Piecing together the U.S. hedge fund industry from 9 public regulatory data sources.
An open-source intelligence project assembling the financial picture of U.S. hedge funds — balance sheets, derivatives, borrowing, positioning, and fund-level holdings — from fragments no one else combines.
Reproducibility: Every claim is backed by claims_ledger.csv with source file, cell reference, and refresh timestamp. Pipeline manifest: run_manifest.json.
The hedge fund industry reports to a dozen different regulators in a dozen different formats. No single source tells the full story. But combined, they do.
This project pulls from 9 public data sources across the Federal Reserve, SEC, CFTC, DTCC, and CBOE to build a unified picture of:
- $3.3 trillion in total assets (Fed Z.1 Q3 2025) — with $12.6T in gross assets via Form PF
- $20.2 trillion in derivative exposure — 3.7x their net asset value
- $415 trillion in interest rate swap notional flowing through the system weekly
- 283,362 long equity/ETF positions across 8 of the largest funds — report periods 2024Q1–2025Q4, amendment-deduped
- Over 1 million OTC derivative trades per day flowing through DTCC
- The complete borrowing, leverage, and counterparty structure of an industry that answers to no single regulator
| # | Source | What It Reveals | Coverage |
|---|---|---|---|
| 1 | Federal Reserve Z.1 | Aggregate balance sheet (Table B.101.f) — assets, liabilities, net worth | Raw FRED series span 1945–2025; usable hedge fund observations begin 2012 Q4 |
| 2 | SEC Form PF | Private fund statistics — GAV, NAV, leverage, derivatives, borrowing by creditor, strategy allocation, concentration | 2013–2025, quarterly + monthly |
| 3 | CFTC Weekly Swaps | OTC derivatives market — interest rate, credit, and FX swap notional, volumes, counterparty splits | 2013–2026, weekly |
| 4 | SEC EDGAR 13F | Fund-level equity holdings for Citadel, Bridgewater, Renaissance, Point72, Two Sigma, D.E. Shaw, Millennium, AQR — amendment-deduped | Bundled local cache currently spans report periods 2024Q1–2025Q4 |
| 5 | SEC EDGAR Submissions | Complete filing history, SC 13G (5%+ ownership stakes), Form ADV registration | 1996–2026 |
| 6 | CFTC COT | Leveraged fund positioning in equity index futures | Weekly |
| 7 | CBOE VIX | Market volatility index | Daily, aggregated quarterly |
| 8 | DTCC Swap Repository | Trade-level OTC derivative transactions — notional, counterparty type, clearing status, block-trade and prime-broker flags | Local snapshot: 2025-03-13 to 2026-03-13, daily |
| 9 | CFTC FCM Financials | Broker-level adjusted net capital, excess capital, customer segregated funds, cleared swap segregation | Local snapshot: 2022-01 to 2026-01, monthly |
The Fed's Z.1 shows $3.26T in hedge fund assets (Q3 2025, all-time high, +16% YoY). SEC Form PF shows $12.6T in gross assets and $20.2T in derivatives. The difference is leverage and off-balance-sheet exposure that the Fed's flow-of-funds framework doesn't capture.
Hedge funds are only part of the picture. Form PF covers all private funds — and the total is staggering:
| Fund Type | Gross Assets (GAV) | Net Assets (NAV) | Leverage (GAV/NAV) |
|---|---|---|---|
| Hedge Funds | $12.6T | $5.4T | 2.32x |
| Private Equity | $7.9T | $7.3T | 1.09x |
| Other Private Funds | $1.9T | $1.6T | 1.14x |
| Securitized Asset Funds | $1.1T | $0.4T | 2.74x |
| Real Estate Funds | $1.1T | $0.9T | 1.28x |
| Venture Capital | $0.5T | $0.4T | 1.09x |
| Liquidity Funds | $0.4T | $0.4T | 1.03x |
| All Private Funds | $25.5T | $16.4T | 1.55x |
That's $25.5 trillion in gross assets across the U.S. private fund industry (Q1 2025) — and hedge funds carry by far the highest leverage at 2.32x. Securitized asset funds are even more leveraged at 2.74x, but at a fraction of the size.
