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

hedgefundmonitor

1
foryourhealth111-pixel/Vibe-Skills·Finance & Fintech·Audit passed·Snapshot 92e3dff99fc1

Summary

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.

SKILL.md

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
fpfSEC Form PF — aggregated stats from qualifying hedge fund filingsQuarterly
tffCFTC Traders in Financial Futures — futures market positioningMonthly
Installs
0
scoos
FRB Senior Credit Officer Opinion Survey on Dealer Financing Terms
Quarterly
ficcFICC 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 mnemonicsGET /metadata/mnemonicsAll series identifiers
Query series infoGET /metadata/query?mnemonic=Full metadata for one series
Search seriesGET /metadata/search?query=Text search with wildcards (*, ?)

Series Data

EndpointPathDescription
Single timeseriesGET /series/timeseries?mnemonic=Date/value pairs for one series
Full singleGET /series/full?mnemonic=Data + metadata for one series
Multi fullGET /series/multifull?mnemonics=A,BData + metadata for multiple series
DatasetGET /series/dataset?dataset=fpfAll series in a dataset
Category CSVGET /categories?category=leverageCSV download for a category
SpreadGET /calc/spread?x=MNE1&y=MNE2Difference between two series

Common Parameters

ParameterDescriptionExample
start_dateStart date YYYY-MM-DD2020-01-01
end_dateEnd date YYYY-MM-DD2024-12-31
periodicityResample frequencyQ, M, A, D, W
howAggregation methodlast (default), first, mean, median, sum
remove_nullsDrop null valuestrue
time_formatDate formatdate (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

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