_match = re.search(r'\{.*\}', raw_response, re.DOTALL)
+            if json_match:
+                data = json.loads(json_match.group(0))
+                symbol = data.get("symbol")
+                scan_intent = data.get("scan_intent")
+                time_range = data.get("time_range")
+            else:
+                print(f"   WARNING: No JSON found in extraction response: {raw_response}")
+                # Fallback to simple cleaning
+                clean_resp = raw_response.strip().upper()
+                if "SCAN" in clean_resp or "GAINERS" in clean_resp or "LOSERS" in clean_resp:
+                    scan_intent = "ALL"
+                elif len(clean_resp) <= 5 and clean_resp.isalpha():
+                    symbol = clean_resp
+        except Exception as e:
+            print(f"   Error parsing symbol extraction: {e}")
+        
+        if symbol: symbol = symbol.upper().replace("$", "")
+        
+        # Default time_range to 1M if null (INTRADAY is premium-only, 1M uses free DAILY endpoint)
+        if time_range is None:
+            time_range = "1M"
+        
+        print(f"   Raw LLM Response: {raw_response}")
+        print(f"   Extracted Symbol: {symbol}")
+        print(f"   Scan Intent: {scan_intent}")
+        print(f"   Time Range: {time_range}")
+        
+        return {"symbol": symbol, "scan_intent": scan_intent, "time_range": time_range}
+
+    def web_research_step(state: AgentState):
+        print("--- 🔎 Web Research ---")
+        if state.get("scan_intent"):
+            return {"web_research_results": "Market Scan initiated. Web research skipped for individual stock."}
+        results = web_agent.research(queries=[state['task']])
+        return {"web_research_results": results}
+
+    def market_data_step(state: AgentState):
+        print("--- 📊 Market Data Retrieval ---")
+        
+        # Handle scan intent
+        if state.get("scan_intent"):
+            print(f"   Scan Intent Detected: {state['scan_intent']}")
+            
+            # Load watchlist
+            import json
+            watchlist_path = "watchlist.json"
+            if not os.path.exists(watchlist_path):
+                return {"market_data_results": {"error": "Watchlist not found. Please add symbols to your watchlist."}}
+            
+            with open(watchlist_path, 'r') as f:
+                watchlist = json.load(f)
+            
+            scan_results = []
+            scan_intent = state['scan_intent']
+            
+            for sym in watchlist:
+                try:
+                    # Use GLOBAL_QUOTE for real-time price (free tier)
+                    quote = market_agent.get_global_quote(symbol=sym)
+                    quote_data = quote.get("data", {})
+                    price = float(quote_data.get("price", 0))
+                    change_pct_str = quote_data.get("change_percent", "0%").replace("%", "")
+                    pct_change = float(change_pct_str) if change_pct_str else 0
+                    
+                    if scan_intent == "UPWARD" and pct_change > 0:
+                        scan_results.append({"symbol": sym, "price": price, "change": pct_change})
+                    elif scan_intent == "DOWNWARD" and pct_change < 0:
+                        scan_results.append({"symbol": sym, "price": price, "change": pct_change})
+                    elif scan_intent == "ALL":
+                        scan_results.append({"symbol": sym, "price": price, "change": pct_change})
+                except Exception as e:
+                    print(f"   ⚠️ Error scanning {sym}: {e}")
+            
+            # Sort by change
+            scan_results.sort(key=lambda x: x['change'], reverse=True)
+            return {"market_data_results": {"scan_results": scan_results}}
+        
+        # Single symbol analysis
+        if not state.get("symbol"):
+            return {"market_data_results": "Skipped."}
+        
+        symbol = state["symbol"]
+        combined_data = {"symbol": symbol}
+        
+        # 1. Get REAL current price via GLOBAL_QUOTE (free tier)
+        try:
+            import time
+            quote = market_agent.get_global_quote(symbol=symbol)
+            combined_data["quote"] = quote.get("data", {})
+            combined_data["quote_source"] = quote.get("source", "Unknown")
+            print(f"   ✅ Real-time quote: ${combined_data['quote'].get('price', 'N/A')}")
+            time.sleep(1)  # Respect rate limit (1 req/sec)
+        except Exception as e:
+            print(f"   ⚠️ Quote fetch failed: {e}")
+            combined_data["quote"] = {}
+        
+        # 2. Get REAL fundamentals via OVERVIEW (free tier)
+        try:
+            overview = market_agent.get_company_overview(symbol=symbol)
+            combined_data["overview"] = overview.get("data", {})
+            combined_data["overview_source"] = overview.get("source", "Unknown")
+            print(f"   ✅ Company: {combined_data['overview'].get('Name', symbol)}, P/E: {combined_data['overview'].get('PERatio', 'N/A')}")
+            import time
+            time.sleep(1)  # Respect rate limit
+        except Exception as e:
+            print(f"   ⚠️ Overview fetch failed: {e}")
+            combined_data["overview"] = {}
+        
+        # 3. Get historical data via DAILY (free tier) for trend analysis
+        try:
+            time_range = state.get("time_range", "1M")
+            # Map INTRADAY to 1M for free tier compatibility
+            if time_range == "INTRADAY":
+                time_range = "1M"
+            print(f"   Fetching DAILY data for {symbol} (time_range={time_range})")
+            results = market_agent.get_market_data(symbol=symbol, time_range=time_range)
+            combined_data["daily_data"] = results
+            source = results.get("meta_data", {}).get("Source", "Unknown")
+            data_points = len(results.get("data", {}))
+            print(f"   ✅ Daily data: {data_points} data points (Source: {source})")
+        except Exception as e:
+            print(f"   ⚠️ Daily data fetch failed: {e}")
+            combined_data["daily_data"] = {}
+        
+        return {"market_data_results": combined_data, "debug_market_data_raw": combined_data}
+
+    def portfolio_data_step(state: AgentState):
+        print("--- 💼 Internal Portfolio Data ---")
+        if state.get("scan_intent"):
+             return {"portfolio_data_results": "Market Scan initiated. Portfolio context skipped."}
+             
+        if not state.get("symbol"):
+            return {"portfolio_data_results": "Skipped: No symbol provided."}
+        
+        try:
+            results = portfolio_agent.query_portfolio(question=f"What is the current exposure to {state['symbol']}?")
+            return {"portfolio_data_results": results}
+        except Exception as e:
+            print(f"   ⚠️ Portfolio data fetch failed (Private MCP may be down): {e}")
+            return {"portfolio_data_results": f"Portfolio data unavailable (service error). Analysis continues without internal portfolio context."}
+
+    def transform_data_step(state: AgentState):
+        print("--- 🔀 Transforming Data for Analysis ---")
+        if state.get("scan_intent"):
+            return {"analysis_dataframe": pd.DataFrame()} # Skip transformation for scan
+            
+        market_data = state.get("market_data_results")
+        
+        if not isinstance(market_data, dict):
+            print("   Skipping transformation: No valid market data received.")
+            return {"analysis_dataframe": pd.DataFrame()}
+        
+        # Extract daily_data from the new combined format
+        daily_data = market_data.get('daily_data', {})
+        time_series_data = daily_data.get('data', {}) if isinstance(daily_data, dict) else {}
+        
+        if not time_series_data:
+            print("   Skipping transformation: No daily time series data available.")
+            return {"analysis_dataframe": pd.DataFrame()}
+            
+        try:
+            df = pd.DataFrame.from_dict(time_series_data, orient='index')
+            df.index = pd.to_datetime(df.index)
+            df.index.name = "timestamp"
+            df.rename(columns={
+                '1. open': 'open', '2. high': 'high', '3. low': 'low',
+                '4. close': 'close', '5. volume': 'volume'
+            }, inplace=True)
+            df = df.apply(pd.to_numeric).sort_index()
+            
+            print(f"   Successfully created DataFrame with shape {df.shape}")
+            return {"analysis_dataframe": df, "debug_dataframe_head": df.head().to_dict()}
+        except Exception as e:
+            print(f"   CRITICAL ERROR during data transformation: {e}")
+            return {"analysis_dataframe": pd.DataFrame()}
+
+    def run_data_analysis_step(state: AgentState):
+        print("--- 🔬 Running Deep-Dive Data Analysis ---")
+        if state.get("scan_intent"):
+            return {"analysis_results": {}} # Skip analysis for scan
+            
+        df = state.get("analysis_dataframe")
+        if df is not None and not df.empty:
+            analysis_results = data_analyzer.run_analysis(df)
+            return {"analysis_results": analysis_results, "debug_analysis_results_full": analysis_results}
+        else:
+            print("   Skipping analysis: No data to analyze.")
+            return {"analysis_results": {}}
+
+    def synthesize_report_step(state: AgentState):
+        print("--- 📝 Synthesizing Final Report ---")
+        
+        # Helper to truncate text to avoid Rate Limits
+        def truncate(text, max_chars=3000):
+            s = str(text)
+            if len(s) > max_chars:
+                return s[:max_chars] + "... (truncated)"
+            return s
+
+        # Check for Scan Results
+        market_data_res = state.get("market_data_results", {})
+        if isinstance(market_data_res, dict) and "scan_results" in market_data_res:
+            scan_results = market_data_res["scan_results"]
+            # Truncate scan results if necessary (though usually small)
+            scan_results_str = truncate(scan_results, 4000)
+            
+            report_prompt = f"""
+            You are a senior financial analyst. The user requested a market scan: "{state['task']}".
+            
+            Scan Results (from Watchlist):
+            {scan_results_str}
+            
+            Generate a "Market Scan Report".
+            1. Summary: Briefly explain the criteria and the overall market status based on these results.
+            2. Results Table: Create a markdown table with columns: Symbol | Price | % Change.
+            3. Conclusion: Highlight the most significant movers.
+            """
+            final_report = call_gemini(report_prompt)
+            return {"final_report": final_report}
+
+        analysis_insights = state.get("analysis_results", {}).get("insights", "Not available.")
+        
+        # Truncate inputs for the main report
+        web_data = truncate(state.get('web_research_results', 'Not available.'), 3000)
+        portfolio_data = truncate(state.get('portfolio_data_results', 'Not available.'), 2000)
+        
+        # Extract rich data from combined market results
+        market_data_raw = state.get("market_data_results", {})
+        data_sources = []
+        
+        # Build rich market context from the new format
+        quote_data = {}
+        overview_data = {}
+        if isinstance(market_data_raw, dict):
+            quote_data = market_data_raw.get("quote", {})
+            overview_data = market_data_raw.get("overview", {})
+            
+            if market_data_raw.get("quote_source"):
+                data_sources.append(f"Price: {market_data_raw['quote_source']}")
+            if market_data_raw.get("overview_source"):
+                data_sources.append(f"Fundamentals: {market_data_raw['overview_source']}")
+            daily = market_data_raw.get("daily_data", {})
+            if isinstance(daily, dict):
+                src = daily.get("meta_data", {}).get("Source", "")
+                if src:
+                    data_sources.append(f"Historical: {src}")
+        
+        data_source = " | ".join(data_sources) if data_sources else "Unknown"
+        
+        # Build a structured market data section
+        market_context = f"""
+--- REAL-TIME PRICE (GLOBAL_QUOTE) ---
+Current Price: ${quote_data.get('price', 'N/A')}
+Change: {quote_data.get('change', 'N/A')} ({quote_data.get('change_percent', 'N/A')})
+Open: ${quote_data.get('open', 'N/A')}
+High: ${quote_data.get('high', 'N/A')}
+Low: ${quote_data.get('low', 'N/A')}
+Volume: {quote_data.get('volume', 'N/A')}
+Previous Close: ${quote_data.get('previous_close', 'N/A')}
+
+--- COMPANY FUNDAMENTALS (OVERVIEW) ---
+Company: {overview_data.get('Name', 'N/A')}
+Sector: {overview_data.get('Sector', 'N/A')} | Industry: {overview_data.get('Industry', 'N/A')}
+Market Cap: ${overview_data.get('MarketCapitalization', 'N/A')}
+Revenue (TTM): ${overview_data.get('RevenueTTM', 'N/A')}
+EPS: ${overview_data.get('EPS', 'N/A')}
+P/E Ratio: {overview_data.get('PERatio', 'N/A')}
+Forward P/E: {overview_data.get('ForwardPE', 'N/A')}
+Profit Margin: {overview_data.get('ProfitMargin', 'N/A')}
+Operating Margin: {overview_data.get('OperatingMarginTTM', 'N/A')}
+Return on Equity: {overview_data.get('ReturnOnEquityTTM', 'N/A')}
+Beta: {overview_data.get('Beta', 'N/A')}
+52-Week High: ${overview_data.get('52WeekHigh', 'N/A')}
+52-Week Low: ${overview_data.get('52WeekLow', 'N/A')}
+Dividend Yield: {overview_data.get('DividendYield', 'N/A')}
+Analyst Target: ${overview_data.get('AnalystTargetPrice', 'N/A')}
+Quarterly Earnings Growth: {overview_data.get('QuarterlyEarningsGrowthYOY', 'N/A')}
+Quarterly Revenue Growth: {overview_data.get('QuarterlyRevenueGrowthYOY', 'N/A')}
+"""
+        
+        report_prompt = f"""
+        You are a senior financial analyst writing a comprehensive "Alpha Report".
+        Your task is to synthesize all available information into a structured, cited report.
+        USE THE REAL FINANCIAL NUMBERS PROVIDED — do NOT say data is unavailable if numbers are given.
+
+        Original User Task: {state['task']}
+        Target Symbol: {state.get('symbol', 'Unknown')}
+        Data Source: {data_source}
+        ---
+        Available Information:
+        - Web Intelligence: {web_data}
+        - Market Data & Fundamentals: {market_context}
+        - Deep-Dive Data Analysis Insights: {analysis_insights}
+        - Internal Portfolio Context: {portfolio_data}
+        ---
+
+        CRITICAL INSTRUCTIONS:
+        1. First, evaluate the "Available Information".
+           - If the Target Symbol is 'Unknown' OR if the Web Intelligence and Market Data contain no meaningful information:
+             You MUST respond with: "I am not sure about this company as I could not find sufficient data."
+             Do NOT generate the rest of the report.
+
+        2. Otherwise, generate the "Alpha Report" with the following sections:
+        
+        > [!NOTE]
+        > **Data Source**: {data_source}
+
+        ## 1. Executive Summary
+        A 2-3 sentence overview of the key findings and current situation.
+
+        ## 2. Internal Context
+        Detail the firm's current exposure:
+        - IF the firm has shares > 0: Present as a markdown table:
+          | Symbol | Shares | Avg Cost | Current Value |
+          |--------|--------|----------|---------------|
+        - IF the firm has 0 shares: State: "The firm has no current exposure to {state.get('symbol')}."
+
+        ## 3. Market Data
+        ALWAYS present as a markdown table:
+        | Metric | Value | Implication |
+        |--------|-------|-------------|
+        | Current Price | $XXX.XX | +/-X.X% vs. open |
+        | 5-Day Trend | Upward/Downward/Flat | Brief note |
+        | Volume | X.XXM | Above/Below average |
+
+        ## 4. Real-Time Intelligence
+        ### News
+        - **[Headline]** - [Brief summary] `[Source: URL]`
+        - **[Headline]** - [Brief summary] `[Source: URL]`
+
+        ### Filings (if any)
+        - **[Filing Type]** - [Brief description] `[Source: URL]`
+
+        ## 5. Sentiment Analysis
+        **Overall Sentiment:** Bullish / Bearish / Neutral
+
+        **Evidence:**
+        - [Specific fact from news/data supporting this sentiment]
+        - [Another supporting fact]
+
+        ## 6. Synthesis & Recommendations
+        Combine all information to provide actionable insights. Focus on:
+        - Key risks and opportunities
+        - Recommended actions (if any)
+        - Items to monitor
+
+        FORMATTING RULES:
+        - Use markdown headers (##, ###)
+        - Include URLs in backticks: `[Source: example.com]`
+        - Use tables for structured data
+        - Be concise but comprehensive
+        """
+        final_report = call_gemini(report_prompt)
+        return {"final_report": final_report}
+
+    # 4. Build the Graph
+    workflow = StateGraph(AgentState)
+
+    workflow.add_node("extract_symbol", extract_symbol_step)
+    workflow.add_node("web_researcher", web_research_step)
+    workflow.add_node("market_data_analyst", market_data_step)
+    workflow.add_node("portfolio_data_fetcher", portfolio_data_step)
+    workflow.add_node("transform_data", transform_data_step)
+    workflow.add_node("data_analyzer", run_data_analysis_step)
+    workflow.add_node("report_synthesizer", synthesize_report_step)
+
+    workflow.set_entry_point("extract_symbol")
+    workflow.add_edge("extract_symbol", "web_researcher")
+    workflow.add_edge("web_researcher", "market_data_analyst")
+    workflow.add_edge("market_data_analyst", "portfolio_data_fetcher")
+    workflow.add_edge("portfolio_data_fetcher", "transform_data")
+    workflow.add_edge("transform_data", "data_analyzer")
+    workflow.add_edge("data_analyzer", "report_synthesizer")
+    workflow.add_edge("report_synthesizer", END)
+
+    return workflow.compile()

agents/tool_calling_agents.py

@@ -0,0 +1,102 @@
+# agents/tool_calling_agents.py (Corrected with longer timeout)
+import httpx
+import logging
+
+# --- Configuration ---
+import os
+MCP_GATEWAY_URL = os.getenv("MCP_GATEWAY_URL", "http://127.0.0.1:8000/route_agent_request")
+
+# --- Logging Setup ---
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("ToolCallingAgents")
+
+class BaseAgent:
+    """A base class for agents that call tools via the MCP Gateway."""
+    def __init__(self):
+        # A reasonable default timeout for fast, external APIs
+        self.client = httpx.Client(timeout=30.0)
+
+    def call_mcp_gateway(self, target_service: str, payload: dict) -> dict:
+        """A standardized method to make a request to the MCP Gateway."""
+        request_body = { "target_service": target_service, "payload": payload }
+        try:
+            logger.info(f"Agent calling MCP Gateway for service '{target_service}' with payload: {payload}")
+            response = self.client.post(MCP_GATEWAY_URL, json=request_body)
+            response.raise_for_status()
+            logger.info(f"Received successful response from MCP Gateway for '{target_service}'.")
+            return response.json()
+        except httpx.HTTPStatusError as e:
+            logger.error(f"Error response {e.response.status_code} from MCP Gateway: {e.response.text}")
+            raise
+        except httpx.RequestError as e:
+            logger.error(f"Failed to connect to MCP Gateway at {MCP_GATEWAY_URL}: {e}")
+            raise
+
+class WebResearchAgent(BaseAgent):
+    """An agent specialized in performing web research using Tavily."""
+    def research(self, queries: list[str], search_depth: str = "basic") -> dict:
+        payload = { "queries": queries, "search_depth": search_depth }
+        return self.call_mcp_gateway("tavily_research", payload)
+
+class MarketDataAgent(BaseAgent):
+    """An agent specialized in fetching financial market data."""
+    def get_market_data(self, symbol: str, time_range: str = "DAILY") -> dict:
+        payload = { "symbol": symbol, "time_range": time_range }
+        return self.call_mcp_gateway("alpha_vantage_market_data", payload)
+
+    def get_company_overview(self, symbol: str) -> dict:
+        """Fetch company fundamentals (Revenue, EPS, P/E, Market Cap, etc.) - FREE tier."""
+        payload = { "symbol": symbol }
+        return self.call_mcp_gateway("alpha_vantage_overview", payload)
+
+    def get_global_quote(self, symbol: str) -> dict:
+        """Fetch real-time price quote (price, change, volume) - FREE tier."""
+        payload = { "symbol": symbol }
+        return self.call_mcp_gateway("alpha_vantage_quote", payload)
+
+class InternalPortfolioAgent(BaseAgent):
+    """An agent specialized in securely querying the internal portfolio database."""
+
+    # --- THIS IS THE FIX ---
+    def __init__(self):
+        # Override the default client with one that has a longer timeout
+        # because local LLM calls can be slow.
+        super().__init__()
+        self.client = httpx.Client(timeout=180.0) # Give it 180 seconds
+
+    def query_portfolio(self, question: str) -> dict:
+        payload = { "question": question }
+        return self.call_mcp_gateway("internal_portfolio_data", payload)
+
+# --- Example Usage (for testing this file directly) ---
+if __name__ == '__main__':
+    print("--- Testing Agents ---")
+    
+    # Make sure all your MCP servers and the gateway are running.
+    
+    # 1. Test the Web Research Agent
+    print("\n[1] Testing Web Research Agent...")
+    try:
+        web_agent = WebResearchAgent()
+        research_results = web_agent.research(queries=["What is the current market sentiment on NVIDIA?"])
+        print("Web Research Result:", research_results['status'])
+    except Exception as e:
+        print("Web Research Agent failed:", e)
+
+    # 2. Test the Market Data Agent
+    print("\n[2] Testing Market Data Agent...")
+    try:
+        market_agent = MarketDataAgent()
+        market_results = market_agent.get_intraday_data(symbol="TSLA", interval="15min")
+        print("Market Data Result:", market_results['status'])
+    except Exception as e:
+        print("Market Data Agent failed:", e)
+
+    # 3. Test the Internal Portfolio Agent
+    print("\n[3] Testing Internal Portfolio Agent...")
+    try:
+        portfolio_agent = InternalPortfolioAgent()
+        portfolio_results = portfolio_agent.query_portfolio(question="How many shares of AAPL do we own?")
+        print("Portfolio Query Result:", portfolio_results['status'])
+    except Exception as e:
+        print("Internal Portfolio Agent failed:", e)

alerts.json

@@ -0,0 +1,1202 @@
+[
+  {
+    "timestamp": "2026-02-23T16:02:50.829004",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc9 DOWN ALERT: GOOGL moved -1.88% to $150.44",
+    "details": {
+      "price": 150.44,
+      "change": -1.88,
+      "timestamp": "2026-02-23T16:02:50.828870",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T16:02:50.588234",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc9 DOWN ALERT: AMZN moved -1.08% to $152.35",
+    "details": {
+      "price": 152.35,
+      "change": -1.08,
+      "timestamp": "2026-02-23T16:02:50.588103",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T16:02:50.065711",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc9 DOWN ALERT: NVDA moved -1.53% to $875.20",
+    "details": {
+      "price": 875.2,
+      "change": -1.53,
+      "timestamp": "2026-02-23T16:02:50.065550",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T16:02:49.485653",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc8 UP ALERT: AAPL moved +0.85% to $177.37",
+    "details": {
+      "price": 177.37,
+      "change": 0.85,
+      "timestamp": "2026-02-23T16:02:49.485551",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:57:49.074923",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc9 DOWN ALERT: GOOGL moved -1.97% to $152.91",
+    "details": {
+      "price": 152.91,
+      "change": -1.97,
+      "timestamp": "2026-02-23T15:57:49.074861",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:57:48.797596",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc9 DOWN ALERT: AMZN moved -1.11% to $151.38",
+    "details": {
+      "price": 151.38,
+      "change": -1.11,
+      "timestamp": "2026-02-23T15:57:48.797543",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:57:48.528998",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc9 DOWN ALERT: MSFT moved -1.41% to $414.94",
+    "details": {
+      "price": 414.94,
+      "change": -1.41,
+      "timestamp": "2026-02-23T15:57:48.528923",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:57:48.275158",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc9 DOWN ALERT: NVDA moved -1.41% to $880.13",
+    "details": {
+      "price": 880.13,
+      "change": -1.41,
+      "timestamp": "2026-02-23T15:57:48.275098",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:57:47.997563",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc8 UP ALERT: TSLA moved +0.99% to $174.76",
+    "details": {
+      "price": 174.76,
+      "change": 0.99,
+      "timestamp": "2026-02-23T15:57:47.997453",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:57:47.646256",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc8 UP ALERT: AAPL moved +0.71% to $178.17",
+    "details": {
+      "price": 178.17,
+      "change": 0.71,
+      "timestamp": "2026-02-23T15:57:47.646185",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:52:47.082500",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc8 UP ALERT: AMZN moved +1.14% to $148.75",
+    "details": {
+      "price": 148.75,
+      "change": 1.14,
+      "timestamp": "2026-02-23T15:52:47.082369",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:52:46.818754",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc9 DOWN ALERT: MSFT moved -1.89% to $405.29",
+    "details": {
+      "price": 405.29,
+      "change": -1.89,
+      "timestamp": "2026-02-23T15:52:46.818681",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:52:46.574255",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc8 UP ALERT: NVDA moved +0.98% to $895.50",
+    "details": {
+      "price": 895.5,
+      "change": 0.98,
+      "timestamp": "2026-02-23T15:52:46.574139",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:52:46.282249",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc8 UP ALERT: TSLA moved +0.63% to $172.37",
+    "details": {
+      "price": 172.37,
+      "change": 0.63,
+      "timestamp": "2026-02-23T15:52:46.282184",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:52:45.997373",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc8 UP ALERT: AAPL moved +1.13% to $177.06",
+    "details": {
+      "price": 177.06,
+      "change": 1.13,
+      "timestamp": "2026-02-23T15:52:45.997297",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:47:45.624160",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc9 DOWN ALERT: GOOGL moved -1.62% to $151.97",
+    "details": {
+      "price": 151.97,
+      "change": -1.62,
+      "timestamp": "2026-02-23T15:47:45.624000",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:47:45.101681",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc8 UP ALERT: MSFT moved +1.15% to $410.48",
+    "details": {
+      "price": 410.48,
+      "change": 1.15,
+      "timestamp": "2026-02-23T15:47:45.101532",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:47:44.843718",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc8 UP ALERT: NVDA moved +0.60% to $877.30",
+    "details": {
+      "price": 877.3,
+      "change": 0.6,
+      "timestamp": "2026-02-23T15:47:44.843592",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:47:44.602609",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc8 UP ALERT: TSLA moved +1.26% to $177.68",
+    "details": {
+      "price": 177.68,
+      "change": 1.26,
+      "timestamp": "2026-02-23T15:47:44.602375",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:42:43.952097",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc8 UP ALERT: GOOGL moved +0.94% to $151.89",
+    "details": {
+      "price": 151.89,
+      "change": 0.94,
+      "timestamp": "2026-02-23T15:42:43.951962",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:42:43.707562",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc8 UP ALERT: AMZN moved +1.21% to $152.90",
+    "details": {
+      "price": 152.9,
+      "change": 1.21,
+      "timestamp": "2026-02-23T15:42:43.707414",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:42:43.202583",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc8 UP ALERT: NVDA moved +0.77% to $876.09",
+    "details": {
+      "price": 876.09,
+      "change": 0.77,
+      "timestamp": "2026-02-23T15:42:43.202373",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:42:42.962266",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc9 DOWN ALERT: TSLA moved -0.91% to $176.54",
+    "details": {
+      "price": 176.54,
+      "change": -0.91,
+      "timestamp": "2026-02-23T15:42:42.962126",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:42:42.631633",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc8 UP ALERT: AAPL moved +1.81% to $174.50",
+    "details": {
+      "price": 174.5,
+      "change": 1.81,
+      "timestamp": "2026-02-23T15:42:42.631549",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:37:41.694310",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc9 DOWN ALERT: MSFT moved -1.16% to $413.01",
+    "details": {
+      "price": 413.01,
+      "change": -1.16,
+      "timestamp": "2026-02-23T15:37:41.694178",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:37:41.180824",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc9 DOWN ALERT: TSLA moved -1.92% to $172.87",
+    "details": {
+      "price": 172.87,
+      "change": -1.92,
+      "timestamp": "2026-02-23T15:37:41.180584",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:32:40.534192",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc8 UP ALERT: GOOGL moved +1.18% to $148.21",
+    "details": {
+      "price": 148.21,
+      "change": 1.18,
+      "timestamp": "2026-02-23T15:32:40.534088",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:32:40.258578",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc8 UP ALERT: AMZN moved +0.77% to $147.86",
+    "details": {
+      "price": 147.86,
+      "change": 0.77,
+      "timestamp": "2026-02-23T15:32:40.258419",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:32:39.966418",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc9 DOWN ALERT: MSFT moved -0.85% to $414.63",
+    "details": {
+      "price": 414.63,
+      "change": -0.85,
+      "timestamp": "2026-02-23T15:32:39.966328",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:32:39.726474",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc9 DOWN ALERT: NVDA moved -1.08% to $895.22",
+    "details": {
+      "price": 895.22,
+      "change": -1.08,
+      "timestamp": "2026-02-23T15:32:39.726372",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:32:39.459972",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc9 DOWN ALERT: TSLA moved -0.71% to $177.73",
+    "details": {
+      "price": 177.73,
+      "change": -0.71,
+      "timestamp": "2026-02-23T15:32:39.457996",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:32:39.076694",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc8 UP ALERT: AAPL moved +1.64% to $178.46",
+    "details": {
+      "price": 178.46,
+      "change": 1.64,
+      "timestamp": "2026-02-23T15:32:39.076604",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:27:38.357548",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc9 DOWN ALERT: AMZN moved -1.04% to $150.17",
+    "details": {
+      "price": 150.17,
+      "change": -1.04,
+      "timestamp": "2026-02-23T15:27:38.357415",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:27:38.086363",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc8 UP ALERT: MSFT moved +1.88% to $408.01",
+    "details": {
+      "price": 408.01,
+      "change": 1.88,
+      "timestamp": "2026-02-23T15:27:38.086275",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:27:37.596664",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc9 DOWN ALERT: TSLA moved -1.24% to $177.29",
+    "details": {
+      "price": 177.29,
+      "change": -1.24,
+      "timestamp": "2026-02-23T15:27:37.596417",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:22:36.708382",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc8 UP ALERT: AMZN moved +0.52% to $151.54",
+    "details": {
+      "price": 151.54,
+      "change": 0.52,
+      "timestamp": "2026-02-23T15:22:36.708264",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:22:36.160011",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc8 UP ALERT: NVDA moved +1.76% to $872.56",
+    "details": {
+      "price": 872.56,
+      "change": 1.76,
+      "timestamp": "2026-02-23T15:22:36.159824",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:22:35.558072",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc8 UP ALERT: AAPL moved +1.35% to $172.60",
+    "details": {
+      "price": 172.6,
+      "change": 1.35,
+      "timestamp": "2026-02-23T15:22:35.557985",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:17:35.159665",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc8 UP ALERT: GOOGL moved +1.76% to $151.76",
+    "details": {
+      "price": 151.76,
+      "change": 1.76,
+      "timestamp": "2026-02-23T15:17:35.159511",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:17:34.866740",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc8 UP ALERT: AMZN moved +0.57% to $151.17",
+    "details": {
+      "price": 151.17,
+      "change": 0.57,
+      "timestamp": "2026-02-23T15:17:34.866570",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:17:34.599685",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc8 UP ALERT: MSFT moved +1.43% to $416.60",
+    "details": {
+      "price": 416.6,
+      "change": 1.43,
+      "timestamp": "2026-02-23T15:17:34.599540",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:17:33.726380",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc9 DOWN ALERT: AAPL moved -0.76% to $172.26",
+    "details": {
+      "price": 172.26,
+      "change": -0.76,
+      "timestamp": "2026-02-23T15:17:33.725968",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:12:33.392945",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc9 DOWN ALERT: GOOGL moved -1.15% to $150.06",
+    "details": {
+      "price": 150.06,
+      "change": -1.15,
+      "timestamp": "2026-02-23T15:12:33.392789",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:12:33.126692",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc8 UP ALERT: AMZN moved +0.73% to $150.46",
+    "details": {
+      "price": 150.46,
+      "change": 0.73,
+      "timestamp": "2026-02-23T15:12:33.126533",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:12:32.881587",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc8 UP ALERT: MSFT moved +1.86% to $408.84",
+    "details": {
+      "price": 408.84,
+      "change": 1.86,
+      "timestamp": "2026-02-23T15:12:32.881434",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:12:32.620286",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc8 UP ALERT: NVDA moved +1.18% to $869.96",
+    "details": {
+      "price": 869.96,
+      "change": 1.18,
+      "timestamp": "2026-02-23T15:12:32.620121",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:12:32.373827",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc9 DOWN ALERT: TSLA moved -0.92% to $176.23",
+    "details": {
+      "price": 176.23,
+      "change": -0.92,
+      "timestamp": "2026-02-23T15:12:32.373656",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:12:32.043015",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc9 DOWN ALERT: AAPL moved -1.89% to $176.38",
+    "details": {
+      "price": 176.38,
+      "change": -1.89,
+      "timestamp": "2026-02-23T15:12:32.042926",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:07:31.615108",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc9 DOWN ALERT: GOOGL moved -1.29% to $152.72",
+    "details": {
+      "price": 152.72,
+      "change": -1.29,
+      "timestamp": "2026-02-23T15:07:31.615039",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:07:31.382651",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc9 DOWN ALERT: AMZN moved -0.77% to $147.18",
+    "details": {
+      "price": 147.18,
+      "change": -0.77,
+      "timestamp": "2026-02-23T15:07:31.382583",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:07:31.150601",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc8 UP ALERT: MSFT moved +1.65% to $409.96",
+    "details": {
+      "price": 409.96,
+      "change": 1.65,
+      "timestamp": "2026-02-23T15:07:31.150548",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:07:30.634724",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc8 UP ALERT: TSLA moved +0.83% to $176.40",
+    "details": {
+      "price": 176.4,
+      "change": 0.83,
+      "timestamp": "2026-02-23T15:07:30.634681",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:07:30.388707",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc9 DOWN ALERT: AAPL moved -0.64% to $177.68",
+    "details": {
+      "price": 177.68,
+      "change": -0.64,
+      "timestamp": "2026-02-23T15:07:30.388036",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:02:29.732837",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc9 DOWN ALERT: AMZN moved -0.75% to $149.74",
+    "details": {
+      "price": 149.74,
+      "change": -0.75,
+      "timestamp": "2026-02-23T15:02:29.732742",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:02:29.174620",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc8 UP ALERT: NVDA moved +0.57% to $869.63",
+    "details": {
+      "price": 869.63,
+      "change": 0.57,
+      "timestamp": "2026-02-23T15:02:29.174440",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T15:02:28.921854",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc8 UP ALERT: TSLA moved +0.62% to $173.35",
+    "details": {
+      "price": 173.35,
+      "change": 0.62,
+      "timestamp": "2026-02-23T15:02:28.921569",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:57:27.984442",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc9 DOWN ALERT: AMZN moved -1.62% to $150.05",
+    "details": {
+      "price": 150.05,
+      "change": -1.62,
+      "timestamp": "2026-02-23T14:57:27.984310",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:57:27.429875",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc9 DOWN ALERT: NVDA moved -0.66% to $868.06",
+    "details": {
+      "price": 868.06,
+      "change": -0.66,
+      "timestamp": "2026-02-23T14:57:27.429712",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:57:27.185181",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc8 UP ALERT: TSLA moved +0.62% to $172.35",
+    "details": {
+      "price": 172.35,
+      "change": 0.62,
+      "timestamp": "2026-02-23T14:57:27.185028",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:57:26.839288",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc8 UP ALERT: AAPL moved +0.97% to $176.88",
+    "details": {
+      "price": 176.88,
+      "change": 0.97,
+      "timestamp": "2026-02-23T14:57:26.839206",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:52:26.559810",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc8 UP ALERT: GOOGL moved +0.78% to $148.74",
+    "details": {
+      "price": 148.74,
+      "change": 0.78,
+      "timestamp": "2026-02-23T14:52:26.559623",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:52:26.279638",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc8 UP ALERT: AMZN moved +1.64% to $151.81",
+    "details": {
+      "price": 151.81,
+      "change": 1.64,
+      "timestamp": "2026-02-23T14:52:26.279497",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:52:25.523873",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc9 DOWN ALERT: TSLA moved -0.88% to $175.64",
+    "details": {
+      "price": 175.64,
+      "change": -0.88,
+      "timestamp": "2026-02-23T14:52:25.523555",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:47:24.792504",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc8 UP ALERT: GOOGL moved +1.88% to $150.00",
+    "details": {
+      "price": 150.0,
+      "change": 1.88,
+      "timestamp": "2026-02-23T14:47:24.792424",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:47:24.541934",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc8 UP ALERT: AMZN moved +1.99% to $152.71",
+    "details": {
+      "price": 152.71,
+      "change": 1.99,
+      "timestamp": "2026-02-23T14:47:24.541832",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:47:24.259374",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc9 DOWN ALERT: MSFT moved -1.15% to $407.15",
+    "details": {
+      "price": 407.15,
+      "change": -1.15,
+      "timestamp": "2026-02-23T14:47:24.259281",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:47:23.975605",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc8 UP ALERT: NVDA moved +0.63% to $881.09",
+    "details": {
+      "price": 881.09,
+      "change": 0.63,
+      "timestamp": "2026-02-23T14:47:23.975490",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:47:23.384567",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc9 DOWN ALERT: AAPL moved -2.00% to $176.61",
+    "details": {
+      "price": 176.61,
+      "change": -2.0,
+      "timestamp": "2026-02-23T14:47:23.384494",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:42:23.055020",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc9 DOWN ALERT: GOOGL moved -1.97% to $151.26",
+    "details": {
+      "price": 151.26,
+      "change": -1.97,
+      "timestamp": "2026-02-23T14:42:23.054873",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:42:22.023432",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc9 DOWN ALERT: TSLA moved -0.61% to $172.10",
+    "details": {
+      "price": 172.1,
+      "change": -0.61,
+      "timestamp": "2026-02-23T14:42:22.023076",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:42:21.688810",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc8 UP ALERT: AAPL moved +1.35% to $177.07",
+    "details": {
+      "price": 177.07,
+      "change": 1.35,
+      "timestamp": "2026-02-23T14:42:21.688749",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:37:21.263534",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc8 UP ALERT: GOOGL moved +1.62% to $148.80",
+    "details": {
+      "price": 148.8,
+      "change": 1.62,
+      "timestamp": "2026-02-23T14:37:21.263372",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:37:20.989225",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc9 DOWN ALERT: AMZN moved -1.93% to $151.14",
+    "details": {
+      "price": 151.14,
+      "change": -1.93,
+      "timestamp": "2026-02-23T14:37:20.989089",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:37:20.487178",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc8 UP ALERT: NVDA moved +1.13% to $864.99",
+    "details": {
+      "price": 864.99,
+      "change": 1.13,
+      "timestamp": "2026-02-23T14:37:20.486972",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:37:19.893301",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc8 UP ALERT: AAPL moved +1.06% to $175.74",
+    "details": {
+      "price": 175.74,
+      "change": 1.06,
+      "timestamp": "2026-02-23T14:37:19.893173",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:32:19.561885",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc9 DOWN ALERT: GOOGL moved -0.70% to $149.11",
+    "details": {
+      "price": 149.11,
+      "change": -0.7,
+      "timestamp": "2026-02-23T14:32:19.561817",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:32:19.270259",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc9 DOWN ALERT: AMZN moved -1.51% to $147.29",
+    "details": {
+      "price": 147.29,
+      "change": -1.51,
+      "timestamp": "2026-02-23T14:32:19.270142",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:32:19.036575",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc8 UP ALERT: MSFT moved +1.31% to $410.11",
+    "details": {
+      "price": 410.11,
+      "change": 1.31,
+      "timestamp": "2026-02-23T14:32:19.036511",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:32:18.740934",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc8 UP ALERT: NVDA moved +0.60% to $874.47",
+    "details": {
+      "price": 874.47,
+      "change": 0.6,
+      "timestamp": "2026-02-23T14:32:18.740876",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:32:18.249400",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc9 DOWN ALERT: AAPL moved -0.89% to $174.08",
+    "details": {
+      "price": 174.08,
+      "change": -0.89,
+      "timestamp": "2026-02-23T14:32:18.249275",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:27:17.888026",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc9 DOWN ALERT: GOOGL moved -1.85% to $152.17",
+    "details": {
+      "price": 152.17,
+      "change": -1.85,
+      "timestamp": "2026-02-23T14:27:17.887973",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:27:17.620195",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc8 UP ALERT: AMZN moved +1.59% to $149.41",
+    "details": {
+      "price": 149.41,
+      "change": 1.59,
+      "timestamp": "2026-02-23T14:27:17.620126",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:27:17.365753",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc8 UP ALERT: MSFT moved +1.35% to $404.62",
+    "details": {
+      "price": 404.62,
+      "change": 1.35,
+      "timestamp": "2026-02-23T14:27:17.365699",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:27:17.139392",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc9 DOWN ALERT: NVDA moved -1.77% to $887.77",
+    "details": {
+      "price": 887.77,
+      "change": -1.77,
+      "timestamp": "2026-02-23T14:27:17.139331",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:27:16.887010",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc8 UP ALERT: TSLA moved +0.55% to $172.70",
+    "details": {
+      "price": 172.7,
+      "change": 0.55,
+      "timestamp": "2026-02-23T14:27:16.886915",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:22:16.284495",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc8 UP ALERT: GOOGL moved +0.61% to $152.62",
+    "details": {
+      "price": 152.62,
+      "change": 0.61,
+      "timestamp": "2026-02-23T14:22:16.284452",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:22:16.013878",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc9 DOWN ALERT: AMZN moved -0.58% to $151.58",
+    "details": {
+      "price": 151.58,
+      "change": -0.58,
+      "timestamp": "2026-02-23T14:22:16.013802",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:22:15.740628",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc8 UP ALERT: MSFT moved +1.52% to $412.66",
+    "details": {
+      "price": 412.66,
+      "change": 1.52,
+      "timestamp": "2026-02-23T14:22:15.740481",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:22:15.202294",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc8 UP ALERT: TSLA moved +1.30% to $175.57",
+    "details": {
+      "price": 175.57,
+      "change": 1.3,
+      "timestamp": "2026-02-23T14:22:15.202058",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:22:14.919608",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc9 DOWN ALERT: AAPL moved -1.62% to $176.88",
+    "details": {
+      "price": 176.88,
+      "change": -1.62,
+      "timestamp": "2026-02-23T14:22:14.919515",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:17:14.582764",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc8 UP ALERT: GOOGL moved +1.43% to $148.96",
+    "details": {
+      "price": 148.96,
+      "change": 1.43,
+      "timestamp": "2026-02-23T14:17:14.582640",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:17:14.333919",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc9 DOWN ALERT: AMZN moved -0.75% to $149.70",
+    "details": {
+      "price": 149.7,
+      "change": -0.75,
+      "timestamp": "2026-02-23T14:17:14.333765",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:17:14.072576",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc8 UP ALERT: MSFT moved +1.65% to $402.59",
+    "details": {
+      "price": 402.59,
+      "change": 1.65,
+      "timestamp": "2026-02-23T14:17:14.072441",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:17:13.281986",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc9 DOWN ALERT: TSLA moved -0.93% to $173.77",
+    "details": {
+      "price": 173.77,
+      "change": -0.93,
+      "timestamp": "2026-02-23T14:17:13.281832",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:17:12.980202",
+    "type": "MARKET",
+    "symbol": "AAPL",
+    "message": "\ud83d\udcc8 UP ALERT: AAPL moved +1.53% to $264.58",
+    "details": {
+      "price": 264.58,
+      "change": 1.535,
+      "timestamp": "2026-02-23T14:17:12.980028",
+      "source": "Alpha Vantage GLOBAL_QUOTE"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:12:12.686003",
+    "type": "MARKET",
+    "symbol": "GOOGL",
+    "message": "\ud83d\udcc9 DOWN ALERT: GOOGL moved -0.81% to $147.37",
+    "details": {
+      "price": 147.37,
+      "change": -0.81,
+      "timestamp": "2026-02-23T14:12:12.685880",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:12:12.443587",
+    "type": "MARKET",
+    "symbol": "AMZN",
+    "message": "\ud83d\udcc8 UP ALERT: AMZN moved +2.56% to $210.11",
+    "details": {
+      "price": 210.11,
+      "change": 2.5627,
+      "timestamp": "2026-02-23T14:12:12.443459",
+      "source": "Alpha Vantage GLOBAL_QUOTE"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:12:12.160320",
+    "type": "MARKET",
+    "symbol": "MSFT",
+    "message": "\ud83d\udcc9 DOWN ALERT: MSFT moved -1.41% to $406.68",
+    "details": {
+      "price": 406.68,
+      "change": -1.41,
+      "timestamp": "2026-02-23T14:12:12.160181",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:12:11.888936",
+    "type": "MARKET",
+    "symbol": "NVDA",
+    "message": "\ud83d\udcc9 DOWN ALERT: NVDA moved -1.95% to $864.92",
+    "details": {
+      "price": 864.92,
+      "change": -1.95,
+      "timestamp": "2026-02-23T14:12:11.888786",
+      "source": "Mocked (API limit reached)"
+    }
+  },
+  {
+    "timestamp": "2026-02-23T14:12:11.641915",
+    "type": "MARKET",
+    "symbol": "TSLA",
+    "message": "\ud83d\udcc8 UP ALERT: TSLA moved +1.01% to $173.40",
+    "details": {
+      "price": 173.4,
+      "change": 1.01,
+      "timestamp": "2026-02-23T14:12:11.641802",
+      "source": "Mocked (API limit reached)"
+    }
+  }
+]

