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‎Project_06_Stock_Prices/README.md‎

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# Comparative Analysis of Tech Giants: Stock Price Performance and Trend Analysis
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## Project Overview
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This project compares the stock prices of APPLE, GOOGLE, MICROSOFT, and AMAZON. It retrieves historical stock prices, calculates percentage changes over a specified period, and identifies the best-performing stock. Additionally, it analyzes trends in the data.
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## Key Features
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- Retrieves stock data using the `pandas_datareader` library.
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- Calculates percentage change in stock prices for each company.
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- Identifies the company with the best stock performance.
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- Displays historical stock prices to analyze trends and patterns.
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## Libraries Used
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- **pandas**: For data manipulation and analysis.
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- **pandas_datareader**: For retrieving stock data from sources like Yahoo Finance.
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- **datetime**: For specifying the date range for stock data retrieval.
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## Code Explanation
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The project performs the following steps:
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1. **Stock Data Retrieval**: The program retrieves stock data for Apple, Google, Microsoft, and Amazon over the year 2023, using their respective stock symbols.
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2. **Percentage Change Calculation**: It calculates the percentage change in stock prices from the beginning to the end of the year for each company.
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3. **Best Performer Identification**: The program identifies which company's stock performed the best during the period.
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4. **Historical Data Display**: The historical stock prices are displayed to analyze trends or patterns over the specified time frame.
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## Code Structure
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- **Importing Libraries**: Import necessary libraries for data retrieval and handling.
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- **Company Dictionary**: Stores company names and their stock symbols.
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- **Date Range**: Defines the start and end dates for data retrieval.
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- **Data Retrieval**: Fetches stock data for each company.
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- **Percentage Change Calculation**: Computes and displays percentage changes in stock prices.
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- **Trend Analysis**: Shows historical stock prices for trend exploration.
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## Prerequisites
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- Python 3.x installed on your machine.
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## Sample Run
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### Percentage Change in Stock Prices
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```python
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Apple: -35.04%
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Google: -36.20%
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Microsoft: -36.29%
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Amazon: -43.52%
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```
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### Best Performer:
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```python
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Apple: -35.04% (Best Performer)
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```
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## Historical Stock Prices (Sample Output)
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- ### Apple
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```python
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Open High Low Close Volume
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Date
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2023-12-29 193.90 194.40 191.725 192.53 42672148
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2023-12-28 194.14 194.66 193.170 193.58 34049898
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2023-12-27 192.49 193.50 191.090 193.15 48087681
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2023-12-26 193.61 193.89 192.830 193.05 28919310
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2023-12-22 195.18 195.41 192.970 193.60 37149570
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```
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- ### Google
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```python
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Open High Low Close Volume
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Date
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2023-12-29 139.63 140.36 138.780 139.69 18733017
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2023-12-28 140.78 141.14 139.750 140.23 16045712
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2023-12-27 141.59 142.08 139.886 140.37 19628618
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2023-12-26 141.59 142.68 141.190 141.52 16780333
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2023-12-22 140.77 141.99 140.710 141.49 26532199
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```
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- ### Microsoft
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```python
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Open High Low Close Volume
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Date
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2023-12-29 376.00 377.160 373.4800 376.04 18730838
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2023-12-28 375.37 376.458 374.1600 375.28 14327013
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2023-12-27 373.69 375.060 372.8116 374.07 14905412
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2023-12-26 375.00 376.940 373.5000 374.66 12673050
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2023-12-22 373.68 375.180 372.7100 374.58 17107484
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```
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- ### Amazon
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```python
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Open High Low Close Volume
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Date
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2023-12-29 153.10 153.890 151.03 151.94 39823204
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2023-12-28 153.72 154.080 152.95 153.38 27057002
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2023-12-27 153.56 154.780 153.12 153.34 31434733
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2023-12-26 153.56 153.975 153.03 153.41 25067222
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2023-12-22 153.77 154.350 152.71 153.42 29514093
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```
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## Explanation
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This project focuses on comparing the stock prices of four major companies: Apple (AAPL), Google (GOOGL), Microsoft (MSFT), and Amazon (AMZN). The program retrieves current and historical stock prices for each company and calculates the percentage change in stock prices over a specified period. It then identifies which company's stock has performed the best.
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The `pandas_datareader` library is used to fetch the stock data from Stooq, a financial data source, within a defined date range (January 1, 2023, to December 31, 2023). The `datetime` module is used to handle the date range.
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A dictionary maps each company's name to its respective stock symbol, which is used to dynamically retrieve stock data for each company. The program calculates the percentage change in stock prices by comparing the closing prices on the start and end dates. The results are stored in a dictionary where the key is the company name and the value is the percentage change.
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The program prints the percentage change in stock prices for each company and identifies the company with the highest percentage change. This helps in determining the best-performing stock. Additionally, the program prints the first five rows of historical stock prices for each company, providing valuable insights into daily price movements and trading volumes.
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The output shows that all four companies experienced significant declines in their stock prices over the specified period, with Amazon showing the most significant decline. This indicates a general downtrend in the market. The historical stock price data provides valuable insights into daily price movements and trading volumes, which can be used for more detailed analysis and forecasting.
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## Insights
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- All four companies experienced a decline in stock prices during the specified period, with **Amazon** showing the most significant drop (-43.52%).
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- The trends in historical data reveal fluctuations influenced by market conditions, highlighting the need for further analysis to understand driving factors.
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## Future Enhancements
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1. **Advanced Data Visualization**:
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- Use libraries like `matplotlib` and `seaborn` to create visual representations of trends and patterns.
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2. **Real-Time Data Retrieval**:
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- Incorporate APIs like Alpha Vantage or Yahoo Finance for real-time stock data updates.
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3. **Sentiment Analysis**:
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- Integrate news sentiment analysis to correlate stock price trends with public sentiment.
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4. **Sector Comparison**:
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- Compare the performance of these companies against their respective sectors.
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5. **Predictive Analysis**:
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- Implement machine learning models to forecast future stock price trends.

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