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.

To install:

git clone [email protected]: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')