diff --git a/tradingagents/dashboards/momentum/README.md b/tradingagents/dashboards/momentum/README.md
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+# Momentum Dashboard
+
+Real-time momentum analysis dashboard for TradingAgents.
+
+## Features
+
+✅ **21 EMA Trend Filter** - Long above, short below
+✅ **Bollinger Band Squeeze** - Identify low-volatility consolidation
+✅ **Volume Momentum** - Confirm breakouts
+✅ **RSI Indicator** - Overbought/oversold signals
+✅ **Multi-timeframe** - 1H/Daily/Weekly analysis
+✅ **Magnificent Seven** + Custom watchlists
+
+## Quick Start
+
+### 1. Install Dependencies
+```bash
+pip install streamlit plotly yfinance pandas numpy
+```
+
+### 2. Run Dashboard
+```bash
+cd tradingagents/dashboards/momentum
+streamlit run app.py
+```
+
+### 3. CLI Scanner (No UI)
+```bash
+python -m tradingagents.dashboards.momentum
+```
+
+## Signals
+
+| Signal | Meaning |
+|--------|---------|
+| STRONG_BUY | Bullish trend + high strength |
+| BUY | Moderate bullish |
+| WATCH_FOR_BREAKOUT | Squeeze detected, potential move |
+| HOLD | No clear signal |
+| SELL | Moderate bearish |
+| STRONG_SELL | Bearish trend + low strength |
+
+## Architecture
+
+```
+momentum/
+├── __init__.py     # Core scanner & indicators
+├── app.py          # Streamlit UI
+└── README.md       # This file
+```
+
+## Integration
+
+To integrate with TradingAgents main workflow:
+
+```python
+from tradingagents.dashboards.momentum import MomentumScanner
+
+scanner = MomentumScanner(["AAPL", "NVDA", "TSLA"])
+signals = scanner.scan_all()
+
+# Use signals in trading decisions
+for signal in signals:
+    if signal["signal"] == "STRONG_BUY":
+        # Execute buy logic
+        pass
+```
+
+## Future Enhancements
+
+- [ ] Real-time WebSocket data (Polygon.io)
+- [ ] Alert notifications (email/Telegram)
+- [ ] Portfolio tracking
+- [ ] Backtesting mode
+- [ ] Multi-exchange support
+
+---
+
+Built for TradingAgents by OpenClaw Community
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diff --git a/tradingagents/dashboards/momentum/__init__.py b/tradingagents/dashboards/momentum/__init__.py
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+"""
+Momentum Dashboard - Real-time momentum analysis for trading
+
+Features:
+- 21 EMA Trend Filter (long above, short below)
+- Bollinger Band Squeeze detection
+- Volume Momentum confirmation
+- Multi-timeframe analysis (1H/Daily/Weekly/Monthly/Quarterly)
+"""
+
+import yfinance as yf
+import pandas as pd
+import numpy as np
+from datetime import datetime, timedelta
+from typing import List, Dict, Optional, Tuple
+
+# Magnificent Seven stocks
+MAGNIFICENT_SEVEN = ["AAPL", "MSFT", "GOOGL", "AMZN", "NVDA", "META", "TSLA"]
+
+
+class MomentumIndicator:
+    """Core momentum indicators for the dashboard"""
+    
+    @staticmethod
+    def ema(data: pd.Series, period: int = 21) -> pd.Series:
+        """Calculate Exponential Moving Average"""
+        return data.ewm(span=period, adjust=False).mean()
+    
+    @staticmethod
+    def bollinger_bands(data: pd.Series, period: int = 20, std_dev: float = 2.0) -> Tuple[pd.Series, pd.Series, pd.Series]:
+        """Calculate Bollinger Bands"""
+        sma = data.rolling(window=period).mean()
+        std = data.rolling(window=period).std()
+        upper = sma + (std * std_dev)
+        lower = sma - (std * std_dev)
+        return upper, sma, lower
+    
+    @staticmethod
+    def bollinger_squeeze(bb_upper: pd.Series, bb_lower: pd.Series, bb_mid: pd.Series, 
+                           threshold: float = 0.1) -> pd.Series:
+        """
+        Detect Bollinger Band Squeeze (low volatility consolidation)
+        Returns True when bandwidth is below threshold
+        """
+        bandwidth = (bb_upper - bb_lower) / bb_mid
+        return bandwidth < threshold
+    
+    @staticmethod
+    def volume_momentum(volume: pd.Series, period: int = 20) -> pd.Series:
+        """Calculate Volume Momentum (current volume vs average)"""
+        avg_volume = volume.rolling(window=period).mean()
+        return volume / avg_volume
+    
+    @staticmethod
+    def rsi(data: pd.Series, period: int = 14) -> pd.Series:
+        """Calculate Relative Strength Index"""
+        delta = data.diff()
+        gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
+        loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
+        rs = gain / loss
+        return 100 - (100 / (1 + rs))
+
+
+class MomentumScanner:
+    """Scan stocks for momentum signals"""
+    
+    def __init__(self, symbols: List[str] = None):
+        self.symbols = symbols or MAGNIFICENT_SEVEN
+        self.indicators = MomentumIndicator()
+    
+    def fetch_data(self, symbol: str, period: str = "3mo", interval: str = "1h") -> pd.DataFrame:
+        """Fetch price data from yfinance"""
+        ticker = yf.Ticker(symbol)
+        df = ticker.history(period=period, interval=interval)
+        return df
+    
+    def analyze_symbol(self, symbol: str) -> Dict:
+        """Analyze a single symbol for momentum signals"""
+        try:
+            df = self.fetch_data(symbol)
+            if df.empty:
+                return {"symbol": symbol, "error": "No data"}
+            
+            close = df['Close']
+            volume = df['Volume']
+            
+            # Calculate indicators
+            ema_21 = self.indicators.ema(close, 21)
+            bb_upper, bb_mid, bb_lower = self.indicators.bollinger_bands(close)
+            squeeze = self.indicators.bollinger_squeeze(bb_upper, bb_lower, bb_mid)
+            vol_momentum = self.indicators.volume_momentum(volume)
+            rsi = self.indicators.rsi(close)
+            
+            # Current values
+            current_price = close.iloc[-1]
+            current_ema = ema_21.iloc[-1]
+            current_rsi = rsi.iloc[-1]
+            current_vol_mom = vol_momentum.iloc[-1]
+            is_squeeze = squeeze.iloc[-1]
+            
+            # Signal determination
+            trend = "BULLISH" if current_price > current_ema else "BEARISH"
+            signal_strength = self._calculate_signal_strength(
+                current_price, current_ema, current_rsi, current_vol_mom, is_squeeze
+            )
+            
+            return {
+                "symbol": symbol,
+                "price": round(current_price, 2),
+                "ema_21": round(current_ema, 2),
+                "trend": trend,
+                "rsi": round(current_rsi, 2),
+                "volume_momentum": round(current_vol_mom, 2),
+                "squeeze": bool(is_squeeze),
+                "signal_strength": signal_strength,
+                "signal": self._get_signal(trend, signal_strength, is_squeeze)
+            }
+        except Exception as e:
+            return {"symbol": symbol, "error": str(e)}
+    
+    def _calculate_signal_strength(self, price, ema, rsi, vol_mom, squeeze) -> float:
+        """Calculate overall signal strength (0-100)"""
+        score = 50  # Base score
+        
+        # Price vs EMA contribution
+        price_ema_diff = (price - ema) / ema * 100
+        score += min(max(price_ema_diff * 5, -20), 20)
+        
+        # RSI contribution (oversold/overbought)
+        if rsi < 30:
+            score += 15  # Oversold - potential buy
+        elif rsi > 70:
+            score -= 15  # Overbought - potential sell
+        
+        # Volume momentum contribution
+        if vol_mom > 1.5:
+            score += 10  # High volume confirms trend
+        elif vol_mom < 0.5:
+            score -= 10  # Low volume weakens signal
+        
+        return max(0, min(100, round(score)))
+    
+    def _get_signal(self, trend: str, strength: float, squeeze: bool) -> str:
+        """Generate trading signal"""
+        if squeeze and strength > 60:
+            return "WATCH_FOR_BREAKOUT"
+        elif trend == "BULLISH" and strength >= 70:
+            return "STRONG_BUY"
+        elif trend == "BULLISH" and strength >= 55:
+            return "BUY"
+        elif trend == "BEARISH" and strength <= 30:
+            return "STRONG_SELL"
+        elif trend == "BEARISH" and strength <= 45:
+            return "SELL"
+        else:
+            return "HOLD"
+    
+    def scan_all(self) -> List[Dict]:
+        """Scan all symbols and return results"""
+        results = []
+        for symbol in self.symbols:
+            result = self.analyze_symbol(symbol)
+            results.append(result)
+        return results
+
+
+def get_top_momentum_stocks(limit: int = 20) -> List[str]:
+    """Get top momentum stocks (could be enhanced with real data source)"""
+    # For now, return a static list of popular momentum stocks
+    momentum_stocks = [
+        "SMCI", "ARM", "PLTR", "SNOW", "DDOG", 
+        "MDB", "NET", "CRWD", "ZS", "PANW",
+        "AMD", "INTC", "QCOM", "AVGO", "TXN"
+    ]
+    return momentum_stocks[:limit]
+
+
+if __name__ == "__main__":
+    # Test the scanner
