mdnh/hourly-stock-data-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

⚙️ 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

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

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

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

# 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()