This dataset presents an extensive record of daily historical stock prices for Tesla, Inc. (TSLA), one of the world’s most innovative and closely watched electric vehicle and clean energy companies. The data was sourced from Yahoo Finance, a widely used and trusted provider of financial market data, and covers a significant period spanning from Tesla’s initial public offering (IPO) to the most recent date available at the time of extraction.

The dataset includes critical trading metrics for each market day, such as the opening price, highest and lowest prices of the day, closing price, adjusted closing price (accounting for dividends and splits), and total trading volume. This rich dataset supports a variety of use cases, including financial market analysis, investment research, time series forecasting, development and backtesting of trading algorithms, and educational projects in data science and finance.

Dataset Features

• Date: The calendar date for each trading session (in YYYY-MM-DD format)
• Open: The opening price of TSLA shares at the start of the trading day
• High: The highest price reached during the trading session
• Low: The lowest price reached during the trading session
• Close: The last price at which the stock traded during the day
• Adj Close: The closing price adjusted for corporate actions (splits, dividends, etc.)
• Volume: The total number of TSLA shares traded on that day

Source and Collection Details

• Source: Yahoo Finance - Tesla (TSLA) Historical Data
• Collection Method: Data was downloaded using Yahoo Finance's CSV export feature for accuracy and completeness.
• Time Range: Covers from Tesla’s IPO (June 2010) to the most recent available trading day.
• Data Integrity: Minimal cleaning was performed—dates were standardized, and any duplicate or empty rows were removed; all values remain as originally reported by Yahoo Finance.

Example Use Cases

• Stock Price Prediction: Train and test time series models (ARIMA, LSTM, Prophet, etc.) to forecast Tesla’s stock prices.
• Algorithmic Trading: Backtest and evaluate trading strategies using historical price and volume data.
• Market Trend Analysis: Analyze price trends, volatility, and return rates over different periods.
• Event Study: Investigate the impact of major announcements (e.g., product launches, earnings releases) on TSLA stock price.
• Educational Projects: Use as a hands-on resource for learning finance, statistics, or machine learning.

License & Acknowledgments

• Intended Use: This dataset is provided for academic, research, and personal projects. For commercial or investment use, please verify data accuracy and consult Yahoo Finance’s terms of use.
• Acknowledgment: Data sourced from Yahoo Finance. All trademarks and copyrights belong to their respective owners.

Tesla stock price, live market quote, shares value, historical data, intraday chart, earnings per share and news.

The price of Tesla shares traded on the Nasdaq stock exchange remained rather stable between July 2010 and January 2020. With the beginning of 2020, the price of Tesla share increased dramatically and stood at ****** U.S. dollars per share in November 2021. Since then, the price of Tesla share fluctuated significantly and reached its peak at ****** U.S. dollars per share in December 2024, before falling dramatically in February 2025. Why did Tesla's stock value go up in 2020? Despite the effects of the pandemic, Tesla share prices experienced a massive increase in 2020. Tesla kept increasing its output levels throughout the year, except for the second quarter, and released its new vehicle Tesla Model Y. Additionally, when the company was added to the S&P 500 index in August 2020, it instilled further trust in investors. In 2020, Tesla was the top-performing stock on the S&P 500 index, and two years later, in 2024, it ranked among the ten largest companies on the index by market capitalization. Steady growth in the last decade Founded in 2003, Tesla primarily focuses on designing and producing electric vehicles, as well as energy generation and storage systems. Since then, Tesla's revenue has steadily increased, reaching nearly ** million U.S. dollars in 2024. Most of the revenue came from automotive sales in 2024. Tesla's first electric car, the Roadster, was sold between 2008 and 2012. Currently, the company offers four primary electric vehicles: Model 3, Model Y, Model S, and Model X.

Tesla reported $14.57B in Stock for its fiscal quarter ending in June of 2025. Data for Tesla | TSLA - Stock including historical, tables and charts were last updated by Trading Economics this last July in 2025.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tesla Surging: Can (TSLA) Stock Reach New Heights?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Content

There's the data of last 5 years of Tesla stock price having attributes such as date itself, it's opening bid price, high and low of the days, close price and the volume of trade.

Certain questions can be answered using the dataset such as:

Q: Enhance the data quality by adding "percent change" attribute (as compared to last day close price of-coarse) Q: How the stock price was impacted in the wake of COVID Pandemic (which came at significant level around 1st week of Mar 2020 onwards) Q: At what days of the week it shows uptrend & downtrend more often (if it shows any such specific trend at all) Q: When it showed dramatic bullish trend and the possible potential reason behind it?

