AI Newsletter Digest improvements: fixed QP soft line break decoding, URL extraction, and content cleaning
This commit is contained in:
Krilly
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#!/usr/bin/env python3
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"""
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Institutional Flow Tracker - Single Stock Deep Dive
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Provides detailed analysis of institutional ownership for a specific stock,
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including historical trends, top holders, and position changes.
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Usage:
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python3 analyze_single_stock.py AAPL
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python3 analyze_single_stock.py MSFT --quarters 12 --api-key YOUR_KEY
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python3 analyze_single_stock.py TSLA --compare-to GM
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Requirements:
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- FMP API key (set FMP_API_KEY environment variable or pass --api-key)
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"""
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import argparse
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import json
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import os
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import sys
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from datetime import datetime
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from typing import List, Dict, Optional
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from collections import defaultdict
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try:
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import requests
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except ImportError:
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print("Error: 'requests' library not installed. Install with: pip install requests")
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sys.exit(1)
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class SingleStockAnalyzer:
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"""Analyze institutional ownership for a single stock"""
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def __init__(self, api_key: str):
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self.api_key = api_key
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self.base_url = "https://financialmodelingprep.com/api/v3"
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def get_institutional_holders(self, symbol: str) -> List[Dict]:
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"""Get all institutional holders data for a stock"""
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url = f"{self.base_url}/institutional-holder/{symbol}"
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params = {"apikey": self.api_key}
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try:
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response = requests.get(url, params=params, timeout=30)
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response.raise_for_status()
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data = response.json()
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return data if isinstance(data, list) else []
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except requests.exceptions.RequestException as e:
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print(f"Error fetching institutional holders for {symbol}: {e}")
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return []
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def get_company_profile(self, symbol: str) -> Dict:
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"""Get company profile information"""
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url = f"{self.base_url}/profile/{symbol}"
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params = {"apikey": self.api_key}
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try:
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response = requests.get(url, params=params, timeout=30)
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response.raise_for_status()
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data = response.json()
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return data[0] if isinstance(data, list) and data else {}
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except requests.exceptions.RequestException as e:
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print(f"Error fetching company profile for {symbol}: {e}")
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return {}
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def analyze_stock(self, symbol: str, quarters: int = 8) -> Dict:
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"""Perform comprehensive institutional analysis on a stock"""
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print(f"Analyzing institutional ownership for {symbol}...")
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# Get company profile
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profile = self.get_company_profile(symbol)
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company_name = profile.get('companyName', symbol)
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sector = profile.get('sector', 'Unknown')
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market_cap = profile.get('mktCap', 0)
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print(f"Company: {company_name}")
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print(f"Sector: {sector}")
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print(f"Market Cap: ${market_cap:,}")
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# Get institutional holders
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holders = self.get_institutional_holders(symbol)
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if not holders:
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print(f"No institutional holder data available for {symbol}")
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return {}
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# Group by quarter
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quarters_data = defaultdict(list)
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for holder in holders:
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date = holder.get('dateReported', '')
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if date:
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quarters_data[date].append(holder)
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# Get most recent N quarters
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sorted_quarters = sorted(quarters_data.keys(), reverse=True)[:quarters]
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if len(sorted_quarters) < 2:
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print(f"Insufficient data (need at least 2 quarters, found {len(sorted_quarters)})")
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return {}
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# Calculate quarterly metrics
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quarterly_metrics = []
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for q in sorted_quarters:
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holders_q = quarters_data[q]
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total_shares = sum(h.get('totalShares', 0) for h in holders_q)
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total_value = sum(h.get('totalInvested', 0) for h in holders_q)
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num_holders = len(holders_q)
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quarterly_metrics.append({
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'quarter': q,
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'total_shares': total_shares,
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'total_value': total_value,
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'num_holders': num_holders,
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'top_holders': sorted(holders_q, key=lambda x: x.get('totalShares', 0), reverse=True)[:20]
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})
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# Calculate trends
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most_recent = quarterly_metrics[0]
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oldest = quarterly_metrics[-1]
