kelly_qp: Quadratic programming approximation to discrete time...
In shill1729/KellyCriterion: Log-optimal stock allocation
Description
Usage
Arguments
Details
Value
View source: R/mean_variance.R
Description
A quadratic programming approximation to the Kelly criterion, i.e. full risk mean-variance optimization.
Usage
1
Arguments
returns
the data-set of (daily) arithmetic returns. Assumes it is a matrix with colnames of tickers
direction
the direction of the bet: long or short
sample_mean
the mean vector
sample_cov
the covariance matrix
Details
The function estimates the entire sample set for mean returns and covariance and then runs the optimization routine by calling solve.qp from quadprog package.
Value
vector
shill1729/KellyCriterion documentation built on Oct. 12, 2020, 4:21 a.m.
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Description Usage Arguments Details Value
View source: R/mean_variance.R
Description
A quadratic programming approximation to the Kelly criterion, i.e. full risk mean-variance optimization.
Usage
1 |
Arguments
returns |
the data-set of (daily) arithmetic returns. Assumes it is a matrix with colnames of tickers |
direction |
the direction of the bet: long or short |
sample_mean |
the mean vector |
sample_cov |
the covariance matrix |
Details
The function estimates the entire sample set for mean returns and covariance and then runs the optimization routine by calling solve.qp from quadprog package.
Value
vector
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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