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Package ‘fmlr’
March 5, 2019
Title Financial Machine Learning using R
Version 0.0.8.0
Author Larry Lei Hua [aut, cre],
Maintainer Larry Lei Hua 
Description This is an R package for tools of machine learning for financial data.
Depends R (>= 3.5)
License file LICENSE
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
LinkingTo Rcpp
Imports stats,
lubridate,
pracma,
zoo,
Rcpp,
stringr,
readr

R topics documented:
acc_lucky . . . . . . .
bar_tick . . . . . . . .
bar_tick_imbalance . .
bar_tick_runs . . . . .
bar_time . . . . . . . .
bar_unit . . . . . . . .
bar_unit_runs . . . . .
bar_volume . . . . . .
bar_volume_imbalance
bar_volume_runs . . .
ema . . . . . . . . . .

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. 2
. 3
. 3
. 4
. 5
. 6
. 6
. 7
. 8
. 9
. 10

2

acc_lucky
fracDiff . . . . . . . . . . . . .
imbalance_tick . . . . . . . . .
imbalance_volume . . . . . . .
istar_CUSUM . . . . . . . . . .
istar_CUSUM_R . . . . . . . .
label_meta . . . . . . . . . . . .
purged_k_CV . . . . . . . . . .
read_algoseek_futures_fullDepth
Tstar_tib . . . . . . . . . . . . .
Tstar_trb_cpp . . . . . . . . . .
Tstar_vib . . . . . . . . . . . .
Tstar_vrb_cpp . . . . . . . . . .
weights_fracDiff . . . . . . . .

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Index

acc_lucky

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10
11
11
12
13
13
14
15
16
17
18
19
20
21

A function to check whether a classification is better than a guess

Description
A function to check whether a classification is better than a guess
Usage
acc_lucky(train_class, test_class, my_acc, s = 1000)
Arguments
train_class

a vector for the distribution of classes in the training set

test_class

a vector for the distribution of classes in the test set

my_acc

a number between 0 and 1 for the classification accuracy to be evaluated

s

sample size of simulations used to check p-values

Author(s)
Larry Lei Hua
Examples
train_class <- c(1223,1322,1144)
test_class <- c(345,544,233)
my_acc <- 0.45
acc_lucky(train_class, test_class, my_acc)

bar_tick

3

bar_tick

Construct tick bars

Description
Construct tick bars
Usage
bar_tick(dat, nTic)
Arguments
dat

dat input with at least the following columns: Price, Size

nTic

the number of ticks of each window

Value
time stamp at the end of each bar (if timestamp is provided), and H,L,O,C,V
Author(s)
Larry Lei Hua

bar_tick_imbalance

Construct tick imbalance bars

Description
Construct tick imbalance bars
Usage
bar_tick_imbalance(dat, w0 = 10, bkw_T = 5, bkw_b = 5)
Arguments
dat

dat input with at least the following column: Price, Size

w0

the time window length of the first bar

bkw_T

backward window length when using pracma::movavg for exponentially weighted
average T

bkw_b

backward window length when using pracma::movavg for exponentially weighted
average b_t

4

bar_tick_runs

Value
a list of vectors for tStamp (if returned), and HLOCV of tick imbalance bars. Note that the remaining data after the latest imbalance time point will be formed as a bar.
Author(s)
Larry Lei Hua
Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 4), runif(50)), Size = rep(10,54))
bar_tick_imbalance(dat)

bar_tick_runs

Construct tick runs bars

Description
Construct tick runs bars
Usage
bar_tick_runs(dat, w0 = 10, de = 1, bkw_T = 5, bkw_Pb1 = 5,
filter = FALSE)
Arguments
dat

dat input with at least the following column: Price, Size

w0

the time window length of the first bar

de

a positive value for adjusting the expected window size, that is, de*E0T; default:
1

bkw_T

backward window length when using pracma::movavg for exponentially weighted
average T

bkw_Pb1

backward window length when using pracma::movavg for exponentially weighted
average P[b_t=1]

filter

whether used as a filter; default FALSE. If TRUE, then only i_feabar, the ending
time index of feature bars, is returned

Value
If filter==FALSE, a list of vectors for tStamp (if returned), and HLOCV of tick runs bars. Note
that the remaining data after the latest ending time point detected will be formed as a bar. If filter==TRUE, i_feabar a vector of integers for the time index.

