- Download Stock Price Data with
tidyquant(2023.06.18) - Arithmetic Mean vs Geometrical Mean 2 (2023.01.24)
- Arithmetic Mean vs Geometrical Mean (2022.12.29)
- Monte Carlo Simulation (2018.03.28)
- Boxplot (2018.01.11)
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Download daily stock price data into a
.csvfile- 8 columns :
symboldateopenhighlowclosevolumeadjusted
Codes : TidyquantInit.r
# 필요한 라이브러리를 로드합니다 if (!requireNamespace("tidyquant")) { install.packages("tidyquant") } library(tidyquant) # 작업 디렉토리를 설정합니다. 해당 경로에 저장될 것입니다. setwd({path}) # 다운로드 받을 종목의 심볼을 정의합니다 symbols <- c("122630.KS", "252670.KS") # KODEX 레버리지, KODEX 200선물인버스2X # 데이터를 다운로드할 기간을 설정합니다 start_date <- "2022-01-01" end_date <- "2022-12-31" # 종목 데이터를 다운로드합니다 data <- tq_get(symbols, from = start_date, to = end_date) head(data) str(data) # 데이터프레임을 CSV 파일로 저장합니다 current_datetime <- format(Sys.time(), "%Y%m%d_%H%M%S") write.csv(data, file = paste0("stock_data_", current_datetime, ".csv"))
Output
# A tibble: 6 x 8 symbol date open high low close volume adjusted <chr> <date> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 122630.KS 2022-01-04 24030 24120 23700 23945 14785180 23945 2 122630.KS 2022-01-05 23820 23885 23005 23265 18355770 23265 3 122630.KS 2022-01-06 22920 23370 22800 22850 21266580 22850 4 122630.KS 2022-01-07 23120 23475 23035 23380 14437010 23380 5 122630.KS 2022-01-10 23295 23335 22670 22985 16135230 22985 6 122630.KS 2022-01-11 23090 23345 22885 23135 15460780 23135tibble [488 x 8] (S3: tbl_df/tbl/data.frame) $ symbol : chr [1:488] "122630.KS" "122630.KS" "122630.KS" "122630.KS" ... $ date : Date[1:488], format: "2022-01-04" "2022-01-05" "2022-01-06" "2022-01-07" ... $ open : num [1:488] 24030 23820 22920 23120 23295 ... $ high : num [1:488] 24120 23885 23370 23475 23335 ... $ low : num [1:488] 23700 23005 22800 23035 22670 ... $ close : num [1:488] 23945 23265 22850 23380 22985 ... $ volume : num [1:488] 14785180 18355770 21266580 14437010 16135230 ... $ adjusted: num [1:488] 23945 23265 22850 23380 22985 ... - 8 columns :
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Although testing some extreme cases, it seems never be able for the geometrical mean to beat the arithmetic mean.
Codes : Mean2.r
# Case 1 case1 <- c(seq(1, 1.2, by=0.01), seq(1, 0.8, by=-0.01)) # case1 <- seq(1.2, 0.8, by=-0.01) plot(case1) abline(h = 1) aMean <- mean(case1) gMean <- exp(mean(log(case1))) print(paste("Arithmetic mean:", aMean)) print(paste("Geometric mean:", gMean))
# Case 2 case2 <- c(2, 0.5) aMean <- mean(case2) gMean <- exp(mean(log(case2))) print(paste("Arithmetic mean:", aMean)) print(paste("Geometric mean:", gMean))
# Case 1 [1] "Arithmetic mean: 1" [1] "Geometric mean: 0.993102769755157" # Case 2 [1] "Arithmetic mean: 1.25" [1] "Geometric mean: 1" -
Stop …… did you forget this formula? Don't disappoint your primary school!
(a + b) / 2 ≥ sqrt(a · b)
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Generally, the geometrical mean tends to be lower than the arithmetic mean.
