Current Status:
- ✅ Position manager supports both stocks and options
- ✅ Market data fetcher handles both asset types
⚠️ Need to verify AI agents analyze both properly⚠️ Need specific metrics for each asset type
Actions Required:
-
Enhance Position Manager
# Add stock-specific metrics - P/E ratio tracking - Dividend yield - Earnings dates - Analyst consensus # Add option-specific metrics - Time decay (theta per day) - IV rank/percentile - Probability of profit - Break-even prices - Max profit/loss
-
Update Market Data Fetcher
# Stock-specific data - Fundamental ratios - Earnings calendar - Dividend schedule - Insider transactions # Option-specific data - IV surface (all strikes/dates) - Skew analysis - Term structure - Historical IV vs current
-
Enhance AI Agents
# Add asset-type detection def analyze_position(position): if isinstance(position, StockPosition): return analyze_stock(position) elif isinstance(position, OptionPosition): return analyze_option(position) # Stock analysis focuses on: - Fundamental valuation - Technical patterns - Earnings potential - Dividend safety # Option analysis focuses on: - Greeks optimization - IV vs HV comparison - Time decay management - Probability analysis
Implementation Steps:
-
Create Sentiment Display Component
// In frontend_enhanced.html function displaySentimentOnDashboard() { // For each position, show: // - Sentiment badge (🟢 Bullish / 🔴 Bearish / ⚪ Neutral) // - Sentiment score (-1.0 to +1.0) // - Sentiment trend (↗️ Improving / ↘️ Declining) // - Key news headlines (top 3) // - Last updated timestamp }
-
Auto-Refresh Sentiment
// Refresh every 5 minutes setInterval(async () => { await updateAllSentiment(); }, 300000);
-
Sentiment Alerts
// Alert on significant sentiment changes if (Math.abs(newSentiment - oldSentiment) > 0.3) { showAlert(`Sentiment shift for ${symbol}: ${oldSentiment} → ${newSentiment}`); }
Key Components to Build:
-
Multi-Factor Scoring System
class MultiFactorScorer: """ Combine fundamental, technical, and sentiment factors """ factors = { 'fundamental': { 'earnings_growth': 0.15, 'revenue_growth': 0.10, 'profit_margin': 0.10, 'pe_ratio': 0.10, 'debt_to_equity': 0.05, }, 'technical': { 'price_momentum': 0.10, 'volume_trend': 0.05, 'rsi': 0.05, 'macd': 0.05, 'support_resistance': 0.05, }, 'sentiment': { 'news_sentiment': 0.10, 'social_sentiment': 0.05, 'analyst_sentiment': 0.05, } } def calculate_score(self, symbol): """ Returns: Score 0-100, breakdown by factor """ pass
-
Machine Learning Prediction Engine
class MLPredictionEngine: """ Predict price movements using ensemble ML models """ models = [ 'LSTM', # Time series prediction 'RandomForest', # Feature importance 'XGBoost', # Gradient boosting 'NeuralNetwork', # Deep learning ] def predict_price_movement(self, symbol, horizon='1d'): """ Predict price movement for next 1d, 1w, 1m Returns: Probability distribution of outcomes """ pass
-
Order Flow Analysis
class OrderFlowAnalyzer: """ Track institutional buying/selling """ def analyze_unusual_activity(self, symbol): """ Detect: - Large block trades - Unusual options volume - Dark pool activity - Institutional accumulation/distribution """ pass
-
Correlation & Portfolio Optimization
class PortfolioOptimizer: """ Optimize portfolio using Modern Portfolio Theory """ def optimize_portfolio(self, positions, target_return, risk_tolerance): """ Find optimal weights to: - Maximize Sharpe ratio - Minimize correlation - Meet risk constraints - Achieve target return """ pass
File: src/data/position_manager.py
Enhancements:
@dataclass
class StockPosition:
# Existing fields...
# Add stock-specific metrics
pe_ratio: Optional[float] = None
dividend_yield: Optional[float] = None
next_earnings_date: Optional[str] = None
analyst_consensus: Optional[str] = None # Buy/Hold/Sell
analyst_target_price: Optional[float] = None
def calculate_metrics(self, market_data):
"""Calculate stock-specific metrics"""
self.current_price = market_data['current_price']
self.unrealized_pnl = (self.current_price - self.entry_price) * self.quantity
self.unrealized_pnl_pct = (self.current_price / self.entry_price - 1) * 100
self.pe_ratio = market_data.get('pe_ratio')
self.dividend_yield = market_data.get('dividend_yield')
@dataclass
class OptionPosition:
