tools-only Series Data Endpoints
1. Single Timeseries — /series/timeseries
URL: GET https://data.financialresearch.gov/hf/v1/series/timeseries
Returns date/value pairs for a single series.
Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
mnemonic |
string | Yes | Series identifier |
label |
string | No | Subseries: aggregation (default) or disclosure_edits |
start_date |
string | No | First date YYYY-MM-DD (default: 1901-01-01) |
end_date |
string | No | Last date YYYY-MM-DD (default: today) |
periodicity |
string | No | Resample to frequency (see parameters.md) |
how |
string | No | Aggregation method: last (default), first, mean, median, sum |
remove_nulls |
string | No | true to remove null values |
time_format |
string | No | date (YYYY-MM-DD, default) or ms (epoch milliseconds) |
Response
Array of [date_string, value] pairs. Values are floats or null.
[
["2013-03-31", -3.0],
["2013-06-30", -2.0],
["2013-09-30", null],
["2013-12-31", -3.0]
]
Examples
import requests
import pandas as pd
BASE = "https://data.financialresearch.gov/hf/v1"
# Full history for a series
resp = requests.get(f"{BASE}/series/timeseries", params={
"mnemonic": "FPF-ALLQHF_LEVERAGERATIO_GAVWMEAN"
})
data = resp.json()
df = pd.DataFrame(data, columns=["date", "leverage"])
df["date"] = pd.to_datetime(df["date"])
# Filtered date range with null removal
resp = requests.get(f"{BASE}/series/timeseries", params={
"mnemonic": "FPF-ALLQHF_NAV_SUM",
"start_date": "2018-01-01",
"end_date": "2024-12-31",
"remove_nulls": "true"
})
# Annual frequency (calendar year end)
resp = requests.get(f"{BASE}/series/timeseries", params={
"mnemonic": "FPF-ALLQHF_GAV_SUM",
"periodicity": "A",
"how": "last"
})
# Epoch milliseconds for charting libraries
resp = requests.get(f"{BASE}/series/timeseries", params={
"mnemonic": "FICC-SPONSORED_REPO_VOL",
"time_format": "ms"
})
2. Series Spread — /calc/spread
URL: GET https://data.financialresearch.gov/hf/v1/calc/spread
Returns the difference (spread) between two series: x - y. Useful for comparing rates or examining basis relationships.
Parameters
| Parameter | Type | Required | Description |
|---|---|---|---|
x |
string | Yes | Base series mnemonic |
y |
string | Yes | Subtracted series mnemonic |
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
Array of [date, value] pairs where value = x - y at each date.
[
["2020-01-02", 0.15],
["2020-03-03", -0.37],
["2020-04-01", 0.60]
]
Examples
# Spread between two repo rates
resp = requests.get(f"{BASE}/calc/spread", params={
"x": "REPO-GCF_AR_G30-P",
"y": "REPO-TRI_AR_AG-P",
"start_date": "2019-01-01",
"remove_nulls": "true"
})
spread = pd.DataFrame(resp.json(), columns=["date", "spread_bps"])
spread["date"] = pd.to_datetime(spread["date"])
# Annual spread with mean aggregation
resp = requests.get(f"{BASE}/calc/spread", params={
"x": "FPF-STRATEGY_EQUITY_LEVERAGERATIO_GAVWMEAN",
"y": "FPF-STRATEGY_CREDIT_LEVERAGERATIO_GAVWMEAN",
"periodicity": "A",
"how": "mean"
})