bar_time

5

Author(s)
Larry Lei Hua

Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 4), runif(1000)), Size = rep(10,1004))
x1 <- bar_tick_runs(dat)
x2 <- bar_tick_runs(dat, filter=TRUE)

bar_time

Construct time bars

Description
Construct time bars

Usage
bar_time(dat, tDur = 1)
Arguments
dat

dat input with at least the following columns: tStamp, Price, Size, where tStamp
should be sorted already

tDur

the time duration in seconds of each window

Value
tStamp, seconds since the starting time point, and H,L,O,C,V

Author(s)
Larry Lei Hua

6

bar_unit_runs

bar_unit

Construct unit bars

Description
Construct unit bars
Usage
bar_unit(dat, unit)
Arguments
dat

dat input with at least the following columns: Price, Size. If timestamp is provided than output also contains timestamp of the unit bars

unit

the total dollar (unit) traded of each window

Value
time stamp at the end of each bar (if timestamp is provided), and H,L,O,C,V
Author(s)
Larry Lei Hua

bar_unit_runs

Construct unit runs bars

Description
Construct unit runs bars
Usage
bar_unit_runs(dat, u_0 = 2000, w0 = 10, de = 1, bkw_T = 5,
bkw_Pb1 = 5, bkw_U = 5, filter = FALSE)
Arguments
dat

dat input with at least the following column: Price, Size

u_0

average unit (volume*price) for each trade, and it is used to create the first bar

w0

the time window length of the first bar

de

a positive value for adjusting the expected window size, that is, de*E0T; default:
1

bar_volume

7

bkw_T

backward window length when using pracma::movavg for exponentially weighted
average T

bkw_Pb1

backward window length when using pracma::movavg for exponentially weighted
average P[b_t=1]

bkw_U

backward window length for exponentially weighted average volumes

filter

whether used as a filter; default FALSE. If TRUE, then only i_feabar, the ending
time index of feature bars, is returned

Value
If filter==FALSE, a list of vectors for tStamp (if returned), and HLOCV of volume runs bars.
Note that the remaining data after the latest ending time point detected will be formed as a bar. If
filter==TRUE, i_feabar a vector of integers for the time index.
Author(s)
Larry Lei Hua
Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 4), runif(50)), Size = floor(runif(54)*100))
bar_unit_runs(dat, u_0=mean(dat$Price)*mean(dat$Size))
bar_unit_runs(dat, u_0=mean(dat$Price)*mean(dat$Size), filter=TRUE)

bar_volume

Construct volume bars

Description
Construct volume bars
Usage
bar_volume(dat, vol)
Arguments
dat

dat input with at least the following columns: Price, Size

vol

the volume of each window

Value
time stamp at the end of each bar (if timestamp is provided), and H,L,O,C,V

8

bar_volume_imbalance

Author(s)
Larry Lei Hua

bar_volume_imbalance

Construct volume imbalance bars

Description
Construct volume imbalance bars
Usage
bar_volume_imbalance(dat, w0 = 10, bkw_T = 5, bkw_b = 5)
Arguments
dat

dat input with at least the following column: Price, Size

w0

the time window length of the first bar

bkw_T

backward window length when using pracma::movavg for exponentially weighted
average T

bkw_b

backward window length when using pracma::movavg for exponentially weighted
average b_tv_t

Value
a list of vectors for tStamp (if returned), and HLOCV of volume imbalance bars. Note that the
remaining data after the latest imbalance time point will be formed as a bar.
Author(s)
Larry Lei Hua
Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 4), runif(50)), Size = rep(10,54))
bar_volume_imbalance(dat)

bar_volume_runs

bar_volume_runs

9

Construct volume runs bars

Description
Construct volume runs bars
Usage
bar_volume_runs(dat, v_0 = 20, w0 = 10, de = 1, bkw_T = 5,
bkw_Pb1 = 5, bkw_V = 5, filter = FALSE)
Arguments
dat

dat input with at least the following column: Price, Size

v_0

average volume for each trade, and it is used to create the first bar

w0

the time window length of the first bar

de

a positive value for adjusting the expected window size, that is, de*E0T; default:
1

bkw_T

backward window length when using pracma::movavg for exponentially weighted
average T

bkw_Pb1

backward window length when using pracma::movavg for exponentially weighted
average P[b_t=1]

bkw_V

backward window length for exponentially weighted average volumes

filter

whether used as a filter; default FALSE. If TRUE, then only i_feabar, the ending
time index of feature bars, is returned

Value
If filter==FALSE, a list of vectors for tStamp (if returned), and HLOCV of volume runs bars.
Note that the remaining data after the latest ending time point detected will be formed as a bar. If
filter==TRUE, i_feabar a vector of integers for the time index.
Author(s)
Larry Lei Hua
Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 4), runif(50)), Size = floor(runif(54)*100))
bar_volume_runs(dat)
bar_volume_runs(dat, filter=TRUE)

10

fracDiff

ema

exponentially weighted moving average; only return the last value

Description
exponentially weighted moving average; only return the last value
Usage
ema(x, n)
Arguments
x
n

a numeric vector
window size

Value
a numeric value
Author(s)
Larry Lei Hua

fracDiff

convert a time series into a fractionally differentiated series

Description
convert a time series into a fractionally differentiated series
Usage
fracDiff(x, d = 0.5, nWei = 10, tau = NULL)
Arguments
x
d
nWei
tau

Author(s)
Larry Lei Hua

a vector of time series to be fractionally differentiated
the order for fractionally differentiated features
number of weights for output
threshold where weights are cut off; default is NULL, if not NULL then use tau
and nWei is not used

imbalance_tick

11

imbalance_tick

The auxiliary function b_t for constructing tick imbalance bars. The
first b_t is assigned the value 0 because no information is available

Description
The auxiliary function b_t for constructing tick imbalance bars. The first b_t is assigned the value
0 because no information is available
Usage
imbalance_tick(dat)
Arguments
dat

dat input with at least the following columns: Price

Author(s)
Larry Lei Hua, ming08108(github)
Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 4), runif(2), 0.5, 0.5, 0.4, runif(2) ))
b_t <- imbalance_tick(dat)

imbalance_volume

The auxiliary function b_tv_t for constructing volume imbalance bars.
The first b_tv_t is assigned the value 0 because no information is available

Description
The auxiliary function b_tv_t for constructing volume imbalance bars. The first b_tv_t is assigned
the value 0 because no information is available
Usage
imbalance_volume(dat)
Arguments
dat

dat input with at least the following columns: Price, Size

12

istar_CUSUM

Author(s)
Larry Lei Hua
Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 4), runif(10)), Size = rep(10,14))
b_tv_t <- imbalance_volume(dat)

istar_CUSUM

time index that triggers a symmetric CUSUM filter

Description
time index that triggers a symmetric CUSUM filter
Usage
istar_CUSUM(x, h)
Arguments
x

a vector of time series to be filtered

h

a vector of the thresholds

Author(s)
Larry Lei Hua
Examples
set.seed(1)
x <- runif(100, 1, 3)
h <- rep(1.5, 100)
i_CUSUM <- istar_CUSUM(x,h)
abline(v=i_CUSUM, lty = 2)
## Comparing C and R versions
# set.seed(1)
# x <- runif(1000000, 1, 3)
# h <- rep(1.5, 100)
#
#
#
#

start_time <- Sys.time()
i_CUSUM <- istar_CUSUM(x,h)
end_time <- Sys.time()
C_time <- end_time - start_time

istar_CUSUM_R

#
#
#
#
#
#

13

start_time <- Sys.time()
i_CUSUM_R <- istar_CUSUM_R(x,h)
end_time <- Sys.time()
R_time <- end_time - start_time
cat("C and R time: ", C_time, R_time)
all(i_CUSUM-i_CUSUM_R==0)

istar_CUSUM_R

time index that triggers a symmetric CUSUM filter (R version for istar_CUSUM(), for shorter x, the R version can be faster than the C
version)

Description
time index that triggers a symmetric CUSUM filter (R version for istar_CUSUM(), for shorter x,
the R version can be faster than the C version)
Usage
istar_CUSUM_R(x, h)
Arguments
x

a vector of time series to be filtered

h

a vector of the thresholds

Author(s)
Larry Lei Hua

label_meta

Meta labeling, including three options: triple barriers, upper and vertical barriers, and lower and vertical barriers.

