alterlab-hedgefund-monitor

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

Scenario	Use Instead
A single filer's raw Form PF/13F/13D documents or holdings line by line	`alterlab-edgartools`
Treasury yields, Fed policy rates, or financial-conditions indices	`alterlab-fred`
Treasury debt, auctions, or Daily Treasury Statement cash balances	`alterlab-usfiscaldata`
Security prices or fund NAV/price histories	`alterlab-alpha-vantage`

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

Key	Dataset	Update Frequency
`fpf`	SEC Form PF — aggregated stats from qualifying hedge fund filings	Quarterly
`tff`	CFTC Traders in Financial Futures — futures market positioning	Monthly
`scoos`	FRB Senior Credit Officer Opinion Survey on Dealer Financing Terms	Quarterly
`ficc`	FICC Sponsored Repo Service Volumes	Daily

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

Endpoint	Path	Description
List mnemonics	`GET /metadata/mnemonics`	All series identifiers
Query series info	`GET /metadata/query?mnemonic=`	Full metadata for one series
Search series	`GET /metadata/search?query=`	Text search with wildcards (`*`, `?`)

Series Data

Endpoint	Path	Description
Single timeseries	`GET /series/timeseries?mnemonic=`	Date/value pairs for one series
Full single	`GET /series/full?mnemonic=`	Data + metadata for one series
Multi full	`GET /series/multifull?mnemonics=A,B`	Data + metadata for multiple series
Dataset	`GET /series/dataset?dataset=fpf`	All series in a dataset
Category CSV	`GET /categories?category=leverage`	CSV download for a category (returned HTTP 502 when checked on 2026-09-23; fall back to `/series/multifull`)
Spread	`GET /calc/spread?x=MNE1&y=MNE2`	Difference between two series

Common Parameters

Parameter	Description	Example
`start_date`	Start date YYYY-MM-DD	`2020-01-01`
`end_date`	End date YYYY-MM-DD	`2024-12-31`
`periodicity`	Resample frequency	`Q`, `M`, `A`, `D`, `W`
`how`	Aggregation method	`last` (default), `first`, `mean`, `median`, `sum`
`remove_nulls`	Drop null values	`true`
`time_format`	Date format	`date` (YYYY-MM-DD) or `ms` (epoch ms)

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 — Base URL, versioning, protocols, response format
references/endpoints-metadata.md — Mnemonics, query, and search endpoints with full parameter details
references/endpoints-series-data.md — Timeseries, spread, and full data endpoints
references/endpoints-combined.md — Full, multifull, dataset, and category endpoints
references/datasets.md — Dataset descriptions (fpf, tff, scoos, ficc) and dataset-specific notes
references/parameters.md — Complete parameter reference with periodicity codes, how values
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.