| --- |
| pretty_name: Stocks Semimonthly Short Interest |
| language: |
| - en |
| license: other |
| task_categories: |
| - time-series-forecasting |
| - tabular-regression |
| tags: |
| - finance |
| - quantitative-trading |
| - backtesting |
| - algorithmic-trading |
| - stocks |
| - equities |
| - semimonthly |
| - short-interest |
| - short-selling |
| 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" |
| --- |
| |
| # Stocks Semimonthly Short Interest |
|
|
| The consolidated short interest of every US-listed name at each semimonthly settlement date, with the average daily volume and the days-to-cover ratio published beside it. December 2017 onward, 38 249 tickers. |
|
|
| <!-- MEASURED:START - rewritten by dataset_cards/refresh.py --> |
| |
| Shape measured at the next refresh of `dataset_cards/facts.json`. Refreshed monthly. |
|
|
| <!-- MEASURED:END --> |
|
|
| ## Why It Matters |
|
|
| Short interest is the oldest sentiment and constraint variable in the cross-section: high short interest predicts low returns, changes in it predict reversals, days-to-cover prices the squeeze. The catalogue carried it inside `Stocks-Weekly-ShortOverShorted`, whose source is frozen at 2026-05-29; this is the raw exchange figure, refreshed. |
|
|
| `date` is the **settlement date** the position is measured at, mid-month and month end. The figure is not public on that day: FINRA disseminates each report about two weeks later. Measured against the weekly series above, each value showed up 15 to 21 days after its settlement date. Lag `date` by at least three weeks before trading on a row, or the backtest sees the short interest before the market did. |
|
|
| `days_to_cover` is the exchange's own ratio, short interest over average daily volume, and does not match the weekly series' column of the same name, which was computed otherwise. |
|
|
| ## Load It |
|
|
| ```bash |
| pip install --upgrade pwb-toolbox |
| ``` |
|
|
| ```python |
| from pwb_toolbox import datasets as pwb_ds |
| |
| df = pwb_ds.load_dataset("Stocks-Semimonthly-ShortInterest", symbols=["GME"]) |
| print(df.iloc[-1, :]) |
| ``` |
|
|
| ## Columns |
|
|
| | Column Name | Description | |
| | :--- | :--- | |
| | **symbol** | Stock ticker. | |
| | **date** | Settlement date of the short-interest report (mid-month or month end). | |
| | **short_interest** | Shares sold short and not yet covered, as reported to FINRA. | |
| | **avg_daily_volume** | Average daily share volume over the reporting period. | |
| | **days_to_cover** | Short interest divided by average daily volume. | |
| |
| Source: FINRA consolidated short interest, as disseminated by the exchanges. |
| |
| ## 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 every dataset in this organisation and the strategy catalogue. |
| |
| ## Elsewhere |
| |
| - [The strategy catalogue](https://paperswithbacktest.com/strategies) |
| - [`pwb-toolbox`](https://github.com/paperswithbacktest/pwb-toolbox), the loader used in the snippet above |
| - [Every dataset in this organisation](https://huggingface.co/datasets/paperswithbacktest) |
| |