# Existing fields...
# Add option-specific metrics
current_price: Optional[float] = None
delta: Optional[float] = None
gamma: Optional[float] = None
theta: Optional[float] = None
vega: Optional[float] = None
iv: Optional[float] = None
iv_rank: Optional[float] = None
probability_of_profit: Optional[float] = None
break_even_price: Optional[float] = None
max_profit: Optional[float] = None
max_loss: Optional[float] = None
def calculate_metrics(self, market_data, option_data):
"""Calculate option-specific metrics"""
self.current_price = option_data['last_price']
self.delta = option_data.get('delta')
self.gamma = option_data.get('gamma')
self.theta = option_data.get('theta')
self.vega = option_data.get('vega')
self.iv = option_data.get('implied_volatility')
# Calculate P&L
self.unrealized_pnl = (self.current_price - self.premium_paid) * self.quantity * 100
self.unrealized_pnl_pct = (self.current_price / self.premium_paid - 1) * 100
# Calculate probability of profit
self.probability_of_profit = self.calculate_pop(market_data, option_data)File: frontend_enhanced.html
Add to Positions Tab:
<div class="position-card">
<div class="position-header">
<h3>AAPL - Apple Inc.</h3>
<span class="sentiment-badge bullish">🟢 Bullish</span>
</div>
<div class="position-details">
<!-- Existing position details -->
</div>
<div class="sentiment-section">
<h4>Sentiment Analysis</h4>
<div class="sentiment-score">
<span class="score">+0.65</span>
<span class="trend">↗️ Improving</span>
</div>
<div class="sentiment-breakdown">
<div class="sentiment-source">
<span class="source-name">News:</span>
<span class="source-score">+0.70</span>
</div>
<div class="sentiment-source">
<span class="source-name">Social:</span>
<span class="source-score">+0.55</span>
</div>
<div class="sentiment-source">
<span class="source-name">Analysts:</span>
<span class="source-score">+0.75</span>
</div>
</div>
<div class="key-headlines">
<h5>Key Headlines:</h5>
<ul>
<li>Apple announces record iPhone sales</li>
<li>Analysts raise price targets to $200</li>
<li>Strong demand in China market</li>
</ul>
</div>
<div class="last-updated">
Last updated: 2 minutes ago
</div>
</div>
</div>JavaScript Functions:
async function loadPositionsWithSentiment() {
// Load positions
const positions = await fetch('/api/positions').then(r => r.json());
// Get unique symbols
const symbols = [...new Set([
...positions.stocks.map(p => p.symbol),
...positions.options.map(p => p.symbol)
])];
// Fetch sentiment for each symbol
const sentimentPromises = symbols.map(symbol =>
fetch(`/api/sentiment/${symbol}`).then(r => r.json())
);
const sentiments = await Promise.all(sentimentPromises);
// Create sentiment map
const sentimentMap = {};
symbols.forEach((symbol, i) => {
sentimentMap[symbol] = sentiments[i];
});
// Display positions with sentiment
displayPositionsWithSentiment(positions, sentimentMap);
}
function displayPositionsWithSentiment(positions, sentimentMap) {
// Display each position with its sentiment data
positions.stocks.forEach(position => {
const sentiment = sentimentMap[position.symbol];
renderPositionCard(position, sentiment, 'stock');
});
positions.options.forEach(position => {
const sentiment = sentimentMap[position.symbol];
renderPositionCard(position, sentiment, 'option');
});
}
function renderPositionCard(position, sentiment, type) {
// Render position card with sentiment badge, score, trend, headlines
const badge = getSentimentBadge(sentiment.sentiment_score);
const trend = getSentimentTrend(sentiment.sentiment_trend);
// ... render HTML
}
function getSentimentBadge(score) {
if (score > 0.3) return '🟢 Bullish';
if (score < -0.3) return '🔴 Bearish';
return '⚪ Neutral';
}
function getSentimentTrend(trend) {
if (trend > 0.1) return '↗️ Improving';
if (trend < -0.1) return '↘️ Declining';
return '➡️ Stable';
}
// Auto-refresh sentiment every 5 minutes
setInterval(async () => {
await loadPositionsWithSentiment();
}, 300000);File: src/agents/sentiment_research_agent.py
Integrate Firecrawl:
def research_with_firecrawl(self, symbol: str) -> Dict[str, Any]:
"""
Use Firecrawl to gather comprehensive sentiment data
"""
results = {
'news': [],
'social_media': [],
'youtube': [],
'analyst_reports': []
}
# Search news
news_query = f"{symbol} stock news latest"
# Call firecrawl_search tool
# results['news'] = firecrawl_search(query=news_query, limit=10)
# Search social media
twitter_query = f"${symbol} site:twitter.com"
reddit_query = f"{symbol} site:reddit.com/r/wallstreetbets OR site:reddit.com/r/stocks"
