See the first installement in this article for instructions from Matthew Smith on which R packages and data sets you need.
I plot the returns series using ggplot.
# Plot some returns – I only plot a random sample of 20 assets for each Synthetic vs Real.
ret_plot0 <- df %>%
filter(class == 0) %>%
group_by(row_id) %>%
nest() %>%
ungroup() %>%
sample_n(20) %>%
unnest() %>%
ggplot(aes(x = variable, y = value)) +
geom_line(aes(group = factor(row_id), color = factor(row_id))) +
ggtitle(“Synthetic Financial Time Series”) +
theme_classic() +
theme(axis.text.x = element_blank(), legend.position = “bottom”, legend.title = element_blank())
ret_plot1 <- df %>%
filter(class == 1) %>%
group_by(row_id) %>%
nest() %>%
ungroup() %>%
sample_n(20) %>%
unnest() %>%
ggplot(aes(x = variable, y = value)) +
geom_line(aes(group = factor(row_id), color = factor(row_id))) +
ggtitle(“Real Financial Time Series”) +
theme_classic() +
theme(axis.text.x = element_blank(), legend.position = “bottom”, legend.title = element_blank())
plot_grid(ret_plot0, ret_plot1)
Next I plot boxplots for the Average returns and secondly the standard deviations.
ave_box <- df %>%
group_by(class, row_id) %>%
summarise(mean = mean(value)) %>%
ggplot(aes(x = factor(class), y = mean, color = factor(class))) +
geom_boxplot(show.legend = FALSE) +
ggtitle(“Syn vs Real Average Returns”) +
xlab(“Class”) +
ylab(“Average Returns”) +
theme_tq()
sd_box <- df %>%
group_by(class, row_id) %>%
summarise(sd = sd(value)) %>%
ggplot(aes(x = factor(class), y = sd, color = factor(class))) +
geom_boxplot(show.legend = FALSE) +
ggtitle(“Syn vs Real Standard Deviations”) +
xlab(“Class”) +
ylab(“Standard Deviation”) +
theme_tq()
plot_grid(ave_box, sd_box)
Visit Matthew Smith – R Blog to see the next step in his analysis, which is calculating the Durbin-Watson statistic: https://lf0.com/post/synth-real-time-series/financial-time-series/
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