Checks whether your backtest data is lying to you: survivorship detection and CI integrity gates for financial price panels.
backtest-bias
Checks whether your backtest data is lying to you.
Most backtests don't fail loudly. They flatter you quietly, because the data underneath them is missing the stocks that died. This library tests your price panel for that, in one line, and tells you roughly what it costs when it finds it.
The measured numbers this library is built on
These are not estimates. I measured them on real Indian market data and published the write-ups:
- 24% of the top-500 Indian stocks (as of 2015) are invisible to yfinance today: delisted,
- Survivor-only universes inflated equal-weight returns by +0.8 to +3.2 pp/yr depending on
- On the most widely used Kaggle NSE dataset, index-membership look-ahead added +10% terminal
- How much of a universe should be dead? Measured across six top-500 vintages (2012-2022,
Citing this
If you use the library or the measured constants in BIAS_TABLE.md, cite it as:
Jain, A. (2026). backtest-bias: survivorship and integrity checks for financial price panels.
Zenodo. https://doi.org/10.5281/zenodo.21770386
That DOI is the concept DOI: it always resolves to the latest version. GitHub also reads CITATION.cff in this repo, so the "Cite this repository" button in the sidebar produces BibTeX and APA directly.
Install
pip install backtest-bias30 seconds to a verdict
import pandas as pd
from backtestbias import checksurvivorship
prices = pd.readcsv("mypanel.csv") # wide (date x symbols) or long (date/symbol/close) report = check_survivorship(prices) print(report.summary())
survivorship check: 412 symbols over 9.2y, 0 died in-window (0%)
verdict: SEVERE - 412 names over 9.2y with zero deaths is the survivor-only signature;
comparable universes lose 22%-28% of names over 9y (measured)
expect EW returns inflated roughly +0.8-3.2 pp/yr vs an honest universe (measured,
vintage-dependent; see backtest_bias.REFERENCES)What v0.1 ships
| function | what it answers | |---|---| | check_survivorship(prices) | does my universe contain the stocks that died, or only the winners? Full report with severity and a measured bias estimate | | deadnameratio(prices) | one number: what fraction of my names end before the panel does. 0.0 = pure survivor panel | | assert_integrity(prices) | CI gate: raise if the panel smells survivor-only, so a silent re-download of bad data fails your pipeline instead of flattering your backtest |
Input handling is forgiving: wide panels, long frames, sniffed column names, NaN-padded histories. Anything the library cannot judge honestly, it raises instead of guessing.
Roadmap
- v0.2: look-ahead / point-in-time violations: fundamentals dated by period instead of
- v0.3: rename-continuity and corporate-action gap detection
Who, and how to get this run on your own data
I'm Ayan Jain. I build point-in-time Indian equity data and audit backtests and datasets for the biases that inflate them. The measured numbers above come from those audits.
If you want this class of check run on your own backtest or dataset by a person instead of a library, that's my Bias Check: you send the backtest or data, and within 48 hours you get a written verdict on survivorship, look-ahead / point-in-time integrity, cost realism, and marking, with what's wrong and roughly what it costs in return terms. Fixed price, Rs 7,500. Start it through the intake form or email ayanjain259@gmail.com. Larger or ongoing work is scoped separately, tell me the problem and I'll send a quote.
Public data and replication: a survivorship-free Indian equity dataset (NSE/BSE) and a runnable notebook that visualizes the bias on a sample are on Kaggle under financebroski (the dataset and the notebook).
The Bias Table: every measured number behind this library, one page, citable: BIAS_TABLE.md. New rows land by email at The Bias Ledger.
MIT licensed. Issues and war stories welcome, especially datasets that fooled you.
