feat: Add Momentum Dashboard with EMA/BB/RSI indicators
- Real-time momentum scanner for Magnificent Seven + custom watchlists - 21 EMA trend filter (long above, short below) - Bollinger Band squeeze detection - Volume momentum confirmation - RSI overbought/oversold signals - Streamlit interactive UI - Multi-timeframe support (1H/Daily/Weekly) Implements #383 🤖 Generated with OpenClaw AI Agent
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# Momentum Dashboard
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Real-time momentum analysis dashboard for TradingAgents.
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## Features
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✅ **21 EMA Trend Filter** - Long above, short below
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✅ **Bollinger Band Squeeze** - Identify low-volatility consolidation
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✅ **Volume Momentum** - Confirm breakouts
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✅ **RSI Indicator** - Overbought/oversold signals
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✅ **Multi-timeframe** - 1H/Daily/Weekly analysis
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✅ **Magnificent Seven** + Custom watchlists
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## Quick Start
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### 1. Install Dependencies
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```bash
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pip install streamlit plotly yfinance pandas numpy
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```
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### 2. Run Dashboard
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```bash
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cd tradingagents/dashboards/momentum
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streamlit run app.py
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```
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### 3. CLI Scanner (No UI)
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```bash
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python -m tradingagents.dashboards.momentum
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```
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## Signals
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| Signal | Meaning |
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|--------|---------|
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| STRONG_BUY | Bullish trend + high strength |
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| BUY | Moderate bullish |
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| WATCH_FOR_BREAKOUT | Squeeze detected, potential move |
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| HOLD | No clear signal |
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| SELL | Moderate bearish |
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| STRONG_SELL | Bearish trend + low strength |
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## Architecture
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```
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momentum/
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├── __init__.py # Core scanner & indicators
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├── app.py # Streamlit UI
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└── README.md # This file
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```
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## Integration
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To integrate with TradingAgents main workflow:
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```python
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from tradingagents.dashboards.momentum import MomentumScanner
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scanner = MomentumScanner(["AAPL", "NVDA", "TSLA"])
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signals = scanner.scan_all()
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# Use signals in trading decisions
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for signal in signals:
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if signal["signal"] == "STRONG_BUY":
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# Execute buy logic
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pass
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```
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## Future Enhancements
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- [ ] Real-time WebSocket data (Polygon.io)
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- [ ] Alert notifications (email/Telegram)
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- [ ] Portfolio tracking
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- [ ] Backtesting mode
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- [ ] Multi-exchange support
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---
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Built for TradingAgents by OpenClaw Community
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"""
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Momentum Dashboard - Real-time momentum analysis for trading
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Features:
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- 21 EMA Trend Filter (long above, short below)
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- Bollinger Band Squeeze detection
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- Volume Momentum confirmation
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- Multi-timeframe analysis (1H/Daily/Weekly/Monthly/Quarterly)
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"""
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import yfinance as yf
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from typing import List, Dict, Optional, Tuple
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# Magnificent Seven stocks
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MAGNIFICENT_SEVEN = ["AAPL", "MSFT", "GOOGL", "AMZN", "NVDA", "META", "TSLA"]
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class MomentumIndicator:
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"""Core momentum indicators for the dashboard"""
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@staticmethod
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def ema(data: pd.Series, period: int = 21) -> pd.Series:
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"""Calculate Exponential Moving Average"""
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return data.ewm(span=period, adjust=False).mean()
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@staticmethod
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def bollinger_bands(data: pd.Series, period: int = 20, std_dev: float = 2.0) -> Tuple[pd.Series, pd.Series, pd.Series]:
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"""Calculate Bollinger Bands"""
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sma = data.rolling(window=period).mean()
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std = data.rolling(window=period).std()
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upper = sma + (std * std_dev)
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lower = sma - (std * std_dev)
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return upper, sma, lower
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@staticmethod
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def bollinger_squeeze(bb_upper: pd.Series, bb_lower: pd.Series, bb_mid: pd.Series,
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threshold: float = 0.1) -> pd.Series:
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"""
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Detect Bollinger Band Squeeze (low volatility consolidation)
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Returns True when bandwidth is below threshold
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"""
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bandwidth = (bb_upper - bb_lower) / bb_mid
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return bandwidth < threshold
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@staticmethod
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def volume_momentum(volume: pd.Series, period: int = 20) -> pd.Series:
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"""Calculate Volume Momentum (current volume vs average)"""
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avg_volume = volume.rolling(window=period).mean()
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return volume / avg_volume
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@staticmethod
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def rsi(data: pd.Series, period: int = 14) -> pd.Series:
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"""Calculate Relative Strength Index"""
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delta = data.diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / loss
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return 100 - (100 / (1 + rs))
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class MomentumScanner:
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"""Scan stocks for momentum signals"""
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def __init__(self, symbols: List[str] = None):
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self.symbols = symbols or MAGNIFICENT_SEVEN
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self.indicators = MomentumIndicator()
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def fetch_data(self, symbol: str, period: str = "3mo", interval: str = "1h") -> pd.DataFrame:
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"""Fetch price data from yfinance"""
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ticker = yf.Ticker(symbol)
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df = ticker.history(period=period, interval=interval)
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return df
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def analyze_symbol(self, symbol: str) -> Dict:
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"""Analyze a single symbol for momentum signals"""
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try:
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df = self.fetch_data(symbol)
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if df.empty:
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return {"symbol": symbol, "error": "No data"}
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close = df['Close']
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volume = df['Volume']
