U.S. Economic and Financial Dataset

Dataset Description

This dataset combines historical U.S. economic and financial indicators, spanning the last 50 years, to facilitate time series analysis and uncover patterns in macroeconomic trends. It is designed for exploring relationships between interest rates, inflation, economic growth, stock market performance, and industrial production.

Key Features

• Frequency: Monthly
• Time Period: Last 50 years from Nov-24
• Sources:
  • Federal Reserve Economic Data (FRED)
  • Yahoo Finance

Dataset Feature Description

1. Interest Rate (Interest_Rate):

   • The effective federal funds rate, representing the interest rate at which depository institutions trade federal funds overnight.

2. Inflation (Inflation):

   • The Consumer Price Index for All Urban Consumers, an indicator of inflation trends.

3. GDP (GDP):

   • Real GDP measures the inflation-adjusted value of goods and services produced in the U.S.

4. Unemployment Rate (Unemployment):

   • The percentage of the labor force that is unemployed and actively seeking work.

5. Stock Market Performance (S&P500):

   • Monthly average of the adjusted close price, representing stock market trends.

6. Industrial Production (Ind_Prod):

   • A measure of real output in the industrial sector, including manufacturing, mining, and utilities.

Dataset Statistics

1. Total Entries: 599
2. Columns: 6
3. Memory usage: 37.54 kB
4. Data types: float64

Feature Overview

• Columns:
  • Interest_Rate: Monthly Federal Funds Rate (%)
  • Inflation: CPI (All Urban Consumers, Index)
  • GDP: Real GDP (Billions of Chained 2012 Dollars)
  • Unemployment: Unemployment Rate (%)
  • Ind_Prod: Industrial Production Index (2017=100)
  • S&P500: Monthly Average of S&P 500 Adjusted Close Prices

Executive Summary

This project explores the interconnected dynamics of key macroeconomic indicators and financial market trends over the past 50 years, leveraging data from the Federal Reserve Economic Data (FRED) and Yahoo Finance. The dataset integrates critical variables such as the Federal Funds Rate, Inflation (CPI), Real GDP, Unemployment Rate, Industrial Production, and the S&P 500 Index, providing a holistic view of the U.S. economy and financial markets.

The analysis focuses on uncovering relationships between these variables through time-series visualization, correlation analysis, and trend decomposition. Key findings are included in the Insights section. This project serves as a robust resource for understanding long-term economic trends, policy impacts, and market behavior. It is particularly valuable for students, researchers, policymakers, and financial analysts seeking to connect macroeconomic theory with real-world data.

Potential Use Cases

• Economic Analysis: Examine relationships between interest rates, inflation, GDP, and unemployment.
• Stock Market Prediction: Study how macroeconomic indicators influence stock market trends.
• Time Series Modeling: Perform ARIMA, VAR, or other models to forecast economic trends.
• Cyclic Pattern Analysis: Identify how economic shocks and recoveries impact key indicators.

Snap of Power Analysis

https://github.com/user-attachments/assets/1b40e0ca-7d2e-4fbc-8cfd-df3f09e4fdb8">

To ensure sufficient power, the dataset covers last 50 years of monthly data i.e., around 600 entries.

Key Insights derived through EDA, time-series visualization, correlation analysis, and trend decomposition

• Interest Rate and Inflation Dynamics: The interest Rate and inflation exhibit an inverse relationship, especially during periods of aggressive monetary tightening by the Federal Reserve.
• Economic Growth and Market Performance: GDP growth and the S&P 500 Index show a positive correlation, reflecting how market performance often aligns with overall economic health.
• Labor Market and Industrial Output: Unemployment and industrial production demonstrate a strong inverse relationship. Higher industrial output is typically associated with lower unemployment
• Market Behavior During Economic Shocks: The S&P 500 experienced sharp declines during significant crises, such as the 2008 financial crash and the COVID-19 pandemic in 2020. These events also triggered increased unemployment and contractions in GDP, highlighting the interplay between markets and the broader economy.
• Correlation Highlights: S&P 500 and GDP have a strong positive correlation. Interest rates negatively correlate with GDP and inflation, reflecting monetary policy impacts. Unemployment is negatively correlated with industrial production but positively correlated with interest rates.

