汇总与合并日内数据

在 R 中导入与管理金融数据

Joshua Ulrich

Quantitative Analyst & quantmod Co-Author and Maintainer

时区

¹ 来源：https://www.shutterstock.com/video/search/time-zone-clocks

时区

内部而言，xts 索引是 UTC 下自 1970-01-01 起的秒数
merge() 使用内部索引
merge() 结果采用第一个对象的时区

时区

datetime <- as.POSIXct("2017-01-18 10:00:00", tz = "UTC")
london <- xts(1, datetime, tzone = "Europe/London")
tokyo <- xts(1, datetime, tzone = "Asia/Tokyo")

merge(london, tokyo)

                    london tokyo
2017-01-18 10:00:00      1     1

merge(tokyo, london)

                    tokyo london
2017-01-18 19:00:00     1      1

创建规则的日内数据

head(dc_trades)

                    Price
2016-01-16 08:00:58 20.85
2016-01-16 08:01:56 20.85
2016-01-16 08:03:35 20.85
2016-01-16 08:07:44 20.84
2016-01-16 08:45:58 20.85
2016-01-16 08:46:49 20.85

创建规则的日内数据

datetimes <- seq(from = as.POSIXct("2016-01-16 08:00"),
                  to = as.POSIXct("2016-01-17 18:00"),
                  by = "1 min")

regular_xts <- xts(, order.by = datetimes)

merged_xts <- merge(dc_trades, regular_xts)
head(merged_xts)

                    Price
2016-01-16 08:00:00    NA
2016-01-16 08:00:58 20.85
2016-01-16 08:01:00    NA
2016-01-16 08:01:56 20.85
2016-01-16 08:02:00    NA
2016-01-16 08:03:00    NA

仅保留交易时段

# 所有观测应为 NA
all(is.na(merged_xts["2016-01-16 19:00/2016-01-17 07:00"]))

TRUE

# xts 按时段子集
merged_trade_day <- merged_xts["T08:00/T18:00"]

# 现在没有观测
nrow(merged_trade_day["2016-01-16 19:00/2016-01-17 07:00"])

按交易日填充缺失值

来自本 DataCamp 课程的 split()-lapply()-rbind() 范式：使用 xts/zoo 操作时间序列

# 用 split() 将数据分为不重叠的块
trade_day_list <- split(merged_trade_day, "days")

# 对每个块用 lapply() 应用函数
filled_trade_day_list <- lapply(trade_day_list, na.locf)

# 用 do.call() 和 rbind() 合并各块
filled_trade_day <- do.call(rbind, filled_trade_day_list)

汇总不规则日内数据

用 to.period() 汇总稠密日内数据
- period：新频率（如秒、小时、天等）
- k：每条新观测包含的周期数

汇总不规则日内数据（1）

head(dc_price)

                    DC.Price
2016-01-16 00:00:07 20.84224
2016-01-16 00:00:08 20.84225
2016-01-16 00:00:08 20.84225
2016-01-16 00:00:11 20.84225
2016-01-16 00:00:25 20.84224
2016-01-16 00:00:44 20.84224

xts_5min <- to.period(dc_price, period = "minutes", k = 5)
head(xts_5min, n = 4)

                    dc_price.Open  dc_price.High  dc_price.Low  dc_price.Close
2016-01-16 00:03:49      20.84224       20.84227      20.84140        20.84160
2016-01-16 00:09:50      20.84160       20.84160      20.84156        20.84156
2016-01-16 00:14:57      20.84156       20.84156      20.84154        20.84154
2016-01-16 00:19:23      20.84154       20.84154      20.83206        20.83211

xts_aligned <- align.time(xts_5min, n = 60 * 5)
head(xts_aligned, n = 4)

                    dc_price.Open  dc_price.High  dc_price.Low  dc_price.Close
2016-01-16 00:05:00      20.84224       20.84227      20.84140        20.84160
2016-01-16 00:05:00      20.84160       20.84160      20.84156        20.84156
2016-01-16 00:15:00      20.84156       20.84156      20.84154        20.84154
2016-01-16 00:20:00      20.84154       20.84154      20.83206        20.83211

让我们来练习！

在 R 中导入与管理金融数据