- Top 10 funds control 8.2% of industry NAV
- Top 500 funds control 54.8%
- Combined 13F long equity value across 8 mega funds: $831B (2025Q4 snapshot, options excluded)
- NVIDIA held by all 8 funds ($19.1B combined); iShares ETFs are the #1 line item ($20.3B)
- Citadel filed 854 SC 13G forms (5%+ ownership in 854 companies)
- 79% of hedge fund borrowing flows through prime brokerage (Fed Z.1, Q3 2025)
- Only 0.7% is unsecured — 99.3% is collateralized (Form PF, 2025-03)
- In 2025Q1, 63.9% of creditors are U.S. financial institutions and 35.3% are non-U.S. financial institutions
- In 2025-03, qualifying hedge funds held $2.8T in reverse repo and $2.6T in prime-broker financing
The Fed's Z.1 leverage ratio (liabilities / net assets) averages 0.43x and appears to oscillate around that mean — suggesting the industry self-corrects. But Z.1 only captures on-balance-sheet leverage.
SEC Form PF tells a different story. The GAV/NAV ratio — gross asset value divided by net asset value — captures the full picture including off-balance-sheet and derivative exposure. It has climbed from 1.76x (2013 Q4) to 2.32x (2025 Q1, all-time high) with a statistically significant upward trend (+0.008x per quarter, p≈0.00). An Augmented Dickey-Fuller test confirms GAV/NAV is non-stationary (p=0.99) — it is not mean-reverting. It has never pulled back to its historical average.
Both measures hit all-time highs simultaneously: Z.1 at 0.485x (Q3 2025), GAV/NAV at 2.32x (Q1 2025). The Z.1 data gives a false sense of safety — the leverage that matters most has been building uninterrupted for 12 years.
- $4.8T long / $4.9T short in interest rate derivatives — nearly perfectly hedged
- $1.8T long / $945B short in equities — net long $883B
- $517B long / $639B short in credit — net short $122B (betting on defaults)
- The weekly CFTC swaps data shows $415T in IR notional outstanding — the plumbing beneath everything
The individual findings above aren't independent — they're links in a statistically verified cascade. Granger causality tests (5/28 significant pairs) show that volatility shocks cause leverage adjustments (VIX → GAV/NAV, p=0.002) and broker capital stress (VIX → FCM excess capital, p=0.002), while leverage shifts feed back into volatility (Z.1 leverage → VIX, p=0.025). This isn't correlation — the causal direction is testable and confirmed.
The accelerants are already in place:
- Liquidity cushion: Portfolio liquidity exceeds investor redemption terms by 46.5 percentage points at the 30-day horizon on average; the cushion narrows in high-VIX quarters but stays positive in the bundled sample
- Rising broker concentration: FCM market HHI is trending upward (p<0.001) — fewer brokers are absorbing more risk each cycle, widening the blast radius when one breaks
- Leverage is at the all-time peak: 0.485x (Q3 2025) — the highest in 52 quarters of Z.1 data, with the fastest 5-quarter buildup on record. Monte Carlo simulation (10K paths, 8Q horizon) gives VaR 95% = -1.7% and P(negative) = 7.1%
The dominoes are: volatility spike → fund deleveraging → broker capital strain → further forced selling — and the system is more concentrated while its liquidity cushion compresses when volatility rises.
The current suite emits 18 result rows: 8 named cross-source tests plus 10 ADF/Mann-Kendall checks on key series. Key findings:
| Test | Result | p-value | What It Means |
|---|---|---|---|
| Liquidity gap vs VIX | PASS | 0.005 | The 30-day portfolio-minus-investor liquidity cushion narrows in high-VIX quarters, but remains positive in the bundled sample |
| VIX → GAV/NAV (Granger) | PASS | 0.002 | Volatility causes leverage changes — fear drives deleveraging |
| Z.1 leverage stationarity | FAIL | 0.139 | Non-stationary under AIC lag selection in the current run (ADF=-2.410); default-lag variants are more favorable |
| Form PF GAV trend | PASS | 0.000 | Industry gross assets trending strongly upward |
| Form PF GAV/NAV trend | PASS | 0.000 | Leverage ratio trending upward — funds are levering up |
| Z.1 ~ Form PF cointegration | FAIL | 0.173 | The two measures of industry size move independently |
| Z.1/Form PF ratio stability | FAIL | 0.944 | The gap between Fed and SEC views of the industry is widening |
| CFTC IR vs DTCC rates clearing | FAIL | 0.993 | Rates clearing measures are not equivalent within a 10pp band in the local 2025Q1–2026Q1 overlap |
| Form PF → Z.1 leverage | FAIL | 0.086 | Borderline — SEC data nearly predicts Fed data at 10% level |
Additionally, the advanced analysis found 3 structural breaks in Form PF GAV/NAV (2017Q3, 2020Q2, 2023Q1) and 2 cointegrating relationships between Form PF GAV and IR/Credit swap notional — the derivatives market and fund leverage are locked in long-run equilibrium. Full test results are saved to outputs/reports/cross_source_tests.csv.