Link to GitHub Repo

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View market daily updates and historical trends for Secured Overnight Financing Rate. from United States. Source: Federal Reserve Bank of New York. Track …

View market daily updates and historical trends for 10-2 Year Treasury Yield Spread. from United States. Source: Department of the Treasury. Track economi…

Historical 3-Month Treasury Bill Rates (2000-2023) Dataset

Track the Pulse of the Financial Market with Detailed T-Bill Data

Dataset Description

This dataset contains historical 3-month Treasury Bill rates, sourced from Yahoo Finance. The dataset spans from January 3, 2000, to December 31, 2023, and provides daily prices along with adjusted close prices and volumes. This data is crucial for financial analysts, economists, and researchers who are interested in interest rate trends and their impact on the economy.

Table of Contents

1. Introduction
2. Data Source
3. Dataset Structure
4. Data Collection and Processing
5. Usage and Applications
6. License
7. Acknowledgements

Introduction

Treasury Bills (T-Bills) are short-term government securities with maturities of one year or less. They are sold at a discount from their face value and do not pay interest before maturity. This dataset specifically focuses on the 3-month T-Bill rates, which are commonly used as a risk-free rate benchmark in various financial models and analyses.

The 3-month T-Bill rate is considered a reliable indicator of short-term interest rates and economic conditions. It is widely used in the valuation of financial instruments, risk management, and macroeconomic analysis.

Data Source

The data was sourced from Yahoo Finance. The Ticker symbol used for the 3-month Treasury Bill rates is ^IRX.

Dataset Structure

The dataset is provided in CSV format with the following columns:

• Date: The date of the recorded interest rate (in YYYY-MM-DD format).
• Open: The opening price of the 3-month T-Bill rate on the given date.
• High: The highest price of the 3-month T-Bill rate on the given date.
• Low: The lowest price of the 3-month T-Bill rate on the given date.
• Close: The closing price of the 3-month T-Bill rate on the given date.
• Adj Close: The adjusted close price for the 3-month T-Bill rate, adjusted for any corporate actions.
• Volume: The trading volume of the 3-month T-Bill rate on the given date (although typically 0 for T-Bills).

Example:

Data Collection and Processing

The data was collected from Yahoo Finance using the Python yfinance library. The following steps were performed to process the data:

1. Data Retrieval: Historical data was fetched for the 3-month Treasury Bill using the yfinance API.
2. Data Cleaning: Missing values were dropped to ensure data consistency.
3. Conversion: The adjusted close prices were used for analysis as they account for any corporate actions.

Usage and Applications

This dataset can be used for various financial analyses and modeling, including but not limited to:

• Interest Rate Modeling: Use the dataset to model interest rate behaviors using stochastic models such as the Vasicek model, Cox-Ingersoll-Ross model, etc.
• Risk Management: Assess the risk-free rate for evaluating investment performance and calculating risk premiums.
• Econometric Analysis: Conduct time series analysis to study trends, seasonality, and economic cycles.
• Financial Education: Serve as a resource for educational purposes in finance and economics courses.
• Macroeconomic Research: Analyze the relationship between short-term interest rates and macroeconomic indicators such as GDP growth, inflation, and employment rates.

License

This dataset is made available under the Creative Commons Attribution 4.0 International License. You are free to use, modify, and distribute the data, provided proper attribution is given.

Acknowledgements

Special thanks to Yahoo Finance for providing the historical data and the Python community for the yfinance library, which facilitated data retrieval and processing.

NASDAQ Stock Data with Economic Indicators

Overview

This dataset comprises historical stock price data for NASDAQ-listed companies, combined with a selection of key economic indicators. It is designed to provide a comprehensive view of market behavior, facilitating financial analysis and predictive modeling. Users can explore relationships between stock performance and various economic factors.

