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 f

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: 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): Sector focused scan: Custom screening: 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: 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: 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 15% QoQ Number of institutions increasing 10% Quality long term investors adding positions Low current ownership (<40%) with room to grow Accumulation happening across multiple quarters Moderate Bullish: Institutional ownership increasing 5 15% QoQ Mix of new buyers and sellers, net positive Current ownership 40 70% Neutral: Minimal change in ownership (<5%) Similar number of buyers and sellers Stable institutional base Moderate Bearish: Institutional ownership decreasing 5 15% QoQ More sellers than buyers High ownership ( 80%) limiting new buyers Strong Bearish (Consider selling/avoiding): Institutional ownership decreasing 15% QoQ Number of institutions decreasing 10% Quality investors exiting positions Distribution happening across multiple quarters Concentration risk (top holder selling large position) Step 5: Portfolio Application For new positions: 1. Run institutional analysis on your stock idea 2. Look for confirmation (institutions also accumulating) 3. If strong bearish signals, reconsider or reduce position size 4. If strong bullish signals, gain confidence in thesis For existing holdings: 1. Quarterly review after 13F filing deadlines 2. Monitor for distribution (early warning system) 3. If institutions are exiting, re evaluate your thesis 4. Consider trimming if widespread institutional selling Screening workflow integration: 1. Use Value Dividend Screener or other screeners to find candidates 2. Run Institutional Flow Tracker on top candidates 3. Prioritize stocks with institutional accumulation 4. Avoid stocks with institutional distribution Output Format All analysis generates structured markdown reports saved to repository root: Filename convention: institutional flow analysis <TICKER/THEME <DATE .md Report sections: 1. Executive Summary (key findings) 2. Institutional Ownership Trend (current vs historical) 3. Top Holders and Changes 4. New Buyers vs Sellers 5. Concentration Analysis 6. Interpretation and Recommendations 7. Data Sources and Timestamp Data Reliability Grades All analysis includes a coverage based reliability grade : Grade A: A comparable prior quarter exists and the stock has = 50 institutional (13F) holders. Dense coverage, safe for ranking. Grade B: A comparable prior quarter exists and the stock has = 10 holders. Usable but thin — reference only. Grade C: No comparable prior quarter (change not measurable) or < 10 holders. EXCLUDED from screening results. The screening script ( track institutional flow.py ) automatically excludes Grade C stocks. The single stock analysis ( analyze single stock.py ) displays the grade with appropriate warnings. Why coverage, not per holder reconciliation: Metrics are sourced from FMP's aggregate 13F summary ( institutional ownership/symbol positions summary ), which reconciles quarter over quarter deltas across all filing managers at source . This replaces the retired /api/v3/institutional holder feed, which returned asymmetric per holder lists across quarters (e.g., 5,415 holders one quarter, 201 the next) and required client side filtering to avoid inflated percent changes. With the reconciled summary, the remaining quality signal that matters in practice is breadth (how many managers hold the name) and whether a prior quarter exists to measure change against — which is what the grade now reflects. Limitations and Caveats Data Lag: 13F filings have 45 day reporting delay Positions may have changed since filing date Use as confirming indicator, not leading signal Coverage: Only institutions managing $100M are required to file Excludes individual investors and smaller funds International institutions may not file 13F Reporting Rules: Only long equity positions reported (no shorts, options, bonds) Holdings as of quarter end snapshot Some positions may be confidential (delayed reporting) Interpretation: Correlation ≠ causation (stocks can fall despite institutional buying) Consider overall market environment and fundamentals Combine with technical analysis and other skills Advanced Use Cases Insider + Institutional Combo: Look for stocks where both insiders AND institutions are buying Particularly powerful signal when aligned Sector Rotation Detection: Track aggregate institutional flows by sector Identify early rotation trends before they appear in price Contrarian Plays: Find quality stocks institutions are selling (potential value) Requires strong fundamental conviction Smart Money Validation: Before major position, check if smart money agrees Gain confidence or find overlooked risks References The references/ folder contains detailed guides: 13f filings guide.md Comprehensive guide to 13F SEC filings, what they include, reporting requirements, and data quality considerations institutional investor types.md Different types of institutional investors (hedge funds, mutual funds, pension funds, etc.), their typical strategies, and how to interpret their moves interpretation framework.md Detailed framework for interpreting institutional ownership changes, signal quality assessment, and integration with other analysis Script Parameters track institutional flow.py Main screening script for finding stocks with significant institutional changes. Required: api key : FMP API key (or set FMP API KEY environment variable) Optional: top N : Return top N stocks by institutional change (default: 50) min change percent X : Minimum % change in institutional ownership (default: 10) min market cap X : Minimum market cap in dollars (default: 1B) sector NAME : Filter by specific sector min institutions N : Minimum number of institutional holders (default: 10) limit N : Number of stocks to fetch from screener (default: 100). Lower values save API calls. output FILE : Output JSON file path output dir DIR : Output directory for reports (default: reports/) sort by FIELD : Sort by 'ownership change' or 'institution count change' analyze single stock.py Deep dive analysis on a specific stock's institutional ownership. Required: Ticker symbol (positional argument) api key : FMP API key (or set FMP API KEY environment variable) Optional: quarters N : Number of quarters to analyze (default: 8, i.e., 2 years) output FILE : Output markdown report path output dir DIR : Output directory for reports (default: reports/) compare to TICKER : Compare institutional ownership to another stock (future feature) track institution portfolio.py Status: NOT YET IMPLEMENTED This script is a placeholder. It prints alternative resources (WhaleWisdom, SEC EDGAR, DataRoma) and exits with error code 1. FMP API organizes institutional holder data by stock (not by institution), making full portfolio reconstruction impractical. For institution specific portfolio tracking, use: 1. WhaleWisdom: https://whalewisdom.com (free tier available) 2. SEC EDGAR: https://www.sec.gov/cgi bin/browse edgar 3. DataRoma: https://www.dataroma.com Data Quality Module (data quality.py) Shared utility module used by both track institutional flow.py and analyze single stock.py : coverage grade(): Assigns

institutional-flow-tracker