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AlterLab-Academic-Skills alterlab-hedgefund-monitor

Part of the AlterLab Academic Skills suite. Query the OFR (Office of Financial Research) Hedge Fund Monitor API for hedge fund data including SEC Form PF aggregated statistics, CFTC Traders in Financial Futures, FICC Sponsored Repo volumes, and FRB SCOOS dealer financing terms. Access time series data on hedge fund size, leverage, counterparties, liquidity, complexity, and risk management. 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.

install
source · Clone the upstream repo
git clone https://github.com/AlterLab-IEU/AlterLab-Academic-Skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/AlterLab-IEU/AlterLab-Academic-Skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/finance-economics/alterlab-hedgefund-monitor" ~/.claude/skills/alterlab-ieu-alterlab-academic-skills-alterlab-hedgefund-monitor && rm -rf "$T"
manifest: skills/finance-economics/alterlab-hedgefund-monitor/SKILL.md
source content

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

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-ALLQHF_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"])

Authentication

None required. The API is fully open and free.

Datasets

KeyDatasetUpdate Frequency
fpf
SEC Form PF — aggregated stats from qualifying hedge fund filingsQuarterly
tff
CFTC Traders in Financial Futures — futures market positioningMonthly
scoos
FRB Senior Credit Officer Opinion Survey on Dealer Financing TermsQuarterly
ficc
FICC Sponsored Repo Service VolumesMonthly

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

EndpointPathDescription
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

EndpointPathDescription
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
Spread
GET /calc/spread?x=MNE1&y=MNE2
Difference between two series

Common Parameters

ParameterDescriptionExample
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), 
    STRATEGY_CREDIT
    , 
    STRATEGY_EQUITY
    , 
    STRATEGY_MACRO
    , etc.
  • Metrics: 
    LEVERAGERATIO
    , 
    GAV
    (gross assets), 
    NAV
    (net assets), 
    GNE
    (gross notional exposure), 
    BORROWING
  • Stats: 
    SUM
    , 
    GAVWMEAN
    , 
    NAVWMEAN
    , 
    P5
    , 
    P50
    , 
    P95
    , 
    PCTCHANGE
    , 
    COUNT
# Common series examples
mnemonics = [
    "FPF-ALLQHF_LEVERAGERATIO_GAVWMEAN",   # All funds: leverage (gross asset-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
]

Reference Files