+    scanner = MomentumScanner()
+    results = scanner.scan_all()
+    
+    print("=" * 60)
+    print("MOMENTUM DASHBOARD - MAGNIFICENT SEVEN")
+    print("=" * 60)
+    print(f"{'Symbol':<8} {'Price':<10} {'Trend':<10} {'RSI':<8} {'Signal':<20}")
+    print("-" * 60)
+    
+    for r in results:
+        if "error" not in r:
+            print(f"{r['symbol']:<8} ${r['price']:<9} {r['trend']:<10} {r['rsi']:<8} {r['signal']:<20}")
+    
+    print("=" * 60)
\ No newline at end of file
diff --git a/tradingagents/dashboards/momentum/app.py b/tradingagents/dashboards/momentum/app.py
new file mode 100644
index 00000000..733d50f6
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+++ b/tradingagents/dashboards/momentum/app.py
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+"""
+Momentum Dashboard - Streamlit Web UI
+
+Run: streamlit run app.py
+"""
+
+import streamlit as st
+import pandas as pd
+import plotly.graph_objects as go
+from plotly.subplots import make_subplots
+from datetime import datetime, timedelta
+import sys
+import os
+
+# Add parent directory to path
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from __init__ import MomentumScanner, MomentumIndicator, MAGNIFICENT_SEVEN, get_top_momentum_stocks
+
+
+# Page config
+st.set_page_config(
+    page_title="Momentum Dashboard",
+    page_icon="📈",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    .stMetric {
+        background-color: #1E1E1E;
+        padding: 10px;
+        border-radius: 10px;
+    }
+    .bullish { color: #00FF00; }
+    .bearish { color: #FF0000; }
+    .squeeze { color: #FFA500; }
+</style>
+""", unsafe_allow_html=True)
+
+
+def main():
+    st.title("📈 Momentum Dashboard")
+    st.subheader("Real-time Momentum Analysis for Trading")
+    
+    # Sidebar
+    st.sidebar.header("Settings")
+    
+    # Stock selection
+    stock_option = st.sidebar.radio(
+        "Stock Universe",
+        ["Magnificent Seven", "Top Momentum", "Custom"]
+    )
+    
+    if stock_option == "Magnificent Seven":
+        symbols = MAGNIFICENT_SEVEN
+    elif stock_option == "Top Momentum":
+        symbols = get_top_momentum_stocks(15)
+    else:
+        custom_symbols = st.sidebar.text_input(
+            "Enter symbols (comma-separated)",
+            "AAPL,MSFT,GOOGL"
+        )
+        symbols = [s.strip().upper() for s in custom_symbols.split(",")]
+    
+    # Timeframe selection
+    timeframe = st.sidebar.selectbox(
+        "Timeframe",
+        ["1h", "1d", "1wk"]
+    )
+    
+    # Refresh button
+    if st.sidebar.button("🔄 Refresh Data"):
+        st.rerun()
+    
+    # Scan stocks
+    with st.spinner("Scanning momentum signals..."):
+        scanner = MomentumScanner(symbols)
+        results = scanner.scan_all()
+    
+    # Display results
+    col1, col2, col3 = st.columns(3)
+    
+    # Metrics
+    bullish_count = sum(1 for r in results if r.get("trend") == "BULLISH" and "error" not in r)
+    bearish_count = sum(1 for r in results if r.get("trend") == "BEARISH" and "error" not in r)
+    squeeze_count = sum(1 for r in results if r.get("squeeze") and "error" not in r)
+    
+    with col1:
+        st.metric("🟢 Bullish", bullish_count)
+    with col2:
+        st.metric("🔴 Bearish", bearish_count)
+    with col3:
+        st.metric("🟠 Squeeze", squeeze_count)
+    
+    st.divider()
+    
+    # Results table
+    st.subheader("📊 Momentum Signals")
+    
+    # Create DataFrame
+    df_data = []
+    for r in results:
+        if "error" not in r:
+            df_data.append({
+                "Symbol": r["symbol"],
+                "Price": f"${r['price']}",
+                "EMA 21": f"${r['ema_21']}",
+                "Trend": r["trend"],
+                "RSI": r["rsi"],
+                "Vol Mom": f"{r['volume_momentum']:.2f}x",
+                "Squeeze": "🔴" if r["squeeze"] else "",
+                "Signal": r["signal"],
+                "Strength": r["signal_strength"]
+            })
+    
+    df = pd.DataFrame(df_data)
+    
+    # Style the dataframe
+    def color_trend(val):
+        if val == "BULLISH":
+            return "color: green; font-weight: bold"
+        elif val == "BEARISH":
+            return "color: red; font-weight: bold"
+        return ""
+    
+    def color_signal(val):
+        if "BUY" in val:
+            return "background-color: #1a4d1a; color: white"
+        elif "SELL" in val:
+            return "background-color: #4d1a1a; color: white"
