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.
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
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"])
None required. The API is fully open and free.
| 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 |
The HFM organizes data into six categories (each downloadable as CSV):
| 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 (*, ?) |
| 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 |
| 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) |
Mnemonics follow the pattern FPF-{SCOPE}_{METRIC}_{STAT}:
ALLQHF (all qualifying hedge funds), STRATEGY_CREDIT, STRATEGY_EQUITY, STRATEGY_MACRO, etc.LEVERAGERATIO, GAV (gross assets), NAV (net assets), GNE (gross notional exposure), BORROWINGSUM, 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
]
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