One can use R and data from e.g quandl.com to feed sierra (EoD).

This way you can turn your little Sierra into an economics machine.
(fuck you Bloomberg ! )

Here is an example/template for the Baltic Dry Index.

(I am not a coder - so feel free to better this embarrassing script)

 

Code

### Baltic Dry Index ### via quandl ### v0.1 share ### ---- # # #_____________________________________________________________________________ ### Meta data ---- name1 <- "bender is great" stamp.date <- Sys.Date() description <- "Baltic Dry Index. Source: DryShips Inc." # #______________________________________________________________________________ ### set variables ---- quandl.auth <- 'GetThisFromYourQuandlAccount' #https://www.quandl.com quandl.symbol <- "OFDP/INDEX_BDI_1" path.to.sierra.data <- "F:/Programme/aaaa Trading/SierraChart5/Data/r.BDI.dly" #_____________________________________________________________________________ ### Libraries ------------------------------------------------------------- library("Quandl") library("xts") library("sqldf") #______________________________________________________________________________ ### Auth for Quandl ---- Quandl.auth(quandl.auth) # #_____________________________________________________________________________ ### DL from quandl ------------------------------------------------------------ d = Quandl(quandl.symbol, collapse=NULL, # NULL for Daily start_date="1900-01-01", type="xts") #plot(qdata[,1]) #______________________________________________________________________________ ### opti data for sierra ---- # convert xts 2 df d.df <- as.data.frame(d) rn <- row.names(d.df) # opti Date rn <- sapply(rn, function(x) {as.character(gsub("-","/", as.character(x)))}) # write back opti row names row.names(d.df) <- rn # make pseudo OHLC d.df[,2] <- d.df[,1] d.df[,3] <- d.df[,1] d.df[,4] <- d.df[,1] d.df[,5] <- d.df[,1] d.df[,1] <- rn # rename col names(d.df) <- c("Date","Open","High","Low","Close") #_____________________________________________________________________________ ### merge with data in (txt-) DB ---- # this preserves the data downloaded earlier - # in case there is more data in local DB, than in the web-source # get downloaded quandl data d_DL <- d.df rm(d.df) # get current data in DB if (file.exists(path.to.sierra.data)){ d_DB <- read.table(path.to.sierra.data, header=T, sep=",", colClasses=c("character","numeric","numeric","numeric", "numeric")) d_DB[,1] <- sapply(d_DB[,1], function(x) {as.character(gsub("-","/", as.character(x)))}) } else { d_DB <- d_DL } # Only the rows which are NOT in both data frames: d_update <- sqldf('SELECT * FROM d_DL EXCEPT SELECT * FROM d_DB') # merge into updated DB d_DBnew <- merge(d_update, d_DB, all.x=T, all=T) rm(d_DB) rm(d_DL) rm(d_update) #_____________________________________________________________________________ ### write to file ---- write.table(d_DBnew,path.to.sierra.data,sep=",",row.names=FALSE, quote=F) #_____________________________________________________________________________ ### clean ---- rm(list=ls()) #_____________________________________________________________________________

I believe @Nicolas11 also posted a lot of R/Sierra code for manipulating data.

Nicolas' trading journal This first message does not reflect any more what I am doing now. More information in the Quick Summary below. ;) Dear fellow traders, Time has also come for me to start a journal. One word of background: I spent some time in the world of automatic …

Mike

Big Mike 

I believe @Nicolas11 also posted a lot of R/Sierra code for manipulating data. Nicolas' trading journal This first message does not reflect any more what I am doing now. More information in the Quick Summary below. ;) Dear fellow traders, Time has also come for me to start a journal. One word of background: I spent some time in the world of automatic … Mike

TY , I will have a look

quandl changed the symbol to:

OFDP/INDEX_BDI