2554 Endpoints Combined 26644ad8

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

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

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

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

Available Categories

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