# results['social_media'] = firecrawl_search(...)
# Search YouTube
youtube_query = f"{symbol} stock analysis"
# results['youtube'] = firecrawl_search(...)
# Search analyst reports
analyst_query = f"{symbol} analyst rating upgrade downgrade"
# results['analyst_reports'] = firecrawl_search(...)
return results
def analyze_sentiment(self, content: str) -> float:
"""
Analyze sentiment of text content
Returns: Score from -1.0 (bearish) to +1.0 (bullish)
"""
# Use NLP to analyze sentiment
# Count bullish vs bearish keywords
# Weight by context and source credibility
passFile: src/analytics/multi_factor_scorer.py
Create new file:
from typing import Dict, Any, List
from dataclasses import dataclass
@dataclass
class FactorScore:
factor_name: str
score: float # 0-100
weight: float
weighted_score: float
details: Dict[str, Any]
class MultiFactorScorer:
"""
Combine fundamental, technical, and sentiment factors
into a single score (0-100)
"""
def __init__(self):
self.factor_weights = {
'fundamental': 0.40,
'technical': 0.30,
'sentiment': 0.30
}
def calculate_score(self, symbol: str, market_data: Dict,
sentiment_data: Dict) -> Dict[str, Any]:
"""
Calculate comprehensive score for a symbol
"""
scores = []
# Fundamental score
fund_score = self.calculate_fundamental_score(market_data)
scores.append(fund_score)
# Technical score
tech_score = self.calculate_technical_score(market_data)
scores.append(tech_score)
# Sentiment score
sent_score = self.calculate_sentiment_score(sentiment_data)
scores.append(sent_score)
# Calculate weighted total
total_score = sum(s.weighted_score for s in scores)
return {
'total_score': total_score,
'factor_scores': scores,
'recommendation': self.get_recommendation(total_score),
'confidence': self.calculate_confidence(scores)
}
def calculate_fundamental_score(self, data: Dict) -> FactorScore:
"""Score based on fundamental metrics"""
pass
def calculate_technical_score(self, data: Dict) -> FactorScore:
"""Score based on technical indicators"""
pass
def calculate_sentiment_score(self, data: Dict) -> FactorScore:
"""Score based on sentiment analysis"""
pass- ✅ Handle both stocks and options seamlessly
- ✅ Real-time sentiment on dashboard for all positions
- ✅ Multi-factor scoring (fundamental + technical + sentiment)
- ✅ Machine learning predictions
- ✅ Risk decomposition (Aladdin-style)
- ✅ Automated daily/hourly analysis
- Signal accuracy: > 50.75%
- Sentiment accuracy: > 60%
- Data latency: < 5 seconds
- Dashboard refresh: Every 5 minutes
- Analysis completion: < 30 seconds
- One-click access to all position data
- Clear sentiment indicators
- Actionable recommendations
- Risk warnings and alerts
- Performance tracking
Week 1:
- ✅ Enhanced position management
- ✅ Sentiment dashboard integration
- ✅ Firecrawl integration for sentiment
Week 2:
- Multi-factor scoring system
- Machine learning prediction engine
- Risk decomposition framework
Week 3:
- Order flow analysis
- Portfolio optimization
- Backtesting engine
Week 4:
- Automated trading signals
- Performance tracking
- System refinement
Next Steps: Start implementing Priority 1 - Enhanced Position Management