alphavantage_mcp.py

@@ -0,0 +1,417 @@
+# alphavantage_mcp.py (Corrected for Free Tier)
+from fastapi import FastAPI, HTTPException
+import uvicorn
+import os
+from dotenv import load_dotenv
+from alpha_vantage.timeseries import TimeSeries
+import logging
+
+# --- Configuration ---
+load_dotenv()
+
+# --- Logging Setup (MUST be before we use logger) ---
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("AlphaVantage_MCP_Server")
+
+# --- Get API Key ---
+ALPHA_VANTAGE_API_KEY = os.getenv("ALPHA_VANTAGE_API_KEY")
+
+# Fallback: Try to read from Streamlit secrets file (for cloud deployment)
+if not ALPHA_VANTAGE_API_KEY:
+    try:
+        import toml
+        secrets_path = os.path.join(os.path.dirname(__file__), ".streamlit", "secrets.toml")
+        if os.path.exists(secrets_path):
+            secrets = toml.load(secrets_path)
+            ALPHA_VANTAGE_API_KEY = secrets.get("ALPHA_VANTAGE_API_KEY")
+            logger.info("Loaded ALPHA_VANTAGE_API_KEY from .streamlit/secrets.toml")
+    except Exception as e:
+        logger.warning(f"Could not load from secrets.toml: {e}")
+
+if not ALPHA_VANTAGE_API_KEY:
+    logger.warning("ALPHA_VANTAGE_API_KEY not found in environment. Market data features will fail.")
+else:
+    logger.info(f"ALPHA_VANTAGE_API_KEY found: {ALPHA_VANTAGE_API_KEY[:4]}...")
+
+# --- FastAPI App & Alpha Vantage Client ---
+app = FastAPI(title="Aegis Alpha Vantage MCP Server")
+ts = TimeSeries(key=ALPHA_VANTAGE_API_KEY, output_format='json')
+
+@app.post("/market_data")
+async def get_market_data(payload: dict):
+    """
+    Fetches market data using the Alpha Vantage API.
+    Supports both intraday and daily data based on time_range.
+    Expects a payload like:
+    {
+        "symbol": "NVDA",
+        "time_range": "INTRADAY" | "1D" | "3D" | "1W" | "1M" | "3M" | "1Y"
+    }
+    """
+    symbol = payload.get("symbol")
+    time_range = payload.get("time_range", "INTRADAY")
+
+    if not symbol:
+        logger.error("Validation Error: 'symbol' is required.")
+        raise HTTPException(status_code=400, detail="'symbol' is required.")
+
+    logger.info(f"Received market data request for symbol: {symbol}, time_range: {time_range}")
+
+    try:
+        # Route to appropriate API based on time range
+        if time_range == "INTRADAY":
+            # Intraday data (last 4-6 hours, 5-min intervals)
+            data, meta_data = ts.get_intraday(symbol=symbol, interval="5min", outputsize='compact')
+            logger.info(f"Successfully retrieved intraday data for {symbol}")
+            meta_data["Source"] = "Real API (Alpha Vantage)"
+        else:
+            # Daily data for historical ranges
+            data, meta_data = ts.get_daily(symbol=symbol, outputsize='full')
+            logger.info(f"Successfully retrieved daily data for {symbol}")
+            
+            # Filter data based on time range
+            data = filter_data_by_time_range(data, time_range)
+            logger.info(f"Filtered to {len(data)} data points for time_range={time_range}")
+            meta_data["Source"] = "Real API (Alpha Vantage)"
+        
+        return {"status": "success", "data": data, "meta_data": meta_data}
+
+    except Exception as e:
+        # Catch ALL exceptions to ensure fallback works
+        logger.error(f"Alpha Vantage API error for symbol {symbol}: {e}")
+        logger.warning(f"Triggering MOCK DATA fallback for {symbol} due to error.")
+        
+        import random
+        import math
+        from datetime import datetime, timedelta
+        
+        # Seed randomness with symbol AND date to ensure it changes daily
+        # But stays consistent within the same day
+        today_str = datetime.now().strftime("%Y-%m-%d %H:%M")
+        seed_value = f"{symbol}_{today_str}"
+        random.seed(seed_value)
+        
+        mock_data = {}
+        current_time = datetime.now()
+        
+        # Generate unique base price
+        symbol_hash = sum(ord(c) for c in symbol)
+        base_price = float(symbol_hash % 500) + 50
+        
+        # Force distinct start prices for common stocks
+        if "AAPL" in symbol: base_price = 150.0
+        if "TSLA" in symbol: base_price = 250.0
+        if "NVDA" in symbol: base_price = 450.0
+        if "MSFT" in symbol: base_price = 350.0
+        if "GOOG" in symbol: base_price = 130.0
+        if "AMZN" in symbol: base_price = 140.0
+        
+        # Add some daily variation to base price
+        daily_noise = (hash(today_str) % 100) / 10.0  # -5 to +5 variation
+        base_price += daily_noise
+        
+        trend_direction = 1 if symbol_hash % 2 == 0 else -1
+        volatility = base_price * 0.02
+        trend_strength = base_price * 0.001
+        current_price = base_price
+        
+        # Determine number of data points based on time range
+        if time_range == "INTRADAY":
+            num_points = 100
+            time_delta = timedelta(minutes=5)
+        elif time_range in ["1D", "3D"]:
+            num_points = int(time_range[0]) if time_range != "1D" else 1
+            time_delta = timedelta(days=1)
+        elif time_range == "1W":
+            num_points = 7
+            time_delta = timedelta(days=1)
+        elif time_range == "1M":
+            num_points = 30
+            time_delta = timedelta(days=1)
+        elif time_range == "3M":
+            num_points = 90
+            time_delta = timedelta(days=1)
+        elif time_range == "1Y":
+            num_points = 365
+            time_delta = timedelta(days=1)
+        else:
+            num_points = 100
+            time_delta = timedelta(minutes=5)
+        
+        for i in range(num_points):
+            noise = random.uniform(-volatility, volatility)
+            cycle_1 = (base_price * 0.02) * math.sin(i / 8.0)
+            cycle_2 = (base_price * 0.01) * math.sin(i / 3.0)
+            change = noise + (trend_direction * trend_strength)
+            current_price += change
+            final_price = current_price + cycle_1 + cycle_2
+            final_price = max(1.0, final_price)
+            
+            t = current_time - (time_delta * (num_points - i - 1))
+            
+            # Format timestamp based on data type
+            if time_range == "INTRADAY":
+                timestamp_str = t.strftime("%Y-%m-%d %H:%M:%S")
+            else:
+                timestamp_str = t.strftime("%Y-%m-%d")
+            
+            mock_data[timestamp_str] = {
+                "1. open": str(round(final_price, 2)),
+                "2. high": str(round(final_price + (volatility * 0.3), 2)),
+                "3. low": str(round(final_price - (volatility * 0.3), 2)),
+                "4. close": str(round(final_price + random.uniform(-0.1, 0.1), 2)),
+                "5. volume": str(int(random.uniform(100000, 5000000)))
+            }
+        
+        return {
+            "status": "success", 
+            "data": mock_data, 
+            "meta_data": {
+                "Information": f"Mock Data ({time_range}) - API Limit/Error",
+                "Source": "Simulated (Fallback)"
+            }
+        }
+
+
+def filter_data_by_time_range(data: dict, time_range: str) -> dict:
+    """Filter daily data to the specified time range."""
+    from datetime import datetime, timedelta
+    
+    # Map time ranges to days
+    range_map = {
+        "1D": 1,
+        "3D": 3,
+        "1W": 7,
+        "1M": 30,
+        "3M": 90,
+        "1Y": 365
+    }
+    
+    days = range_map.get(time_range, 30)
+    cutoff_date = datetime.now() - timedelta(days=days)
+    
+    # Filter data
+    filtered = {}
+    for timestamp_str, values in data.items():
+        try:
+            timestamp = datetime.strptime(timestamp_str, "%Y-%m-%d")
+            if timestamp >= cutoff_date:
+                filtered[timestamp_str] = values
+        except:
+            # If parsing fails, include the data point
+            filtered[timestamp_str] = values
+    
+    return filtered
+        
+
+@app.post("/company_overview")
+async def get_company_overview(payload: dict):
+    """
+    Fetches company fundamentals from Alpha Vantage OVERVIEW endpoint.
+    Returns: Revenue, EPS, P/E, Market Cap, Margins, Dividend Yield, etc.
+    Expects: {"symbol": "AAPL"}
+    """
+    import requests as req
+
+    symbol = payload.get("symbol")
+    if not symbol:
+        raise HTTPException(status_code=400, detail="'symbol' is required.")
+
+    logger.info(f"Fetching company overview for {symbol}")
+
+    try:
+        url = "https://www.alphavantage.co/query"
+        params = {
+            "function": "OVERVIEW",
+            "symbol": symbol,
+            "apikey": ALPHA_VANTAGE_API_KEY,
+        }
+        resp = req.get(url, params=params, timeout=15)
+        resp.raise_for_status()
+        data = resp.json()
+
+        # Check for error/empty response
+        if "Symbol" not in data:
+            raise ValueError(f"No overview data returned: {data.get('Note', data.get('Information', 'Unknown error'))}")
+
+        logger.info(f"Successfully retrieved company overview for {symbol}")
+        return {
+            "status": "success",
+            "source": "Alpha Vantage OVERVIEW",
+            "data": {
+                "Name": data.get("Name", symbol),
+                "Symbol": data.get("Symbol", symbol),
+                "Description": data.get("Description", ""),
+                "Sector": data.get("Sector", ""),
+                "Industry": data.get("Industry", ""),
+                "MarketCapitalization": data.get("MarketCapitalization", "N/A"),
+                "PERatio": data.get("PERatio", "N/A"),
+                "EPS": data.get("EPS", "N/A"),
+                "RevenuePerShareTTM": data.get("RevenuePerShareTTM", "N/A"),
+                "RevenueTTM": data.get("RevenueTTM", "N/A"),
+                "GrossProfitTTM": data.get("GrossProfitTTM", "N/A"),
+                "ProfitMargin": data.get("ProfitMargin", "N/A"),
+                "OperatingMarginTTM": data.get("OperatingMarginTTM", "N/A"),
+                "ReturnOnEquityTTM": data.get("ReturnOnEquityTTM", "N/A"),
+                "DividendPerShare": data.get("DividendPerShare", "N/A"),
+                "DividendYield": data.get("DividendYield", "N/A"),
+                "Beta": data.get("Beta", "N/A"),
+                "52WeekHigh": data.get("52WeekHigh", "N/A"),
+                "52WeekLow": data.get("52WeekLow", "N/A"),
+                "50DayMovingAverage": data.get("50DayMovingAverage", "N/A"),
+                "200DayMovingAverage": data.get("200DayMovingAverage", "N/A"),
+                "SharesOutstanding": data.get("SharesOutstanding", "N/A"),
+                "BookValue": data.get("BookValue", "N/A"),
+                "PriceToBookRatio": data.get("PriceToBookRatio", "N/A"),
+                "TrailingPE": data.get("TrailingPE", "N/A"),
+                "ForwardPE": data.get("ForwardPE", "N/A"),
+                "AnalystTargetPrice": data.get("AnalystTargetPrice", "N/A"),
+                "AnalystRatingBuy": data.get("AnalystRatingBuy", "N/A"),
+                "AnalystRatingHold": data.get("AnalystRatingHold", "N/A"),
+                "AnalystRatingSell": data.get("AnalystRatingSell", "N/A"),
+                "QuarterlyEarningsGrowthYOY": data.get("QuarterlyEarningsGrowthYOY", "N/A"),
+                "QuarterlyRevenueGrowthYOY": data.get("QuarterlyRevenueGrowthYOY", "N/A"),
+            }
+        }
+
+    except Exception as e:
+        logger.error(f"Company overview error for {symbol}: {e}")
+        logger.warning(f"Returning fallback overview for {symbol}")
+        # Simulate realistic fallback data when API limit is hit
+        base_mc = 10000000000  # 10B default
+        base_rev = 5000000000
+        base_eps = 2.50
+        base_pe = 15.0
+        
+        if "AAPL" in symbol:
+            base_mc, base_rev, base_eps, base_pe = 3000000000000, 380000000000, 6.42, 28.5
+            name, sector = "Apple Inc.", "Technology"
+        elif "MSFT" in symbol:
+            base_mc, base_rev, base_eps, base_pe = 3100000000000, 240000000000, 11.50, 35.2
+            name, sector = "Microsoft Corporation", "Technology"
+        elif "NVDA" in symbol:
+            base_mc, base_rev, base_eps, base_pe = 2200000000000, 60000000000, 12.30, 75.0
+            name, sector = "NVIDIA Corporation", "Technology"
+        elif "TSLA" in symbol:
+            base_mc, base_rev, base_eps, base_pe = 600000000000, 95000000000, 3.12, 45.0
+            name, sector = "Tesla Inc.", "Consumer Discretionary"
+        elif "AMZN" in symbol:
+            base_mc, base_rev, base_eps, base_pe = 1800000000000, 570000000000, 2.90, 60.0
+            name, sector = "Amazon.com Inc.", "Consumer Discretionary"
+        else:
+            name = symbol
+            sector = "General Market"
+            
+        import random
+        # Add tiny randomization to make it look alive
+        mc = base_mc * random.uniform(0.98, 1.02)
+        rev = base_rev * random.uniform(0.98, 1.02)
+        
+        return {
+            "status": "success",
+            "source": "Mocked (API limit reached)",
+            "data": {
+                "Name": name, "Symbol": symbol,
+                "Description": f"{name} is a major player in the {sector} sector. (Note: Data mocked due to Alpha Vantage API limits).",
+                "Sector": sector, "Industry": sector,
+                "MarketCapitalization": str(int(mc)), 
+                "PERatio": f"{base_pe:.1f}", 
+                "EPS": f"{base_eps:.2f}",
+                "RevenueTTM": str(int(rev)), 
+                "GrossProfitTTM": str(int(rev * 0.4)),
+                "ProfitMargin": "0.15",
+                "OperatingMarginTTM": "0.20", 
+                "ReturnOnEquityTTM": "0.25",
+                "DividendYield": "0.015", 
+                "Beta": "1.1",
+                "52WeekHigh": "150.0", 
+                "52WeekLow": "100.0",
+                "AnalystTargetPrice": "160.0",
+                "QuarterlyEarningsGrowthYOY": "0.10",
+                "QuarterlyRevenueGrowthYOY": "0.08",
+            }
+        }
+
+
+@app.post("/global_quote")
+async def get_global_quote(payload: dict):
+    """
+    Fetches real-time quote from Alpha Vantage GLOBAL_QUOTE endpoint.
+    Returns: latest price, change, change%, volume, etc.
+    Expects: {"symbol": "AAPL"}
+    """
+    import requests as req
+
+    symbol = payload.get("symbol")
+    if not symbol:
+        raise HTTPException(status_code=400, detail="'symbol' is required.")
+
+    logger.info(f"Fetching global quote for {symbol}")
+
+    try:
+        url = "https://www.alphavantage.co/query"
+        params = {
+            "function": "GLOBAL_QUOTE",
+            "symbol": symbol,
+            "apikey": ALPHA_VANTAGE_API_KEY,
+        }
+        resp = req.get(url, params=params, timeout=15)
+        resp.raise_for_status()
+        data = resp.json()
+
+        quote = data.get("Global Quote", {})
+        if not quote:
+            raise ValueError(f"No quote data returned: {data.get('Note', 'Unknown error')}")
+
+        logger.info(f"Successfully retrieved global quote for {symbol}")
+        return {
+            "status": "success",
+            "source": "Alpha Vantage GLOBAL_QUOTE",
+            "data": {
+                "symbol": quote.get("01. symbol", symbol),
+                "price": quote.get("05. price", "0"),
+                "open": quote.get("02. open", "0"),
+                "high": quote.get("03. high", "0"),
+                "low": quote.get("04. low", "0"),
+                "volume": quote.get("06. volume", "0"),
+                "previous_close": quote.get("08. previous close", "0"),
+                "change": quote.get("09. change", "0"),
+                "change_percent": quote.get("10. change percent", "0%"),
+            }
+        }
+
+    except Exception as e:
+        logger.error(f"Global quote error for {symbol}: {e}")
+        import random
+        base_price = 150.0
+        if "AAPL" in symbol: base_price = 175.50
+        elif "MSFT" in symbol: base_price = 410.20
+        elif "NVDA" in symbol: base_price = 880.00
+        elif "TSLA" in symbol: base_price = 175.00
+        
+        price = base_price * random.uniform(0.98, 1.02)
+        change = price * random.uniform(-0.02, 0.02)
+        
+        return {
+            "status": "success",
+            "source": "Mocked (API limit reached)",
+            "data": {
+                "symbol": symbol, 
+                "price": f"{price:.2f}", 
+                "open": f"{(price - change):.2f}",
+                "high": f"{(price * 1.01):.2f}",
+                "low": f"{(price * 0.99):.2f}",
+                "change": f"{change:.2f}",
+                "change_percent": f"{(change / base_price * 100):.2f}%", 
+                "volume": str(int(random.uniform(1000000, 50000000))),
+            }
+        }
+
+
+@app.get("/")
+def read_root():
+    return {"message": "Aegis Alpha Vantage MCP Server is operational."}
+
+# --- Main Execution ---
+if __name__ == "__main__":
+    uvicorn.run(app, host="127.0.0.1", port=8002)

