Monthly and long-term United States Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term Canada Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term Japan Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term Mexico Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term Indonesia Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term India Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term Philippines Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term Poland Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

Monthly and long-term South Africa Interest Rate data: historical series and analyst forecasts curated by FocusEconomics.

🇺🇸 Alphabet Inc. (GOOGL) Comprehensive Financial Dataset

📌 Overview

Welcome to the GOOGL Financial Dataset! This dataset provides clear and easy-to-use quarterly financial statements (income statement, balance sheet, and cash flow) along with daily historical stock prices.

As a data engineer double majored with economics, I'll personally analyze and provide constructive feedback on all your work using this dataset. Let's dive in and explore Google's financial journey together!

🗃 Files Included

• googl_daily_prices.csv: Historical daily stock prices.
• googl_income_statement.csv: Quarterly income statements.
• googl_balance_sheet.csv: Quarterly balance sheets.
• googl_cash_flow_statement.csv: Quarterly cash flow statements.

📘 About This Dataset

This dataset offers a unique blend of long-term market performance and detailed financial metrics:

• Time Series of Daily Prices: Track the historical performance of GOOGL stock from its early days up until now.
• Quarterly Financial Statements: Dive into the income statements, balance sheets, and cash flow statements that reflect the company’s financial evolution.
• Integrated Insights: Ideal for comprehensive financial analyses, forecasting, model building, and exploring the dynamic interplay between market performance and underlying business operations.

Whether you're building predictive models, performing deep-dive financial analysis, or exploring the evolution of one of the world's most innovative tech giants, this dataset is your go-to resource for clean, well-organized, and rich financial data.

💡 Tips for Using the Dataset

• Visualize Stock Trends: Plot daily prices to quickly understand stock movements.
• Financial Analysis: Compare income, balance sheet, and cash flow data to spot financial trends and health.
• Predictive Modeling: Use this dataset to build forecasting models and predict future performance.
• Combine Data: Merge price data with financial statements to analyze relationships and uncover deeper insights.

🔗 Works Great with My GOOGL News Dataset!

For a more comprehensive financial analysis, pair this dataset with my other Kaggle dataset:
👉 Google (Alphabet Inc.) Daily News — 2000 to 2025

That dataset includes:

• Daily news articles from Finnhub
• Headlines, summaries, sources, and timestamps
• Covering GOOGL from 2000 to 2025

Combining both datasets unlocks powerful analysis such as:

• Correlating news sentiment with stock price movements
• Studying the impact of earnings reports and product launches
• Developing event-driven forecasting models

Together, they give you everything you need for news + financial signal modeling.

📝 Column Descriptions

📈 googl_daily_prices.csv

• date: Trading date.
• 1. open: Opening stock price on the trading day.
• 2. high: Highest stock price on the trading day.
• 3. low: Lowest stock price on the trading day.
• 4. close: Closing stock price on the trading day.
• 5. volume: Number of shares traded on the day.

📊 googl_income_statement.csv

• fiscalDateEnding: Date marking the end of fiscal quarter.
• reportedCurrency: Currency used in reporting (USD).
• grossProfit: Revenue minus the cost of goods sold.
• totalRevenue: Total income generated from operations.
• costOfRevenue: Direct costs attributable to the production of goods.
• costofGoodsAndServicesSold: Costs directly associated with goods sold.
• operatingIncome: Earnings after operating expenses deducted.
• sellingGeneralAndAdministrative: Administrative and general sales costs.
• researchAndDevelopment: Expenses related to research and innovation.
• operatingExpenses: Total operational costs.
• investmentIncomeNet: Net income from investments.
• netInterestIncome: Income earned from interest after deducting interest paid.
• interestIncome: Income generated from interest-bearing investments.
• interestExpense: Expenses from interest payments.
• nonInterestIncome: Income from non-interest-bearing activities.
• otherNonOperatingIncome: Additional income outside regular operations.
• depreciation: Reduction in value of assets over time.
• depreciationAndAmortization: Combined depreciation and amortization costs.
• incomeBeforeTax: Income before taxation.
• incomeTaxExpense: Taxes paid on earnings.
• interestAndDebtExpense: Interest paid on debts.
• netIncomeFromContinuingOperations: Profit from ongoing operations.
• comprehensiveIncomeNetOfTax: Income after comprehensive expenses.
• ebit: Earnings before interest and taxes.
• ebitda: Earnings before interest, taxes, depreciation, and...

