R/mod_finder_utils_server.R
In frdanconia/shiny_etf_portfolio: ShinyETF
Defines functions
import.etf.data
import.onvista.price.data
# Price data --------------------------------------------------------------
import.onvista.price.data <- function(df, start.date = lubridate::today() - lubridate::years(5)) {
# onvista provides 5 years of time series historie for free
start.date.formated <- paste(str_sub(start.date, 9L,10L), str_sub(start.date, 6L, 7L), lubridate::year(start.date), sep = '.')
df <- df %>%
# group_nest(idNotation, .key = 'meta') %>%
mutate(prices.daily = map2(.x = idNotation, .y= start.date.formated,
.f = ~ vroom(
file = paste0(
'https://www.onvista.de/etf/snapshotHistoryCSV?idNotation=',
.x,
'&datetimeTzStartRange=',
.y,
'&timeSpan=5Y&codeResolution=1D'
),
delim = ';',
col_types = list(
Datum = vroom::col_character(include_na = TRUE),
Volumen = vroom::col_double(include_na = TRUE)
)
)
)
) %>%
dplyr::mutate(prices.daily = purrr::map(.x = prices.daily,
.f = ~ dplyr::mutate(., Datum = lubridate::dmy(Datum)) %>%
dplyr::mutate_at(., .vars = c('Eröffnung', 'Hoch', 'Tief', 'Schluss'), ~round(./100, digits = 3)) %>%
dplyr::rename(., date = Datum, open = Eröffnung, high = Hoch, low = Tief, close = Schluss, volume = Volumen)
)
)
return(df)
}
import.etf.data <- function(df, all.etfs = FALSE, n.complete.days = 300) {
# import time series data from onvista finance
# future::plan("multiprocess")
df <- df %>%
dplyr::group_nest(asset.class,
idNotation, isin,
etf, benchmark,
region, smartBeta, currencyHeadge, marketCap, listing_date,
.key = 'meta.data') %>%
import.onvista.price.data(start.date = lubridate::today() - lubridate::years(5)) %>%
adjust.price.periods(period = 'weeks', name = 'weekly') %>%
adjust.price.periods(period = 'months', name = 'monthly') %>%
adjust.price.periods(period = 'quarters', name = 'quarterly') %>%
adjust.price.periods(period = 'years', name = 'yearly') %>%
calculate.return(period = 'daily') %>%
calculate.return(period = 'weekly') %>%
calculate.return(period = 'monthly') %>%
calculate.return(period = 'quarterly') %>%
calculate.return(period = 'yearly') %>%
calculate.Annualized.return.stats() %>%
dplyr::mutate(n.trade.days = purrr::map_int(prices.daily, nrow),
n.complete.days = purrr::map_int(prices.daily, ~na.omit(.) %>% nrow),
n.na.days = purrr::map_int(prices.daily, ~filter(., is.na(.$close)) %>% nrow),
recent.trading.date = purrr::map(prices.daily, ~max(.$date)),
complete.ratio = round(n.complete.days/n.trade.days, digits = 3),
missing.ratio = round(n.na.days/n.trade.days, digits = 3)) %>%
tidyr::unnest(recent.trading.date)
