Datasetpublic

paperswithbacktest/Stocks-Weekly-PharmaClinicalPredict

Stocks Weekly PharmaClinicalPredict Modelled outcome, duration and economic impact for pharmaceutical clinical trials, one row per trial. 91,981 rows, 9 columns. Updated by Papers With Backtest. Why It Matters A biotech's value is a probability-weighted pipeline, and the probabilities are what this dataset estimates: Event odds ahead of the readout: success_prediction is a modelled probability that a trial reaches its endpoint. Priced against the sponsor's market… See the full description on the dataset page: https://huggingface.co/datasets/paperswithbacktest/Stocks-Weekly-PharmaClinicalPredict.

sourceHugging Faceotherupdated 17d agoView on Hugging Face

0likes71downloads

Dataset Card

Stocks Weekly PharmaClinicalPredict

Modelled outcome, duration and economic impact for pharmaceutical clinical trials, one row per trial.

91,981 rows, 9 columns. Updated by Papers With Backtest.

Why It Matters

A biotech's value is a probability-weighted pipeline, and the probabilities are what this dataset estimates:

—Event odds ahead of the readout: success_prediction is a modelled probability that a trial reaches its endpoint. Priced against the sponsor's market capitalisation, it is a view on how much of the outcome is already in the stock.
—Timing: duration_prediction estimates how long the trial still has to run, which is what decides whether an event is inside or outside a holding period.
—Size of the move: economic_effect scores how material the trial is to the sponsor, separating a phase III lead asset from a minor indication.

Load It

Installation/Upgrade:

bash

pip install --upgrade pwb-toolbox

Load the Dataset:

python

from pwb_toolbox import datasets as pwb_ds

predictions = pwb_ds.load_dataset("Stocks-Weekly-PharmaClinicalPredict")
trials = pwb_ds.load_dataset("Stocks-Weekly-PharmaClinicalTrials")

# The predictions carry no ticker; the trial register is what maps them to one.
linked = predictions.merge(
    trials[["trial_id", "symbol", "datetime"]].drop_duplicates("trial_id"),
    on="trial_id",
    how="inner",
)

Columns

Column Name	Description
trial_id	Registry identifier for the trial. The join key to `Stocks-Weekly-PharmaClinicalTrials`.
source	Registry the trial was read from.
sponsor	Sponsoring organisation.
subsidiary	Subsidiary running the trial, where it differs from the sponsor.
official_title	Registered title of the trial.
success_prediction	Modelled probability, between 0 and 1, that the trial meets its endpoint.
success_composite	Composite score combining the success model with trial characteristics.
duration_prediction	Modelled remaining duration, in days.
economic_effect	Unitless index of how material the outcome is to the sponsor.

What This Data Does Not Cover

There is no ticker and no date column. The file is one row per trial, keyed by trial_id, and it carries neither symbol nor datetime. Mapping a prediction to a listed company means joining Stocks-Weekly-PharmaClinicalTrials, which holds both. Anything that needs a point-in-time view has to take its timestamps from that side too.

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.