Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
message = FALSE,
warning = FALSE,
collapse = TRUE,
comment = "#>",
out.width = "100%"
)
## ---- eval = TRUE-------------------------------------------------------------
library(dispositionEffect)
library(dplyr)
library(tidyr)
library(purrr)
library(ggplot2)
## ---- eval = TRUE-------------------------------------------------------------
portfolio_results_ts <- portfolio_compute(
portfolio_transactions = investor,
market_prices = marketprices,
time_series_DE = TRUE
)
## ---- eval=TRUE---------------------------------------------------------------
portfolio <- portfolio_results_ts$portfolio
dplyr::select(portfolio, -datetime)
## ---- eval=TRUE---------------------------------------------------------------
timeseries <- portfolio_results_ts$timeseries
head(timeseries)
## ---- eval=TRUE---------------------------------------------------------------
disposition_summary_ts(timeseries)
## ---- eval=TRUE, fig.width=8, fig.height=5, fig.align='center'----------------
timeseries %>%
tidyr::pivot_longer(cols = dplyr::starts_with("DE")) %>%
ggplot2::ggplot(ggplot2::aes(x = datetime, y = value, col = name)) +
ggplot2::geom_line(size = 1.5) +
ggplot2::scale_colour_viridis_d(alpha = 1) +
ggplot2::labs(
title = "Time Series Disposition Effect results",
subtitle = "Method Count",
x = "", y = ""
) +
ggplot2::theme(legend.position = "bottom")
## ---- eval=TRUE---------------------------------------------------------------
portfolio_results_ts_assets <- portfolio_compute(
portfolio_transactions = investor,
market_prices = marketprices,
time_series_DE = TRUE,
assets_time_series_DE = c("ACO", "LSUG")
)
## ---- eval=TRUE---------------------------------------------------------------
timeseries_assets <- portfolio_results_ts_assets$timeseries
head(timeseries_assets)[, 2:6]
head(timeseries_assets)[, c(2:4, 7:8)]
## ---- eval=TRUE---------------------------------------------------------------
disposition_summary_ts(timeseries_assets)[, 2:6]
disposition_summary_ts(timeseries_assets)[, c(2:4, 7:8)]
## ---- eval=TRUE, fig.width=8, fig.height=5, fig.align='center'----------------
timeseries_assets %>%
tidyr::pivot_longer(cols = dplyr::contains("DE")) %>%
ggplot2::ggplot(ggplot2::aes(x = datetime, y = value, col = name)) +
ggplot2::geom_line(size = 1.5) +
ggplot2::scale_colour_viridis_d(alpha = 1) +
ggplot2::facet_wrap(~ name, ncol = 2) +
ggplot2::labs(
title = "Assets Time Series Disposition Effect results",
subtitle = "Method Count",
x = "", y = ""
) +
ggplot2::theme(legend.position = "bottom")
## ---- eval=FALSE--------------------------------------------------------------
# trx <- DEanalysis$transactions
# mkt <- DEanalysis$marketprices
# investor_id <- unique(trx$investor)
#
# res_list <- vector(mode = "list", length = length(investor_id))
# for (i in seq_along(investor_id)) {
# tmp_trx <- trx %>%
# dplyr::filter(investor == investor_id[i])
# tmp_res <- tryCatch(
# dispositionEffect::portfolio_compute(
# portfolio_transactions = tmp_trx,
# market_prices = mkt,
# time_series_DE = TRUE
# ),
# error = function(e) "Error"
# )
# res_list[[i]] <- tmp_res # save results
# rm(tmp_trx, tmp_res)
# }
#
# # extract time series results for each investor
# timeseries_10_investors <- res_list %>%
# purrr::map("timeseries")
## ---- eval=TRUE, include=FALSE------------------------------------------------
load("figures/ts_res.RData")
## ---- eval=TRUE---------------------------------------------------------------
purrr::map(timeseries_10_investors, disposition_summary_ts) %>%
dplyr::bind_rows() %>%
dplyr::filter(stat == "Mean") %>%
dplyr::arrange(desc(DETs_count))
## ---- eval=TRUE, fig.width=8, fig.height=5, fig.align='center'----------------
timeseries_10_investors %>%
dplyr::bind_rows() %>%
tidyr::pivot_longer(cols = dplyr::contains("DE")) %>%
ggplot2::ggplot(ggplot2::aes(x = datetime, y = value, col = investor)) +
ggplot2::geom_line(size = 0.75) +
ggplot2::scale_colour_viridis_d(alpha = 0.9) +
ggplot2::facet_wrap(~ name, nrow = 2, ncol = 1) +
ggplot2::labs(
title = "10 Investors Time Series Disposition Effect results",
subtitle = "Method Count",
x = "", y = ""
) +
ggplot2::theme(legend.position = "bottom")
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