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# Calculate indicators
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ema_21 = self.indicators.ema(close, 21)
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bb_upper, bb_mid, bb_lower = self.indicators.bollinger_bands(close)
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squeeze = self.indicators.bollinger_squeeze(bb_upper, bb_lower, bb_mid)
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vol_momentum = self.indicators.volume_momentum(volume)
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rsi = self.indicators.rsi(close)
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# Current values
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current_price = close.iloc[-1]
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current_ema = ema_21.iloc[-1]
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current_rsi = rsi.iloc[-1]
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current_vol_mom = vol_momentum.iloc[-1]
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is_squeeze = squeeze.iloc[-1]
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# Signal determination
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trend = "BULLISH" if current_price > current_ema else "BEARISH"
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signal_strength = self._calculate_signal_strength(
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current_price, current_ema, current_rsi, current_vol_mom, is_squeeze
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)
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return {
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"symbol": symbol,
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"price": round(current_price, 2),
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"ema_21": round(current_ema, 2),
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"trend": trend,
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"rsi": round(current_rsi, 2),
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"volume_momentum": round(current_vol_mom, 2),
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"squeeze": bool(is_squeeze),
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"signal_strength": signal_strength,
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"signal": self._get_signal(trend, signal_strength, is_squeeze)
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}
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except Exception as e:
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return {"symbol": symbol, "error": str(e)}
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def _calculate_signal_strength(self, price, ema, rsi, vol_mom, squeeze) -> float:
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"""Calculate overall signal strength (0-100)"""
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score = 50 # Base score
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# Price vs EMA contribution
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price_ema_diff = (price - ema) / ema * 100
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score += min(max(price_ema_diff * 5, -20), 20)
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# RSI contribution (oversold/overbought)
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if rsi < 30:
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score += 15 # Oversold - potential buy
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elif rsi > 70:
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score -= 15 # Overbought - potential sell
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# Volume momentum contribution
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if vol_mom > 1.5:
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score += 10 # High volume confirms trend
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elif vol_mom < 0.5:
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score -= 10 # Low volume weakens signal
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return max(0, min(100, round(score)))
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def _get_signal(self, trend: str, strength: float, squeeze: bool) -> str:
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"""Generate trading signal"""
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if squeeze and strength > 60:
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return "WATCH_FOR_BREAKOUT"
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elif trend == "BULLISH" and strength >= 70:
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return "STRONG_BUY"
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elif trend == "BULLISH" and strength >= 55:
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return "BUY"
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elif trend == "BEARISH" and strength <= 30:
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return "STRONG_SELL"
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elif trend == "BEARISH" and strength <= 45:
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return "SELL"
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else:
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return "HOLD"
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def scan_all(self) -> List[Dict]:
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"""Scan all symbols and return results"""
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results = []
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for symbol in self.symbols:
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result = self.analyze_symbol(symbol)
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results.append(result)
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return results
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def get_top_momentum_stocks(limit: int = 20) -> List[str]:
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"""Get top momentum stocks (could be enhanced with real data source)"""
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# For now, return a static list of popular momentum stocks
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momentum_stocks = [
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"SMCI", "ARM", "PLTR", "SNOW", "DDOG",
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"MDB", "NET", "CRWD", "ZS", "PANW",
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"AMD", "INTC", "QCOM", "AVGO", "TXN"
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]
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return momentum_stocks[:limit]
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if __name__ == "__main__":
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# Test the scanner
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scanner = MomentumScanner()
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results = scanner.scan_all()
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print("=" * 60)
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print("MOMENTUM DASHBOARD - MAGNIFICENT SEVEN")
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print("=" * 60)
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print(f"{'Symbol':<8} {'Price':<10} {'Trend':<10} {'RSI':<8} {'Signal':<20}")
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print("-" * 60)
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for r in results:
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if "error" not in r:
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print(f"{r['symbol']:<8} ${r['price']:<9} {r['trend']:<10} {r['rsi']:<8} {r['signal']:<20}")
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print("=" * 60)
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"""
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Momentum Dashboard - Streamlit Web UI
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Run: streamlit run app.py
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"""
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import streamlit as st
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import pandas as pd
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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from datetime import datetime, timedelta
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import sys
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import os
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# Add parent directory to path
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from __init__ import MomentumScanner, MomentumIndicator, MAGNIFICENT_SEVEN, get_top_momentum_stocks
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# Page config
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st.set_page_config(
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page_title="Momentum Dashboard",
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page_icon="📈",
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layout="wide",
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initial_sidebar_state="expanded"
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)
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# Custom CSS
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st.markdown("""
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<style>
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.stMetric {
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background-color: #1E1E1E;
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padding: 10px;
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border-radius: 10px;
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}
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.bullish { color: #00FF00; }
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.bearish { color: #FF0000; }
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.squeeze { color: #FFA500; }
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</style>
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""", unsafe_allow_html=True)
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def main():
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st.title("📈 Momentum Dashboard")
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st.subheader("Real-time Momentum Analysis for Trading")
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# Sidebar
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st.sidebar.header("Settings")
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# Stock selection
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stock_option = st.sidebar.radio(
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"Stock Universe",
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["Magnificent Seven", "Top Momentum", "Custom"]