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shares_trend = ((most_recent['total_shares'] - oldest['total_shares']) / oldest['total_shares'] * 100) if oldest['total_shares'] > 0 else 0
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holders_trend = most_recent['num_holders'] - oldest['num_holders']
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# Analyze position changes (recent quarter vs previous)
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if len(quarterly_metrics) >= 2:
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current_q = quarterly_metrics[0]
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previous_q = quarterly_metrics[1]
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# Create holder dictionaries for comparison
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current_holders_map = {h.get('holder', ''): h for h in current_q['top_holders']}
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previous_holders_map = {h.get('holder', ''): h for h in previous_q['top_holders']}
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# Categorize changes
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new_positions = []
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increased_positions = []
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decreased_positions = []
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closed_positions = []
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# Check current holders
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for name, holder in current_holders_map.items():
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current_shares = holder.get('totalShares', 0)
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if name in previous_holders_map:
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previous_shares = previous_holders_map[name].get('totalShares', 0)
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change = current_shares - previous_shares
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pct_change = (change / previous_shares * 100) if previous_shares > 0 else 0
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if change > 0:
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increased_positions.append({
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'name': name,
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'current_shares': current_shares,
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'change': change,
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'pct_change': pct_change
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})
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elif change < 0:
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decreased_positions.append({
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'name': name,
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'current_shares': current_shares,
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'change': change,
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'pct_change': pct_change
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})
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else:
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new_positions.append({
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'name': name,
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'shares': current_shares
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})
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# Check for closed positions
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for name, holder in previous_holders_map.items():
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if name not in current_holders_map:
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closed_positions.append({
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'name': name,
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'previous_shares': holder.get('totalShares', 0)
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})
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# Sort by magnitude
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increased_positions.sort(key=lambda x: x['change'], reverse=True)
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decreased_positions.sort(key=lambda x: x['change'])
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else:
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new_positions = []
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increased_positions = []
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decreased_positions = []
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closed_positions = []
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return {
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'symbol': symbol,
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'company_name': company_name,
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'sector': sector,
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'market_cap': market_cap,
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'quarterly_metrics': quarterly_metrics,
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'shares_trend': shares_trend,
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'holders_trend': holders_trend,
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'new_positions': new_positions,
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'increased_positions': increased_positions,
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'decreased_positions': decreased_positions,
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'closed_positions': closed_positions
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}
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def generate_report(self, analysis: Dict, output_file: Optional[str] = None):
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"""Generate detailed markdown report"""
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if not analysis:
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print("No analysis data available")
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return
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timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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symbol = analysis['symbol']
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report = f"""# Institutional Ownership Analysis: {symbol}
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**Company:** {analysis['company_name']}
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**Sector:** {analysis['sector']}
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**Market Cap:** ${analysis['market_cap']:,}
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**Analysis Date:** {timestamp}
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## Executive Summary
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"""
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# Determine overall signal
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shares_trend = analysis['shares_trend']
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holders_trend = analysis['holders_trend']
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if shares_trend > 15 and holders_trend > 5:
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signal = "🟢 **STRONG ACCUMULATION**"
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interpretation = "Strong institutional buying with increasing participation. Positive signal."
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elif shares_trend > 7 and holders_trend > 0:
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signal = "🟢 **MODERATE ACCUMULATION**"
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interpretation = "Steady institutional buying. Moderately positive signal."
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elif shares_trend < -15 or holders_trend < -5:
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signal = "🔴 **STRONG DISTRIBUTION**"
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interpretation = "Significant institutional selling. Warning sign - investigate further."
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elif shares_trend < -7:
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signal = "🔴 **MODERATE DISTRIBUTION**"
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interpretation = "Institutional selling detected. Monitor closely."
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else:
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signal = "⚪ **NEUTRAL**"
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interpretation = "No significant institutional flow changes. Stable ownership."
