Amazon - Stock Market Shares (2014 - 2024)

Asset Price Dataset Description

This dataset is a comprehensive collection of historical financial data on a specific asset, covering a wide range of information related to daily prices, trading volume and technical indicators. It is designed to provide a detailed, multi-faceted view of asset performance over time, enabling in-depth analysis and the application of various financial strategies.

Information on the columns of the dataset

1. Date: The specific date of the entry.
2. Opening: The opening price of the asset at the beginning of the day.
3. High: The highest price reached by the asset during the day.
4. Low: The lowest price reached by the asset during the day.
5. Closing: The price of the asset at the end of the day.
6. Adjusted Closing: The closing price adjusted for dividends and stock splits.
7. Volume: The number of shares traded during the day.
8. Amplitude: The difference between the highest and lowest price of the day (High - Low).
9. MA7: Moving average of the closing price of the last 7 days.
10. MA14: Moving average of the closing price of the last 14 days.
11. MA30: Moving average of the closing price over the last 30 days.
12. Daily Return: The percentage change in the closing price in relation to the previous day.
13. ATR (Average True Range): Moving average of the True Range (TR) for a given period, used to measure volatility.
14. RSI (Relative Strength Index): Relative Strength Index, a momentum indicator that measures the speed and change of price movements.
15. Annual growth percentage: Percentage of annual growth.
16. Percentage of daily growth: Percentage of daily growth.
17. Absolute Daily Growth: Daily absolute growth, the absolute difference in the closing price compared to the previous day.
18. Day: The day of the week.
19. Month: The month of the year.
20. TR (True Range): The biggest difference between:

• The maximum price of the day minus the minimum price of the day.
• The maximum price of the day minus the closing price of the previous day.
• The minimum price of the day minus the closing price of the previous day.

Applicability

1. Trend Analysis:

• Through historical data, it is possible to identify short and long-term price trends, helping analysts and investors make informed decisions about buying and selling assets.

2. Development of Negotiation Strategies:

• The data can be used to develop and test automated trading strategies, including the use of moving averages, relative strength indexes (RSI), and other technical indicators.

3. Volatility Study:

• With metrics such as Average True Range (ATR), the dataset allows measuring asset volatility over time, essential for risk management strategies and understanding asset stability.

4. Performance Assessment:

• The detailed history of opening, closing, high and low prices, as well as trading volume, allows an accurate assessment of the asset's performance in different periods.

5. Modeling and Forecasting:

• The data can be used to build predictive models using machine learning and statistical analysis techniques, providing predictions about future price movements.

6. Education and Research:

• For students and researchers, the dataset offers a rich source of real data to study financial markets, test hypotheses and perform simulations.

Importance

1. Informed Decision Making:

• Access to detailed historical data allows investors and analysts to make evidence-based decisions, reducing uncertainty and risk associated with financial markets.

2. Backtesting:

• It is possible to apply trading strategies to historical data to verify their effectiveness before implementing them in the real market, a crucial process for developing robust trading systems.

3. Comparative Performance Analysis:

• With consistent data, you can compare the asset's performance over different periods or with other assets, providing a clear perspective on its relative performance.

4. Pattern Identification:

• The dataset allows the identification of patterns and anomalies in price movements, which can be explored to develop trading strategies or to better understand the factors that influence the market.

5. Risk Management:

• Analyzing volatility and price behavior over time helps in building risk management strategies, essential for preserving capital and optimizing returns.

This dataset is a valuable tool for anyone involved in financial markets, from individual investors to market analysts and academic researchers, providing the necessary foundation for detailed analysis and informed financial decisions.