app.py

@@ -0,0 +1,471 @@
+import streamlit as st
+import sys
+import os
+import httpx
+import pandas as pd
+import json
+import time
+from datetime import datetime
+import base64
+import subprocess
+
+# --- Path Setup ---
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '.')))
+
+# --- Configuration ---
+WATCHLIST_FILE = "watchlist.json"
+ALERTS_FILE = "alerts.json"
+
+# --- Page Configuration ---
+st.set_page_config(
+    page_title="Sentinel - AI Financial Intelligence",
+    page_icon="🛡️",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# --- Custom CSS ---
+def load_css(file_name):
+    with open(file_name) as f:
+        st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True)
+
+load_css("style.css")
+
+# --- Auto-Start Backend Services ---
+# --- Auto-Start Backend Services ---
+# --- Auto-Start Backend Services ---
+@st.cache_resource
+def start_background_services():
+    # Managed by main.py in production
+    pass
+
+# Trigger startup (cached, runs once per container)
+start_background_services()
+
+# --- Helper Functions ---
+@st.cache_data(ttl=60)
+def check_server_status():
+    # All services are mounted as sub-apps under the gateway on port 8000
+    urls = {
+        "Gateway": "http://127.0.0.1:8000/",
+        "Tavily": "http://127.0.0.1:8000/tavily/",
+        "Alpha Vantage": "http://127.0.0.1:8000/alphavantage/",
+        "Private DB": "http://127.0.0.1:8000/private/",
+    }
+    statuses = {}
+    with httpx.Client(timeout=3.0) as client:
+        for name, url in urls.items():
+            try:
+                response = client.get(url)
+                statuses[name] = "✅ Online" if response.status_code == 200 else "⚠️ Error"
+            except: statuses[name] = "❌ Offline"
+    return statuses
+
+def load_watchlist():
+    if not os.path.exists(WATCHLIST_FILE): return []
+    try:
+        with open(WATCHLIST_FILE, 'r') as f:
+            return json.load(f)
+    except:
+        return []
+
+def save_watchlist(watchlist):
+    with open(WATCHLIST_FILE, 'w') as f: json.dump(watchlist, f)
+
+def load_alerts():
+    if not os.path.exists(ALERTS_FILE): return []
+    try:
+        with open(ALERTS_FILE, 'r') as f:
+            return json.load(f)
+    except:
+        return []
+
+def get_base64_image(image_path):
+    try:
+        with open(image_path, "rb") as img_file:
+            return base64.b64encode(img_file.read()).decode()
+    except Exception:
+        return ""
+
+# --- Session State ---
+if 'page' not in st.session_state:
+    st.session_state.page = 'home'
+if 'analysis_complete' not in st.session_state:
+    st.session_state.analysis_complete = False
+if 'final_state' not in st.session_state:
+    st.session_state.final_state = None
+if 'error_message' not in st.session_state:
+    st.session_state.error_message = None
+
+# --- UI Components ---
+
+def render_sidebar():
+    with st.sidebar:
+        # Logo Area
+        logo_base64 = get_base64_image("assets/logo.png")
+        if logo_base64:
+            st.markdown(f"""
+            <div style="text-align: center; margin-bottom: 2rem;">
+                <img src="data:image/png;base64,{logo_base64}" style="width: 80px; height: 80px; margin-bottom: 10px;">
+                <h2 style="margin:0; font-size: 1.5rem;">SENTINEL</h2>
+                <p style="color: var(--text-secondary); font-size: 0.8rem;">AI Financial Intelligence</p>
+            </div>
+            """, unsafe_allow_html=True)
+        
+        # Navigation
+        if st.button("🏠 Home", use_container_width=True):
+            st.session_state.page = 'home'
+            st.rerun()
+        
+        if st.button("⚡ Analysis Console", use_container_width=True):
+            st.session_state.page = 'analysis'
+            st.rerun()
+
+        st.markdown("---")
+        st.markdown("### 🧩 Add-On Features")
+
+        if st.button("📄 Research Reports", use_container_width=True):
+            st.session_state.page = 'research_report'
+            st.rerun()
+
+        if st.button("💼 Portfolio Analyzer", use_container_width=True):
+            st.session_state.page = 'portfolio_analyzer'
+            st.rerun()
+
+        if st.button("🎙️ Earnings Sentiment", use_container_width=True):
+            st.session_state.page = 'earnings_sentiment'
+            st.rerun()
+
+        if st.button("📬 Weekly Digest", use_container_width=True):
+            st.session_state.page = 'weekly_digest'
+            st.rerun()
+
+        if st.button("🌍 Macro Impact", use_container_width=True):
+            st.session_state.page = 'macro_impact'
+            st.rerun()
+
+        st.markdown("---")
+        
+        # Settings - Completely Redesigned
+        st.markdown("### 🎯 Intelligence Configuration")
+        
+        # Analysis Depth
+        st.select_slider(
+            "Analysis Depth",
+            options=["Quick Scan", "Standard", "Deep Dive", "Comprehensive"],
+            value="Standard"
+        )
+        
+        # Risk Profile
+        st.selectbox(
+            "Risk Tolerance",
+            ["Conservative", "Moderate", "Aggressive", "Custom"],
+            help="Adjusts recommendation thresholds"
+        )
+        
+        # Time Horizon
+        st.radio(
+            "Investment Horizon",
+            ["Short-term (< 1 year)", "Medium-term (1-5 years)", "Long-term (5+ years)"],
+            index=1
+        )
+        
+        # Market Sentiment Tracking
+        st.toggle("Track Market Sentiment", value=True, help="Include social media and news sentiment analysis")
+        
+        st.markdown("---")
+        
+        # System Status
+        with st.expander("📡 System Status", expanded=False):
+            server_statuses = check_server_status()
+            for name, status in server_statuses.items():
+                dot_class = "status-ok" if status == "✅ Online" else "status-err"
+                st.markdown(f"""
+                <div style="display: flex; align-items: center; justify-content: space-between; margin-bottom: 8px;">
+                    <span style="font-size: 0.9rem;">{name}</span>
+                    <div><span class="status-dot {dot_class}"></span><span style="font-size: 0.8rem; color: var(--text-secondary);">{status.split(' ')[1]}</span></div>
+                </div>
+                """, unsafe_allow_html=True)
+
+        # Watchlist
+        with st.expander("🛡️ Watchlist", expanded=False):
+            watchlist = load_watchlist()
+            new_symbol = st.text_input("Add Symbol:", placeholder="e.g. MSFT").upper()
+            if st.button("Add"):
+                if new_symbol and new_symbol not in watchlist:
+                    watchlist.append(new_symbol)
+                    save_watchlist(watchlist)
+                    st.rerun()
+            
+            if watchlist:
+                st.markdown("---")
+                for symbol in watchlist:
+                    col1, col2 = st.columns([3, 1])
+                    col1.markdown(f"**{symbol}**")
+                    if col2.button("❌", key=f"del_{symbol}"):
+                        watchlist.remove(symbol)
+                        save_watchlist(watchlist)
+                        st.rerun()
+
+def render_home():
+    # Auto-refresh logic (Every 10s)
+    if 'last_refresh_home' not in st.session_state:
+        st.session_state.last_refresh_home = time.time()
+
+    if time.time() - st.session_state.last_refresh_home > 10:
+        st.session_state.last_refresh_home = time.time()
+        st.rerun()
+
+    # Hero Section with Logo
+    logo_base64 = get_base64_image("assets/logo.png")
+    
+    if logo_base64:
+        st.markdown(f"""
+        <div class="hero-container">
+            <div style="margin-bottom: 24px;">
+                <span class="theme-pill">🌟 SENTINEL V2.0 BETA</span>
+            </div>
+            <div style="display: flex; align-items: center; justify-content: center; gap: 24px; margin-bottom: 1.5rem;">
+                <img src="data:image/png;base64,{logo_base64}" style="width: 85px; height: 85px; filter: drop-shadow(0 0 20px rgba(167, 139, 250, 0.4));">
+                <h1 class="hero-title" style="margin: 0;">Sentinel AI<br>Financial Intelligence</h1>
+            </div>
+            <p class="hero-subtitle">
+                Transform raw market data into actionable business insights with the power of ultra-fast AI.
+                Analyze stocks, macro news, and private portfolios through autonomous agentic workflows.
+            </p>
+        </div>
+        """, unsafe_allow_html=True)
+    else:
+        # Fallback without logo
+        st.markdown("""
+        <div class="hero-container">
+            <div style="margin-bottom: 24px;">
+                <span class="theme-pill">🌟 SENTINEL V2.0 BETA</span>
+            </div>
+            <h1 class="hero-title">Sentinel AI<br>Financial Intelligence</h1>
+            <p class="hero-subtitle">
+                Transform raw market data into actionable business insights with the power of ultra-fast AI.
+                Analyze stocks, macro news, and private portfolios through autonomous agentic workflows.
+            </p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    col1, col2, col3 = st.columns([1, 1.5, 1])
+    with col2:
+        if st.button("🚀 INITIATE ANALYSIS SEQUENCE", type="primary", use_container_width=True):
+            st.session_state.page = 'analysis'
+            st.rerun()
+
+    # Feature Cards Base
+    st.markdown("""
+    <div style="margin-top: 4rem;">
+        <h3 style="text-align: center; margin-bottom: 1rem; color: #fff;">Core Subsystems Active</h3>
+        <div class="feature-grid">
+            <div class="feature-card">
+                <div class="feature-icon">🧠</div>
+                <div class="feature-title">Agentic Reasoning</div>
+                <div class="feature-desc">
+                    Our AI automatically understands market structures, identifies patterns, and generates insights via LangChain without manual oversight.
+                </div>
+            </div>
+            <div class="feature-card">
+                <div class="feature-icon">�</div>
+                <div class="feature-title">Live Data Injection</div>
+                <div class="feature-desc">
+                    Direct real-time connections to Wall Street terminals, Alpha Vantage, and global news aggregators via blazing fast MCP microservices.
+                </div>
+            </div>
+            <div class="feature-card">
+                <div class="feature-icon">🛡️</div>
+                <div class="feature-title">Private Execution</div>
+                <div class="feature-desc">
+                    Analyze completely private Brokerage statements directly on your local machine using encrypted LangGraph vector embeddings.
+                </div>
+            </div>
+        </div>
+    </div>
+    """, unsafe_allow_html=True)
+
+    # --- Live Wire on Home Page ---
+    st.markdown("---")
+    st.markdown("### 🚨 Live Wire Trending")
+    
+    alerts_container = st.container()
+    alerts = load_alerts()
+    if not alerts:
+        alerts_container.caption("No active alerts in feed.")
+    else:
+        for alert in reversed(alerts[-10:]): # Show last 10 on home
+            alert_type = alert.get("type", "INFO")
+            css_class = "alert-market" if alert_type == "MARKET" else "alert-news" if alert_type == "NEWS" else ""
+            icon = "📉" if alert_type == "MARKET" else "📰"
+            timestamp = datetime.fromisoformat(alert.get("timestamp", datetime.now().isoformat())).strftime("%H:%M:%S")
+            
+            html = f"""
+            <div class="alert-card {css_class}">
+                <div class="alert-header">
+                    <span>{icon} {alert.get("symbol")}</span>
+                    <span>{timestamp}</span>
+                </div>
+                <div class="alert-body">
+                    {alert.get("message")}
+                </div>
+            </div>
+            """
+            alerts_container.markdown(html, unsafe_allow_html=True)
+
+    # Footer
+    st.markdown("<br><br><br>", unsafe_allow_html=True)
+    st.markdown("""
+    <div style="text-align: center; color: var(--text-secondary); font-size: 0.9rem;">
+        Powered by <b>Google Gemini</b> • Built with <b>LangGraph</b> • Designed with <b>Streamlit</b>
+    </div>
+    """, unsafe_allow_html=True)
+
+def render_analysis():
+    st.markdown("## ⚡ Intelligence Directive")
+    
+    # Error Display
+    if st.session_state.error_message:
+        st.error(st.session_state.error_message)
+        if st.button("Dismiss Error"):
+            st.session_state.error_message = None
+            st.rerun()
+
+    col_main, col_alerts = st.columns([3, 1.2])
+
+    with col_main:
+        with st.form("research_form", clear_on_submit=False):
+            task_input = st.text_area("Enter directive:", placeholder="e.g., Analyze the recent volatility for Tesla ($TSLA) and summarize news.", height=100)
+            submitted = st.form_submit_button("EXECUTE ANALYSIS", use_container_width=True)
+
+        if submitted and task_input:
+            st.session_state.error_message = None
+            server_statuses = check_server_status()
+            all_online = all(s == "✅ Online" for s in server_statuses.values())
+            
+            if not all_online:
+                st.error("SYSTEM HALTED: Core services offline. Check sidebar status.")
+            else:
+                with st.status("🚀 SENTINEL ORCHESTRATOR ENGAGED...", expanded=True) as status:
+                    try:
+                        from agents.orchestrator_v3 import get_orchestrator
+                        # Use default provider or env var
+                        orchestrator = get_orchestrator(llm_provider="gemini")
+                        
+                        final_state_result = {}
+                        for event in orchestrator.stream({"task": task_input}):
+                            agent_name = list(event.keys())[0]
+                            state_update = list(event.values())[0]
+                            final_state_result.update(state_update)
+                            
+                            status.write(f"🛡️ Agent Active: {agent_name}...")
+                            
+                        status.update(label="✅ Analysis Complete!", state="complete", expanded=False)
+                        st.session_state.final_state = final_state_result
+                        st.session_state.analysis_complete = True
+                        st.rerun()
+                    except Exception as e:
+                        status.update(label="❌ System Failure", state="error")
+                        st.session_state.error_message = f"RUNTIME ERROR: {e}"
+                        st.rerun()
+
+        if st.session_state.analysis_complete:
+            final_state = st.session_state.final_state
+            symbol = final_state.get('symbol', 'N/A') if final_state else 'N/A'
+            
+            st.markdown(f"### 📝 Report: {symbol}")
+            
+            # Executive Summary
+            st.info(final_state.get("final_report", "No report generated."))
+            
+            # Deep-Dive Insights
+            with st.expander("🔍 Deep-Dive Insights", expanded=True):
+                insights = final_state.get("analysis_results", {}).get("insights")
+                if insights: st.markdown(insights)
+                else: st.warning("No deep-dive insights available.")
+            
+            # Charts
+            with st.expander("📊 Market Telemetry"):
+                charts = final_state.get("analysis_results", {}).get("charts", [])
+                if charts:
+                    for chart in charts:
+                        st.plotly_chart(chart, use_container_width=True)
+                else:
+                    st.caption("No telemetry data available.")
+            
+            # Raw Data
+            with st.expander("💾 Raw Intelligence Logs"):
+                tab1, tab2, tab3 = st.tabs(["Web Intelligence", "Market Data", "Internal Portfolio"])
+                with tab1: st.json(final_state.get('web_research_results', '{}'))
+                with tab2: st.json(final_state.get('market_data_results', '{}'))
+                with tab3: st.json(final_state.get('portfolio_data_results', '{}'))
+
+            if st.button("🛡️ New Analysis"):
+                st.session_state.analysis_complete = False
+                st.session_state.final_state = None
+                st.rerun()
+
+    # Live Alerts Feed
+    with col_alerts:
+        st.markdown("### 🚨 Live Wire")
+        alerts_container = st.container()
+        
+        # Auto-refresh logic
+        if 'last_refresh' not in st.session_state:
+            st.session_state.last_refresh = time.time()
+
+        if time.time() - st.session_state.last_refresh > 10:
+            st.session_state.last_refresh = time.time()
+            st.rerun()
+
+        alerts = load_alerts()
+        if not alerts:
+            alerts_container.caption("No active alerts in feed.")
+        else:
+            for alert in reversed(alerts[-20:]):
+                alert_type = alert.get("type", "INFO")
+                css_class = "alert-market" if alert_type == "MARKET" else "alert-news" if alert_type == "NEWS" else ""
+                icon = "📉" if alert_type == "MARKET" else "📰"
+                timestamp = datetime.fromisoformat(alert.get("timestamp", datetime.now().isoformat())).strftime("%H:%M:%S")
+                
+                html = f"""
+                <div class="alert-card {css_class}">
+                    <div class="alert-header">
+                        <span>{icon} {alert.get("symbol")}</span>
+                        <span>{timestamp}</span>
+                    </div>
+                    <div class="alert-body">
+                        {alert.get("message")}
+                    </div>
+                </div>
+                """
+                alerts_container.markdown(html, unsafe_allow_html=True)
+
+
+# ---------------------------------------------------------------------------
+# Page Router — dispatches to the correct page based on session state
+# ---------------------------------------------------------------------------
+render_sidebar()
+
+if st.session_state.page == 'home':
+    render_home()
+elif st.session_state.page == 'analysis':
+    render_analysis()
+elif st.session_state.page == 'research_report':
+    from features.research_report import render_research_report
+    render_research_report()
+elif st.session_state.page == 'portfolio_analyzer':
+    from features.portfolio_analyzer import render_portfolio_analyzer
+    render_portfolio_analyzer()
+elif st.session_state.page == 'earnings_sentiment':
+    from features.earnings_sentiment import render_earnings_sentiment
+    render_earnings_sentiment()
+elif st.session_state.page == 'weekly_digest':
+    from features.weekly_digest import render_weekly_digest
+    render_weekly_digest()
+elif st.session_state.page == 'macro_impact':
+    from features.macro_impact import render_macro_impact
+    render_macro_impact()
+else:
+    render_home()

app_command_center.py

@@ -0,0 +1,318 @@
+# app_command_center.py
+import streamlit as st
+import sys
+import os
+import httpx
+import time
+import json
+from datetime import datetime
+
+# --- Path Setup ---
+sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '.')))
+from agents.orchestrator_v3 import SentinelOrchestratorV3
+
+# --- Configuration ---
+WATCHLIST_FILE = "watchlist.json"
+ALERTS_FILE = "alerts.json"
+
+# --- Page Configuration ---
+st.set_page_config(
+    page_title="Aegis Digital Briefing",
+    page_icon="🛡️",
+    layout="wide"
+)
+
+# --- Custom CSS for the Briefing Room Theme ---
+st.markdown("""
+<style>
+    @import url('https://fonts.googleapis.com/css2?family=Source+Serif+4:wght@600;700&family=Open+Sans:wght@400;600&display=swap');
+    
+    html, body, [class*="st-"] {
+        font-family: 'Open Sans', sans-serif;
+    }
+    
+    /* Main Headers */
+    h1, h2, h3 {
+        font-family: 'Source Serif 4', serif;
+    }
+    .main-header {
+        font-size: 2.8rem;
+        font-weight: 700;
+        color: #1A202C;
+        text-align: center;
+        margin-bottom: 0.5rem;
+    }
+    .subtitle {
+        text-align: center;
+        color: #718096;
+        font-size: 1.1rem;
+        margin-bottom: 2.5rem;
+    }
+    
+    /* Card/Widget styling */
+    .card {
+        background-color: #FFFFFF;
+        border-radius: 8px;
+        padding: 25px;
+        border: 1px solid #E2E8F0;
+    }
+    .metric-card {
+        border-radius: 8px;
+        padding: 1.5rem;
+        text-align: center;
+        border: 1px solid #E2E8F0;
+    }
+    .metric-value {
+        font-size: 2rem;
+        font-weight: 700;
+        color: #2D3748;
+    }
+    .metric-label {
+        font-size: 0.9rem;
+        color: #A0AEC0;
+        font-weight: 600;
+    }
+
+    /* Sidebar "Analyst Notes" */
+    .sidebar .st-emotion-cache-16txtl3 {
+        font-size: 1.2rem;
+        font-weight: 600;
+        color: #2D3748;
+    }
+    .note-entry {
+        background-color: #F7FAFC;
+        border-left: 4px solid #4299E1;
+        padding: 1rem;
+        border-radius: 4px;
+        margin-bottom: 0.75rem;
+    }
+    .note-title { font-weight: 600; color: #2C5282; margin-bottom: 0.25rem; }
+    .note-content { font-size: 0.85rem; color: #4A5568; }
+    
+    /* Alerts Styling */
+    .alert-card {
+        padding: 1rem;
+        border-radius: 6px;
+        margin-bottom: 0.8rem;
+        border-left: 5px solid #CBD5E0;
+        background-color: #F7FAFC;
+    }
+    .alert-market { border-left-color: #E53E3E; background-color: #FFF5F5; } /* Red for Market */
+    .alert-news { border-left-color: #3182CE; background-color: #EBF8FF; } /* Blue for News */
+    .alert-header { display: flex; justify-content: space-between; font-size: 0.85rem; color: #718096; margin-bottom: 0.5rem; }
+    .alert-body { font-weight: 600; color: #2D3748; }
+
+</style>
+""", unsafe_allow_html=True)
+
+# --- Helper Functions & State ---
+@st.cache_data(ttl=60)
+def check_server_status():
+    urls = {"Gateway": "http://127.0.0.1:8000/", "Tavily": "http://127.0.0.1:8001/",
+            "Alpha Vantage": "http://127.0.0.1:8002/", "Private DB": "http://127.0.0.1:8003/"}
+    statuses = {}
+    with httpx.Client(timeout=2.0) as client:
+        for name, url in urls.items():
+            try:
+                response = client.get(url)
+                statuses[name] = "✅ Online" if response.status_code == 200 else "⚠️ Error"
+            except:
+                statuses[name] = "❌ Offline"
+    return statuses
+
+def load_watchlist():
+    if not os.path.exists(WATCHLIST_FILE): return []
+    try:
+        with open(WATCHLIST_FILE, 'r') as f: return json.load(f)
+    except: return []
+
+def save_watchlist(watchlist):
+    with open(WATCHLIST_FILE, 'w') as f: json.dump(watchlist, f)
+
+def load_alerts():
+    if not os.path.exists(ALERTS_FILE): return []
+    try:
+        with open(ALERTS_FILE, 'r') as f: return json.load(f)
+    except: return []
+
+if 'final_state' not in st.session_state:
+    st.session_state.final_state = None
+
+# --- UI Rendering ---
+
+# Header
+st.markdown('<h1 class="main-header">Aegis Digital Briefing Room</h1>', unsafe_allow_html=True)
+st.markdown('<p class="subtitle">Automated Intelligence Reports for Modern Finance</p>', unsafe_allow_html=True)
+
+# --- SIDEBAR: Watchlist & Notes ---
+sidebar = st.sidebar
+sidebar.title("🛡️ Command Center")
+
+# 1. Watchlist Manager
+sidebar.subheader("Active Watchlist")
+watchlist = load_watchlist()
+new_symbol = sidebar.text_input("Add Symbol:", placeholder="e.g. MSFT").upper()
+if sidebar.button("Add to Watchlist"):
+    if new_symbol and new_symbol not in watchlist:
+        watchlist.append(new_symbol)
+        save_watchlist(watchlist)
+        st.rerun()
+
+symbol_to_remove = sidebar.selectbox("Remove Symbol:", ["Select..."] + watchlist)
+if symbol_to_remove != "Select..." and sidebar.button("Remove"):
+    watchlist.remove(symbol_to_remove)
+    save_watchlist(watchlist)
+    st.rerun()
+
+sidebar.markdown("---")
+
+# 2. Analyst Notes
+sidebar.title("👨‍💼 Analyst's Live Notes")
+notes_placeholder = sidebar.empty()
+notes_placeholder.info("Awaiting new directive...")
+
+# --- MAIN CONTENT ---
+main_col, alerts_col = st.columns([3, 1])
+
+with main_col:
+    # Main container for Research
+    main_container = st.container(border=True)
+    
+    # Input Form
+    with main_container:
+        st.subheader("🚀 Launch On-Demand Analysis")
+        with st.form("research_form"):
+            task_input = st.text_input("", placeholder="Enter your directive, e.g., 'Analyze market reaction to the latest Apple ($AAPL) product launch'", label_visibility="collapsed")
+            submitted = st.form_submit_button("Generate Briefing", use_container_width=True)
+
+    # --- Main Logic ---
+    if submitted and task_input:
+        server_statuses = check_server_status()
+        if not all(s == "✅ Online" for s in server_statuses.values()):
+            main_container.error("Analysis cannot proceed. One or more backend services are offline. Please check the status.")
+        else:
+            # main_container.empty() # Don't clear, just show results below
+            
+            final_state_result = {}
+            analyst_notes = []
+            
+            try:
+                with st.spinner("Your AI Analyst is compiling the briefing... This may take a moment."):
+                    for event in SentinelOrchestratorV3.stream({"task": task_input}):
+                        node_name = list(event.keys())[0]
+                        final_state_result.update(event[node_name])
+                        
+                        # --- Generate and Display Live Analyst Notes ---
+                        note = ""
+                        if node_name == "extract_symbol":
+                            note = f"Identified target entity: **{event[node_name].get('symbol', 'N/A')}**"
+                        elif node_name == "web_researcher":
+                            note = "Sourced initial open-source intelligence from the web."
+                        elif node_name == "market_data_analyst":
+                            note = "Retrieved latest intraday market performance data."
+                        elif node_name == "data_analyzer":
+                            note = "Commenced deep-dive quantitative analysis of time-series data."
+                        elif node_name == "report_synthesizer":
+                            note = "Synthesizing all findings into the final executive briefing."
+                        
+                        if note:
+                            analyst_notes.append(f'<div class="note-entry"><div class="note-title">{node_name.replace("_", " ").title()}</div><div class="note-content">{note}</div></div>')
+                            notes_placeholder.markdown("".join(analyst_notes), unsafe_allow_html=True)
+                            time.sleep(0.5)
+
+                # --- Display the Final Briefing ---
+                st.session_state.final_state = final_state_result
+                final_state = st.session_state.final_state
+                symbol = final_state.get("symbol", "N/A")
+
+                # HEADLINE
+                st.markdown(f"## Briefing: {symbol} - {datetime.now().strftime('%B %d, %Y')}")
+                st.markdown("---")
+                
+                # KEY METRICS WIDGET
+                st.subheader("Key Performance Indicators")
+                df = final_state.get("analysis_results", {}).get("dataframe")
+                if df is not None and not df.empty:
+                    m_col1, m_col2, m_col3, m_col4 = st.columns(4)
+                    with m_col1:
+                        st.markdown(f'<div class="metric-card"><div class="metric-value">${df["close"].iloc[-1]:.2f}</div><div class="metric-label">Latest Close Price</div></div>', unsafe_allow_html=True)
+                    with m_col2:
+                        st.markdown(f'<div class="metric-card"><div class="metric-value">{df["volume"].sum()/1e6:.2f}M</div><div class="metric-label">Total Volume</div></div>', unsafe_allow_html=True)
+                    with m_col3:
+                        st.markdown(f'<div class="metric-card"><div class="metric-value">${df["high"].max():.2f}</div><div class="metric-label">Intraday High</div></div>', unsafe_allow_html=True)
+                    with m_col4:
+                        st.markdown(f'<div class="metric-card"><div class="metric-value">${df["low"].min():.2f}</div><div class="metric-label">Intraday Low</div></div>', unsafe_allow_html=True)
+                else:
+                    st.info("Quantitative market data was not applicable for this briefing.")
+                
+                st.markdown("<br>", unsafe_allow_html=True)
+                
+                # MAIN BRIEFING (REPORT + CHARTS)
+                brief_col1, brief_col2 = st.columns([7, 5]) # 70/50 split
+                with brief_col1:
+                    st.subheader("Executive Summary & Analysis")
+                    report_html = final_state.get("final_report", "No report generated.").replace("\n", "<br>")
+                    st.markdown(f'<div class="card" style="height: 100%;">{report_html}</div>', unsafe_allow_html=True)
+                
+                with brief_col2:
+                    st.subheader("Visual Data Debrief")
+                    charts = final_state.get("analysis_results", {}).get("charts", [])
+                    if charts:
+                        for chart in charts:
+                            st.plotly_chart(chart, use_container_width=True)
+                    else:
+                        st.markdown('<div class="card" style="height: 100%;"><p>No visualizations were generated for this briefing.</p></div>', unsafe_allow_html=True)
+                        
+                # EVIDENCE LOG
+                with st.expander("Show Evidence Log & Methodology"):
+                    st.markdown("#### Open Source Intelligence (Web Research)")
+                    st.json(final_state.get('web_research_results', '{}'))
+                    st.markdown("#### Deep-Dive Analysis Insights")
+                    st.text(final_state.get("analysis_results", {}).get("insights", "No insights."))
+
+                if st.button("Start New Briefing"):
+                    st.session_state.final_state = None
+                    st.rerun()
+
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+
+# --- LIVE ALERTS FEED ---
+with alerts_col:
+    st.subheader("🚨 Live Alerts")
+    st.caption("Real-time monitoring feed")
+    
+    alerts_container = st.container(height=600)
+    
+    # Auto-refresh logic for alerts
+    if 'last_refresh' not in st.session_state:
+        st.session_state.last_refresh = time.time()
+
+    # Refresh every 10 seconds
+    if time.time() - st.session_state.last_refresh > 10:
+        st.session_state.last_refresh = time.time()
+        st.rerun()
+
+    alerts = load_alerts()
+    if not alerts:
+        alerts_container.info("No active alerts.")
+    else:
+        for alert in alerts:
+            alert_type = alert.get("type", "INFO")
+            css_class = "alert-market" if alert_type == "MARKET" else "alert-news" if alert_type == "NEWS" else ""
+            icon = "📉" if alert_type == "MARKET" else "📰"
+            
+            timestamp = datetime.fromisoformat(alert.get("timestamp", datetime.now().isoformat())).strftime("%H:%M")
+            
+            html = f"""
+            <div class="alert-card {css_class}">
+                <div class="alert-header">
+                    <span>{icon} {alert.get("symbol")}</span>
+                    <span>{timestamp}</span>
+                </div>
+                <div class="alert-body">
+                    {alert.get("message")}
+                </div>
+            </div>
+            """
+            alerts_container.markdown(html, unsafe_allow_html=True)

create_dummy_db.py

@@ -0,0 +1,36 @@
+# create_dummy_db.py
+import sqlite3
+
+# Connect to the SQLite database (this will create the file if it doesn't exist)
+conn = sqlite3.connect('portfolio.db')
+cursor = conn.cursor()
+
+# --- Create the holdings table ---
+cursor.execute('''
+CREATE TABLE IF NOT EXISTS holdings (
+    id INTEGER PRIMARY KEY,
+    symbol TEXT NOT NULL UNIQUE,
+    shares INTEGER NOT NULL,
+    average_cost REAL NOT NULL
+)
+''')
+print("Table 'holdings' created successfully.")
+
+# --- Insert some sample data ---
+# Using INSERT OR IGNORE to prevent errors if you run the script multiple times
+holdings_data = [
+    ('NVDA', 1500, 250.75),
+    ('AAPL', 5000, 180.20),
+    ('IBM', 2500, 155.45),
+    ('TSLA', 1000, 220.90)
+]
+
+cursor.executemany('''
+INSERT OR IGNORE INTO holdings (symbol, shares, average_cost) VALUES (?, ?, ?)
+''', holdings_data)
+print(f"{len(holdings_data)} sample holdings inserted.")
+
+# --- Commit the changes and close the connection ---
+conn.commit()
+conn.close()
+print("Database 'portfolio.db' is set up and ready.")

deployment_guide.md

@@ -0,0 +1,64 @@
+# Aegis Deployment Guide
+
+This guide explains how to deploy the Aegis Financial Analyst Agent using Docker and Docker Compose.
+
+## Prerequisites
+- **Docker** and **Docker Compose** installed on your machine.
+- An `.env` file with valid API keys (see `.env.example` or your existing `.env`).
+
+## Deployment Steps
+
+1.  **Build and Start Services**
+    Run the following command in the project root directory:
+    ```bash
+    docker-compose up --build -d
+    ```
+    This will:
+    - Build the Docker image for the application.
+    - Create a network `aegis-net`.
+    - Start all services (Gateway, Microservices, Monitor, Frontend) in detached mode.
+
+2.  **Verify Deployment**
+    - **Frontend**: Access the Streamlit UI at `http://localhost:8501`.
+    - **Gateway**: `http://localhost:8000`
+    - **Services**:
+        - Tavily: `http://localhost:8001`
+        - Alpha Vantage: `http://localhost:8002`
+        - Portfolio: `http://localhost:8003`
+
+3.  **View Logs**
+    To see logs for all services:
+    ```bash
+    docker-compose logs -f
+    ```
+    To see logs for a specific service (e.g., frontend):
+    ```bash
+    docker-compose logs -f frontend
+    ```
+
+4.  **Stop Services**
+    To stop and remove containers:
+    ```bash
+    docker-compose down
+    ```
+
+## Environment Variables
+Ensure your `.env` file contains:
+
+- `GOOGLE_API_KEY`
+- `TAVILY_API_KEY`
+- `ALPHA_VANTAGE_API_KEY`
+
+Docker Compose automatically reads these from the `.env` file in the same directory.
+
+## Alternative Deployment (No Docker)
+If you cannot run Docker, use the local deployment script:
+```bash
+./deploy_local.sh
+```
+This runs all services in the background and saves logs to a `logs/` folder.
+
+## Troubleshooting
+- **"Cannot connect to the Docker daemon"**: This means Docker is not running. Open **Docker Desktop** on your Mac and wait for it to start (the whale icon in the menu bar should stop animating).
+- **Port Conflicts**: Ensure ports 8000-8003 and 8501 are free.
+- **Database Persistence**: The `portfolio.db` file is mounted as a volume, so your internal portfolio data persists across restarts.

digests/digest_20260221_052320.json

@@ -0,0 +1,80 @@
+{
+  "date": "2026-02-21T05:23:20.102485",
+  "date_display": "February 21, 2026",
+  "watchlist": [
+    "AAPL",
+    "TSLA",
+    "NVDA",
+    "MSFT",
+    "AMZN",
+    "GOOGL"
+  ],
+  "ticker_summaries": [
+    {
+      "ticker": "AAPL",
+      "weekly_change_pct": 6.15,
+      "latest_close": 158.31,
+      "volume_anomaly_pct": -45.5
+    },
+    {
+      "ticker": "TSLA",
+      "weekly_change_pct": 0.5,
+      "latest_close": 257.7,
+      "volume_anomaly_pct": -94.3
+    },
+    {
+      "ticker": "NVDA",
+      "weekly_change_pct": -1.19,
+      "latest_close": 446.05,
+      "volume_anomaly_pct": -22.5
+    },
+    {
+      "ticker": "MSFT",
+      "weekly_change_pct": 3.3,
+      "latest_close": 363.03,
+      "volume_anomaly_pct": -43.4
+    },
+    {
+      "ticker": "AMZN",
+      "weekly_change_pct": 0.12,
+      "latest_close": 139.17,
+      "volume_anomaly_pct": -3.9
+    },
+    {
+      "ticker": "GOOGL",
+      "weekly_change_pct": 3.81,
+      "latest_close": 138.6,
+      "volume_anomaly_pct": -17.8
+    }
+  ],
+  "winners": [
+    {
+      "ticker": "AAPL",
+      "weekly_change_pct": 6.15,
+      "latest_close": 158.31,
+      "volume_anomaly_pct": -45.5
+    },
+    {
+      "ticker": "GOOGL",
+      "weekly_change_pct": 3.81,
+      "latest_close": 138.6,
+      "volume_anomaly_pct": -17.8
+    },
+    {
+      "ticker": "MSFT",
+      "weekly_change_pct": 3.3,
+      "latest_close": 363.03,
+      "volume_anomaly_pct": -43.4
+    }
+  ],
+  "losers": [
+    {
+      "ticker": "NVDA",
+      "weekly_change_pct": -1.19,
+      "latest_close": 446.05,
+      "volume_anomaly_pct": -22.5
+    }
+  ],
+  "macro_news": "- [05:23] Market Update: Volatile Sentiment for major financial market news this week economy stocks: Live market data at 05:23 indicates a Volatile trend for major financial market news this week economy stocks. Analysts are tracking a potential Secto\n- [05:23] Sector Alert: Sector Rotation affecting major financial market news this week economy stocks: Breaking: A significant Sector Rotation is rippling through the sector, heavily influencing major financial market news this week economy stocks. Expe",
+  "narrative": "## Weekly Market Briefing: February 21, 2026\n\n**To:** Valued Clients\n**From:** [Your Name/Department], Chief Market Strategist\n**Date:** February 21, 2026\n\n**Subject:** Navigating Volatility: A Look Back at the Week and Ahead\n\nThis week, the market navigated a landscape characterized by **volatile sentiment**, as indicated by live market data. While major indices experienced fluctuations, our focus remains on understanding the underlying drivers and identifying opportunities within this dynamic environment. We observed a notable **sector rotation** at play, influencing the performance of various market segments and individual equities.\n\n### Market Overview: A Week of Shifting Sands\n\nThe overarching theme this week was one of **volatility**. While specific index movements are not provided, the sentiment data suggests a cautious approach from investors as they digest evolving economic signals and sector-specific developments. The observed **sector rotation** indicates a potential recalibration of investment strategies, with capital potentially moving from areas of perceived overvaluation to those offering more attractive growth prospects or defensive qualities. This dynamic environment underscores the importance of a well-diversified portfolio and a keen eye on emerging trends.\n\n### Watchlist Highlights: Tech Titans Show Mixed Fortunes\n\nOur watchlist, comprising key technology giants, presented a mixed bag of performance this week.\n\nLeading the pack was **Apple (AAPL)**, which posted a robust **6.15% gain**, closing the week at **$158.31**. This strong performance suggests renewed investor confidence in the company's prospects. Following closely was **Alphabet (GOOGL)**, which climbed **3.81%** to **$138.60**, indicating positive sentiment towards its search and cloud businesses. **Microsoft (MSFT)** also demonstrated resilience, adding **3.3%** to its value and finishing the week at **$363.03**.\n\nOn the other end of the spectrum, **NVIDIA (NVDA)** experienced a slight pullback, declining by **1.19%** to **$446.05**. While a modest loss, it's worth noting given NVDA's recent strong performance. **Amazon (AMZN)** and **Tesla (TSLA)** showed more muted gains, with AMZN up **0.12%** to **$139.17** and TSLA up **0.5%** to **$257.70**, respectively. These performances suggest that while the broader tech sector remains a focus, individual company narratives and sector-specific headwinds or tailwinds are playing a significant role.\n\n### Volume Alerts: A Quieter Trading Environment\n\nInterestingly, our watchlist experienced significant **volume anomalies**, with most tickers showing substantially lower trading volumes than their typical averages. **Tesla (TSLA)** stood out with a **-94.3%** volume anomaly, indicating a remarkably subdued trading interest. **Apple (AAPL)** also saw a considerable drop in volume at **-45.5%**, followed by **Microsoft (MSFT)** at **-43.4%**. **Alphabet (GOOGL)** and **NVIDIA (NVDA)** also registered negative volume anomalies at **-17.8%** and **-22.5%**, respectively. **Amazon (AMZN)** showed the least deviation with a **-3.9%** anomaly.\n\nThis widespread reduction in trading volume, particularly for the market leaders, could suggest a period of consolidation or a \"wait-and-see\" attitude among traders and investors. It might indicate that significant market participants are holding their positions, awaiting clearer directional signals or more definitive economic news.\n\n### Macro Landscape: Economic Crosscurrents\n\nThe macro environment this week was marked by **volatile sentiment** surrounding economic news. The mention of a potential \"Secto\" (likely referring to a specific sector or economic indicator) suggests that analysts are closely monitoring particular areas for signs of strength or weakness. The broader theme of **sector rotation** further reinforces the idea that investors are actively reallocating capital based on evolving economic conditions and sector-specific outlooks. While specific economic data releases are not detailed, these overarching themes point to a market that is sensitive to incoming information and is actively seeking clarity on the economic trajectory.\n\n### Week Ahead: Key Catalysts to Monitor\n\nLooking ahead to the coming week, investors will be keenly focused on any further developments related to the **sector rotation** and the underlying economic indicators driving it. We will be closely watching for any official statements or data releases that could provide more clarity on the \"Secto\" mentioned in this week's news.\n\nFurthermore, the subdued trading volumes observed this week warrant attention. A sustained period of low volume could precede a significant price move, and understanding the catalysts for such a move will be crucial. We will be monitoring corporate earnings reports, any significant geopolitical developments, and key economic data releases for potential market-moving information.\n\nIn conclusion, while this week presented a degree of market volatility and shifting sector dynamics, our watchlist highlights the resilience of some of the largest technology companies. The reduced trading volumes suggest a market in a period of observation. We remain committed to providing you with timely analysis and strategic guidance as we navigate the evolving market landscape.\n\nSincerely,\n\n[Your Name]\nChief Market Strategist\n[Your Financial Institution]"
+}

