highfrequency (version 0.7.0.1)

tradesCleanupUsingQuotes: Perform a final cleaning procedure on trade data

Description

Function performs cleaning procedure rmTradeOutliersUsingQuotes for the trades of all stocks data in "dataDestination". Note that preferably the input data for this function is trade and quote data cleaned by respectively e.g. tradesCleanup and quotesCleanup.

Usage

tradesCleanupUsingQuotes(
  tradeDataSource = NULL,
  quoteDataSource = NULL,
  dataDestination = NULL,
  tData = NULL,
  qData = NULL,
  lagQuotes = 2
)

Arguments

tradeDataSource

character indicating the folder in which the original trade data is stored.

quoteDataSource

character indicating the folder in which the original quote data is stored.

dataDestination

character indicating the folder in which the cleaned data is stored, folder of dataSource by default.

tData

data.table or xts object containing (ONE day and for ONE stock only) trade data cleaned by tradesCleanup. This argument is NULL by default. Enabling it, means the arguments from, to, dataSource and dataDestination will be ignored. (only advisable for small chunks of data)

qData

data.table or xts object containing (ONE day and for ONE stock only) cleaned quote data. This argument is NULL by default. Enabling it means the arguments from, to, dataSource, dataDestination will be ignored. (only advisable for small chunks of data)

lagQuotes

passed through to rmTradeOutliersUsingQuotes. rmTradeOutliersUsingQuotes

Value

For each day an xts object is saved into the folder of that date, containing the cleaned data.

Details

In case you supply the arguments "tData" and "qData", the on-disk functionality is ignored and the function returns cleaned trades as a data.table or xts object (see examples).

When using the on-disk functionality and tradeDataSource and quoteDataSource are the same, the quote files are all files in the folder that contains 'quote', and the rest are treated as containing trade data.

References

Barndorff-Nielsen, O. E., P. R. Hansen, A. Lunde, and N. Shephard (2009). Realized kernels in practice: Trades and quotes. Econometrics Journal 12, C1-C32.

Brownlees, C.T. and Gallo, G.M. (2006). Financial econometric analysis at ultra-high frequency: Data handling concerns. Computational Statistics & Data Analysis, 51, pages 2232-2245.

Examples

# NOT RUN {
# Consider you have raw trade data for 1 stock for 2 days 
tDataAfterFirstCleaning <- tradesCleanup(tDataRaw = sampleTDataRawMicroseconds, 
                                          exchanges = "N", report = FALSE)
# 
qData <- quotesCleanup(qDataRaw = sampleQDataRawMicroseconds, 
                       exchanges = "N", report = FALSE)
dim(tDataAfterFirstCleaning)
tDataAfterFinalCleaning <- 
  tradesCleanupUsingQuotes(qData = qData[as.Date(DT) == "2018-01-02"],
                           tData = tDataAfterFirstCleaning[as.Date(DT) == "2018-01-02"])
dim(tDataAfterFinalCleaning)
#In case you have more data it is advised to use the on-disk functionality
#via the "tradeDataSource", "quoteDataSource", and "dataDestination" arguments
# }