In the high-stakes world of investment management, information is currency. The ability to gather, analyze, and act on data faster than competitors can mean the difference between outsized returns and missed opportunities. While institutional investors have long relied on expensive data terminals and proprietary feeds, web scraping has democratized access to financial intelligence—enabling individual investors, hedge funds, and fintech startups to build sophisticated research systems at a fraction of the cost.
This comprehensive guide explores how modern investors leverage web scraping for investment research, portfolio monitoring, and market intelligence. From extracting real-time stock data to monitoring SEC filings and analyzing market sentiment, we'll cover the techniques, tools, and best practices that drive data-driven investment decisions in 2026.
Traditional financial data sources come with significant limitations:
Web scraping addresses these gaps by enabling custom data collection from thousands of sources: stock exchanges, financial news sites, analyst platforms, regulatory filings, social media, and alternative data providers. The result is a competitive edge through proprietary datasets that others don't have.
Real-time and historical price data forms the foundation of quantitative analysis. Scraping targets include:
# Stock data extraction with technical indicator calculation
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import asyncio
import aiohttp
class StockDataScraper:
def __init__(self):
self.session = None
self.base_headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
}
async def __aenter__(self):
self.session = aiohttp.ClientSession(headers=self.base_headers)
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
if self.session:
await self.session.close()
async def fetch_historical_data(self, symbol: str, period: str = "1y") -> pd.DataFrame:
"""
Fetch historical OHLCV data for a stock symbol
"""
# Example using Yahoo Finance-like endpoint
url = f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}"
params = {
'period1': int((datetime.now() - timedelta(days=365)).timestamp()),
'period2': int(datetime.now().timestamp()),
'interval': '1d',
'events': 'history'
}
async with self.session.get(url, params=params) as response:
data = await response.json()
result = data['chart']['result'][0]
timestamps = result['timestamp']
quote = result['indicators']['quote'][0]
df = pd.DataFrame({
'date': [datetime.fromtimestamp(ts) for ts in timestamps],
'open': quote['open'],
'high': quote['high'],
'low': quote['low'],
'close': quote['close'],
'volume': quote['volume']
})
return df
def calculate_technical_indicators(self, df: pd.DataFrame) -> pd.DataFrame:
"""
Calculate common technical indicators
"""
# Simple Moving Averages
df['sma_20'] = df['close'].rolling(window=20).mean()
df['sma_50'] = df['close'].rolling(window=50).mean()
df['sma_200'] = df['close'].rolling(window=200).mean()
# Exponential Moving Average
df['ema_12'] = df['close'].ewm(span=12).mean()
df['ema_26'] = df['close'].ewm(span=26).mean()
# MACD
df['macd'] = df['ema_12'] - df['ema_26']
df['macd_signal'] = df['macd'].ewm(span=9).mean()
df['macd_histogram'] = df['macd'] - df['macd_signal']
# RSI
delta = df['close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
rs = gain / loss
df['rsi'] = 100 - (100 / (1 + rs))
# Bollinger Bands
df['bb_middle'] = df['close'].rolling(window=20).mean()
bb_std = df['close'].rolling(window=20).std()
df['bb_upper'] = df['bb_middle'] + (bb_std * 2)
df['bb_lower'] = df['bb_middle'] - (bb_std * 2)
return df
async def fetch_multiple_stocks(self, symbols: list) -> dict:
"""
Fetch data for multiple stocks concurrently
"""
tasks = [self.fetch_historical_data(sym) for sym in symbols]
results = await asyncio.gather(*tasks, return_exceptions=True)
return {
symbol: df if not isinstance(df, Exception) else None
for symbol, df in zip(symbols, results)
}
# Usage
async def main():
symbols = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA']
async with StockDataScraper() as scraper:
data = await scraper.fetch_multiple_stocks(symbols)
for symbol, df in data.items():
if df is not None:
df = scraper.calculate_technical_indicators(df)
print(f"\n{symbol} - Latest Data:")
print(f"Close: ${df['close'].iloc[-1]:.2f}")
print(f"RSI: {df['rsi'].iloc[-1]:.2f}")
print(f"MACD: {df['macd'].iloc[-1]:.4f}")
if __name__ == "__main__":
asyncio.run(main())
SEC filings contain critical information about company performance, risks, and insider activity. Key filings to monitor:
