The price of Amazon shares traded on the Nasdaq stock exchange fluctuated significantly but increased for the most part during the period between 2010 and 2025, peaking at ****** U.S. dollar per share in January 2025. Expansion during the pandemic Due to the rise of online shopping worldwide during the Covid-19 pandemic, Amazon's share prices saw an increase as the company experienced dramatic growth. As a result, the company's net sales revenue increased by almost *** billion U.S. dollars between 2019 to 2024, growing ever since. However, the surge in Amazon's operations significantly increased the company's fulfillment expenses and shipping costs after 2020. The shift towards offline shopping and cost increases after the pandemic resulted in significant layoffs in 2022. Amazon Web Services Amazon is not only the world's most valuable retailer but also the leader in the cloud computing industry through Amazon Web Services (AWS). AWS is a platform that offers storage, servers, and networking to individuals, businesses, and organizations. Amazon's success is driven by its excellence in diverse sectors, but AWS stands as the primary source of profit. The cloud service has consistently grown in profitability, generating nearly ** billion U.S. dollars in profit in 2024.

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

This dataset contains 10 years of daily stock price data (2015–2025) for four of the world’s leading technology companies: NVIDIA (NVDA), Google (GOOGL), Amazon (AMZN), and Microsoft (MSFT).

The dataset includes key financial metrics with the following columns:

• Date – Trading day
• Open – Price at the start of the trading day
• High – Highest price reached that day
• Low – Lowest price reached that day
• Close – Final price at market close
• Volume – Number of shares traded
• Ticker – Stock symbol (e.g., NVDA, MSFT)

Data was retrieved using the yfinance Python library and cleaned to ensure consistency and usability. All data is stored in a single excel file. Prices have been adjusted for dividends and stock splits using auto_adjust=True to reflect true historical value

Use Cases: - Time-series forecasting
- Trend analysis across multiple tech stocks
- Portfolio performance modeling
- Building finance-related dashboards and ML models

Context

Amazon has become a house hold name now and has been around for quite sometime. It comes under the popular FAANG companies and a dream company for many. Today almost anything that you need today is available on Amazon. From groceries to electronics. But it has not only benefited the people purchasing from them. It has benefited those too who invested in the company back then and continue to do till today.

Content

This data set has 7 columns with all the necessary values such as opening price of the stock, the closing price of it, its highest in the day and much more. It has date wise data of the stock starting from 1997 to 2020(August).

Amazon reported $180.2B in Sales Revenues for its fiscal quarter ending in September of 2025. Data for Amazon | AMZN - Sales Revenues including historical, tables and charts were last updated by Trading Economics this last December in 2025.

The dataset was acquired from the free version of the Yahoo Finance API in Python, containing the close price of Bitcoin (BTC) from 2014-10-15 to 2020-01-01 and Amazon (AMZN) from 2010-01-01 to 2021-06-01 with the daily time step.

Amazon reported $1.95 in EPS Earnings Per Share for its fiscal quarter ending in September of 2025. Data for Amazon | AMZN - EPS Earnings Per Share including historical, tables and charts were last updated by Trading Economics this last December in 2025.

• This is the Historical Stock Market Data of five major Big Tech companies: NVIDIA (NVDA), Apple (AAPL), Microsoft (MSFT), Google (GOOGL), and Amazon (AMZN) over a 15 years from January 1, 2010 to January 1, 2025.
• It includes daily stock data with opening and closing prices, highs, lows and trading volume.
• This dataset serves as a valuable resource for analyzing long term growth trends, volatility and market behavior of leading tech giants.
• By analyzing this dataset, we can gain a deeper understanding of NVDA, AAPL, MSFT, GOOGL, and AMZN's historical stock behavior over 15 years and make predictions about their future performance.

