Downloading data

First lets load our packages

Next lets select a few stocks to build our portfolios.

We will choose the following 4 stocks 430139.BJ(华岭股份) 430198.BJ(微创光电) 430425.BJ(乐创技术) 430300.BJ(辰光医疗) Read the price data.

##选取投资组合对象
#430139.BJ(华岭股份) 430198.BJ(微创光电) 430425.BJ(乐创技术) 430300.BJ
#(辰光医疗) 
##读取数据
price <-read.csv("D:/投资组合管理/4.28/data_zuoye.csv")
#修改数据格式
price[,1] = as.POSIXct(price[,1])
price = xts(price[,-1], order.by=price[,1])
##绘制走势图
names(price)<-c("HL","WC","LC","CG")
dygraph(price) 
#plot(price)
##获取月汇报
#过滤数据
#pr_monthly <- to.monthly(price, indexAt = "lastof", OHLC = FALSE) 
#head(pr_monthly)
#计算回报
#rt_monthly <- na.omit(Return.calculate(pr_monthly, method = "log")) 
head(price)
##               HL   WC   LC   CG
## 2022-01-04 20.26 9.70 24.5 8.86
## 2022-01-05 19.47 9.77 24.7 8.88
## 2022-01-06 19.49 9.66 24.7 8.91
## 2022-01-07 19.35 9.53 24.7 8.67
## 2022-01-10 19.61 9.50 24.7 8.70
## 2022-01-11 20.00 9.44 24.6 8.61
pr_daily <- to.daily(price, indexAt = "lastof", OHLC = FALSE) 
head(pr_daily)
##               HL   WC   LC   CG
## 2022-01-31 20.26 9.70 24.5 8.86
## 2022-01-31 19.47 9.77 24.7 8.88
## 2022-01-31 19.49 9.66 24.7 8.91
## 2022-01-31 19.35 9.53 24.7 8.67
## 2022-01-31 19.61 9.50 24.7 8.70
## 2022-01-31 20.00 9.44 24.6 8.61
#计算回报
rt_daily <- na.omit(Return.calculate(pr_daily, method = "log")) 

head(rt_daily)
##                      HL           WC           LC           CG
## 2022-01-31 -0.039773679  0.007190581  0.008130126  0.002254792
## 2022-01-31  0.001026694 -0.011322818  0.000000000  0.003372684
## 2022-01-31 -0.007209094 -0.013548931  0.000000000 -0.027305451
## 2022-01-31  0.013347221 -0.003152919  0.000000000  0.003454235
## 2022-01-31  0.019692633 -0.006335818 -0.004056801 -0.010398707
## 2022-01-31  0.012422520  0.006335818  0.004461577  0.021827403
dygraph(rt_daily)
mean_ret <- colMeans(rt_daily)
print(round(mean_ret, 4))
##      HL      WC      LC      CG 
## -0.0021 -0.0024 -0.0021 -0.0013
cov_mat <- cov(rt_daily) * 252
print(round(cov_mat,4)) 
##        HL      WC      LC     CG
## HL 0.1414  0.0222  0.0154 0.0105
## WC 0.0222  0.0667 -0.0150 0.0228
## LC 0.0154 -0.0150  0.5691 0.0018
## CG 0.0105  0.0228  0.0018 0.0950
tick <- c("HL","WC","LC","CG")
wts <- runif(n = length(tick))
wts <- wts/sum(wts)
print(wts)
## [1] 0.3078549 0.2099996 0.2095977 0.2725478
port_returns <- (sum(wts * mean_ret) + 1)^252 - 1
port_risk <- sqrt(t(wts) %*% (cov_mat %*% wts))
sharpe_ratio <- port_returns/port_risk


num_port <- 1000

# Creating a matrix to store the weights

all_wts <- matrix(nrow = num_port,
                  ncol = length(tick))

# Creating an empty vector to store
# Portfolio returns

port_returns <- vector('numeric', length = num_port)

# Creating an empty vector to store
# Portfolio Standard deviation

port_risk <- vector('numeric', length = num_port)