digests/digest_20260221_210307.json

@@ -0,0 +1,86 @@
+{
+  "date": "2026-02-21T21:03:07.971541",
+  "date_display": "February 21, 2026",
+  "watchlist": [
+    "AAPL",
+    "TSLA",
+    "NVDA",
+    "MSFT",
+    "AMZN",
+    "GOOGL"
+  ],
+  "ticker_summaries": [
+    {
+      "ticker": "AAPL",
+      "weekly_change_pct": 1.25,
+      "latest_close": 154.55,
+      "volume_anomaly_pct": -59.5
+    },
+    {
+      "ticker": "TSLA",
+      "weekly_change_pct": -0.27,
+      "latest_close": 252.91,
+      "volume_anomaly_pct": -38.8
+    },
+    {
+      "ticker": "NVDA",
+      "weekly_change_pct": 4.6,
+      "latest_close": 466.62,
+      "volume_anomaly_pct": 33.0
+    },
+    {
+      "ticker": "MSFT",
+      "weekly_change_pct": 0.1,
+      "latest_close": 346.11,
+      "volume_anomaly_pct": -77.7
+    },
+    {
+      "ticker": "AMZN",
+      "weekly_change_pct": -0.37,
+      "latest_close": 138.85,
+      "volume_anomaly_pct": -4.8
+    },
+    {
+      "ticker": "GOOGL",
+      "weekly_change_pct": 4.41,
+      "latest_close": 138.32,
+      "volume_anomaly_pct": 15.0
+    }
+  ],
+  "winners": [
+    {
+      "ticker": "NVDA",
+      "weekly_change_pct": 4.6,
+      "latest_close": 466.62,
+      "volume_anomaly_pct": 33.0
+    },
+    {
+      "ticker": "GOOGL",
+      "weekly_change_pct": 4.41,
+      "latest_close": 138.32,
+      "volume_anomaly_pct": 15.0
+    },
+    {
+      "ticker": "AAPL",
+      "weekly_change_pct": 1.25,
+      "latest_close": 154.55,
+      "volume_anomaly_pct": -59.5
+    }
+  ],
+  "losers": [
+    {
+      "ticker": "AMZN",
+      "weekly_change_pct": -0.37,
+      "latest_close": 138.85,
+      "volume_anomaly_pct": -4.8
+    },
+    {
+      "ticker": "TSLA",
+      "weekly_change_pct": -0.27,
+      "latest_close": 252.91,
+      "volume_anomaly_pct": -38.8
+    }
+  ],
+  "macro_news": "- [21:02] Market Update: Bearish Sentiment for major financial market news this week economy stocks: Live market data at 21:02 indicates a Bearish trend for major financial market news this week economy stocks. Analysts are tracking a potential New Pr\n- [21:02] Sector Alert: New Product Launch affecting major financial market news this week economy stocks: Breaking: A significant New Product Launch is rippling through the sector, heavily influencing major financial market news this week economy stocks. E",
+  "narrative": "**Weekly Market Briefing - February 21, 2026**\n\n**Market Overview**\nThe market exhibited a mixed sentiment this week, with some major stocks experiencing significant gains while others faced declines. The overall trend was slightly bearish, with analysts tracking a potential downturn in major financial market news. Despite this, some sectors saw a surge in activity due to new product launches, which heavily influenced the market. The week's key moves were led by technology stocks, with NVIDIA (NVDA) and Alphabet (GOOGL) emerging as the biggest winners.\n\n**Watchlist Highlights**\nOur watchlist saw a notable performance from NVIDIA (NVDA), which rose by 4.6% to close at $466.62. This significant gain was accompanied by a 33.0% increase in volume, indicating strong investor interest. Alphabet (GOOGL) followed closely, with a 4.41% increase to $138.32 and a 15.0% rise in volume. Apple (AAPL) also made it to the winners' list, with a 1.25% gain to $154.55, although its volume declined by 59.5%. On the other hand, the biggest losers were Amazon (AMZN) and Tesla (TSLA), which declined by 0.37% to $138.85 and 0.27% to $252.91, respectively. Microsoft (MSFT) saw a minimal gain of 0.1% to $346.11, while its volume plummeted by 77.7%.\n\n**Volume Alerts**\nThis week saw unusual volume activity in several stocks. NVIDIA's (NVDA) 33.0% increase in volume was a notable highlight, indicating strong investor interest in the stock. On the other hand, Microsoft (MSFT) experienced a significant decline in volume, with a 77.7% drop. Apple (AAPL) also saw a substantial decline in volume, with a 59.5% decrease. These volume anomalies warrant close monitoring, as they may signal changes in investor sentiment or upcoming market movements.\n\n**Macro Landscape**\nThe macro landscape was marked by bearish sentiment, with analysts tracking a potential downturn in major financial market news. A significant new product launch rippled through the sector, heavily influencing the market. This launch is expected to have a lasting impact on the industry, and investors should closely watch its developments. The current market conditions are characterized by uncertainty, and investors should remain cautious and adapt to the changing landscape.\n\n**Week Ahead**\nAs we look ahead to the next week, investors should keep a close eye on the technology sector, particularly NVIDIA (NVDA) and Alphabet (GOOGL), which are expected to continue their upward trend. The new product launch is likely to have a lasting impact on the market, and its effects should be closely monitored. Additionally, investors should watch for any changes in volume activity, as these can signal shifts in investor sentiment. The overall market sentiment is expected to remain bearish, and investors should be prepared for potential downturns. With the current uncertainty in the market, it is essential to stay informed and adapt to the changing landscape to make informed investment decisions."
+}

digests/digest_20260222_013724.json

@@ -0,0 +1,80 @@
+{
+  "date": "2026-02-22T01:37:24.760694",
+  "date_display": "February 22, 2026",
+  "watchlist": [
+    "AAPL",
+    "TSLA",
+    "NVDA",
+    "MSFT",
+    "AMZN",
+    "GOOGL"
+  ],
+  "ticker_summaries": [
+    {
+      "ticker": "AAPL",
+      "weekly_change_pct": 2.74,
+      "latest_close": 156.59,
+      "volume_anomaly_pct": 37.2
+    },
+    {
+      "ticker": "TSLA",
+      "weekly_change_pct": 3.24,
+      "latest_close": 272.3,
+      "volume_anomaly_pct": 7.4
+    },
+    {
+      "ticker": "NVDA",
+      "weekly_change_pct": 3.82,
+      "latest_close": 475.35,
+      "volume_anomaly_pct": 18.3
+    },
+    {
+      "ticker": "MSFT",
+      "weekly_change_pct": -0.26,
+      "latest_close": 352.35,
+      "volume_anomaly_pct": 56.0
+    },
+    {
+      "ticker": "AMZN",
+      "weekly_change_pct": 1.08,
+      "latest_close": 148.48,
+      "volume_anomaly_pct": -11.5
+    },
+    {
+      "ticker": "GOOGL",
+      "weekly_change_pct": 5.5,
+      "latest_close": 144.52,
+      "volume_anomaly_pct": -28.6
+    }
+  ],
+  "winners": [
+    {
+      "ticker": "GOOGL",
+      "weekly_change_pct": 5.5,
+      "latest_close": 144.52,
+      "volume_anomaly_pct": -28.6
+    },
+    {
+      "ticker": "NVDA",
+      "weekly_change_pct": 3.82,
+      "latest_close": 475.35,
+      "volume_anomaly_pct": 18.3
+    },
+    {
+      "ticker": "TSLA",
+      "weekly_change_pct": 3.24,
+      "latest_close": 272.3,
+      "volume_anomaly_pct": 7.4
+    }
+  ],
+  "losers": [
+    {
+      "ticker": "MSFT",
+      "weekly_change_pct": -0.26,
+      "latest_close": 352.35,
+      "volume_anomaly_pct": 56.0
+    }
+  ],
+  "macro_news": "- [01:37] Market Update: Cautious Sentiment for major financial market news this week economy stocks: Live market data at 01:37 indicates a Cautious trend for major financial market news this week economy stocks. Analysts are tracking a potential New P\n- [01:37] Sector Alert: New Product Launch affecting major financial market news this week economy stocks: Breaking: A significant New Product Launch is rippling through the sector, heavily influencing major financial market news this week economy stocks. E",
+  "narrative": "**Weekly Market Briefing - February 22, 2026**\n\n**Market Overview**\nThe market exhibited a mixed sentiment this week, with cautious optimism prevailing among investors. The overall trend was upward, with several major stocks posting significant gains. The biggest winners of the week were GOOGL, NVDA, and TSLA, which saw weekly changes of 5.5%, 3.82%, and 3.24%, respectively. On the other hand, MSFT was the only notable loser, with a weekly decline of 0.26%. The latest closing prices for these stocks were $144.52, $475.35, $272.30, and $352.35, respectively.\n\n**Watchlist Highlights**\nOur watchlist stocks saw a significant amount of activity this week. GOOGL was the top performer, with a 5.5% gain, driven by a significant New Product Launch in the sector. NVDA followed closely, with a 3.82% increase, as the company continues to benefit from the growing demand for its graphics processing units. TSLA also had a strong week, with a 3.24% gain, as investors remain optimistic about the company's electric vehicle prospects. On the other hand, MSFT was the only loser, with a 0.26% decline, despite a significant volume anomaly of 56.0%. AAPL and AMZN also saw gains of 2.74% and 1.08%, respectively, with latest closing prices of $156.59 and $148.48.\n\n**Volume Alerts**\nSeveral stocks on our watchlist exhibited unusual volume activity this week. MSFT saw a significant volume anomaly of 56.0%, indicating a high level of investor interest in the stock. AAPL also saw a notable volume anomaly of 37.2%, while NVDA and TSLA saw more moderate anomalies of 18.3% and 7.4%, respectively. On the other hand, GOOGL and AMZN saw volume anomalies of -28.6% and -11.5%, respectively, indicating lower-than-expected trading activity.\n\n**Macro Landscape**\nThe macroeconomic landscape was marked by cautious sentiment this week, with analysts tracking a potential new trend in the economy. A significant New Product Launch in the sector was also announced, which is expected to have a ripple effect on the market. The launch is likely to influence investor sentiment and drive market activity in the coming weeks. Additionally, live market data at 01:37 indicated a cautious trend for major financial market news, with analysts advising investors to remain vigilant and adapt to changing market conditions.\n\n**Week Ahead**\nAs we look ahead to the next week, investors will be closely watching the market for signs of continued growth or potential corrections. The New Product Launch is expected to remain a key driver of market activity, and investors will be monitoring its impact on the sector. Additionally, investors will be keeping a close eye on economic data releases and earnings reports from major companies. With the market exhibiting cautious optimism, investors will need to remain agile and adapt to changing market conditions to navigate the week ahead. Key stocks to watch include GOOGL, NVDA, and TSLA, which are expected to continue their upward trend, while MSFT will be closely monitored for any signs of recovery. Overall, the market is expected to remain volatile, and investors will need to stay informed and up-to-date to make informed investment decisions."
+}

digests/digest_20260224_165133.json

@@ -0,0 +1,73 @@
+{
+  "date": "2026-02-24T16:51:33.906305",
+  "date_display": "February 24, 2026",
+  "watchlist": [
+    "AAPL",
+    "TSLA",
+    "NVDA",
+    "MSFT",
+    "AMZN",
+    "GOOGL"
+  ],
+  "ticker_summaries": [
+    {
+      "ticker": "AAPL",
+      "weekly_change_pct": 2.06,
+      "latest_close": 160.25,
+      "volume_anomaly_pct": -90.3
+    },
+    {
+      "ticker": "TSLA",
+      "weekly_change_pct": 1.03,
+      "latest_close": 255.17,
+      "volume_anomaly_pct": -18.4
+    },
+    {
+      "ticker": "NVDA",
+      "weekly_change_pct": 6.42,
+      "latest_close": 492.18,
+      "volume_anomaly_pct": 47.0
+    },
+    {
+      "ticker": "MSFT",
+      "weekly_change_pct": 1.54,
+      "latest_close": 355.13,
+      "volume_anomaly_pct": -73.8
+    },
+    {
+      "ticker": "AMZN",
+      "weekly_change_pct": 2.01,
+      "latest_close": 153.05,
+      "volume_anomaly_pct": 16.9
+    },
+    {
+      "ticker": "GOOGL",
+      "weekly_change_pct": 2.27,
+      "latest_close": 144.19,
+      "volume_anomaly_pct": -68.8
+    }
+  ],
+  "winners": [
+    {
+      "ticker": "NVDA",
+      "weekly_change_pct": 6.42,
+      "latest_close": 492.18,
+      "volume_anomaly_pct": 47.0
+    },
+    {
+      "ticker": "GOOGL",
+      "weekly_change_pct": 2.27,
+      "latest_close": 144.19,
+      "volume_anomaly_pct": -68.8
+    },
+    {
+      "ticker": "AAPL",
+      "weekly_change_pct": 2.06,
+      "latest_close": 160.25,
+      "volume_anomaly_pct": -90.3
+    }
+  ],
+  "losers": [],
+  "macro_news": "- [16:51] Market Update: Bullish Sentiment for major financial market news this week economy stocks: Live market data at 16:51 indicates a Bullish trend for major financial market news this week economy stocks. Analysts are tracking a potential Macro \n- [16:51] Sector Alert: Macro Headwinds affecting major financial market news this week economy stocks: Breaking: A significant Macro Headwinds is rippling through the sector, heavily influencing major financial market news this week economy stocks. Expe",
+  "narrative": "## Weekly Market Briefing: February 24, 2026\n\n**To:** Our Valued Clients and Investment Professionals\n**From:** [Your Name/Department], Senior Market Analyst\n**Date:** February 24, 2026\n\n### Market Overview: A Week of Resilient Gains Amidst Nuanced Signals\n\nThe past week, concluding February 24, 2026, saw a broadly positive performance across our core watchlist, reflecting a prevailing bullish sentiment in the broader market. Despite some underlying macro headwinds, major indices likely closed higher, driven by continued strength in key technology and growth sectors. Investor confidence appears to be holding firm, with capital flowing into established market leaders. However, a closer look at volume metrics suggests a more complex picture, warranting careful consideration as we move forward.\n\n### Watchlist Highlights: Tech Leaders Drive Performance\n\nOur watchlist demonstrated robust performance this week, with all tracked tickers posting positive returns. This broad-based strength underscores the current market's appetite for growth and innovation.\n\n**Biggest Winners:**\n*   **NVIDIA (NVDA)** led the charge, surging an impressive **6.42%** to close at $492.18. This significant outperformance highlights the continued investor enthusiasm for the AI and semiconductor sector, which remains a powerful narrative in the current market cycle.\n*   **Alphabet (GOOGL)** also posted a strong gain of **2.27%**, closing the week at $144.19. This reflects the resilience of the digital advertising and cloud computing giants, which continue to demonstrate robust business models.\n*   **Apple (AAPL)** contributed to the positive momentum with a **2.06%** increase, ending the week at $160.25. Despite recent concerns over specific product cycles, Apple's ecosystem strength continues to attract investor interest.\n\nOther notable performers included **Amazon (AMZN)**, up **2.01%** to $153.05, **Microsoft (MSFT)**, gaining **1.54%** to $355.13, and **Tesla (TSLA)**, which advanced **1.03%** to $255.17.\n\n**Biggest Losers:**\nNotably, our watchlist recorded **no significant losers** this week, a testament to the strong positive momentum observed across these bellwether technology stocks. This uniform upward movement suggests a broad-based conviction among investors in these market leaders.\n\n### Volume Alerts: A Tale of Two Extremes\n\nWhile price action was overwhelmingly positive, volume metrics presented a mixed and intriguing picture, signaling areas of both strong conviction and potential caution.\n\n*   **NVIDIA (NVDA)**'s stellar **6.42%** gain was accompanied by a significant **47.0% positive volume anomaly**. This indicates strong institutional and retail interest, lending credibility to its price surge and suggesting robust demand for the stock. High volume on a strong upward move is typically a bullish signal.\n*   In stark contrast, several other major players experienced unusually low trading volumes despite their positive price movements:\n    *   **Apple (AAPL)** saw an alarming **-90.3% volume anomaly**, meaning trading activity was exceptionally light. While the stock gained over 2%, this low conviction behind the move could raise questions about the sustainability of the rally or suggest a lack of strong institutional participation.\n    *   **Microsoft (MSFT)** also traded on significantly reduced volume, with a **-73.8% anomaly**.\n    *   **Alphabet (GOOGL)** experienced a **-68.8% anomaly**.\n    *   **Tesla (TSLA)** had a more moderate, but still notable, **-18.4% anomaly**.\n\nThese low volume gains for some of the market's largest companies suggest that while prices moved higher, the conviction behind these moves might be weaker than implied by the percentage gains alone. This could indicate a \"climbing a wall of worry\" scenario or simply a period of reduced liquidity, which bears close watching. **Amazon (AMZN)**, on the other hand, saw a modest **16.9% positive volume anomaly**, aligning with its solid price performance.\n\n### Macro Landscape: Bullish Sentiment Navigates Headwinds\n\nThe broader macro environment presented a somewhat contradictory narrative this week. Live market data indicated a prevailing **\"Bullish Sentiment\"** for major financial markets, suggesting optimism regarding the economy and corporate earnings. This aligns with the positive performance observed in our watchlist.\n\nHowever, this bullishness appears to be coexisting with, or perhaps even overcoming, identified **\"Macro Headwinds\"** that are reportedly \"rippling through the sector.\" While the specific nature of these headwinds was not detailed, their presence suggests that the market's current upward trajectory is not without underlying challenges. Analysts are actively tracking a \"potential Macro\" shift, implying that while current sentiment is positive, there are significant economic developments on the horizon that could influence future market direction. This creates a nuanced environment where investors are balancing optimism with a need for vigilance.\n\n### Week Ahead: Navigating Mixed Signals\n\nAs we look ahead, the market will likely continue to grapple with these mixed signals. Investors should remain attentive to any further details regarding the \"Macro Headwinds\" and the \"potential Macro\" developments that analysts are tracking. Key areas to watch include:\n\n*   **Economic Data Releases:** Any upcoming inflation reports, employment figures, or manufacturing data could provide clarity on the strength of the economy and the potential trajectory of interest rates.\n*   **Corporate Commentary:** While earnings season may be winding down for some, any forward-looking statements or guidance from major corporations could offer insights into the impact of the macro environment on business performance.\n*   **Geopolitical Developments:** Global events continue to hold the potential to introduce volatility and shift market sentiment rapidly.\n\nGiven the strong price performance on mixed volume signals and the presence of acknowledged macro headwinds, a cautious yet opportunistic approach is advisable. We recommend focusing on companies with strong fundamentals and clear growth catalysts, while closely monitoring the evolving macro landscape for shifts in sentiment or underlying economic conditions.\n\n---\n*Disclaimer: This briefing is for informational purposes only and does not constitute investment advice. Investors should consult with a qualified financial professional before making any investment decisions.*"
+}

docker-compose.yml

@@ -0,0 +1,78 @@
+
+
+services:
+  gateway:
+    build: .
+    command: uvicorn mcp_gateway:app --host 0.0.0.0 --port 8000
+    ports:
+      - "8000:8000"
+    environment:
+      - TAVILY_MCP_URL=http://tavily:8001/research
+      - ALPHAVANTAGE_MCP_URL=http://alphavantage:8002/market_data
+      - PRIVATE_MCP_URL=http://portfolio:8003/portfolio_data
+      - TAVILY_API_KEY=${TAVILY_API_KEY}
+      - ALPHA_VANTAGE_API_KEY=${ALPHA_VANTAGE_API_KEY}
+      - GOOGLE_API_KEY=${GOOGLE_API_KEY}
+
+    networks:
+      - aegis-net
+
+  tavily:
+    build: .
+    command: uvicorn tavily_mcp:app --host 0.0.0.0 --port 8001
+    ports:
+      - "8001:8001"
+    environment:
+      - TAVILY_API_KEY=${TAVILY_API_KEY}
+    networks:
+      - aegis-net
+
+  alphavantage:
+    build: .
+    command: uvicorn alphavantage_mcp:app --host 0.0.0.0 --port 8002
+    ports:
+      - "8002:8002"
+    environment:
+      - ALPHA_VANTAGE_API_KEY=${ALPHA_VANTAGE_API_KEY}
+    networks:
+      - aegis-net
+
+  portfolio:
+    build: .
+    command: uvicorn private_mcp:app --host 0.0.0.0 --port 8003
+    ports:
+      - "8003:8003"
+    volumes:
+      - ./portfolio.db:/app/portfolio.db
+    networks:
+      - aegis-net
+
+  monitor:
+    build: .
+    command: python monitor.py
+    environment:
+      - MCP_GATEWAY_URL=http://gateway:8000/route_agent_request
+      - TAVILY_API_KEY=${TAVILY_API_KEY}
+      - ALPHA_VANTAGE_API_KEY=${ALPHA_VANTAGE_API_KEY}
+    depends_on:
+      - gateway
+    networks:
+      - aegis-net
+
+  frontend:
+    build: .
+    command: streamlit run app.py --server.port 8501 --server.address 0.0.0.0
+    ports:
+      - "8501:8501"
+    environment:
+      - MCP_GATEWAY_URL=http://gateway:8000/route_agent_request
+      - GOOGLE_API_KEY=${GOOGLE_API_KEY}
+
+    depends_on:
+      - gateway
+    networks:
+      - aegis-net
+
+networks:
+  aegis-net:
+    driver: bridge

features/__init__.py

@@ -0,0 +1 @@
+# Sentinel Add-On Features Package

features/earnings_sentiment.py

@@ -0,0 +1,293 @@
+"""
+features/earnings_sentiment.py — Earnings Call Sentiment Intelligence
+Analyzes earnings call transcripts for sentiment, confidence, guidance tone.
+"""
+import streamlit as st
+import json
+import re
+import logging
+from datetime import datetime, timedelta
+
+logger = logging.getLogger("EarningsSentiment")
+
+
+# ---------------------------------------------------------------------------
+# Transcript fetching
+# ---------------------------------------------------------------------------
+def _fetch_transcript(ticker: str, quarter: int = None, year: int = None) -> str:
+    """Fetch earnings call transcript via Tavily search."""
+    from features.utils import run_tavily_search
+
+    now = datetime.now()
+    if not quarter:
+        quarter = (now.month - 1) // 3 + 1
+        # Last quarter
+        if quarter == 1:
+            quarter = 4
+            year = (year or now.year) - 1
+        else:
+            quarter -= 1
+    if not year:
+        year = now.year
+
+    query = f"{ticker} earnings call transcript Q{quarter} {year}"
+    try:
+        result = run_tavily_search(query, search_depth="advanced")
+        texts = []
+        for qr in result.get("data", []):
+            for r in qr.get("results", []):
+                texts.append(r.get("content", ""))
+        return "\n\n".join(texts[:5]) if texts else ""
+    except Exception as e:
+        logger.error(f"Transcript fetch failed: {e}")
+        return ""
+
+
+# ---------------------------------------------------------------------------
+# Sentiment analysis via Gemini
+# ---------------------------------------------------------------------------
+def _analyze_sentiment(ticker: str, transcript: str) -> dict:
+    """Run Gemini to analyze sentiment of earnings call."""
+    from features.utils import call_gemini
+
+    prompt = f"""You are an expert sentiment analyst specializing in earnings.
+
+Analyze the following text regarding the earnings call for {ticker}.
+Note: The text may be the raw transcript OR market commentary/news about the call. 
+Analyze whatever is provided to determine the sentiment, guidance, and key themes as accurately as possible.
+
+---
+{transcript[:6000]}
+---
+
+Provide your analysis as a VALID JSON object with this exact structure:
+{{
+    "management_sentiment": {{
+        "score": <float from -1.0 to 1.0>,
+        "label": "Positive" | "Neutral" | "Negative",
+        "confidence_level": <int from 0-100>,
+        "forward_guidance": "Optimistic" | "Cautious" | "Withdrawn",
+        "key_quotes": ["quote1", "quote2"]
+    }},
+    "qa_sentiment": {{
+        "score": <float from -1.0 to 1.0>,
+        "label": "Positive" | "Neutral" | "Negative",
+        "confidence_level": <int from 0-100>,
+        "analyst_concerns": ["concern1", "concern2"]
+    }},
+    "key_themes": ["theme1", "theme2", "theme3", "theme4", "theme5"],
+    "positive_words": ["word1", "word2", "word3", "word4", "word5", "word6", "word7", "word8"],
+    "negative_words": ["word1", "word2", "word3", "word4", "word5", "word6", "word7", "word8"],
+    "divergence_alerts": ["alert1 if any"],
+    "between_the_lines": "A 2-3 paragraph analysis of what management is really communicating between the lines."
+}}
+
+Be precise with scores. Detect hedging language, overconfidence, and tone shifts.
+Return ONLY the JSON, no markdown formatting."""
+
+    raw = call_gemini(prompt, "You are a senior NLP analyst at a hedge fund specializing in earnings call analysis.")
+
+    # Force JSON format cleanup if AI included markdown blocks
+    raw = raw.replace("```json", "").replace("```", "").strip()
+
+    try:
+        # Match from first { to last }
+        json_match = re.search(r'\{.*\}', raw, re.DOTALL)
+        if json_match:
+            return json.loads(json_match.group(0))
+    except (json.JSONDecodeError, ValueError) as e:
+        logger.error(f"Sentiment parse error: {e}")
+
+    # Fallback structure
+    return {
+        "management_sentiment": {"score": 0, "label": "Neutral", "confidence_level": 50, "forward_guidance": "Cautious", "key_quotes": []},
+        "qa_sentiment": {"score": 0, "label": "Neutral", "confidence_level": 50, "analyst_concerns": []},
+        "key_themes": ["Unable to parse"],
+        "positive_words": [], "negative_words": [],
+        "divergence_alerts": [],
+        "between_the_lines": raw,
+    }
+
+
+# ---------------------------------------------------------------------------
+# Visualization helpers
+# ---------------------------------------------------------------------------
+def _render_gauge(score: float, label: str, title: str):
+    """Render a Plotly gauge chart for sentiment score."""
+    import plotly.graph_objects as go
+
+    color = "#10b981" if score > 0.2 else "#ef4444" if score < -0.2 else "#f59e0b"
+    fig = go.Figure(go.Indicator(
+        mode="gauge+number+delta",
+        value=score,
+        title={"text": title, "font": {"size": 16, "color": "white"}},
+        number={"font": {"color": "white"}},
+        gauge={
+            "axis": {"range": [-1, 1], "tickcolor": "white"},
+            "bar": {"color": color},
+            "bgcolor": "#1e1e1e",
+            "bordercolor": "#333",
+            "steps": [
+                {"range": [-1, -0.3], "color": "rgba(239,68,68,0.2)"},
+                {"range": [-0.3, 0.3], "color": "rgba(245,158,11,0.2)"},
+                {"range": [0.3, 1], "color": "rgba(16,185,129,0.2)"},
+            ],
+        },
+    ))
+    fig.update_layout(
+        paper_bgcolor="rgba(0,0,0,0)", font_color="white",
+        height=250, margin=dict(l=20, r=20, t=50, b=20),
+    )
+    return fig
+
+
+def _render_wordcloud(words: list, title: str, colormap: str = "Greens"):
+    """Generate a word cloud image from a list of words."""
+    import matplotlib
+    matplotlib.use("Agg")
+    import matplotlib.pyplot as plt
+    from wordcloud import WordCloud
+
+    if not words:
+        return None
+
+    text = " ".join(words)
+    wc = WordCloud(
+        width=400, height=200, background_color="black",
+        colormap=colormap, max_words=50, prefer_horizontal=0.7,
+    ).generate(text)
+
+    fig, ax = plt.subplots(figsize=(6, 3))
+    ax.imshow(wc, interpolation="bilinear")
+    ax.axis("off")
+    ax.set_title(title, color="white", fontsize=12, pad=10)
+    fig.patch.set_facecolor("black")
+    plt.tight_layout()
+    return fig
+
+
+# ---------------------------------------------------------------------------
+# Streamlit page renderer
+# ---------------------------------------------------------------------------
+def render_earnings_sentiment():
+    st.markdown("## 🎙️ Earnings Call Sentiment Intelligence")
+    st.caption("Analyze earnings call transcripts for hidden sentiment signals, management confidence, "
+               "and forward guidance shifts that predict future price moves.")
+
+    col1, col2, col3 = st.columns([2, 1, 1])
+    with col1:
+        ticker = st.text_input("Ticker Symbol:", placeholder="e.g. AAPL", key="es_ticker").upper().strip()
+    with col2:
+        quarter = st.selectbox("Quarter:", [None, 1, 2, 3, 4], format_func=lambda x: f"Q{x}" if x else "Auto-detect", key="es_q")
+    with col3:
+        year = st.number_input("Year:", min_value=2020, max_value=2026, value=datetime.now().year, key="es_year")
+
+    analyze_btn = st.button("🔍 Analyze Earnings Call", use_container_width=True, key="es_analyze")
+
+    if analyze_btn and ticker:
+        with st.status("🎙️ Analyzing earnings call...", expanded=True) as status:
+            status.write(f"📡 Searching for {ticker} Q{quarter or 'latest'} {year} transcript...")
+            transcript = _fetch_transcript(ticker, quarter, year)
+
+            if not transcript:
+                status.update(label="⚠️ No transcript found", state="error")
+                st.warning(f"Could not find earnings call transcript for {ticker}. "
+                           "Try specifying a different quarter or year.")
+                return
+
+            status.write("🧠 Running deep sentiment analysis...")
+            analysis = _analyze_sentiment(ticker, transcript)
+            st.session_state["es_analysis"] = analysis
+            st.session_state["es_display_ticker"] = ticker
+            status.update(label="✅ Analysis Complete!", state="complete", expanded=False)
+
+    # Display results
+    analysis = st.session_state.get("es_analysis")
+    if not analysis:
+        return
+
+    ticker_display = st.session_state.get("es_display_ticker", "")
+    st.markdown(f"### 📊 Sentiment Analysis: **{ticker_display}**")
+
+    mgmt = analysis.get("management_sentiment", {})
+    qa = analysis.get("qa_sentiment", {})
+
+    # Side-by-side gauges
+    col1, col2 = st.columns(2)
+    with col1:
+        st.markdown("#### 🎤 Management Prepared Remarks")
+        fig = _render_gauge(mgmt.get("score", 0), mgmt.get("label", "N/A"), "Management Sentiment")
+        st.plotly_chart(fig, use_container_width=True)
+
+        st.markdown(f"**Confidence Level:** {mgmt.get('confidence_level', 'N/A')}/100")
+        st.markdown(f"**Forward Guidance:** {mgmt.get('forward_guidance', 'N/A')}")
+
+        if mgmt.get("key_quotes"):
+            st.markdown("**Key Quotes:**")
+            for q in mgmt["key_quotes"]:
+                st.markdown(f'> *"{q}"*')
+
+    with col2:
+        st.markdown("#### ❓ Q&A Session")
+        fig = _render_gauge(qa.get("score", 0), qa.get("label", "N/A"), "Q&A Sentiment")
+        st.plotly_chart(fig, use_container_width=True)
+
+        st.markdown(f"**Confidence Level:** {qa.get('confidence_level', 'N/A')}/100")
+
+        if qa.get("analyst_concerns"):
+            st.markdown("**Analyst Concerns:**")
+            for c in qa["analyst_concerns"]:
+                st.markdown(f"- ⚠️ {c}")
+
+    # Key Themes
+    st.markdown("---")
+    st.markdown("#### 🏷️ Key Themes Mentioned")
+    themes = analysis.get("key_themes", [])
+    if themes:
+        cols = st.columns(min(len(themes), 5))
+        for i, theme in enumerate(themes[:5]):
+            with cols[i % 5]:
+                st.markdown(f"""
+                <div style="background: #1e1e1e; border: 1px solid #333; border-radius: 8px;
+                     padding: 12px; text-align: center; margin: 4px 0;">
+                    <span style="font-size: 0.9rem; color: #a78bfa;">{theme}</span>
+                </div>
+                """, unsafe_allow_html=True)
+
+    # Word Clouds
+    col1, col2 = st.columns(2)
+    with col1:
+        fig = _render_wordcloud(analysis.get("positive_words", []), "Positive Language", "Greens")
+        if fig:
+            st.pyplot(fig)
+    with col2:
+        fig = _render_wordcloud(analysis.get("negative_words", []), "Negative / Hedging Language", "Reds")
+        if fig:
+            st.pyplot(fig)
+
+    # Divergence Alerts
+    alerts = analysis.get("divergence_alerts", [])
+    if alerts:
+        st.markdown("---")
+        st.markdown("#### 🚨 Divergence Alerts")
+        for alert in alerts:
+            st.error(f"⚠️ {alert}")
+
+    # Between the Lines
+    st.markdown("---")
+    with st.expander("🔮 What Management Is Really Saying", expanded=True):
+        st.markdown(analysis.get("between_the_lines", "No analysis available."))
+
+    # PDF Export
+    st.markdown("---")
+    if st.button("📥 Download Sentiment Report as PDF", key="es_pdf"):
+        from features.utils import export_to_pdf
+        sections = [
+            {"title": f"Earnings Sentiment: {ticker_display}", "body": f"Management: {mgmt.get('label', 'N/A')} ({mgmt.get('score', 0):.2f})\nQ&A: {qa.get('label', 'N/A')} ({qa.get('score', 0):.2f})"},
+            {"title": "Key Themes", "body": ", ".join(themes)},
+            {"title": "Between the Lines", "body": analysis.get("between_the_lines", "")},
+        ]
+        pdf_bytes = export_to_pdf(sections, f"{ticker_display}_sentiment.pdf")
+        st.download_button("⬇️ Download PDF", data=pdf_bytes,
+                           file_name=f"{ticker_display}_Sentiment_Report.pdf",
+                           mime="application/pdf", key="es_pdf_dl")

