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