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highfrequency (version 0.6.4)

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

1,419

Version

0.6.4

License

GPL (>= 2)

Maintainer

Kris Boudt

Last Published

February 26th, 2020

Functions in highfrequency (0.6.4)

HAR model estimation (Heterogeneous Autoregressive model for Realized volatility)

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

autoSelectExchangeQuotes

Retain only data from the stock exchange with the highest volume

Delete the observations where the bid or ask is zero

listAvailableKernels

Available Kernels

Get price column(s) from a timeseries

aggregateQuotes

Aggregate a data.table or xts object containing quote data

aggregateTrades

Aggregate a data.table or xts object containing trades data

HEAVY Model estimation

Aggregate a time series

Realized Covariance

Aggregate a time series but keep first and last observation

Cleans quote data

tradesCleanupUsingQuotes

Perform a final cleaning procedure on trade data

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.

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

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

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

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

autoSelectExchangeTrades

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

getTradeDirection

Get trade direction

highfrequency-package

highfrequency: Tools for Highfrequency Data Analysis

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

Compute log returns

Robust two time scale covariance estimation

Realized Outlyingness Weighted Covariance

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

mergeTradesSameTimestamp

Merge multiple transactions with the same time stamp

Threshold Covariance

matchTradesQuotes

Match trade and quote data

Realized quarticity of highfrequency return series.

mergeQuotesSameTimestamp

Merge multiple quote entries with the same time stamp

rmNegativeSpread

Delete entries for which the spread is negative

realized_library

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

Realized BiPower Covariance

sample_qdataraw

Sample of raw quotes for stock XXX for 1 day

sample_tdataraw

Sample of raw trades for stock XXX for 1 day

Realized kurtosis of highfrequency return series.

Realized skewness of highfrequency return series.

Realized semivariance of highfrequency return series.

sample_5minprices_jumps

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

Sample of cleaned quotes for stock XXX for 1 day

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

Delete the observations where the price is zero

Hayashi-Yoshida Covariance

Spot volatility estimation

Realized quad-power variation of highfrequency return series.

sample_tdata_microseconds

Sample of cleaned trades for stock XXX for 2 days

Realized Covariance: Kernel

Realized Covariance: Average Subsample

rmTradeOutliers

Delete transactions with unlikely transaction prices

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

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

sample_qdata_microseconds

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

Retain only data from a single stock exchange

rmTradeOutliersUsingQuotes

Delete transactions with unlikely transaction prices

Spot Drift Estimation

Two time scale covariance estimation

Synchronize (multiple) irregular timeseries by refresh time

sample_tdataraw_microseconds

Sample of raw trades for stock XXX for 2 days

sample_5minprices

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

Cleans trade data

sample_qdataraw_microseconds

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

rmOutliersQuotes

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

Delete entries with abnormal Sale Condition.

sample_real5minprices

Sample of imaginary price data for 61 days

sample_returns_5min

Sample returns data

Sample of cleaned trades for stock XXX for 1 day

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

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

Calculate the realized tripower quarticity

An estimator of realized variance.

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

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

SP500 Realized Measures calculated with 5 minute sampling

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))

exchangeHoursOnly

Extract data from an xts object for the Exchange Hours Only