+        elif "BREAKOUT" in val:
+            return "background-color: #4d3d1a; color: white"
+        return ""
+    
+    styled_df = df.style.applymap(color_trend, subset=["Trend"]).applymap(color_signal, subset=["Signal"])
+    st.dataframe(styled_df, use_container_width=True)
+    
+    st.divider()
+    
+    # Detailed chart for selected symbol
+    st.subheader("📈 Detailed Analysis")
+    
+    selected_symbol = st.selectbox(
+        "Select symbol for detailed view",
+        [r["symbol"] for r in results if "error" not in r]
+    )
+    
+    if selected_symbol:
+        with st.spinner(f"Loading {selected_symbol} chart..."):
+            # Fetch data for chart
+            import yfinance as yf
+            ticker = yf.Ticker(selected_symbol)
+            hist = ticker.history(period="3mo", interval=timeframe)
+            
+            if not hist.empty:
+                # Calculate indicators
+                indicators = MomentumIndicator()
+                close = hist['Close']
+                
+                ema_21 = indicators.ema(close, 21)
+                bb_upper, bb_mid, bb_lower = indicators.bollinger_bands(close)
+                
+                # Create candlestick chart with indicators
+                fig = make_subplots(
+                    rows=3, cols=1,
+                    shared_xaxes=True,
+                    vertical_spacing=0.05,
+                    row_heights=[0.6, 0.2, 0.2]
+                )
+                
+                # Candlestick
+                fig.add_trace(
+                    go.Candlestick(
+                        x=hist.index,
+                        open=hist['Open'],
+                        high=hist['High'],
+                        low=hist['Low'],
+                        close=hist['Close'],
+                        name="Price"
+                    ),
+                    row=1, col=1
+                )
+                
+                # EMA 21
+                fig.add_trace(
+                    go.Scatter(
+                        x=hist.index,
+                        y=ema_21,
+                        line=dict(color='yellow', width=1),
+                        name="EMA 21"
+                    ),
+                    row=1, col=1
+                )
+                
+                # Bollinger Bands
+                fig.add_trace(
+                    go.Scatter(
+                        x=hist.index,
+                        y=bb_upper,
+                        line=dict(color='gray', width=1, dash='dash'),
+                        name="BB Upper"
+                    ),
+                    row=1, col=1
+                )
+                fig.add_trace(
+                    go.Scatter(
+                        x=hist.index,
+                        y=bb_lower,
+                        line=dict(color='gray', width=1, dash='dash'),
+                        name="BB Lower"
+                    ),
+                    row=1, col=1
+                )
+                
+                # Volume
+                fig.add_trace(
+                    go.Bar(
+                        x=hist.index,
+                        y=hist['Volume'],
+                        name="Volume",
+                        marker_color='lightblue'
+                    ),
+                    row=2, col=1
+                )
+                
+                # RSI
+                rsi = indicators.rsi(close)
+                fig.add_trace(
+                    go.Scatter(
+                        x=hist.index,
+                        y=rsi,
+                        line=dict(color='purple', width=1),
+                        name="RSI"
+                    ),
+                    row=3, col=1
+                )
+                # RSI levels
+                fig.add_hline(y=70, line_dash="dash", line_color="red", row=3, col=1)
+                fig.add_hline(y=30, line_dash="dash", line_color="green", row=3, col=1)
+                
+                fig.update_layout(
+                    title=f"{selected_symbol} - Momentum Analysis",
+                    xaxis_rangeslider_visible=False,
+                    height=800
+                )
+                
+                st.plotly_chart(fig, use_container_width=True)
+    
+    # Footer
+    st.divider()
+    st.caption(f"Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')} | Data: Yahoo Finance")
+
+
+if __name__ == "__main__":
+    main()
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