version: "1.0.1" name: alterlab-hedgefund-monitor description: Queries the OFR (Office of Financial Research) Hedge Fund Monitor API for time series on hedge fund size, leverage, counterparties, liquidity, complexity, and risk management, including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms (no API key or registration required). Use when working with hedge fund data, systemic risk monitoring, financial stability research, hedge fund leverage or leverage ratios, counterparty concentration, Form PF statistics, repo market data, or OFR financial research data. Part of the AlterLab Academic Skills suite. license: MIT allowed-tools: Read WebFetch Bash(curl:) Bash(python:) compatibility: No API key or registration required. Queries the open OFR Hedge Fund Monitor REST API; needs network access. metadata: skill-author: AlterLab version: "1.1.1" last_updated: "2026-09-23"

OFR Hedge Fund Monitor API

Free, open REST API from the U.S. Office of Financial Research (OFR) providing aggregated hedge fund time series data. No API key or registration required.

Base URL: https://data.financialresearch.gov/hf/v1

When to Use This Skill

• Aggregate hedge-fund size, leverage, liquidity, counterparty, or stress-test statistics (SEC Form PF via OFR)
• CFTC Traders in Financial Futures positioning, FICC sponsored-repo volumes, or FRB SCOOS dealer-financing terms
• Systemic-risk or financial-stability research that needs these series as clean time series

Does NOT Trigger

Quick Start

import requests
import pandas as pd
BASE = "https://data.financialresearch.gov/hf/v1"
# List all available datasets
resp = requests.get(f"{BASE}/series/dataset")
datasets = resp.json()
# Returns: {"ficc": {...}, "fpf": {...}, "scoos": {...}, "tff": {...}}
# Search for series by keyword
resp = requests.get(f"{BASE}/metadata/search", params={"query": "*leverage*"})
results = resp.json()
# Each result: {mnemonic, dataset, field, value, type}
# Fetch a single time series
resp = requests.get(f"{BASE}/series/timeseries", params={
    "mnemonic": "FPF-STRATEGY_EQUITY_LEVERAGERATIO_GAVWMEAN",
    "start_date": "2015-01-01"
})
series = resp.json()  # [[date, value], ...]
df = pd.DataFrame(series, columns=["date", "value"])
df["date"] = pd.to_datetime(df["date"])

Mnemonics are exact — guessing fails. The API rejects unknown identifiers with a plain Invalid mnemonic string (HTTP 200, not JSON). Always discover real mnemonics via /metadata/search or /metadata/mnemonics before requesting data. Note in particular that there is no FPF-ALLQHF_LEVERAGERATIO_* series: aggregate leverage is published per gross-asset cohort (see below), while the GAVWMEAN/NAVWMEAN asset-weighted leverage means exist only at the strategy level.

Authentication

None required. The API is fully open and free.

Datasets

The /series/dataset index returns long_name: "FormPF" for every dataset (an upstream API

label quirk); the real source names are above. Confirm a series' true cadence via

schedule/observation_frequency in its metadata.

Data Categories

The HFM organizes data into six categories (each downloadable as CSV):

• size — Hedge fund industry size (AUM, count of funds, net/gross assets)
• leverage — Leverage ratios, borrowing, gross notional exposure
• counterparties — Counterparty concentration, prime broker lending
• liquidity — Financing maturity, investor redemption terms, portfolio liquidity
• complexity — Open positions, strategy distribution, asset class exposure
• risk_management — Stress test results (CDS, equity, rates, FX scenarios)

Core Endpoints

Metadata

Series Data

Common Parameters

Key FPF Mnemonic Patterns

Mnemonics follow the pattern FPF-{SCOPE}_{METRIC}_{STAT}:

• Scope: ALLQHF (all qualifying hedge funds), or a strategy such as STRATEGY_CREDIT,

STRATEGY_EQUITY, STRATEGY_MACRO, STRATEGY_RV (relative value), STRATEGY_FUTURES, etc. ALLQHF leverage/cash metrics carry a gross-asset cohort segment: GAVN10 (10 largest funds), GAVN11TO50, GAVN51 (rest).

• Metrics: GAV (gross assets), NAV (net assets), GNE (gross notional exposure),

LEVERAGERATIO, CASHRATIO, COUNT, plus stress-test scenarios (CDSUP250BPS, etc.)

• Stats: SUM, AVERAGE (equal-weighted, used for ALLQHF cohorts), GAVWMEAN/NAVWMEAN

(asset-weighted, strategy-level only), P5, P50, P95, PCTCHANGE

# Verified series examples (all return data as of 2026)
mnemonics = [
    "FPF-ALLQHF_GAVN10_LEVERAGERATIO_AVERAGE",    # 10 largest funds: leverage (equal-weighted)
    "FPF-STRATEGY_EQUITY_LEVERAGERATIO_GAVWMEAN", # Equity strategy: leverage (GAV-weighted)
    "FPF-ALLQHF_GAV_SUM",                         # All funds: gross assets (total $)
    "FPF-ALLQHF_NAV_SUM",                         # All funds: net assets (total $)
    "FPF-ALLQHF_GNE_SUM",                         # All funds: gross notional exposure
    "FICC-SPONSORED_REPO_VOL",                    # FICC: sponsored repo volume (daily)
]

Reference Files

• [references/api-overview.md](references/api-overview.md) — Base URL, versioning, protocols, response format
• [references/endpoints-metadata.md](references/endpoints-metadata.md) — Mnemonics, query, and search endpoints with full parameter details
• [references/endpoints-series-data.md](references/endpoints-series-data.md) — Timeseries, spread, and full data endpoints
• [references/endpoints-combined.md](references/endpoints-combined.md) — Full, multifull, dataset, and category endpoints
• [references/datasets.md](references/datasets.md) — Dataset descriptions (fpf, tff, scoos, ficc) and dataset-specific notes
• [references/parameters.md](references/parameters.md) — Complete parameter reference with periodicity codes, how values
• [references/examples.md](references/examples.md) — Python examples: discovery, bulk download, spread analysis, DataFrame workflows

Cite the OFR Hedge Fund Monitor, the dataset (e.g. SEC Form PF), the mnemonic, and the retrieval date for every series used.

Part of the AlterLab Academic Skills suite.