---
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`:

```python
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](https://xiaoyaoblob.gumroad.com/l/aygokj).

---

📬 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**.