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 shares increased dramatically and stood at 381.59 U.S. dollars per share in November 2021. Since then, the price of Tesla shares has fluctuated significantly and reached its peak at 444.72 U.S. dollars per share in September 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, the Tesla Model Y. Additionally, when the company was added to the S&P 500 index in December 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 98 billion 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.

The “Tesla Stock Price Data (Last One Year)” dataset is a comprehensive collection of historical stock market information, focusing on Tesla Inc. (TSLA) for the past year. This dataset serves as a valuable resource for financial analysts, investors, researchers, and data enthusiasts who are interested in studying the trends, patterns, and performance of Tesla’s stock in the financial markets.It consists of 9 columns referring to date, high and low prices, open and closing value, volume, cumulative open and of course changing of price.At a first glance in order to better understand the data we should plot the time series of each attribute.The cumulative Open Interest(OI) is the total open contracts that are being held in a particular Future or Call or Put contracts on the Exchange. We can see that the biggest drop of the stock happened in January of 2023 and after 5 to 6 months it regained its stock value round the summer of the same year with opening and closing price around 300.As a next step we are going to plot some more plots in order ro better understand the relation between our target column(change price) with every other attribute. In order to interpret the results:

Linear Regression:

Mean Absolute Error (MAE): 6.28 This model, on average, predicts the “Price Change” within approximately 6.28 units of the true value. Mean Squared Error (MSE): 52.97 MSE measures the average of squared differences, and this value suggests some variability in prediction errors. Root Mean Squared Error (RMSE): 7.28 RMSE is the square root of MSE and is in the same units as the target variable. An RMSE of 7.28 indicates the typical prediction error. R-squared (R2): 0.0868 R-squared represents the proportion of the variance in the target variable explained by the model. An R2 of 0.0868 suggests that the model explains only a small portion of the variance, indicating limited predictive power. Decision Tree Regression:

Mean Absolute Error (MAE): 9.21 This model, on average, predicts the “Price Change” within approximately 9.21 units of the true value, which is higher than the Linear Regression model. Mean Squared Error (MSE): 150.69 The MSE is relatively high, indicating larger prediction errors and more variability. Root Mean Squared Error (RMSE): 12.28 RMSE of 12.28 is notably higher, suggesting that this model has larger prediction errors. R-squared (R2): -1.598 The negative R-squared value indicates that the model performs worse than a horizontal line as a predictor, indicating a poor fit. Random Forest Regression:

Mean Absolute Error (MAE): 6.99 This model, on average, predicts the “Price Change” within approximately 6.99 units of the true value, similar to Linear Regression. Mean Squared Error (MSE): 62.79 MSE is lower than the Decision Tree model but higher than Linear Regression, suggesting intermediate prediction accuracy Root Mean Squared Error (RMSE): 7.92 RMSE is also intermediate, indicating moderate prediction errors. R-squared (R2): -0.0824 The negative R-squared suggests that the Random Forest model does not perform well and has limited predictive power.

This dataset provides a decade of historical stock price data for Tesla, Inc., spanning from December 7, 2014, to December 5, 2024. It includes essential financial metrics that are valuable for trend analysis, forecasting, and machine learning projects.

This dataset contains historical stock price data for Tesla Inc. (TSLA) spanning from 1995 to 2024. It provides an in-depth look at the performance of Tesla's stock over nearly three decades, covering various key financial indicators and metrics that have shaped the company's growth story.

Tesla, Inc. (TSLA) is one of the most recognized electric vehicle manufacturers in the world, and its stock has experienced substantial volatility, making it a popular asset for investors, analysts, and enthusiasts. From its IPO in 2010 to its meteoric rise in the following years, this dataset captures the evolution of its stock price and trading volume.

Dataset Contents:

The dataset includes the following key columns:

Date: The date of the stock data.

Open: The opening price of Tesla's stock on a given date.

High: The highest price reached by Tesla's stock on that date.

Low: The lowest price reached by Tesla's stock on that date.

Close: The closing price of Tesla's stock on that date.

Adj Close: The adjusted closing price, which accounts for stock splits and dividends.

Volume: The total number of shares traded on that date.

Key Features and Insights:

Tesla's IPO and Early Performance: The dataset starts in 1995, a few years before Tesla's IPO in 2010. This gives users insight into the pre-IPO trading environment for the company and the broader market trends.

Post-IPO Growth: After Tesla went public in 2010, it experienced significant volatility, with periods of rapid growth and significant dips. The stock price and volume data reflect these shifts, helping users track Tesla's journey from a niche electric vehicle startup to one of the most valuable companies globally.

Stock Splits & Adjusted Close: The data includes adjusted close values, which provide a clear view of the stock's performance over time, accounting for stock splits and dividends. Notably, Tesla has undergone stock splits in recent years, and the "Adj Close" column allows users to view a consistent series of values.

