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pretty_name: Stocks Weekly ShortVolume
language:
- en
license: other
task_categories:
- time-series-forecasting
- tabular-regression
tags:
- finance
- quantitative-trading
- backtesting
- algorithmic-trading
- stocks
- equities
- weekly
size_categories:
- 1M<n<10M
extra_gated_prompt: "This dataset is free to browse and gated for download. Approval is tied to a Papers With Backtest subscription, which also covers the other datasets in this organisation and the strategy catalogue at https://paperswithbacktest.com. Plans and what each one includes: https://paperswithbacktest.com/pricing"
dataset_info:
features:
- name: symbol
dtype: string
- name: datetime
dtype: string
- name: short_volume
dtype: int64
- name: total_volume
dtype: int64
- name: short_volume_ratio_exchange
dtype: float64
- name: retail_short_ratio
dtype: float64
- name: institutional_short_ratio
dtype: float64
- name: market_maker_short_ratio
dtype: float64
splits:
- name: train
num_examples: 2569339
---
# Stocks Weekly ShortVolume
Weekly short-selling volume for US equities, split by the type of participant behind the trade.
**2,569,339 rows** over 6,402 symbols, 8 columns, covering **2011-01-07 to 2026-07-03**. Refreshed monthly.
## Why It Matters
Short volume is the flow side of short interest, and it arrives weekly rather than twice a month:
- **Pressure, not positioning:** `short_volume_ratio_exchange` measures how much of the week's trading was sold short. It moves before short interest does, because it counts trades rather than open positions.
- **Who is doing it:** The retail, institutional and market-maker splits separate genuine directional selling from the market-making leg that offsets a customer buy. The market-maker share is the part that carries the least information about direction.
- **Crowding:** A rising short share on falling volume is a different setup from a rising short share on rising volume, and both are visible here.
## Load It
**Installation/Upgrade:**
```bash
pip install --upgrade pwb-toolbox
```
**Load the Dataset:**
```python
from pwb_toolbox import datasets as pwb_ds
df = pwb_ds.load_dataset("Stocks-Weekly-ShortVolume", symbols=["AAPL"])
print(df.iloc[-1, :])
```
## Columns
| Column Name | Description |
| :--- | :--- |
| **symbol** | Stock ticker. |
| **datetime** | End of the reporting week. |
| **short_volume** | Shares sold short during the week. |
| **total_volume** | Total shares traded during the week. |
| **short_volume_ratio_exchange** | Short volume over total volume, at the exchange level. |
| **retail_short_ratio** | Share of the short volume attributed to retail flow. |
| **institutional_short_ratio** | Share attributed to institutional flow. |
| **market_maker_short_ratio** | Share attributed to market making, which is largely mechanical. |
Data provided by [SOV.AI](https://sov.ai).
## Access
Browsing the card and the schema is open to anyone. Downloading the files needs an approved request, tied to a subscription: [what each plan includes](https://paperswithbacktest.com/pricing). The same subscription covers the other datasets in this organisation.
## Elsewhere
- [Dataset page and coverage charts](https://paperswithbacktest.com/datasets/stocks-weekly-shortvolume)
- [The strategy catalogue](https://paperswithbacktest.com/strategies), 3,806 papers and 4,837 replicated strategies
- [`pwb-toolbox`](https://github.com/paperswithbacktest/pwb-toolbox), the loader used in the snippet above
- [`awesome-systematic-trading`](https://github.com/paperswithbacktest/awesome-systematic-trading), the replicated strategies with their measured Sharpe
- [Every dataset in this organisation](https://huggingface.co/datasets/paperswithbacktest)
Papers With Backtest publishes 32 datasets on the Hub and codes the papers that use them. Every strategy in the catalogue is run over its own full history before it is published, which is where the numbers above come from.
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