README.md

metadata

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:

pip install --upgrade pwb-toolbox

Load the Dataset:

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.

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. The same subscription covers the other datasets in this organisation.

Elsewhere

• Dataset page and coverage charts
• The strategy catalogue, 3,806 papers and 4,837 replicated strategies
• pwb-toolbox, the loader used in the snippet above
• awesome-systematic-trading, the replicated strategies with their measured Sharpe
• Every dataset in this organisation

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.