# code <- "004477.OF"
code <- "001878.OF"
fields <- c("fund_benchmark", "fund_benchindexcode", "sec_name")
meta <- wsd_meta(code, fields)
bm_code <- meta[["fund_benchindexcode"]]
args <- tibble(
codes = c(code, bm_code),
start = wsd_start_date(codes),
# start = "2017-05-01",
end = "2018-07-09",
options = "period=W;PriceAdj=F"
)
ret <-
invoke(wsd_return_xts, args)
ret_bear <-
ret[ret[, bm_code]<0, ]
ret %>%
as_tibble() %>%
mutate(!!bm_code < 0)
ret_split <- split(ret, ifelse(ret[, bm_code]>0, "bull", "bear"))
res <-
ret_split %>%
map_df(nav_metrics_risk_return, return_type = "tibble", .id = "市场")
res %>%
write.csv(file = "clipboard", fileEncoding = "native.enc", row.names = FALSE)
res <- ret %>%
nav_metrics_risk_return
res %>%
rbind() %>%
as_tibble() %>%
write.csv(file = "clipboard", fileEncoding = "native.enc", row.names = FALSE)
ans <-
apply.yearly(ret[], nav_metrics_risk_return)
ans %>%
xts_to_df() %>%
write.csv(file = "./foo.csv", fileEncoding = "native.enc", row.names = FALSE)
# ret["2018/2018-06-01"] %>%
# {
# map_df(
# list(
# 累积收益 = Return.cumulative,
# 年化收益 = Return.annualized,
# 年化波动 = sd.annualized
# ), function(f) f(.) %>% as.data.frame,
# .id = "指标"
# )
# }
# ret["2018/2018-06-01"] %>% index %>% range()
# cor(ret)
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