Python Client for Interfacing with the Federal Reserve Bank of St. Louis' Economic Data API (FRED®)
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Updated
Jul 7, 2023 - Python
Python Client for Interfacing with the Federal Reserve Bank of St. Louis' Economic Data API (FRED®)
Give your AI agent accurate economic data. 330K indicators from FRED, World Bank, IMF, Eurostat & more. MCP server + web UI.
Support financial data science workflow, manage large structured and unstructured data sets, and apply financial econometrics and machine learning
A fully-featured FRED Command Line Interface & Python API wrapper.
CLI tool to interface with the FRED (Federal Reserve Economic Data) API.
A feature-rich python package for interacting with the Federal Reserve Bank of St. Louis Economic Database: FRED
Professional stock market analyzer using LLM
Automatic discovery of non-trivial statistical truths from 500+ public time series — mutual information, Granger causality, FDR correction
This Python code for interact with the Federal Reserve Economic Data (FRED) API to fetch, save, and manage economic data categories
A comprehensive pipeline for downloading, processing, and cleaning Federal Reserve Economic Data (FRED) series.
Python engine that analyzes 25 years of WTI oil and Henry Hub gas prices to output BUY/WAIT/WATCH procurement recommendations using FRED API data.
Production-grade data pipeline that converts raw CMS Medical Loss Ratio filings into reproducible, inflation-adjusted issuer-level panel datasets for advanced analytics and risk modeling.
FRED Python MCP server using fred-py-api
Detect U.S. housing market bubbles using macroeconomic signals. Forecast HPI, score speculative risk, and visualize insights using a fully modular, cloud-native GCP pipeline.
The FRED MCP Server is a Model Context Protocol (MCP) implementation that bridges AI assistants with the Federal Reserve Economic Data (FRED) API, providing simple access to find and analyze economic and financial data from the St. Louis Federal Reserve.
Sextant is a local Python Financial Backtest app with a Streamlit interface. The core is a strict bar-by-bar event loop (MarketEvent → SignalEvent → OrderEvent → FillEvent) — fully deterministic, with a complete JSON audit trail of every event.
Inflation, Officially: Official Data & Central Bank Expectations. No analysis. No predictions. Just the data.
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