Create README.md

README.md

@@ -0,0 +1,124 @@
+---
+language:
+- en
+license: cc-by-4.0
+size_categories:
+- 10K<n<100K
+task_categories:
+- time-series-forecasting
+- tabular-classification
+tags:
+- finance
+- stock-market
+- technical-indicators
+- time-series
+- trading
+- OHLCV
+pretty_name: Hourly Stock Prices with Technical Indicators (2023)
+---
+
+# Hourly Stock Prices + Technical Indicators (2023)
+
+This dataset contains **hourly OHLCV price data** and key **technical indicators**
+for 8 major U.S. tickers across different sectors. Perfect for time series forecasting,
+technical analysis, and machine learning projects.
+
+**Coverage:** January 3, 2023 – December 18, 2023  
+**Symbols:** AAPL, MSFT, NVDA, JPM, XOM, SPY, TSLA, AMZN  
+**Records:** 11,202  
+**Size:** 2.16 MB  
+
+---
+
+## 📊 Columns
+
+| Column | Description |
+|---------|-------------|
+| timestamp | Date & time in UTC (YYYY-MM-DD HH:MM:SS) |
+| symbol | Stock ticker |
+| open, high, low, close, volume | OHLCV data |
+| sma_10, sma_50 | Simple moving averages |
+| ema_20 | Exponential moving average |
+| rsi_14 | Relative Strength Index |
+| macd, macd_signal, macd_hist | MACD components |
+| volatility_20 | Rolling volatility (20-hour window) |
+| target_up_next | Binary target: 1 if next hour close ≥ 0.05% higher |
+
+---
+
+## ⚙️ Technical Details
+
+- **Data source:** Publicly available financial market data (2023), aggregated and preprocessed to include technical indicators and binary movement labels.
+- **Interval:** 1 hour (aggregated from minute-level data)
+- **Technical indicators:** Calculated using pandas with proper groupby operations per symbol
+- **Missing values:** 16 rows (0.14%) in `volatility_20` column - occurs at the start of each symbol's time series where insufficient history exists for 20-hour rolling window
+- **Timestamps:** UTC format, ISO 8601 compliant (`YYYY-MM-DD HH:MM:SS`)
+- **Metadata:** `metadata.json` contains full dataset generation details including date ranges, symbols, and target threshold
+
+## 📈 Data Quality
+
+- ✅ No duplicate records
+- ✅ All prices positive and valid
+- ✅ All volumes positive
+- ✅ Timestamps properly formatted
+- ✅ Target variable balanced (41.75% ups, 58.25% downs)
+
+---
+
+## 🚀 Quick Start
+
+### Load from Hugging Face
+```python
+from datasets import load_dataset
+import pandas as pd
+
+# Load dataset
+dataset = load_dataset("YOUR_USERNAME/hourly-stock-data-2023")
+df = pd.DataFrame(dataset['train'])
+
+# Convert timestamp to datetime
+df['timestamp'] = pd.to_datetime(df['timestamp'])
+
+print(df.head())
+```
+
+### Direct CSV loading
+```python
+import pandas as pd
+
+df = pd.read_csv('hf://datasets/YOUR_USERNAME/hourly-stock-data-2023/hourly_stock_prices_technical_indicators.csv')
+df['timestamp'] = pd.to_datetime(df['timestamp'])
+```
+
+---
+
+## 🧠 Example Usage
+
+### Load and explore
+```python
+import pandas as pd
+
+# Load dataset
+df = pd.read_csv('hourly_stock_prices_technical_indicators.csv')
+df['timestamp'] = pd.to_datetime(df['timestamp'])
+
+# Basic statistics
+print(f"Total records: {len(df):,}")
+print(f"Symbols: {df['symbol'].nunique()}")
+print(f"Date range: {df['timestamp'].min()} to {df['timestamp'].max()}")
+
+# Target distribution per symbol
+df.groupby('symbol')['target_up_next'].mean()
+```
+
+### Time series analysis
+```python
+# Filter for specific symbol
+aapl = df[df['symbol'] == 'AAPL'].set_index('timestamp')
+
+# Plot price with moving averages
+import matplotlib.pyplot as plt
+aapl[['close', 'sma_10', 'sma_50', 'ema_20']].plot(figsize=(12, 6))
+plt.title('AAPL Price with Technical Indicators')
+plt.show()
+```