# SEC EDGAR filing scraper
import requests
from bs4 import BeautifulSoup
from dataclasses import dataclass
from datetime import datetime
import feedparser
@dataclass
class SECFiling:
company_name: str
cik: str
form_type: str
filing_date: datetime
accession_number: str
filing_url: str
description: str
class SECFilingScraper:
def __init__(self):
self.base_url = "https://www.sec.gov"
self.headers = {
'User-Agent': 'YourCompanyName YourEmail@example.com' # SEC requires identification
}
def get_company_filings(self, cik: str, form_types: list = None) -> list:
"""
Retrieve recent filings for a specific company by CIK
"""
# Pad CIK to 10 digits
cik_padded = cik.zfill(10)
url = f"{self.base_url}/cgi-bin/browse-edgar?action=getcompany&CIK={cik_padded}&type=&dateb=&owner=include&count=40&search_text="
response = requests.get(url, headers=self.headers)
soup = BeautifulSoup(response.content, 'html.parser')
filings = []
table = soup.find('table', class_='tableFile2')
if table:
rows = table.find_all('tr')[1:] # Skip header
for row in rows:
cols = row.find_all('td')
if len(cols) >= 4:
form_type = cols[0].text.strip()
# Filter by form type if specified
if form_types and form_type not in form_types:
continue
filing_date = datetime.strptime(cols[3].text.strip(), '%Y-%m-%d')
description = cols[2].text.strip()
# Get filing URL
links = cols[1].find_all('a')
if links:
filing_url = self.base_url + links['href']
accession = links['href'].split('/')[-1].replace('-index.htm', '')
filings.append(SECFiling(
company_name="", # Would need separate lookup
cik=cik,
form_type=form_type,
filing_date=filing_date,
accession_number=accession,
filing_url=filing_url,
description=description
))
return filings
def get_latest_filings_feed(self, form_type: str = None) -> list:
"""
Get latest filings via RSS feed
"""
if form_type:
feed_url = f"{self.base_url}/cgi-bin/browse-edgar?action=getcurrent&type={form_type}&company=&dateb=&owner=include&start=0&count=100&output=atom"
else:
feed_url = f"{self.base_url}/cgi-bin/browse-edgar?action=getcurrent&company=&dateb=&owner=include&start=0&count=100&output=atom"
feed = feedparser.parse(feed_url)
filings = []
for entry in feed.entries:
filing = SECFiling(
company_name=entry.get('title', '').split(' - ')[0],
cik="",
form_type=entry.get('form_type', ''),
filing_date=datetime.strptime(entry.published, '%a, %d %b %Y %H:%M:%S %Z'),
accession_number="",
filing_url=entry.link,
description=entry.get('summary', '')
)
filings.append(filing)
return filings
def extract_filing_text(self, filing_url: str) -> str:
"""
Extract full text content from a filing
"""
response = requests.get(filing_url, headers=self.headers)
soup = BeautifulSoup(response.content, 'html.parser')
# Find the document link
doc_link = soup.find('a', {'href': lambda x: x and x.endswith('.txt')})
if doc_link:
doc_url = self.base_url + doc_link['href']
doc_response = requests.get(doc_url, headers=self.headers)
return doc_response.text
return ""
def monitor_insider_trading(self, symbols: list) -> dict:
"""
Monitor Form 4 filings for insider trading activity
"""
insider_activity = {}
for symbol in symbols:
# Get CIK for symbol (simplified - would need mapping)
cik = self._get_cik_from_symbol(symbol)
if cik:
filings = self.get_company_filings(cik, ['4', '4/A'])
insider_activity[symbol] = filings
return insider_activity
def _get_cik_from_symbol(self, symbol: str) -> str:
"""
Map stock symbol to CIK (would typically use a lookup table or API)
"""
# Simplified mapping - in production, use SEC's company tickers file
ticker_to_cik = {
'AAPL': '0000320193',
'MSFT': '0000789019',
'GOOGL': '0001652044',
'AMZN': '0001018724',
'TSLA': '0001318605'
}
return ticker_to_cik.get(symbol, '')
# Usage
scraper = SECFilingScraper()
# Get latest 10-K filings
latest_10k = scraper.get_latest_filings_feed('10-K')
print(f"Found {len(latest_10k)} recent 10-K filings")
# Monitor insider trading for specific stocks
insider_data = scraper.monitor_insider_trading(['AAPL', 'TSLA'])
for symbol, filings in insider_data.items():
print(f"{symbol}: {len(filings)} recent insider transactions")
Analyst recommendations and price targets provide valuable sentiment indicators. Scraping sources include:
# Analyst ratings aggregator
from dataclasses import dataclass
from typing import Optional
from datetime import datetime
import json
@dataclass
class AnalystRating:
symbol: str
firm: str
analyst: str
rating: str # Buy, Hold, Sell, etc.