Columns Description:

1. Date: The trading date of the stock data entry.
2. Close_AAPL: Apple’s stock price at market close at the end of the trading days.
3. Close_AMZN: Amazon’s stock price at market close at the end of the trading days.
4. Close_GOOGL: Google’s stock price at market close at the end of the trading days.
5. Close_MSFT: Microsoft’s stock price at the end of the trading days.
6. Close_NVDA: NVIDIA’s stock price at the end of the trading days.
7. High_AAPL: The highest price of Apple’s stock reached during the trading days.
8. High_AMZN: The highest price of Amazon’s stock reached during the trading days.
9. High_GOOGL: The highest price of Google’s stock reached during the trading days.
10. High_MSFT: The highest price of Microsoft’s stock reached during the trading days.
11. High_NVDA: The highest price of NVIDIA’s stock reached during the trading days.
12. Low_AAPL: The lowest price of Apple’s stock reached during the trading days.
13. Low_AMZN: The lowest price of Amazon’s stock reached during the trading days.
14. Low_GOOGL: The lowest price of Google’s stock reached during the trading days.
15. Low_MSFT: The lowest price of Microsoft’s stock reached during the trading days.
16. Low_NVDA: The lowest price NVIDIA’s stock reached during the trading days.
17. Open_AAPL: Apple’s opening stock price at the beginning of the trading days.
18. Open_AMZN: Amazon’s opening stock price at the beginning of the trading days.
19. Open_GOOGL: Google’s opening stock price at the beginning of the trading days.
20. Open_MSFT: Microsoft’s opening stock price at the beginning of the trading days.
21. Open_NVDA: NVIDIA’s opening stock price at the beginning of the trading days.
22. Volume_AAPL: The number of shares traded of Apple’s stock during the trading days.
23. Volume_AMZN: The number of shares traded of Amazon’s stock during the trading days.
24. Volume_GOOGL: The number of shares traded of Google’s stock during the trading days.
25. Volume_MSFT: The number of shares traded of Microsoft’s stock during the trading days.
26. Volume_NVDA: The number of shares traded of NVIDIA’s stock during the trading days.

Usefulness of Data:

1. Trend Analysis: This dataset can be used for the analysis of long term stock price trends for major 5 tech companies. By analyzing this dataset and taking deep insights about the data and stock patterns over 15 years, investors can identify potential opportunities.
2. Volatility and Risk Assessment: The data helps to assess the volatility of 5 big tech companies' stocks by comparing highs and lows and provides the management strategies to the investors.
3. Predictive Modeling: With stock prices, this dataset can be used for developing predictive models such as forecasting future stock prices using techniques such as ARIMA, SARIMAX, or Deep Learning Models.
4. Comparative Analysis: By analyzing this Dataset, researchers and analysts can compare the performance of NVIDIA, Apple, Microsoft, Google, and Amazon over 15 years, which helps to identify trends in the stock market and relative growth between these companies.
5. Market Behavior Understanding: By analyzing how each stock reacts to major market events (e.g., earnings reports & macroeconomic changes, etc.), we can understand the companies' growth & patterns.

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Further details about the study are available at the following links:https://osf.io/pcwahhttps://osf.io/xsb9t

Context

The subject matter of this dataset contains the stock prices of the 10 popular companies ( Apple, Amazon, Netflix, Microsoft, Google, Facebook, Tesla, Walmart, Uber and Zoom)

Content

Within the dataset one will encounter the following: The date - "Date" The opening price of the stock - "Open" The high price of that day - "High" The low price of that day - "Low" The closed price of that day - "Close" The amount of stocks traded during that day - "Volume" The stock's closing price that has been amended to include any distributions/corporate actions that occurs before next days open - "Adj[usted] Close" Time period - 2015 to 2021 (day level)

Tasks - Exploratory Data Analysis - Tell a visualization story - Compare stock price growth between companies - Stock price prediction - Time series analysis

Further details about the study are available at the following links:https://osf.io/pcwahhttps://osf.io/xsb9t

According to estimates, Amazon claimed the top spot among online retailers in the United States in 2023, capturing 37.6 percent of the market. Second place was occupied by the e-commerce site of the retail chain Walmart, with a 6.4 percent market share, followed in third place by Apple, with 3.6 percent.

