alg_EG: Exponential Gradient Algorithm (UP)

In ngloe/olpsR: Algorithms and functions for On-line Portfolio Selection

Description

computes the Exponential Gradient Algorithm by Helmbold et al., 1998

Usage

alg_EG(returns, eta)

Arguments

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).
eta	learning rate

Value

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

Note

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

References

Helmbold, D. P.; Schapire, R. E.; Singer, Y. & Warmuth, M. K., On-Line Portfolio Selection Using Multiplicative Updates, Mathematical Finance, Blackwell Publishers Inc, 1998, 8, 325-347

Examples

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

# compute Exponential Gradient algorithm
EG = alg_EG(returns, eta=0.05)

# plot portfolio wealth
plot(EG)
# plot portfolio weights of kinar
plot(EG$Weights[,1], type="l")

ngloe/olpsR documentation built on May 23, 2019, 4:42 p.m.