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highfrequency (version 0.6.3)

Tools for Highfrequency Data Analysis

Description

Provide functionality to manage, clean and match highfrequency trades and quotes data, calculate various liquidity measures, estimate and forecast volatility, detect price jumps and investigate microstructure noise and intraday periodicity.

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Version

Install

install.packages('highfrequency')

Monthly Downloads

1,607

Version

0.6.3

License

GPL (>= 2)

Maintainer

Kris Boudt

Last Published

January 15th, 2020

Functions in highfrequency (0.6.3)

Jiang and Oomen (2008) tests for the presence of jumps in the price series.

Ait-Sahalia and Jacod (2009) tests for the presence of jumps in the price series.

Calculate the realized tripower quarticity

SP500 Realized Measures calculated with 5 minute sampling

Aggregate a time series but keep first and last observation

autoSelectExchangeQuotes

Retain only data from the stock exchange with the highest volume

getLiquidityMeasures

Compute Liquidity Measure Function returns an xts or data.table object containing 23 liquidity measures. Please see details below. Note that this assumes a regular time grid. The Lee + Ready measure uses two lags for the Tick Rule.

exchangeHoursOnly

Extract data from an xts object for the Exchange Hours Only

Get price column(s) from a timeseries

An estimator of realized variance.

autoSelectExchangeTrades

etain only data from the stock exchange with the highest trading volume

An estimator of integrated quarticity from applying the median operator on blocks of three returns.

matchTradesQuotes

Match trade and quote data

Compute log returns

An estimator of integrated quarticity from applying the minimum operator on blocks of two returns.

Aggregate a time series

aggregateTrades

Aggregate a data.table or xts object containing trades data

Realized Covariance: Average Subsample

Realized BiPower Covariance

HAR model estimation (Heterogeneous Autoregressive model for Realized volatility)

mergeTradesSameTimestamp

Merge multiple transactions with the same time stamp

Function returns the value, the standard error and the confidence band of the integrated variance (IV) estimator.

Realized semivariance of highfrequency return series.

getTradeDirection

Get trade direction

Hayashi-Yoshida Covariance

sample_5minprices_jumps

Ten artificial time series (including jumps) for the NYSE trading days during January 2010

Returns the positive semidinite projection of a symmetric matrix using the eigenvalue method

Delete the observations where the bid or ask is zero

Realized quarticity of highfrequency return series.

Check for Trade, Bid, and Ask/Offer (BBO/TBBO), Quantity, and Price data

Delete entries for which the spread is more than "maxi" times the median spread

Realized beta: a tool in measuring risk with respect to the market.

HEAVY Model estimation

## mukp: to use when p,k different from range [4,6]

highfrequency-package

highfrequency: Tools for Highfrequency Data Analysis

Delete the observations where the price is zero

Realized Covariance

Threshold Covariance

Robust two time scale covariance estimation

realized_library

The realized library from the Oxford-Man Institute of Quantitative Finance

Realized Covariance: Kernel

sample_qdata_microseconds

Sample of cleaned quotes for stock XXX for 2 days measured in microseconds

listAvailableKernels

Available Kernels

sample_returns_5min

Sample returns data

Sample of cleaned trades for stock XXX for 1 day

rmTradeOutliersUsingQuotes

Delete transactions with unlikely transaction prices

Synchronize (multiple) irregular timeseries by refresh time

rmTradeOutliers

Delete transactions with unlikely transaction prices

sample_tdata_microseconds

Sample of cleaned trades for stock XXX for 2 days

Realized tri-power variation estimator of quarticity for a highfrequency return series.

mergeQuotesSameTimestamp

Merge multiple quote entries with the same time stamp

rmNegativeSpread

Delete entries for which the spread is negative

Realized kurtosis of highfrequency return series.

Realized multipower variation (MPV), an estimator of integrated power variation.

rmOutliersQuotes

Delete entries for which the mid-quote is outlying with respect to surrounding entries

sample_qdataraw

Sample of raw quotes for stock XXX for 1 day

sample_tdataraw_microseconds

Sample of raw trades for stock XXX for 2 days

sample_qdataraw_microseconds

Sample of raw quotes for stock XXX for 2 days measured in microseconds

sample_real5minprices

Sample of imaginary price data for 61 days

Spot volatility estimation

Two time scale covariance estimation

sample_5minprices

Ten artificial time series for the NYSE trading days during January 2010

tradesCleanupUsingQuotes

Perform a final cleaning procedure on trade data

Delete entries with abnormal Sale Condition.

Realized Outlyingness Weighted Covariance

sample_tdataraw

Sample of raw trades for stock XXX for 1 day

Realized quad-power variation of highfrequency return series.

Retain only data from a single stock exchange

Sample of cleaned quotes for stock XXX for 1 day

Cleans quote data

Cleans trade data

Realized skewness of highfrequency return series.

Spot Drift Estimation

Modulated Realized Covariance (MRC): Return univariate or multivariate preaveraged estimator.

Barndorff-Nielsen and Shephard (2006) tests for the presence of jumps in the price series.

rvKernel <- function(x, # Tick Data kernel.type = "rectangular", # Kernel name (or number) kernel.param = 1, # Kernel parameter (usually lags) kernel.dofadj = TRUE, # Kernel Degree of freedom adjustment align.by = "seconds", # Align the tick data to [seconds|minutes|hours] align.period = 1) # Align the tick data to this many [seconds|minutes|hours] # Multiday adjustment: multixts <- multixts(x) if (multixts == TRUE) result <- apply.daily(x, rv.kernel,kernel.type,kernel.param,kernel.dofadj, align.by, align.period, cts, makeReturns) return(result) else #Daily estimation: align.period <- .getAlignPeriod(align.period, align.by) cdata <- .convertData(x, cts = cts, makeReturns = makeReturns) x <- cdata$data x <- .alignReturns(x, align.period) type <- kernelCharToInt(kernel.type) kernelEstimator(as.double(x), as.double(x), as.integer(length(x)), as.integer(kernel.param), as.integer(ifelse(kernel.dofadj, 1, 0)), as.integer(type), ab = double(kernel.param + 1), ab2 = double(kernel.param + 1))

aggregateQuotes

Aggregate a data.table or xts object containing quote data