alg_SCRP: Successive Constant Rebalanced Portfolio Algorithm (SCRP)
In ngloe/olpsR: Algorithms and functions for On-line Portfolio Selection

Description Usage Arguments Details Value Note References Examples

copmutes the SCRP algorithm that directly adopts the Best Constant Rebalanced Portfolio until time t.

1	alg_SCRP(returns, method = "rand", ...)

`returns`	Matrix of price relatives, i.e. the ratio of the closing (opening) price today and the day before (use function `get_price_relatives` to calculate from asset prices).
`method`	The method used to calculate BCRP. "`rand`" generates random CRPs to find BCRP. By default the number of random portfolios is "`samplings=1000`". "`approx`" limits the set of CRPs to the portfolios of the form b=(b_1, 1-b_1), where b_1 runs from 0 to 1 in steps of length "`step=0.05`".
`...`	further arguments (`samplings`, `step`) dependend on the "`method`" argument.

For the "approx" method the calculation may require very much memory dependend on the number of assets and the "step" argument. If an error occurs due to memory problems the "rand" method may work.

Object of class OLP containing

`Alg`	Name of the Algorithm
`Names`	vector of asset names in the portfolio
`Weights`	calculated portfolio weights as a vector
`Wealth`	wealth achieved by the portfolio as a vector
`mu`	exponential growth rate
`APY`	annual percantage yield (252 trading days)
`sigma`	standard deviation of exponential growth rate
`ASTDV`	annualized standard deviation (252 trading days)
`MDD`	maximum draw down (downside risk)
`SR`	Sharpe ratio
`CR`	Calmar ratio

see also print.OLP, plot.OLP

The print method for OLP objects prints only a short summary.

Gaivoronski & Stella 2000, Stochastic Nonstationary Optimization for Finding Universal Portfolios http://mpra.ub.uni-muenchen.de/21913/

# load data
data(NYSE)
# select stocks
returns = cbind(comme=NYSE$comme, kinar=NYSE$kinar)

# calculate BCRP
SCRP_rnd = alg_SCRP(returns, method="rand", samplings=1000); SCRP_rnd
SCRP_approx = alg_SCRP(returns, method="approx", step=0.05); SCRP_approx
plot(SCRP_rnd, SCRP_approx)
plot(SCRP_approx$Weights, type="l")