/institutional-flow-tracker

name: institutional-flow-tracker
description: Use this skill to track institutional investor ownership changes and portfolio flows using 13F filings data. Analyzes hedge funds, mutual funds, and other institutional holders to identify stocks with significant smart money accumulation or distribution. Helps discover stocks before major moves by following where sophisticated investors are deploying capital.

Institutional Flow Tracker

Overview

This skill tracks institutional investor activity through 13F SEC filings to identify "smart money" flows into and out of stocks. By analyzing quarterly changes in institutional ownership, you can discover stocks that sophisticated investors are accumulating before major price moves, or identify potential risks when institutions are reducing positions.

**Key Insight:** Institutional investors (hedge funds, pension funds, mutual funds) manage trillions of dollars and conduct extensive research. Their collective buying/selling patterns often precede significant price movements by 1-3 quarters.

Prerequisites

• **FMP API Key:** Set `FMP_API_KEY` environment variable or pass `--api-key` to scripts
• **Python 3.9+:** Required for running analysis scripts
• **Dependencies:** `pip install requests` (scripts handle missing dependencies gracefully)

When to Use This Skill

Use this skill when:

• Validating investment ideas (checking if smart money agrees with your thesis)
• Discovering new opportunities (finding stocks institutions are accumulating)
• Risk assessment (identifying stocks institutions are exiting)
• Portfolio monitoring (tracking institutional support for your holdings)
• Following specific investors (tracking Warren Buffett, Cathie Wood, etc.)
• Sector rotation analysis (identifying where institutions are rotating capital)

**Do NOT use when:**

• Seeking real-time intraday signals (13F data has 45-day reporting lag)
• Analyzing micro-cap stocks (<$100M market cap with limited institutional interest)
• Looking for short-term trading signals (<3 months horizon)

Data Sources & Requirements

Required: FMP API Key

This skill uses Financial Modeling Prep (FMP) API to access 13F filing data:

**Setup:**

# Set environment variable (preferred)
export FMP_API_KEY=your_key_here

# Or provide when running scripts
python3 scripts/track_institutional_flow.py --api-key YOUR_KEY

**API Tier Requirements:**

• **Free Tier:** 250 requests/day (sufficient for analyzing 20-30 stocks quarterly)
• **Paid Tiers:** Higher limits for extensive screening

**13F Filing Schedule:**

• Filed quarterly within 45 days after quarter end
• Q1 (Jan-Mar): Filed by mid-May
• Q2 (Apr-Jun): Filed by mid-August
• Q3 (Jul-Sep): Filed by mid-November
• Q4 (Oct-Dec): Filed by mid-February

Analysis Workflow

Step 1: Identify Stocks with Significant Institutional Changes

Execute the main screening script to find stocks with notable institutional activity:

**Quick scan (top 50 stocks by institutional change):**

python3 scripts/track_institutional_flow.py \
  --top 50 \
  --min-change-percent 10

**Sector-focused scan:**

python3 scripts/track_institutional_flow.py \
  --sector Technology \
  --min-institutions 20

**Custom screening:**

python3 scripts/track_institutional_flow.py \
  --min-market-cap 2000000000 \
  --min-change-percent 15 \
  --top 100 \
  --output institutional_flow_results.json

**Output includes:**

• Stock ticker and company name
• Current institutional ownership % (of shares outstanding)
• Quarter-over-quarter change in shares held
• Number of institutions holding
• Change in number of institutions (new buyers vs sellers)
• Top institutional holders

Step 2: Deep Dive on Specific Stocks

For detailed analysis of a specific stock's institutional ownership:

python3 scripts/analyze_single_stock.py AAPL

**This generates:**

• Historical institutional ownership trend (8 quarters)
• Top 20 institutional holders with position changes
• Concentration analysis (top 10 holders' % of total institutional ownership)
• New / increased / decreased positions among the largest holders
• Data quality assessment with coverage-based reliability grade

**Key metrics to evaluate:**

• **Ownership %:** Higher institutional ownership (>70%) = more stability but limited upside
• **Ownership Trend:** Rising ownership = bullish, falling = bearish
• **Concentration:** High concentration (top 10 > 50%) = risk if they sell
• **Quality of Holders:** Presence of quality long-term investors (Berkshire, Fidelity) vs momentum funds

Step 3: Track Specific Institutional Investors

> **Note:** `track_institution_portfolio.py` is **not yet implemented**. FMP API organizes > institutional holder data by stock (not by institution), making full portfolio reconstruction > impractical via this API alone.

**Alternative approach — use `analyze_single_stock.py` to check if a specific institution holds a stock:**

# Analyze a stock and look for a specific institution in the output
python3 institutional-flow-tracker/scripts/analyze_single_stock.py AAPL
# Then search the report for "Berkshire" or "ARK" in the Top 20 holders table

**For full institution-level portfolio tracking, use these external resources:** 1. **WhaleWisdom:** https://whalewisdom.com (free tier available, 13F portfolio viewer) 2. **SEC EDGAR:** https://www.sec.gov/cgi-bin/browse-edgar (official 13F filings) 3. **DataRoma:** https://www.dataroma.com (superinvestor portfolio tracker)

Step 4: Interpretation and Action

Read the references for interpretation guidance:

• `references/13f_filings_guide.md` - Understanding 13F data and limitations
• `references/institutional_investor_types.md` - Different investor types and their strategies
• `references/interpretation_framework.md` - How to interpret institutional flow signals

**Signal Strength Framework:**

**Strong Bullish (Consider buying):**

• Institutional ownership increasing