The package mladv provide tools to clean, convert and analyze the
financial data. It also provides various tools to apply machine learning
to financial data.
We expect to release the package as soon as possible in CRAN. At the mean time, you can install it with devtools:
devtools::install_github("thanhuwe8/mladv")
First you load the data from the package
library(mladv)
head(data1,3)
#> Time Price Size cum_volume ppt
#> 1 2018-12-17 10:15:36 21.6 123460 123460 1.31
#> 2 2018-12-17 10:15:47 21.6 400 123860 0.00
#> 3 2018-12-17 10:16:07 21.6 3000 126860 0.03
With the data_set, we can convert the raw market data into different
bars such as time bars or volume bars as examples below with SSI_data
volume6000 <- volumebarr(data1, 15000)
head(volume6000)
#> Time Open High Low Close Volume Transaction
#> 1 2018-12-17 12:15:36 21.60 21.60 21.60 21.60 123460 1
#> 2 2018-12-17 12:16:22 21.60 21.60 21.60 21.60 50340 4
#> 3 2018-12-17 12:17:12 21.60 21.60 21.55 21.60 15530 4
#> 4 2018-12-17 12:18:01 21.60 21.60 21.60 21.60 21930 9
#> 5 2018-12-17 12:18:43 21.60 21.65 21.60 21.65 31330 4
#> 6 2018-12-17 12:20:56 21.65 21.65 21.60 21.65 16410 12
minute_bar <- timebar(data1, "minute")
head(minute_bar)
#> Time Open High Low Close Volume
#> 1 2018-12-17 12:16:22 21.60 21.60 21.60 21.60 173800
#> 2 2018-12-17 12:17:25 21.60 21.60 21.55 21.60 23270
#> 3 2018-12-17 12:18:31 21.60 21.60 21.60 21.60 15470
#> 4 2018-12-17 12:19:34 21.65 21.65 21.60 21.65 33930
#> 5 2018-12-17 12:20:30 21.65 21.65 21.60 21.60 5060
#> 6 2018-12-17 12:21:33 21.60 21.65 21.60 21.65 37130
We can still use functions from popular packages such as quantmod or
ggplot2 with the return data.frame
We will add more useful functions later.
This project is licensed under the GPL3 License
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