foryourhealth111-pixel/hedgefundmonitor
bundled/skills/hedgefundmonitor/SKILL.md
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 --global
skillsauthnpx skillsauth add foryourhealth111-pixel/vco-skills-codex hedgefundmonitorInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- hedgefundmonitor
- description:
- 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.
- license:
- MIT
- skill-author:
- K-Dense Inc.
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
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