Amazon’s continued success

Amazon has long dominated the e-commerce market as the world’s favorite online marketplace. In 2022, company hit over half a trillion U.S. dollars in net sales. The United States is by far Amazon’s most profitable market, as the U.S. branch generated over 356 billion U.S. dollars in sales in 2022. Germany ranked second, with 33 billion dollars, followed closely by the United Kingdom with 30 billion dollars.

Online shopping on the rise

Online shopping has grown significantly over the past decade, with more people turning to the internet for their shopping needs. The proof is in the numbers: the U.S. e-commerce industry was worth almost a trillion dollars in 2023. By 2027, forecasts show that the online market will grow to more than 50 percent. U.S. online shoppers purchase fashion and food and beverages the most via the internet.

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.

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.

Amazon is one of the most recognisable brands in the world, and the third largest by revenue. It was the fourth tech company to reach a $1 trillion market cap, and a market leader in e-commerce,...

Amazon enjoyed staggering sales growth in United Kingdom over the past decade, taking net sales from roughly four billion to almost 33.6 billion U.S. dollars in 2023. That makes the UK the retail behemoth’s second biggest European market, sitting behind Germany where the company reported total net sales of about 37.6 billion U.S. dollars in 2023.

Amazon’s other UK presence Amazon runs 20 distribution services in the UK, where Amazon has its largest European logistics and fulfillment presence. Operating under the “Amazon UK Services” name, the retailer generated over two billion British pounds in 2018. This represented over 200 percent turnover growth since 2015.

Consumers have no problem shopping with Amazon

In proportion to the pace Amazon’s retail empire is expanding, worries are voiced within the industry about the monopoly held by the retailer, not to mention the privacy concerns revolving around Amazon’s own brand smart devices. Yet shoppers seem unfazed, as convenience and variety offered by the retailer convert more and more people into being Amazon shoppers. A recent survey conducted with UK shoppers found out that only a small share of consumers felt guilty about or actively chose not shopping with Amazon. In comparison, nearly one quarter of those surveyed said they “loved” shopping with Amazon.

Index Time Series for Direxion Daily AMZN Bear 1X Shares. The frequency of the observation is daily. Moving average series are also typically included. The fund, under normal circumstances, invests at least 80% of the it"s net assets (plus borrowings for investment purposes) in financial instruments, including swap agreements and options, that, in combination, provide 1X daily inverse (opposite) or short exposure to AMZN, consistent with the fund"s investment objective. It is non-diversified.

This Dataset contains the Stock prices of Amazon.com Company the opening price, closing price, low price etc.. Use these Data and Predict the Stock Prices for upcoming years. Available timeframes: Monthly(MN1), Weekly(W1), Daily(D1), 4-Hourly(H4), Hourly(H1), 30-Minutes(M30), 15-Minutes(M15), 10-Minutes(M10), 5-Minutes(M5).

Amazon.com D1 Daily timeframe

   datetime open high  low close  volume
0 1998-01-02 3.00 3.00 2.87  2.97 2646000
1 1998-01-05 2.93 2.96 2.82  2.85 5762000
2 1998-01-06 2.81 2.92 2.80  2.89 6258000
3 1998-01-07 2.90 2.90 2.81  2.86 4500000
4 1998-01-08 2.81 2.82 2.71  2.76 9898000


... ... ... ... ... ... ... ...

  datetime  open  high   low  close  volume


6634 2024-03-08 176.87 178.79 174.33 175.33 24047313
6635 2024-03-09 175.32 175.36 175.31 175.35   21048
6636 2024-03-11 174.31 174.47 171.47 171.97 17955062
6637 2024-03-12 173.50 176.76 171.98 175.39 22960027
6638 2024-03-13 175.90 177.62 175.55 176.54 18901563

