license: cc-by-nc-4.0
task_categories:
- tabular-regression
- time-series-forecasting
language:
- en
tags:
- finance
- stock-market
- quant
- feature-engineering
pretty_name: QuantAlpha NASDAQ-100 Feature-Engineered Sample (NVDA)
short_description: >-
Explore a clean, ML-ready NVDA sample from the QuantAlpha NASDAQ-100
dataset—perfect for testing and research.
size_categories:
- n<1K
🚀 QuantAlpha NASDAQ-100 Feature-Engineered Sample
This dataset provides a representative sample of the full QuantAlpha NASDAQ-100 dataset. It allows researchers and quantitative traders to explore the schema and feature richness of the NVDA dataset before accessing the full NASDAQ-100 universe.
📁 Dataset Contents
The sample consists of one Parquet file containing a randomly selected month of data from 2024 for NVIDIA (NVDA):
| Ticker | Filename | Rows | Date Range |
|---|---|---|---|
| NVDA | NVDA_2024_month01.parquet |
~21 | January 2024 |
⚠️ Note: This is a limited sample. The full dataset includes all NASDAQ-100 constituents and multi-year historical coverage.
📊 Feature Overview
Each record contains 53 machine-learning-ready features, including:
- Trend Indicators: SMA ratios, MACD, ADX, Trend Persistence
- Momentum & Volatility: RSI, Stochastic Oscillator, ROC, Normalized ATR, Bollinger Band metrics
- Volume Metrics: On-Balance Volume (OBV), Volume Ratios
- Performance Metrics: Log Returns, 30-day Sharpe Ratio, 30-day Sortino Ratio
- Benchmark Analysis: Relative returns, Alpha, Beta vs. SPY and QQQ
- Market Microstructure: Price over Control (POC), Gap percentages, Z-scores
All features are cleaned, normalized, and free of look-ahead bias, making them ready for ML pipelines with XGBoost, LightGBM, or neural networks.
🛠 Usage
You can load the sample file directly into a Pandas DataFrame using fastparquet or pyarrow:
import pandas as pd
# Load the sample file
df = pd.read_parquet("NVDA_2024_month01.parquet")
# Inspect the data
print(df.info())
display(df.head())
📜 License
This sample is provided under the Creative Commons Attribution Non-Commercial 4.0 (CC BY-NC 4.0) license. For commercial licensing of the full NASDAQ-100 universe, please visit our Gumroad storefront.
📬 Contact & Support
If you have any questions about this dataset, licensing, or access to the full version, feel free to reach out:
📧 Email: quantalpha.global@gmail.com
Please note that this email is intended for dataset-related inquiries only.
We aim to respond within 1–2 business days.