Introduction: The "Cryptocurrency Price Analysis Dataset: BTC, ETH, XRP, LTC (2018-2023)" is a comprehensive dataset that captures the daily price movements of six popular cryptocurrencies. It covers a period from January 1, 2018, to May 31, 2023, providing a valuable resource for researchers, analysts, and enthusiasts interested in studying the historical price behavior of these digital assets.

Description: This dataset contains a wealth of information for six major cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Litecoin (LTC). The data spans a time frame of over five years, enabling users to explore long-term trends, analyze volatility patterns, and gain insights into market dynamics.

Columns:

1. Crypto: This column specifies the name of the cryptocurrency (e.g., BTC, ETH, XRP, LTC).
2. Date: The date on which the price data was recorded.
3. Open: The opening price of the cryptocurrency at the beginning of the day.
4. High: The highest price reached by the cryptocurrency during the day.
5. Low: The lowest price reached by the cryptocurrency during the day.
6. Close: The closing price of the cryptocurrency at the end of the day.

Use Cases: The dataset offers numerous possibilities for analysis and research within the field of cryptocurrencies. Here are a few potential use cases:

1. Price Analysis: Researchers can investigate the historical price movements of each cryptocurrency to identify trends, patterns, and potential correlations between different assets.
2. Volatility Study: The dataset enables the study of volatility in cryptocurrency markets, helping users understand the frequency and magnitude of price fluctuations.
3. Market Performance: Analysts can analyze the performance of individual cryptocurrencies over time, comparing returns and risk measures to assess their investment potential.
4. Trading Strategies: Traders can utilize the dataset to develop and backtest trading strategies based on technical indicators, price patterns, or machine learning algorithms.
5. Sentiment Analysis: Combine this dataset with external sentiment data to explore the relationship between market sentiment and cryptocurrency price movements. By sharing this dataset on Kaggle, you are providing a valuable resource to the data science community, encouraging collaborative research, and enabling the development of innovative models and solutions within the cryptocurrency domain.

Please note that this dataset is for educational and research purposes only and should not be used for making financial decisions without thorough analysis and consultation with financial professionals.

Tesla is an American electric vehicle and clean energy company founded in 2003 by Elon Musk. It produces electric cars, energy storage systems, and solar products. Tesla is known for its cutting-edge technology, sustainability, and innovation in autonomous driving technology.

Tesla's Stock Tesla's stock, traded as TSLA on NASDAQ, has been one of the most closely watched and volatile stocks in recent years. The company's stock price has experienced significant fluctuations due to various factors, including investor sentiment, company-specific news and events, and global economic conditions. Tesla's market capitalization has grown rapidly in recent years, making it one of the most valuable car companies in the world. Despite the volatility, Tesla's stock has been popular with both institutional and retail investors who are bullish on the company's long-term prospects for growth and innovation.

About the dataset This dataset contains historical stock prices of an asset from March 2013 to the present. The dataset provides a detailed view of the fluctuations and trends in the price of the asset over time. The data includes information on daily stock prices, high and low values, closing prices, trading volumes, and adjusted closing prices.

The dataset consists of seven columns - Date, Price, High, Low, Close, Volume, and Adj Close. The Date column contains the date of the trading day, while the Price column contains the opening price of the asset on that day. The High column provides the highest price of the asset on that trading day, while the Low column provides the lowest price of the asset on that trading day. The Close column contains the closing price of the asset on that day. The Volume column contains the number of shares of the asset traded on that day, while the Adj Close column provides the adjusted closing price of the asset on that day, taking into account any dividends or other corporate actions that may have affected the price.

This dataset is a valuable resource for researchers, investors, and analysts who are interested in analyzing the performance of the asset over time. The data can be used to identify patterns and trends in the price of the asset, which can help in making informed investment decisions. The data can also be used to develop trading strategies and forecast future prices of the asset.

It is important to note that the performance of the asset is subject to various external factors, such as global economic conditions and company-specific events, which can impact its price. Therefore, it is recommended to exercise caution when interpreting the data and making investment decisions based on it.

Overall, this dataset provides a comprehensive view of the historical prices of an asset from March 2013 to the present and is an essential tool for anyone interested in understanding the performance of the asset and gaining insights into its long-term trends.