Datasets:

paperswithbacktest
/

Stocks-Weekly-LiquidityPriceImprovement

pretty_name: Stocks Weekly LiquidityPriceImprovement
language:
  - en
license: other
task_categories:
  - time-series-forecasting
  - tabular-regression
tags:
  - finance
  - quantitative-trading
  - backtesting
  - algorithmic-trading
  - stocks
  - equities
  - weekly
size_categories:
  - 100K<n<1M
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: total_price_improvement
      dtype: float64
    - name: shares
      dtype: int64
    - name: price_improvement_per_share
      dtype: float64
    - name: average_price_improvement
      dtype: float64
  splits:
    - name: train
      num_examples: 772556

Stocks Weekly LiquidityPriceImprovement

Weekly metrics on price improvement and execution quality for US equities.

772,556 rows over 13,506 symbols, 6 columns, covering 2022-11-25 to 2026-07-10. Refreshed monthly.

Why It Matters

This dataset adds execution quality insights to trading models by:

Cost control: Price-improvement statistics quantify execution quality relative to quotes.
Venue selection: Use improvement rates to refine routing strategies and broker evaluation.
Model realism: Incorporate achievable price improvement into backtests for more accurate P&L expectations.

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

Example Output:

symbol                                        AAPL
datetime                       2022-11-25 00:00:00
total_price_improvement                   9294.824
shares                                     9701773
price_improvement_per_share                0.00075
average_price_improvement                 0.000958

Columns

Column Name	Description
symbol	Stock ticker.
datetime	Week-ending date (YYYY-MM-DD).
total_price_improvement	Total notional price improvement captured.
shares	Number of shares associated with the measurement.
price_improvement_per_share	Price improvement per share.
average_price_improvement	Average price improvement per trade.

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.