Kindly upvote if it helps. Will be appreciated. Thank You Happy Learning ^_^

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tesla Stock Price Predicted to Reach $350 in One Year

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Tesla reported $1.02T in Market Capitalization this July of 2025, considering the latest stock price and the number of outstanding shares.Data for Tesla | TSLA - Market Capitalization including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Explore the fascinating journey of Tesla's stock performance over the past 5 years and gain valuable insights into its growth, trends, and market behavior. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2655536%2F077eab1e897d10453e7fdfe1619a3d05%2FTesla-Logo-PNG-HD-Isolated.png?generation=1687460082377704&alt=media" alt=""> Tesla, the renowned electric vehicle manufacturer, has captured the world's attention with its groundbreaking innovations and exponential growth. In this blog post, we will dive into Tesla's stock performance over the past five years, unraveling key trends and providing valuable insights for investors and enthusiasts alike.

The Dataset holds Tesla Stock Prices from last 5 years.

Date: First Column represents the data.

Open: Tesla Stock Opening Price for the given date.

High: Tesla Stock price highest price point hit.

Low: Tesla Stock price lowest price for the given date Tesla Stock price lowest price for the given date.

adj Close: Adjusted stock closing price of Tesla after taking dividends, stock splits, and new stock offerings into account.

Volume: Amount of an Tesla Stock that changed hands over the course of the trading d

Source: https://finance.yahoo.com

Tesla reported 3.18B in Outstanding Shares in April of 2024. Data for Tesla | TSLA - Outstanding Shares including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Your dataset contains historical stock prices for the company Tesla. This information can be used to analyze the performance of the company's stock over time and make predictions about future performance. It may include data such as date, opening price, closing price, high and low prices, and trading volume. This information can be used to study trends and patterns in the stock market and make informed investment decisions.

Date: Represents the date of the stock price. Open: Represents the opening stock price on that date. High: Represents the highest stock price on that date. Low: Represents the lowest stock price on that date. Close: Represents the closing stock price on that date. Adj close: Represents the adjusted closing stock price on that date (taking into account corporate actions such as splits). Volume: Represents the number of shares traded on that date.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tesla Stock: An Analysis of its Growth, Volatility, and Future Prospects

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Explore Tesla's stock dynamics with a focus on recent losses, future delivery reports, and technical outlook, amidst Wall Street's projections and market trends.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tesla Stock: Machine Learning Predicts 10% Increase in the Next Month

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

**Dataset Name: ** Tesla (TSLA) Stock Data

Data Source: The data is obtained from Yahoo Finance, a leading provider of historical stock market data.

Time Period: The dataset covers the daily stock trading data for Tesla, Inc. from 2015 to 2022. This 10-year period provides a comprehensive view of Tesla's stock performance over an extended time horizon.

Data Fields: - Date - The calendar date for each stock trading day in the format YYYY-MM-DD. - Open - The opening stock price for the trading day. - High - The highest stock price reached during the trading day. - Low - The lowest stock price reached during the trading day. - Close - The closing stock price for the trading day. - Adjusted Close - The closing price is adjusted for any corporate actions like splits, dividends, etc. - Volume - The trading volume (number of shares traded) for the day.

Background on Tesla, Inc.: Tesla, Inc. is an American electric vehicle and clean energy company based in Palo Alto, California. Founded in 2003, Tesla designs and manufactures electric cars, battery energy storage from home to grid-scale, solar panels and related products. Tesla's vehicles include the Model S, Model 3, Model X, and Model Y. The company is led by Elon Musk, who is also the CEO of SpaceX.

Tesla reported 184.9 in PE Price to Earnings for its fiscal quarter ending in June of 2025. Data for Tesla | TSLA - PE Price to Earnings including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Tesla reported $0.4 in EPS Earnings Per Share for its fiscal quarter ending in June of 2025. Data for Tesla | TSLA - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last July in 2025.

This horizontal bar chart displays highest price by date using the aggregation sum. The data is filtered where the stock is TSLA. The data is about stocks per day.

How many Tesla vehicles were delivered in 2025? Tesla's vehicle deliveries in the first quarter of 2025 amounted to around 336,700 units. Quarterly deliveries decreased by around 32.1 percent during the first quarter of 2025, compared with the fourth quarter of 2024. Between October and December 2024, deliveries crossed the 495,500 unit threshold, a new record for the brand. World's most valuable brand As of March 2025, Tesla was the most valuable brand within the global automotive sector. The brand was over double the brand value of Toyota, which was second in the ranking. April 2025 also recorded Tesla among the ten leading companies in the S&P 500 Index based on market capitalization, with a market cap around 798.1 billion U.S. dollars. Tesla enters the mainstream segment The initial rise in Tesla's market value was largely due to the release of its top-selling Model 3. The Model 3 was Tesla’s successful attempt to tap into the mainstream segment. By 2024, this Model consistently ranked among the world’s best-selling all-electric vehicle models, along with the bestseller Model Y. The Model 3 faces tough competition from other Tesla models, including the Model Y and the refreshed Model S Plaid.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tesla Stock: Hold for Now, But Watch for Opportunities

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data