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report += f"""**Signal:** {signal}
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**Interpretation:** {interpretation}
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**Trend ({len(analysis['quarterly_metrics'])} Quarters):**
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- Institutional Shares: {shares_trend:+.2f}%
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- Number of Institutions: {holders_trend:+d}
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## Historical Institutional Ownership Trend
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| Quarter | Total Shares Held | Total Value | Number of Institutions | QoQ Change |
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|---------|-------------------|-------------|----------------------|------------|
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"""
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# Add quarterly data
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metrics = analysis['quarterly_metrics']
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for i, q in enumerate(metrics):
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if i < len(metrics) - 1:
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prev_shares = metrics[i+1]['total_shares']
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qoq_change = ((q['total_shares'] - prev_shares) / prev_shares * 100) if prev_shares > 0 else 0
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qoq_str = f"{qoq_change:+.2f}%"
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else:
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qoq_str = "N/A"
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report += f"| {q['quarter']} | {q['total_shares']:,} | ${q['total_value']:,} | {q['num_holders']} | {qoq_str} |\n"
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# Recent changes
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report += f"""
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## Recent Quarter Changes ({metrics[0]['quarter']} vs {metrics[1]['quarter']})
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### New Positions (Institutions that newly initiated)
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"""
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if analysis['new_positions']:
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report += "| Institution | Shares Acquired |\n"
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report += "|-------------|----------------|\n"
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for pos in analysis['new_positions'][:10]:
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report += f"| {pos['name']} | {pos['shares']:,} |\n"
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else:
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report += "No new institutional positions detected.\n"
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report += "\n### Increased Positions (Top 10)\n\n"
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if analysis['increased_positions']:
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report += "| Institution | Current Shares | Change | % Change |\n"
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report += "|-------------|----------------|--------|----------|\n"
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for pos in analysis['increased_positions'][:10]:
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report += f"| {pos['name']} | {pos['current_shares']:,} | {pos['change']:+,} | {pos['pct_change']:+.2f}% |\n"
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else:
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report += "No significant position increases detected.\n"
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report += "\n### Decreased Positions (Top 10)\n\n"
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if analysis['decreased_positions']:
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report += "| Institution | Current Shares | Change | % Change |\n"
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report += "|-------------|----------------|--------|----------|\n"
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for pos in analysis['decreased_positions'][:10]:
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report += f"| {pos['name']} | {pos['current_shares']:,} | {pos['change']:,} | {pos['pct_change']:.2f}% |\n"
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else:
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report += "No significant position decreases detected.\n"
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report += "\n### Closed Positions (Institutions that exited)\n\n"
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if analysis['closed_positions']:
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report += "| Institution | Previous Shares |\n"
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report += "|-------------|-----------------|\n"
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for pos in analysis['closed_positions'][:10]:
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report += f"| {pos['name']} | {pos['previous_shares']:,} |\n"
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else:
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report += "No institutional exits detected.\n"
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# Top current holders
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report += f"\n## Top 20 Current Institutional Holders ({metrics[0]['quarter']})\n\n"
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report += "| Rank | Institution | Shares Held | % of Institutional | Latest Change |\n"
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report += "|------|-------------|-------------|-------------------|---------------|\n"
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total_inst_shares = metrics[0]['total_shares']
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for i, holder in enumerate(metrics[0]['top_holders'], 1):
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shares = holder.get('totalShares', 0)
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pct_of_inst = (shares / total_inst_shares * 100) if total_inst_shares > 0 else 0
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change = holder.get('change', 0)
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report += f"| {i} | {holder.get('holder', 'Unknown')} | {shares:,} | {pct_of_inst:.2f}% | {change:+,} |\n"
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# Concentration analysis
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if len(metrics[0]['top_holders']) >= 10:
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top_10_shares = sum(h.get('totalShares', 0) for h in metrics[0]['top_holders'][:10])
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concentration = (top_10_shares / total_inst_shares * 100) if total_inst_shares > 0 else 0
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report += f"""
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## Concentration Analysis
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**Top 10 Holders Concentration:** {concentration:.2f}%
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**Interpretation:**
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"""
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if concentration > 60:
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report += "- **High Concentration** - Top 10 institutions control majority of institutional ownership\n"
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report += "- **Risk:** Significant price impact if top holders sell\n"
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report += "- **Opportunity:** May indicate high conviction by quality investors\n"
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elif concentration > 40:
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report += "- **Moderate Concentration** - Balanced institutional ownership\n"
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report += "- **Risk:** Moderate concentration risk\n"