features/macro_impact.py

@@ -0,0 +1,333 @@
+"""
+features/macro_impact.py — Macro Event Impact Analyzer
+How upcoming economic events (Fed, CPI, GDP, Jobs) will impact your watchlist.
+"""
+import streamlit as st
+import json
+import logging
+from datetime import datetime, timedelta
+
+logger = logging.getLogger("MacroImpact")
+
+# ---------------------------------------------------------------------------
+# Sector impact map (hardcoded domain knowledge)
+# ---------------------------------------------------------------------------
+EVENT_SECTOR_MAP = {
+    "Fed Rate Decision": {
+        "icon": "🏦",
+        "description": "Federal Reserve interest rate decision",
+        "impacted_sectors": ["Financials", "Technology", "Real Estate"],
+        "direction_hint": "Rate hikes typically pressure high-duration assets (Tech) and benefit Financials",
+    },
+    "CPI Release": {
+        "icon": "📊",
+        "description": "Consumer Price Index inflation data",
+        "impacted_sectors": ["Consumer Staples", "Energy", "Consumer Discretionary"],
+        "direction_hint": "Higher CPI benefits inflation hedges (Energy), pressures consumer spending",
+    },
+    "Jobs Report": {
+        "icon": "👷",
+        "description": "Non-Farm Payrolls employment data",
+        "impacted_sectors": ["Consumer Discretionary", "Financials", "Industrials"],
+        "direction_hint": "Strong jobs data supports consumer spending; may trigger rate hike fears",
+    },
+    "GDP Report": {
+        "icon": "📈",
+        "description": "Gross Domestic Product growth data",
+        "impacted_sectors": ["Industrials", "Materials", "Financials"],
+        "direction_hint": "Strong GDP supports cyclical sectors; weak GDP triggers defensive rotation",
+    },
+    "Retail Sales": {
+        "icon": "🛒",
+        "description": "Monthly retail sales data",
+        "impacted_sectors": ["Consumer Discretionary", "Consumer Staples"],
+        "direction_hint": "Direct indicator of consumer spending health",
+    },
+    "Housing Data": {
+        "icon": "🏠",
+        "description": "New home sales and housing starts",
+        "impacted_sectors": ["Real Estate", "Financials", "Materials"],
+        "direction_hint": "Key indicator for housing-related sectors and mortgage rates",
+    },
+}
+
+# Ticker to sector mapping (extends what portfolio_analyzer has)
+TICKER_SECTOR = {
+    "AAPL": "Technology", "MSFT": "Technology", "GOOGL": "Technology",
+    "AMZN": "Consumer Discretionary", "TSLA": "Consumer Discretionary",
+    "NVDA": "Technology", "META": "Technology", "JPM": "Financials",
+    "V": "Financials", "JNJ": "Healthcare", "WMT": "Consumer Staples",
+    "PG": "Consumer Staples", "UNH": "Healthcare", "HD": "Consumer Discretionary",
+    "DIS": "Communication Services", "BAC": "Financials", "XOM": "Energy",
+    "KO": "Consumer Staples", "PFE": "Healthcare", "NFLX": "Communication Services",
+    "INTC": "Technology", "AMD": "Technology", "CRM": "Technology",
+    "MA": "Financials", "BA": "Industrials", "CAT": "Industrials",
+    "GS": "Financials", "CVX": "Energy", "LMT": "Industrials",
+}
+
+
+# ---------------------------------------------------------------------------
+# Event calendar fetching
+# ---------------------------------------------------------------------------
+def _fetch_economic_calendar() -> list[dict]:
+    """Fetch upcoming economic events via Tavily search."""
+    from features.utils import run_tavily_search, call_gemini
+
+    now = datetime.now()
+    query = f"economic calendar {now.strftime('%B %Y')} Fed CPI GDP jobs report schedule"
+
+    try:
+        result = run_tavily_search(query, search_depth="advanced")
+        articles = []
+        for qr in result.get("data", []):
+            for r in qr.get("results", []):
+                articles.append(r.get("content", "")[:500])
+        calendar_text = "\n".join(articles[:5])
+    except Exception:
+        calendar_text = ""
+
+    prompt = f"""Based on the following economic calendar information and your knowledge of the {now.strftime('%B %Y')} 
+economic calendar, list the upcoming major US economic events for the next 30 days.
+
+Research data:
+{calendar_text}
+
+Return a JSON array of events. Each event should have:
+{{
+    "event": "Event Name" (must match one of: Fed Rate Decision, CPI Release, Jobs Report, GDP Report, Retail Sales, Housing Data),
+    "date": "YYYY-MM-DD" (estimated date),
+    "importance": "High" | "Medium" | "Low",
+    "consensus": "Brief expected outcome"
+}}
+
+Return 5-8 events. Use realistic dates in {now.strftime('%B-%March %Y')} timeframe.
+Return ONLY the JSON array, no markdown."""
+
+    raw = call_gemini(prompt, "You are an economic calendar analyst.")
+
+    import re
+    try:
+        json_match = re.search(r'\[.*\]', raw, re.DOTALL)
+        if json_match:
+            events = json.loads(json_match.group(0))
+            return events
+    except (json.JSONDecodeError, ValueError):
+        pass
+
+    # Fallback: generate reasonable defaults
+    return [
+        {"event": "CPI Release", "date": (now + timedelta(days=5)).strftime("%Y-%m-%d"), "importance": "High", "consensus": "Expected 3.1% YoY"},
+        {"event": "Fed Rate Decision", "date": (now + timedelta(days=12)).strftime("%Y-%m-%d"), "importance": "High", "consensus": "Expected hold at current range"},
+        {"event": "Jobs Report", "date": (now + timedelta(days=8)).strftime("%Y-%m-%d"), "importance": "High", "consensus": "Expected 180K new jobs"},
+        {"event": "GDP Report", "date": (now + timedelta(days=20)).strftime("%Y-%m-%d"), "importance": "Medium", "consensus": "Expected 2.1% annualized"},
+        {"event": "Retail Sales", "date": (now + timedelta(days=15)).strftime("%Y-%m-%d"), "importance": "Medium", "consensus": "Expected +0.3% MoM"},
+    ]
+
+
+# ---------------------------------------------------------------------------
+# Historical impact analysis
+# ---------------------------------------------------------------------------
+def _analyze_historical_impact(ticker: str, event_type: str) -> dict:
+    """Analyze historical price impact around past event occurrences."""
+    from features.utils import fetch_stock_data
+
+    try:
+        data = fetch_stock_data(ticker, "1Y")
+        ts = data.get("data", {})
+        sorted_times = sorted(ts.keys())
+
+        if len(sorted_times) < 30:
+            return {"avg_impact": 0, "occurrences": 0, "direction": "insufficient data"}
+
+        # Sample 5 evenly-spaced points as proxy for past events
+        prices = [float(ts[t]["4. close"]) for t in sorted_times]
+        impacts = []
+        step = len(prices) // 6
+        for i in range(1, 6):
+            idx = i * step
+            if idx + 3 < len(prices) and idx > 0:
+                before = prices[idx - 1]
+                after = prices[min(idx + 3, len(prices) - 1)]
+                pct = ((after - before) / before) * 100
+                impacts.append(pct)
+
+        if impacts:
+            avg = sum(impacts) / len(impacts)
+            return {
+                "avg_impact": round(avg, 2),
+                "occurrences": len(impacts),
+                "direction": "📈 Up" if avg > 0 else "📉 Down",
+                "max_impact": round(max(impacts), 2),
+                "min_impact": round(min(impacts), 2),
+            }
+    except Exception as e:
+        logger.warning(f"Historical analysis failed for {ticker}: {e}")
+
+    return {"avg_impact": 0, "occurrences": 0, "direction": "N/A"}
+
+
+# ---------------------------------------------------------------------------
+# Streamlit page renderer
+# ---------------------------------------------------------------------------
+def render_macro_impact():
+    st.markdown("## 🌍 Macro Event Impact Analyzer")
+    st.caption("See how upcoming economic events — Fed meetings, CPI, jobs reports — will specifically "
+               "impact your watchlist holdings, with historical correlation data.")
+
+    # Fetch calendar
+    if st.button("🔄 Refresh Economic Calendar", use_container_width=True, key="mi_refresh"):
+        with st.status("🌍 Fetching economic calendar...", expanded=True) as status:
+            status.write("📡 Searching for upcoming events...")
+            try:
+                events = _fetch_economic_calendar()
+                st.session_state["mi_events"] = events
+                status.update(label=f"✅ Found {len(events)} upcoming events", state="complete", expanded=False)
+            except Exception as e:
+                status.update(label="⚠️ Error fetching calendar", state="error")
+                st.warning(f"Could not fetch economic calendar: {e}")
+                return
+
+    events = st.session_state.get("mi_events", [])
+    if not events:
+        st.info("Click **Refresh Economic Calendar** to load upcoming events.")
+        return
+
+    # Load watchlist
+    from features.utils import load_watchlist
+    watchlist = load_watchlist()
+
+    # Timeline view
+    st.markdown("### 📅 Economic Calendar — Next 30 Days")
+
+    # Render as visual timeline
+    for event in sorted(events, key=lambda e: e.get("date", "")):
+        event_name = event.get("event", "Unknown")
+        event_info = EVENT_SECTOR_MAP.get(event_name, {})
+        icon = event_info.get("icon", "📌")
+        importance = event.get("importance", "Medium")
+        imp_color = "#ef4444" if importance == "High" else "#f59e0b" if importance == "Medium" else "#10b981"
+
+        # Find affected watchlist tickers
+        impacted_sectors = event_info.get("impacted_sectors", [])
+        affected_tickers = [t for t in watchlist if TICKER_SECTOR.get(t, "") in impacted_sectors]
+
+        st.markdown(f"""
+        <div style="background: #121212; border: 1px solid #333; border-left: 4px solid {imp_color};
+             border-radius: 8px; padding: 16px; margin-bottom: 12px;">
+            <div style="display: flex; justify-content: space-between; align-items: center;">
+                <div>
+                    <span style="font-size: 1.2rem;">{icon} <b>{event_name}</b></span>
+                    <span style="color: {imp_color}; margin-left: 8px; font-size: 0.8rem;
+                           background: {imp_color}22; padding: 2px 8px; border-radius: 4px;">
+                        {importance}
+                    </span>
+                </div>
+                <span style="color: #9ca3af; font-size: 0.9rem;">📅 {event.get('date', 'TBD')}</span>
+            </div>
+            <p style="color: #9ca3af; margin: 8px 0 4px 0; font-size: 0.9rem;">
+                {event_info.get('description', '')}
+            </p>
+            <p style="color: #a78bfa; font-size: 0.85rem; margin: 4px 0;">
+                📌 Consensus: {event.get('consensus', 'N/A')}
+            </p>
+            <p style="color: #f59e0b; font-size: 0.85rem; margin: 4px 0;">
+                🎯 Impacted sectors: {', '.join(impacted_sectors) if impacted_sectors else 'General market'}
+            </p>
+            <p style="color: #10b981; font-size: 0.85rem; margin: 4px 0;">
+                🛡️ Your affected tickers: <b>{', '.join(affected_tickers) if affected_tickers else 'None in watchlist'}</b>
+            </p>
+        </div>
+        """, unsafe_allow_html=True)
+
+    # Detailed impact analysis
+    st.markdown("---")
+    st.markdown("### 🔬 Historical Impact Analysis")
+    st.caption("Select an event to see how your watchlist tickers have historically performed around similar events.")
+
+    event_names = list(set(e.get("event", "") for e in events))
+    selected_event = st.selectbox("Select Event Type:", event_names, key="mi_event_select")
+
+    if selected_event and watchlist:
+        event_info = EVENT_SECTOR_MAP.get(selected_event, {})
+        impacted_sectors = event_info.get("impacted_sectors", [])
+        affected = [t for t in watchlist if TICKER_SECTOR.get(t, "") in impacted_sectors]
+
+        if not affected:
+            affected = watchlist[:3]  # Analyze top 3 if no sector match
+            st.info(f"No direct sector match. Analyzing top watchlist tickers instead.")
+
+        if st.button(f"📊 Analyze Impact on {len(affected)} Tickers", key="mi_analyze", use_container_width=True):
+            results = []
+            progress = st.progress(0)
+            for i, ticker in enumerate(affected):
+                impact = _analyze_historical_impact(ticker, selected_event)
+                impact["ticker"] = ticker
+                impact["sector"] = TICKER_SECTOR.get(ticker, "Other")
+                results.append(impact)
+                progress.progress((i + 1) / len(affected))
+
+            st.session_state["mi_results"] = results
+            st.session_state["mi_selected_event"] = selected_event
+
+    # Display results
+    results = st.session_state.get("mi_results", [])
+    if results:
+        selected_evt = st.session_state.get("mi_selected_event", "")
+        st.markdown(f"#### Historical Impact: **{selected_evt}**")
+
+        import pandas as pd
+        df = pd.DataFrame(results)
+        display_cols = [c for c in ["ticker", "sector", "avg_impact", "direction", "max_impact", "min_impact"] if c in df.columns]
+        st.dataframe(df[display_cols], use_container_width=True, hide_index=True)
+
+        # Visual bar chart
+        if "avg_impact" in df.columns:
+            import plotly.express as px
+            fig = px.bar(df, x="ticker", y="avg_impact",
+                         color="avg_impact",
+                         color_continuous_scale=["#ef4444", "#f59e0b", "#10b981"],
+                         template="plotly_dark",
+                         title=f"Average 3-Day Price Impact After {selected_evt}",
+                         labels={"avg_impact": "Avg Impact (%)", "ticker": ""})
+            fig.add_hline(y=0, line_dash="dash", line_color="white")
+            st.plotly_chart(fig, use_container_width=True)
+
+            # Key insight
+            for r in results:
+                ticker = r["ticker"]
+                avg = r.get("avg_impact", 0)
+                direction = "dropped" if avg < 0 else "gained"
+                st.markdown(f"- Based on historical analysis, **{ticker}** {direction} an average of "
+                            f"**{abs(avg):.1f}%** in 3 days after {selected_evt}")
+
+    # AI Pre-Event Briefing
+    st.markdown("---")
+    st.markdown("### 🤖 AI Pre-Event Briefing")
+    if st.button("Generate Pre-Event Briefing", key="mi_briefing", use_container_width=True):
+        from features.utils import call_gemini
+
+        events_summary = json.dumps(events, indent=2)
+        watchlist_str = ", ".join(watchlist) if watchlist else "None"
+        results_str = json.dumps(results, indent=2) if results else "No historical data yet."
+
+        prompt = f"""You are a macro strategist preparing a client for upcoming economic events.
+
+UPCOMING EVENTS (next 30 days):
+{events_summary}
+
+CLIENT'S WATCHLIST: {watchlist_str}
+
+HISTORICAL IMPACT DATA:
+{results_str}
+
+Write a 2-3 paragraph "Pre-Event Briefing" that:
+1. Highlights the most critical upcoming event and why it matters
+2. Identifies which watchlist holdings are most at risk/opportunity
+3. Provides specific positioning recommendations (what to hedge, what to hold)
+4. Assigns a RISK SCORE (1-10) for the overall 30-day macro window
+
+Be specific, actionable, and data-driven."""
+
+        with st.spinner("🤖 Generating briefing..."):
+            briefing = call_gemini(prompt, "You are a senior macro strategist at a global asset management firm.")
+            st.markdown(briefing)

features/portfolio_analyzer.py

@@ -0,0 +1,527 @@
+"""
+features/portfolio_analyzer.py — Personal Portfolio Document Analyzer
+Upload CSV/PDF brokerage statements, get AI-driven portfolio insights.
+"""
+import streamlit as st
+import pandas as pd
+import json
+import logging
+import io
+from datetime import datetime
+
+logger = logging.getLogger("PortfolioAnalyzer")
+
+# ---------------------------------------------------------------------------
+# Sector mapping for common tickers (fallback)
+# ---------------------------------------------------------------------------
+SECTOR_MAP = {
+    "AAPL": "Technology", "MSFT": "Technology", "GOOGL": "Technology",
+    "AMZN": "Consumer Discretionary", "TSLA": "Consumer Discretionary",
+    "NVDA": "Technology", "META": "Technology", "JPM": "Financials",
+    "V": "Financials", "JNJ": "Healthcare", "WMT": "Consumer Staples",
+    "PG": "Consumer Staples", "UNH": "Healthcare", "HD": "Consumer Discretionary",
+    "DIS": "Communication Services", "BAC": "Financials", "XOM": "Energy",
+    "KO": "Consumer Staples", "PFE": "Healthcare", "NFLX": "Communication Services",
+    "INTC": "Technology", "AMD": "Technology", "CRM": "Technology",
+    "MA": "Financials", "BA": "Industrials", "CAT": "Industrials",
+}
+
+# ---------------------------------------------------------------------------
+# CSV parsers for common brokerage formats
+# ---------------------------------------------------------------------------
+COLUMN_ALIASES = {
+    "ticker": ["ticker", "symbol", "stock", "instrument", "security"],
+    "shares": ["shares", "quantity", "qty", "units", "amount", "open_quantity", "net_quantity", "quantity_available"],
+    "avg_cost": ["avg_cost", "average_cost", "cost_basis", "avg_price",
+                 "average_price", "purchase_price", "cost_per_share", "buy_average"],
+    "current_price": ["current_price", "market_price", "price", "last_price",
+                      "current_value_per_share", "mark"],
+    "description": ["description", "action", "activity", "type", "transaction", "details"]
+}
+
+
+def _normalize_columns(df: pd.DataFrame) -> pd.DataFrame | None:
+    """Try to map brokerage-specific columns to standard names."""
+    col_lower = {c: str(c).lower().strip().replace(" ", "_").replace(".", "") for c in df.columns}
+    df = df.rename(columns=col_lower)
+
+    # Custom handling for Zerodha P&L format
+    if "open_quantity" in df.columns and "open_value" in df.columns:
+        df["open_quantity"] = pd.to_numeric(df["open_quantity"], errors="coerce").fillna(0)
+        # Keep non-zero positions (handle negative quantities for short/accounting entries)
+        df = df[df["open_quantity"] != 0].copy()
+        df["shares"] = df["open_quantity"].abs()
+        
+        df["open_value"] = pd.to_numeric(df["open_value"], errors="coerce").fillna(0).abs()
+        df["avg_cost"] = df["open_value"] / df["shares"]
+
+
+    mapping = {}
+    for standard, aliases in COLUMN_ALIASES.items():
+        if standard in df.columns:
+            continue  # Already mapped via custom logic above
+        for alias in aliases:
+            if alias in df.columns:
+                mapping[alias] = standard
+                break
+
+    if "ticker" not in df.columns and "ticker" not in mapping.values():
+        return None
+
+    df = df.rename(columns=mapping)
+    
+    # Flag to check if this is an activity log (has tickers/instruments but no shares)
+    is_activity_log = "shares" not in df.columns
+
+    # Keep only mapped + extra columns
+    available = [c for c in ["ticker", "shares", "avg_cost", "current_price", "description"] if c in df.columns]
+    if len(available) < 2:
+        return None
+        
+    df = df[available].copy()
+    
+    if is_activity_log:
+        df["shares"] = 1.0 # Default to 1 so the analyzer can still fetch prices and analyze the asset
+        if "avg_cost" not in df.columns:
+            df["avg_cost"] = 0.0
+        # Drop duplicate transactions so we just get a unique list of assets traded
+        df = df.drop_duplicates(subset=["ticker"]).copy()
+    
+    # Ensure numeric columns are forced to float to prevent missing data errors
+    for col in ["shares", "avg_cost", "current_price"]:
+        if col in df.columns:
+            df[col] = pd.to_numeric(df[col], errors="coerce").fillna(0)
+            
+    # Final filter: remove rows with 0 shares (closed positions)
+    if "shares" in df.columns:
+        df = df[df["shares"] > 0]
+        
+    # Cleanup empty tickers which might be generated from summary rows
+    df = df[df["ticker"].notna()]
+    df = df[df["ticker"].astype(str).str.strip() != ""]
+        
+    if df.empty:
+        return None
+        
+    return df
+
+
+def _find_header_and_normalize(df: pd.DataFrame) -> pd.DataFrame | None:
+    """Find the actual table header (skipping metadata rows at top) and normalize."""
+    import streamlit as st
+    st.write("DEBUG: Raw DataFrame Head:", df.head())
+    
+    target_keywords = set()
+    for aliases in COLUMN_ALIASES.values():
+        target_keywords.update(aliases)
+        
+    header_idx = -1
+    max_matches = 0
+    
+    # Search the first 50 rows for the row with the most matching target columns
+    for idx, row in df.head(50).iterrows():
+        # Clean up cell text for comparison
+        row_vals = [str(val).lower().strip().replace(" ", "_").replace(".", "") for val in row.values]
+        matches = sum(1 for val in row_vals if val in target_keywords)
+        
+        if matches > max_matches:
+            max_matches = matches
+            header_idx = idx
+
+    # If we found a row with at least 2 matching columns (e.g. Symbol and Quantity)
+    if header_idx != -1 and max_matches >= 2:
+        df.columns = [str(c).strip() for c in df.iloc[header_idx].values]
+        df = df.iloc[header_idx + 1:].reset_index(drop=True)
+    elif header_idx == -1:
+        # Fallback if we didn't search with header=None or couldn't find matches
+        pass
+
+    return _normalize_columns(df)
+
+
+def _parse_csv(uploaded_file) -> pd.DataFrame | None:
+    """Parse uploaded CSV and normalize columns, skipping metadata at top."""
+    try:
+        content = uploaded_file.getvalue()
+        with open("debug_raw_file.csv", "wb") as f:
+            f.write(content)
+        df = pd.read_csv(io.BytesIO(content), header=None)
+        return _find_header_and_normalize(df)
+    except Exception as e:
+        logger.error(f"CSV parse error: {e}")
+        return None
+
+
+def _parse_excel(uploaded_file) -> pd.DataFrame | None:
+    """Parse uploaded Excel and normalize columns, skipping metadata at top."""
+    try:
+        content = uploaded_file.getvalue()
+        with open("debug_raw_file.xlsx", "wb") as f:
+            f.write(content)
+        df = pd.read_excel(io.BytesIO(content), header=None)
+        return _find_header_and_normalize(df)
+    except Exception as e:
+        logger.error(f"Excel parse error: {e}")
+        return None
+def _parse_pdf(uploaded_file) -> pd.DataFrame | None:
+    """Extract holdings from a PDF brokerage statement.
+    
+    Strategy:
+    1. Try pdfplumber table extraction first (structured PDFs)
+    2. Fall back to Gemini AI extraction from raw text (any format)
+    """
+    try:
+        import pdfplumber
+    except ImportError:
+        logger.error("pdfplumber not installed")
+        return None
+
+    # --- Stage 1: Try structured table extraction ---
+    full_text = ""
+    try:
+        text_rows = []
+        uploaded_file.seek(0)
+        with pdfplumber.open(uploaded_file) as pdf:
+            for page in pdf.pages:
+                full_text += (page.extract_text() or "") + "\n"
+                tables = page.extract_tables()
+                for table in tables:
+                    if not table or len(table) < 2:
+                        continue
+                        
+                    # Clean up rows
+                    cleaned_table = []
+                    for row in table:
+                        if row and any(row):
+                            cleaned_table.append([str(c).strip() if c else "" for c in row])
+                            
+                    if len(cleaned_table) > 1:
+                        # Test this specific table
+                        df = pd.DataFrame(cleaned_table[1:], columns=cleaned_table[0])
+                        result = _normalize_columns(df)
+                        if result is not None and not result.empty:
+                            return result  # We found a valid holdings table!
+                            
+        # If we loop through all tables and find nothing valid
+        logger.info("PDF table extraction yielded no valid holdings. Falling back to AI.")
+    except Exception as e:
+        logger.warning(f"PDF table extraction failed, falling back to AI: {e}")
+        # Try to get raw text anyway if it wasn't extracted
+        if not full_text:
+            try:
+                uploaded_file.seek(0)
+                with pdfplumber.open(uploaded_file) as pdf:
+                    full_text = "\n".join(page.extract_text() or "" for page in pdf.pages)
+            except Exception:
+                return None
+
+    # --- Stage 2: AI-powered extraction from raw text ---
+    if not full_text or len(full_text.strip()) < 20:
+        return None
+
+    try:
+        from features.utils import call_gemini
+        import re
+
+        # Truncate to avoid token limits
+        text_chunk = full_text[:8000]
+
+        prompt = f"""You are a senior financial analyst and data extraction expert. Extract the final, current stock/ETF equity holdings from this brokerage statement text.
+
+DOCUMENT TEXT:
+---
+{text_chunk}
+---
+
+Extract ALL current investment holdings you can find. 
+CRITICAL RULES FOR EXTRACTION:
+1. **Holdings Snapshots:** Look first for a "Positions", "Holdings", or "Asset Allocation" summary table showing current shares owned.
+2. **Transaction Ledgers (Acorns/etc):** If the document ONLY lists "Securities Bought" and "Securities Sold" without a final summary table, you MUST calculate the net holdings yourself.
+   - For each ticker, sum the shares Bought and subtract the shares Sold.
+   - If the net shares are > 0.0001, include it as a current holding.
+   - To estimate `avg_cost`, take the total $ Amount Bought divided by total Shares Bought.
+3. **Valid Assets:** Include stocks, equity ETFs, and bond ETFs (like AGG, ISTB, BND). Do not include raw cash/MMFs.
+4. **Data Formatting:**
+   - ticker: The standard ticker symbol (e.g., AAPL, VOO, AGG, IXUS). Do not use full names, ONLY the 1-5 letter ticker.
+   - shares: Number of shares currently held (as a plain number, no commas).
+   - avg_cost: Average cost per share (as a plain number, no $ sign). If unknown, use 0.
+
+Return ONLY a valid JSON array. If you find NO absolute current holdings (or if net shares = 0), return an empty array: []
+Example format:
+[
+    {{"ticker": "VOO", "shares": 1.55, "avg_cost": 415.25}},
+    {{"ticker": "AGG", "shares": 3.2, "avg_cost": 98.10}}
+]
+
+Return ONLY the JSON array, no markdown formatting or explanation."""
+
+        raw = call_gemini(prompt, "You are a precise financial document parser. Extract data accurately.")
+
+        # Parse JSON from response
+        json_match = re.search(r'\[.*\]', raw, re.DOTALL)
+        if json_match:
+            holdings_list = json.loads(json_match.group(0))
+            if holdings_list:
+                df = pd.DataFrame(holdings_list)
+                # Clean up columns
+                for col in ["ticker", "shares", "avg_cost"]:
+                    if col not in df.columns:
+                        df[col] = 0 if col != "ticker" else "UNKNOWN"
+                df["shares"] = pd.to_numeric(df["shares"], errors="coerce").fillna(0)
+                df["avg_cost"] = pd.to_numeric(df["avg_cost"], errors="coerce").fillna(0)
+                df["ticker"] = df["ticker"].astype(str).str.upper().str.strip()
+                # Filter out invalid rows
+                df = df[df["ticker"].str.len() > 0]
+                df = df[df["ticker"] != "UNKNOWN"]
+                df = df[df["shares"] > 0]
+                if not df.empty:
+                    logger.info(f"AI extracted {len(df)} holdings from PDF")
+                    return df
+    except Exception as e:
+        logger.error(f"AI PDF extraction failed: {e}")
+
+    return None
+
+
+# ---------------------------------------------------------------------------
+# Analysis logic
+# ---------------------------------------------------------------------------
+def _enrich_holdings(holdings: pd.DataFrame) -> pd.DataFrame:
+    """Fetch current prices and compute P&L metrics."""
+    from features.utils import fetch_stock_data
+
+    if "shares" in holdings.columns:
+        holdings["shares"] = pd.to_numeric(holdings["shares"], errors="coerce").fillna(0)
+    if "avg_cost" in holdings.columns:
+        holdings["avg_cost"] = pd.to_numeric(holdings["avg_cost"], errors="coerce").fillna(0)
+
+    current_prices = []
+    for _, row in holdings.iterrows():
+        ticker = str(row.get("ticker", "")).upper().strip()
+        if "current_price" in holdings.columns and pd.notna(row.get("current_price")):
+            current_prices.append(float(row["current_price"]))
+            continue
+        try:
+            data = fetch_stock_data(ticker, "INTRADAY")
+            ts = data.get("data", {})
+            sorted_times = sorted(ts.keys())
+            if sorted_times:
+                current_prices.append(float(ts[sorted_times[-1]]["4. close"]))
+            else:
+                current_prices.append(0.0)
+        except Exception:
+            current_prices.append(0.0)
+
+    holdings["current_price"] = current_prices
+    if "shares" in holdings.columns and "avg_cost" in holdings.columns:
+        holdings["market_value"] = holdings["shares"] * holdings["current_price"]
+        holdings["cost_basis_total"] = holdings["shares"] * holdings["avg_cost"]
+        holdings["unrealized_pnl"] = holdings["market_value"] - holdings["cost_basis_total"]
+        holdings["pnl_pct"] = ((holdings["unrealized_pnl"] / holdings["cost_basis_total"]) * 100).round(2)
+        total_value = holdings["market_value"].sum()
+        holdings["weight_pct"] = ((holdings["market_value"] / total_value) * 100).round(2) if total_value > 0 else 0
+    else:
+        holdings["market_value"] = 0
+        holdings["weight_pct"] = 0
+        holdings["unrealized_pnl"] = 0
+        holdings["pnl_pct"] = 0
+
+    # Assign base sectors
+    holdings["sector"] = holdings["ticker"].apply(
+        lambda t: SECTOR_MAP.get(str(t).upper(), "Other")
+    )
+
+    # Dynamically resolve "Other" sectors via AI
+    unknown_tickers = holdings[holdings["sector"] == "Other"]["ticker"].unique().tolist()
+    if unknown_tickers:
+        try:
+            from features.utils import call_gemini
+            import json
+            import re
+            
+            prompt = f"""Categorize these stock tickers into their standard GICS sectors (e.g., Technology, Financials, Energy, Consumer Staples, Healthcare, Utilities, Basic Materials, etc.). 
+If they are international or Indian stocks, classify them correctly based on their real-world industry.
+Return ONLY a valid JSON dictionary mapping the ticker to its sector string.
+Example: {{"AAPL": "Technology", "COALINDIA": "Energy"}}
+Tickers to classify: {unknown_tickers}"""
+            
+            response = call_gemini(prompt, "You are a financial data categorizer. Return only JSON.")
+            json_match = re.search(r'\{.*\}', response, re.DOTALL)
+            if json_match:
+                sector_updates = json.loads(json_match.group(0))
+                holdings["sector"] = holdings.apply(
+                    lambda row: sector_updates.get(row["ticker"], row["sector"]) if row["sector"] == "Other" else row["sector"],
+                    axis=1
+                )
+        except Exception as e:
+            logger.warning(f"Failed to dynamically fetch sectors: {e}")
+
+    return holdings
+
+
+def _generate_ai_analysis(holdings: pd.DataFrame) -> dict:
+    """Run Gemini to generate portfolio health narrative + recommendations."""
+    from features.utils import call_gemini
+
+    summary = holdings.to_string(index=False)
+    total_value = holdings["market_value"].sum()
+    total_pnl = holdings.get("unrealized_pnl", pd.Series([0])).sum()
+    over_concentrated = holdings[holdings["weight_pct"] > 20]["ticker"].tolist() if "weight_pct" in holdings.columns else []
+
+    prompt = f"""You are a certified financial planner analyzing a personal portfolio.
+
+Portfolio Summary:
+{summary}
+
+Total Portfolio Value: ${total_value:,.2f}
+Total Unrealized P&L: ${total_pnl:,.2f}
+Over-concentrated positions (>20% weight): {over_concentrated if over_concentrated else 'None'}
+
+Provide:
+1. **Portfolio Health Narrative** (2-3 paragraphs): Overall assessment, diversification quality, risk level
+2. **Rebalancing Recommendations** (numbered list of 3-5 specific actions)
+3. **Risk Flags** (any issues to address urgently)
+
+Be specific with ticker names and percentages. Be actionable."""
+
+    narrative = call_gemini(prompt, "You are a senior portfolio advisor at a wealth management firm.")
+    return {"narrative": narrative, "over_concentrated": over_concentrated}
+
+
+# ---------------------------------------------------------------------------
+# Streamlit page renderer
+# ---------------------------------------------------------------------------
+def render_portfolio_analyzer():
+    st.markdown("## 💼 Portfolio Document Analyzer")
+    st.caption("Upload your brokerage CSV or PDF statement to get AI-driven portfolio insights, "
+               "sector allocation, and personalized rebalancing recommendations.")
+
+    uploaded = st.file_uploader(
+        "Upload Brokerage Statement",
+        type=["csv", "pdf", "xlsx", "xls"],
+        help="Supported: Robinhood, Schwab, Fidelity CSV/Excel exports, or any PDF with holdings tables.",
+        key="pa_upload",
+    )
+
+    if uploaded is not None:
+        # Parse based on file type
+        if uploaded.name.lower().endswith(".csv"):
+            holdings = _parse_csv(uploaded)
+        elif uploaded.name.lower().endswith((".xlsx", ".xls")):
+            holdings = _parse_excel(uploaded)
+        else:
+            holdings = _parse_pdf(uploaded)
+
+        if holdings is None or holdings.empty:
+            st.warning("⚠️ Could not parse holdings from this file. "
+                       "Please ensure your CSV has columns like: ticker/symbol, shares/quantity, avg_cost/cost_basis.")
+            st.info("**Supported column names:** ticker, symbol, shares, quantity, avg_cost, cost_basis, current_price, instrument, description")
+            st.write("DEBUG: I tried to parse it but `holdings` returned empty. Is Streamlit running the latest code?")
+            return
+
+        st.success(f"✅ Parsed {len(holdings)} holdings from **{uploaded.name}**")
+        st.write("DEBUG: Successfully parsed holdings DataFrame:", holdings)
+
+        with st.status("📊 Analyzing portfolio...", expanded=True) as status:
+            status.write("💰 Fetching current prices...")
+            holdings = _enrich_holdings(holdings)
+            status.write("🤖 Running AI analysis...")
+            ai_result = _generate_ai_analysis(holdings)
+            status.update(label="✅ Analysis Complete!", state="complete", expanded=False)
+
+        st.session_state["pa_holdings"] = holdings
+        st.session_state["pa_ai"] = ai_result
+
+    # Display results
+    holdings = st.session_state.get("pa_holdings")
+    ai_result = st.session_state.get("pa_ai")
+
+    if holdings is not None and not holdings.empty:
+        st.markdown("### 📋 Holdings Overview")
+
+        # Color-coded holdings table
+        def _color_pnl(val):
+            if isinstance(val, (int, float)):
+                color = "#10b981" if val >= 0 else "#ef4444"
+                return f"color: {color}; font-weight: 600"
+            return ""
+
+        display_cols = [c for c in ["ticker", "shares", "avg_cost", "current_price",
+                                     "market_value", "unrealized_pnl", "pnl_pct",
+                                     "weight_pct", "sector"] if c in holdings.columns]
+        styled = holdings[display_cols].style.applymap(
+            _color_pnl, subset=[c for c in ["unrealized_pnl", "pnl_pct"] if c in display_cols]
+        ).format({
+            c: "${:,.2f}" for c in ["avg_cost", "current_price", "market_value",
+                                     "unrealized_pnl"] if c in display_cols
+        } | {c: "{:.1f}%" for c in ["pnl_pct", "weight_pct"] if c in display_cols})
+
+        st.dataframe(styled, use_container_width=True, hide_index=True)
+
+        # Sector allocation pie chart
+        col1, col2 = st.columns(2)
+        with col1:
+            st.markdown("### 🥧 Sector Allocation")
+            if "sector" in holdings.columns and "market_value" in holdings.columns:
+                import plotly.express as px
+                sector_data = holdings.groupby("sector")["market_value"].sum().reset_index()
+                fig = px.pie(sector_data, values="market_value", names="sector",
+                             template="plotly_dark",
+                             color_discrete_sequence=px.colors.qualitative.Set2)
+                fig.update_traces(textposition="inside", textinfo="percent+label")
+                st.plotly_chart(fig, use_container_width=True)
+
+        with col2:
+            st.markdown("### 📊 Position Weights")
+            if "weight_pct" in holdings.columns:
+                import plotly.express as px
+                fig = px.bar(holdings.sort_values("weight_pct", ascending=True),
+                             x="weight_pct", y="ticker", orientation="h",
+                             template="plotly_dark",
+                             labels={"weight_pct": "Weight (%)", "ticker": ""},
+                             color="weight_pct",
+                             color_continuous_scale="Viridis")
+                # Add 20% concentration line
+                fig.add_vline(x=20, line_dash="dash", line_color="#ef4444",
+                              annotation_text="20% threshold", annotation_position="top")
+                st.plotly_chart(fig, use_container_width=True)
+
+        # AI narrative
+        if ai_result:
+            st.markdown(f"""
+            <div class="report-section" style="border-left: 3px solid #8b5cf6; margin-top: 2rem;">
+                <h4 style="color: #a78bfa;">🤖 AI Portfolio Health Assessment</h4>
+                <div class="alert-body" style="font-size: 1.05rem;">
+                    {ai_result.get("narrative", "")}
+                </div>
+            </div>
+            """, unsafe_allow_html=True)
+
+            if ai_result.get("over_concentrated"):
+                st.markdown(f"""
+                <div class="alert-card alert-market" style="margin-top: 1rem;">
+                    <div class="alert-header">
+                        <span>⚠️ Concentration Alert</span>
+                    </div>
+                    <div class="alert-body">
+                        **Over-concentrated positions detected:** {', '.join(ai_result['over_concentrated'])} (> 20% portfolio weight)
+                    </div>
+                </div>
+                """, unsafe_allow_html=True)
+
+        # PDF export
+        st.markdown("---")
+        if st.button("📥 Download Analysis as PDF", key="pa_pdf"):
+            from features.utils import export_to_pdf
+            sections = [
+                {"title": "Portfolio Summary", "body": holdings.to_string(index=False)},
+                {"title": "AI Health Assessment", "body": ai_result.get("narrative", "") if ai_result else ""},
+            ]
+            pdf_bytes = export_to_pdf(sections, "portfolio_analysis.pdf")
+            st.download_button(
+                label="⬇️ Download PDF",
+                data=pdf_bytes,
+                file_name="Portfolio_Analysis.pdf",
+                mime="application/pdf",
+                key="pa_pdf_dl",
+            )

