我使用这个脚本是为了从 tiingo 检索 etfs 价格数据并计算滚动回报和夏普。
library(quantmod)
library(httr)
library(jsonlite)
library(tidyverse)
library(hrbrthemes)
library(tidyquant)
library(timetk)
library(dplyr)
library(roll)
###
tickers = c('spy', 'qqq', 'dia', 'splv'); forex = c('EURUSD')
tiingo_api_key("XXXX")
price_data <- tq_get(tickers, from = '2019-08-19', to = '2021-01-29',
get = "tiingo")
forex_data <- tq_get(forex, from = '2019-08-19', to = '2021-01-29',
get = "tiingo")
forex_data <- forex_data[ ,c(2, 8)]
price_data <- price_data %>%
# join datasets using date column.
# non key columns from rhs that have the same names as columns in
# the lhs get assigned the suffix "_forex_data"
left_join(forex_data, by = c("date"), suffix = c("", "_forex_data")) %>%
# calculate new value of adjusted by dividing by forex value
mutate(adjusted = adjusted / adjusted_forex_data) %>%
# exclude adjusted_forex_data column from result
select(-adjusted_forex_data)
price_data <- na.omit(price_data)
price_data <- price_data[ ,c(1, 2,8)]
colnames(price_data)[3] <- "adjClose"
by_etf <- price_data %>% group_by(symbol)
price_data_roll <- by_etf %>%
mutate(lagx = lag(adjClose)) %>%
mutate(pct_change = (adjClose - lagx)/adjClose)%>%
mutate(rollmeanx = roll_mean(pct_change, width = 22),rollsdx = roll_sd(pct_change, width=22)) %>%
mutate(roll_sharpe = rollmeanx / rollsdx^5)
price_data_change <- price_data_roll[ ,c(1, 2, 5)]
library(lubridate)
#price_data_eom <- na.omit(price_data_eom)
price_data_eom <- price_data_change %>% mutate(Month = month(date), Year = year(date)) %>%
mutate(Ini_date = ymd(paste(Year, Month, "01", sep = "-"))) %>%
group_by(Ini_date) %>%
filter(date == max(date))
price_data_eom <- price_data_eom[ ,c(1, 2, 3)]
我需要的是建立一个新的回报时间序列，比如 price_data_change 。
Price_data_change dput：
structure(list(symbol = c("SPY", "SPY", "SPY", "SPY", "SPY",
"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
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"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
"SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY", "SPY",
"QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ",
"QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ",
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"QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ",
"QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "QQQ",
"QQQ", "QQQ", "QQQ", "QQQ", "QQQ", "DIA", "DIA", "DIA", "DIA",
"DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA",
"DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA",
"DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA",
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"DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA",
"DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA",
"DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA",
"DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA",
"DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA",
"DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA",
"DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA", "DIA",
"DIA", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
"SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV", "SPLV",
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在这个新的时间序列中，我需要在 1 月份获得在上个月最后一天具有最高 roll_sharpe 的 ETF 的每日回报，其他月份依此类推。 感谢所有贡献者。如果您需要进一步的数据或解释，请告诉我。