hedgefundmonitor

Files (8)

• SKILL.md
• api-overview.md · references/
• datasets.md · references/
• endpoints-combined.md · references/
• endpoints-metadata.md · references/
• endpoints-series-data.md · references/
• examples.md · references/
• parameters.md · references/

SKILL.md (first lines)

# 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

```python
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`

```python
# 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](references/api-overview.md)** — Base URL, versioning, protocols, response format
- **[references/endpoints-metadata.md](references/endpoints-metadata.md)** — Mnemonics, query, and search endpoints with full parameter details
- **[references/endpoints-series-data.md](references/endpoints-series-data.md)** — Timeseries, spread, and full data endpoints
- **[references/endpoints-combined.md](references/endpoints-combined.md)** — Full, multifull, dataset, and category endpoints
- **[references/datasets.md](references/datasets.md)** — Dataset descriptions (fpf, tff, scoos, ficc) and dataset-specific notes
- **[references/parameters.md](references/parameters.md)** — Complete parameter reference with periodicity codes, how values
- **[references/examples.md](references/examples.md)** — Python examples: discovery, bulk download, spread analysis, DataFrame workflows

From foryourhealth111-pixel/Vibe-Skills, MIT.

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