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Stocks Weekly Earnings Surprise
Weekly earnings surprise probabilities and outcomes for publicly traded companies.
2,204,032 rows over 6,067 symbols, 8 columns, covering 2016-12-30 to 2026-07-03. Refreshed monthly.
Why It Matters
This dataset supplies high-frequency earnings-surprise context for equity strategies by:
- Pre-event positioning: Surprise probabilities guide sizing and hedging ahead of earnings announcements.
- Post-event drift: Actual vs. estimated EPS deltas enable backtests of drift and reversal behaviors.
- Cross-sectional filters: Combine probabilities with market cap to target investable, liquid names.
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-EarningSurprise", symbols=["MSFT"])
print(df.iloc[0, :])
Example Output:
symbol MSFT
datetime 2016-12-30 00:00:00
surprise_probability -0.441381
eps_surprise 0.05
actual_earning_result 0.84
estimated_earning 0.79
date_pub 2017-01-26T00:00:00
market_cap None
Columns
| Column Name | Description |
|---|---|
| symbol | Stock ticker. |
| datetime | Week-ending snapshot date (YYYY-MM-DD). |
| surprise_probability | Model-estimated probability of an earnings surprise. |
| eps_surprise | Difference between reported and estimated EPS. |
| actual_earning_result | Reported EPS value. |
| estimated_earning | Consensus EPS estimate. |
| date_pub | Publication timestamp of the earnings release. |
| market_cap | Market capitalization (USD). |
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 aboveawesome-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.
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