features/research_report.py

@@ -0,0 +1,472 @@
+"""
+features/research_report.py — AI-Generated Investment Research Reports
+Uses LangGraph multi-agent pipeline: Fundamentals, News, Risk, Verdict agents.
+Data from SEC EDGAR, Tavily, Alpha Vantage.
+"""
+import streamlit as st
+import json
+import re
+import time
+import requests
+import logging
+from typing import TypedDict, Dict, Any
+from datetime import datetime
+from functools import lru_cache
+
+from langgraph.graph import StateGraph, END
+
+logger = logging.getLogger("ResearchReport")
+
+# ---------------------------------------------------------------------------
+# SEC EDGAR — Dynamic CIK lookup (supports ALL US public companies)
+# ---------------------------------------------------------------------------
+SEC_HEADERS = {"User-Agent": "SentinelAI research@sentinel-ai.app", "Accept-Encoding": "gzip, deflate"}
+
+_cik_cache: dict = {}  # in-memory cache: ticker -> CIK
+
+
+def _get_cik_for_ticker(ticker: str) -> str | None:
+    """Look up CIK number for any US public company ticker via SEC EDGAR."""
+    global _cik_cache
+    ticker = ticker.upper().strip()
+
+    # Return from cache if available
+    if ticker in _cik_cache:
+        return _cik_cache[ticker]
+
+    # Fetch the full SEC ticker→CIK mapping (cached after first call)
+    if not _cik_cache:
+        try:
+            url = "https://www.sec.gov/files/company_tickers.json"
+            resp = requests.get(url, headers=SEC_HEADERS, timeout=15)
+            resp.raise_for_status()
+            data = resp.json()
+            for entry in data.values():
+                t = str(entry.get("ticker", "")).upper()
+                cik = str(entry.get("cik_str", "")).zfill(10)
+                _cik_cache[t] = cik
+            logger.info(f"Loaded {len(_cik_cache)} ticker→CIK mappings from SEC EDGAR")
+        except Exception as e:
+            logger.error(f"Failed to fetch SEC ticker mappings: {e}")
+            return None
+
+    return _cik_cache.get(ticker)
+
+
+# ---------------------------------------------------------------------------
+# Smart ticker resolution (supports company names AND ticker symbols)
+# ---------------------------------------------------------------------------
+# Common company names → tickers (fast path)
+_COMMON_NAMES = {
+    "AMAZON": "AMZN", "APPLE": "AAPL", "GOOGLE": "GOOGL", "ALPHABET": "GOOGL",
+    "MICROSOFT": "MSFT", "TESLA": "TSLA", "NVIDIA": "NVDA", "META": "META",
+    "FACEBOOK": "META", "NETFLIX": "NFLX", "AMD": "AMD", "INTEL": "INTC",
+    "DISNEY": "DIS", "WALMART": "WMT", "JPMORGAN": "JPM", "GOLDMAN": "GS",
+    "BERKSHIRE": "BRK-B", "VISA": "V", "MASTERCARD": "MA", "PAYPAL": "PYPL",
+    "UBER": "UBER", "AIRBNB": "ABNB", "SNOWFLAKE": "SNOW", "PALANTIR": "PLTR",
+    "COINBASE": "COIN", "SPOTIFY": "SPOT", "SHOPIFY": "SHOP", "SALESFORCE": "CRM",
+    "ORACLE": "ORCL", "IBM": "IBM", "CISCO": "CSCO", "ADOBE": "ADBE",
+    "BOEING": "BA", "FORD": "F", "GM": "GM", "TOYOTA": "TM",
+    "COCA-COLA": "KO", "COCACOLA": "KO", "PEPSI": "PEP", "NIKE": "NKE",
+    "STARBUCKS": "SBUX", "MCDONALDS": "MCD", "PFIZER": "PFE", "JOHNSON": "JNJ",
+    "EXXON": "XOM", "CHEVRON": "CVX", "COSTCO": "COST", "TARGET": "TGT",
+    "BROADCOM": "AVGO", "QUALCOMM": "QCOM", "MICRON": "MU", "RIVIAN": "RIVN",
+    "ROBINHOOD": "HOOD", "SOFI": "SOFI", "BLOCK": "SQ", "SQUARE": "SQ",
+}
+
+# Name-to-ticker cache from SEC EDGAR
+_name_to_ticker_cache: dict = {}
+
+
+def _resolve_ticker(user_input: str) -> str:
+    """Resolve user input (company name or ticker) to a valid ticker symbol."""
+    global _name_to_ticker_cache
+    cleaned = user_input.upper().strip()
+
+    # 1. Check if it's already a valid short ticker (1-5 chars, all alpha)
+    if len(cleaned) <= 5 and cleaned.replace("-", "").isalpha():
+        # Verify it exists in SEC data (if cache is loaded)
+        if _cik_cache and cleaned in _cik_cache:
+            return cleaned
+        # If cache is empty, trust the user
+        if not _cik_cache:
+            return cleaned
+
+    # 2. Fast path: common names
+    if cleaned in _COMMON_NAMES:
+        logger.info(f"Resolved '{user_input}' → '{_COMMON_NAMES[cleaned]}' (common name)")
+        return _COMMON_NAMES[cleaned]
+
+    # 3. Check partial matches in common names
+    for name, ticker in _COMMON_NAMES.items():
+        if name in cleaned or cleaned in name:
+            logger.info(f"Resolved '{user_input}' → '{ticker}' (partial match: {name})")
+            return ticker
+
+    # 4. Search SEC EDGAR company names (lazy load)
+    if not _name_to_ticker_cache:
+        try:
+            url = "https://www.sec.gov/files/company_tickers.json"
+            resp = requests.get(url, headers=SEC_HEADERS, timeout=15)
+            resp.raise_for_status()
+            data = resp.json()
+            for entry in data.values():
+                name = str(entry.get("title", "")).upper()
+                ticker = str(entry.get("ticker", "")).upper()
+                _name_to_ticker_cache[name] = ticker
+        except Exception as e:
+            logger.warning(f"SEC name lookup failed: {e}")
+
+    # Exact match on SEC company name
+    if cleaned in _name_to_ticker_cache:
+        resolved = _name_to_ticker_cache[cleaned]
+        logger.info(f"Resolved '{user_input}' → '{resolved}' (SEC EDGAR exact)")
+        return resolved
+
+    # Partial match on SEC company name
+    for name, ticker in _name_to_ticker_cache.items():
+        if cleaned in name:
+            logger.info(f"Resolved '{user_input}' → '{ticker}' (SEC EDGAR partial: {name})")
+            return ticker
+
+    # 5. Fallback: return as-is (user probably typed a valid ticker we don't have cached)
+    logger.warning(f"Could not resolve '{user_input}' — using as-is")
+    return cleaned
+
+
+# ---------------------------------------------------------------------------
+# SEC EDGAR filing fetcher
+# ---------------------------------------------------------------------------
+# ---------------------------------------------------------------------------
+# LangGraph state
+# ---------------------------------------------------------------------------
+class ReportState(TypedDict):
+    ticker: str
+    sec_data: Dict[str, Any]
+    fundamentals_output: str
+    news_output: str
+    risk_output: str
+    verdict_output: str
+    final_report: Dict[str, str]
+
+
+def _fetch_sec_filings(ticker: str) -> dict:
+    """Fetch company filing metadata from SEC EDGAR (supports ALL tickers)."""
+    cik = _get_cik_for_ticker(ticker.upper())
+    if not cik:
+        return {"error": f"CIK not found for {ticker}. SEC data unavailable."}
+    url = f"https://data.sec.gov/submissions/CIK{cik}.json"
+    try:
+        resp = requests.get(url, headers=SEC_HEADERS, timeout=15)
+        resp.raise_for_status()
+        data = resp.json()
+        company_name = data.get("name", ticker)
+        recent = data.get("filings", {}).get("recent", {})
+        forms = recent.get("form", [])
+        dates = recent.get("filingDate", [])
+        descriptions = recent.get("primaryDocDescription", [])
+        accession = recent.get("accessionNumber", [])
+        # Get last 10-K and 10-Q
+        filings_summary = []
+        for i, form in enumerate(forms[:50]):
+            if form in ("10-K", "10-Q", "8-K"):
+                filings_summary.append({
+                    "form": form,
+                    "date": dates[i] if i < len(dates) else "N/A",
+                    "description": descriptions[i] if i < len(descriptions) else "",
+                    "accession": accession[i] if i < len(accession) else "",
+                })
+        return {"company_name": company_name, "filings": filings_summary[:10]}
+    except Exception as e:
+        logger.error(f"SEC EDGAR fetch failed: {e}")
+        return {"error": str(e)}
+
+
+# ---------------------------------------------------------------------------
+# Build the LangGraph pipeline
+# ---------------------------------------------------------------------------
+def _build_report_pipeline():
+    from features.utils import call_gemini, run_tavily_search, fetch_stock_data, fetch_company_overview, fetch_global_quote
+
+    def fundamentals_agent(state: ReportState):
+        ticker = state["ticker"]
+        sec = state.get("sec_data", {})
+
+        # Fetch company fundamentals (Revenue, EPS, P/E, Margins, Market Cap)
+        overview_data = {}
+        try:
+            overview_result = fetch_company_overview(ticker)
+            overview_data = overview_result.get("data", {})
+            overview_source = overview_result.get("source", "Unknown")
+        except Exception as e:
+            logger.warning(f"Company overview fetch failed: {e}")
+            overview_source = "Unavailable"
+
+        # Fetch real-time price quote
+        quote_data = {}
+        try:
+            quote_result = fetch_global_quote(ticker)
+            quote_data = quote_result.get("data", {})
+        except Exception as e:
+            logger.warning(f"Global quote fetch failed: {e}")
+
+        # Build a rich data summary for the LLM
+        financials_summary = f"""
+Company: {overview_data.get('Name', ticker)} ({overview_data.get('Symbol', ticker)})
+Sector: {overview_data.get('Sector', 'N/A')} | Industry: {overview_data.get('Industry', 'N/A')}
+Description: {overview_data.get('Description', 'N/A')[:300]}
+
+--- FINANCIAL METRICS (Source: {overview_source}) ---
+Market Cap: ${overview_data.get('MarketCapitalization', 'N/A')}
+Revenue (TTM): ${overview_data.get('RevenueTTM', 'N/A')}
+Gross Profit (TTM): ${overview_data.get('GrossProfitTTM', 'N/A')}
+EPS: ${overview_data.get('EPS', 'N/A')}
+P/E Ratio: {overview_data.get('PERatio', 'N/A')}
+Forward P/E: {overview_data.get('ForwardPE', 'N/A')}
+Profit Margin: {overview_data.get('ProfitMargin', 'N/A')}
+Operating Margin: {overview_data.get('OperatingMarginTTM', 'N/A')}
+Return on Equity: {overview_data.get('ReturnOnEquityTTM', 'N/A')}
+Revenue Per Share: ${overview_data.get('RevenuePerShareTTM', 'N/A')}
+Book Value: ${overview_data.get('BookValue', 'N/A')}
+Price to Book: {overview_data.get('PriceToBookRatio', 'N/A')}
+Dividend Yield: {overview_data.get('DividendYield', 'N/A')}
+Beta: {overview_data.get('Beta', 'N/A')}
+
+--- GROWTH ---
+Quarterly Earnings Growth (YoY): {overview_data.get('QuarterlyEarningsGrowthYOY', 'N/A')}
+Quarterly Revenue Growth (YoY): {overview_data.get('QuarterlyRevenueGrowthYOY', 'N/A')}
+
+--- PRICE DATA ---
+Current Price: ${quote_data.get('price', 'N/A')}
+Today's Change: {quote_data.get('change', 'N/A')} ({quote_data.get('change_percent', 'N/A')})
+Today's Open: ${quote_data.get('open', 'N/A')}
+Today's High: ${quote_data.get('high', 'N/A')}
+Today's Low: ${quote_data.get('low', 'N/A')}
+Volume: {quote_data.get('volume', 'N/A')}
+Previous Close: ${quote_data.get('previous_close', 'N/A')}
+52-Week High: ${overview_data.get('52WeekHigh', 'N/A')}
+52-Week Low: ${overview_data.get('52WeekLow', 'N/A')}
+50-Day MA: ${overview_data.get('50DayMovingAverage', 'N/A')}
+200-Day MA: ${overview_data.get('200DayMovingAverage', 'N/A')}
+
+--- ANALYST CONSENSUS ---
+Target Price: ${overview_data.get('AnalystTargetPrice', 'N/A')}
+Buy Ratings: {overview_data.get('AnalystRatingBuy', 'N/A')}
+Hold Ratings: {overview_data.get('AnalystRatingHold', 'N/A')}
+Sell Ratings: {overview_data.get('AnalystRatingSell', 'N/A')}
+"""
+
+        prompt = f"""You are a financial fundamentals analyst. Analyze {ticker}.
+
+{financials_summary}
+
+SEC Filings Summary: {json.dumps(sec.get('filings', [])[:5], indent=2)}
+
+Based on ALL the data above, provide:
+1. Business overview (2-3 sentences)
+2. Key financial metrics analysis — use the ACTUAL numbers provided (Revenue, EPS, Margins, P/E, etc.)
+3. Year-over-year growth assessment using the quarterly growth data
+4. A markdown table of key metrics with their actual values
+5. Valuation assessment (is it overvalued/undervalued based on P/E, P/B, analyst targets?)
+
+Use the real numbers. Be specific and data-driven."""
+
+        result = call_gemini(prompt, "You are a senior equity research analyst specializing in fundamental analysis.")
+        return {"fundamentals_output": result}
+
+    def news_agent(state: ReportState):
+        ticker = state["ticker"]
+        try:
+            search_result = run_tavily_search(f"{ticker} stock news last 30 days analysis")
+            articles = []
+            for qr in search_result.get("data", []):
+                for r in qr.get("results", []):
+                    articles.append(f"- **{r.get('title', '')}**: {r.get('content', '')[:200]}...")
+            news_text = "\n".join(articles[:8]) if articles else "No recent news found."
+        except Exception:
+            news_text = "News search unavailable."
+
+        prompt = f"""Summarize the last 30 days of news for {ticker}:
+
+{news_text}
+
+Provide:
+1. Overall news sentiment (Bullish/Bearish/Neutral)
+2. Top 3-5 key headlines with brief explanations
+3. Any catalysts or upcoming events mentioned
+Be concise and factual."""
+        result = call_gemini(prompt, "You are a financial news analyst summarizing market intelligence.")
+        return {"news_output": result}
+
+    def risk_agent(state: ReportState):
+        ticker = state["ticker"]
+        sec = state.get("sec_data", {})
+        filings_text = json.dumps(sec.get("filings", []), indent=2)
+
+        try:
+            search_result = run_tavily_search(f"{ticker} 10-K risk factors annual report risks")
+            risk_articles = []
+            for qr in search_result.get("data", []):
+                for r in qr.get("results", []):
+                    risk_articles.append(r.get("content", "")[:300])
+            risk_text = "\n".join(risk_articles[:5])
+        except Exception:
+            risk_text = "Risk search unavailable."
+
+        prompt = f"""You are a risk analyst. Identify key risk factors for {ticker}.
+
+SEC Filing History: {filings_text}
+Risk-Related Research: {risk_text}
+
+Provide:
+1. Top 5 risk factors (ranked by severity)
+2. Risk category for each (Operational, Financial, Regulatory, Market, Competitive)
+3. Brief mitigation outlook for each
+Format as a numbered list."""
+        result = call_gemini(prompt, "You are a senior risk analyst at a major investment bank.")
+        return {"risk_output": result}
+
+    def verdict_agent(state: ReportState):
+        prompt = f"""You are the lead analyst writing the final investment verdict for {state['ticker']}.
+
+FUNDAMENTALS ANALYSIS:
+{state.get('fundamentals_output', 'N/A')}
+
+NEWS & SENTIMENT:
+{state.get('news_output', 'N/A')}
+
+RISK ASSESSMENT:
+{state.get('risk_output', 'N/A')}
+
+Based on ALL the above analysis, provide:
+1. **Recommendation**: Buy / Hold / Sell (with conviction level: High/Medium/Low)
+2. **Price Target**: Estimated 12-month price target with brief methodology
+3. **Bull Case** (2-3 sentences)
+4. **Bear Case** (2-3 sentences)
+5. **Key Catalysts to Watch** (3-5 bullet points)
+
+Be specific and data-driven. Reference specific findings from the analysis above."""
+        result = call_gemini(prompt, "You are a senior investment strategist issuing a formal recommendation.")
+        return {"verdict_output": result}
+
+    def compile_report(state: ReportState):
+        return {
+            "final_report": {
+                "executive_summary": f"Research report for **{state['ticker']}** generated on {datetime.now().strftime('%Y-%m-%d %H:%M')}.",
+                "fundamentals": state.get("fundamentals_output", ""),
+                "news": state.get("news_output", ""),
+                "risks": state.get("risk_output", ""),
+                "verdict": state.get("verdict_output", ""),
+            }
+        }
+
+    workflow = StateGraph(ReportState)
+    workflow.add_node("fundamentals", fundamentals_agent)
+    workflow.add_node("news", news_agent)
+    workflow.add_node("risk", risk_agent)
+    workflow.add_node("verdict", verdict_agent)
+    workflow.add_node("compile", compile_report)
+
+    workflow.set_entry_point("fundamentals")
+    workflow.add_edge("fundamentals", "news")
+    workflow.add_edge("news", "risk")
+    workflow.add_edge("risk", "verdict")
+    workflow.add_edge("verdict", "compile")
+    workflow.add_edge("compile", END)
+
+    return workflow.compile()
+
+
+# ---------------------------------------------------------------------------
+# Cached report generation
+# ---------------------------------------------------------------------------
+@lru_cache(maxsize=128)
+def generate_report(ticker: str) -> dict:
+    # Resolve company names to ticker symbols
+    resolved = _resolve_ticker(ticker)
+    sec_data = _fetch_sec_filings(resolved)
+    pipeline = _build_report_pipeline()
+    result = pipeline.invoke({"ticker": resolved.upper(), "sec_data": sec_data})
+    report = result.get("final_report", {})
+    report["_resolved_ticker"] = resolved.upper()
+    return report
+
+
+# ---------------------------------------------------------------------------
+# Streamlit page renderer
+# ---------------------------------------------------------------------------
+def render_research_report():
+    st.markdown("## 🌳💰 AI-Generated Research Report")
+    st.caption("Generate a comprehensive, multi-agent investment research report for any stock. "
+               "Powered by SEC EDGAR, Tavily news search, Alpha Vantage, and Google Gemini.")
+
+    col1, col2 = st.columns([3, 1])
+    with col1:
+        ticker = st.text_input("Enter Ticker or Company Name:", placeholder="e.g. AAPL, Tesla, Amazon, NVDA", key="rr_ticker").strip()
+    with col2:
+        st.markdown("<br>", unsafe_allow_html=True)
+        generate_btn = st.button("🔬 Generate Report", use_container_width=True, key="rr_generate")
+
+    if generate_btn and ticker:
+        with st.status("🚀 Multi-Agent Research Pipeline Active...", expanded=True) as status:
+            status.write("📡 Fetching SEC filings...")
+            time.sleep(0.5)
+            status.write("🔬 FundamentalsAgent analyzing financials...")
+            status.write("📰 NewsAgent scanning last 30 days...")
+            status.write("⚠️ RiskAgent evaluating risk factors...")
+            status.write("🎯 VerdictAgent synthesizing recommendation...")
+
+            try:
+                report = generate_report(ticker)
+                resolved = report.get("_resolved_ticker", ticker.upper())
+                st.session_state["rr_report"] = report
+                st.session_state["rr_display_ticker"] = resolved
+                if resolved != ticker.upper():
+                    status.write(f"🔄 Resolved '{ticker}' → {resolved}")
+                status.update(label=f"✅ Report Complete for {resolved}!", state="complete", expanded=False)
+            except Exception as e:
+                status.update(label="❌ Pipeline Error", state="error")
+                st.error(f"Failed to generate report: {e}")
+                return
+
+    # Display report
+    report = st.session_state.get("rr_report")
+    if report:
+        ticker_display = st.session_state.get("rr_display_ticker", "")
+        st.markdown(f"### 🌳💰 Research Report: **{ticker_display}**")
+        st.info(report.get("executive_summary", ""))
+
+        st.subheader("📋 Business Overview & Financial Health")
+        st.markdown(report.get('fundamentals', 'No data available.'))
+        st.markdown("---")
+
+        st.subheader("📰 Recent News & Sentiment")
+        st.markdown(report.get('news', 'No data available.'))
+        st.markdown("---")
+
+        st.subheader("⚠️ Risk Factors")
+        st.markdown(report.get('risks', 'No data available.'))
+        st.markdown("---")
+
+        st.subheader("🎯 Analyst Verdict & Price Target")
+        st.markdown(report.get('verdict', 'No data available.'))
+
+        # PDF Download
+        st.markdown("---")
+        if st.button("📥 Download as PDF", key="rr_pdf"):
+            from features.utils import export_to_pdf
+            sections = [
+                {"title": "Executive Summary", "body": report.get("executive_summary", "")},
+                {"title": "Business Overview & Financial Health", "body": report.get("fundamentals", "")},
+                {"title": "Recent News & Sentiment", "body": report.get("news", "")},
+                {"title": "Risk Factors", "body": report.get("risks", "")},
+                {"title": "Analyst Verdict & Price Target", "body": report.get("verdict", "")},
+            ]
+            pdf_bytes = export_to_pdf(sections, f"{ticker_display}_report.pdf")
+            st.download_button(
+                label="⬇️ Download PDF",
+                data=pdf_bytes,
+                file_name=f"{ticker_display}_Research_Report.pdf",
+                mime="application/pdf",
+                key="rr_pdf_dl",
+            )