previous_rating: Optional[str]
price_target: Optional[float]
previous_target: Optional[float]
date: datetime
reasoning: str
class AnalystRatingScraper:
def __init__(self):
self.session = requests.Session()
self.session.headers.update({
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
})
def aggregate_ratings(self, symbol: str) -> dict:
"""
Aggregate analyst ratings from multiple sources
"""
ratings = {
'symbol': symbol,
'consensus_rating': None,
'average_target': None,
'ratings_distribution': {},
'recent_upgrades': [],
'recent_downgrades': [],
'all_ratings': []
}
# This would scrape from multiple sources
# Example structure for aggregated data
sources = [
self._scrape_marketwatch(symbol),
self._scrape_yahoo_finance(symbol),
self._scrape_benzinga(symbol)
]
all_ratings = []
for source_ratings in sources:
all_ratings.extend(source_ratings)
# Calculate consensus
rating_scores = {'Strong Buy': 5, 'Buy': 4, 'Overweight': 4, 'Hold': 3,
'Neutral': 3, 'Underweight': 2, 'Sell': 1, 'Strong Sell': 1}
scores = []
targets = []
distribution = {}
for rating in all_ratings:
if rating.rating in rating_scores:
scores.append(rating_scores[rating.rating])
distribution[rating.rating] = distribution.get(rating.rating, 0) + 1
if rating.price_target:
targets.append(rating.price_target)
# Identify upgrades/downgrades
if rating.previous_rating:
prev_score = rating_scores.get(rating.previous_rating, 3)
curr_score = rating_scores.get(rating.rating, 3)
if curr_score > prev_score:
ratings['recent_upgrades'].append(rating)
elif curr_score < prev_score:
ratings['recent_downgrades'].append(rating)
if scores:
avg_score = sum(scores) / len(scores)
ratings['consensus_rating'] = self._score_to_rating(avg_score)
ratings['ratings_distribution'] = distribution
if targets:
ratings['average_target'] = sum(targets) / len(targets)
ratings['all_ratings'] = all_ratings
return ratings
def _scrape_marketwatch(self, symbol: str) -> list:
"""Scrape analyst ratings from MarketWatch"""
# Implementation would go here
return []
def _scrape_yahoo_finance(self, symbol: str) -> list:
"""Scrape analyst ratings from Yahoo Finance"""
# Implementation would go here
return []
def _scrape_benzinga(self, symbol: str) -> list:
"""Scrape analyst ratings from Benzinga"""
# Implementation would go here
return []
def _score_to_rating(self, score: float) -> str:
"""Convert numeric score to rating"""
if score >= 4.5:
return 'Strong Buy'
elif score >= 3.5:
return 'Buy'
elif score >= 2.5:
return 'Hold'
elif score >= 1.5:
return 'Sell'
else:
return 'Strong Sell'
def detect_rating_changes(self, new_ratings: list, old_ratings: list) -> list:
"""
Detect changes in analyst ratings
"""
changes = []
old_by_firm = {r.firm: r for r in old_ratings}
for new_rating in new_ratings:
if new_rating.firm in old_by_firm:
old_rating = old_by_firm[new_rating.firm]
if new_rating.rating != old_rating.rating or \
new_rating.price_target != old_rating.price_target:
changes.append({
'firm': new_rating.firm,
'symbol': new_rating.symbol,
'old_rating': old_rating.rating,
'new_rating': new_rating.rating,
'old_target': old_rating.price_target,
'new_target': new_rating.price_target,
'change_date': new_rating.date
})
return changes
# Usage
scraper = AnalystRatingScraper()
ratings = scraper.aggregate_ratings('AAPL')
print(f"Consensus: {ratings['consensus_rating']}")
print(f"Average Target: ${ratings['average_target']:.2f}")
print(f"Distribution: {ratings['ratings_distribution']}")
Beyond traditional financial metrics, alternative data sources provide early signals:
# Market sentiment analysis from social media
import re
from textblob import TextBlob
from collections import Counter
import asyncio
class SentimentAnalyzer:
def __init__(self):