if (all.etfs) {
df <- df %>%
dplyr::filter(recent.trading.date > lubridate::today()-5, n.complete.days > {{n.complete.days}})
}
return(df)
}
assign.asset.class <- function(df, complete.ratio = 0.0) {
df <- df %>%
dplyr::filter(complete.ratio >= {{complete.ratio}}) %>%
# asset class classification
dplyr::mutate(asset.class = dplyr::case_when(stringr::str_detect(benchmark, 'Corporate|Fallen Angel Bond|High Yield') ~'Corporate',
stringr::str_detect(benchmark, 'EONIA|FED') ~ 'Money Market',
stringr::str_detect(benchmark, 'iBoxx|Government|Treasury|Sovereign|Inflation|Bund|Morgan GBI EMU Index|MTS|Bond|iTraxx') ~ 'Bond',
stringr::str_detect(benchmark, 'STOXX|MSCI|S&P|Dow Jones|FTSE|Nikkei|Nasdaq|NASDAQ|DAX|Equity|Russell|Quality|Sustainability|Solactive|ATX|SmallCap|NYSE Arca Gold Bugs Index|OMX Stockholm|Low Carbon 100 Europe|TA-25|CSI 300|NMX 30|Gold Miners|Wide Moat|CAC|TOPIX|Nifty|Hang Seng|Minimum Variance|Scientific Beta|EMQQ') ~ 'Equity',
stringr::str_detect(benchmark, 'Commodity|Agriculture|Enhanced Roll|RICI|Livestock') ~ 'Commodity',
TRUE ~ 'Other')) %>%
# regional classification
dplyr::mutate(region = dplyr::case_when(asset.class == 'Commodity' ~ 'Commodity',
stringr::str_detect(benchmark, 'SMIT|Malaysia|Thailand|ex Japan|Asia|Africa|East|CSI|Emerging Markets|Nifty|China|Russia|Mexico|Philippines|Taiwan|Pakistan|India|Brazil|Vietnam|Poland|Turkey|Indonesia|Kuwait|Bangladesh|LatAM') ~ 'Emerging Markets',
stringr::str_detect(benchmark, 'World|WORLD|Global|FactSet') ~ 'Global',
stringr::str_detect(benchmark, 'Japanese|Japan|Nikkei|TOPIX|Singapore|Australian|TA-25|MSCI Pacific|Australia|Canada|Korea')~ 'Other Developed',
stringr::str_detect(benchmark, 'Europe|EURO|Eurozone|EUR|EMU|DAX|Germany|Bund|Italy|FTSE MIB|Spain|ATX|OMX|United Kingdom|FTSE All-Share|FTSE 100|Euro|EONIA|France|STOXX') ~ 'Europe',
stringr::str_detect(benchmark, 'USA|US|North America|S&P|KLD|U.S.|Wide Moat|Russell|NASDAQ|Nasdaq|United States|Dow Jones Industrial|FED') ~ 'US',
TRUE ~ 'Other')) %>%
# smart Beta classification
dplyr::mutate(smartBeta = case_when(stringr::str_detect(benchmark, 'Value') ~ 'Value',
stringr::str_detect(benchmark, 'Quality') ~ 'Quality',
stringr::str_detect(benchmark, 'Volatility|Low Vol|Low Risk|Minimum Variance') ~ 'Volatility',
stringr::str_detect(benchmark, 'Buyback') ~ 'Buyback',
stringr::str_detect(benchmark, 'High Dividend|Income') ~ 'High Dividend',
stringr::str_detect(benchmark, 'Multi-Factor|Multi Factor|Multiple-Factor|Equity Factor|Scientific Beta') ~ 'Multi-Factor',
stringr::str_detect(benchmark, 'Wide Moat') ~ 'Wide Moat',