2020-2024 Surge: Tesla's stock price saw a remarkable rise between 2020 and 2024, driven by its strong earnings reports, market optimism, and the overall growth of the electric vehicle and clean energy sectors. This period saw some of the most significant increases in Tesla's stock price, reflecting investor sentiment and broader trends in the stock market.

Market Volatility and External Factors: Users can analyze how external factors, such as changes in the global economy, the electric vehicle industry, and global events (like the COVID-19 pandemic), affected Tesla’s stock price.

Potential Use Cases:

Stock Price Prediction Models: Data scientists and machine learning practitioners can use this dataset to build models that predict Tesla's stock price based on historical data.

Technical Analysis: The dataset provides enough detail to perform technical analysis, such as moving averages, volatility analysis, and trend recognition.

Comparative Analysis: Analysts can compare Tesla's performance with other electric vehicle manufacturers or traditional automakers to gauge the company's market position.

Financial Insights and Investment Research: Investors can analyze key financial indicators, trading volume, and stock price movement to make informed decisions or study Tesla's financial growth.

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 ^_^

Tesla reported $0.5 in EPS Earnings Per Share for its fiscal quarter ending in September of 2025. Data for Tesla | TSLA - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Tesla Stock Price Dataset: A Comprehensive Historical Record

This dataset provides a detailed and meticulously compiled history of Tesla's stock performance, starting from its IPO on June 29, 2010, up until December 5, 2024. It offers a rich trove of information for analysts, researchers, and enthusiasts interested in studying the evolution of Tesla's stock and its journey through years of innovation and market dynamics.

Features of the Dataset Date: The trading date for each record. Open: The stock's opening price on the given date. High: The highest price Tesla's stock reached during the trading session. Low: The lowest price Tesla's stock fell to during the trading session. Close: The stock's closing price at the end of the trading day. Adj Close: The adjusted closing price, accounting for corporate actions like stock splits and dividends. Volume: The total number of shares traded on that particular day.

This dataset contains the predicted prices of the asset Tesla tokenized stock (xStock) over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Content

Weekly data of Tesla Historical Share Price from 2016. Prices given in US dollar($). Data is good for time series analysis and EDA.

Source

Link : https://yhoo.it/31RLBzt

If you find it useful, please support by upvoting

How many Tesla vehicles were delivered in 2025? Tesla's vehicle deliveries in the third quarter of 2025 amounted to around 497,120 units. Quarterly deliveries increased by around seven percent during the third quarter of 2025, compared with the third quarter of 2024. 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.

Tesla reported $1.37B in Net Income for its fiscal quarter ending in September of 2025. Data for Tesla | TSLA - Net Income including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Tesla reported $28.1B in Sales Revenues for its fiscal quarter ending in September of 2025. Data for Tesla | TSLA - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last December in 2025.

This dataset contains the predicted prices of the asset Tesla Tokenized Stock Defichain over the next 16 years. This data is calculated initially using a default 5 percent annual growth rate, and after page load, it features a sliding scale component where the user can then further adjust the growth rate to their own positive or negative projections. The maximum positive adjustable growth rate is 100 percent, and the minimum adjustable growth rate is -100 percent.

Overview

This dataset collection contains detailed information related to Tesla's stock prices (USD) and stock splits. It is an excellent resource for analysts, researchers, and enthusiasts interested in studying the stock performance of one of the world's most innovative companies.

Stock Price Date Range: From June 29, 2010 to September 20, 2024.

Potential Uses:

These datasets can be used for various purposes, such as:

• Historical Analysis: Track and analyze stock price trends and the impact of stock splits over time.
• Financial Modeling: Develop predictive models for stock prices based on historical data.
• Educational Purposes: Use the data to understand the implications of stock splits and their effects on stock performance.

Datasets Included

1. TeslaStockPrice.csv
2. TeslaStockSplit.csv

1. TeslaStockPrice.csv

Description: This dataset provides daily stock prices of Tesla, Inc. Note: All prices are in USD.

Columns: - Date: Date of the trading day. - Open: Stock price at market open. - High: Highest stock price during the trading day. - Low: Lowest stock price during the trading day. - Close: Stock price at market close. - Adj Close: Adjusted closing price accounting for dividends and stock splits. - Volume: Number of shares traded.

2. TeslaStockSplit.csv:

Description: This dataset details the history of stock splits conducted by Tesla, Inc.

Columns: - Date: The date of the stock split. - Split Ratio: The ratio by which stock was split (e.g., 5:1).

Acknowledgment: Data is sourced from publicly available financial records and is provided as-is for educational and research purposes.

