alg_EG: Exponential Gradient Algorithm (UP)
In ngloe/olpsR: Algorithms and functions for On-line Portfolio Selection

Description Usage Arguments Value Note References Examples

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

1	alg_EG(returns, eta)

`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

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.

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

# 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")