Net Liquidity Index — Daily CSV Download (WALCL – TGA – RRP)

The Net Liquidity Index — calculated as the Fed balance sheet (WALCL) minus the Treasury General Account (TGA) minus Reverse Repo usage (ON RRP) — is the composite measure of effective financial system liquidity tracked by macro analysts like Darius Dale, Andy Constan, and Raoul Pal. This dataset is unavailable from any single source; Eco3min computes it from three FRED series.

Dataset: US Net Liquidity Index (2015–2026) · Updated —



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Source: FRED series WALCL · 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

IndicatorUS Net Liquidity Index (2015–2026)
GeographyUnited States
FrequencyWeekly
Period2015–2026
Variablesdate, fed_assets, tga, on_rrp, net_liquidity
FormatCSV, Excel (XLSX)
SourcesFederal Reserve Bank of St. Louis — FRED
Last updated

Dataset Variables

The CSV and Excel files contain the following columns.

ColumnTypeDescription
dateDate (YYYY-MM-DD)Observation date
fed_assetsFloatfed_assets value
tgaFloattga value
on_rrpFloaton_rrp value
net_liquidityFloatNet Liquidity = WALCL − TGA − ON RRP, millions of dollars

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 WALCL:

https://fred.stlouisfed.org/graph/fredgraph.csv?id=WALCL

Direct CSV Access — Eco3min Structured Dataset

https://eco3min.fr/dataset/net-liquidity-index.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/net-liquidity-index.csv"
df = pd.read_csv(url, parse_dates=["date"])

print(df.head())
print(df["net_liquidity"].describe())

Using the Dataset in R

library(readr)

url <- "https://eco3min.fr/dataset/net-liquidity-index.csv"
df <- read_csv(url)

head(df)
summary(df$net_liquidity)

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 WALCL. 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 Fed balance sheet is one of three components of the Net Liquidity framework. Cross-reference with the two other “pipes” — TGA and ON RRP — to understand effective system liquidity rather than headline balance sheet size.

Related Macroeconomic Datasets

Net Liquidity is computed from three underlying FRED series. Each component page provides the raw data, while this page provides the composite. Cross-reference with equity and rate datasets to understand the macro-financial transmission.

Related Research

This raw dataset provides the weekly composite. The in-depth research study below analyzes the QT offset mechanism, introduces the “Stealth Easing” regime classification, and documents the historical turning points where plumbing moved markets.


Sources

  • Federal Reserve Bank of St. Louis — FRED database

Suggested Citation