Statistical methods used and why
| Method | Where Used | What It Tests | Why This Test |
|---|---|---|---|
| Augmented Dickey-Fuller (ADF) | Z.1 leverage ratio, Form PF GAV, GAV/NAV, VIX, COT net positioning | Tests whether a time series has a unit root (non-stationary). Null hypothesis: the series is non-stationary. | Determines whether metrics like leverage mean-revert to a long-run average or trend indefinitely. A stationary leverage ratio implies self-correcting behavior; a non-stationary one implies structural drift. |
| Mann-Kendall trend test | Same series as ADF | Non-parametric test for monotonic trend. Does not assume normality. | Complements ADF — a series can be stationary (ADF) but still have a significant trend (Mann-Kendall). Used because financial time series often violate normality assumptions. |
| Granger causality | All pairwise combinations of VIX, Z.1 leverage, GAV/NAV, COT positioning, swap notional, FCM excess capital | Tests whether past values of series X improve predictions of series Y beyond Y's own history. | Establishes directional causation between data sources — e.g., does a VIX spike cause subsequent deleveraging, or do they just co-move? Identifying causal chains is critical for understanding systemic transmission. |
| Engle-Granger cointegration | Z.1 total assets vs Form PF GAV; Form PF GAV vs swap notional | Tests whether two non-stationary series share a long-run equilibrium — they can diverge short-term but are bound together over time. | If the Fed and SEC measures of industry size are cointegrated, they're measuring the same thing with different lags. If not, they're capturing fundamentally different phenomena. |
| Two-sample t-test (Welch's) | Liquidity gap in high-VIX vs low-VIX quarters | Tests whether the mean of a metric differs between two groups. Welch's variant does not assume equal variance. | Determines whether the liquidity mismatch (investor-redeemable minus portfolio-liquid) is significantly worse during stress periods. A significant result means liquidity risk is procyclical. |
| TOST equivalence test | CFTC swap clearing % vs DTCC clearing % | Tests whether two measures are equivalent within a specified margin (10 percentage points). | Standard hypothesis tests can only reject equality — they can't confirm it. TOST flips this: it tests whether two data sources agree closely enough to be interchangeable. |
| Spearman rank correlation | FCM customer segregation vs COT net positioning | Non-parametric correlation that measures monotonic (not just linear) relationships. | Used for cross-source validation where the relationship may be non-linear — e.g., does broker capital move in the same direction as futures positioning? |
| Bai-Perron structural break detection | Form PF GAV/NAV ratio | Identifies dates where the statistical properties of a time series change abruptly. | Locates regime shifts — points where the leverage relationship fundamentally changed (e.g., post-COVID, post-rate-hikes). These are not gradual trends but discrete structural changes. |
| Monte Carlo simulation | Z.1 total assets, liabilities, net assets | Generates 10,000 forward paths using historical return distributions to estimate Value-at-Risk and probability of drawdown. | Provides probabilistic risk estimates rather than point forecasts. VaR 95% tells you the worst-case quarterly loss you'd expect 19 out of 20 times. |
| Vector Autoregression (VAR) | Cross-source aligned quarterly data | Models multiple time series simultaneously, capturing how each variable responds to shocks in the others. | Enables impulse response analysis — if VIX spikes by 1 standard deviation, how do leverage, capital, and positioning respond over the next 8 quarters? |
20+ publication-quality charts generated to outputs/figures/:
| Category | Charts |
|---|---|
| Z.1 Balance Sheet | Total assets, asset composition, debt securities, liability structure, balance sheet overview, derivative exposure, borrowing patterns, correlation heatmap |
| Form PF | GAV/NAV leverage, strategy allocation, concentration trends |
| CFTC Swaps | Clearing rates, notional outstanding |
| FCM | Capital & adequacy, market concentration |
| DTCC | Notional by asset class, clearing rates |
| EDGAR | Filing volume by fund |
| Cross-Source | Z.1 vs Form PF leverage comparison |
All processed CSVs are written to data/processed/. Monetary values are in billions USD unless noted. Dates use quarterly (2025Q1) or monthly (2025-03) format.
