{"id":48727,"date":"2020-06-13T09:50:00","date_gmt":"2020-06-13T13:50:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=48727"},"modified":"2022-11-21T09:45:42","modified_gmt":"2022-11-21T14:45:42","slug":"time-series-classification-synthetic-vs-real-financial-time-series-part-iv","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/time-series-classification-synthetic-vs-real-financial-time-series-part-iv\/","title":{"rendered":"Time Series Classification Synthetic vs Real Financial Time Series \u2013 Part IV"},"content":{"rendered":"\n

See\u00a0Part I<\/a>,\u00a0Part II<\/a>\u00a0and\u00a0<\/em>Part III<\/a><\/em> in this article for instructions from Matthew Smith on which R packages and data sets you need.<\/em><\/p>\n\n\n\n

I compute the 10 day rolling mean and standard deviation using the tq_mutate<\/code> function from the tidyquant<\/code> package. value<\/code> corresponds to the returns of the financial time series and is plotted in blue with the 10 day rolling average and standard deviation plotted over the returns. (I use melt<\/code> again here but look into the pivot_longer<\/code> function for a more intuitive application)<\/p>\n\n\n\n

\n# Rolling mean and standard deviations
\n# I only use a random sample of 1 of each class of the grouped observations to save on memory and to make the plot more readable.
\n# The rollowing window is 10 days
\n# I use the tq_mutate functionality from the \u201ctidyquant\u201d package to keep things in a \u201ctidy\u201d format as per the \u201ctidyverse\u201d \u2018rules\u2019.
\n# In the plot \u201cvalue\u201d is the returns, \u201cmean_10\u201d is the 10 day rolling mean and \u201csd_10\u201d is the 10 day rolling standard deviation.\n<\/p>\n\n\n\n\n

\nplot0 <- df %>%
\n filter(class == 0) %>%
\n as_tibble() %>%
\n group_by(row_id) %>%
\n nest() %>%
\n ungroup() %>%
\n sample_n(1) %>%
\n unnest() %>%
\n mutate(variable = as.Date(variable)) %>%
\n tq_mutate(
\n select = value,
\n mutate_fun = rollapply,
\n width = 10,
\n align = “right”,
\n FUN = mean,
\n na.rm = TRUE,
\n col_rename = “mean_10”
\n ) %>%
\n tq_mutate(
\n select = value,
\n mutate_fun = rollapply,
\n width = 10,
\n align = “right”,
\n FUN = sd,
\n na.rm = TRUE,
\n col_rename = “sd_10”
\n ) %>%
\n melt(measure.vars = 5:7) %>%
\n setNames(c(“row_id”, “class”, “data set”, “date”, “variable”, “value”)) %>%
\n ggplot(aes(x = date)) +
\n geom_line(aes(y = value, colour = variable)) +
\n ggtitle(“Synthetic Financial Time Series Rolling Mean and Standard Deviation”) +
\n theme_classic() +
\n scale_colour_manual(values = c(“#1f77b4”, “red”, “black”)) +
\n theme(axis.text.x = element_blank(), legend.position = “bottom”, legend.title = element_blank())

\n\nplot1 <- df %>%
\n filter(class == 1) %>%
\n as_tibble() %>%
\n group_by(row_id) %>%
\n nest() %>%
\n ungroup() %>%
\n sample_n(1) %>%
\n unnest() %>%
\n mutate(variable = as.Date(variable)) %>%
\n tq_mutate(
\n select = value,
\n mutate_fun = rollapply,
\n width = 10,
\n align = “right”,
\n FUN = mean,
\n na.rm = TRUE,
\n col_rename = “mean_10”
\n ) %>%
\n tq_mutate(
\n select = value,
\n mutate_fun = rollapply,
\n width = 10,
\n align = “right”,
\n FUN = sd,
\n na.rm = TRUE,
\n col_rename = “sd_10”
\n ) %>%
\n melt(measure.vars = 5:7) %>%
\n setNames(c(“row_id”, “class”, “data set”, “date”, “variable”, “value”)) %>%
\n ggplot(aes(x = date)) +
\n geom_line(aes(y = value, colour = variable)) +
\n ggtitle(“Real Financial Time Series Rolling Mean and Standard Deviation”) +
\n theme_classic() +
\n scale_colour_manual(values = c(“#1f77b4”, “red”, “black”)) +
\n theme(axis.text.x = element_blank(), legend.position = “bottom”, legend.title = element_blank())

\n\nplot_grid(plot0, plot1)\n<\/p>\n\n\n\n

\"\"<\/figure>\n\n\n\n

Visit\u00a0Matthew Smith \u2013 R Blog<\/a>\u00a0to see the next step in his analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"

Matthew Smith computes the 10 day rolling mean and standard deviation using the tq_mutate function from the tidyquant package.<\/p>\n","protected":false},"author":372,"featured_media":48731,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,338,341,351,344,342],"tags":[6989,806,7487,6613,6988,6614,852,7811,1044,1045,5519,2536],"contributors-categories":[13694],"class_list":{"0":"post-48727","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-ibkr-quant-news","10":"category-quant-development","11":"category-quant-europe","12":"category-quant-regions","13":"category-r-development","14":"tag-asset-pricing","15":"tag-data-science","16":"tag-durbin-watson-box-plot","17":"tag-financial-data","18":"tag-financial-markets","19":"tag-jupyter-notebook","20":"tag-machine-learning","21":"tag-r-rstats","22":"tag-tidyquant","23":"tag-tidyverse","24":"tag-time-series","25":"tag-visualization","26":"contributors-categories-matthew-smith-r-blog"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"\nTime Series Classification Synthetic vs Real Financial Time Series \u2013 Part IV<\/title>\n<meta name=\"description\" content=\"Matthew Smith computes the 10 day rolling mean and standard deviation using the tq_mutate function from the tidyquant package.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.interactivebrokers.com\/campus\/wp-json\/wp\/v2\/posts\/48727\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Time Series Classification Synthetic vs Real Financial Time Series \u2013 Part IV | IBKR Quant Blog\" \/>\n<meta property=\"og:description\" content=\"Matthew Smith computes the 10 day rolling mean and standard deviation using the tq_mutate function from the tidyquant package.\" \/>\n<meta property=\"og:url\" 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