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AAPL Multi-Class Trend Prediction Data
Stock & Market Data
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About
Historical Apple stock data spanning ten years, structured for multi-class classification designed to predict the following day's price movement. This data product combines daily price, volume, and a curated set of technical indicators. The defined prediction objective classifies the trend as bullish (price increase greater than 0.5%), bearish (price decrease greater than 0.5%), or neutral (price movement within a $\pm$0.5% range). This resource is valuable for developing predictive models using machine learning techniques.
Columns
The data contains 20 columns, providing fundamental trading information alongside technical analysis metrics:
- date, stock date: Date fields for the trading day.
- open, high, low, close: Standard daily trading prices.
- volume: Daily trading volume.
- SMA (Simple Moving Average) (sma_50, sma_100): Used to assess whether a price will continue or reverse a trend.
- EMA (Exponential Moving Average) (ema_50, ema_100): Weighted average that places greater emphasis on recent price data points.
- RSI (Relative Strength Index) (rsi_7, rsi_14): Measures the speed and magnitude of recent price changes, helpful for evaluating overvalued or undervalued conditions.
- Bollinger Band (bollinger): Generates signals for oversold or overbought conditions.
- MACD (Moving Average Convergence Divergence): A momentum indicator showing the relationship between two exponential moving averages.
- CCI (Commodity Channel Index) (cci_7, cci_14): Measures the difference between the current price and the historical average price.
- TR (True Range): Measures daily price range incorporating any gaps from the preceding day's closing price.
- ATR (Average True Range) (atr_7, atr_14): Measures volatility, averaging the true ranges over a specified period.
- target: The multi-class outcome (bullish, neutral, or bearish) for the next trading day's trend.
Distribution
The information is provided in a tabular format, contained within a CSV file named
aapl_2014_2023.csv. It includes 2,516 valid records across 20 distinct columns. The data integrity is high, with no reported mismatched or missing values. The overall file size is approximately 744 KB.Usage
This data product is perfectly suited for training and validating classification algorithms aimed at financial market prediction. It can be applied in studies determining optimal entry and exit points for trading, or in evaluating volatility using metrics like Average True Range. Specific indicators, such as MACD, can be isolated to track momentum, while Bollinger Bands can be used to develop trading strategies based on identifying overbought or oversold market conditions.
Coverage
The dataset focuses on Apple stock prices and indicators spanning a decade, from the start of 2014 through to the end of 2023. The subject matter relates to investments in the United States market. Updates are anticipated annually.
License
CC0: Public Domain
Who Can Use It
Machine Learning Developers seeking labelled data for training stock trend classification models. Financial Analysts interested in quantifying market risk and volatility using established technical metrics. Algorithmic Traders designing and backtesting decision-making rules based on multiple moving averages and momentum indicators.
Dataset Name Suggestions
- Apple Stock Price Trend Indicators (2014-2023)
- AAPL Multi-Class Trend Prediction Data
- Ten Years of Apple Stock Indicators for ML
Attributes
Original Data Source: AAPL Multi-Class Trend Prediction Data
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