Federal Reserve Z.1 (3 files)
hedge_fund_analysis.csv — 52 rows, quarterly (Q4 2012 – Q3 2025)
The primary analysis dataset. Fed Z.1 Table B.101.f balance sheet items joined with VIX and derived metrics.
| Column | Description |
|---|---|
Total assets |
Aggregate hedge fund assets ($B) |
Total liabilities |
Aggregate liabilities ($B) |
Total net assets |
Assets minus liabilities ($B) |
Corporate equities; asset |
Equity holdings ($B) |
Derivatives (long value) |
Derivative exposure, long side ($B) |
Loans, total secured borrowing via prime brokerage; liability |
Prime brokerage borrowing ($B) |
VIX_mean, VIX_max, VIX_end |
Quarterly VIX statistics |
leverage_ratio |
Total liabilities / total net assets |
cash_to_assets |
(Deposits + cash + MMF) / total assets |
equity_pct |
Corporate equities / total assets |
derivative_to_assets |
Derivatives (long) / total assets |
prime_brokerage_pct |
Prime brokerage / total loans (liability) |
foreign_borrowing_share |
Foreign / (domestic + foreign) borrowing |
total_assets_qoq, total_assets_yoy |
Quarter-over-quarter and year-over-year growth |
leverage_change |
Quarter-over-quarter change in leverage ratio |
hedge_fund_metrics.csv — 319 rows. Same schema, includes pre-2012 quarters (many zeros).
statistical_analysis.csv — 319 rows. Same as metrics plus regime column from regime detection.
SEC Form PF (19 files)
form_pf_gav_nav.csv — 392 rows
| Column | Description |
|---|---|
fund_type |
Hedge Fund, Private Equity, Liquidity Fund, etc. |
quarter |
e.g., 2025Q1 |
gav |
Gross asset value ($B) |
nav |
Net asset value ($B) |
gav_nav_ratio |
GAV / NAV — true leverage proxy |
form_pf_borrowing_detail.csv — 882 rows, monthly
| Column | Description |
|---|---|
type |
Secured, Unsecured, or Total |
subtype |
Reverse Repo, Prime Broker, Other Secured, or Subtotal |
month |
e.g., 2025-03 |
amount_bn |
Borrowing amount ($B) |
form_pf_borrowing_creditor.csv — 196 rows, quarterly
| Column | Description |
|---|---|
creditor_type |
US Financial, Non-US Financial, US Non-Financial, Non-US Non-Financial |
share |
Fraction of total borrowing (0–1) |
form_pf_notional.csv — 5,145 rows, monthly
| Column | Description |
|---|---|
investment_type |
e.g., Interest Rate Derivatives, Credit Derivatives, Listed Equities |
long_notional |
Long exposure ($B) |
short_notional |
Short exposure ($B) |
net_exposure |
Long minus short ($B) |
form_pf_concentration.csv — 294 rows, quarterly
| Column | Description |
|---|---|
top_n |
Top 10, 25, 50, 100, 250, or 500 |
nav_share |
Share of industry NAV (0–1) |
gav_share, borrowing_share, derivative_share |
Corresponding shares |
form_pf_strategy.csv — 441 rows, quarterly
| Column | Description |
|---|---|
strategy |
Equity, Credit, Macro, Multi-Strategy, Relative Value, etc. |
gav, nav, borrowing |
Strategy-level aggregates ($B) |
form_pf_liquidity.csv — 882 rows, quarterly
| Column | Description |
|---|---|
period |
At most 1 day, 7 days, 30 days, 90 days, 180 days, 365 days |
cumulative_pct |
Cumulative fraction liquidatable/redeemable (0–1) |
liquidity_type |
investor_liquidity, portfolio_liquidity, or financing_liquidity |
form_pf_metric_liquidity_mismatch.csv — 49 rows, quarterly
| Column | Description |
|---|---|
portfolio_30d |
Fraction of portfolio liquidatable in 30 days |
investor_30d |
Fraction of investor capital redeemable in 30 days |
liquidity_mismatch_30d |
portfolio_30d minus investor_30d |
Other Form PF files: form_pf_derivatives.csv (derivative value by fund type), form_pf_fund_counts.csv (fund counts by type), form_pf_fair_value.csv (Level 1/2/3 fair value), form_pf_geography.csv (geographic allocation), form_pf_leverage_dist.csv (leverage ratio distribution), form_pf_sector.csv (sector allocation), form_pf_borrowing_pct.csv (borrowing as % of GAV), form_pf_metric_concentration_top10.csv, form_pf_metric_hf_gav_nav.csv, form_pf_metric_strategy_hhi.csv, form_pf_metric_latest_notional.csv.