stringr::str_detect(benchmark, 'Momentum') ~ 'Momentum',
stringr::str_detect(benchmark, 'Low Beta') ~ 'Low Beta',
stringr::str_detect(benchmark, 'Lev|Leverage') ~ 'Leverage',
stringr::str_detect(benchmark, 'Short') ~ 'Short',
TRUE ~ 'None')) %>%
dplyr::mutate(currencyHeadge = case_when(stringr::str_detect(benchmark, 'Hedge|Hedged|hedged') ~ 'Hedged',
TRUE ~ 'None')) %>%
dplyr::mutate(marketCap = dplyr::case_when(stringr::str_detect(benchmark, 'small|Small|TecDAX|SDAX') ~ 'SmallCap',
stringr::str_detect(benchmark, 'Mid-Cap|Mid Cap|MDAX') ~ 'MidCap',
stringr::str_detect(benchmark, 'Large|EMQQ') ~ 'LargeCap',
stringr::str_detect(benchmark, 'All-Cap|All Cap') ~ 'AllCap',
asset.class == 'Equity' ~ 'LargeCap',
TRUE ~ 'Not applicable')) %>%
dplyr::mutate(asset.class = dplyr::case_when(marketCap != 'Not applicable' ~ 'Equity',
TRUE ~ asset.class),
asset.class = dplyr::case_when(isin %in% c('DE000A14ZT85',
'DE000A12GJD2',
'LU1287022708',
'LU1838002480',
'LU1079842321',
'LU1079841273',
'IE00BDFBTQ78',
'IE00BDFBTK17',
'IE00BDFBTR85') ~ 'Equity',
isin %in% c('IE00BDS67326') ~ 'Bond',
isin %in% c('LU0292106167') ~ 'Commodity',
TRUE ~ asset.class),
region = dplyr::case_when(isin %in% c('DE000A14ZT85',
'IE00BDFBTK17',
'IE00BDFBTR85',
'LU0322250712',
'LU0378437767',
'DE000A2H9VH7',
'DE000A2N5XE0',
'LU1838002480',
'DE000A2H9VJ3',
'DE000A2H9VG9') ~ 'Global',
isin %in% c('IE00BDS67326',
'LU1681043912',
'LU1681043086',
'LU0292109005',
'IE00BFNM3P36',
'IE00BQXKVQ19') ~ 'Emerging Markets',
isin %in% c('DE000A2N5XC4') ~ 'Other Developed',
isin %in% c('LU1681046931',
'LU1681044647',
'LU1681044720',
'LU1287023342',
'LU1829219556',
'LU1829219713',
'LU1829219986',
'LU0321462870',
'LU0290359032',
'LU1686830065') ~ 'Europe',
asset.class == 'Commodity' ~ 'Commodity',
TRUE ~ region)
) %>%
dplyr::select(asset.class, etf, isin, benchmark, region, smartBeta, currencyHeadge, marketCap, everything())
return(df)
}
adjust.price.periods <- function(df, period = 'weeks', name = 'weekly') {
df <- df %>%
dplyr::mutate(price = purrr::map(prices.daily, ~ tidyquant::tq_transmute(.,
select = close,
mutate_fun = to.period,
period = {{period}},
indexAt = "last")
)
) %>%
dplyr::rename_at(.vars = c('price'), ~paste(., name, sep = '.'))
return(df)
}
calculate.return <- function(df, period = 'weekly') {
# compute periodical returns
df <- df %>%
dplyr::mutate(return = map(prices.daily, ~ tidyquant::tq_transmute(.,
select = close,
mutate_fun = periodReturn,
period = {{period}},
type = "arithmetic") # arithmetic/log
)) %>%
dplyr::rename_at(.vars = c('return'), ~paste(., period, sep = '.'))