Features

The dataset includes the following features:

• Date: The date of the recorded stock prices (formatted as YYYY-MM-DD).

• Open: The price at which the stock opened for trading on a given day.

• High: The highest price reached by the stock during the trading day.

• Low: The lowest price recorded during the trading day.

• Close: The price at which the stock closed at the end of the trading day.

• Volume: The total number of shares traded during the day.

• Interest Rate: The prevailing interest rate, which influences economic activity and stock performance.

• Exchange Rate: The exchange rate for the USD against other currencies, reflecting international market influences.

• VIX: The Volatility Index, a measure of market risk and investor sentiment, often referred to as the "fear index."

• Gold: The price of gold per ounce, which serves as a traditional safe-haven asset and is often inversely correlated with stock prices.

• Oil: The price of crude oil, an essential commodity that influences various sectors, especially transportation and manufacturing.

• TED Spread: The difference between the interest rates on interbank loans and short-term U.S. government debt, which indicates credit risk in the banking system.

• EFFR (Effective Federal Funds Rate): The interest rate at which depository institutions lend reserve balances to other depository institutions overnight, influencing overall economic activity.

Use Cases

This dataset is suitable for a variety of applications, including: - Financial Analysis: Evaluate historical trends in stock prices relative to economic indicators. - Predictive Modeling: Develop machine learning models to forecast stock price movements based on historical data and economic variables. - Time Series Analysis: Conduct analyses over different time frames (daily, weekly, monthly, yearly) to identify patterns and anomalies.

Data Source

The data is sourced from reputable financial APIs and databases: - Yahoo Finance: Historical stock prices. - Federal Reserve Economic Data (FRED): Economic indicators such as interest rates and VIX. - Alpha Vantage / Quandl: Commodity prices for gold and oil.

Conclusion

This dataset provides a rich foundation for analysts, researchers, and data scientists interested in the intersection of stock market performance and macroeconomic conditions. Its structured features and comprehensive nature make it a valuable resource for both academic and practical financial inquiries.

Dataset Description Overview

This dataset contains historical daily exchange rates between the US Dollar (USD) and the Indonesian Rupiah (IDR), sourced from Yahoo Finance. Each row represents one trading day, making it suitable for time-series analysis, forecasting, and financial research. Context

The USD/IDR exchange rate is widely used for:

Monitoring currency risk and hedging USD–IDR exposure
Evaluating Indonesia’s macroeconomic and financial conditions
Backtesting FX trading strategies
Teaching time-series and financial modeling

Because Indonesia is an emerging market, USD/IDR often exhibits notable volatility, driven by global interest rates, commodity prices, and domestic policy changes. Source and Collection

Data provider: Yahoo Finance
Instrument: USD/IDR exchange rate (e.g., ticker USDIDR=X on Yahoo Finance)
Frequency: Daily (one record per trading day)
Fields: Standard Yahoo Finance OHLC data (Open, High, Low, Close, Adjusted Close, Volume)
Collection method: Downloaded programmatically via a Python library that wraps Yahoo Finance data (e.g., yfinance)
Retrieval: Data was fetched in Python and then exported to CSV for this dataset
Coverage period: From the earliest available date on Yahoo Finance for USD/IDR up to the download date (please add the exact start and end dates if you want, e.g., YYYY‑MM‑DD to YYYY‑MM‑DD)

Please check Yahoo Finance’s terms of use before using the dataset in commercial or production settings. Possible Use Cases

Time-series forecasting models (ARIMA, Prophet, LSTM, etc.)
Volatility and risk analysis (e.g., rolling volatility, VaR)
Studying the impact of macroeconomic news or events on IDR
Feature in multi-asset or macroeconomic research datasets
Educational projects in finance, econometrics, and data science

Notes

This dataset contains no personal or sensitive information.
Values are provided as-is from Yahoo Finance; minor discrepancies may exist compared with other FX data vendors.