CFTC Weekly Swaps (3 files)
swaps_weekly.csv — 605 rows, weekly (2013–2026)
| Column | Description |
|---|---|
date |
Report date |
ir_total |
Interest rate swap notional outstanding ($B) |
ir_cleared, ir_uncleared |
Cleared vs uncleared IR notional ($B) |
ir_cleared_pct |
Fraction cleared (0–1) |
credit_total, fx_total, equity_total, commodity_total |
Notional by asset class ($B) |
credit_cleared_pct, fx_cleared_pct |
Clearing rates by asset class |
swaps_quarterly.csv — 51 rows. Quarterly aggregation with weeks count.
swaps_weekly_long.csv — 5,733 rows. Long-format with metric, value_millions, value_billions.
DTCC Swap Repository (2 files)
dtcc_daily_summary.csv — 1,309 rows, daily (2025–2026)
| Column | Description |
|---|---|
date |
Trading date |
asset_class |
Commodity, Credit, Equity, ForeignExchange, InterestRate |
trade_count |
Number of trades |
total_notional_bn |
Total notional ($B) |
cleared_pct |
Fraction of trades cleared (0–1) |
pb_pct |
Fraction involving prime brokerage (0–1) |
block_pct |
Fraction that are block trades (0–1) |
dtcc_quarterly.csv — 25 rows. Quarter-end snapshots by asset class.
CFTC FCM Financials (5 files)
fcm_monthly_industry.csv — 49 rows, monthly (2022–2026)
| Column | Description |
|---|---|
adj_net_capital |
Industry adjusted net capital (raw USD) |
net_capital_requirement |
Regulatory minimum (raw USD) |
excess_net_capital |
Capital above requirement (raw USD) |
customer_assets_seg |
Customer segregated assets (raw USD) |
cleared_swap_seg |
Cleared swap customer segregation (raw USD) |
capital_adequacy_ratio |
adj_net_capital / requirement |
swap_seg_share |
Cleared swap seg / (customer + swap seg) |
fcm_count |
Number of registered FCMs |
fcm_concentration.csv — 49 rows, monthly
| Column | Description |
|---|---|
hhi |
Herfindahl-Hirschman Index of customer seg market share |
top5_share |
Top 5 FCM share of customer segregated assets |
fcm_monthly_all.csv — 3,083 rows. Individual FCM-level monthly data.
fcm_top_brokers.csv — 490 rows. Top 10 FCMs per month with market share.
fcm_quarterly.csv — 17 rows. Quarter-end industry snapshots.
Requires Python 3.10+.
pip install -r requirements.txtIf you want to refresh raw source data, add a FRED API key:
echo "FRED_API_KEY=your_key_here" > .envGet a free FRED API key at https://fred.stlouisfed.org/docs/api/api_key.html
Note: Form PF data requires a manual download from the SEC Form PF Statistics page. Place the
.xlsxfile indata/raw/form_pf/. The portfolio-facing artifact workflow does not require live fetches and runs from the trackeddata/processed/snapshot plus safe local caches.
# Full local refresh: fetch -> parse -> analyze -> artifacts
python -m src.pipeline
# Refresh public figures, reports, provenance files, and the notebook from the tracked snapshot
python -m src.pipeline --artifacts
# Update the local raw cache only
python -m src.pipeline --fetch
# Reparse raw inputs into processed CSVs
python -m src.pipeline --parse
# Recompute analysis outputs only
python -m src.pipeline --analyze
# Optional source-specific fetchers
python -m src.data.fetch --13f
python -m src.data.fetch_swaps
python -m src.data.fetch_dtcc
python -m src.data.fetch_fcmThis repo now distinguishes between:
data/raw/: an untracked local cache that can exceed 11 GB and is used for live fetch/parse workdata/processed/*.csv: the tracked compact snapshot used for portfolio-facing analysis and artifact refreshes
The canonical public artifact command is:
python -m src.pipeline --artifactsIt regenerates the tracked figures in outputs/figures/, selected reports in outputs/reports/, the rendered notebook in notebooks/hedge_fund_analysis.ipynb, and the provenance files:
outputs/reports/claims_ledger.csvoutputs/reports/run_manifest.json
Every headline number in the README and executive summary is intended to be traceable through outputs/reports/claims_ledger.csv, while outputs/reports/run_manifest.json records input file hashes, source coverage windows, the artifact command, and the git commit SHA when available.