Codes : Mean.r
# Set the number of simulations n_simulations <- 1000 # Set the sample size sample_size <- 240 # Set the distribution of values for the random sample mean = 1 sd = 0.3
# Initialize vectors to store the results of the simulations arithmetic_mean1 <- numeric(n_simulations) arithmetic_mean2 <- numeric(n_simulations) geometric_mean1 <- numeric(n_simulations) geometric_mean2 <- numeric(n_simulations) # Run the simulations for (i in 1:n_simulations) { # Generate a random sample sample1 <- rnorm(sample_size, mean = mean, sd = sd) sample2 <- rlnorm(sample_size, mean = log(mean), sd = sd) # Calculate the arithmetic mean of the sample arithmetic_mean1[i] <- mean(sample1) arithmetic_mean2[i] <- mean(sample2) # Calculate the geometric mean of the sample geometric_mean1[i] <- exp(mean(log(sample1))) geometric_mean2[i] <- exp(mean(log(sample2))) }
# Calculate the mean and standard deviation of the arithmetic means arithmetic_mean_mean1 <- mean(arithmetic_mean1) arithmetic_mean_mean2 <- mean(arithmetic_mean2) arithmetic_mean_sd1 <- sd(arithmetic_mean1) arithmetic_mean_sd2 <- sd(arithmetic_mean2) # Calculate the mean and standard deviation of the geometric means geometric_mean_mean1 <- mean(geometric_mean1) geometric_mean_mean2 <- mean(geometric_mean2) geometric_mean_sd1 <- sd(geometric_mean1) geometric_mean_sd2 <- sd(geometric_mean2) # Print the results print(paste("Arithmetic mean 1:", arithmetic_mean_mean1, "±", arithmetic_mean_sd1)) print(paste("Geometric mean 1:", geometric_mean_mean1, "±", geometric_mean_sd1)) print(paste("Arithmetic mean 2:", arithmetic_mean_mean2, "±", arithmetic_mean_sd2)) print(paste("Geometric mean 2:", geometric_mean_mean2, "±", geometric_mean_sd2))
# Plot windows(width = 11, height = 6, title = "Arithmetic Mean vs Geometric Mean") # title argument does not work par(mfrow = c(1, 2)) plot(arithmetic_mean1, geometric_mean1, # xlim = c(0.99, 1.01), ylim = c(0.99, 1.01), col = "red") abline(h = 1); abline(v = 1) plot(arithmetic_mean2, geometric_mean2, # xlim = c(0.99, 1.01), ylim = c(0.99, 1.01), col = "blue") abline(h = 1); abline(v = 1)
[1] "Arithmetic mean 1: 0.999924804528105 ± 0.0128838398954078" [1] "Geometric mean 1: 0.978778509142553 ± 0.0132069503869906" [1] "Arithmetic mean 2: 1.0204070827279 ± 0.0131895453967714" [1] "Geometric mean 2: 1.00029970882788 ± 0.0128234253639529"
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Suppose a Binomial dist., n=100, p=0.3333 / win -> +100, lose -> -50 / run 1,000 times
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It seems …… useless???
Codes : Monte_Carlo_Simulation.R
m <- 1000; n <- 100; p <- 0.3333 win <- 100; lose <- -50 binom.raw <- matrix(nrow=m, ncol=n) earn <- matrix(nrow=m, ncol=n) earn.avg <-c() for (i in 1:m) { binom.raw[i,] <- rbinom(n, 1, p) for (j in 1:n ) { ifelse(binom.raw[i,j] == 1, earn[i,j] <- win, earn[i,j] <- lose) } earn.avg[i] <- mean(earn[i,]) } summary(earn.avg) windows(width=12, height=7) par(mfrow=c(1,2)) plot(rank(earn.avg),earn.avg) abline(h=mean(earn.avg), col="red") hist(earn.avg)
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Drawing boxplots divided by groups and months for monitoring multi-strategy investment performance
Codes : Boxplot.R
## Set working directory (not necessary) setwd(""~/your path"") ## Generating file & dataframe names by each month ## Target Period : '17.1 ~ '18.01 file.yymm <- c(1701:1712, 1801:1802) file.name <- sprintf('stock_history_%s.csv', file.yymm) df.name <- sprintf('stk.history.%s', file.yymm) ## Making dataframes by each month data for (i in 1:length(file.yymm)) { assign(df.name[i], read.csv(file.name[i], header=T)) print(sprintf('stk.history.%s', file.yymm[i])) } ## Merging mothly data ## These ugly codes should be upgraded! stk.history <- c() for (i in 1:length(file.yymm)) { stk.history <- rbind(stk.history.1701, stk.history.1702, stk.history.1703, stk.history.1704, stk.history.1705, stk.history.1706, stk.history.1707, stk.history.1708, stk.history.1709, stk.history.1710, stk.history.1711, stk.history.1712, stk.history.1801, stk.history.1802) } ## Checking the structure of the merged dataframe str(stk.history) attach(stk.history) ## Boxplot 1 windows(width=10, height=7) boxplot(수익률 ~ YYMM, main="Monthly Performace (Total)") abline(h=0, col='red') ## Boxplot 2 windows(width=10, height=7) boxplot(수익률 ~ 그룹 + YYMM, main="Comparing Groups : Traditional vs DayTrading", col=c('skyblue','pink')) abline(h=0, col='red') ## Boxplot 3 windows(width=10, height=7) boxplot(수익률 ~ YYMM, subset=그룹=='DayTrading', main="DayTrading Performance", col=c('pink')) abline(h=0, col='red') detach(stk.history)