self.bullish_keywords = ['moon', 'rocket', 'bull', 'long', 'calls', 'buy', 'undervalued',
'growth', 'breakout', 'support', 'accumulate']
self.bearish_keywords = ['bear', 'short', 'puts', 'sell', 'overvalued', 'crash',
'resistance', 'dump', 'bubble', 'correction']
def analyze_text_sentiment(self, text: str) -> dict:
"""
Analyze sentiment of a text using multiple methods
"""
# TextBlob sentiment
blob = TextBlob(text)
polarity = blob.sentiment.polarity
subjectivity = blob.sentiment.subjectivity
# Keyword-based sentiment
text_lower = text.lower()
bullish_count = sum(1 for word in self.bullish_keywords if word in text_lower)
bearish_count = sum(1 for word in self.bearish_keywords if word in text_lower)
# Determine overall sentiment
if polarity > 0.1 or bullish_count > bearish_count:
sentiment = 'bullish'
elif polarity < -0.1 or bearish_count > bullish_count:
sentiment = 'bearish'
else:
sentiment = 'neutral'
return {
'sentiment': sentiment,
'polarity': polarity,
'subjectivity': subjectivity,
'bullish_keywords': bullish_count,
'bearish_keywords': bearish_count,
'confidence': abs(polarity) + abs(bullish_count - bearish_count) * 0.1
}
def aggregate_sentiment(self, texts: list) -> dict:
"""
Aggregate sentiment across multiple texts
"""
sentiments = [self.analyze_text_sentiment(text) for text in texts]
sentiment_counts = Counter([s['sentiment'] for s in sentiments])
total = len(sentiments)
avg_polarity = sum(s['polarity'] for s in sentiments) / total
avg_confidence = sum(s['confidence'] for s in sentiments) / total
return {
'bullish_pct': (sentiment_counts['bullish'] / total) * 100,
'bearish_pct': (sentiment_counts['bearish'] / total) * 100,
'neutral_pct': (sentiment_counts['neutral'] / total) * 100,
'average_polarity': avg_polarity,
'average_confidence': avg_confidence,
'overall_sentiment': 'bullish' if avg_polarity > 0.05 else 'bearish' if avg_polarity < -0.05 else 'neutral',
'sample_size': total
}
class SocialMediaScraper:
def __init__(self):
self.sentiment_analyzer = SentimentAnalyzer()
async def scrape_stocktwits_sentiment(self, symbol: str) -> dict:
"""
Scrape sentiment data from StockTwits
"""
url = f"https://api.stocktwits.com/api/2/streams/symbol/{symbol}.json"
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
data = await response.json()
messages = data.get('messages', [])
texts = [msg['body'] for msg in messages]
sentiment = self.sentiment_analyzer.aggregate_sentiment(texts)
sentiment['watchlist_count'] = data.get('symbol', {}).get('watchlist_count', 0)
return sentiment
async def scrape_reddit_sentiment(self, symbol: str, subreddits: list = None) -> dict:
"""
Scrape sentiment from Reddit investing communities
"""
if subreddits is None:
subreddits = ['wallstreetbets', 'stocks', 'investing', 'SecurityAnalysis']
all_texts = []
for subreddit in subreddits:
# Would use Reddit API (PRAW) in production
# This is a simplified example
url = f"https://www.reddit.com/r/{subreddit}/search.json?q={symbol}&restrict_sr=1&sort=new"
async with aiohttp.ClientSession() as session:
async with session.get(url, headers={'User-Agent': 'InvestmentBot/1.0'}) as response:
data = await response.json()
posts = data.get('data', {}).get('children', [])
for post in posts:
all_texts.append(post['data'].get('title', ''))
all_texts.append(post['data'].get('selftext', ''))
return self.sentiment_analyzer.aggregate_sentiment(all_texts)
def calculate_sentiment_score(self, social_data: dict) -> float:
"""
Calculate a composite sentiment score (-1 to 1)
"""
bullish = social_data.get('bullish_pct', 0)
bearish = social_data.get('bearish_pct', 0)
polarity = social_data.get('average_polarity', 0)
# Weighted composite
score = ((bullish - bearish) / 100 * 0.6) + (polarity * 0.4)
return max(-1, min(1, score))
# Usage
async def analyze_market_sentiment(symbol: str):
