Python Client for Interfacing with the Federal Reserve Bank of St. Louis' Economic Data API (FRED®)

An R client for the Federal Reserve Economic Data (FRED) API

Jupyter notebooks for analysis of US federal debt, tax revenues, GDP, budget deficit, evolution of yields on treasury borrowings, treasury yield curves and inflation expectations, unemployment and participation rates, quantitative easing, industrial production, personal consumption and savings, stock market. Using APIs from FRED and Yahoo-Finance.

Pull data from Federal Reserve Economic Data (FRED) directly into Julia

Practical financial data science examples applying statistics, time series analysis, graph analytics, backtesting, machine learning, natural language processing, neural networks and LLMs

Support financial data science workflow, manage large structured and unstructured data sets, and apply financial econometrics and machine learning

Python code for pricing European and American options with examples for individual stock, index, and FX options denominated in USD and Euro. Jupyter notebooks for pricing options using free publicly available datasets.

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This Python script provides two main functionalities: stock and economic indicators analysis. It utilizes the Yahoo Finance to fetch historical stock price data for multiple tickers and the fredapi library to fetch economic indicator data from the FRED API.

A fully-featured FRED Command Line Interface & Python API wrapper.

💸 A comprehensive AI-powered data explorer that combines FRED economic data & insights with vector search, regression analysis, and interactive RAG chatbot via Pinecone Vector DB, OpenAI, Claude, and Gemini. Built with TypeScript, React, and Express for seamless full-stack performance.

A Discord bot that integrates real-time economic calendar tracking with stock chart analysis. Get instant alerts for market-moving events, Finviz stock charts, and Federal Reserve (FRED) economic data - all within your Discord server.

CLI tool to interface with the FRED (Federal Reserve Economic Data) API.

:tada: How to crawl the FRED big data in a rapid and methodical way

Easy-to-use client for accessing Federal Reserve Bank of St. Louis FRED® API

Download FRED data in Julia.

Professional stock market analyzer using LLM

FRED (Federal Reserve Economic Data) API integration with Model Context Protocol (MCP)

Files to access economic indicators, nowcasts, and recession dates from FRED

This Python code for interact with the Federal Reserve Economic Data (FRED) API to fetch, save, and manage economic data categories

A feature-rich python package for interacting with the Federal Reserve Bank of St. Louis Economic Database: FRED

Financial Data Analysis with FRED API - USA Federal Reserve Economic Data

OpenAPI Specification (YAML and JSON) for the Federal Reserve Economic Data (FRED) API

This project explores how U.S. economic metrics (such as GDP and Consumer Price Index) impact the performance of major world stock exchanges. Using Python and the FRED API, we retrieve economic data and compare it with stock price data obtained via the Yahoo Finance API.

This project utilizes the Federal Reserve Economic Data (FRED) API and Python's Pandas library to gather, clean, and analyze economic data. The project walks through the process of pulling down data for various economic indicators, cleaning and joining the data, and using the FRED API to obtain up-to-date data.

A command-line tool for analyzing Federal Reserve policy scenarios by finding historical analogues based on unemployment and inflation conditions.

Extends the core methodology from "Large Bayesian Vector Auto Regressions" by Bańbura et al. (2010), focusing on how Bayesian shrinkage enables estimation of high-dimensional VARs without overfitting.

A comprehensive pipeline for downloading, processing, and cleaning Federal Reserve Economic Data (FRED) series.

This project shows the data analysis of economic data using Fred API (Federal Reserve Economic Data) by cleaning the data, fetching data from the API, exploring and analyzing.

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.