Bayesian models for alpha estimation.
This project is no longer actively developed but pull requests will be evaluated.
There are currently two models:
the returns model, which ingests a returns-stream. It computes (among other things) a forwards-looking gains parameter (which is basically a Sharpe ratio). Of interest is
P(gains > 0); that is, the probability that the algorithm will make money. Originally authored by Adrian Seyboldt.
the author model, which ingests the in-sample Sharpe ratios of user-run backtests. It computes (among other things) average Sharpe delivered at a population-, author- and algorithm-level. Originally authored by George Ho.
Installation and Usage
git clone firstname.lastname@example.org:quantopian/bayesalpha.git cd bayesalpha pip install -e .
To use (this snippet should demonstrate 95% of all use cases):
import bayesalpha as ba # Fit returns model trace = ba.fit_returns_population(data, ...) trace = ba.fit_returns_single(data, ...) # Fit author model trace = ba.fit_authors(data, ...) # Save to netcdf file trace.save('foo.nc') del trace # Load from netcdf file trace = ba.load('foo.nc')