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)
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if stock_option == "Magnificent Seven":
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symbols = MAGNIFICENT_SEVEN
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elif stock_option == "Top Momentum":
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symbols = get_top_momentum_stocks(15)
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else:
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custom_symbols = st.sidebar.text_input(
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"Enter symbols (comma-separated)",
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"AAPL,MSFT,GOOGL"
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)
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symbols = [s.strip().upper() for s in custom_symbols.split(",")]
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# Timeframe selection
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timeframe = st.sidebar.selectbox(
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"Timeframe",
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["1h", "1d", "1wk"]
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)
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# Refresh button
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if st.sidebar.button("🔄 Refresh Data"):
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st.rerun()
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# Scan stocks
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with st.spinner("Scanning momentum signals..."):
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scanner = MomentumScanner(symbols)
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results = scanner.scan_all()
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# Display results
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col1, col2, col3 = st.columns(3)
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# Metrics
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bullish_count = sum(1 for r in results if r.get("trend") == "BULLISH" and "error" not in r)
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bearish_count = sum(1 for r in results if r.get("trend") == "BEARISH" and "error" not in r)
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squeeze_count = sum(1 for r in results if r.get("squeeze") and "error" not in r)
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with col1:
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st.metric("🟢 Bullish", bullish_count)
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with col2:
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st.metric("🔴 Bearish", bearish_count)
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with col3:
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st.metric("🟠 Squeeze", squeeze_count)
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st.divider()
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# Results table
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st.subheader("📊 Momentum Signals")
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# Create DataFrame
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df_data = []
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for r in results:
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if "error" not in r:
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df_data.append({
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"Symbol": r["symbol"],
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"Price": f"${r['price']}",
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"EMA 21": f"${r['ema_21']}",
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"Trend": r["trend"],
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"RSI": r["rsi"],
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"Vol Mom": f"{r['volume_momentum']:.2f}x",
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"Squeeze": "🔴" if r["squeeze"] else "",
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"Signal": r["signal"],
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"Strength": r["signal_strength"]
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})
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df = pd.DataFrame(df_data)
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# Style the dataframe
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def color_trend(val):
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if val == "BULLISH":
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return "color: green; font-weight: bold"
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elif val == "BEARISH":
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return "color: red; font-weight: bold"
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return ""
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def color_signal(val):
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if "BUY" in val:
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return "background-color: #1a4d1a; color: white"
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elif "SELL" in val:
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return "background-color: #4d1a1a; color: white"
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elif "BREAKOUT" in val:
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return "background-color: #4d3d1a; color: white"
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return ""
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styled_df = df.style.applymap(color_trend, subset=["Trend"]).applymap(color_signal, subset=["Signal"])
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st.dataframe(styled_df, use_container_width=True)
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st.divider()
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# Detailed chart for selected symbol
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st.subheader("📈 Detailed Analysis")
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selected_symbol = st.selectbox(
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"Select symbol for detailed view",
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[r["symbol"] for r in results if "error" not in r]
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)
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if selected_symbol:
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with st.spinner(f"Loading {selected_symbol} chart..."):
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# Fetch data for chart
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import yfinance as yf
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ticker = yf.Ticker(selected_symbol)
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hist = ticker.history(period="3mo", interval=timeframe)
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if not hist.empty:
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# Calculate indicators
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indicators = MomentumIndicator()
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close = hist['Close']
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ema_21 = indicators.ema(close, 21)
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bb_upper, bb_mid, bb_lower = indicators.bollinger_bands(close)
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# Create candlestick chart with indicators
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fig = make_subplots(
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rows=3, cols=1,
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shared_xaxes=True,
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vertical_spacing=0.05,
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row_heights=[0.6, 0.2, 0.2]
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)
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# Candlestick
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fig.add_trace(
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go.Candlestick(
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x=hist.index,
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open=hist['Open'],
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high=hist['High'],
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low=hist['Low'],
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close=hist['Close'],
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name="Price"
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),
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row=1, col=1
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)
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# EMA 21
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fig.add_trace(
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go.Scatter(
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x=hist.index,
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y=ema_21,
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line=dict(color='yellow', width=1),
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name="EMA 21"
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),
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row=1, col=1
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)
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# Bollinger Bands
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fig.add_trace(
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go.Scatter(
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x=hist.index,
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y=bb_upper,
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line=dict(color='gray', width=1, dash='dash'),
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name="BB Upper"
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),
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row=1, col=1
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)
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fig.add_trace(
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go.Scatter(
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x=hist.index,
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y=bb_lower,
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line=dict(color='gray', width=1, dash='dash'),
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name="BB Lower"
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),
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row=1, col=1
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)
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# Volume
|
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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()
|
||||
Loading…
Reference in New Issue