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title: README
emoji: 📈
colorFrom: indigo
colorTo: green
sdk: static
pinned: false
---
# QuantFeatures AI
**Institutional-quality crypto market features from tick data, for independent quants and small teams: nearly 7 years of history, built for backtesting and live trading alike.**
Large funds have data teams that turn raw tick data into clean, systematically prepared research features. QuantFeatures provides that feature data without a data-engineering team: trade flow, trade-size statistics, covariance, liquidations, funding, open interest, and top of book.
- **Long, consistent history:** December 2019 to today, cleaned and prepared the same way across venues and years.
- **Backtest to production:** historical and real-time features come from the same computation, so what you backtest is what you run live.
## Free samples
Hourly features for the first UTC day of each month, December 2019 to October 2026. Access is instant after a short form.
| Dataset | Markets | Modules |
| --- | --- | --- |
| [binance-hourly-features](https://huggingface.co/datasets/QuantFeatures/binance-hourly-features) | Spot and perpetual (USDT) | 19 |
| [okx-hourly-features](https://huggingface.co/datasets/QuantFeatures/okx-hourly-features) | Perpetual (USDT) | 17 |
| [bybit-hourly-features](https://huggingface.co/datasets/QuantFeatures/bybit-hourly-features) | Perpetual (USDT) | 17 |
| [hyperliquid-hourly-features](https://huggingface.co/datasets/QuantFeatures/hyperliquid-hourly-features) | Perpetual (USDC) | 15 |
- **Cleaned and documented:** exchange outages and missing data are handled the same way everywhere; NaN means not computable, never a silent zero.
- **Easy to load:** Parquet, one config per module, one split per market.
```python
import pandas as pd
df = pd.read_parquet("hf://datasets/QuantFeatures/hyperliquid-hourly-features/data/TradeFlow/perp.parquet")
```
Field definitions, timestamp semantics, and coverage: [Feature Catalog](https://github.com/QuantFeatures/FeatureCatalog).
## Built for AI
- **Model-ready inputs:** consistent features across venues and years, explicit missing values, and standard Parquet that loads with `datasets` or pandas.
- **Agent-ready:** an MCP (Model Context Protocol) server lets AI assistants and agents discover features by meaning, read exact definitions, check coverage, and fetch rows as of any time. Available to customers on request.
## Full data on request
Continuous history for Hyperliquid and Binance, in two ways:
1. **Delivered datasets:** we source the tick data and deliver features at intervals from 10 minutes to 3 hours.
2. **Your own tick data:** if you license Tardis data, we compute features at any interval from 30 seconds to 1 hour.
Real-time features run on your own infrastructure with your own data feeds.
## Contact
[QuantFeatures@gmail.com](mailto:QuantFeatures@gmail.com) · [GitHub](https://github.com/QuantFeatures)
*QuantFeatures is a trade name of ValueFuse LLC. Research data, not trading recommendations or a promise of predictive performance.*
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