├── data/
│ ├── raw/
│ │ ├── swaps/ # ~600 weekly CFTC swap reports (xlsx)
│ │ ├── dtcc/ # Daily DTCC cumulative swap reports (zip/csv)
│ │ ├── fcm/ # Monthly FCM financial reports (xlsx)
│ │ ├── form_pf/ # SEC Form PF statistics (xlsx + pdf)
│ │ ├── form_adv/ # Fund profiles from EDGAR Submissions API
│ │ ├── 13f_*.csv # Fund-level holdings
│ │ ├── cftc_cot.csv # Futures positioning
│ │ └── vix_quarterly.csv # Volatility index
│ └── processed/ # Tracked compact snapshot used by public artifacts
├── src/
│ ├── data/
│ │ ├── fetch.py # FRED, SEC EDGAR, CFTC, VIX fetchers
│ │ ├── fetch_swaps.py # CFTC weekly swap report downloader
│ │ ├── fetch_dtcc.py # DTCC trade-level swap data downloader
│ │ ├── fetch_fcm.py # CFTC FCM financial report downloader
│ │ ├── parse_form_pf.py # Form PF Excel parser (141 sheets → 19 CSVs)
│ │ ├── parse_fcm.py # FCM financial report parser (49 files → 5 CSVs)
│ │ ├── parse_dtcc.py # DTCC daily swap report parser (available ZIPs → 2 CSVs + log)
│ │ ├── parse_swaps.py # CFTC weekly swap report parser (available files → 3 CSVs)
│ │ └── prepare.py # Data cleaning and transformation
│ ├── analysis/
│ │ ├── metrics.py # Derived metrics and statistics
│ │ ├── advanced.py # Granger causality, VAR, Monte Carlo, structural breaks
│ │ └── cross_source.py # Cross-source alignment, reconciliation, 18 hypothesis tests
│ └── visualization/
│ └── plots.py # 18 matplotlib/seaborn chart functions
├── notebooks/
│ └── hedge_fund_analysis.ipynb # Rendered, executed notebook artifact
└── outputs/
├── figures/ # Tracked portfolio-facing charts
└── reports/ # Tracked reports, claims ledger, run manifest
Python 3.10+ — pandas, numpy, matplotlib, seaborn, fredapi, openpyxl, requests, python-dotenv
Core tracked snapshot outputs in data/processed/:
| Source | Files | Key Outputs |
|---|---|---|
| Form PF | 19 | GAV/NAV, strategy allocation, concentration, leverage distribution, notional exposure, liquidity, fair value, geography, sector, borrowing, fund counts |
| FCM | 5 | Monthly industry totals, quarterly aggregates, top brokers, concentration (HHI) |
| DTCC | 2 CSVs + log | Daily summary and quarterly quarter-end snapshots by asset class |
| CFTC Swaps | 3 | Weekly time series, long format, quarterly aggregates |
| Z.1 | 2 | Canonical analysis dataset plus compatibility copy |
Active development. All 9 data sources are acquired and parsed, the cross-source analysis runs end-to-end, and the public artifact path is now python -m src.pipeline --artifacts. The current bundled local 13F window spans 2024Q1–2025Q4 with 384,723 amendment-deduped rows (283,362 long equity/ETF positions). Tracked figures, reports, and the notebook are regenerated from the compact processed snapshot, and headline numbers are traced through outputs/reports/claims_ledger.csv.
Note:
data/raw/13f_all_holdings.csvis not treated as canonical if fresher per-fund caches exist. The loader prefers the newest coherent local 13F window and the artifact pipeline snapshots that intodata/processed/13f_holdings.csv.
This project is licensed under CC BY-SA 4.0.
You must give appropriate credit if you use, remix, or build upon this work. Derivatives must be shared under the same license.
This project includes a CITATION.cff file for automated citation. You can also cite manually:
Ortiz, C. (2026). Hedge Fund Mosaic: Piecing together the U.S. hedge fund industry
from public regulatory data (v1.1.0). Zenodo. https://doi.org/10.5281/zenodo.19187969
@dataset{ortiz2026hedgefundmosaic,
author = {Ortiz, Christopher},
title = {Hedge Fund Mosaic: Piecing Together the U.S. Hedge Fund Industry from Public Regulatory Data},
year = {2026},
publisher = {Zenodo},
version = {1.1.0},
doi = {10.5281/zenodo.19187969},
url = {https://doi.org/10.5281/zenodo.19187969}
}