This dataset encapsulates a detailed examination of market dynamics over a five-year period, focusing on the fluctuation of prices and trading volumes across a diversified portfolio. It covers various sectors including energy commodities like natural gas and crude oil, metals such as copper, platinum, silver, and gold, cryptocurrencies including Bitcoin and Ethereum, and key stock indices and companies like the S&P 500, Nasdaq 100, Apple, Tesla, Microsoft, Google, Nvidia, Berkshire Hathaway, Netflix, Amazon, and Meta Platforms. This dataset serves as a valuable resource for analyzing trends and patterns in global markets.

Date: The date of the recorded data, formatted as DD-MM-YYYY. Natural_Gas_Price: Price of natural gas in USD per million British thermal units (MMBtu). Natural_Gas_Vol.: Trading volume of natural gas Crude_oil_Price: Price of crude oil in USD per barrel. Crude_oil_Vol.: Trading volume of crude oil Copper_Price: Price of copper in USD per pound. Copper_Vol.: Trading volume of copper Bitcoin_Price: Price of Bitcoin in USD. Bitcoin_Vol.: Trading volume of Bitcoin Platinum_Price: Price of platinum in USD per troy ounce. Platinum_Vol.: Trading volume of platinum Ethereum_Price: Price of Ethereum in USD. Ethereum_Vol.: Trading volume of Ethereum S&P_500_Price: Price index of the S&P 500. Nasdaq_100_Price: Price index of the Nasdaq 100. Nasdaq_100_Vol.: Trading volume for the Nasdaq 100 index Apple_Price: Stock price of Apple Inc. in USD. Apple_Vol.: Trading volume of Apple Inc. stock Tesla_Price: Stock price of Tesla Inc. in USD. Tesla_Vol.: Trading volume of Tesla Inc. stock Microsoft_Price: Stock price of Microsoft Corporation in USD. Microsoft_Vol.: Trading volume of Microsoft Corporation stock Silver_Price: Price of silver in USD per troy ounce. Silver_Vol.: Trading volume of silver Google_Price: Stock price of Alphabet Inc. (Google) in USD. Google_Vol.: Trading volume of Alphabet Inc. stock Nvidia_Price: Stock price of Nvidia Corporation in USD. Nvidia_Vol.: Trading volume of Nvidia Corporation stock Berkshire_Price: Stock price of Berkshire Hathaway Inc. in USD. Berkshire_Vol.: Trading volume of Berkshire Hathaway Inc. stock Netflix_Price: Stock price of Netflix Inc. in USD. Netflix_Vol.: Trading volume of Netflix Inc. stock Amazon_Price: Stock price of Amazon.com Inc. in USD. Amazon_Vol.: Trading volume of Amazon.com Inc. stock Meta_Price: Stock price of Meta Platforms, Inc. (formerly Facebook) in USD. Meta_Vol.: Trading volume of Meta Platforms, Inc. stock Gold_Price: Price of gold in USD per troy ounce. Gold_Vol.: Trading volume of gold

Image attribute : Image by Freepik

The dataset contains a total of 25,161 rows, each row representing the stock market data for a specific company on a given date. The information collected through web scraping from www.nasdaq.com includes the stock prices and trading volumes for the companies listed, such as Apple, Starbucks, Microsoft, Cisco Systems, Qualcomm, Meta, Amazon.com, Tesla, Advanced Micro Devices, and Netflix.

Data Analysis Tasks:

1) Exploratory Data Analysis (EDA): Analyze the distribution of stock prices and volumes for each company over time. Visualize trends, seasonality, and patterns in the stock market data using line charts, bar plots, and heatmaps.

2)Correlation Analysis: Investigate the correlations between the closing prices of different companies to identify potential relationships. Calculate correlation coefficients and visualize correlation matrices.

3)Top Performers Identification: Identify the top-performing companies based on their stock price growth and trading volumes over a specific time period.

4)Market Sentiment Analysis: Perform sentiment analysis using Natural Language Processing (NLP) techniques on news headlines related to each company. Determine whether positive or negative news impacts the stock prices and volumes.

5)Volatility Analysis: Calculate the volatility of each company's stock prices using metrics like Standard Deviation or Bollinger Bands. Analyze how volatile stocks are in comparison to others.

Machine Learning Tasks:

1)Stock Price Prediction: Use time-series forecasting models like ARIMA, SARIMA, or Prophet to predict future stock prices for a particular company. Evaluate the models' performance using metrics like Mean Squared Error (MSE) or Root Mean Squared Error (RMSE).

2)Classification of Stock Movements: Create a binary classification model to predict whether a stock will rise or fall on the next trading day. Utilize features like historical price changes, volumes, and technical indicators for the predictions. Implement classifiers such as Logistic Regression, Random Forest, or Support Vector Machines (SVM).

3)Clustering Analysis: Cluster companies based on their historical stock performance using unsupervised learning algorithms like K-means clustering. Explore if companies with similar stock price patterns belong to specific industry sectors.

