Graph and download economic data for Interest Rates and Price Indexes; Dow Jones U.S. Total Market Index, Level (BOGZ1FL073164013A) from 1970 to 2024 about mutual funds, equity, liabilities, interest rate, interest, rate, price index, indexes, price, and USA.

TraditionData’s Interest Rate Swaps service offers comprehensive coverage across 33 currencies, focusing on portfolio interest rate risk management and yield enhancement. This service includes:

• Real-time, intraday, and end-of-day pricing for interest rate markets.
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For further details, you can visit TraditionData Interest Rate Swaps.

The main stock market index of United States, the US500, rose to 6818 points on December 2, 2025, gaining 0.08% from the previous session. Over the past month, the index has declined 0.50%, though it remains 12.70% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on December of 2025.

The "Stock Market Dataset for AI-Driven Prediction and Trading Strategy Optimization" is designed to simulate real-world stock market data for training and evaluating machine learning models. This dataset includes a combination of technical indicators, market metrics, sentiment scores, and macroeconomic factors, providing a comprehensive foundation for developing and testing AI models for stock price prediction and trading strategy optimization.

Key Features Market Metrics:

Open, High, Low, Close Prices: Daily stock price movement. Volume: Represents the trading activity during the day. Technical Indicators:

RSI (Relative Strength Index): A momentum oscillator to measure the speed and change of price movements. MACD (Moving Average Convergence Divergence): An indicator to reveal changes in strength, direction, momentum, and duration of a trend. Bollinger Bands: Upper and lower bands around a stock price to measure volatility. Sentiment Analysis:

Sentiment Score: Simulated sentiment derived from financial news and social media, ranging from -1 (negative) to 1 (positive). Macroeconomic Factors:

GDP Growth: Indicates the overall health and growth of the economy. Inflation Rate: Reflects changes in purchasing power and economic stability. Target Variable:

Buy/Sell Signal: Binary classification (1 = Buy, 0 = Sell) based on price movement thresholds, simulating actionable trading decisions. Use Cases AI Model Training: Ideal for building stock prediction models using LSTM, Gradient Boosting, Random Forest, etc. Trading Strategy Optimization: Enables testing of trading algorithms and strategies in a simulated environment. Sentiment Analysis Research: Useful for understanding how sentiment influences stock movements. Feature Engineering and Selection: Provides a diverse set of features for experimentation with advanced techniques like PCA and LDA. Dataset Highlights Synthetic Yet Realistic: Carefully designed to mimic real-world financial data trends and relationships. Comprehensive Coverage: Includes key indicators and metrics used by traders and analysts. Scalable: Suitable for use in both small-scale academic projects and larger AI-driven trading platforms. Accessible for All Levels: The intuitive structure ensures that even beginners can utilize this dataset for financial machine learning applications. File Format The dataset is provided in CSV format, where:

Rows represent individual trading days. Columns represent features (technical indicators, market metrics, etc.) and the target variable. Acknowledgments This dataset is synthetically generated and is intended for research and educational purposes. It is not based on real market data and should not be used for actual trading.

This dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

Abstract

Currency exchange rate is an important metric to inform economic policy but traditional sources are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual rate trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes currency exchange rate estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: Ouaddai, Salamat, Wadi Fira, Sila, Ennedi Est, Batha, Tibesti, Logone Oriental, Logone Occidental, Guera, Hadjer Lamis, Lac, Mayo Kebbi Est, Chari Baguirmi, Ennedi Ouest, Borkou, Tandjile, Mandoul, Moyen Chari, Mayo Kebbi Ouest, Kanem, Barh El Gazal, Ndjaména, Market Average

Graph and download economic data for Exchange Rate (market+estimated) for Croatia (XRNCUSHRA618NRUG) from 1990 to 2019 about Croatia, exchange rate, rate, and USA.

Abstract

Currency exchange rate is an important metric to inform economic policy but traditional sources are often produced with delay during crises and only at an aggregate level. This may poorly reflect the actual rate trends in rural or poverty-stricken areas, where large populations reside in fragile situations. This data set includes currency exchange rate estimates and is intended to help gain insight in price developments beyond what can be formally measured by traditional methods. The estimates are generated using a machine-learning approach that imputes ongoing subnational price surveys, often with accuracy similar to direct measurement of prices. The data set provides new opportunities to investigate local price dynamics in areas where populations are sensitive to localized price shocks and where traditional data are not available.

Geographic coverage notes

The data cover the following sub-national areas: North, South, Artibonite, Centre, South-East, Grande'Anse, North-East, West, North-West, Market Average

Learn more about the Graphics Double Data Rate Market Report by Market Research Intellect, which stood at USD 15.4 billion in 2024 and is forecast to expand to USD 25.1 billion by 2033, growing at a CAGR of 7.2%.Discover how new strategies, rising investments, and top players are shaping the future.

Actual value and historical data chart for China Annual Percentage Growth Rate Of GDP At Market Prices Based On Constant 2010 Us Dollars

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

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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

https:/...

