FRED TOTRESNS — Daily CSV Download (US Bank Reserves at the Federal Reserve)
Bank reserves held at the Federal Reserve are the foundation of the monetary system. Before 2008, total reserves were ~$45 billion. After QE1, they exploded to $2.8 trillion — and peaked at $4.2 trillion in 2022. Reserves determine banks’ ability to lend, the plumbing of the overnight market, and the practical limit of QT before “reserve scarcity” triggers stress.
Dataset: US Bank Reserves at the Federal Reserve (2001–2026) · Updated —
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Source: FRED series TOTRESNS · Federal Reserve Bank of St. Louis — FRED (TOTRESNS)
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 Fed balance sheet and the Net Liquidity Index helps situate this indicator within the broader macro regime.
Dataset Overview
| Indicator | US Bank Reserves at the Federal Reserve (2001–2026) |
|---|---|
| Geography | United States |
| Frequency | Monthly |
| Period | 2001–2026 |
| Variables | date, total_reserves_billions |
| Format | CSV, Excel (XLSX) |
| Sources | Federal Reserve Bank of St. Louis — FRED (TOTRESNS) |
| Last updated | — |
Dataset Variables
The CSV and Excel files contain the following columns.
| Column | Type | Description |
|---|---|---|
date | Date (YYYY-MM-DD) | Observation date |
total_reserves_billions | Float | Total reserves of depository institutions at the Fed (billions USD) |
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 TOTRESNS:
https://fred.stlouisfed.org/graph/fredgraph.csv?id=TOTRESNS
Direct CSV Access — Eco3min Structured Dataset
https://eco3min.fr/dataset/us-bank-reserves.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/us-bank-reserves.csv" df = pd.read_csv(url, parse_dates=["date"]) print(df.head()) print(df["total_reserves_billions"].describe())
Using the Dataset in R
library(readr) url <- "https://eco3min.fr/dataset/us-bank-reserves.csv" df <- read_csv(url) head(df) summary(df$total_reserves_billions)
Both examples load the dataset directly from the URL — no download or API key required.
Methodology
FRED series TOTRESNS — total reserves of depository institutions held at the Federal Reserve, not seasonally adjusted. Includes required and excess reserves (the required/excess distinction was eliminated in March 2020).
This dataset is updated monthly 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
Bank reserves are the plumbing beneath M2 and Net Liquidity. QE creates reserves; QT destroys them. The critical question in every QT cycle is: at what reserve level does scarcity emerge and overnight markets seize up? The 2019 repo crisis provided a painful answer.
- Fed Balance Sheet (WALCL) — Total Fed assets — reserves are one component of the liability side
- Net Liquidity Index — The composite that captures reserve availability
- Treasury General Account (TGA) — TGA movements absorb/release reserves
- Overnight Reverse Repo (ON RRP) — ON RRP absorbs reserves from the banking system
- M2 Money Supply — Broad money that reserves enable banks to create
Related Research
Reserves are the building block of the Net Liquidity decomposition. Understanding reserve levels helps predict when QT must end — before the plumbing breaks.
Macroeconomic Dataset Hub
This dataset is part of the Eco3min macro-financial data repository.
Explore the Eco3min Dataset Hub
Sources
- Federal Reserve Bank of St. Louis — FRED (TOTRESNS)