Description
Meta labeling, including three options: triple barriers, upper and vertical barriers, and lower and
vertical barriers.
Usage
label_meta(x, events, ptSl, ex_vert = T, n_ex = 0)

14

purged_k_CV

Arguments
x

a vector of prices series to be labeled.

events

a dataframe that has the following columns:
• t0: event’s starting time index.
• t1: event’s ending time index; if t1==Inf then no vertical barrier, i.e., last
observation in x is the vertical barrier.
• trgt: the unit absolute return used to set up the upper and lower barriers.
• side: 0: both upper and lower barriers; 1: only upper; -1: only lower.

ptSl

a vector of two multipliers for the upper and lower barriers, respectively.

ex_vert

whether exclude the output when the vertical barrier is hit; default is T.

n_ex

number of excluded observations at the begining of x; default is 0.

Value
data frame with the following columns:
• T_up: local time index when the upper barrier is hit; Inf means that upper is not hit.
• T_lo: local time index when the lower barrier is hit; Inf means that lower is not hit.
• t1: local time index when the vertical barrier is hit.
• ret: return associated with the event.
• label: low:-1, vertical:0, upper:1.
• t0Fea: begining time index of feature bars.
• t1Fea: ending time index of feature bars.
• tLabel: ending time index of events, i.e., when the labels are created. Both t1Fea and tLabel
will be useful for sequential bootstrap.
Author(s)
Larry Lei Hua

purged_k_CV

Purged k-fold CV with embargo

Description
Purged k-fold CV with embargo
Usage
purged_k_CV(feaMat, k = 5, gam = 0.01)

read_algoseek_futures_fullDepth
Arguments
feaMat

a data.frame for feature matrix with the first column being the label

k

number of folds for k-fold CV

gam

gamma for embargo

Value
a list of k data.frame, each containing a test set and a training set
Author(s)
Larry Lei Hua
Examples
feaMat <- data.frame(Y = c(1,1,0,1,0),
V = c(2,4,2,4,1),
t1Fea = c(2,5,8,14,20),
tLabel = c(4,12,16,23,38))
purged_k_CV(feaMat, k=2, gam=0.1)

read_algoseek_futures_fullDepth
Load AlgoSeek Futures Full Depth data from zip files

Description
Load AlgoSeek Futures Full Depth data from zip files
Usage
read_algoseek_futures_fullDepth(zipdata, whichData = NULL)
Arguments
zipdata

the original zip data provided by AlgoSeek

whichData

the specific data to be loaded; by default load all data in the zip file

Author(s)
Larry Lei Hua

15

16

Tstar_tib

Examples
zipdata <- tempfile()
download.file("https://www.algoseek.com/static/files/sample_data/
futures_and_future_options/ESH5.Futures.FullDepth.20150128.csv.zip",zipdata)
dat <- read_algoseek_futures_fullDepth(zipdata)
# Do not run unless the file 20160104.zip is avaliable
# dat <- read_algoseek_futures_fullDepth("20160104.zip", whichData="ES/ESH6.csv")

Tstar_tib

Tstar index for Tick Imbalance Bars (bar_tib)

Description
Tstar index for Tick Imbalance Bars (bar_tib)
Usage
Tstar_tib(dat, w0 = 10, bkw_T = 5, bkw_b = 5)
Arguments
dat

dat input with at least the following columns: Price

w0

the time window length of the first bar

bkw_T

backward window length when using pracma::movavg for exponentially weighted
average T

bkw_b

backward window length when using pracma::movavg for exponentially weighted
average b_t

Value
a vector for the lengths of the tick imbalance bars. For example, if the return is c(10,26), then the 2
tick imbalance bars are (0,10] and (10, 36]
Author(s)
Larry Lei Hua

Tstar_trb_cpp

17

Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 4), runif(50)))
T_tib <- Tstar_tib(dat)
b_t <- imbalance_tick(dat)
cumsum(b_t)[cumsum(T_tib)] # check the accumulated b_t's where the imbalances occur

Tstar_trb_cpp

Tstar index for Tick Runs Bars (bar_trb)

Description
Tstar index for Tick Runs Bars (bar_trb)
Usage
Tstar_trb_cpp(b_t, w0, de, bkw_T, bkw_Pb1)
Arguments
b_t

output of imbalance_tick(dat) with the dat has at least the following columns:
Price

w0

the time window length of the first bar

de

a positive value for adjusting the expected window size, that is, de*E0

bkw_T

backward window length for exponentially weighted average T

bkw_Pb1

backward window length for exponentially weighted average P[b_t=1]

