FRED RRPONTSYD — Daily CSV Download (Overnight Reverse Repo Facility)
The Fed’s Overnight Reverse Repo Facility (ON RRP) allows money market funds and other eligible counterparties to park cash at the Fed overnight. Usage peaked at $2.5 trillion in late 2022 — a measure of excess liquidity in the financial system. As usage declines, those funds re-enter the market. FRED series RRPONTSYD.
Dataset: Overnight Reverse Repo Facility (2013–2026) · Updated —
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Source: FRED series RRPONTSYD · Federal Reserve Bank of St. Louis
Macro Takeaway
This indicator is a key component of the macro-financial monitoring framework. Its current level relative to its historical distribution — captured in the percentile and z-score above — provides immediate context for whether conditions are historically normal, stretched, or compressed.
Cross-referencing with the 10-year Treasury yield and the yield curve spread helps situate this indicator within the broader macro regime.
Dataset Overview
| Indicator | Overnight Reverse Repo Facility (2013–2026) |
|---|---|
| Geography | United States |
| Frequency | Daily (business days) |
| Period | 2003–2026 |
| Variables | date, on_rrp_billions |
| Format | CSV, Excel (XLSX) |
| Sources | Federal Reserve Bank of St. Louis — FRED |
| Last updated | — |
Dataset Variables
The CSV and Excel files contain the following columns.
| Column | Type | Description |
|---|---|---|
date | Date (YYYY-MM-DD) | Observation date |
on_rrp_billions | Float | on_rrp_billions value |
Column names match the CSV headers exactly.
Download the Complete Dataset
The full dataset is available in CSV and Excel formats.
FRED Direct CSV Access
The underlying data is available from FRED under series code RRPONTSYD:
https://fred.stlouisfed.org/graph/fredgraph.csv?id=RRPONTSYD
Direct CSV Access — Eco3min Structured Dataset
https://eco3min.fr/dataset/reverse-repo-facility.csv
This URL returns the complete dataset in CSV format. It can be used directly in pandas, R, curl, or any data tool.
Using the Dataset in Python
import pandas as pd url = "https://eco3min.fr/dataset/reverse-repo-facility.csv" df = pd.read_csv(url, parse_dates=["date"]) print(df.head()) print(df["rrpontsyd"].describe())
Using the Dataset in R
library(readr) url <- "https://eco3min.fr/dataset/reverse-repo-facility.csv" df <- read_csv(url) head(df) summary(df$rrpontsyd)
Both examples load the dataset directly from the URL — no download or API key required.
Methodology
The primary data source is the Federal Reserve’s FRED database, series RRPONTSYD. The data is published by the relevant US government agency and made available through FRED with consistent formatting and metadata.
This dataset is updated weekly (Saturday 08:00 UTC) via automated pull from the FRED API.
Historical Regimes
Historical regime analysis for this dataset will be added in a future update. The key stats block above provides immediate context for the current reading relative to the full historical distribution.
Related Macroeconomic Datasets
Related Macroeconomic Datasets
The ON RRP is the third and most consequential “pipe” in the Net Liquidity framework. Its $2.37 trillion drain between 2022 and 2026 is the single largest factor explaining why quantitative tightening did not produce the financial stress that historical precedent suggested.
- Net Liquidity Index (WALCL – TGA – RRP) — The composite showing the ON RRP’s impact on effective liquidity
- Fed Balance Sheet (WALCL) — The QT that the ON RRP drain offset
- Treasury General Account (TGA) — The other reserve drain alongside ON RRP
- M2 Money Supply — Broad money: distinct from reserve-level plumbing
- Federal Funds Rate — The rate that determines ON RRP attractiveness vs T-bills
Related Research
The ON RRP peaked at $2.37 trillion in September 2022. By March 2026, it was fully depleted — near $0. This $2.37 trillion drain offset QT by 110%, keeping Net Liquidity essentially flat while the S&P 500 rose 78%. The buffer is now exhausted. The full analysis — including “Stealth Easing” regime classification and correlation breakdown — is in the study below.
Sources
- Federal Reserve Bank of St. Louis — FRED database