Downloading data

First lets load our packages

Next lets select a few stocks to build our portfolios.

We will choose the following 4 stocks 430198.BJ(微创光电) 430300.BJ(辰光医疗) 430476.BJ(海能技术) 430478.BJ(峆一药业) Read the price data.

##选取投资组合对象
#430139.BJ(微创光电) 430198.BJ(辰光医疗) 430425.BJ(海能技术) 430300.BJ(峆一药业) 
##读取数据
price <-read.csv("D:/zhangxy/data_work1.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("WC","CG","HN","QY")
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)
##              WC   CG    HN    QY
## 2022-01-04 9.70 8.86 10.05 21.99
## 2022-01-05 9.77 8.88  9.90 21.51
## 2022-01-06 9.66 8.91  9.91 21.51
## 2022-01-07 9.53 8.67 10.04 21.99
## 2022-01-10 9.50 8.70 10.01 21.99
## 2022-01-11 9.44 8.61 10.05 22.00
pr_daily <- to.daily(price, indexAt = "lastof", OHLC = FALSE) 
head(pr_daily)
##              WC   CG    HN    QY
## 2022-01-31 9.70 8.86 10.05 21.99
## 2022-01-31 9.77 8.88  9.90 21.51
## 2022-01-31 9.66 8.91  9.91 21.51
## 2022-01-31 9.53 8.67 10.04 21.99
## 2022-01-31 9.50 8.70 10.01 21.99
## 2022-01-31 9.44 8.61 10.05 22.00
#计算回报
rt_daily <- na.omit(Return.calculate(pr_daily, method = "log")) 

head(rt_daily)
##                      WC           CG           HN            QY
## 2022-01-31  0.007190581  0.002254792 -0.015037877 -0.0220698613
## 2022-01-31 -0.011322818  0.003372684  0.001009591  0.0000000000
## 2022-01-31 -0.013548931 -0.027305451  0.013032766  0.0220698613
## 2022-01-31 -0.003152919  0.003454235 -0.002992521  0.0000000000
## 2022-01-31 -0.006335818 -0.010398707  0.003988041  0.0004546488
## 2022-01-31  0.006335818  0.021827403  0.000000000  0.0095001845
dygraph(rt_daily)
mean_ret <- colMeans(rt_daily)
print(round(mean_ret, 4))
##      WC      CG      HN      QY 
## -0.0024 -0.0013  0.0010 -0.0006
cov_mat <- cov(rt_daily) * 252
print(round(cov_mat,4)) 
##        WC     CG     HN     QY
## WC 0.0667 0.0228 0.0133 0.0172
## CG 0.0228 0.0950 0.0177 0.0347
## HN 0.0133 0.0177 0.1250 0.0119
## QY 0.0172 0.0347 0.0119 1.3291
tick <- c("WC","CG","HN","QY")
wts <- runif(n = length(tick))
wts <- wts/sum(wts)
print(wts)
## [1] 0.3683467 0.2166561 0.1613975 0.2535998
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(WC:QY, 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(WC:QY, 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)