In [24]:

    

using JuMP, Mosek, DataFrames, PyPlot


    

In [25]:

    

data = readtable("seudo_stock_data.csv")
data=data[2:end]


    

    
Out[25]:




stock1
stock2
stock3
1
0.42
5.87
7.59
2
1.75
8.79
16.13
3
0.3
5.33
5.8
4
0.24
5.84
9.14
5
1.65
9.03
13.25
6
0.0
3.88
3.23
7
0.66
7.08
9.0
8
0.3
5.33
5.8
9
0.24
5.84
9.14
10
0.28
5.51
6.96
11
1.65
9.03
13.25
12
0.38
5.73
7.35
13
-1.24
3.91
6.71
14
-0.74
0.16
-2.63
15
1.4
13.24
18.78
16
-0.74
5.47
11.65
17
4.21
3.91
5.98
18
-3.59
2.94
9.36
19
1.78
9.83
14.11
20
-0.85
2.27
6.81
21
-0.54
-3.34
-6.56
22
-2.88
2.48
15.69
23
0.54
4.35
8.43
24
7.39
10.98
4.51
25
5.66
7.4
10.72
26
1.48
2.99
6.75
27
2.26
5.35
8.05
28
3.59
12.03
15.61
29
3.45
2.49
-0.33
30
1.8
7.61
12.52
&vellip
&vellip
&vellip
&vellip

In [26]:

    

data = convert(Array{Float64}, data)
plot([i for i in 1:length(data[:,1])],data[:,1],color="r", label="asset1")
plot(data[:,2],color="b",label="asset2")
plot(data[:,3],color="g",label="asset3")
legend()


    

    













    
Out[26]:





PyObject <matplotlib.legend.Legend object at 0x14b524fd0>

In [40]:

    

n, μ, Σ = length(data[1,:]), vec(mean(data,1)), cov(data)
result=[]
figure(figsize=(4.0,3.0))  
for r_min in 0.0:1.0:5.0
    port = Model(solver=MosekSolver(LOG=0))
    @variable(port, 0 ≤ x[1:n] ≤ 1)
    @constraint(port, sum(x) == 1)
    @constraint(port, dot(μ,x) ≥ r_min)
    @objective(port, Min, x' * Σ * x)
    solve(port)
    plot([i for i in 1:n], getvalue(x), label=r_min)
end
legend()


    

    













    
Out[40]:





PyObject <matplotlib.legend.Legend object at 0x14d021fd0>

In [ ]: