INTRODUÇÃO
Este relatório tem por objetivo organizar o estudo de otimização de portfólio:
- Etapa 1: Selecionar Ativos;
- Etapa 2:Otimizar o Portfólio;
- Etapa 3:Realizar Análise de Desempenho e Risco.
ETAPA 1 - SELEÇÃO DE ATIVOS
Inicialmente, serão considerados alguns ativos, sem maior aprofundamento. São eles:
- Ibovespa (^BVSP);
- Minerva (BEEF3.SA);
- Itaú (ITSA3.SA);
- Prio (PRIO3.SA);
- Weg (WEGE3.SA);
- CSN (CMIN3.SA);
- Renner (LREN3.SA);
- Transmissora Aliança de Energia Elétrica (TAEE11.SA);
- Banco do Brasil (BBAS3.SA);
- Cemig (CMIG3.SA).
Para obtenção dos dados será utilizada a função getSymbols do pacote quantmod.
# PACOTES UTILIZADOS:
library(quantmod)
prazo <- 180
# IMPORTAÇÃO DOS DADOS:
ibovespa <- getSymbols(Symbols = '^BVSP',
from = Sys.Date()-prazo,
to = Sys.Date(),
auto.assign = FALSE)
minerva <- getSymbols(Symbols = 'BEEF3.SA',
from = Sys.Date()-prazo,
to = Sys.Date(),
auto.assign = FALSE)
itau <- getSymbols(Symbols = 'ITSA3.SA',
from = Sys.Date()-prazo,
to = Sys.Date(),
auto.assign = FALSE)
prio <- getSymbols(Symbols = 'PRIO3.SA',
from = Sys.Date()-prazo,
to = Sys.Date(),
auto.assign = FALSE)
weg <- getSymbols(Symbols = 'WEGE3.SA',
from = Sys.Date()-prazo,
to = Sys.Date(),
auto.assign = FALSE)
csn <- getSymbols(Symbols = 'CMIN3.SA',
from = Sys.Date()-prazo,
to = Sys.Date(),
auto.assign = FALSE)
renner <- getSymbols(Symbols = 'LREN3.SA',
from = Sys.Date()-prazo,
to = Sys.Date(),
auto.assign = FALSE)
taesa <- getSymbols(Symbols = 'TAEE11.SA',
from = Sys.Date()-prazo,
to = Sys.Date(),
auto.assign = FALSE)
brasil <- getSymbols(Symbols = 'BBAS3.SA',
from = Sys.Date()-prazo,
to = Sys.Date(),
auto.assign = FALSE)
cemig <- getSymbols(Symbols = 'CMIG3.SA',
from = Sys.Date()-prazo,
to = Sys.Date(),
auto.assign = FALSE)O código acima “cria” objetos com os preços de abertura, fechamento, mais alto, mais baixo, ajustado e volume de operações para cada ação. O objeto é do tipo xts (série temporal). O próximo passo consiste em “juntar” o preço ajustado de todas os ativos em uma tabela única contendo uma coluna com as datas.
# PACOTES NECESSÁRIOS:
library(magrittr)
library(tibble)
library(janitor)
library(dplyr)
# ORGANIZANDO AS TABELAS:
tabela_ibovespa <- ibovespa %>%
as.data.frame() %>% # transforma em tabela
rownames_to_column() %>% # transforma a data da linha em coluna
clean_names() %>% # altera o nome das colunas
select(rowname, bvsp_adjusted) %>%
rename("data" = rowname)
tabela_minerva <- minerva %>%
as.data.frame() %>% # transforma em tabela
rownames_to_column() %>% # transforma a data da linha em coluna
clean_names() %>% # altera o nome das colunas
select(rowname, beef3_sa_adjusted) %>%
rename("data" = rowname)
tabela_itau <- itau %>%
as.data.frame() %>% # transforma em tabela
rownames_to_column() %>% # transforma a data da linha em coluna
clean_names() %>% # altera o nome das colunas
select(rowname, itsa3_sa_adjusted) %>%
rename("data" = rowname)
tabela_prio <- prio %>%
as.data.frame() %>% # transforma em tabela
rownames_to_column() %>% # transforma a data da linha em coluna
clean_names() %>% # altera o nome das colunas
select(rowname, prio3_sa_adjusted) %>%
rename("data" = rowname)
tabela_weg <- weg %>%
as.data.frame() %>% # transforma em tabela
rownames_to_column() %>% # transforma a data da linha em coluna
clean_names() %>% # altera o nome das colunas
select(rowname, wege3_sa_adjusted) %>%
rename("data" = rowname)
tabela_csn <- csn %>%
as.data.frame() %>% # transforma em tabela
rownames_to_column() %>% # transforma a data da linha em coluna
clean_names() %>% # altera o nome das colunas
select(rowname, cmin3_sa_adjusted) %>%
rename("data" = rowname)
tabela_renner <- renner %>%
as.data.frame() %>% # transforma em tabela
rownames_to_column() %>% # transforma a data da linha em coluna
clean_names() %>% # altera o nome das colunas
select(rowname, lren3_sa_adjusted) %>%
rename("data" = rowname)
tabela_taesa <- taesa %>%
as.data.frame() %>% # transforma em tabela
rownames_to_column() %>% # transforma a data da linha em coluna
clean_names() %>% # altera o nome das colunas
select(rowname, taee11_sa_adjusted) %>%
rename("data" = rowname)
tabela_brasil <- brasil %>%
as.data.frame() %>% # transforma em tabela
rownames_to_column() %>% # transforma a data da linha em coluna
clean_names() %>% # altera o nome das colunas
select(rowname, bbas3_sa_adjusted) %>%
rename("data" = rowname)
tabela_cemig <- cemig %>%
as.data.frame() %>% # transforma em tabela
rownames_to_column() %>% # transforma a data da linha em coluna
clean_names() %>% # altera o nome das colunas
select(rowname, cmig3_sa_adjusted) %>%
rename("data" = rowname)
consolidado <- tabela_ibovespa %>%
bind_cols(tabela_minerva[2],
tabela_itau[2],
tabela_prio[2],
tabela_weg[2],
tabela_csn[2],
tabela_renner[2],
tabela_taesa[2],
tabela_brasil[2],
tabela_cemig[2])
consolidado <- tabela_ibovespa %>%
full_join(tabela_minerva, by = c('data' = 'data')) %>%
full_join(tabela_itau, by = c('data' = 'data')) %>%
full_join(tabela_prio, by = c('data' = 'data')) %>%
full_join(tabela_weg, by = c('data' = 'data')) %>%
full_join(tabela_csn, by = c('data' = 'data')) %>%
full_join(tabela_renner, by = c('data' = 'data')) %>%
full_join(tabela_taesa, by = c('data' = 'data')) %>%
full_join(tabela_brasil, by = c('data' = 'data')) %>%
full_join(tabela_cemig, by = c('data' = 'data'))
consolidado## data bvsp_adjusted beef3_sa_adjusted itsa3_sa_adjusted
## 1 2023-05-15 109029 9.490742 8.702969
## 2 2023-05-16 108194 9.196302 8.635727
## 3 2023-05-17 109460 8.931309 8.770209
## 4 2023-05-18 110108 9.549629 8.741394
## 5 2023-05-19 110745 10.010917 8.712574
## 6 2023-05-22 110213 10.099248 8.693364
## 7 2023-05-23 109929 9.971658 8.702969
## 8 2023-05-24 108800 9.549629 8.616516
## 9 2023-05-25 110054 9.618332 8.750998
## 10 2023-05-26 110906 9.745922 8.702969
## 11 2023-05-29 110333 9.539815 8.693364
## 12 2023-05-30 108967 9.657590 8.683758
## 13 2023-05-31 108335 10.030546 8.558878
## 14 2023-06-01 110565 9.952029 8.639331
## 15 2023-06-02 112558 10.167951 8.860851
## 16 2023-06-05 112696 10.226838 8.880115
## 17 2023-06-06 114610 10.501648 8.880115
## 18 2023-06-07 115488 10.462389 8.899377
## 19 2023-06-09 117019 10.805902 9.043848
## 20 2023-06-12 117336 10.747013 8.995690
## 21 2023-06-13 116743 10.913862 9.092006
## 22 2023-06-14 119069 10.943306 9.217211
## 23 2023-06-15 119221 11.031637 9.390577
## 24 2023-06-16 118758 10.953120 9.294262
## 25 2023-06-19 119858 11.178857 9.525416
## 26 2023-06-20 119622 10.992379 9.583203
## 27 2023-06-21 120420 10.972750 9.631361
## 28 2023-06-22 118934 10.462389 9.486891
## 29 2023-06-23 118977 10.001101 9.520412
## 30 2023-06-26 118243 9.873512 9.491178
## 31 2023-06-27 117523 10.266097 9.432711
## 32 2023-06-28 116681 9.932400 9.335265
## 33 2023-06-29 118383 10.187580 9.520412
## 34 2023-06-30 118087 10.432945 9.491178
## 35 2023-07-03 119673 10.226838 9.637346
## 36 2023-07-04 119076 10.059990 9.549644
## 37 2023-07-05 119549 10.305355 9.656835
## 38 2023-07-06 117426 10.246468 9.559389
## 39 2023-07-07 118898 10.324986 9.617856
## 40 2023-07-10 117942 9.991288 9.695813
## 41 2023-07-11 117220 9.598702 9.452199
## 42 2023-07-12 117666 9.569259 9.422966
## 43 2023-07-13 119264 9.814624 9.598367
## 44 2023-07-14 117711 9.696849 9.500922
## 45 2023-07-17 118219 9.687034 9.598367
## 46 2023-07-18 117841 9.480927 9.627601
## 47 2023-07-19 117552 9.539815 9.569134
## 48 2023-07-20 118083 9.657590 9.637346
## 49 2023-07-21 120217 9.853883 9.725046
## 50 2023-07-24 121342 9.422040 9.666580
## 51 2023-07-25 122008 9.166859 9.841982
## 52 2023-07-26 122560 9.323893 9.794260
## 53 2023-07-27 119990 9.372966 9.676727
## 54 2023-07-28 120187 9.598702 9.764877
## 55 2023-07-31 121943 9.902956 9.706111
## 56 2023-08-01 121248 10.030546 9.706111
## 57 2023-08-02 120859 10.050175 9.784465
## 58 2023-08-03 120586 10.344614 9.725700
## 59 2023-08-04 119508 10.501648 9.637550
## 60 2023-08-07 119380 10.423131 9.696317
## 61 2023-08-08 119090 10.413316 9.578785
## 62 2023-08-09 118409 10.550721 9.449340
## 63 2023-08-10 118350 10.854975 9.381079
## 64 2023-08-11 118065 10.609610 9.429837
## 65 2023-08-14 116810 10.285726 9.429837
## 66 2023-08-15 116171 10.240000 9.390830
## 67 2023-08-16 115592 10.440000 9.400582
## 68 2023-08-17 114982 10.600000 9.283562
## 69 2023-08-18 115409 10.660000 9.249152
## 70 2023-08-21 114429 10.570000 9.190240
## 71 2023-08-22 116156 11.200000 9.278608
## 72 2023-08-23 118135 10.960000 9.425888
## 73 2023-08-24 117026 10.720000 9.366977
## 74 2023-08-25 115837 10.460000 9.288427
## 75 2023-08-28 117121 10.900000 9.416070
## 76 2023-08-29 118404 8.910000 9.445525
## 77 2023-08-30 117535 8.600000 9.308064
## 78 2023-08-31 115742 8.400000 9.209878
## 79 2023-09-01 117893 8.560000 9.308064
## 80 2023-09-04 117777 8.950000 9.219697
## 81 2023-09-05 117331 8.990000 9.121511
## 82 2023-09-06 115985 8.730000 8.974231
## 83 2023-09-08 115313 8.700000 8.934958
## 84 2023-09-11 116883 8.560000 9.121511
## 85 2023-09-12 117968 8.600000 9.190240
## 86 2023-09-13 118176 8.710000 9.249152
## 87 2023-09-14 119392 8.670000 9.258971
## 88 2023-09-15 118758 8.580000 9.249152
## 89 2023-09-18 118288 8.650000 9.258971
## 90 2023-09-19 117846 8.620000 9.239333
## 91 2023-09-20 118695 8.580000 9.298246
## 92 2023-09-21 116145 8.270000 9.170604
## 93 2023-09-22 116009 8.240000 9.047812
## 94 2023-09-25 115925 8.160000 9.117411
## 95 2023-09-26 114193 8.140000 8.978213
## 96 2023-09-27 114327 8.150000 8.988155
## 97 2023-09-28 115731 8.150000 9.017983
## 98 2023-09-29 116565 8.090000 9.097525
## 99 2023-10-02 115057 8.230000 8.978213
## 100 2023-10-03 113419 8.130000 8.908614
## 101 2023-10-04 113607 8.150000 8.968271
## 102 2023-10-05 113284 8.070000 9.027926
## 103 2023-10-06 114170 8.130000 9.067697
## 104 2023-10-09 115156 8.100000 8.948385
## 105 2023-10-10 116737 8.160000 9.087583
## 106 2023-10-11 117051 8.130000 9.167124
## 107 2023-10-13 115754 7.930000 9.067697
## 108 2023-10-16 116534 7.910000 9.057754
## 109 2023-10-17 115908 7.800000 8.858901
## 110 2023-10-18 114060 7.710000 8.998098
## 111 2023-10-19 114004 7.460000 8.928499
## 112 2023-10-20 113155 7.430000 8.820000
## 113 2023-10-23 112785 7.380000 8.880000
## 114 2023-10-24 113762 7.510000 8.910000
## 115 2023-10-25 112830 7.370000 8.870000
## 116 2023-10-26 114777 7.600000 9.020000
## 117 2023-10-27 113301 7.610000 8.840000
## 118 2023-10-30 112532 7.550000 8.750000
## 119 2023-10-31 113144 7.570000 8.780000
## 120 2023-11-01 115053 7.600000 8.880000
## 121 2023-11-03 118160 7.790000 9.130000
## 122 2023-11-06 118487 8.050000 9.190000
## 123 2023-11-07 119268 8.050000 9.350000
## 124 2023-11-08 119099 7.880000 9.410000
## 125 2023-11-09 119012 6.820000 9.430000
## 126 2023-11-10 120636 6.840000 9.500000
## prio3_sa_adjusted wege3_sa_adjusted cmin3_sa_adjusted lren3_sa_adjusted
## 1 34.47 39.36090 4.47 16.95712
## 2 34.50 39.42049 4.45 16.60507
## 3 36.50 39.18217 4.69 18.20885
## 4 36.35 39.02330 4.73 18.43378
## 5 36.97 39.84746 4.90 18.98141
## 6 36.19 40.07584 4.78 18.51201
## 7 36.16 39.93682 4.67 18.20885
## 8 35.95 39.53964 4.52 17.98393
## 9 35.63 38.76513 4.53 19.06942
## 10 35.35 39.23182 4.58 19.59750
## 11 35.12 38.60626 4.57 19.25523
## 12 34.13 38.07006 4.44 18.89340
## 13 34.19 37.34520 4.40 19.02053
## 14 34.17 36.92815 4.47 20.10602
## 15 35.32 37.40477 4.67 20.01800
## 16 35.35 37.36506 4.73 20.06690
## 17 34.28 37.93105 4.77 20.65365
## 18 34.79 37.88140 4.61 21.17195
## 19 34.48 37.53386 4.56 21.34797
## 20 33.14 37.48421 4.48 22.31611
## 21 34.34 37.08703 4.41 21.02526
## 22 35.40 37.60337 4.49 21.01548
## 23 35.60 37.57358 4.49 20.60476
## 24 36.27 37.58350 4.47 19.88110
## 25 36.06 37.40477 4.53 20.32116
## 26 36.16 36.68985 4.45 21.10349
## 27 38.67 36.19337 4.38 21.27952
## 28 38.30 36.14372 4.32 20.92747
## 29 38.21 36.34231 4.35 21.81738
## 30 37.04 36.20138 4.37 21.16217
## 31 35.80 36.30083 4.35 20.62432
## 32 36.14 36.38039 4.25 20.64422
## 33 36.29 36.63898 4.26 21.09793
## 34 37.08 37.54401 4.18 19.72692
## 35 37.42 37.48434 4.23 20.01296
## 36 38.04 36.98706 4.21 20.00309
## 37 38.62 37.29538 4.19 20.17077
## 38 38.15 36.68870 4.12 19.52965
## 39 39.35 36.79810 4.21 20.14118
## 40 39.49 36.09198 4.12 18.83920
## 41 40.70 35.75383 4.12 18.60248
## 42 41.55 35.86323 4.12 18.31644
## 43 42.89 35.86323 4.21 18.19808
## 44 41.52 35.80356 4.21 17.58654
## 45 41.99 35.10738 4.23 17.71477
## 46 43.18 35.74389 4.15 17.80354
## 47 44.14 37.70314 4.12 17.53723
## 48 44.20 37.95177 4.08 17.63586
## 49 45.17 38.67779 4.15 18.05013
## 50 46.17 41.46816 4.32 18.60248
## 51 44.96 40.42995 4.42 18.38548
## 52 45.59 40.40999 4.46 18.74057
## 53 45.09 40.28021 4.34 18.37562
## 54 45.16 40.25027 4.29 18.38548
## 55 45.62 39.85096 4.45 18.49398
## 56 45.80 39.79106 4.40 18.39535
## 57 45.25 39.96077 4.32 18.74057
## 58 47.16 40.36008 4.29 18.29671
## 59 47.57 40.77935 4.21 19.35210
## 60 47.81 40.85921 4.27 18.74057
## 61 47.86 40.58968 4.20 18.88852
## 62 48.12 40.70947 4.14 18.05013
## 63 47.38 40.17041 4.13 18.46439
## 64 48.36 39.73117 4.14 18.25726
## 65 48.59 39.24202 4.12 17.33010
## 66 46.82 39.28194 4.08 17.20187
## 67 45.98 37.54496 4.05 17.25119
## 68 45.38 36.84616 4.05 16.87638
## 69 45.17 36.19729 4.06 17.12296
## 70 44.75 35.97767 4.06 16.87638
## 71 45.10 36.40693 4.11 16.94542
## 72 45.85 36.38696 4.19 17.17228
## 73 46.06 35.91777 4.10 16.65938
## 74 46.39 36.29712 4.09 16.38320
## 75 46.69 36.55666 4.14 16.47198
## 76 45.85 36.42689 4.23 16.47198
## 77 45.81 36.23722 4.35 16.21552
## 78 46.42 35.81795 4.28 15.81112
## 79 46.95 35.88783 4.36 16.54102
## 80 46.61 36.06752 4.41 16.21552
## 81 47.49 35.84789 4.33 15.85058
## 82 46.79 35.48852 4.29 15.35740
## 83 47.49 35.33878 4.21 15.30809
## 84 47.71 35.21898 4.38 15.53495
## 85 48.11 35.58834 4.30 15.79140
## 86 48.20 36.86613 4.29 16.02812
## 87 49.25 36.01760 4.43 15.84071
## 88 49.40 36.03756 4.49 15.36727
## 89 48.16 35.72810 4.47 15.21932
## 90 48.71 35.91777 4.46 14.40065
## 91 48.71 35.72810 4.55 14.45983
## 92 46.44 34.91950 4.62 13.75952
## 93 46.76 34.53018 4.68 13.70034
## 94 46.98 36.13000 4.62 13.61157
## 95 46.93 36.08000 4.56 12.98031
## 96 48.30 36.02000 4.60 12.90000
## 97 47.24 36.39000 4.68 13.42000
## 98 47.04 36.27000 4.85 13.40000
## 99 45.40 36.15000 4.79 12.85000
## 100 45.17 35.17000 4.74 12.53000
## 101 43.90 35.16000 4.66 12.93000
## 102 43.71 34.98000 4.67 12.73000
## 103 44.54 34.80000 4.66 12.59000
## 104 48.45 34.85000 4.66 12.68000
## 105 48.25 35.30000 4.73 13.00000
## 106 47.60 35.17000 4.82 13.10000
## 107 50.00 34.54000 4.83 12.69000
## 108 49.80 34.65000 4.92 12.96000
## 109 50.20 34.28000 4.93 12.81000
## 110 50.20 34.29000 4.77 12.24000
## 111 50.03 33.94000 4.78 12.34000
## 112 49.70 33.81000 4.70 12.22000
## 113 48.84 33.90000 4.80 12.40000
## 114 48.85 35.01000 4.93 12.38000
## 115 48.36 31.47000 4.84 12.35000
## 116 48.00 32.66000 4.93 12.68000
## 117 47.77 31.63000 4.95 12.21000
## 118 46.36 32.30000 5.07 11.94000
## 119 47.70 33.01000 5.18 12.26000
## 120 48.07 33.08000 5.33 12.27000
## 121 48.20 33.53000 5.63 13.24000
## 122 48.01 33.21000 5.69 12.61000
## 123 46.82 32.78000 5.77 13.18000
## 124 45.48 32.45000 5.90 13.45000
## 125 44.73 32.41000 5.89 13.55000
## 126 46.47 33.17000 6.03 12.96000
## taee11_sa_adjusted bbas3_sa_adjusted cmig3_sa_adjusted
## 1 36.23963 42.57689 19.34996
## 2 36.09339 41.35056 18.84023
## 3 36.16164 41.41762 18.50695
## 4 36.22988 41.38888 18.52655
## 5 36.15189 42.19366 18.67359
## 6 36.15189 41.63798 18.14426
## 7 36.31763 42.61522 17.83058
## 8 36.39563 42.73977 17.58552
## 9 36.67837 42.78767 17.62473
## 10 36.73687 42.53857 17.35027
## 11 36.91236 42.46192 17.28164
## 12 36.64912 42.82599 17.01698
## 13 35.99589 42.74935 16.79153
## 14 36.15189 42.86432 16.75232
## 15 36.00564 43.50061 17.07580
## 16 35.75214 43.77285 16.96797
## 17 36.42488 44.27844 17.27184
## 18 36.52237 44.25900 17.38947
## 19 37.05861 46.41750 18.14426
## 20 37.20485 48.09958 19.07549
## 21 36.70762 47.59894 18.42853
## 22 37.15611 47.83391 18.47754
## 23 37.05861 48.81290 18.18347
## 24 36.45412 48.45068 17.47769
## 25 36.86361 49.35135 17.77177
## 26 36.88311 49.34156 17.93841
## 27 36.90261 50.89817 18.36971
## 28 36.63937 49.92896 17.76197
## 29 37.09761 49.94854 18.52655
## 30 36.86361 50.06602 18.33234
## 31 36.52237 49.14577 18.58985
## 32 36.34688 48.54858 18.64927
## 33 36.23963 48.99891 19.04543
## 34 36.69787 48.36256 19.13457
## 35 36.57112 49.33177 18.73841
## 36 36.46387 49.01849 18.31253
## 37 36.19088 48.69542 18.02532
## 38 36.10313 47.98075 17.77772
## 39 36.23963 48.91080 17.79752
## 40 35.83014 48.01012 17.78762
## 41 35.56690 47.32483 17.90647
## 42 35.31341 46.56121 17.72820
## 43 35.31341 47.00175 18.09465
## 44 35.26466 46.68848 17.83714
## 45 35.49865 47.09966 17.99561
## 46 35.44991 47.25629 17.91637
## 47 35.28416 46.43394 17.39146
## 48 35.46941 46.94302 17.33204
## 49 35.69365 47.50105 17.34194
## 50 35.83014 46.57100 18.02532
## 51 35.33291 46.32624 18.13426
## 52 35.41091 46.71785 18.66908
## 53 35.19641 45.75843 18.18378
## 54 35.28416 46.28708 18.22340
## 55 35.09892 47.17797 18.26301
## 56 35.22567 46.36541 18.31253
## 57 35.29391 46.57100 18.72850
## 58 35.50840 46.64931 18.71860
## 59 35.58640 46.40457 18.77802
## 60 35.44016 46.32624 18.49081
## 61 35.31000 46.00318 18.76812
## 62 34.69000 45.74864 19.26332
## 63 34.88000 45.98360 19.53073
## 64 34.83000 46.48288 19.62977
## 65 34.82000 46.02275 19.70900
## 66 34.50000 45.77800 19.57034
## 67 34.45000 46.16961 19.70900
## 68 34.50000 46.60036 19.65948
## 69 34.72000 46.78637 19.08505
## 70 34.45000 46.61994 19.33265
## 71 34.50000 47.63164 19.70900
## 72 34.57000 47.84016 19.53073
## 73 34.90000 47.21460 18.84735
## 74 34.77000 46.59898 19.08505
## 75 34.73000 47.93945 19.11476
## 76 34.94000 48.20755 19.21380
## 77 34.81000 47.51249 19.66939
## 78 34.20000 46.76778 19.50102
## 79 34.91000 47.08552 19.57034
## 80 34.86000 46.93658 19.44159
## 81 34.30000 46.76778 19.17418
## 82 34.00000 46.45004 18.81764
## 83 34.25000 46.31102 18.85726
## 84 34.70000 47.04580 19.14447
## 85 34.78000 47.30000 19.37226
## 86 34.78000 47.26000 19.63967
## 87 34.92000 47.39000 19.80804
## 88 35.31000 47.55000 19.80804
## 89 35.15000 47.35000 19.80804
## 90 35.01000 47.28000 19.80804
## 91 35.09000 47.70000 19.71890
## 92 34.98000 46.61000 19.80804
## 93 34.93000 46.69000 19.50102
## 94 34.73000 46.42000 19.60006
## 95 34.47000 45.76000 19.68000
## 96 34.39000 46.16000 19.51000
## 97 34.55000 47.64000 19.50000
## 98 34.56000 47.18000 19.10000
## 99 34.50000 47.05000 18.89000
## 100 33.83000 46.45000 18.70000
## 101 34.25000 46.80000 19.31000
## 102 34.24000 46.94000 19.05000
## 103 34.14000 48.85000 18.80000
## 104 33.97000 48.59000 18.89000
## 105 34.16000 48.51000 18.63000
## 106 34.02000 48.97000 18.61000
## 107 33.50000 48.82000 17.98000
## 108 33.78000 49.81000 18.02000
## 109 33.46000 49.36000 17.16000
## 110 33.60000 49.12000 16.30000
## 111 33.77000 49.50000 16.36000
## 112 33.76000 49.34000 16.00000
## 113 33.89000 49.27000 15.92000
## 114 33.89000 49.04000 15.52000
## 115 33.81000 49.05000 15.28000
## 116 34.31000 50.07000 15.31000
## 117 33.96000 48.80000 14.94000
## 118 33.75000 48.46000 15.17000
## 119 33.79000 48.35000 15.40000
## 120 33.99000 48.40000 16.48000
## 121 34.73000 50.10000 16.83000
## 122 35.19000 50.45000 17.00000
## 123 35.80000 51.02000 17.01000
## 124 35.71000 51.67000 16.99000
## 125 35.65000 49.51000 17.45000
## 126 36.03000 50.24000 17.44000ETAPA 2 - OTIMIZAÇÃO DE PORTFÓLIO
Nesta seção, o objeto “consolidado” será utilizado para otimização de portfólio. Em primeiro lugar, vamos transformar esta tabela em um objeto de série temporal (para tanto, será utilizada a função timeSeries do pacote com o mesmo nome). Em seguida, será utilizada a função returns do pacote timeSeries para cálculo dos retornos diários de cada uma das ações.
## Carregando pacotes exigidos: timeDate##
## Attaching package: 'timeSeries'## The following object is masked from 'package:zoo':
##
## time<-## Carregando pacotes exigidos: fBasics##
## Attaching package: 'fBasics'## The following object is masked from 'package:TTR':
##
## volatility## Carregando pacotes exigidos: fAssetsconsolidado_ts <- timeSeries(consolidado[,-1], consolidado[,1]) # Série temporal de preços ajustados
retornos_ts <- returns(consolidado_ts) #
# CÁLCULO DO RISCO E RETORNO INDIVIDUAL (CADA AÇÃO)
retornos_acao <- basicStats(retornos_ts) %>%
as.data.frame() %>%
rownames_to_column() %>%
rename("estatistica" = rowname) %>%
dplyr::filter(estatistica == 'Mean' | estatistica == 'Stdev')
# CÁLCULO DO RISCO E RETORNO DO PORTFÓLIO
fronteira_eficiente <- portfolioFrontier(retornos_ts)
frontierPlot(fronteira_eficiente)
points(x = 0.015,
y = - 0.00051,
col = "Red",
pch = 19,
cex = 1.5)
points(x = 0.015,
y = 0.0009,
col = "Green",
pch = 19,
cex = 1.5)
VISUALIZAÇÃO INTERATIVA
##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:timeSeries':
##
## filter## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutlibrary(magrittr)
tabela_retorno <- retornos_ts %>%
as.data.frame() %>%
rownames_to_column() %>%
rename("data" = rowname) %>%
mutate(data = as.Date(data))
grafico <- tabela_retorno %>%
ggplot() +
geom_line(mapping = aes(x = data, y = bvsp_adjusted)) +
geom_point(mapping = aes(x = data, y = bvsp_adjusted))
ggplotly(grafico)ANÁLISE DA CARTEIRA DE MENOR VARIÂNCIA (MENOR RISCO)
Para identificar os principais “atributos” da carteira de menor variância (menor risco), será utilizada a função “minvariancePortfolio()” do pacote “fPortfolio”. Conforme exemplo apresentado no código abaixo:
# PACOTES UTILIZADOS:
library(fPortfolio)
carteira_min_risco <- minvariancePortfolio(retornos_ts)
frontierPlot(fronteira_eficiente,
frontier = "upper")
minvariancePoints(fronteira_eficiente,
col = "green",
cex = 1.5,
pch = 19)
##
## Title:
## MV Minimum Variance Portfolio
## Estimator: covEstimator
## Solver: solveRquadprog
## Optimize: minRisk
## Constraints: LongOnly
##
## Portfolio Weights:
## bvsp_adjusted beef3_sa_adjusted itsa3_sa_adjusted prio3_sa_adjusted
## 0.0000 0.0168 0.1511 0.1093
## wege3_sa_adjusted cmin3_sa_adjusted lren3_sa_adjusted taee11_sa_adjusted
## 0.0463 0.0057 0.0000 0.6537
## bbas3_sa_adjusted cmig3_sa_adjusted
## 0.0000 0.0172
##
## Covariance Risk Budgets:
## bvsp_adjusted beef3_sa_adjusted itsa3_sa_adjusted prio3_sa_adjusted
## 0.0000 0.0168 0.1511 0.1093
## wege3_sa_adjusted cmin3_sa_adjusted lren3_sa_adjusted taee11_sa_adjusted
## 0.0463 0.0057 0.0000 0.6537
## bbas3_sa_adjusted cmig3_sa_adjusted
## 0.0000 0.0172
##
## Target Returns and Risks:
## mean Cov CVaR VaR
## 0.0002 0.0071 0.0131 0.0111
##
## Description:
## Sat Nov 11 16:56:33 2023 by user: Pessoal
CARTEIRA ÓTIMA E ÍNDICE DE SHARPE
ÍNDICE SHARPE = RC - RL RC = RETORNO DA CARTEIRA RL = RETORNO LIVRE DE RISCO (RENDA FIXA)
SIGMA = RISCO DA CARTEIRA
frontierPlot(fronteira_eficiente)
sharpeRatioLines(fronteira_eficiente,
col = "blue",
cex = 1.5,
pch = 19)
ALTERANDO CONFIGURAÇÕES DE OTIMIZAÇÃO
configuracao_01 <- portfolioSpec(
model = list(
type = "MV",
optimize = "minRisk",
estimator = "covEstimator",
tailRisk = list(),
params = list(alpha = 0.05, a = 2)),
portfolio = list(
weights = NULL,
targetReturn = NULL,
targetRisk = NULL,
riskFreeRate = 0,
nFrontierPoints = 50,
status = NA),
optim = list(
solver = "solveRshortExact",
objective = NULL,
parames = list(),
control = list(meq = 2),
trace = FALSE)
)
carteira_config_01 <- efficientPortfolio(retornos_ts,
spec = configuracao_01)## Warning in as.vector(invSigma %*% one)/(one %*% invSigma %*% one): Recycling array of length 1 in vector-array arithmetic is deprecated.
## Use c() or as.vector() instead.
frontierPlot(fronteira_eficiente)
points(x = frontierPoints(carteira_config_01)[,1],
y = frontierPoints(carteira_config_01)[,2],
col = "Orange",
cex = 1.5,
pch = 19)
VENDAS A DESCOBERTO
carteira_otima_vendido <- tangencyPortfolio(retornos_ts,
spec = configuracao_01,
constraints = 'short')
tailoredFrontierPlot(portfolioFrontier(retornos_ts,
spec = configuracao_01,
constraints = 'short'))## Warning in as.vector(invSigma %*% one)/(one %*% invSigma %*% one): Recycling array of length 1 in vector-array arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in as.vector(invSigma %*% one)/(one %*% invSigma %*% one): Recycling array of length 1 in vector-array arithmetic is deprecated.
## Use c() or as.vector() instead.
## Warning in as.vector(invSigma %*% one)/(one %*% invSigma %*% one): Recycling array of length 1 in vector-array arithmetic is deprecated.
## Use c() or as.vector() instead.