R Markdown

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When you click the Knit button a document will be generated that includes both content as well as the output of any embedded R code chunks within the document. You can embed an R code chunk like this:

summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

Including Plots

You can also embed plots, for example:

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

library(tidyverse)
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
library(ggrepel)
new <- mpg %>%
  group_by(class) %>%
  filter(row_number(desc(hwy)) == 1)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(colour = class)) +
  geom_point(data = new, size = 3, shape = 1) +
  geom_label_repel(data =new, aes(label = model))

new_carat <- diamonds %>% filter(carat < 3)
ggplot(data = new_carat, aes(x = carat, y = price)) +
  geom_boxplot(aes(group = cut_number(carat, 20)))

为了方便呈现，筛选出carat大于3的数据，并使用cut_number()将carat划分为20组。

ggplot(data = new_carat, aes(x = carat, y = price)) +
  geom_boxplot(aes(group = cut_width(carat, 0.1)))

使用cut_width(carat, 0.1)将carat以0.1为间隔划分。

之前的code报错,evaluate函数有误，缺少大括号，已修改。修改代码后运行

# install.packages("lattice")
library(lattice)
# install.packages("zoo")
library(zoo)
## 
## 载入程辑包：'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
evaluate <- function(PARAM, minVal = NA, maxVal = NA, y = 2014,
                     transform = FALSE, verbose = FALSE,
                     negative = FALSE, transformOnly = FALSE,
                     returnData = FALSE, accountParams = NULL){
  
  # Step 1
  # Convert and declare parameters if they exist on unbounded (-inf,inf) domain
  if( transform | transformOnly ){
    PARAM <- minVal +
      (maxVal - minVal) * unlist(lapply( PARAM, function(v) (1 + exp(-v))^(-1) ))
    if( transformOnly ){

    }
  }
 return(PARAM)  
}  ## 修改
  
 
# Declare bounds and step size for optimization
lowerBound <- c(n1 = 5, nFact = 3, nSharpe = 22, shThresh = 0.05)
upperBound <- c(n1 = 80, nFact = 3, nSharpe = 22, shThresh = 0.95)
stepSize <- c(n1 = 5, nFact = 1, nSharpe = 1, shThresh = 0.05)

pnames <- names(stepSize)
np <- length(pnames)

# Declare list of all test points
POINTS <- list()
for( p in pnames ){
  POINTS[[p]] <- seq(lowerBound[[p]], upperBound[[p]], stepSize[[p]])
}

OPTIM <- data.frame(matrix(NA, nrow = prod(unlist(lapply(POINTS, length))),
                           ncol = np + 1))
names(OPTIM)[1:np] <- names(POINTS)
names(OPTIM)[np+1] <- "obj"

# Store all possible combinations of parameters
for( i in 1:np ){
  each <- prod(unlist(lapply(POINTS, length))[-(1:i)])
  times <- prod(unlist(lapply(POINTS, length))[-(i:length(pnames))])
  OPTIM[,i] <- rep(POINTS[[pnames[i]]], each = each, times = times)
}

# Test each row of OPTIM
timeLapse <- proc.time()[3]
for( i in 1:nrow(OPTIM) ){
  OPTIM[i,np+1] <- evaluate(OPTIM[i,1:np], transform = FALSE, y = 2014)
  cat(paste0("##  ", floor( 100 * i / nrow(OPTIM)), "% complete\n"))
  cat(paste0("##  ",
             round( ((proc.time()[3] - timeLapse) * 
                       ((nrow(OPTIM) - i)/ i))/60, 2),
             " minutes remaining\n\n"))
}
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library(lattice)
wireframe(obj ~ n1*shThresh, data = OPTIM,
          xlab = "n1", ylab = "shThresh",
          main = "Long-Only MACD Exhaustive Optimization",
          drape = TRUE,
          colorkey = TRUE,
          screen = list(z = 15, x = -60)
)

levelplot(obj ~ n1*shThresh, data = OPTIM,
          xlab = "n1", ylab = "shThresh",
          main = "Long-Only MACD Exhaustive Optimization"
)