Duplicate from Adilbai/stock-dataset

Co-authored-by: Baidalin Adilzhan <Adilbai@users.noreply.huggingface.co>

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README.md

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+---
+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>

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