4)Anomaly Detection: Detect anomalies in stock prices or trading volumes that deviate significantly from the historical trends. Use techniques like Isolation Forest or One-Class SVM for anomaly detection.

5)Reinforcement Learning for Portfolio Optimization: Formulate the stock market data as a reinforcement learning problem to optimize a portfolio's performance. Apply algorithms like Q-Learning or Deep Q-Networks (DQN) to learn the optimal trading strategy.

The dataset provided on Kaggle, titled "Stock Market Stars: Historical Data of Top 10 Companies," is intended for learning purposes only. The data has been gathered from public sources, specifically from web scraping www.nasdaq.com, and is presented in good faith to facilitate educational and research endeavors related to stock market analysis and data science.

It is essential to acknowledge that while we have taken reasonable measures to ensure the accuracy and reliability of the data, we do not guarantee its completeness or correctness. The information provided in this dataset may contain errors, inaccuracies, or omissions. Users are advised to use this dataset at their own risk and are responsible for verifying the data's integrity for their specific applications.

This dataset is not intended for any commercial or legal use, and any reliance on the data for financial or investment decisions is not recommended. We disclaim any responsibility or liability for any damages, losses, or consequences arising from the use of this dataset.

By accessing and utilizing this dataset on Kaggle, you agree to abide by these terms and conditions and understand that it is solely intended for educational and research purposes.

Please note that the dataset's contents, including the stock market data and company names, are subject to copyright and other proprietary rights of the respective sources. Users are advised to adhere to all applicable laws and regulations related to data usage, intellectual property, and any other relevant legal obligations.

In summary, this dataset is provided "as is" for learning purposes, without any warranties or guarantees, and users should exercise due diligence and judgment when using the data for any purpose.

This dataset contains 5 years historical data of Big 6 companies listed in NASDAQ composite index. The stocks are: Amazon, Apple, Google, Meta, Microsoft, and Tesla. These are the big names with huge yearly revenues. The data is an indication of the fluctuations of prices but doesn't contain macro/micro economic conditions that caused the fluctuations.

With a market capitalization of 3.12 trillion U.S. dollars as of May 2024, Microsoft was the world’s largest company that year. Rounding out the top five were some of the world’s most recognizable brands: Apple, NVIDIA, Google’s parent company Alphabet, and Amazon. Saudi Aramco led the ranking of the world's most profitable companies in 2023, with a pre-tax income of nearly 250 billion U.S. dollars. How are market value and market capitalization determined? Market value and market capitalization are two terms frequently used – and confused - when discussing the profitability and viability of companies. Strictly speaking, market capitalization (or market cap) is the worth of a company based on the total value of all their shares; an important metric when determining the comparative value of companies for trading opportunities. Accordingly, many stock exchanges such as the New York or London Stock Exchange release market capitalization data on their listed companies. On the other hand, market value technically refers to what a company is worth in a much broader context. It is determined by multiple factors, including profitability, corporate debt, and the market environment as a whole. In this sense it aims to estimate the overall value of a company, with share price only being one element. Market value is therefore useful for determining whether a company’s shares are over- or undervalued, and in arriving at a price if the company is to be sold. Such valuations are generally made on a case-by-case basis though, and not regularly reported. For this reason, market capitalization is often reported as market value. What are the top companies in the world? The answer to this question depends on the metric used. Although the largest company by market capitalization, Microsoft's global revenue did not manage to crack the top 20 companies. Rather, American multinational retailer Walmart was ranked as the largest company in the world by revenue. Walmart also had the highest number of employees in the world.

Dataset Description This dataset contains historical stock price data across multiple timeframes, ranging from intraday to higher timeframes. The available timeframes include:

1-minute (1m) 5-minute (5m) 30-minute (30m) 60-minute (1h) 240-minute (4h) Daily (1D) Weekly (1W) Monthly (1M)

Each dataset file represents price movements at a specific timeframe, enabling traders and analysts to perform technical analysis across different granularities.

Columns - datetime: The timestamp or date of the recorded price data. - open: The opening price at the beginning of the timeframe. - high: The highest price reached within the timeframe. - low: The lowest price reached within the timeframe. - close: The closing price at the end of the timeframe. - % change: The percentage change in price from the previous timeframe, indicating the rate of price movement.

Exchange Information The dataset is sourced from the IC Market exchange, ensuring reliable and up-to-date market data. https://in.tradingview.com/broker/ICmarkets/

Time Period Covered The dataset spans from the inception of the company to 2025, covering complete historical stock price data.

Potential Use Cases - Technical analysis: Identifying trends, support/resistance levels, and chart patterns. - Algorithmic trading: Backtesting strategies across multiple timeframes. - Machine learning models: Training predictive models for stock price movements. - Volatility analysis: Understanding market behavior across different timeframes.