• Stars
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  • Rank 329,721 (Top 7 %)
  • Language
    Python
  • License
    Other
  • Created over 6 years ago
  • Updated about 1 year ago

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Repository Details

Bayesian models to compute performance and uncertainty of returns and alpha.

bayesalpha

BayesAlpha

Bayesian models for alpha estimation.

This project is no longer actively developed but pull requests will be evaluated.

Models

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

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')

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