Adilbai/stock-dataset

📈 S&P 500 Comprehensive Stock Market Dataset

🎯 Dataset Overview

This comprehensive dataset contains 620,095 daily observations of S&P 500 companies with 73 meticulously engineered features spanning the last 5 years. Designed specifically for time series forecasting, stock price prediction, and advanced financial modeling tasks.

📊 Key Statistics

Metric	Value
Total Records	620,095 daily observations
Features	73 comprehensive features
Time Period	Last 5 years
Companies	S&P 500 constituents
Data Source	Yahoo Finance API
Update Frequency	Daily market data

🚀 Quick Start

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Adilbai/stock-dataset")
df = dataset["train"].to_pandas()

# Basic info
print(f"Dataset shape: {df.shape}")
print(f"Date range: {df['Date'].min()} to {df['Date'].max()}")
print(f"Unique tickers: {df['Ticker'].nunique()}")

🔧 Feature Categories

📈 Basic Market Data (9 features)

• Date: Trading date timestamp
• OHLC Data: Open, High, Low, Close prices
• Volume: Number of shares traded
• Corporate Actions: Dividends, Stock Splits
• Ticker: Stock symbol identifier

📊 Technical Analysis Indicators (16 features)

Moving Averages

• SMA_5, SMA_10, SMA_20, SMA_50: Simple Moving Averages
• EMA_12, EMA_26: Exponential Moving Averages

Momentum Indicators

• MACD, MACD_Signal, MACD_Histogram: MACD components
• RSI: Relative Strength Index (14-period)

Volatility Indicators

• BB_Middle, BB_Upper, BB_Lower: Bollinger Bands
• BB_Width, BB_Position: Bollinger Bands metrics
• Volatility: Historical volatility measure

⚙️ Engineered Features (16 features)

• Price_Change: Daily price change
• Price_Change_5d: 5-day price change
• High_Low_Ratio: High to low price ratio
• Open_Close_Ratio: Open to close price ratio
• Volume_SMA: Volume moving average
• Volume_Ratio: Volume to average ratio

⏳ Lagged Features (32 features)

Historical context with 10-period lags for:

• Price Lags: Close_lag_1 to Close_lag_10
• Volume Lags: Volume_lag_1 to Volume_lag_10
• Price Change Lags: Price_Change_lag_1 to Price_Change_lag_10
• RSI Lags: RSI_lag_1 to RSI_lag_10
• MACD Lags: MACD_lag_1 to MACD_lag_10
• Volatility Lags: Volatility_lag_1 to Volatility_lag_10

🎯 Target Variables (12 features)

Time Horizon	Return	Direction	Category
1-Day	Future_Return_1d	Future_Up_1d	Future_Category_1d
5-Day	Future_Return_5d	Future_Up_5d	Future_Category_5d
10-Day	Future_Return_10d	Future_Up_10d	Future_Category_10d
20-Day	Future_Return_20d	Future_Up_20d	Future_Category_20d

🎯 Use Cases

🔮 Primary Applications

• Stock Price Prediction: Forecast future prices using technical indicators
• Direction Classification: Predict price movement direction
• Risk Assessment: Analyze volatility and market risk patterns
• Trading Strategy Development: Backtest algorithmic strategies
• Financial Research: Academic computational finance research

🤖 Machine Learning Tasks

• Regression: Predict continuous returns (Future_Return_*)
• Binary Classification: Predict direction (Future_Up_*)
• Multi-class Classification: Predict movements (Future_Category_*)
• Time Series Forecasting: Leverage lagged features
• Anomaly Detection: Identify unusual market patterns

📝 Example Usage

Data Exploration

# View dataset structure
print(f"Dataset shape: {df.shape}")
print(f"Features: {df.columns.tolist()}")

# Target distribution
print(df['Future_Up_1d'].value_counts())
print(df['Future_Category_1d'].value_counts())

Feature Selection

# Technical indicators
technical_features = [
    'SMA_5', 'SMA_10', 'RSI', 'MACD', 
    'BB_Position', 'Volatility'
]

# Lagged features
lag_features = [col for col in df.columns if 'lag' in col]

# All targets
targets = [col for col in df.columns if 'Future_' in col]

Model Training Example

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import TimeSeriesSplit

# Prepare features and target
features = technical_features + lag_features
X = df[features].fillna(method='ffill')
y = df['Future_Return_1d']

# Time series split
tscv = TimeSeriesSplit(n_splits=5)
model = RandomForestRegressor(n_estimators=100)

# Train model
for train_idx, test_idx in tscv.split(X):
    X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]
    y_train, y_test = y.iloc[train_idx], y.iloc[test_idx]
    
    model.fit(X_train, y_train)
    predictions = model.predict(X_test)

⚠️ Important Considerations

🔴 Data Limitations

• Survivorship Bias: Only current S&P 500 constituents included
• Market Hours: Regular trading session data only
• Corporate Actions: Historical adjustments may affect patterns

⚡ Usage Guidelines

• Temporal Order: Maintain chronological order in train/test splits
• Look-ahead Bias: Avoid using future information in features
• Market Regimes: Performance may vary across market conditions
• Feature Correlation: Technical indicators share underlying price data

📊 Data Quality

✅ Quality Assurance

• Industry-standard technical indicator calculations
• Comprehensive historical context with multiple time horizons
• Robust data validation pipelines
• Proper handling of corporate actions and market holidays

🔧 Data Processing

• Forward-fill methodology for missing data
• Vectorized operations for consistency
• No look-ahead bias in feature construction
• Dividend and split adjustments included

📖 Citation

If you use this dataset in your research, please cite:

@dataset{adilbai_sp500_dataset,
  title={S&P 500 Comprehensive Stock Market Dataset},
  author={Adilbai},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/Adilbai/stock-dataset}
}

📄 License

This dataset is released under the MIT License. While the dataset compilation and feature engineering are provided under MIT license, users should be aware of Yahoo Finance's terms of service for the underlying data.

⚠️ Disclaimer

Important: This dataset is provided for educational and research purposes only. It should not be used as the sole basis for investment decisions. Past performance does not guarantee future results. Users should conduct their own research and consider consulting with financial advisors before making investment decisions.

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