scraper = SocialMediaScraper()
stocktwits_data = await scraper.scrape_stocktwits_sentiment(symbol)
reddit_data = await scraper.scrape_reddit_sentiment(symbol)
print(f"\n{symbol} Sentiment Analysis:")
print(f"StockTwits - Bullish: {stocktwits_data['bullish_pct']:.1f}%, Bearish: {stocktwits_data['bearish_pct']:.1f}%")
print(f"Reddit - Bullish: {reddit_data['bullish_pct']:.1f}%, Bearish: {reddit_data['bearish_pct']:.1f}%")
# Combined score
combined_score = (scraper.calculate_sentiment_score(stocktwits_data) +
scraper.calculate_sentiment_score(reddit_data)) / 2
print(f"Combined Sentiment Score: {combined_score:.3f}")
# asyncio.run(analyze_market_sentiment('TSLA'))
Here's a complete architecture for an automated investment research system:
# Complete investment intelligence pipeline
from dataclasses import dataclass, field
from typing import List, Dict
from datetime import datetime, timedelta
import asyncio
import json
@dataclass
class InvestmentSignal:
symbol: str
signal_type: str # 'technical', 'fundamental', 'sentiment', 'insider'
direction: str # 'bullish', 'bearish', 'neutral'
strength: float # 0-1
timestamp: datetime
source: str
details: dict = field(default_factory=dict)
class InvestmentIntelligenceEngine:
def __init__(self):
self.stock_scraper = StockDataScraper()
self.sec_scraper = SECFilingScraper()
self.analyst_scraper = AnalystRatingScraper()
self.social_scraper = SocialMediaScraper()
self.signals_db = []
async def analyze_stock(self, symbol: str) -> dict:
"""
Comprehensive analysis of a single stock
"""
analysis = {
'symbol': symbol,
'timestamp': datetime.now(),
'technical': {},
'fundamental': {},
'sentiment': {},
'analyst': {},
'insider': {},
'composite_score': 0,
'signals': []
}
# Technical Analysis
try:
async with self.stock_scraper as scraper:
price_data = await scraper.fetch_historical_data(symbol)
price_data = scraper.calculate_technical_indicators(price_data)
latest = price_data.iloc[-1]
analysis['technical'] = {
'current_price': latest['close'],
'rsi': latest['rsi'],
'macd': latest['macd'],
'sma_20': latest['sma_20'],
'sma_50': latest['sma_50'],
'trend': 'uptrend' if latest['sma_20'] > latest['sma_50'] else 'downtrend'
}
# Generate technical signals
if latest['rsi'] < 30:
analysis['signals'].append(InvestmentSignal(
symbol=symbol,
signal_type='technical',
direction='bullish',
strength=0.7,
timestamp=datetime.now(),
source='RSI Oversold',
details={'rsi': latest['rsi']}
))
elif latest['rsi'] > 70:
analysis['signals'].append(InvestmentSignal(
symbol=symbol,
signal_type='technical',
direction='bearish',
strength=0.7,
timestamp=datetime.now(),
source='RSI Overbought',
details={'rsi': latest['rsi']}
))
except Exception as e:
analysis['technical']['error'] = str(e)
# Sentiment Analysis
try:
stocktwits = await self.social_scraper.scrape_stocktwits_sentiment(symbol)
analysis['sentiment'] = stocktwits
sentiment_score = self.social_scraper.calculate_sentiment_score(stocktwits)
if sentiment_score > 0.5:
analysis['signals'].append(InvestmentSignal(
symbol=symbol,
signal_type='sentiment',
direction='bullish',
strength=sentiment_score,
timestamp=datetime.now(),
source='Social Media Sentiment',
details={'score': sentiment_score}
))
elif sentiment_score < -0.5:
analysis['signals'].append(InvestmentSignal(
symbol=symbol,
signal_type='sentiment',
direction='bearish',
strength=abs(sentiment_score),
timestamp=datetime.now(),
source='Social Media Sentiment',
details={'score': sentiment_score}
))
except Exception as e:
analysis['sentiment']['error'] = str(e)
# Analyst Ratings
try:
ratings = self.analyst_scraper.aggregate_ratings(symbol)
analysis['analyst'] = ratings
if ratings['consensus_rating'] in ['Strong Buy', 'Buy']:
analysis['signals'].append(InvestmentSignal(
symbol=symbol,
