kafka399

#aws ec2 describe-spot-price-history --instance-types m4.xlarge --start-time 2017-05-16T01:08:09 --end-time 2017-07-16T12:09:10 --product-descriptions "Linux/UNIX" >dump.txt

require(jsonlite)
require(ggplot2)
require(dplyr)
prices = fromJSON('/projects/dump.txt')

prices = data.frame(prices$SpotPriceHistory)
prices$Timestamp =  strptime(prices$Timestamp, '%Y-%m-%dT%H:%M:%S')
prices$Timestamp = as.POSIXct(prices$Timestamp)

kafka399

find . -name "*.jsp" -print | xargs sed -i 's/old_stuff/new_one/g'

kafka399

Title
========================================================

This is an R Markdown document. Markdown is a simple formatting syntax for authoring web pages (click the **MD** toolbar button for help on Markdown).

When you click the **Knit HTML** button a web page 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:

```{r}
summary(cars)
```

kafka399

sudo tshark -T fields -E separator=, -E quote=d -e ip.src -e ip.dst -e tcp.srcport -e tcp.dstport -e udp.srcport -e upd.dstport -e tcp.len -e ip.len -e eth.type -e frame.time_epoch -e frame.len

kafka399

First graph below shows how GitHub users actions are spread over time during the day. The X axis on the graph is labeled with the hours of the day (GMT) and the Y axis represent median values of the actions for each hour. From it, we can make a deduction, that highest load for GitHub can be expected between 15:00 and 17:00 GMT and lowest to be expected between 05:00 and 07:00 GMT. The color of the line indicates how busy was the day based on quantiles: green are calm days (20% of days), blue – normal days (50% quantile) and red are busy days (80% quantile). I should to mention, that auto-correlation or serial correlation is high (70% for following hour), which means, that busy hours tend to be followed by busy hours and calm hours tend to be followed by calm hours. Moreover, busy days tend happen after busy days.

Second graph below shows median of actions divided by weekdays. There is not big surprise – weekends are more slow than

kafka399

rsi2Vix<-function(ticker,vix)
{
        
        getSymbols(c('SPY','VIX'),from='1995-01-01',index.class=c("POSIXt","POSIXct"))
        rsi2<-RSI(Cl(SPY),2)
        
        signal<-ifelse(rsi2>90,-1,0)
        
        temp<-ifelse(rsi2<10,1,0)