features/utils.py

@@ -0,0 +1,504 @@
+"""
+features/utils.py — Shared utilities for all Sentinel add-on features.
+Wraps existing MCP gateway calls, Gemini client, and PDF export.
+"""
+import os
+import time
+import json
+import logging
+import functools
+import httpx
+from dotenv import load_dotenv
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+load_dotenv()
+logger = logging.getLogger("SentinelFeatures")
+
+# ---------------------------------------------------------------------------
+# Configuration
+# ---------------------------------------------------------------------------
+MCP_GATEWAY_URL = os.getenv("MCP_GATEWAY_URL", "http://127.0.0.1:8000/route_agent_request")
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY", "")
+AV_RATE_LIMIT_DELAY = 12  # seconds between Alpha Vantage calls (free tier)
+
+_last_av_call = 0.0  # module-level timestamp for rate-limiting
+
+
+# ---------------------------------------------------------------------------
+# Retry decorator
+# ---------------------------------------------------------------------------
+def retry_with_backoff(max_retries: int = 3, base_delay: float = 2.0):
+    """Decorator: retries a function with exponential back-off."""
+    def decorator(fn):
+        @functools.wraps(fn)
+        def wrapper(*args, **kwargs):
+            last_exc = None
+            for attempt in range(max_retries):
+                try:
+                    return fn(*args, **kwargs)
+                except Exception as exc:
+                    last_exc = exc
+                    wait = base_delay * (2 ** attempt)
+                    logger.warning(f"[retry {attempt+1}/{max_retries}] {fn.__name__} failed: {exc} — retrying in {wait:.1f}s")
+                    time.sleep(wait)
+            raise last_exc  # type: ignore
+        return wrapper
+    return decorator
+
+
+# ---------------------------------------------------------------------------
+# MCP Gateway helpers (mirrors tool_calling_agents.py pattern)
+# ---------------------------------------------------------------------------
+def _call_gateway(target_service: str, payload: dict, timeout: float = 60.0) -> dict:
+    """Low-level POST to MCP Gateway."""
+    body = {"target_service": target_service, "payload": payload}
+    with httpx.Client(timeout=timeout) as client:
+        resp = client.post(MCP_GATEWAY_URL, json=body)
+        resp.raise_for_status()
+        return resp.json()
+
+
+@retry_with_backoff(max_retries=3)
+def fetch_stock_data(ticker: str, time_range: str = "INTRADAY") -> dict:
+    """Fetch stock data via the MCP gateway → Alpha Vantage microservice.
+    Respects rate-limiting (12 s between calls).
+    """
+    global _last_av_call
+    elapsed = time.time() - _last_av_call
+    if elapsed < AV_RATE_LIMIT_DELAY:
+        time.sleep(AV_RATE_LIMIT_DELAY - elapsed)
+    result = _call_gateway("alpha_vantage_market_data", {"symbol": ticker, "time_range": time_range})
+    _last_av_call = time.time()
+    return result
+
+
+@retry_with_backoff(max_retries=3)
+def run_tavily_search(query: str, search_depth: str = "basic", max_results: int = 5) -> dict:
+    """Run a web search via the MCP gateway → Tavily microservice."""
+    return _call_gateway("tavily_research", {"queries": [query], "search_depth": search_depth})
+
+
+@retry_with_backoff(max_retries=2)
+def fetch_company_overview(ticker: str) -> dict:
+    """Fetch company fundamentals (Revenue, EPS, P/E, Market Cap, Margins) via AV OVERVIEW."""
+    return _call_gateway("alpha_vantage_overview", {"symbol": ticker}, timeout=20.0)
+
+
+@retry_with_backoff(max_retries=2)
+def fetch_global_quote(ticker: str) -> dict:
+    """Fetch real-time price quote via AV GLOBAL_QUOTE."""
+    return _call_gateway("alpha_vantage_quote", {"symbol": ticker}, timeout=15.0)
+
+
+# ---------------------------------------------------------------------------
+# Gemini LLM helper (with automatic model fallback for rate limits)
+# ---------------------------------------------------------------------------
+# Ordered fallback chain: each model has its own 20 req/day free-tier limit
+_GEMINI_MODELS = [
+    "gemini-2.5-flash",
+    "gemini-2.0-flash",
+    "gemini-2.5-flash-lite",
+    "gemini-1.5-flash",
+    "gemini-1.5-flash-8b"
+]
+
+def get_gemini_llm(temperature: float = 0.0, model: str = None):
+    """Return a ChatGoogleGenerativeAI instance. Uses the specified model or the first in the chain."""
+    api_key = GOOGLE_API_KEY
+    if not api_key:
+        raise ValueError("GOOGLE_API_KEY not set. Cannot call Gemini.")
+    model_name = model or _GEMINI_MODELS[0]
+    return ChatGoogleGenerativeAI(
+        model=model_name,
+        google_api_key=api_key,
+        temperature=temperature,
+        max_retries=2,
+    )
+
+
+def call_gemini(prompt: str, system_prompt: str = "") -> str:
+    """One-shot Gemini call with automatic model fallback on rate limits."""
+    from langchain_core.messages import SystemMessage, HumanMessage
+    import time as _time
+
+    messages = []
+    if system_prompt:
+        messages = [SystemMessage(content=system_prompt), HumanMessage(content=prompt)]
+    else:
+        messages = [HumanMessage(content=prompt)]
+
+    last_error = None
+    for model_name in _GEMINI_MODELS:
+        try:
+            llm = get_gemini_llm(model=model_name)
+            result = llm.invoke(messages).content.strip()
+            return result
+        except Exception as e:
+            error_str = str(e)
+            last_error = e
+            if "429" in error_str or "quota" in error_str.lower() or "rate" in error_str.lower() or "404" in error_str:
+                logger.warning(f"Model {model_name} failed ({error_str[:50]}), trying next Gemini model...")
+                _time.sleep(2)  # Brief pause before trying next model
+                continue
+            else:
+                logger.warning(f"Model {model_name} failed with non-rate-limit error: {error_str[:50]}")
+                continue  # Keep trying other Gemini models just in case
+
+    # All Gemini models exhausted. Try Groq/Llama fallback if available.
+    import os
+    from dotenv import load_dotenv
+    env_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), ".env")
+    load_dotenv(dotenv_path=env_path, override=True)
+    
+    groq_api_key = os.getenv("GROQ_API_KEY")
+    if groq_api_key:
+        logger.info("All Gemini models failed. Falling back to Groq (Llama 3 70B)...")
+        try:
+            from langchain_groq import ChatGroq
+            groq_llm = ChatGroq(
+                model="llama-3.3-70b-versatile", 
+                api_key=groq_api_key, 
+                temperature=0.0,
+                max_retries=2
+            )
+            result = groq_llm.invoke(messages).content.strip()
+            return result
+        except ImportError:
+            logger.error("langchain_groq not installed. Cannot use Groq fallback.")
+        except Exception as e:
+            logger.error(f"Groq fallback also failed: {e}")
+            raise last_error
+            
+    # All models exhausted and no fallback available
+    raise last_error
+
+
+# ---------------------------------------------------------------------------
+# PDF export (fpdf2)
+# ---------------------------------------------------------------------------
+def _sanitize_for_pdf(text: str) -> str:
+    """Replace Unicode characters unsupported by Helvetica (latin-1) with safe equivalents."""
+    import re
+    replacements = {
+        "\u2014": "--",   # em dash —
+        "\u2013": "-",    # en dash –
+        "\u2018": "'",    # left single quote '
+        "\u2019": "'",    # right single quote '
+        "\u201c": '"',    # left double quote "
+        "\u201d": '"',    # right double quote "
+        "\u2026": "...",  # ellipsis …
+        "\u2022": "*",    # bullet •
+        "\u2023": ">",    # triangle bullet ‣
+        "\u2027": "-",    # hyphenation point ‧
+        "\u00a0": " ",    # non-breaking space
+        "\u200b": "",     # zero-width space
+        "\u2032": "'",    # prime ′
+        "\u2033": '"',    # double prime ″
+        "\u2212": "-",    # minus sign −
+        "\u00b7": "*",    # middle dot ·
+        "\u25cf": "*",    # black circle ●
+        "\u25cb": "o",    # white circle ○
+        "\u2713": "[x]",  # check mark ✓
+        "\u2717": "[ ]",  # cross mark ✗
+    }
+    for char, replacement in replacements.items():
+        text = text.replace(char, replacement)
+    # Remove markdown bold/italic asterisks (but keep list bullet asterisks at start of lines)
+    text = re.sub(r'(?<!^)\*\*(.*?)\*\*', r'\1', text)
+    text = re.sub(r'(?<!^)\*(.*?)\*', r'\1', text)
+    
+    # Final fallback: strip any remaining non-latin-1 chars
+    return text.encode("latin-1", errors="replace").decode("latin-1")
+
+
+def export_to_pdf(sections: list[dict], filename: str = "report.pdf") -> bytes:
+    """Generate a professional, Wall Street-grade PDF report with logo and styling.
+
+    Each section dict:  {"title": "...", "body": "..."}
+    Returns the PDF as bytes.
+    """
+    from fpdf import FPDF
+    import re
+
+    # --- Color palette ---
+    NAVY = (43, 58, 103)        # #2B3A67
+    GOLD = (197, 165, 90)       # #C5A55A
+    DARK_TEXT = (30, 30, 30)
+    LIGHT_GRAY = (230, 230, 235)
+    MID_GRAY = (160, 160, 170)
+    WHITE = (255, 255, 255)
+    SECTION_BG = (240, 242, 248)
+
+    # --- Custom PDF class with header/footer ---
+    class SentinelPDF(FPDF):
+        def __init__(self):
+            super().__init__()
+            self.logo_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "assets", "sentinel_logo.png")
+
+        def header(self):
+            if self.page_no() == 1:
+                return  # Skip header on title page
+            # Logo
+            if os.path.exists(self.logo_path):
+                self.image(self.logo_path, 10, 6, 12)
+            # Company name
+            self.set_font("Times", "B", 9)
+            self.set_text_color(*NAVY)
+            self.set_xy(24, 8)
+            self.cell(0, 5, "SENTINEL AI", align="L")
+            self.set_font("Times", "", 7)
+            self.set_text_color(*MID_GRAY)
+            self.set_xy(24, 13)
+            self.cell(0, 4, "Equity Research Division", align="L")
+            # Right side: CONFIDENTIAL stamp
+            self.set_font("Times", "B", 7)
+            self.set_text_color(*GOLD)
+            self.set_xy(-50, 10)
+            self.cell(40, 5, "CONFIDENTIAL", align="R")
+            # Divider line
+            self.set_draw_color(*NAVY)
+            self.set_line_width(0.5)
+            self.line(10, 20, self.w - 10, 20)
+            self.ln(16)
+
+        def footer(self):
+            self.set_y(-15)
+            self.set_draw_color(*LIGHT_GRAY)
+            self.set_line_width(0.3)
+            self.line(10, self.get_y(), self.w - 10, self.get_y())
+            self.ln(2)
+            self.set_font("Times", "", 7)
+            self.set_text_color(*MID_GRAY)
+            self.cell(0, 5, "Generated by Sentinel AI | For Authorized Use Only", align="L")
+            self.set_font("Times", "B", 7)
+            self.cell(0, 5, f"Page {self.page_no()}", align="R")
+
+    pdf = SentinelPDF()
+    pdf.set_auto_page_break(auto=True, margin=20)
+
+    # ===== TITLE PAGE =====
+    pdf.add_page()
+    pdf.ln(30)
+
+    # Logo centered
+    if os.path.exists(pdf.logo_path):
+        pdf.image(pdf.logo_path, (pdf.w - 40) / 2, pdf.get_y(), 40)
+        pdf.ln(45)
+
+    # Title
+    pdf.set_font("Times", "B", 28)
+    pdf.set_text_color(*NAVY)
+    pdf.cell(0, 14, _sanitize_for_pdf("SENTINEL AI"), new_x="LMARGIN", new_y="NEXT", align="C")
+
+    pdf.set_font("Times", "", 12)
+    pdf.set_text_color(*GOLD)
+    pdf.cell(0, 8, _sanitize_for_pdf("Equity Research Report"), new_x="LMARGIN", new_y="NEXT", align="C")
+    pdf.ln(5)
+
+    # Gold accent line
+    pdf.set_draw_color(*GOLD)
+    pdf.set_line_width(1)
+    pdf.line(70, pdf.get_y(), pdf.w - 70, pdf.get_y())
+    pdf.ln(10)
+
+    # Extract ticker from first section
+    first_body = sections[0].get("body", "") if sections else ""
+    ticker_match = re.search(r'\(([A-Z]{1,5})\)', first_body)
+    ticker_display = ticker_match.group(1) if ticker_match else ""
+
+    if ticker_display:
+        pdf.set_font("Times", "B", 20)
+        pdf.set_text_color(*DARK_TEXT)
+        pdf.cell(0, 12, ticker_display, new_x="LMARGIN", new_y="NEXT", align="C")
+        pdf.ln(2)
+
+    # Date
+    from datetime import datetime
+    pdf.set_font("Times", "", 10)
+    pdf.set_text_color(*MID_GRAY)
+    pdf.cell(0, 8, f"Generated: {datetime.now().strftime('%B %d, %Y at %H:%M')}", new_x="LMARGIN", new_y="NEXT", align="C")
+    pdf.ln(15)
+
+    # Disclaimer box
+    pdf.set_fill_color(*SECTION_BG)
+    pdf.rect(20, pdf.get_y(), pdf.w - 40, 18, style="F")
+    pdf.set_font("Times", "I", 7)
+    pdf.set_text_color(*MID_GRAY)
+    pdf.set_xy(25, pdf.get_y() + 3)
+    pdf.multi_cell(pdf.w - 50, 4,
+        _sanitize_for_pdf("This report is generated by Sentinel AI using real-time market data from Alpha Vantage, "
+        "SEC EDGAR filings, and AI-powered analysis. It is for informational purposes only and does not "
+        "constitute financial advice. Past performance is not indicative of future results."))
+
+    # ===== CONTENT PAGES =====
+
+    def _render_markdown_line(line: str):
+        """Render a single markdown line with professional formatting."""
+        line = _sanitize_for_pdf(line)
+        stripped = line.strip()
+
+        if not stripped:
+            pdf.ln(2)
+            return
+
+        # Headers
+        if stripped.startswith("### "):
+            pdf.ln(2)
+            pdf.set_font("Times", "B", 11)
+            pdf.set_text_color(*NAVY)
+            pdf.multi_cell(0, 6, stripped[4:])
+            pdf.ln(1)
+            return
+        if stripped.startswith("## "):
+            pdf.ln(3)
+            pdf.set_font("Times", "B", 13)
+            pdf.set_text_color(*NAVY)
+            pdf.multi_cell(0, 7, stripped[3:])
+            pdf.set_draw_color(*GOLD)
+            pdf.set_line_width(0.3)
+            pdf.line(pdf.l_margin, pdf.get_y(), pdf.l_margin + 40, pdf.get_y())
+            pdf.ln(2)
+            return
+        if stripped.startswith("# "):
+            pdf.ln(4)
+            pdf.set_font("Times", "B", 15)
+            pdf.set_text_color(*NAVY)
+            pdf.multi_cell(0, 8, stripped[2:])
+            pdf.set_draw_color(*NAVY)
+            pdf.set_line_width(0.5)
+            pdf.line(pdf.l_margin, pdf.get_y(), pdf.w - pdf.r_margin, pdf.get_y())
+            pdf.ln(3)
+            return
+
+        # Horizontal rule
+        if stripped in ("---", "***", "___"):
+            pdf.set_draw_color(*LIGHT_GRAY)
+            pdf.set_line_width(0.3)
+            pdf.line(pdf.l_margin, pdf.get_y(), pdf.w - pdf.r_margin, pdf.get_y())
+            pdf.ln(3)
+            return
+
+        # Table rows
+        if "|" in stripped and not stripped.startswith("|--"):
+            # Skip separator lines like |---|---|
+            if re.match(r'^[\|\-\:\s]+$', stripped):
+                return
+            cells = [c.strip() for c in stripped.split("|") if c.strip()]
+            if cells:
+                col_width = (pdf.w - pdf.l_margin - pdf.r_margin) / max(len(cells), 1)
+                is_header = any(c.startswith("**") or c in ("Metric", "Value", "Rank", "Risk Factor", "Implication") for c in cells)
+                if is_header:
+                    pdf.set_font("Times", "B", 8)
+                    pdf.set_fill_color(*NAVY)
+                    pdf.set_text_color(*WHITE)
+                else:
+                    pdf.set_font("Times", "", 8)
+                    pdf.set_fill_color(*SECTION_BG)
+                    pdf.set_text_color(*DARK_TEXT)
+                for cell in cells:
+                    cell = re.sub(r'\*\*(.*?)\*\*', r'\1', cell)  # strip bold
+                    cell = cell[:50]  # truncate long cells
+                    pdf.cell(col_width, 6, cell, border=1, fill=True)
+                pdf.ln()
+                return
+
+        # Skip pure table separator lines
+        if re.match(r'^[\|\-\:\s]+$', stripped):
+            return
+
+        # Bullet points
+        if stripped.startswith(("- ", "* ")):
+            prefix = stripped[:2]
+            text = stripped[2:]
+            # Handle bold text within bullets
+            parts = re.split(r'(\*\*.*?\*\*)', text)
+            pdf.set_text_color(*DARK_TEXT)
+            pdf.cell(6)  # indent
+            pdf.set_font("Times", "", 9)
+            pdf.cell(4, 5, chr(8226).encode("latin-1", errors="replace").decode("latin-1"))  # bullet char
+            # Build the full text (strip markdown bold markers)
+            full_text = re.sub(r'\*\*(.*?)\*\*', r'\1', text)
+            pdf.multi_cell(pdf.w - pdf.l_margin - pdf.r_margin - 12, 5, full_text)
+            pdf.ln(1)
+            return
+
+        # Numbered lists
+        num_match = re.match(r'^(\d+)\.\s+', stripped)
+        if num_match:
+            text = stripped[num_match.end():]
+            text = re.sub(r'\*\*(.*?)\*\*', r'\1', text)
+            pdf.set_font("Times", "", 9)
+            pdf.set_text_color(*DARK_TEXT)
+            pdf.cell(4)  # indent
+            pdf.multi_cell(pdf.w - pdf.l_margin - pdf.r_margin - 6, 5, f"{num_match.group(1)}. {text}")
+            pdf.ln(1)
+            return
+
+        # Regular text
+        text = re.sub(r'\*\*(.*?)\*\*', r'\1', stripped)
+        pdf.set_font("Times", "", 9)
+        pdf.set_text_color(*DARK_TEXT)
+        pdf.multi_cell(0, 5, text)
+        pdf.ln(1)
+
+    for idx, sec in enumerate(sections):
+        pdf.add_page()
+
+        # Section header with colored left bar
+        title = _sanitize_for_pdf(sec.get("title", ""))
+
+        # Section number badge
+        section_icons = {
+            "Executive Summary": "01",
+            "Business Overview": "02",
+            "Recent News": "03",
+            "Risk Factors": "04",
+            "Analyst Verdict": "05",
+        }
+        badge_num = section_icons.get(title, f"{idx + 1:02d}")
+
+        # Navy accent bar on the left
+        y_start = pdf.get_y()
+        pdf.set_fill_color(*NAVY)
+        pdf.rect(pdf.l_margin, y_start, 3, 12, style="F")
+
+        # Section number
+        pdf.set_font("Times", "B", 8)
+        pdf.set_text_color(*GOLD)
+        pdf.set_xy(pdf.l_margin + 6, y_start)
+        pdf.cell(10, 5, f"SECTION {badge_num}")
+
+        pdf.set_font("Times", "B", 15)
+        pdf.set_text_color(*NAVY)
+        pdf.set_xy(pdf.l_margin + 6, y_start + 5)
+        pdf.cell(0, 8, title)
+        pdf.ln(8)
+
+        # Gold underline
+        pdf.set_draw_color(*GOLD)
+        pdf.set_line_width(0.5)
+        pdf.line(pdf.l_margin + 6, pdf.get_y(), pdf.l_margin + 80, pdf.get_y())
+        pdf.ln(6)
+
+        # Render body
+        body = sec.get("body", "")
+        for line in body.split("\n"):
+            _render_markdown_line(line)
+        pdf.ln(4)
+
+    return bytes(pdf.output())
+
+
+# ---------------------------------------------------------------------------
+# Watchlist helper
+# ---------------------------------------------------------------------------
+WATCHLIST_FILE = "watchlist.json"
+
+def load_watchlist() -> list[str]:
+    if not os.path.exists(WATCHLIST_FILE):
+        return []
+    try:
+        with open(WATCHLIST_FILE, "r") as f:
+            return json.load(f)
+    except Exception:
+        return []

features/weekly_digest.py

@@ -0,0 +1,338 @@
+"""
+features/weekly_digest.py — Automated Weekly Market Digest
+Background scheduler generates weekly briefings from watchlist data.
+"""
+import streamlit as st
+import json
+import os
+import logging
+from datetime import datetime, timedelta
+from pathlib import Path
+
+logger = logging.getLogger("WeeklyDigest")
+
+DIGESTS_DIR = "digests"
+Path(DIGESTS_DIR).mkdir(exist_ok=True)
+
+
+# ---------------------------------------------------------------------------
+# Digest generation pipeline
+# ---------------------------------------------------------------------------
+def _generate_digest_data() -> dict:
+    """Gather watchlist data, news, and generate the digest."""
+    from features.utils import fetch_stock_data, run_tavily_search, call_gemini, load_watchlist
+
+    watchlist = load_watchlist()
+    if not watchlist:
+        return {"error": "Watchlist is empty. Add tickers to your watchlist first."}
+
+    ticker_summaries = []
+    winners = []
+    losers = []
+
+    for ticker in watchlist:
+        try:
+            data = fetch_stock_data(ticker, "1W")
+            ts = data.get("data", {})
+            sorted_times = sorted(ts.keys())
+            if len(sorted_times) >= 2:
+                first_close = float(ts[sorted_times[0]].get("4. close", 0))
+                last_close = float(ts[sorted_times[-1]].get("4. close", 0))
+                pct_change = ((last_close - first_close) / first_close * 100) if first_close > 0 else 0
+
+                volumes = [int(ts[t].get("5. volume", 0)) for t in sorted_times]
+                avg_vol = sum(volumes) / len(volumes) if volumes else 0
+                latest_vol = volumes[-1] if volumes else 0
+                vol_anomaly = (latest_vol / avg_vol - 1) * 100 if avg_vol > 0 else 0
+
+                summary = {
+                    "ticker": ticker,
+                    "weekly_change_pct": round(pct_change, 2),
+                    "latest_close": round(last_close, 2),
+                    "volume_anomaly_pct": round(vol_anomaly, 1),
+                }
+                ticker_summaries.append(summary)
+
+                if pct_change > 0:
+                    winners.append(summary)
+                else:
+                    losers.append(summary)
+        except Exception as e:
+            logger.warning(f"Failed to fetch data for {ticker}: {e}")
+            ticker_summaries.append({"ticker": ticker, "error": str(e)})
+
+    winners.sort(key=lambda x: x.get("weekly_change_pct", 0), reverse=True)
+    losers.sort(key=lambda x: x.get("weekly_change_pct", 0))
+
+    # Fetch macro news
+    try:
+        macro_result = run_tavily_search("major financial market news this week economy stocks")
+        macro_articles = []
+        for qr in macro_result.get("data", []):
+            for r in qr.get("results", []):
+                macro_articles.append(f"- {r.get('title', '')}: {r.get('content', '')[:150]}")
+        macro_news = "\n".join(macro_articles[:6])
+    except Exception:
+        macro_news = "Macro news unavailable."
+
+    # Generate narrative with Gemini
+    prompt = f"""You are a senior market analyst writing a Weekly Market Briefing for {datetime.now().strftime('%B %d, %Y')}.
+
+WATCHLIST PERFORMANCE THIS WEEK:
+{json.dumps(ticker_summaries, indent=2)}
+
+BIGGEST WINNERS: {json.dumps(winners[:3], indent=2)}
+BIGGEST LOSERS: {json.dumps(losers[:3], indent=2)}
+
+MACRO NEWS:
+{macro_news}
+
+Write a professional 500-700 word "Weekly Market Briefing" that covers:
+1. **Market Overview** - Overall sentiment and key moves
+2. **Watchlist Highlights** - Winners and losers with context
+3. **Volume Alerts** - Any unusual volume activity
+4. **Macro Landscape** - Key economic developments
+5. **Week Ahead** - What to watch for next week
+
+Use a professional but accessible tone. Include specific numbers and percentages.
+Do NOT use placeholders — use the actual data provided."""
+
+    narrative = call_gemini(prompt, "You are a chief market strategist at a major financial institution.")
+
+    return {
+        "date": datetime.now().isoformat(),
+        "date_display": datetime.now().strftime("%B %d, %Y"),
+        "watchlist": watchlist,
+        "ticker_summaries": ticker_summaries,
+        "winners": winners[:3],
+        "losers": losers[:3],
+        "macro_news": macro_news,
+        "narrative": narrative,
+    }
+
+
+def _save_digest(digest: dict):
+    """Save digest to JSON file."""
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filepath = os.path.join(DIGESTS_DIR, f"digest_{timestamp}.json")
+    with open(filepath, "w") as f:
+        json.dump(digest, f, indent=2)
+    return filepath
+
+
+def _load_all_digests() -> list[dict]:
+    """Load all saved digests, sorted newest first."""
+    digests = []
+    if not os.path.exists(DIGESTS_DIR):
+        return digests
+    for fname in sorted(os.listdir(DIGESTS_DIR), reverse=True):
+        if fname.endswith(".json"):
+            try:
+                with open(os.path.join(DIGESTS_DIR, fname)) as f:
+                    d = json.load(f)
+                    d["_filename"] = fname
+                    digests.append(d)
+            except Exception:
+                pass
+    return digests
+
+
+# ---------------------------------------------------------------------------
+# Email delivery (optional)
+# ---------------------------------------------------------------------------
+def _send_email(recipient: str, digest: dict):
+    """Send digest as HTML email via SMTP."""
+    import smtplib
+    from email.mime.text import MIMEText
+    from email.mime.multipart import MIMEMultipart
+    from dotenv import load_dotenv
+
+    env_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), ".env")
+    load_dotenv(dotenv_path=env_path, override=True)
+
+    smtp_user = os.getenv("SMTP_USER", "")
+    smtp_pass = os.getenv("SMTP_PASSWORD", "")
+    smtp_host = os.getenv("SMTP_HOST", "smtp.gmail.com")
+    smtp_port = int(os.getenv("SMTP_PORT", "587"))
+
+    if not smtp_user or not smtp_pass:
+        return False, "SMTP credentials not configured. Set SMTP_USER and SMTP_PASSWORD environment variables."
+
+    try:
+        html_body = f"""
+        <html>
+        <body style="background:#111; color:#fff; font-family:Arial,sans-serif; padding:20px;">
+            <h1 style="color:#a78bfa;">📊 Sentinel Weekly Market Digest</h1>
+            <h3>{digest.get('date_display', '')}</h3>
+            <hr style="border-color:#333;">
+            <div style="white-space:pre-wrap;">{digest.get('narrative', '')}</div>
+            <hr style="border-color:#333;">
+            <p style="color:#888; font-size:12px;">Generated by Sentinel AI Financial Intelligence</p>
+        </body>
+        </html>
+        """
+        msg = MIMEMultipart("alternative")
+        msg["Subject"] = f"Sentinel Weekly Digest — {digest.get('date_display', '')}"
+        msg["From"] = smtp_user
+        msg["To"] = recipient
+        msg.attach(MIMEText(html_body, "html"))
+
+        with smtplib.SMTP(smtp_host, smtp_port) as server:
+            server.starttls()
+            server.login(smtp_user, smtp_pass)
+            server.sendmail(smtp_user, recipient, msg.as_string())
+        return True, "Email sent successfully!"
+    except Exception as e:
+        return False, str(e)
+
+
+# ---------------------------------------------------------------------------
+# Background scheduler
+# ---------------------------------------------------------------------------
+_scheduler_started = False
+
+def _start_scheduler():
+    """Start APScheduler for weekly digests (Sunday 8 AM)."""
+    global _scheduler_started
+    if _scheduler_started:
+        return
+    try:
+        from apscheduler.schedulers.background import BackgroundScheduler
+
+        def _scheduled_job():
+            try:
+                digest = _generate_digest_data()
+                if "error" not in digest:
+                    _save_digest(digest)
+                    logger.info("Scheduled weekly digest generated successfully.")
+            except Exception as e:
+                logger.error(f"Scheduled digest generation failed: {e}")
+
+        scheduler = BackgroundScheduler()
+        scheduler.add_job(_scheduled_job, "cron", day_of_week="sun", hour=8, minute=0)
+        scheduler.start()
+        _scheduler_started = True
+        logger.info("Weekly digest scheduler started (Sunday 8:00 AM)")
+    except Exception as e:
+        logger.warning(f"Failed to start scheduler: {e}")
+
+
+# ---------------------------------------------------------------------------
+# Streamlit page renderer
+# ---------------------------------------------------------------------------
+def render_weekly_digest():
+    st.markdown("## 📬 Weekly Market Digest")
+    st.caption("Automated weekly intelligence briefings covering your watchlist performance, "
+               "macro trends, and AI-generated market commentary. Auto-generates every Sunday at 8 AM.")
+
+    # Start background scheduler
+    _start_scheduler()
+
+    # Controls
+    col1, col2, col3 = st.columns([2, 1, 1])
+    with col1:
+        if st.button("🔄 Regenerate Now", use_container_width=True, key="wd_regen"):
+            with st.status("📊 Generating fresh digest...", expanded=True) as status:
+                status.write("📡 Fetching watchlist data...")
+                status.write("📰 Scanning macro environment...")
+                status.write("✍️ Writing market briefing...")
+                digest = _generate_digest_data()
+                if "error" in digest:
+                    status.update(label="⚠️ Error", state="error")
+                    st.error(digest["error"])
+                    return
+                filepath = _save_digest(digest)
+                st.session_state["wd_current"] = digest
+                status.update(label="✅ Digest Generated!", state="complete", expanded=False)
+                st.rerun()
+
+    # Email settings
+    with col2:
+        email = st.text_input("📧 Email:", placeholder="your@email.com", key="wd_email", label_visibility="collapsed")
+    with col3:
+        if st.button("📤 Send Email", key="wd_send", use_container_width=True):
+            current = st.session_state.get("wd_current")
+            if current and email:
+                ok, msg = _send_email(email, current)
+                if ok:
+                    st.success(msg)
+                else:
+                    st.error(f"Email failed: {msg}")
+            else:
+                st.warning("Generate a digest first, then enter your email.")
+
+    st.markdown("---")
+
+    # Archive selector
+    all_digests = _load_all_digests()
+
+    if all_digests:
+        digest_options = {d.get("date_display", d.get("_filename", "Unknown")): i for i, d in enumerate(all_digests)}
+        selected = st.selectbox(
+            "📚 Browse Archive:",
+            options=list(digest_options.keys()),
+            key="wd_archive",
+        )
+        if selected:
+            idx = digest_options[selected]
+            st.session_state["wd_current"] = all_digests[idx]
+
+    # Display current digest
+    current = st.session_state.get("wd_current")
+    if not current and all_digests:
+        current = all_digests[0]  # Show latest
+        st.session_state["wd_current"] = current
+
+    if current:
+        st.markdown(f"### 📅 {current.get('date_display', 'Unknown Date')}")
+
+        # Quick stats
+        summaries = current.get("ticker_summaries", [])
+        winners = current.get("winners", [])
+        losers = current.get("losers", [])
+
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.metric("📈 Watchlist Tickers", len(summaries))
+        with col2:
+            best = winners[0] if winners else {}
+            st.metric("🏆 Best Performer",
+                      best.get("ticker", "N/A"),
+                      f"{best.get('weekly_change_pct', 0):+.2f}%" if best else None)
+        with col3:
+            worst = losers[0] if losers else {}
+            st.metric("📉 Worst Performer",
+                      worst.get("ticker", "N/A"),
+                      f"{worst.get('weekly_change_pct', 0):+.2f}%" if worst else None)
+
+        # Performance table
+        if summaries:
+            import pandas as pd
+            df = pd.DataFrame([s for s in summaries if "error" not in s])
+            if not df.empty:
+                with st.expander("📊 Watchlist Performance Table", expanded=True):
+                    st.dataframe(df, use_container_width=True, hide_index=True)
+
+        # Narrative
+        st.markdown("---")
+        st.markdown("### 📝 Market Briefing")
+        
+        # Escape dollar signs so Streamlit doesn't render the paragraph as a LaTeX math equation
+        safe_narrative = current.get("narrative", "No narrative available.").replace("$", r"\$")
+        st.markdown(safe_narrative)
+
+        # PDF Export
+        st.markdown("---")
+        if st.button("📥 Download Digest as PDF", key="wd_pdf"):
+            from features.utils import export_to_pdf
+            sections = [
+                {"title": f"Weekly Digest — {current.get('date_display', '')}", "body": ""},
+                {"title": "Market Briefing", "body": current.get("narrative", "")},
+                {"title": "Watchlist Data", "body": json.dumps(summaries, indent=2)},
+            ]
+            pdf_bytes = export_to_pdf(sections, "weekly_digest.pdf")
+            st.download_button("⬇️ Download PDF", data=pdf_bytes,
+                               file_name=f"Weekly_Digest_{current.get('date_display', 'report').replace(' ', '_')}.pdf",
+                               mime="application/pdf", key="wd_pdf_dl")
+    else:
+        st.info("📭 No digests yet. Click **Regenerate Now** to create your first weekly digest.")

linkedin_post.md

@@ -0,0 +1,59 @@
+# 🚀 LinkedIn Post Drafts for Aegis / Sentinel
+
+Here are three variations for your LinkedIn post, depending on the angle you want to take.
+
+## Option 1: The "Technical Deep Dive" (Best for Engineering Networks)
+**Headline:** Building a Proactive Financial AI Agent with LangGraph & Microservices 🛡️
+
+I just wrapped up building **Aegis**, a "Premium Financial Terminal" powered by AI. It’s not just a chatbot; it’s a fully orchestrated agentic system.
+
+**The Tech Stack:**
+*   **Brain:** LangGraph for stateful orchestration (Google Gemini).
+*   **Architecture:** Microservices pattern using the **Model Context Protocol (MCP)**.
+*   **Real-Time:** A background monitor that watches my portfolio and pushes alerts.
+*   **Frontend:** Streamlit for that Bloomberg Terminal vibe.
+
+**How it works:**
+The "Orchestrator" breaks down natural language directives (e.g., "Analyze TSLA and check my exposure"). It routes tasks through a FastAPI Gateway to specialized agents: a **Web Researcher** (Tavily), a **Market Analyst** (Alpha Vantage), and a **Portfolio Manager** (Local DB).
+
+It’s been a great journey learning how to decouple AI tools from the core logic.
+
+#AI #LangGraph #Python #Microservices #FinTech #LLM #OpenSource
+
+---
+
+## Option 2: The "Product Showcase" (Best for General Audience)
+**Headline:** Meet Sentinel: My Personal AI Financial Analyst ⚡
+
+Tired of switching between news sites, stock charts, and my brokerage app, I decided to build my own solution.
+
+Introducing **Sentinel** (Project Aegis) – an AI agent that acts as a proactive financial analyst.
+
+**What it does:**
+✅ **Deep Dives:** I ask "Why is Apple down?", and it reads the news, checks the charts, and writes a report.
+✅ **24/7 Monitoring:** It watches my watchlist and alerts me to price spikes or breaking news.
+✅ **Portfolio Context:** It knows what I own, so its advice is personalized.
+
+The power of Agentic AI is that it doesn't just talk; it *does*. It plans, researches, and synthesizes information faster than I ever could.
+
+#ArtificialIntelligence #FinTech #Productivity #Coding #Streamlit
+
+---
+
+## Option 3: The "Learning Journey" (Best for Engagement)
+**Headline:** From "Chatbot" to "Agentic System" – My latest build 🧠
+
+I spent the last few days building **Aegis**, and it completely changed how I think about AI applications.
+
+I started with a simple script, but realized that for complex tasks like financial analysis, a single LLM call isn't enough. You need **Agents**.
+
+**Key Lessons Learned:**
+1.  **State Management is King:** Using LangGraph to pass context between a "Researcher" and a "Data Analyst" is a game changer.
+2.  **Decoupling Matters:** I built a "Gateway" so I can swap out my News provider without breaking the whole app.
+3.  **Latency vs. Accuracy:** Orchestrating multiple AI calls takes time, but the depth of insight is worth the wait.
+
+Check out the architecture in the comments! 👇
+
+What are you building with Agents right now?
+
+#BuildInPublic #AI #Learning #SoftwareEngineering #TechTrends

logo_helper.py

@@ -0,0 +1,13 @@
+# Logo as base64 encoded string
+# This avoids binary file issues with Hugging Face Spaces
+
+def get_logo_base64():
+    """Returns the Sentinel logo as a base64 encoded string"""
+    # If logo file exists, read it
+    try:
+        import base64
+        with open("assets/logo.png", "rb") as f:
+            return base64.b64encode(f.read()).decode()
+    except:
+        # Fallback: return empty string if file not found
+        return ""

main.py

@@ -0,0 +1,54 @@
+import subprocess
+import sys
+import os
+import time
+import signal
+
+def cleanup(signum, frame):
+    print("Stopping services...")
+    # Add cleanup logic here if needed
+    sys.exit(0)
+
+signal.signal(signal.SIGINT, cleanup)
+signal.signal(signal.SIGTERM, cleanup)
+
+def main():
+    print("🚀 Starting Sentinel Monolith...")
+    
+    # 1. Start the MCP Gateway (which now includes all microservices)
+    # running on port 8000
+    gateway_cmd = [sys.executable, "mcp_gateway.py"]
+    gateway_process = subprocess.Popen(gateway_cmd, cwd=os.getcwd())
+    print(f"✅ Gateway started (PID: {gateway_process.pid})")
+    
+    # 2. Start the Monitor (runs in background loop)
+    # Using the same interpreter
+    monitor_cmd = [sys.executable, "monitor.py"]
+    monitor_process = subprocess.Popen(monitor_cmd, cwd=os.getcwd())
+    print(f"✅ Monitor started (PID: {monitor_process.pid})")
+
+    # Give backend a moment to initialize
+    time.sleep(5)
+    
+    # 3. Start Streamlit (Frontend)
+    # This commands blocks until Streamlit exits
+    print("✅ Starting Streamlit on port 7860...")
+    streamlit_cmd = [
+        "streamlit", "run", "app.py",
+        "--server.port", "7860",
+        "--server.address", "0.0.0.0",
+        "--server.headless", "true",
+        "--browser.serverAddress", "0.0.0.0",
+        "--server.enableCORS", "false",
+        "--server.enableXsrfProtection", "false"
+    ]
+    
+    # We use subprocess.run for the foreground process
+    subprocess.run(streamlit_cmd, check=False)
+    
+    # Cleanup when streamlit exits
+    gateway_process.terminate()
+    monitor_process.terminate()
+
+if __name__ == "__main__":
+    main()

mcp_gateway.py

@@ -0,0 +1,220 @@
+# mcp_gateway.py
+from fastapi import FastAPI, HTTPException, Request, UploadFile, File
+from fastapi.responses import JSONResponse
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+import uvicorn
+import httpx
+import logging
+import os
+import io
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# --- Logging Setup ---
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("MCP_Gateway")
+
+# --- Import Microservices for Consolidation ---
+try:
+    from tavily_mcp import app as tavily_app
+    from alphavantage_mcp import app as alphavantage_app
+    from private_mcp import app as private_app
+    logger.info("Successfully imported microservices for consolidation.")
+except ImportError as e:
+    logger.critical(f"Failed to import microservices: {e}")
+    raise
+
+# --- Configuration (Updated for Monolithic Mode) ---
+# Default to internal mounted paths on the same port (8000)
+TAVILY_MCP_URL = os.getenv("TAVILY_MCP_URL", "http://127.0.0.1:8000/tavily/research")
+ALPHAVANTAGE_MCP_URL = os.getenv("ALPHAVANTAGE_MCP_URL", "http://127.0.0.1:8000/alphavantage/market_data")
+PRIVATE_MCP_URL = os.getenv("PRIVATE_MCP_URL", "http://127.0.0.1:8000/private/portfolio_data")
+
+# --- FastAPI App ---
+app = FastAPI(title="Aegis MCP Gateway (Monolith)")
+
+# --- CORS Configuration ---
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"], 
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --- Mount Microservices ---
+app.mount("/tavily", tavily_app)
+app.mount("/alphavantage", alphavantage_app)
+app.mount("/private", private_app)
+
+client = httpx.AsyncClient()
+
+@app.middleware("http")
+async def audit_log_middleware(request: Request, call_next):
+    # Skip logging for internal sub-app calls to reduce noise if needed, 
+    # but strictly speaking this middleware triggers for the parent app.
+    # Requests to mounted apps might bypass this or trigger it depending on path matching.
+    logger.info(f"Request received: {request.method} {request.url}")
+    response = await call_next(request)
+    return response
+
+# --- New REST Endpoints for Next.js ---
+class ResearchRequest(BaseModel):
+    ticker: str
+
+class ChatMessage(BaseModel):
+    role: str
+    content: str
+
+class ChatRequest(BaseModel):
+    message: str
+    history: list[ChatMessage] = []
+
+@app.post("/api/chat")
+async def api_chat_orchestrator(request: ChatRequest):
+    try:
+        from features.utils import call_gemini
+        from features.research_report import generate_report
+        import re
+
+        user_msg = request.message
+        
+        # 1. Routing Agent: Determine intent
+        routing_prompt = f"""You are Sentinel's routing agent. The user said: "{user_msg}"
+Determine if they want a deep research report on a specific stock ticker. 
+If YES, reply ONLY with the stock ticker symbol (e.g. AAPL, TSLA, NVDA).
+If NO (they are just asking a general question or chatting), reply ONLY with the word "CHAT".
+"""
+        intent = call_gemini(routing_prompt, "You are a precise routing system.").strip().upper()
+
+        if intent != "CHAT" and len(intent) <= 5 and intent.isalpha():
+            # Trigger Research Pipeline
+            logger.info(f"Routing to Research Report Pipeline for: {intent}")
+            report = generate_report(intent)
+            
+            # Format the JSON report beautifully into Markdown for the Chat UI
+            reply = f"### 📊 Sentinel Analysis Sequence Complete: **{report.get('_resolved_ticker', intent)}**\n\n"
+            reply += f"**Executive Summary**\n{report.get('executive_summary', '')}\n\n"
+            reply += f"***\n**Fundamentals**\n{report.get('fundamentals', '')}\n\n"
+            reply += f"***\n**Latest Intelligence**\n{report.get('news', '')}\n\n"
+            reply += f"***\n**⚠️ Risk Assessment**\n{report.get('risks', '')}\n\n"
+            reply += f"***\n**🎯 Final Verdict & Price Target**\n{report.get('verdict', '')}"
+            return {"reply": reply}
+
+        else:
+            # 2. General Conversation Agent
+            logger.info("Routing to General Chat Agent")
+            chat_context = ""
+            for msg in request.history[-5:]: # Keep last 5 messages for context
+                chat_context += f"{msg.role.capitalize()}: {msg.content}\n"
+            
+            chat_prompt = f"""You are Sentinel, an elite AI financial intelligence operating system.
+You are talking to a user through a sleek, neon 'Generative UI' terminal.
+Keep your responses concise, sharp, and highly technical. Use markdown extensively.
+
+Conversation History:
+{chat_context}
+
+User's new message:
+{user_msg}
+"""
+            reply = call_gemini(chat_prompt, "You are Sentinel, an elite financial AI.")
+            return {"reply": reply}
+
+    except Exception as e:
+        logger.error(f"Chat Orchestrator Error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/api/research")
+async def api_research_report(request: ResearchRequest):
+    try:
+        from features.research_report import generate_report
+        report = generate_report(request.ticker)
+        return {"status": "success", "data": report}
+    except Exception as e:
+        logger.error(f"Research Report Error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/api/portfolio")
+async def api_portfolio_analyzer(file: UploadFile = File(...)):
+    try:
+        from features.portfolio_analyzer import _parse_csv, _parse_excel, _parse_pdf, _enrich_holdings, _generate_ai_analysis
+        content = await file.read()
+        file_obj = io.BytesIO(content)
+        file_obj.name = file.filename 
+
+        if file.filename.lower().endswith('.csv'):
+            holdings = _parse_csv(file_obj)
+        elif file.filename.lower().endswith(('.xlsx', '.xls')):
+            holdings = _parse_excel(file_obj)
+        elif file.filename.lower().endswith('.pdf'):
+            holdings = _parse_pdf(file_obj)
+        else:
+            raise HTTPException(status_code=400, detail="Unsupported file format.")
+
+        if holdings is None or holdings.empty:
+            raise HTTPException(status_code=400, detail="Could not parse holdings from the uploaded file.")
+        
+        enriched = _enrich_holdings(holdings)
+        ai_result = _generate_ai_analysis(enriched)
+        
+        # Convert df to dict
+        enriched_dict = enriched.to_dict(orient="records")
+        return {
+            "status": "success",
+            "data": {
+                "holdings": enriched_dict,
+                "analysis": ai_result
+            }
+        }
+    except Exception as e:
+        logger.error(f"Portfolio Analyzer Error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/route_agent_request")
+async def route_agent_request(request_data: dict):
+    target_service = request_data.get("target_service")
+    payload = request_data.get("payload", {})
+    
+    logger.info(f"Routing request for target service: {target_service}")
+
+    url_map = {
+        "tavily_research": TAVILY_MCP_URL,
+        "alpha_vantage_market_data": ALPHAVANTAGE_MCP_URL,
+        "alpha_vantage_overview": os.getenv("AV_OVERVIEW_URL", "http://127.0.0.1:8000/alphavantage/company_overview"),
+        "alpha_vantage_quote": os.getenv("AV_QUOTE_URL", "http://127.0.0.1:8000/alphavantage/global_quote"),
+        "internal_portfolio_data": PRIVATE_MCP_URL,
+    }
+
+    target_url = url_map.get(target_service)
+
+    if not target_url:
+        logger.error(f"Invalid target service specified: {target_service}")
+        raise HTTPException(status_code=400, detail=f"Invalid target service: {target_service}")
+
+    try:
+        # Self-referential call (Gateway -> Mounted App on same server)
+        # We must ensure we don't block. HTTPX AsyncClient handles this well.
+        response = await client.post(target_url, json=payload, timeout=180.0)
+        response.raise_for_status()
+        return JSONResponse(content=response.json(), status_code=response.status_code)
+
+    except httpx.HTTPStatusError as e:
+        logger.error(f"Error from microservice {target_service}: {e.response.text}")
+        raise HTTPException(status_code=e.response.status_code, detail=e.response.json())
+    except httpx.RequestError as e:
+        logger.error(f"Could not connect to microservice {target_service}: {e}")
+        raise HTTPException(status_code=503, detail=f"Service '{target_service}' is unavailable.")
+    except Exception as e:
+        logger.critical(f"An unexpected error occurred during routing: {e}")
+        raise HTTPException(status_code=500, detail="Internal server error in MCP Gateway.")
+
+@app.get("/")
+def read_root():
+    return {"message": "Aegis MCP Gateway (Monolithic) is operational."}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="127.0.0.1", port=8000)
+    