Value
a list of the following two vectors: a vector for the lengths of the tick imbalance bars. For example,
if the return is c(10,26), then the 2 tick imbalance bars are (0,10] and (10, 36] a vector indicating
up runs or down runs
Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 5), runif(100)))
b_t <- imbalance_tick(dat)
T_trb <- Tstar_trb_cpp(b_t, 10, 1.0, 10, 10)
col <- ifelse(T_trb$Type==1, "red", "blue")
T <- cumsum(T_trb$Tstar)
plot(dat$Price)
for(i in 1:length(T)) abline(v = T[i], col = col[i])

18

Tstar_vib

Tstar_vib

Tstar index for Volume Imbalance Bars (bar_vib)

Description
Tstar index for Volume Imbalance Bars (bar_vib)

Usage
Tstar_vib(dat, w0 = 10, bkw_T = 5, bkw_b = 5)
Arguments
dat

dat input with at least the following columns: Price, Size

w0

the time window length of the first bar

bkw_T

backward window length when using pracma::movavg for exponentially weighted
average T

bkw_b

backward window length when using pracma::movavg for exponentially weighted
average b_tv_t

Value
a vector for the lengths of the tick imbalance bars. For example, if the return is c(10,26), then the 2
tick imbalance bars are (0,10] and (10, 36]

Author(s)
Larry Lei Hua

Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 4), runif(50)), Size = rep(10, 54))
T_vib <- Tstar_vib(dat)
b_tv_t <- imbalance_volume(dat)
cumsum(b_tv_t)[cumsum(T_vib)] # check the accumulated b_t's where the imbalances occur

Tstar_vrb_cpp

Tstar_vrb_cpp

19

Tstar index for Volume Runs Bars (bar_vrb)

Description
Tstar index for Volume Runs Bars (bar_vrb)
Usage
Tstar_vrb_cpp(b_t, v_t, v_0, w0, de, bkw_T, bkw_Pb1, bkw_V)
Arguments
b_t

output of imbalance_tick(dat) with the data ’dat’ has at least the following columns:
Price

v_t

volume of the same data

v_0

average volume for each trade, and it is used to create the first bar

w0

the time window length of the first bar

de

a positive value for adjusting the expected window size, that is, de*E0T; default:
1.

bkw_T

backward window length for exponentially weighted average T

bkw_Pb1

backward window length for exponentially weighted average P[b_t=1]

bkw_V

backward window length for exponentially weighted average volumes

Value
a list of the following two vectors: a vector for the lengths of the tick imbalance bars. For example,
if the return is c(10,26), then the 2 tick imbalance bars are (0,10] and (10, 36] a vector indicating
up runs or down runs
Examples
set.seed(1)
dat <- data.frame(Price = c(rep(0.5, 5), runif(100)), Size = runif(105, 10, 100))
b_t <- imbalance_tick(dat)
v_t <- dat$Size
T_vrb <- Tstar_vrb_cpp(b_t, v_t, 55, 10, 1.0, 10, 10, 10)
col <- ifelse(T_vrb$Type==1, "red", "blue")
T <- cumsum(T_vrb$Tstar)
plot(dat$Price)
for(i in 1:length(T)) abline(v = T[i], col = col[i])

20

weights_fracDiff

weights_fracDiff

calculate the weights for deriving fractionally differentiated series

Description
calculate the weights for deriving fractionally differentiated series
Usage
weights_fracDiff(d = 0.5, nWei = 10, tau = NULL)
Arguments
d

the order for fractionally differentiated features

nWei

number of weights for output

tau

threshold where weights are cut off; default is NULL, if not NULL then use tau
and nWei is not used

Author(s)
Larry Lei Hua
Examples
weights_fracDiff(0.5,tau=1e-3)

Index
acc_lucky, 2
bar_tick, 3
bar_tick_imbalance, 3
bar_tick_runs, 4
bar_time, 5
bar_unit, 6
bar_unit_runs, 6
bar_volume, 7
bar_volume_imbalance, 8
bar_volume_runs, 9
ema, 10
fracDiff, 10
imbalance_tick, 11
imbalance_volume, 11
istar_CUSUM, 12
istar_CUSUM_R, 13
label_meta, 13
purged_k_CV, 14
read_algoseek_futures_fullDepth, 15
Tstar_tib, 16
Tstar_trb_cpp, 17
Tstar_vib, 18
Tstar_vrb_cpp, 19
weights_fracDiff, 20

21



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