signal_type='analyst',
direction='bullish',
strength=0.6,
timestamp=datetime.now(),
source='Analyst Consensus',
details={'rating': ratings['consensus_rating']}
))
except Exception as e:
analysis['analyst']['error'] = str(e)
# Calculate composite score
analysis['composite_score'] = self._calculate_composite_score(analysis)
return analysis
def _calculate_composite_score(self, analysis: dict) -> float:
"""
Calculate weighted composite investment score
"""
weights = {
'technical': 0.25,
'sentiment': 0.20,
'analyst': 0.25,
'fundamental': 0.30
}
scores = []
# Technical score
tech = analysis.get('technical', {})
if 'rsi' in tech:
# Normalize RSI to -1 to 1 scale
tech_score = (50 - tech['rsi']) / 50
scores.append(('technical', tech_score))
# Sentiment score
sent = analysis.get('sentiment', {})
if 'average_polarity' in sent:
scores.append(('sentiment', sent['average_polarity']))
# Analyst score
analyst = analysis.get('analyst', {})
rating_scores = {'Strong Buy': 1, 'Buy': 0.5, 'Hold': 0, 'Sell': -0.5, 'Strong Sell': -1}
if analyst.get('consensus_rating') in rating_scores:
scores.append(('analyst', rating_scores[analyst['consensus_rating']]))
if not scores:
return 0
# Weighted average
total_weight = sum(weights.get(s[0], 0.25) for s in scores)
weighted_sum = sum(s[1] * weights.get(s[0], 0.25) for s in scores)
return weighted_sum / total_weight if total_weight > 0 else 0
async def scan_opportunities(self, symbols: list, min_score: float = 0.5) -> list:
"""
Scan multiple stocks for investment opportunities
"""
tasks = [self.analyze_stock(sym) for sym in symbols]
results = await asyncio.gather(*tasks, return_exceptions=True)
opportunities = []
for symbol, result in zip(symbols, results):
if isinstance(result, Exception):
continue
if abs(result['composite_score']) >= min_score:
opportunities.append(result)
# Sort by absolute composite score
opportunities.sort(key=lambda x: abs(x['composite_score']), reverse=True)
return opportunities
def generate_report(self, analysis: dict) -> str:
"""
Generate human-readable investment report
"""
report = f"""
Investment Analysis Report: {analysis['symbol']}
Generated: {analysis['timestamp'].strftime('%Y-%m-%d %H:%M:%S')}
COMPOSITE SCORE: {analysis['composite_score']:.3f} ({'BULLISH' if analysis['composite_score'] > 0 else 'BEARISH' if analysis['composite_score'] < 0 else 'NEUTRAL'})
TECHNICAL ANALYSIS:
- Current Price: ${analysis['technical'].get('current_price', 'N/A')}
- RSI: {analysis['technical'].get('rsi', 'N/A')}
- Trend: {analysis['technical'].get('trend', 'N/A')}
SENTIMENT ANALYSIS:
- Bullish: {analysis['sentiment'].get('bullish_pct', 0):.1f}%
- Bearish: {analysis['sentiment'].get('bearish_pct', 0):.1f}%
- Watchlist Count: {analysis['sentiment'].get('watchlist_count', 'N/A')}
ANALYST RATINGS:
- Consensus: {analysis['analyst'].get('consensus_rating', 'N/A')}
- Average Target: ${analysis['analyst'].get('average_target', 'N/A')}
ACTIVE SIGNALS:
"""
for signal in analysis['signals']:
report += f"- [{signal.signal_type.upper()}] {signal.direction.upper()} from {signal.source} (strength: {signal.strength:.2f})\n"
return report
# Usage example
async def main():
engine = InvestmentIntelligenceEngine()
# Analyze single stock
analysis = await engine.analyze_stock('AAPL')
print(engine.generate_report(analysis))
# Scan for opportunities
watchlist = ['AAPL', 'MSFT', 'GOOGL', 'AMZN', 'TSLA', 'NVDA', 'META', 'NFLX']
opportunities = await engine.scan_opportunities(watchlist, min_score=0.3)
print("\n\nTOP OPPORTUNITIES:")
for opp in opportunities[:5]:
direction = "📈 BUY" if opp['composite_score'] > 0 else "📉 SELL"
print(f"{direction} {opp['symbol']} (Score: {opp['composite_score']:.3f})")
# asyncio.run(main())
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