monitor.py

@@ -0,0 +1,171 @@
+# monitor.py
+import time
+import json
+import os
+import sys
+import logging
+from datetime import datetime
+from agents.tool_calling_agents import MarketDataAgent, WebResearchAgent
+
+# --- Configuration ---
+WATCHLIST_FILE = "watchlist.json"
+ALERTS_FILE = "alerts.json"
+CHECK_INTERVAL = 300  # 5 minutes (preserves API quota; free tier = 25 requests/day)
+PRICE_ALERT_THRESHOLD = 0.5  # More sensitive alerts
+
+# --- Logging Setup ---
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
+    handlers=[
+        logging.StreamHandler(sys.stdout)
+    ]
+)
+logger = logging.getLogger("Aegis_Monitor")
+
+# --- Initialize Agents ---
+market_agent = MarketDataAgent()
+web_agent = WebResearchAgent()
+
+def load_watchlist():
+    if not os.path.exists(WATCHLIST_FILE):
+        return []
+    try:
+        with open(WATCHLIST_FILE, 'r') as f:
+            return json.load(f)
+    except Exception as e:
+        logger.error(f"Error loading watchlist: {e}")
+        return []
+
+def save_alert(alert):
+    alerts = []
+    if os.path.exists(ALERTS_FILE):
+        try:
+            with open(ALERTS_FILE, 'r') as f:
+                alerts = json.load(f)
+        except:
+            pass
+    
+    # Prepend new alert
+    alerts.insert(0, alert)
+    # Keep only last 100 alerts (increased from 50)
+    alerts = alerts[:100]
+    
+    with open(ALERTS_FILE, 'w') as f:
+        json.dump(alerts, f, indent=2)
+
+def check_market_data(symbol):
+    try:
+        logger.info(f"Checking market data for {symbol}...")
+        # Use GLOBAL_QUOTE (free tier) instead of INTRADAY (premium)
+        result = market_agent.get_global_quote(symbol=symbol)
+        
+        if result.get("status") != "success":
+            logger.warning(f"Failed to get market data for {symbol}")
+            return None
+
+        data = result.get("data", {})
+        if not data or data.get("price") == "0":
+            return None
+
+        price = float(data.get("price", 0))
+        change_str = data.get("change_percent", "0%").replace("%", "")
+        pct_change = float(change_str) if change_str else 0
+        
+        return {
+            "price": price,
+            "change": pct_change,
+            "timestamp": datetime.now().isoformat(),
+            "source": result.get("source", "Alpha Vantage")
+        }
+    except Exception as e:
+        logger.error(f"Error checking market data for {symbol}: {e}")
+        return None
+    except Exception as e:
+        logger.error(f"Error checking market data for {symbol}: {e}")
+        return None
+
+def check_news(symbol):
+    try:
+        logger.info(f"Checking news for {symbol}...")
+        query = f"breaking news {symbol} stock today"
+        result = web_agent.research(queries=[query], search_depth="basic")
+        
+        if result.get("status") != "success":
+            return None
+            
+        # Just return the first result title for now as a "headline"
+        data = result.get("data", [])
+        if data and data[0].get("results"):
+            first_hit = data[0]["results"][0]
+            return {
+                "title": first_hit.get("title"),
+                "url": first_hit.get("url"),
+                "content": first_hit.get("content")[:200] + "..."
+            }
+        return None
+    except Exception as e:
+        logger.error(f"Error checking news for {symbol}: {e}")
+        return None
+
+def run_monitor_loop():
+    logger.info("--- 🛡️ Aegis Proactive Monitor Started ---")
+    logger.info(f"Monitoring watchlist every {CHECK_INTERVAL} seconds ({CHECK_INTERVAL/60:.0f} minutes).")
+    logger.info(f"Price alert threshold: {PRICE_ALERT_THRESHOLD}%")
+    
+    while True:
+        watchlist = load_watchlist()
+        if not watchlist:
+            logger.info("Watchlist is empty. Waiting...")
+        
+        for symbol in watchlist:
+            try:
+                # 1. Market Check
+                market_info = check_market_data(symbol)
+                if market_info:
+                    # Alert Logic: Price moved more than threshold
+                    if abs(market_info['change']) > PRICE_ALERT_THRESHOLD:
+                        direction = "📈 UP" if market_info['change'] > 0 else "📉 DOWN"
+                        alert_msg = f"{direction} ALERT: {symbol} moved {market_info['change']:+.2f}% to ${market_info['price']:.2f}"
+                        logger.info(alert_msg)
+                        
+                        save_alert({
+                            "timestamp": datetime.now().isoformat(),
+                            "type": "MARKET",
+                            "symbol": symbol,
+                            "message": alert_msg,
+                            "details": market_info
+                        })
+
+                # 2. News Check (Simplified: Just log latest headline)
+                news_info = check_news(symbol)
+                if news_info:
+                    # Check if this is "significant" news based on keywords
+                    keywords = [
+                        "acquisition", "merger", "earnings", "crash", "surge", "plunge",
+                        "fda", "lawsuit", "sec", "filing", "8-k", "10-k", "insider",
+                        "partnership", "deal", "bankruptcy", "recall", "investigation",
+                        "upgrade", "downgrade", "target", "buyback", "dividend"
+                    ]
+                    if any(k in news_info['title'].lower() for k in keywords):
+                        alert_msg = f"📰 NEWS ALERT: {symbol} - {news_info['title']}"
+                        logger.info(alert_msg)
+                        
+                        save_alert({
+                            "timestamp": datetime.now().isoformat(),
+                            "type": "NEWS",
+                            "symbol": symbol,
+                            "message": alert_msg,
+                            "details": news_info
+                        })
+                        
+            except Exception as e:
+                logger.error(f"Error processing {symbol}: {e}")
+                
+        logger.info(f"Cycle complete. Sleeping for {CHECK_INTERVAL}s...")
+        time.sleep(CHECK_INTERVAL)
+
+if __name__ == "__main__":
+    # Ensure we can import agents
+    sys.path.append(os.path.abspath(os.path.dirname(__file__)))
+    run_monitor_loop()

private_mcp.py

@@ -0,0 +1,175 @@
+# private_mcp.py
+from fastapi import FastAPI, HTTPException
+import uvicorn
+import sqlite3
+import logging
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_ollama import ChatOllama
+
+# --- Logging Setup ---
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("Private_MCP_Server")
+
+# --- Database Configuration ---
+DB_FILE = "portfolio.db"
+
+# --- LLM Configuration (Local Llama 3) ---
+# This connects to the Ollama application running on your machine.
+# Make sure Ollama and the llama3 model are running.
+llm = ChatOllama(model="llama3", temperature=0)
+
+# --- Text-to-SQL Prompt Engineering ---
+# This prompt is carefully designed to make Llama 3 generate ONLY safe SQL queries.
+text_to_sql_prompt = ChatPromptTemplate.from_messages([
+    ("system", 
+     """You are a Text-to-SQL assistant. Convert the question to a read-only SQLite query for the 'holdings' table.
+Schema: symbol (TEXT), shares (INTEGER), average_cost (REAL).
+RULES:
+1. SELECT only. No INSERT/UPDATE/DELETE.
+2. Output ONLY the SQL query. No markdown.
+"""),
+    ("human", "Question: {question}")
+])
+
+# Create the LangChain chain for Text-to-SQL
+sql_generation_chain = text_to_sql_prompt | llm | StrOutputParser()
+
+# --- FastAPI App ---
+app = FastAPI(title="Aegis Private MCP Server")
+
+@app.on_event("startup")
+async def startup_db():
+    """Initialize the database with dummy data if it doesn't exist."""
+    try:
+        with sqlite3.connect(DB_FILE) as conn:
+            cursor = conn.cursor()
+            cursor.execute("""
+                CREATE TABLE IF NOT EXISTS holdings (
+                    symbol TEXT PRIMARY KEY,
+                    shares INTEGER,
+                    average_cost REAL
+                )
+            """)
+            
+            # Check if data exists
+            cursor.execute("SELECT count(*) FROM holdings")
+            if cursor.fetchone()[0] == 0:
+                logger.info("Populating database with diverse dummy data...")
+                # Expanded list of companies across sectors
+                dummy_data = [
+                    # Tech
+                    ('AAPL', 5000, 180.20), ('MSFT', 3000, 350.50), ('GOOGL', 1500, 140.10), ('NVDA', 800, 450.00), ('AMD', 2000, 110.30),
+                    ('INTC', 4000, 35.40), ('CRM', 1200, 220.10), ('ADBE', 600, 550.20), ('ORCL', 2500, 115.50), ('CSCO', 3500, 52.10),
+                    # Finance
+                    ('JPM', 2000, 150.40), ('BAC', 5000, 32.10), ('GS', 500, 340.50), ('V', 1000, 240.20), ('MA', 800, 380.10),
+                    # Retail & Consumer
+                    ('WMT', 1500, 160.30), ('TGT', 1000, 130.50), ('COST', 400, 550.10), ('KO', 3000, 58.20), ('PEP', 2500, 170.40),
+                    ('PG', 2000, 150.10), ('NKE', 1200, 105.30), ('SBUX', 1800, 95.40),
+                    # Healthcare
+                    ('JNJ', 2500, 160.20), ('PFE', 4000, 35.10), ('UNH', 600, 480.50), ('LLY', 400, 580.10), ('MRK', 2000, 110.20),
+                    # Energy & Industrial
+                    ('XOM', 3000, 105.40), ('CVX', 2000, 150.20), ('GE', 1500, 110.50), ('CAT', 800, 280.10), ('BA', 500, 210.30),
+                    # Auto
+                    ('TSLA', 1000, 220.90), ('F', 5000, 12.10), ('GM', 4000, 35.40)
+                ]
+                cursor.executemany("INSERT INTO holdings (symbol, shares, average_cost) VALUES (?, ?, ?)", dummy_data)
+                conn.commit()
+                logger.info("Database populated successfully.")
+            else:
+                logger.info("Database already contains data.")
+    except Exception as e:
+        logger.error(f"Failed to initialize database: {e}")
+
+
+def execute_safe_query(query: str, params=None):
+    """
+    Executes a SQL query after a basic safety check.
+    This is a critical security function.
+    """
+    # SECURITY CHECK: Ensure the query is read-only.
+    if not query.strip().upper().startswith("SELECT"):
+        logger.error(f"SECURITY VIOLATION: Attempted to execute non-SELECT query: {query}")
+        raise HTTPException(status_code=403, detail="Forbidden: Only SELECT queries are allowed.")
+    
+    try:
+        with sqlite3.connect(DB_FILE) as conn:
+            conn.row_factory = sqlite3.Row # Makes results dict-like
+            cursor = conn.cursor()
+            if params:
+                cursor.execute(query, params)
+            else:
+                cursor.execute(query)
+            
+            results = [dict(row) for row in cursor.fetchall()]
+            # Sanitize results: Replace None with 0 (common for SUM on empty set)
+            for row in results:
+                for key, value in row.items():
+                    if value is None:
+                        row[key] = 0
+            return results
+    except sqlite3.Error as e:
+        logger.error(f"Database error executing query '{query}': {e}")
+        raise HTTPException(status_code=500, detail=f"Database query failed: {e}")
+
+@app.post("/portfolio_data")
+async def get_portfolio_data(payload: dict):
+    """
+    Takes a natural language question, converts it to SQL using Llama 3,
+    and executes it against the internal portfolio database.
+    """
+    question = payload.get("question")
+    if not question:
+        raise HTTPException(status_code=400, detail="'question' is a required field.")
+
+    logger.info(f"Received portfolio data question: '{question}'")
+
+    try:
+        # Step 1: Generate the SQL query using the local LLM
+        try:
+            generated_sql = await sql_generation_chain.ainvoke({"question": question})
+            logger.info(f"Llama 3 generated SQL: {generated_sql}")
+        except Exception as llm_error:
+            logger.warning(f"LLM generation failed (likely Ollama offline): {llm_error}. Using fallback logic.")
+            # Fallback Logic: Dynamic symbol extraction
+            import re
+            q_upper = question.upper()
+            # Look for common ticker patterns (1-5 uppercase letters)
+            matches = re.findall(r'\b[A-Z]{1,5}\b', q_upper)
+            
+            found_symbol = None
+            ignored_words = ["WHAT", "IS", "THE", "TO", "OF", "FOR", "IN", "AND", "OR", "SHOW", "ME", "DATA", "STOCK", "PRICE", "DO", "WE", "OWN", "HAVE", "ANY", "EXPOSURE", "CURRENT"]
+            
+            for match in matches:
+                if match not in ignored_words:
+                    found_symbol = match
+                    break
+            
+            if found_symbol:
+                generated_sql = f"SELECT * FROM holdings WHERE symbol='{found_symbol}'"
+            else:
+                generated_sql = "SELECT * FROM holdings" # Default to showing all
+            logger.info(f"Fallback SQL generated: {generated_sql}")
+
+        # Step 2: Execute the query using our secure function
+        results = execute_safe_query(generated_sql)
+        logger.info(f"Successfully executed query and found {len(results)} records.")
+
+        return {"status": "success", "question": question, "generated_sql": generated_sql, "data": results}
+
+    except HTTPException as http_exc:
+        # Re-raise HTTP exceptions from our secure executor
+        raise http_exc
+    except Exception as e:
+        logger.critical(f"An unexpected error occurred in the portfolio data endpoint: {e}")
+        # Don't crash the client, return an empty success with error note
+        return {"status": "error", "message": str(e), "data": []}
+
+@app.get("/")
+def read_root():
+    return {"message": "Aegis Private MCP Server is operational."}
+
+# --- Main Execution ---
+if __name__ == "__main__":
+    # This server runs on port 8003
+    uvicorn.run(app, host="127.0.0.1", port=8003)

requirements.txt

@@ -1,3 +1,23 @@
-altair
+streamlit
+langchain
+langchain-core
+langgraph
+pydantic<3,>=2
 pandas
-streamlit
+plotly
+python-dotenv
+httpx
+alpha_vantage
+fastapi
+uvicorn[standard]
+tavily
+langchain_ollama
+langchain-google-genai
+fpdf2
+pdfplumber
+wordcloud
+APScheduler
+fredapi
+jinja2
+matplotlib
+requests

src/streamlit_app.py

@@ -1,40 +0,0 @@
-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))

start_all.sh

@@ -0,0 +1,52 @@
+#!/bin/bash
+
+# Function to kill all background processes on exit
+cleanup() {
+    echo "Stopping all services..."
+    kill $(jobs -p) 2>/dev/null
+    exit
+}
+
+# Trap SIGINT (Ctrl+C) and call cleanup
+trap cleanup SIGINT
+
+# Cleanup existing processes to prevent port conflicts
+echo "🧹 Cleaning up existing processes..."
+lsof -ti:8000,8001,8002,8003,7860 | xargs kill -9 2>/dev/null || true
+pkill -f "uvicorn" || true
+pkill -f "streamlit" || true
+sleep 2
+
+echo "🚀 Starting Aegis System..."
+
+# Check if venv exists and activate it
+if [ -d "venv" ]; then
+    echo "🔌 Activating virtual environment..."
+    source venv/bin/activate
+else
+    echo "⚠️  No virtual environment found. Running with system python..."
+fi
+
+# Start Microservices
+echo "Starting MCP Gateway (Port 8000)..."
+python mcp_gateway.py > mcp_gateway.log 2>&1 &
+
+echo "Starting Tavily MCP (Port 8001)..."
+python tavily_mcp.py > tavily_mcp.log 2>&1 &
+
+echo "Starting Alpha Vantage MCP (Port 8002)..."
+python alphavantage_mcp.py > alphavantage_mcp.log 2>&1 &
+
+echo "Starting Private Portfolio MCP (Port 8003)..."
+python private_mcp.py > private_mcp.log 2>&1 &
+
+# Start Monitor
+echo "Starting Proactive Monitor..."
+python monitor.py > monitor.log 2>&1 &
+
+# Wait a moment for services to spin up
+sleep 3
+
+# Start Streamlit App
+echo "🛡️  Launching Sentinel Interface..."
+streamlit run app.py --server.port 7860 > streamlit.log 2>&1

style.css

@@ -0,0 +1,404 @@
+@import url('https://fonts.googleapis.com/css2?family=Outfit:wght@300;400;500;600;700;800&family=Inter:wght@300;400;500;600&display=swap');
+
+:root {
+    /* Deep Zinc Space Palette */
+    --bg-color: #09090b;
+    --card-bg: rgba(24, 24, 27, 0.6);
+    --sidebar-bg: rgba(9, 9, 11, 0.85);
+
+    /* Neon Accents */
+    --primary: #ffffff;
+    --primary-hover: #e4e4e7;
+    --accent: #a78bfa;
+    --accent-glow: rgba(167, 139, 250, 0.2);
+
+    /* Text */
+    --text-primary: #fafafa;
+    --text-secondary: #a1a1aa;
+
+    /* Borders */
+    --border-color: rgba(255, 255, 255, 0.08);
+    --border-glow: rgba(255, 255, 255, 0.15);
+
+    /* Semantics */
+    --success: #10b981;
+    --warning: #f59e0b;
+    --danger: #ef4444;
+
+    /* Typography */
+    --font-heading: 'Outfit', sans-serif;
+    --font-body: 'Inter', sans-serif;
+}
+
+/* Global Reset & Background Magic */
+.stApp {
+    background-color: var(--bg-color);
+    background-image:
+        radial-gradient(circle at 15% 50%, rgba(167, 139, 250, 0.04), transparent 30%),
+        radial-gradient(circle at 85% 30%, rgba(56, 189, 248, 0.03), transparent 30%);
+    color: var(--text-primary);
+    font-family: var(--font-body);
+}
+
+h1,
+h2,
+h3,
+h4,
+h5,
+h6 {
+    font-family: var(--font-heading);
+    font-weight: 600;
+    color: var(--text-primary);
+    letter-spacing: -0.02em;
+}
+
+/* Sidebar Glassmorphism */
+section[data-testid="stSidebar"] {
+    background-color: var(--sidebar-bg);
+    border-right: 1px solid var(--border-color);
+    backdrop-filter: blur(20px);
+    -webkit-backdrop-filter: blur(20px);
+}
+
+/* Inputs & Form Overrides */
+.stTextInput input,
+.stTextArea textarea,
+.stSelectbox div[data-baseweb="select"] {
+    background-color: rgba(24, 24, 27, 0.8) !important;
+    color: white !important;
+    border: 1px solid var(--border-color) !important;
+    border-radius: 8px;
+    font-family: var(--font-body);
+    transition: all 0.2s ease;
+}
+
+.stTextInput input:focus,
+.stTextArea textarea:focus {
+    border-color: var(--accent) !important;
+    box-shadow: 0 0 0 1px var(--accent) !important;
+}
+
+/* Dynamic Buttons */
+.stButton button {
+    background: rgba(255, 255, 255, 0.05);
+    color: var(--text-primary);
+    border: 1px solid var(--border-color);
+    font-family: var(--font-heading);
+    font-weight: 500;
+    letter-spacing: 0.01em;
+    border-radius: 8px;
+    padding: 0.75rem 2rem;
+    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+    backdrop-filter: blur(10px);
+}
+
+.stButton button:hover {
+    background: rgba(255, 255, 255, 0.1);
+    border-color: var(--border-glow);
+    transform: translateY(-2px);
+    box-shadow: 0 4px 12px rgba(0, 0, 0, 0.4);
+}
+
+/* High-Impact Hero Buttons (Like "Execute Analysis") */
+button[kind="primary"] {
+    background: linear-gradient(135deg, #a78bfa 0%, #8b5cf6 100%);
+    color: white !important;
+    border: none;
+    box-shadow: 0 4px 15px rgba(139, 92, 246, 0.3);
+}
+
+button[kind="primary"]:hover {
+    transform: translateY(-2px) scale(1.02);
+    box-shadow: 0 6px 20px rgba(139, 92, 246, 0.5);
+}
+
+/* Hero Section */
+.hero-container {
+    display: flex;
+    flex-direction: column;
+    align-items: center;
+    justify-content: center;
+    text-align: center;
+    padding: 7rem 1rem 5rem 1rem;
+    width: 100%;
+}
+
+.hero-title {
+    font-family: var(--font-heading);
+    font-size: 5rem;
+    font-weight: 800;
+    margin-bottom: 1.5rem;
+    color: white;
+    letter-spacing: -0.04em;
+    line-height: 1.1;
+    text-shadow: 0 0 30px rgba(255, 255, 255, 0.1);
+}
+
+.hero-subtitle {
+    font-family: var(--font-body);
+    font-size: 1.25rem;
+    color: var(--text-secondary);
+    max-width: 650px;
+    margin: 0 auto 3rem auto;
+    text-align: center;
+    line-height: 1.7;
+    font-weight: 300;
+}
+
+/* Feature Grid Cards */
+.feature-grid {
+    display: grid;
+    grid-template-columns: repeat(auto-fit, minmax(320px, 1fr));
+    gap: 24px;
+    margin-top: 2rem;
+    padding: 0 1rem;
+}
+
+.feature-card {
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: 16px;
+    padding: 32px;
+    display: flex;
+    flex-direction: column;
+    align-items: flex-start;
+    height: 100%;
+    transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
+    backdrop-filter: blur(16px);
+    -webkit-backdrop-filter: blur(16px);
+    position: relative;
+    overflow: hidden;
+}
+
+/* Glass Edge Highlight */
+.feature-card::before {
+    content: '';
+    position: absolute;
+    top: 0;
+    left: 0;
+    right: 0;
+    height: 1px;
+    background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.1), transparent);
+}
+
+.feature-card:hover {
+    border-color: rgba(167, 139, 250, 0.4);
+    transform: translateY(-4px);
+    box-shadow: 0 10px 30px -10px rgba(0, 0, 0, 0.5),
+        0 0 20px var(--accent-glow);
+}
+
+.feature-icon {
+    font-size: 2.5rem;
+    margin-bottom: 20px;
+    background: rgba(255, 255, 255, 0.05);
+    padding: 12px;
+    border-radius: 12px;
+    border: 1px solid rgba(255, 255, 255, 0.05);
+}
+
+.feature-title {
+    font-family: var(--font-heading);
+    font-size: 1.4rem;
+    font-weight: 600;
+    margin-bottom: 12px;
+    color: white;
+    letter-spacing: -0.01em;
+}
+
+.feature-desc {
+    font-family: var(--font-body);
+    font-size: 0.95rem;
+    color: var(--text-secondary);
+    line-height: 1.6;
+    font-weight: 400;
+}
+
+/* Live Wire Alerts (HUD Style) */
+.alert-card {
+    background: rgba(24, 24, 27, 0.4);
+    border: 1px solid var(--border-color);
+    padding: 16px;
+    margin-bottom: 12px;
+    border-radius: 10px;
+    transition: all 0.2s ease;
+    backdrop-filter: blur(8px);
+    font-family: var(--font-body);
+}
+
+.alert-card:hover {
+    background: rgba(39, 39, 42, 0.6);
+    border-color: var(--border-glow);
+    transform: translateX(2px);
+}
+
+.alert-market {
+    border-left: 3px solid var(--danger);
+}
+
+.alert-news {
+    border-left: 3px solid #3b82f6;
+}
+
+.alert-header {
+    display: flex;
+    justify-content: space-between;
+    color: var(--text-secondary);
+    font-size: 0.8rem;
+    margin-bottom: 6px;
+    font-weight: 500;
+    font-family: var(--font-heading);
+    text-transform: uppercase;
+    letter-spacing: 0.05em;
+}
+
+.alert-body {
+    color: var(--text-primary);
+    font-size: 0.95rem;
+    line-height: 1.5;
+}
+
+/* Feature Page specific elements (Metric Cards, Sections) */
+.metric-card {
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: 14px;
+    padding: 24px 20px;
+    text-align: center;
+    transition: all 0.3s ease;
+    backdrop-filter: blur(12px);
+    position: relative;
+}
+
+.metric-card:hover {
+    border-color: var(--accent);
+    transform: translateY(-2px);
+    box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3);
+}
+
+.metric-card .metric-value {
+    font-family: var(--font-heading);
+    font-size: 2.2rem;
+    font-weight: 700;
+    color: #fff;
+    margin: 8px 0;
+    background: linear-gradient(to right, #fff, #a1a1aa);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+}
+
+.metric-card .metric-label {
+    font-size: 0.8rem;
+    color: var(--text-secondary);
+    text-transform: uppercase;
+    letter-spacing: 0.08em;
+    font-weight: 600;
+}
+
+/* Glass Section Wrappers */
+.report-section,
+.sentiment-gauge,
+.digest-card,
+.timeline-event {
+    background: var(--card-bg);
+    border: 1px solid var(--border-color);
+    border-radius: 14px;
+    padding: 24px;
+    backdrop-filter: blur(12px);
+    transition: all 0.2s ease;
+}
+
+.report-section h4 {
+    font-family: var(--font-heading);
+    color: #fff;
+    margin-bottom: 16px;
+    font-weight: 600;
+    font-size: 1.25rem;
+    display: flex;
+    align-items: center;
+    gap: 8px;
+}
+
+/* Timeline specific */
+.timeline-strip {
+    display: flex;
+    align-items: stretch;
+    gap: 16px;
+    overflow-x: auto;
+    padding: 16px 4px;
+    /* Custom Scrollbar */
+    scrollbar-width: thin;
+    scrollbar-color: #3f3f46 transparent;
+}
+
+.timeline-strip::-webkit-scrollbar {
+    height: 6px;
+}
+
+.timeline-strip::-webkit-scrollbar-thumb {
+    background-color: #3f3f46;
+    border-radius: 10px;
+}
+
+.timeline-event:hover {
+    border-color: var(--accent);
+    transform: translateY(-4px);
+    box-shadow: 0 8px 24px rgba(0, 0, 0, 0.4);
+}
+
+.timeline-event .event-date {
+    font-family: var(--font-heading);
+    color: var(--accent);
+    letter-spacing: 0.05em;
+}
+
+/* Status Indicators (Online/Offline dots) */
+.status-dot {
+    height: 8px;
+    width: 8px;
+    border-radius: 50%;
+    display: inline-block;
+    margin-right: 6px;
+}
+
+.status-ok {
+    background-color: var(--success);
+    box-shadow: 0 0 8px var(--success);
+}
+
+.status-err {
+    background-color: var(--danger);
+    box-shadow: 0 0 8px var(--danger);
+}
+
+/* Theme Pill (Sector Badges, etc) */
+.theme-pill {
+    font-family: var(--font-body);
+    font-weight: 500;
+    letter-spacing: 0.02em;
+    border: 1px solid rgba(255, 255, 255, 0.1);
+    background: rgba(255, 255, 255, 0.03);
+    color: #e4e4e7;
+}
+
+/* Markdown overrides */
+.stMarkdown p {
+    line-height: 1.7;
+    color: #d4d4d8;
+}
+
+.stMarkdown a {
+    color: var(--accent);
+    text-decoration: none;
+    transition: color 0.2s;
+}
+
+.stMarkdown a:hover {
+    color: #c4b5fd;
+    text-decoration: underline;
+}
+
+hr {
+    border-color: var(--border-color);
+    margin: 2rem 0;
+}

tavily_mcp.py

@@ -0,0 +1,117 @@
+# tavily_mcp.py (Corrected Version)
+from fastapi import FastAPI, HTTPException
+import uvicorn
+import os
+from dotenv import load_dotenv
+from tavily import TavilyClient
+import logging
+
+# --- Configuration ---
+load_dotenv()
+
+# --- Logging Setup (MUST be before we use logger) ---
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("Tavily_MCP_Server")
+
+# --- Get API Key ---
+TAVILY_API_KEY = os.getenv("TAVILY_API_KEY")
+
+# Fallback: Try to read from Streamlit secrets file (for cloud deployment)
+if not TAVILY_API_KEY:
+    try:
+        import toml
+        secrets_path = os.path.join(os.path.dirname(__file__), ".streamlit", "secrets.toml")
+        if os.path.exists(secrets_path):
+            secrets = toml.load(secrets_path)
+            TAVILY_API_KEY = secrets.get("TAVILY_API_KEY")
+            logger.info("Loaded TAVILY_API_KEY from .streamlit/secrets.toml")
+    except Exception as e:
+        logger.warning(f"Could not load from secrets.toml: {e}")
+
+if not TAVILY_API_KEY:
+    logger.warning("TAVILY_API_KEY not found in environment. Search features will fail.")
+else:
+    logger.info(f"TAVILY_API_KEY found: {TAVILY_API_KEY[:4]}...")
+
+# --- FastAPI App & Tavily Client ---
+app = FastAPI(title="Aegis Tavily MCP Server")
+tavily = TavilyClient(api_key=TAVILY_API_KEY)
+
+@app.post("/research")
+async def perform_research(payload: dict):
+    """
+    Performs a search for each query using the Tavily API.
+    Expects a payload like:
+    {
+        "queries": ["query1", "query2"],
+        "search_depth": "basic" or "advanced" (optional, default basic)
+    }
+    """
+    queries = payload.get("queries")
+    search_depth = payload.get("search_depth", "basic")
+
+    if not queries or not isinstance(queries, list):
+        logger.error("Validation Error: 'queries' must be a non-empty list.")
+        raise HTTPException(status_code=400, detail="'queries' must be a non-empty list.")
+
+    logger.info(f"Received research request for {len(queries)} queries. Search depth: {search_depth}")
+    
+    # --- THIS IS THE CORRECTED LOGIC ---
+    all_results = []
+    try:
+        # Loop through each query and perform a search
+        for query in queries:
+            logger.info(f"Performing search for query: '{query}'")
+            # The search method takes a single query string
+            response = tavily.search(
+                query=query,
+                search_depth=search_depth,
+                max_results=5 
+            )
+            # Add the results for this query to our collection
+            all_results.append({"query": query, "results": response["results"]})
+            
+        logger.info(f"Successfully retrieved results for all queries from Tavily API.")
+        return {"status": "success", "data": all_results}
+
+    except Exception as e:
+        logger.error(f"Tavily API Error (likely rate limit): {e}. Switching to MOCK DATA fallback.")
+        # --- FALLBACK MECHANISM ---
+        mock_results = []
+        import random
+        from datetime import datetime
+        
+        # Dynamic market sentiments to rotate through
+        sentiments = ["Bullish", "Bearish", "Neutral", "Volatile", "Cautious"]
+        events = ["Earnings Surprise", "New Product Launch", "Regulatory Update", "Sector Rotation", "Macro Headwinds"]
+        
+        current_time = datetime.now().strftime("%H:%M")
+        
+        for query in queries:
+            # Pick random sentiment and event to diversify the "news"
+            s = random.choice(sentiments)
+            e = random.choice(events)
+            
+            mock_results.append({
+                "query": query,
+                "results": [
+                    {
+                        "title": f"[{current_time}] Market Update: {s} Sentiment for {query}",
+                        "content": f"Live market data at {current_time} indicates a {s} trend for {query}. Analysts are tracking a potential {e} that could impact short-term price action. Volume remains high as traders adjust positions.",
+                        "url": "http://mock-source.com/market-update"
+                    },
+                    {
+                        "title": f"[{current_time}] Sector Alert: {e} affecting {query}",
+                        "content": f"Breaking: A significant {e} is rippling through the sector, heavily influencing {query}. Experts advise monitoring key resistance levels. (Simulated Real-Time Data)",
+                        "url": "http://mock-source.com/sector-alert"
+                    }
+                ]
+            })
+        return {"status": "success", "data": mock_results}
+
+@app.get("/")
+def read_root():
+    return {"message": "Aegis Tavily MCP Server is operational."}
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="127.0.0.1", port=8001)   

watchlist.json

@@ -0,0 +1 @@
+["AAPL", "TSLA", "NVDA", "MSFT", "AMZN", "GOOGL"]