Amazon.com H1 Hourly timeframe

       datetime open high  low close volume
0 1998-01-02 16:00:00 3.00 3.00 2.95  2.95 100000
1 1998-01-02 17:00:00 2.95 2.95 2.87  2.93 976000
2 1998-01-02 18:00:00 2.90 2.93 2.88  2.90  74000
3 1998-01-02 19:00:00 2.93 2.95 2.88  2.91 714000
4 1998-01-02 20:00:00 2.90 2.93 2.90  2.90 188000


... ... ... ... ... ... ... ...

       datetime  open  high   low  close  volume


46649 2024-02-27 20:00:00 174.29 174.39 172.94 173.11 2943912
46650 2024-02-27 21:00:00 173.09 173.45 172.86 173.13 2306746
46651 2024-02-27 22:00:00 173.13 173.89 173.13 173.87 2262580
46652 2024-02-27 23:00:00 173.87 173.89 173.28 173.53 3071271
46653 2024-02-28 00:00:00 173.53 173.55 173.48 173.54 3140440

Amazon reported $162.75B in Operating Expenses for its fiscal quarter ending in September of 2025. Data for Amazon | AMZN - Operating Expenses including historical, tables and charts were last updated by Trading Economics this last December in 2025.

Bowlero (BOWL) is another post-pandemic cyclical vs structural setup within discretionary that appears asymmetric to the downside on increasing awareness that fundamental boost from highly favorable reopening dynamics including 10ppts of margin improvement is unsustainable. This also looks like another SPAC management team running yesterday’s “top line growth at any cost” playbook with deteriorating same-center growth behind more aggressive M&A cadence and taking 9ppts of price despite posting record margins in F23 (Jun) nearly +10ppts above F19. Timing appears favorable despite YTD declines as downward revision cycle has just gotten started with F4Q miss/F1Q below and the reopening surge in OOH entertainment rapidly normalizing across both consumer and corporate end markets. BOWL is especially vulnerable given magnitude of overperformance vs F19 in part on underappreciated heavy use of surge pricing to exploit one-time demand spike/price-inelastic consumer behavior that boosted operating leverage as evidenced by near-50% F22 AEBITDA flowthrough that’s already reverted to 28% in F23. Deteriorating margins should accelerate the current downward revision cycle and usher in hefty giveback uncertainty especially given F23 AEBITDA of $354M is >2x F19’s $161M. On persistent negative comps/falling estimates premium 8x NTM EV/AEBITDA multiple appears at risk with closest peers including theme parks at 6-7x and LBEs i.e. close peer PLAY at 5x and every turn ~$2/share. At 7x F24 AEBITDA -15% below MP of just-issued guide which is still 2x F18-19 levels this stock would trade at $7/share representing -30% additional downside. Not hard to imagine further downside toward the SPAC graveyard sub-$5 if the strategy to provide exit liquidity for mom & pops at the top of an unprecedented demand spike unravels, potentially reckless capital misallocation to keep the stock promote alive persists and/or consumer macro deteriorates materially which would likely make financial leverage increasingly top of mind. Apple Stock Valuation Netflix Stock Valuation Microsoft Stock Valuation Meta Stock Valuation Tesla Stock Valuation Amazon Stock Valuation Citibank Stock Valuation Nvidia Stock Valuation AMD Stock Valuation Best Buy Stock Valuation Alphabet Stock Valuation Home Depot Stock Valuation JPMorgan Stock Valuation Red Flags Consistent with its SPAC asset-class heritage there are also multiple red flags here including: 1/ Management team lacking public company experience and recent leadership reshuffling including longtime CFO relinquishing his role right in front of fiscal year end (apparently for medical reasons) and being replaced by former BALY CFO. 2/ Business model to aggressively roll up (primarily) mom & pop bowling center operators despite post- deSPAC material weaknesses in internal control over financial reporting (since remediated per company mgmt with auditor not req’d to sign off given EGC status). 