View market daily updates and historical trends for 4 Week Treasury Bill Rate. from United States. Source: Federal Reserve. Track economic data with YChar…

Context

If you are satisfied in data and code, please upvote :)👍 The investing is necessary for everyone's future. I think that just knowing the meaning of the variables without interpreting this dataset is enough to study. This data is an Nasdaq index, taken from yahoo finance. Contains multiple financial statements and represents prices over a period of about 10 years(2010-01-01 - 2021-06-30) we can analyze price of stocks by time series with comparing financial statements that it is expected to be good measurement of correlation! Have you fun!🎉

The data format is received as json and can be used as a data frame. The script used can be checked at Github repository and if you want longer time scale data or up-to-date data, please run the script from the link. And also, if you want another list of stock, you should check the link which can analysis like Dow (tickers are 30), S&P500 (ticker are 500).

If you interest this data and code, Pleases see notebooks of strategy :)

I'm still learning Python, so if you find messy code execution or have a better way of doing it, let me know!! and Please contact me :) I think it will be a good study.

Content

• In FS_nasdaq_Value.json(csv) It is presented by price like 'Open', 'Close' and so on.

• In FS_nasdaq_Recent+Value.json(csv) It is presented by recent price (2021-06-30)

All data is presented recently. If you want the statements before, Pleases check and fix below code.

Acknowledgements

I'm studying physics and writing code of python and c++. However I'm not used to it yet and also English :(. Let you know if it is not correctly for code and English :🙏

Inspiration

In interpreting the stock market, there are traditionally low PER and PBR strategies. Prior to this, an ML model using various statements and a price estimation model using time series data have been proposed recently, but we know that they are of little use. This data is highly likely to be used for various analyzes, and it is considered to be basic data for understanding the stock's market. Let's study together and find the best model!

If you are satisfied in data and code, please upvote :)👍

The yield on France 10Y Bond Yield rose to 3.49% on December 1, 2025, marking a 0.07 percentage points increase from the previous session. Over the past month, the yield has edged up by 0.04 points and is 0.57 points higher than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. France 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on December of 2025.

Lumber futures trading activity was minimal, with prices steady at $27.50 and reduced open interest, reflecting subdued market conditions.

EGPB - An Event-based Gold Price Benchmark Dataset

This benchmark dataset consists of 8030 rows and 36 variables sourced from multiple credible economic websites, covering a period from January 2001 to December 2022. This dataset can be utilized to predict gold prices specifically or to aid any economic field that is influenced by the variables in this dataset.

Key variables & Features include:

• Previous gold prices

• Future gold prices with predictions for one day, one week, and one month

• Oil prices

• Standard & Poor's 500 Index (S&P 500)

• Dow Jones Industrial (DJI)

• US dollar index

• US treasury

• Inflation rate

• Consumer price index (CPI)

• Federal funds rate

• Silver prices

• Copper prices

• Iron prices

• Platinum prices

• Palladium prices

Additionally, the dataset considers global events that may impact gold prices, which were categorized into groups and collected from three distinct sources: the Al-Jazeera website spanning from 2022 to 2019, the Investing website spanning from 2018 to 2016, and the Yahoo Finance website spanning from 2007 to 2001.

These events data were then divided into multiple groups:

• Economic data

• Politics

• logistics

• Oil

• OPEC

• Dollar currency

• Sterling pound currency

• Russian ruble currency

• Yen currency

• Euro currency

• US stocks

• Global stocks

• Inflation

• Job reports

• Unemployment rates

• CPI rate

• Interest rates

• Bonds

These events were encoded using a numeric value, where 0 represented no events, 1 represented low events, 2 represented high events, 3 represented stable events, 4 represented unstable events, and 5 represented events that were observed during the day but had no effect on the dataset.

Cite this dataset: Farah Mansour and Wael Etaiwi, "EGPBD: An Event-based Gold Price Benchmark Dataset," 2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), Tenerife, Canary Islands, Spain, 2023, pp. 1-7, doi: 10.1109/ICECCME57830.2023.10252987.