# Creating an empty vector to store
# Portfolio Sharpe Ratio

sharpe_ratio <- vector('numeric', length = num_port)

for (i in seq_along(port_returns)) {
  
  wts <- runif(length(tick))
  wts <- wts/sum(wts)
  
  # Storing weight in the matrix
  all_wts[i,] <- wts
  
  # Portfolio returns
  
  port_ret <- sum(wts * mean_ret)
  port_ret <- ((port_ret + 1)^252) - 1
  
  # Storing Portfolio Returns values
  port_returns[i] <- port_ret
  
  
  # Creating and storing portfolio risk
  port_sd <- sqrt(t(wts) %*% (cov_mat  %*% wts))
  port_risk[i] <- port_sd
  
  # Creating and storing Portfolio Sharpe Ratios
  # Assuming 0% Risk free rate
  
  sr <- port_ret/port_sd
  sharpe_ratio[i] <- sr
}

# Storing the values in the table
portfolio_values <- tibble(Return = port_returns,
                           Risk = port_risk,
                           SharpeRatio = sharpe_ratio)


# Converting matrix to a tibble and changing column names
all_wts <- tk_tbl(all_wts)
## Warning in tk_tbl.data.frame(as.data.frame(data), preserve_index, rename_index,
## : Warning: No index to preserve. Object otherwise converted to tibble
## successfully.
colnames(all_wts) <- colnames(pr_daily)
# Combing all the values together
portfolio_values <- tk_tbl(cbind(all_wts, portfolio_values))
## Warning in tk_tbl.data.frame(cbind(all_wts, portfolio_values)): Warning: No
## index to preserve. Object otherwise converted to tibble successfully.
min_var <- portfolio_values[which.min(portfolio_values$Risk),]
max_sr <- portfolio_values[which.max(portfolio_values$SharpeRatio),]


p <- min_var %>%
  gather(HL:CG, key = Asset,
         value = Weights) %>%
  mutate(Asset = as.factor(Asset)) %>%
  ggplot(aes(x = fct_reorder(Asset,Weights), y = Weights, fill = Asset)) +
  geom_bar(stat = 'identity') +
  theme_minimal() +
  labs(x = 'Assets', y = 'Weights', title = "Minimum Variance Portfolio Weights") +
  scale_y_continuous(labels = scales::percent) 


ggplotly(p)
p <- max_sr %>%
  gather(HL:CG, key = Asset,
         value = Weights) %>%
  mutate(Asset = as.factor(Asset)) %>%
  ggplot(aes(x = fct_reorder(Asset,Weights), y = Weights, fill = Asset)) +
  geom_bar(stat = 'identity') +
  theme_minimal() +
  labs(x = 'Assets', y = 'Weights', title = "Tangency Portfolio Weights") +
  scale_y_continuous(labels = scales::percent) 


ggplotly(p)
p <- portfolio_values %>%
  ggplot(aes(x = Risk, y = Return, color = SharpeRatio)) +
  geom_point() +
  theme_classic() +
  scale_y_continuous(labels = scales::percent) +
  scale_x_continuous(labels = scales::percent) +
  labs(x = 'Annualized Risk',
       y = 'Annualized Returns',
       title = "Portfolio Optimization & Efficient Frontier") +
  geom_point(aes(x = Risk,
                 y = Return), data = min_var, color = 'red') +
  geom_point(aes(x = Risk,
                 y = Return), data = max_sr, color = 'red') #+
#annotate('text', x = 3.2, y = 550, label = "Tangency Portfolio") +
#annotate('text', x = 1.3, y = 40, label = "Min Var portfolio") #+
#annotate(geom = 'segment', x = 0.2, xend = 0.185,  y = 0.03, 
#         yend = 0.11, color = 'red', arrow = arrow(type = "open")) +
#annotate(geom = 'segment', x = 0.285, xend = 0.26,  y = 0.34, 
#         yend = 0.31, color = 'red', arrow = arrow(type = "open"))

ggplotly(p)