The benchmark interest rate in the United States was last recorded at 4 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Average Rate on 3-Month Negotiable Certificates of Deposit (Secondary Market), Quoted on an Investment Basis (DISCONTINUED) (DCD90) from 1964-06-11 to 2013-06-28 about CD, secondary market, 3-month, interest rate, interest, rate, and USA.

Graph and download economic data for 6-Month Treasury Bill Secondary Market Rate, Discount Basis (WTB6MS) from 1958-12-12 to 2025-10-24 about 6-month, secondary market, bills, Treasury, interest rate, interest, rate, and USA.

Brazil Market Expectation: Over Selic Rate: Long Term: Standard Deviation data was reported at 0.910 % pa in 28 Jun 2019. This records an increase from the previous number of 0.900 % pa for 27 Jun 2019. Brazil Market Expectation: Over Selic Rate: Long Term: Standard Deviation data is updated daily, averaging 1.330 % pa from Nov 2001 (Median) to 28 Jun 2019, with 4416 observations. The data reached an all-time high of 3.890 % pa in 05 Jan 2004 and a record low of 0.000 % pa in 13 Dec 2018. Brazil Market Expectation: Over Selic Rate: Long Term: Standard Deviation data remains active status in CEIC and is reported by Central Bank of Brazil. The data is categorized under Brazil Premium Database’s Business and Economic Survey – Table BR.SA038: Market Expectation: Over Selic Rate. Market Expectations System was implemented in November 2001, previous projections were collected from incipient through telephone contacts, transcribed into spreadsheets and consolidated manually. Some empty time points occurred because the Market didn´t have the expectation for those days. The Over-Selic interest rate is set by the monetary authorities and has the role of signaling to economic agents the basic level of remuneration of federal securities. Its effective level is defined by the weighted average daily volume of operations backed by federal public short, medium and long-term securities at the present time. Such securities are issued by the Treasury or the Central Bank, negotiated and registered by the Special Settlement and Custody Service, Selic, in the form of repo operations. Notably, the Over-Selic interest rate has the function of guiding the other short-term interest rates of the economy, acting as a minimum limit.

This statistic shows the year on year apparel sales growth rate in the womenswear and menswear segments worldwide from 2012 to 2017, with a forecast from 2018 to 2021. Global menswear sales grew by *** percent between 2016 and 2017.

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.

Euro Area - Day-to-day money market interest rates was -0.49% in December of 2021, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for Euro Area - Day-to-day money market interest rates - last updated from the EUROSTAT on December of 2025. Historically, Euro Area - Day-to-day money market interest rates reached a record high of -0.48% in August of 2021 and a record low of -0.49% in December of 2021.

Source is Federal Reserve Bank of St. Louis. Retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/"NAME OF MEASURE" Column names are "Name of Measure" from FRED's catalog.

Group 1: Yield Curve Indicators These focus on the shape of the Treasury yield curve, comparing longer-term to shorter-term rates. They are primarily used to: Signal Economic Expectations: A normal curve (longer-term rates higher) suggests expectations of growth and possibly inflation. A flattening or inverted curve (short-term rates near or above long-term) could signal a potential slowdown or recession.

Group 2: Monetary Policy and Market Expectations These spreads look at the difference between Treasury yields and the Federal Funds Rate, the primary tool of monetary policy. They indicate: Market vs. Fed Outlook: Widening spreads could suggest the market expects faster rate hikes or higher long-term inflation than the Fed is signaling. Narrowing spreads could mean the opposite. Risk-Taking: When these spreads widen, it can be a sign of investors moving from safe Treasuries to riskier assets in search of yield.

Group 3: Credit Risk and Market Sentiment These spreads focus on corporate bond yields relative to Treasuries, highlighting the added compensation investors require for holding riskier corporate debt. They signal: Credit Conditions: Widening spreads suggest deteriorating credit conditions or lower risk tolerance among investors. Narrowing spreads suggest the opposite. Economic Confidence: Investors often demand higher premiums for corporate bonds during economic uncertainty, widening these spreads.

Group 4: Breakeven Inflation Rates The breakeven inflation rate represents a measure of expected inflation derived from 30-Year Treasury Constant Maturity Securities (BC_30YEAR) and 30-Year Treasury Inflation-Indexed Constant Maturity Securities (TC_30YEAR). The latest value implies what market participants expect inflation to be in the next 30 years, on average.

AR: Central Bank Policy Rate: End of Period data was reported at 55.000 % pa in 2019. This records a decrease from the previous number of 59.252 % pa for 2018. AR: Central Bank Policy Rate: End of Period data is updated yearly, averaging 9.500 % pa from Dec 2002 (Median) to 2019, with 18 observations. The data reached an all-time high of 59.252 % pa in 2018 and a record low of 1.670 % pa in 2003. AR: Central Bank Policy Rate: End of Period data remains active status in CEIC and is reported by International Monetary Fund. The data is categorized under Global Database’s Argentina – Table AR.IMF.IFS: Money Market and Policy Rates: Annual.