@INPROCEEDINGS{10252987, author={Mansour, Farah and Etaiwi, Wael}, booktitle={2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)}, title={EGPBD: An Event-based Gold Price Benchmark Dataset}, year={2023}, volume={}, number={}, pages={1-7}, doi={10.1109/ICECCME57830.2023.10252987}}

Disclaimer

This dataset was compiled from publicly available financial data sourced from Yahoo Finance for research and educational purposes only. Redistribution of raw data may be subject to Yahoo Finance’s terms of service. Users are responsible for complying with all applicable data usage policies and regulations.

Description

This dataset contains two CSV files for companies listed on the London Stock Exchange (next, LSE) * raw_financial_metrics_2025_march.csv - latest available financial data (as of March 2025) including metrics such as revenue, net income, P/E ratio, total debt, market capitalisation and industry classification. * historical_stock_prices_2015_2025.csv - 10 years of daily closing stock prices (2015 April 1st - 2025 March 28) for the same set of companies

The dataset is designed to support: * Financial valuation research * Time-series forecasting (e.g., LSTM ARIMA) * Multi-modal learning (e.g., combining static metrics and price trends) * Exploratory analysis by sector, market capitalisation, etc.

Columns Overview

Financial Metrics:

General Company Information

• Ticker: Unique stock symbol used to identify the company on the stock exchange.
• Name: Full name of the company.
• Website: Official company website.
• Address, City, Postcode, Country: Headquarters location details.
• Exchange: Stock exchange where the company is listed (e.g. LSE for London Stock Exchange).

Market & Share Information

• Market Cap: Total market value of a company’s outstanding shares.
• Shares Outstanding: Total number of shares currently issued and held by shareholders.
• Float Shares: Shares available for public trading (excludes insider holdings).

Stock Performance

• Regular Market Price: Latest trading price during regular market hours.
• Regular Market Change / (%): Absolute and percentage change in price from the previous trading day.
• 50 Day Average / 200 Day Average: Average stock prices over the past 50 and 200 days, respectively.
• 52 Week Change: Percentage change in stock price over the past year.
• 52 Week High / Low: Highest and lowest stock prices over the past year.

Valuation Ratios

• Enterprise to Revenue / EBITDA: Valuation multiples showing how the company is priced relative to its revenue or earnings before interest, taxes, depreciation, and amortization.
• P/E Ratio (Price-to-Earnings): Valuation based on earnings per share.
• P/B Ratio (Price-to-Book): Compares market value to book value.
• Price to Sales (TTM): Market cap divided by total sales over the trailing twelve months.
• PEG Ratio: Price/Earnings ratio adjusted for expected earnings growth.

Financial Strength

• Book Value: Total company equity per share.
• Beta: A Measure of stock volatility relative to the market.
• Debt-to-Equity Ratio: Leverage ratio comparing total debt to shareholder equity.
• Quick Ratio / Current Ratio: Liquidity indicators for meeting short-term obligations.

Cash Flows & Liquidity

• Operating Cash Flow: Cash generated from core business operations.
• Free Cash Flow: Cash available after capital expenditures.
• Total Cash: The Company’s total cash reserves.
• Total Debt: Total liabilities in the form of debt.
• Current Total Assets: Total assets expected to be converted into cash within a year.
• Total Cash Per Share: Cash reserves divided by the number of outstanding shares.

Earnings & Profitability

• Gross Profits: Revenue minus cost of goods sold.
• EBITDA: Earnings before interest, taxes, depreciation, and amortization.
• Operating Margins / Profit Margins: Efficiency and profitability ratios.
• Forward EPS / Trailing EPS: Projected vs actual earnings per share.
• Earnings Quarterly Growth: Year-over-year change in earnings.
• Revenue Growth: Year-over-year change in revenue.

