Hedgefundmonitor

Skill foryourhealth111-pixel/Vibe-Skills/bundled/skills/hedgefundmonitor

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.From its SKILL.md

Install

npx -y skills add foryourhealth111-pixel/Vibe-Skills --skill hedgefundmonitor

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SKILL.md

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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

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	Monthly

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
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), 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

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

What ships with it: 7 files

32.6 KB alongside SKILL.md