[高频数据案例研究]Density Estimation of High-Frequency Financial Data

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Frequently we will want to estimate the empirical probability density function of real-world data and compare it to the theoretical density from one or more probability distributions. The following example shows the empirical and theoretical normal density for EUR/USD high-frequency tick data

1. # Function that reads Reuters CSV tick data and converts Reuters dates
   
2. # Assumes format is Date,Tick
   
3. readRTD <- function(filename) {
   
4. tickData <- read.csv(file=filename, header=TRUE, col.names=c("Date","Tick"))
   
5. tickData$Date <- as.POSIXct(strptime(tickData$Date, format="%d/%m/%Y %H:%M:%S"))
   
6. tickData
   
7. }
   
8. 
   
9. # Boilerplate function for Reuters FX tick data transformation and density plot
   
10. plot.reutersFXDensity <- function() {
    
11. filenames <- c("data/eur_usd_tick_26_10_2007.csv",
    
12.     "data/eur_usd_1min_26_10_2007.csv",
    
13.     "data/eur_usd_5min_26_10_2007.csv",
    
14.     "data/eur_usd_hourly_26_10_2007.csv",
    
15.     "data/eur_usd_daily_26_10_2007.csv")
    
16. labels <- c("Tick", "1 Minute", "5 Minutes", "Hourly", "Daily")
    
17. 
    
18. par(mfrow=c(length(filenames), 2),mar=c(0,0,2,0), cex.main=2)
    
19. tickData <- c()
    
20. i <- 1
    
21. for (filename in filenames) {
    
22. tickData[[i]] <- readRTD(filename)
    
23. # Transform: `$Y = \nabla\log(X_i)[color=#373737][b]Frequently we will want to estimate the empirical probability density function of real-world data and compare it to the theoretical density from one or more probability distributions. The following example shows the empirical and theoretical normal density for EUR/USD high-frequency tick data [/b][/color]
    
24. logtick <- diff(log(tickData[[i]]$Tick))
    
25. # Normalize: `$\frac{(Y-\mu_Y)}{\sigma_Y}[color=#373737][b]Frequently we will want to estimate the empirical probability density function of real-world data and compare it to the theoretical density from one or more probability distributions. The following example shows the empirical and theoretical normal density for EUR/USD high-frequency tick data [/b][/color]
    
26. logtick <- (logtick-mean(logtick))/sd(logtick)
    
27. # Theoretical density range: `$\left[\lfloor\mathrm{min}(Y)\rfloor,\lceil\mathrm{max}(Y)\rceil\right][color=#373737][b]Frequently we will want to estimate the empirical probability density function of real-world data and compare it to the theoretical density from one or more probability distributions. The following example shows the empirical and theoretical normal density for EUR/USD high-frequency tick data [/b][/color]
    
28. x <- seq(floor(min(logtick)), ceiling(max(logtick)), .01)
    
29. plot(density(logtick), xlab="", ylab="", axes=FALSE, main=labels[i])
    
30. lines(x,dnorm(x), lty=2)
    
31. #legend("topleft", legend=c("Empirical","Theoretical"), lty=c(1,2))
    
32. plot(density(logtick), log="y", xlab="", ylab="", axes=FALSE, main="Log Scale")
    
33. lines(x,dnorm(x), lty=2)
    
34. i <- i + 1
    
35. }
    
36. par(op)
    
37. }

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上面的代码解疑：

https://bbs.pinggu.org/thread-3622629-1-1.html

大数据研究：

https://bbs.pinggu.org/thread-3621697-1-1.html

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Density Estimation of HighFrequency Financial Data.pdf (717.84 KB)

2015-3-20 08:28:06 上传

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关键词：Estimation financial Frequency inancial Financia following function converts compare example

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