README.md

---
dataset_info:
  features:
  - name: Date
    dtype: string
  - name: Open
    dtype: float64
  - name: High
    dtype: float64
  - name: Low
    dtype: float64
  - name: Close
    dtype: float64
  - name: Volume
    dtype: int64
  - name: Dividends
    dtype: float64
  - name: Stock Splits
    dtype: float64
  - name: Ticker
    dtype: string
  - name: SMA_5
    dtype: float64
  - name: SMA_10
    dtype: float64
  - name: SMA_20
    dtype: float64
  - name: SMA_50
    dtype: float64
  - name: EMA_12
    dtype: float64
  - name: EMA_26
    dtype: float64
  - name: MACD
    dtype: float64
  - name: MACD_Signal
    dtype: float64
  - name: MACD_Histogram
    dtype: float64
  - name: RSI
    dtype: float64
  - name: BB_Middle
    dtype: float64
  - name: BB_Upper
    dtype: float64
  - name: BB_Lower
    dtype: float64
  - name: BB_Width
    dtype: float64
  - name: BB_Position
    dtype: float64
  - name: Volatility
    dtype: float64
  - name: Price_Change
    dtype: float64
  - name: Price_Change_5d
    dtype: float64
  - name: High_Low_Ratio
    dtype: float64
  - name: Open_Close_Ratio
    dtype: float64
  - name: Volume_SMA
    dtype: float64
  - name: Volume_Ratio
    dtype: float64
  - name: Close_lag_1
    dtype: float64
  - name: Close_lag_2
    dtype: float64
  - name: Close_lag_3
    dtype: float64
  - name: Close_lag_5
    dtype: float64
  - name: Close_lag_10
    dtype: float64
  - name: Volume_lag_1
    dtype: float64
  - name: Volume_lag_2
    dtype: float64
  - name: Volume_lag_3
    dtype: float64
  - name: Volume_lag_5
    dtype: float64
  - name: Volume_lag_10
    dtype: float64
  - name: Price_Change_lag_1
    dtype: float64
  - name: Price_Change_lag_2
    dtype: float64
  - name: Price_Change_lag_3
    dtype: float64
  - name: Price_Change_lag_5
    dtype: float64
  - name: Price_Change_lag_10
    dtype: float64
  - name: RSI_lag_1
    dtype: float64
  - name: RSI_lag_2
    dtype: float64
  - name: RSI_lag_3
    dtype: float64
  - name: RSI_lag_5
    dtype: float64
  - name: RSI_lag_10
    dtype: float64
  - name: MACD_lag_1
    dtype: float64
  - name: MACD_lag_2
    dtype: float64
  - name: MACD_lag_3
    dtype: float64
  - name: MACD_lag_5
    dtype: float64
  - name: MACD_lag_10
    dtype: float64
  - name: Volatility_lag_1
    dtype: float64
  - name: Volatility_lag_2
    dtype: float64
  - name: Volatility_lag_3
    dtype: float64
  - name: Volatility_lag_5
    dtype: float64
  - name: Volatility_lag_10
    dtype: float64
  - name: Future_Return_1d
    dtype: float64
  - name: Future_Up_1d
    dtype: int64
  - name: Future_Category_1d
    dtype: float64
  - name: Future_Return_5d
    dtype: float64
  - name: Future_Up_5d
    dtype: int64
  - name: Future_Category_5d
    dtype: float64
  - name: Future_Return_10d
    dtype: float64
  - name: Future_Up_10d
    dtype: int64
  - name: Future_Category_10d
    dtype: float64
  - name: Future_Return_20d
    dtype: float64
  - name: Future_Up_20d
    dtype: int64
  - name: Future_Category_20d
    dtype: float64
  splits:
  - name: train
    num_bytes: 374644429
    num_examples: 620095
  download_size: 335534650
  dataset_size: 374644429
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
task_categories:
- time-series-forecasting
- reinforcement-learning
- tabular-regression
language:
- en
tags:
- finance
- time-series
- stocks
- technical-analysis
- yahoo-finance
- reinforcement-learning
pretty_name: S&P 500 Comprehensive Stock Market Dataset
size_categories:
- 100K<n<1M
---
# 📈 S&P 500 Comprehensive Stock Market Dataset

<div align="center">
  
![Dataset](https://img.shields.io/badge/Dataset-S%26P%20500-blue)
![Records](https://img.shields.io/badge/Records-620K+-green)
![Features](https://img.shields.io/badge/Features-73-orange)
![License](https://img.shields.io/badge/License-MIT-yellow)
![Time Period](https://img.shields.io/badge/Time%20Period-5%20Years-purple)

</div>

## 🎯 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

```python
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
```python
# 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
```python
# 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
```python
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:

```bibtex
@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.

---

<div align="center">
  
**Built with ❤️ for the financial ML community**

[🤗 Hugging Face](https://huggingface.co/datasets/Adilbai/stock-dataset) • [📊 Dataset](https://huggingface.co/datasets/Adilbai/stock-dataset) • [🐛 Issues](https://huggingface.co/datasets/Adilbai/stock-dataset/discussions)

</div>