Dividends

• Dividend Rate: Annual dividend per share.
• Dividend Yield: Dividend as a percentage of the share price.
• Payout Ratio: Proportion of earnings paid out as dividends.

Analyst Sentiment

• Recommendation: Consensus recommendation from financial analysts (e.g., Buy, Hold, Sell).
• Recommendation Mean: Average numerical value of analyst ratings (lower means more favorable).
• Number of Analyst Opinions: Number of analyst reports used to generate recommendation scores.

Stock Prices

• Company Name - the name of the company
• Date (daily) - the dates in the "yyyy-mm-dd" format

Potential Use Cases

• Stock price prediction
• Deep learning models combining tabular and time-series data
• Graham-style intrinsic value estimation
• Sector-wise performance benchmarking
• Feature engineering and data preprocessing experiments

Inspiration

This dataset was created as part of a Data Science research project exploring the use of financial fundamentals and historical price movements to evaluate company value and predict future performance.

Context

Understand the influence of a company's financial reports on its stock price

Content

Each line represents a financial report for a given date. For each company there are 4 annual reports with 4 different dates: - 2020-12-31 - 2021-03-31 - 2021-06-30 - 2021-09-30

The columns are : - firm : company name - Ticker : company ticker (the symbol) - Research Development - Income Before Tax - Net Income
- Selling General - Administrative
- Gross Profit
- Ebit
- Operating Income
- Interest Expense
- Income Tax Expense
- Total Revenue - Total Operating Expenses
- Cost Of Revenue
- Total Other Income Expense Net
- Net Income From Continuing Ops
- Net Income Applicable To Common Shares

Acknowledgements

The Data is scrapped from the yahoo finance API.

Inspiration

It could be interesting to analyze the evolution of the features for each company but also to compare the evolution between similar companies (in the same sector for example).

It could also be interesting to couple this dataset with the evolution of the share price for each company and see how the financial report influences the share price.

A kernel with nice visualizations showing the evolution of each of the features would be very instructive

Gold dataset is created by calling API from Fred and Yahoo Finance. It contains 4517 rows x 11 columns: 1.Unnamed: 0 →

Likely represents the Date of observation.

Format: MM/DD/YYYY.

2.Gold →

The gold price in U.S. dollars per troy ounce.

Gold is a safe-haven asset often used to hedge against inflation and currency risk.

3.USD_Index →

The U.S. Dollar Index (DXY).

Measures the value of the U.S. dollar against a basket of six major currencies (EUR, JPY, GBP, CAD, SEK, CHF).

Used to gauge dollar strength globally.

4.Oil →

The crude oil price in U.S. dollars per barrel.

Likely West Texas Intermediate (WTI) benchmark.

Important for global energy markets and inflation.

5.Silver →

The silver price in U.S. dollars per troy ounce.

Like gold, silver is a precious metal used both as an investment and in industry.

6.SP500 →

The S&P 500 Index.

A stock market index that tracks the performance of 500 of the largest publicly traded companies in the U.S.

A key indicator of overall U.S. stock market performance.

7.Bitcoin →

The Bitcoin price in U.S. dollars.

First decentralized cryptocurrency, highly volatile.

Note: Missing data before 2011 since Bitcoin did not exist in markets before then.

8.Interest_Rate →

The U.S. Federal Funds Effective Rate (%).

The short-term interest rate at which banks lend to each other overnight.

Set by the Federal Reserve as a key monetary policy tool.

9.10Y_Treasury_Yield →

The yield (%) on U.S. Treasury Bonds with a 10-year maturity.

Reflects government borrowing costs and investor expectations for inflation and growth.

Often seen as the “risk-free rate” benchmark.

10.Inflation_CPI →

The Consumer Price Index (CPI).

Measures the average change in prices paid by consumers for goods and services (inflation indicator).

Higher CPI → higher inflation.

11.Unemployment →

The U.S. unemployment rate (%).

Measures the percentage of the labor force that is jobless but actively seeking work.

Key economic health indicator.