168 OLPS: A TOOLBOX FOR ONLINE PORTFOLIO SELECTION
• c: similarity threshold;
• λ ∈[0, 1): transaction cost rates; and
• opts: options for behavioral control.
Example Call the B
K
algorithm on the “NYSE (O)” dataset with default parameters
and a transaction cost rate of 0.
1: >> manager(’bk’, ’nyse-o’, {5, 10, 1, 0}, opts);
A.3.4.2 Nonparametric Nearest-Neighbor Log-Optimal Strategy
Description “Nonparametric nearest-neighbor-based sample selection” (B
NN
)
(Györfi et al. 2008) searches the price relatives whose preceding market windows
are within the nearest neighbor of latest market window in terms of Euclidean
distance:
C
N
(x
t
1
,w) ={w<i<t+1 : x
i−1
i−w
is among the NNs of x
t
t−w+1
},
where is a threshold parameter. Then, the strategy obtains an optimal portfolio via
solving Equation A.2.
Usage
bnn(fid, data, {K, L, λ}, opts)
• fid: file handle for writing log file;
• data: market sequence matrix;
• K: maximal window size;
• L: parameter to split the parameter space of each k;
• λ ∈[0, 1): transaction cost rates; and
• opts: options for behavioral control.
Example Call the B
NN
algorithm on the “NYSE (O)” dataset with default
parameters and a transaction cost rate of 0.
1: >> manager(’bnn’, ’nyse-o’, {5, 10, 0}, opts);
A.3.4.3 Correlation-Driven Nonparametric Learning Strategy
“Correlation-driven nonparametric sample selection” (CORN) (Li et al. 2011a)
identifies the similarity among two market windows via a correlation coefficient:
C
C
(x
t
1
,w) =
w<i<t+1 :
cov(x
i−1
i−w
, x
t
t−w+1
)
std(x
i−1
i−w
)std(x
t
t−w+1
)
≥ ρ
!
,
where ρ is a predefined threshold. Then, it obtains an optimal portfolio via solving
Equation A.2.
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SUMMARY 169
Usage
corn(fid, data, {w, c, λ}, opts);
cornu(fid, data, {K, L, c, λ}, opts);
cornk_run(fid, data, {K, L, pc, λ}, opts)
• fid: file handle for writing log file;
• data: market sequence matrix;
• w: window size;
• K: maximal window size;
• L: used to split the parameter space of each k;
• c: correlation threshold;
• pc: percentage of experts to be selected;
• λ ∈[0, 1): transaction cost rates; and
• opts: options for behavioral control.
Example Below we call three CORN algorithms with their default parameters.
1: >> manager(’corn’, ’nyse-o’, {5, 0.1, 0}, opts);
2: >> manager(’cornu’, ’nyse-o’, {5, 1, 0.1, 0}, opts);
3: >> manager(’cornk’, ’nyse-o’, {5, 10, 0.1, 0}, opts);
A.4 Summary
In this manual, we describe the OLPS toolbox in detail. OLPS is the first toolbox for
the research of OLPS problems. It is easy to use and can be extended to include new
algorithms and datasets. We hope this toolbox can facilitate further research on this
topic.
T&F Cat #K23731 — K23731_A001 — page 169 — 9/28/2015 — 20:46
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