claude-scientific-skills/scientific-skills/hedgefundmonitor/references/endpoints-combined.md

Combined Data & Metadata Endpoints

1. Full Single Series — /series/full

URL: GET https://data.financialresearch.gov/hf/v1/series/full

Returns both timeseries data and all metadata for one series in a single call.

Parameters

Parameter	Type	Required	Description
mnemonic	string	Yes	Series identifier
start_date	string	No	Start date YYYY-MM-DD
end_date	string	No	End date YYYY-MM-DD
periodicity	string	No	Resample frequency
how	string	No	Aggregation: last, first, mean, median, sum
remove_nulls	string	No	true to remove nulls
time_format	string	No	date or ms

Response

{
  "FPF-ALLQHF_NAV_SUM": {
    "timeseries": {
      "aggregation": [["2013-03-31", 1143832916], ...],
      "disclosure_edits": [...]
    },
    "metadata": { ... }
  }
}

Examples

import requests
import pandas as pd

BASE = "https://data.financialresearch.gov/hf/v1"

resp = requests.get(f"{BASE}/series/full", params={
    "mnemonic": "FPF-ALLQHF_NAV_SUM",
    "start_date": "2018-01-01"
})
result = resp.json()
mnemonic = "FPF-ALLQHF_NAV_SUM"

# Extract timeseries
ts = result[mnemonic]["timeseries"]["aggregation"]
df = pd.DataFrame(ts, columns=["date", "nav_sum"])

# Extract metadata
meta = result[mnemonic]["metadata"]
print(meta["description"]["name"])
print(meta["schedule"]["observation_frequency"])

---

2. Multiple Series Full — /series/multifull

URL: GET https://data.financialresearch.gov/hf/v1/series/multifull

Returns data + metadata for multiple series in one request. Response is keyed by mnemonic, same structure as /series/full.

Parameters

Parameter	Type	Required	Description
mnemonics	string	Yes	Comma-separated mnemonics, no spaces
start_date	string	No	Start date YYYY-MM-DD
end_date	string	No	End date YYYY-MM-DD
periodicity	string	No	Resample frequency
how	string	No	Aggregation method
remove_nulls	string	No	true to remove nulls
time_format	string	No	date or ms

Examples

# Fetch multiple leverage series at once
resp = requests.get(f"{BASE}/series/multifull", params={
    "mnemonics": "FPF-ALLQHF_LEVERAGERATIO_GAVWMEAN,FPF-STRATEGY_EQUITY_LEVERAGERATIO_GAVWMEAN,FPF-STRATEGY_CREDIT_LEVERAGERATIO_GAVWMEAN",
    "start_date": "2015-01-01",
    "remove_nulls": "true"
})
results = resp.json()

# Build a combined DataFrame
frames = []
for mne, data in results.items():
    ts = data["timeseries"]["aggregation"]
    df = pd.DataFrame(ts, columns=["date", mne])
    df["date"] = pd.to_datetime(df["date"])
    df = df.set_index("date")
    frames.append(df)

combined = pd.concat(frames, axis=1)

---

3. Full Dataset — /series/dataset

URL: GET https://data.financialresearch.gov/hf/v1/series/dataset

Without parameters: returns basic info about all datasets. With dataset=: returns all series in that dataset with full data.

Warning: Dataset responses can be very large. Use start_date to limit the data range for performance.

Parameters

Parameter	Type	Required	Description
dataset	string	No	Dataset key: fpf, tff, scoos, ficc
vintage	string	No	p (preliminary), f (final), a (as of). Default: all
start_date	string	No	Start date YYYY-MM-DD
end_date	string	No	End date YYYY-MM-DD
periodicity	string	No	Resample frequency
how	string	No	Aggregation method
remove_nulls	string	No	true to remove nulls
time_format	string	No	date or ms

Examples

# List all available datasets
resp = requests.get(f"{BASE}/series/dataset")
datasets = resp.json()
# {"ficc": {"long_name": "...", "short_name": "..."}, "fpf": {...}, ...}

# Download full FPF dataset (recent data only)
resp = requests.get(f"{BASE}/series/dataset", params={
    "dataset": "fpf",
    "start_date": "2020-01-01"
})
fpf_data = resp.json()
# fpf_data["short_name"], fpf_data["long_name"]
# fpf_data["timeseries"]["FPF-ALLQHF_NAV_SUM"]["timeseries"]["aggregation"]

# Annual data with custom periodicity
resp = requests.get(f"{BASE}/series/dataset", params={
    "dataset": "fpf",
    "start_date": "2015-01-01",
    "end_date": "2024-12-31",
    "periodicity": "A",
    "how": "last"
})

# Only final vintage
resp = requests.get(f"{BASE}/series/dataset", params={
    "dataset": "ficc",
    "vintage": "f"
})

---

4. Category Data — /categories

URL: GET https://data.financialresearch.gov/hf/v1/categories

Returns a CSV file with all series data for a given category.

Parameters

Parameter	Type	Required	Description
category	string	Yes	Category key

Available Categories

Key	Description
complexity	Open positions, strategy distribution, asset class exposure
counterparties	Counterparty concentration and prime broker lending
leverage	Leverage ratios, borrowing, gross notional exposure
liquidity	Financing maturity, investor redemption terms, portfolio liquidity
risk_management	Stress test results
size	Industry size (AUM, fund count, net/gross assets)

Examples

# Download leverage category as CSV
resp = requests.get(f"{BASE}/categories", params={"category": "leverage"})
# Response is CSV text
import io
df = pd.read_csv(io.StringIO(resp.text))

# Also accessible via direct URL:
# https://data.financialresearch.gov/hf/v1/categories?category=leverage