Datasets:

paperswithbacktest
/

Stocks-Quarterly-WikipediaViews

pretty_name: Stocks Quarterly WikipediaViews
language:
  - en
license: other
task_categories:
  - time-series-forecasting
  - tabular-regression
tags:
  - finance
  - quantitative-trading
  - backtesting
  - algorithmic-trading
  - stocks
  - equities
  - quarterly
  - fundamentals
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: views
      dtype: float64
    - name: relative_views
      dtype: float64
    - name: alpha_short
      dtype: float64
    - name: beta_short
      dtype: float64
    - name: alpha_long
      dtype: float64
    - name: beta_long
      dtype: float64
    - name: long_short_alpha
      dtype: float64
    - name: long_short_beta
      dtype: float64
    - name: search_pressure
      dtype: float64
  splits:
    - name: train
      num_examples: 8360636

Stocks Quarterly WikipediaViews

Quarterly Wikipedia page view volumes for US-listed companies.

8,360,636 rows over 2,193 symbols, 11 columns, covering 2015-07-31 to 2026-01-07. The most recent observation is 2026-01-07.

Why It Matters

This dataset brings attention-based signals into equity models by:

Attention signals: Spikes in page views can precede volatility, news coverage, or retail trading activity.
Event detection: Quarterly aggregation smooths noise while highlighting sustained interest trends.
Alternative data overlay: Combine with price/volume to craft attention-adjusted signals and risk alerts.

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-Quarterly-WikipediaViews", symbols=["AAPL"])
print(df.iloc[0, :])

Example Output:

symbol                             AAPL
datetime            2015-07-31 00:00:00
views                            9935.0
relative_views                 0.999732
alpha_short                    0.663812
beta_short                     0.675321
alpha_long                     0.554069
beta_long                      0.638651
long_short_alpha               0.474706
long_short_beta                 0.49371
search_pressure                0.541194

Columns

Column Name	Description
symbol	Stock ticker.
datetime	Quarter-end timestamp.
views	Total Wikipedia page views during the quarter.
relative_views	Page views scaled relative to the broader universe.
alpha_short	Short-term alpha estimate derived from view trends.
beta_short	Short-term beta estimate derived from view trends.
alpha_long	Long-term alpha estimate derived from view trends.
beta_long	Long-term beta estimate derived from view trends.
long_short_alpha	Combined long/short alpha estimate.
long_short_beta	Combined long/short beta estimate.
search_pressure	Normalized search pressure signal.

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.