return(df)
}
calculate.Annualized.return.stats <- function(df) {
df <- df %>%
dplyr::mutate(table.AnnualizedReturns = purrr::map(return.daily, ~ tidyquant::tq_performance(.,
Ra = daily.returns,
performance_fun = table.AnnualizedReturns,
scale = 252,
geometric = TRUE) %>%
dplyr::select(., AnnualizedReturn, AnnualizedStdDev, `AnnualizedSharpe(Rf=0%)`) %>%
dplyr::rename(.,
'Return p.a.' = AnnualizedReturn,
'StdDev p.a' = AnnualizedStdDev,
'Shape-Ratio p.a.' = `AnnualizedSharpe(Rf=0%)`)
)
)
return(df)
}
# Vizualization -----------------------------------------------------------
plot.daily.return <- function(df) {
min.date <- df %>%
dplyr::select(asset, return.daily) %>%
dplyr::mutate(min_date = purrr::map(return.daily, ~summarise(.,min_date = min(date)))) %>%
tidyr::unnest(min_date) %>%
dplyr::select(-return.daily) %>%
tidyr::spread(key = asset, value = min_date)
df <- df %>%
dplyr::select(asset, return.daily) %>%
tidyr::unnest(cols = c(return.daily)) %>%
tidyr::spread(key = asset, value = daily.returns) %>%
replace(., is.na(.), 0) %>%
dplyr::mutate(Core_Equity = ifelse(date < min.date$Core_Equity, NA, Core_Equity),
Core_Bonds = ifelse(date < min.date$Core_Bonds, NA, Core_Bonds),
Core_Commodity = ifelse(date < min.date$Core_Commodity, NA, Core_Commodity),
Satelite_Corporate = ifelse(date < min.date$Satelite_Corporate, NA, Satelite_Corporate),
Satelite_Equity = ifelse(date < min.date$Satelite_Equity, NA, Satelite_Equity)) %>%
timetk::tk_xts(date_var = date, select = -date)
hc <- highcharter::highchart(type = "stock") %>%
highcharter::hc_title(text = "Daily Returns") %>%
highcharter::hc_add_series(df$Core_Equity, name = "Core Equity") %>%
highcharter::hc_add_series(df$Core_Bonds, name = "Core Bonds") %>%
highcharter::hc_add_series(df$Core_Commodity, name = "Core Commodity") %>%
highcharter::hc_add_series(df$Satelite_Corporate, name = "Satelite Corporate") %>%
highcharter::hc_add_series(df$Satelite_Equity, color = "pink", name = 'Satelite Equity') %>%
highcharter::hc_add_theme(highcharter::hc_theme_google()) %>%
highcharter::hc_navigator(enabled = FALSE) %>%
highcharter::hc_scrollbar(enabled = FALSE) %>%
highcharter::hc_legend(enabled = TRUE) %>%
highcharter::hc_exporting(enabled = TRUE)
return(hc)
}
plot.growth <- function(df) {
start.date <- df %>%
dplyr::select(asset, return.daily) %>%
dplyr::mutate(start.date = furrr::future_map(return.daily, ~min(.$date, na.rm = TRUE))) %>%
tidyr::unnest(start.date) %>%
dplyr::summarise(start.date = max(start.date)) %>%
dplyr::pull()
df <- df %>%
dplyr::mutate(growth = purrr::map(return.daily, ~
dplyr::filter(., date >= start.date) %>%
replace(., is.na(.), 0) %>%
dplyr::transmute(date, growth = accumulate(1 + .$daily.returns, `*`)))) %>%
dplyr::select(asset, growth) %>%
unnest(cols = c(growth)) %>%
tibble::rowid_to_column() %>%
tidyr::spread(key = asset, value = growth) %>%
dplyr::select(-rowid) %>%
tidyr::fill(everything(), .direction = "down") %>%
timetk::tk_xts(date_var = date, select = -date)
hc <- highcharter::highchart(type = "stock") %>%
highcharter::hc_title(text = "1 Euro Growth") %>%
highcharter::hc_add_series(df$Core_Equity, name = "Core Equity") %>%
highcharter::hc_add_series(df$Core_Bonds, name = "Core Bonds") %>%
highcharter::hc_add_series(df$Core_Commodity, name = "Core Commodity") %>%
highcharter::hc_add_series(df$Satelite_Corporate, name = "Satelite Corporate") %>%
highcharter::hc_add_series(df$Satelite_Equity, color = "pink", name = 'Satelite Equity') %>%
highcharter::hc_add_theme(highcharter::hc_theme_google()) %>%
highcharter::hc_navigator(enabled = FALSE) %>%
highcharter::hc_scrollbar(enabled = FALSE) %>%
highcharter::hc_legend(enabled = TRUE) %>%
highcharter::hc_exporting(enabled = TRUE)
return(hc)
}
frdanconia/shiny_etf_portfolio documentation built on Jan. 24, 2021, 12:44 a.m.