3/ Anticipated future acquisitions baked into guide/Street estimates and recent expansion of M&A targets beyond bowling alleys to include broader LBE space such as go-kart and laser-tag venues to accommodate more aggressive acquisition pace. 4/ Cap table contingencies including earnout thresholds of 11.4M shares at $15 (satisfied; 50% to sponsor and 50% to CEO T Shannon) and another 11.4M at $17.50 which combined with $144M of PIK convertible preferred. In true SPAC fashion this had masked true fully diluted shares out which mgmt seemingly attempted to subsequently address by ramping up share repo and buying 30% of the convertible preferred following the initial earnout award. 5/ Capital misallocation seemingly driven by optics including aggressive return of capital despite 3x net leverage. This includes $134M of share repo despite GAAP losses/lack of tax shield/premium valuation and initial aggressive repurchase of convertible preferred immediately after broader increased awareness of contingencies (Atairos sale). Recently announced $90M Lucky Strike buy – its largest acquisition thus far - also appears to be driven by desire to offset expected giveback within existing base. 6/ Heavy YTD insider selling including nearly 1.5M shares by CEO and 4.9M-share block in Mar by sponsor Atairos immediately following satisfaction of initial contingency threshold. Both CEO (2.3M shares) and former CFO (4.4M shares) implemented new 10b-5s prior to F4Q end. 7/ Aggressive adjustments including 36% of $354M F22 AEBITDA vs CFFO $218M and $75M FCF (including $7M proceeds from sale of PP&E) before $111M of completed acquisitions. 8/ Selective disclosure including change in QTD revenues vs pre-pandemic disclosure from weekly to trailing 13 weeks at F2Q likely to obscure massive comp wall that it hit starting in late Feb on reopening surge a year ago and support insider sales. This disclosure was eliminated as of the just- reported F4Q. 9/ Ongoing legal overhang from civil suit alleging widespread discriminatory business practices including dozens of age discrimination and retaliation claims that authorities apparently want to settle for $60M according to CNBC. Given the typical red flags here the thesis could just as easily be that <10 out of 200-strong class of 2021 deSPACs trade at/above $10/share and BOWL is one of them. Catalysts Potential catalysts include: 1/ Continued downward revision cycle that just commenced with F4Q 2023 miss/F1Q 2024 below against seemingly aggressive guidance that assumes same-center SSS recover to flat in F2Q-3Q and inflect positively in F4Q, $160M of acquisitions vs $112M in F23 and $73M in F22 and just 50bps of AEBITDA margin decline to 33% at MP +900bps vs F19. 2/ Increasing evidence of discretionary downturn including return of price elasticity/consumer fatigue in LBEs and further slowing in corporate/events business against extremely tough F23 compares. 3/ Introduction of competition into narrative as localized LBE expansion accelerated coming out of the pandemic driven by initial post-pandemic surge in group OOH entertainment demand and BOWL benefitted from outsized ancillary growth including two-year +70% F&B and +72% amusements CAGRs. Portfolio Optimization Efficient Frontier Calculator Asset Correlations Backtest Portfolio Portfolio Visualizer Online Monte Carlo Simulation Risks 1/ Pandemic margin lift proves sustainable and mgmt proves able to execute against F24 guide. 2/ Despite heavily favorable sellside skew positioning has become increasingly bearish incl mid-teens SI up from HSD% in 1Q. BOWL reported F4Q yesterday and expected weak results including negative inflection in sales/SSS/AEBITDA got defended as simply reflecting tough initial reopening comps. Description Bowlero is the world’s largest operator of bowling entertainment centers that went public Dec 2021 in a deSPAC transaction with ISOS Acquisition. The company operates 328 centers as of F23 end (Jun) and recently agreed to acquire high-end operator Lucky Strike for $90M (342 centers PF). Even more recently a two-venue buy expanded its targets to laser tag and go-kart venues because according to CEO T Shannon they "align seamlessly" with bowling alley rollup strategy.