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Time Series Classification Synthetic vs Real Financial Time Series – Part IV

Posted June 13, 2020 at 9:50 am
Matthew Smith
Matthew Smith - R Blog

See Part IPart II and Part III in this article for instructions from Matthew Smith on which R packages and data sets you need.

I compute the 10 day rolling mean and standard deviation using the `tq_mutate` function from the `tidyquant` package. `value` 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` again here but look into the `pivot_longer` function for a more intuitive application)

# Rolling mean and standard deviations
# 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.
# The rollowing window is 10 days
# I use the tq_mutate functionality from the “tidyquant” package to keep things in a “tidy” format as per the “tidyverse” ‘rules’.
# In the plot “value” is the returns, “mean_10” is the 10 day rolling mean and “sd_10” is the 10 day rolling standard deviation.

plot0 <- df %>%
filter(class == 0) %>%
as_tibble() %>%
group_by(row_id) %>%
nest() %>%
ungroup() %>%
sample_n(1) %>%
unnest() %>%
mutate(variable = as.Date(variable)) %>%
tq_mutate(
select = value,
mutate_fun = rollapply,
width = 10,
align = “right”,
FUN = mean,
na.rm = TRUE,
col_rename = “mean_10”
) %>%
tq_mutate(
select = value,
mutate_fun = rollapply,
width = 10,
align = “right”,
FUN = sd,
na.rm = TRUE,
col_rename = “sd_10”
) %>%
melt(measure.vars = 5:7) %>%
setNames(c(“row_id”, “class”, “data set”, “date”, “variable”, “value”)) %>%
ggplot(aes(x = date)) +
geom_line(aes(y = value, colour = variable)) +
ggtitle(“Synthetic Financial Time Series Rolling Mean and Standard Deviation”) +
theme_classic() +
scale_colour_manual(values = c(“#1f77b4”, “red”, “black”)) +
theme(axis.text.x = element_blank(), legend.position = “bottom”, legend.title = element_blank())

plot1 <- df %>%
filter(class == 1) %>%
as_tibble() %>%
group_by(row_id) %>%
nest() %>%
ungroup() %>%
sample_n(1) %>%
unnest() %>%
mutate(variable = as.Date(variable)) %>%
tq_mutate(
select = value,
mutate_fun = rollapply,
width = 10,
align = “right”,
FUN = mean,
na.rm = TRUE,
col_rename = “mean_10”
) %>%
tq_mutate(
select = value,
mutate_fun = rollapply,
width = 10,
align = “right”,
FUN = sd,
na.rm = TRUE,
col_rename = “sd_10”
) %>%
melt(measure.vars = 5:7) %>%
setNames(c(“row_id”, “class”, “data set”, “date”, “variable”, “value”)) %>%
ggplot(aes(x = date)) +
geom_line(aes(y = value, colour = variable)) +
ggtitle(“Real Financial Time Series Rolling Mean and Standard Deviation”) +
theme_classic() +
scale_colour_manual(values = c(“#1f77b4”, “red”, “black”)) +
theme(axis.text.x = element_blank(), legend.position = “bottom”, legend.title = element_blank())

plot_grid(plot0, plot1)

Visit Matthew Smith – R Blog to see the next step in his analysis.

Disclosure: Interactive Brokers

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