R Package Documentation
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Defines functions import.etf.data import.onvista.price.data
# Price data --------------------------------------------------------------
import.onvista.price.data <- function(df, start.date = lubridate::today() - lubridate::years(5)) {
# onvista provides 5 years of time series historie for free
start.date.formated <- paste(str_sub(start.date, 9L,10L), str_sub(start.date, 6L, 7L), lubridate::year(start.date), sep = '.')
df <- df %>%
# group_nest(idNotation, .key = 'meta') %>%
mutate(prices.daily = map2(.x = idNotation, .y= start.date.formated,
.f = ~ vroom(
file = paste0(
'https://www.onvista.de/etf/snapshotHistoryCSV?idNotation=',
.x,
'&datetimeTzStartRange=',
.y,
'&timeSpan=5Y&codeResolution=1D'
),
delim = ';',
col_types = list(
Datum = vroom::col_character(include_na = TRUE),
Volumen = vroom::col_double(include_na = TRUE)
)
)
)
) %>%
dplyr::mutate(prices.daily = purrr::map(.x = prices.daily,
.f = ~ dplyr::mutate(., Datum = lubridate::dmy(Datum)) %>%
dplyr::mutate_at(., .vars = c('Eröffnung', 'Hoch', 'Tief', 'Schluss'), ~round(./100, digits = 3)) %>%
dplyr::rename(., date = Datum, open = Eröffnung, high = Hoch, low = Tief, close = Schluss, volume = Volumen)
)
)
return(df)
}
import.etf.data <- function(df, all.etfs = FALSE, n.complete.days = 300) {
# import time series data from onvista finance
# future::plan("multiprocess")
df <- df %>%
dplyr::group_nest(asset.class,
idNotation, isin,
etf, benchmark,
region, smartBeta, currencyHeadge, marketCap, listing_date,
.key = 'meta.data') %>%
import.onvista.price.data(start.date = lubridate::today() - lubridate::years(5)) %>%
adjust.price.periods(period = 'weeks', name = 'weekly') %>%
adjust.price.periods(period = 'months', name = 'monthly') %>%
adjust.price.periods(period = 'quarters', name = 'quarterly') %>%
adjust.price.periods(period = 'years', name = 'yearly') %>%
calculate.return(period = 'daily') %>%
calculate.return(period = 'weekly') %>%
calculate.return(period = 'monthly') %>%
calculate.return(period = 'quarterly') %>%
calculate.return(period = 'yearly') %>%
calculate.Annualized.return.stats() %>%
dplyr::mutate(n.trade.days = purrr::map_int(prices.daily, nrow),
n.complete.days = purrr::map_int(prices.daily, ~na.omit(.) %>% nrow),
n.na.days = purrr::map_int(prices.daily, ~filter(., is.na(.$close)) %>% nrow),
recent.trading.date = purrr::map(prices.daily, ~max(.$date)),
complete.ratio = round(n.complete.days/n.trade.days, digits = 3),
missing.ratio = round(n.na.days/n.trade.days, digits = 3)) %>%
tidyr::unnest(recent.trading.date)
if (all.etfs) {
df <- df %>%
dplyr::filter(recent.trading.date > lubridate::today()-5, n.complete.days > {{n.complete.days}})
}
return(df)
}
assign.asset.class <- function(df, complete.ratio = 0.0) {
df <- df %>%
dplyr::filter(complete.ratio >= {{complete.ratio}}) %>%
# asset class classification
dplyr::mutate(asset.class = dplyr::case_when(stringr::str_detect(benchmark, 'Corporate|Fallen Angel Bond|High Yield') ~'Corporate',
stringr::str_detect(benchmark, 'EONIA|FED') ~ 'Money Market',
stringr::str_detect(benchmark, 'iBoxx|Government|Treasury|Sovereign|Inflation|Bund|Morgan GBI EMU Index|MTS|Bond|iTraxx') ~ 'Bond',
stringr::str_detect(benchmark, 'STOXX|MSCI|S&P|Dow Jones|FTSE|Nikkei|Nasdaq|NASDAQ|DAX|Equity|Russell|Quality|Sustainability|Solactive|ATX|SmallCap|NYSE Arca Gold Bugs Index|OMX Stockholm|Low Carbon 100 Europe|TA-25|CSI 300|NMX 30|Gold Miners|Wide Moat|CAC|TOPIX|Nifty|Hang Seng|Minimum Variance|Scientific Beta|EMQQ') ~ 'Equity',
stringr::str_detect(benchmark, 'Commodity|Agriculture|Enhanced Roll|RICI|Livestock') ~ 'Commodity',
TRUE ~ 'Other')) %>%
# regional classification
dplyr::mutate(region = dplyr::case_when(asset.class == 'Commodity' ~ 'Commodity',
stringr::str_detect(benchmark, 'SMIT|Malaysia|Thailand|ex Japan|Asia|Africa|East|CSI|Emerging Markets|Nifty|China|Russia|Mexico|Philippines|Taiwan|Pakistan|India|Brazil|Vietnam|Poland|Turkey|Indonesia|Kuwait|Bangladesh|LatAM') ~ 'Emerging Markets',
stringr::str_detect(benchmark, 'World|WORLD|Global|FactSet') ~ 'Global',
stringr::str_detect(benchmark, 'Japanese|Japan|Nikkei|TOPIX|Singapore|Australian|TA-25|MSCI Pacific|Australia|Canada|Korea')~ 'Other Developed',
stringr::str_detect(benchmark, 'Europe|EURO|Eurozone|EUR|EMU|DAX|Germany|Bund|Italy|FTSE MIB|Spain|ATX|OMX|United Kingdom|FTSE All-Share|FTSE 100|Euro|EONIA|France|STOXX') ~ 'Europe',
stringr::str_detect(benchmark, 'USA|US|North America|S&P|KLD|U.S.|Wide Moat|Russell|NASDAQ|Nasdaq|United States|Dow Jones Industrial|FED') ~ 'US',
TRUE ~ 'Other')) %>%
# smart Beta classification
dplyr::mutate(smartBeta = case_when(stringr::str_detect(benchmark, 'Value') ~ 'Value',
stringr::str_detect(benchmark, 'Quality') ~ 'Quality',
stringr::str_detect(benchmark, 'Volatility|Low Vol|Low Risk|Minimum Variance') ~ 'Volatility',
stringr::str_detect(benchmark, 'Buyback') ~ 'Buyback',
stringr::str_detect(benchmark, 'High Dividend|Income') ~ 'High Dividend',
stringr::str_detect(benchmark, 'Multi-Factor|Multi Factor|Multiple-Factor|Equity Factor|Scientific Beta') ~ 'Multi-Factor',
stringr::str_detect(benchmark, 'Wide Moat') ~ 'Wide Moat',