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else:
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report += "- **Low Concentration** - Widely distributed institutional ownership\n"
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report += "- **Risk:** Lower concentration risk, more stable ownership\n"
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report += """
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## Interpretation Guide
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**For detailed interpretation framework, see:**
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`institutional-flow-tracker/references/interpretation_framework.md`
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**Next Steps:**
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1. Validate institutional signal with fundamental analysis
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2. Check technical setup for entry timing
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3. Review sector-wide institutional trends
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4. Monitor quarterly for trend continuation/reversal
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---
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**Data Source:** FMP API (13F SEC Filings)
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**Data Lag:** ~45 days after quarter end
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**Note:** Use as confirming indicator alongside fundamental and technical analysis
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"""
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# Save report
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if output_file:
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output_path = output_file if output_file.endswith('.md') else f"{output_file}.md"
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else:
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output_path = f"institutional_analysis_{symbol}_{datetime.now().strftime('%Y%m%d')}.md"
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with open(output_path, 'w') as f:
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f.write(report)
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print(f"\n✅ Report saved to: {output_path}")
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return report
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def main():
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parser = argparse.ArgumentParser(
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description='Analyze institutional ownership for a specific stock',
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formatter_class=argparse.RawDescriptionHelpFormatter,
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epilog="""
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Examples:
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# Basic analysis
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python3 analyze_single_stock.py AAPL
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# Extended history (12 quarters)
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python3 analyze_single_stock.py MSFT --quarters 12
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# With custom output
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python3 analyze_single_stock.py TSLA --output tesla_analysis.md
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"""
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)
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parser.add_argument(
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'symbol',
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type=str,
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help='Stock ticker symbol to analyze'
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)
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parser.add_argument(
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'--api-key',
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type=str,
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default=os.getenv('FMP_API_KEY'),
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help='FMP API key (or set FMP_API_KEY environment variable)'
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)
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parser.add_argument(
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'--quarters',
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type=int,
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default=8,
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help='Number of quarters to analyze (default: 8, i.e., 2 years)'
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)
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parser.add_argument(
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'--output',
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type=str,
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help='Output file path for markdown report'
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)
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parser.add_argument(
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'--compare-to',
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type=str,
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help='Compare to another stock (optional, future feature)'
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)
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args = parser.parse_args()
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# Validate API key
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if not args.api_key:
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print("Error: FMP API key required")
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print("Set FMP_API_KEY environment variable or pass --api-key argument")
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print("Get free API key at: https://financialmodelingprep.com/developer/docs")
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sys.exit(1)
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# Initialize analyzer
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analyzer = SingleStockAnalyzer(args.api_key)
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# Run analysis
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analysis = analyzer.analyze_stock(args.symbol.upper(), quarters=args.quarters)
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if not analysis:
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print(f"Unable to complete analysis for {args.symbol}")
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sys.exit(1)
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# Generate report
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analyzer.generate_report(analysis, output_file=args.output)
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# Print summary
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print("\n" + "="*80)
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print(f"INSTITUTIONAL OWNERSHIP SUMMARY: {args.symbol}")
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print("="*80)
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print(f"Trend ({args.quarters} quarters): {analysis['shares_trend']:+.2f}% shares, {analysis['holders_trend']:+d} institutions")
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print(f"Recent Activity:")
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print(f" - New Positions: {len(analysis['new_positions'])}")
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print(f" - Increased: {len(analysis['increased_positions'])}")
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print(f" - Decreased: {len(analysis['decreased_positions'])}")
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print(f" - Exited: {len(analysis['closed_positions'])}")
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if __name__ == '__main__':
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main()
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Reference in New Issue
Block a user