stringr::str_detect(benchmark, 'Momentum') ~ 'Momentum',
stringr::str_detect(benchmark, 'Low Beta') ~ 'Low Beta',
stringr::str_detect(benchmark, 'Lev|Leverage') ~ 'Leverage',
stringr::str_detect(benchmark, 'Short') ~ 'Short',
TRUE ~ 'None')) %>%
dplyr::mutate(currencyHeadge = case_when(stringr::str_detect(benchmark, 'Hedge|Hedged|hedged') ~ 'Hedged',
TRUE ~ 'None')) %>%
dplyr::mutate(marketCap = dplyr::case_when(stringr::str_detect(benchmark, 'small|Small|TecDAX|SDAX') ~ 'SmallCap',
stringr::str_detect(benchmark, 'Mid-Cap|Mid Cap|MDAX') ~ 'MidCap',
stringr::str_detect(benchmark, 'Large|EMQQ') ~ 'LargeCap',
stringr::str_detect(benchmark, 'All-Cap|All Cap') ~ 'AllCap',
asset.class == 'Equity' ~ 'LargeCap',
TRUE ~ 'Not applicable')) %>%
dplyr::mutate(asset.class = dplyr::case_when(marketCap != 'Not applicable' ~ 'Equity',
TRUE ~ asset.class),
asset.class = dplyr::case_when(isin %in% c('DE000A14ZT85',
'DE000A12GJD2',
'LU1287022708',
'LU1838002480',
'LU1079842321',
'LU1079841273',
'IE00BDFBTQ78',
'IE00BDFBTK17',
'IE00BDFBTR85') ~ 'Equity',
isin %in% c('IE00BDS67326') ~ 'Bond',
isin %in% c('LU0292106167') ~ 'Commodity',
TRUE ~ asset.class),
region = dplyr::case_when(isin %in% c('DE000A14ZT85',
'IE00BDFBTK17',
'IE00BDFBTR85',
'LU0322250712',
'LU0378437767',
'DE000A2H9VH7',
'DE000A2N5XE0',
'LU1838002480',
'DE000A2H9VJ3',
'DE000A2H9VG9') ~ 'Global',
isin %in% c('IE00BDS67326',
'LU1681043912',
'LU1681043086',
'LU0292109005',
'IE00BFNM3P36',
'IE00BQXKVQ19') ~ 'Emerging Markets',
isin %in% c('DE000A2N5XC4') ~ 'Other Developed',
isin %in% c('LU1681046931',
'LU1681044647',
'LU1681044720',
'LU1287023342',
'LU1829219556',
'LU1829219713',
'LU1829219986',
'LU0321462870',
'LU0290359032',
'LU1686830065') ~ 'Europe',
asset.class == 'Commodity' ~ 'Commodity',
TRUE ~ region)
) %>%
dplyr::select(asset.class, etf, isin, benchmark, region, smartBeta, currencyHeadge, marketCap, everything())
return(df)
}
adjust.price.periods <- function(df, period = 'weeks', name = 'weekly') {
df <- df %>%
dplyr::mutate(price = purrr::map(prices.daily, ~ tidyquant::tq_transmute(.,
select = close,
mutate_fun = to.period,
period = {{period}},
indexAt = "last")
)
) %>%
dplyr::rename_at(.vars = c('price'), ~paste(., name, sep = '.'))
return(df)
}
calculate.return <- function(df, period = 'weekly') {
# compute periodical returns
df <- df %>%
dplyr::mutate(return = map(prices.daily, ~ tidyquant::tq_transmute(.,
select = close,
mutate_fun = periodReturn,
period = {{period}},
type = "arithmetic") # arithmetic/log
)) %>%
dplyr::rename_at(.vars = c('return'), ~paste(., period, sep = '.'))
return(df)
}
calculate.Annualized.return.stats <- function(df) {
df <- df %>%
dplyr::mutate(table.AnnualizedReturns = purrr::map(return.daily, ~ tidyquant::tq_performance(.,
Ra = daily.returns,
performance_fun = table.AnnualizedReturns,
scale = 252,
geometric = TRUE) %>%
dplyr::select(., AnnualizedReturn, AnnualizedStdDev, `AnnualizedSharpe(Rf=0%)`) %>%
dplyr::rename(.,
'Return p.a.' = AnnualizedReturn,
'StdDev p.a' = AnnualizedStdDev,
'Shape-Ratio p.a.' = `AnnualizedSharpe(Rf=0%)`)
)
)
return(df)
}
# Vizualization -----------------------------------------------------------
plot.daily.return <- function(df) {
min.date <- df %>%
dplyr::select(asset, return.daily) %>%
dplyr::mutate(min_date = purrr::map(return.daily, ~summarise(.,min_date = min(date)))) %>%
tidyr::unnest(min_date) %>%
dplyr::select(-return.daily) %>%
tidyr::spread(key = asset, value = min_date)
df <- df %>%
dplyr::select(asset, return.daily) %>%
tidyr::unnest(cols = c(return.daily)) %>%
tidyr::spread(key = asset, value = daily.returns) %>%
replace(., is.na(.), 0) %>%
dplyr::mutate(Core_Equity = ifelse(date < min.date$Core_Equity, NA, Core_Equity),
Core_Bonds = ifelse(date < min.date$Core_Bonds, NA, Core_Bonds),
Core_Commodity = ifelse(date < min.date$Core_Commodity, NA, Core_Commodity),
Satelite_Corporate = ifelse(date < min.date$Satelite_Corporate, NA, Satelite_Corporate),
Satelite_Equity = ifelse(date < min.date$Satelite_Equity, NA, Satelite_Equity)) %>%
timetk::tk_xts(date_var = date, select = -date)
hc <- highcharter::highchart(type = "stock") %>%
highcharter::hc_title(text = "Daily Returns") %>%
highcharter::hc_add_series(df$Core_Equity, name = "Core Equity") %>%
highcharter::hc_add_series(df$Core_Bonds, name = "Core Bonds") %>%
highcharter::hc_add_series(df$Core_Commodity, name = "Core Commodity") %>%
highcharter::hc_add_series(df$Satelite_Corporate, name = "Satelite Corporate") %>%
highcharter::hc_add_series(df$Satelite_Equity, color = "pink", name = 'Satelite Equity') %>%
highcharter::hc_add_theme(highcharter::hc_theme_google()) %>%
highcharter::hc_navigator(enabled = FALSE) %>%
highcharter::hc_scrollbar(enabled = FALSE) %>%
highcharter::hc_legend(enabled = TRUE) %>%
highcharter::hc_exporting(enabled = TRUE)
return(hc)
}
plot.growth <- function(df) {
start.date <- df %>%
dplyr::select(asset, return.daily) %>%
dplyr::mutate(start.date = furrr::future_map(return.daily, ~min(.$date, na.rm = TRUE))) %>%
tidyr::unnest(start.date) %>%
dplyr::summarise(start.date = max(start.date)) %>%
dplyr::pull()
df <- df %>%
dplyr::mutate(growth = purrr::map(return.daily, ~
dplyr::filter(., date >= start.date) %>%
replace(., is.na(.), 0) %>%
dplyr::transmute(date, growth = accumulate(1 + .$daily.returns, `*`)))) %>%
dplyr::select(asset, growth) %>%
unnest(cols = c(growth)) %>%
tibble::rowid_to_column() %>%
tidyr::spread(key = asset, value = growth) %>%
dplyr::select(-rowid) %>%
tidyr::fill(everything(), .direction = "down") %>%
timetk::tk_xts(date_var = date, select = -date)
hc <- highcharter::highchart(type = "stock") %>%
highcharter::hc_title(text = "1 Euro Growth") %>%
highcharter::hc_add_series(df$Core_Equity, name = "Core Equity") %>%
highcharter::hc_add_series(df$Core_Bonds, name = "Core Bonds") %>%
highcharter::hc_add_series(df$Core_Commodity, name = "Core Commodity") %>%
highcharter::hc_add_series(df$Satelite_Corporate, name = "Satelite Corporate") %>%
highcharter::hc_add_series(df$Satelite_Equity, color = "pink", name = 'Satelite Equity') %>%
highcharter::hc_add_theme(highcharter::hc_theme_google()) %>%
highcharter::hc_navigator(enabled = FALSE) %>%
highcharter::hc_scrollbar(enabled = FALSE) %>%
highcharter::hc_legend(enabled = TRUE) %>%
highcharter::hc_exporting(enabled = TRUE)
return(hc)
}
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