junit - How to plot TimeArray in julia with zoom in for hourly, zoom out for daily/monthly?

Question

There is a sample csv data like (real data is in millisecond percision)

using TimeSeries, Plots
s="DateTime,Open,High,Low,Close,Volume
2020/01/05 16:14:01,20,23,19,20,30
2020/01/05 16:14:11,23,27,19,22,20
2020/01/05 17:14:01,24,28,19,23,10
2020/01/05 18:14:01,25,29,20,24,40
2020/01/06 08:02:01,26,30,22,25,50"
ta=readtimearray(IOBuffer(s),format="yyyy/mm/dd HH:MM:SS")
plot(ta.Volume)

I found the package TimeSeries and Temporal are based on daily plot. Is there any easy way to aggregate them into minutes/hourly/daily/weekly... and plot them?

• For the Open value, it should keep the first value during the period.
• For the High value, it should be the maximum value during the period.
• For the Low value, it should be the minimum value during the period.
• For the Close value, it should be the last value during the period.
• For the Volume value, it should be the sum value during the period.

I expect it could the volume like tb

s="DateTime,Volume
2020/01/05 16:00:00,50
2020/01/05 17:00:00,10
2020/01/05 18:00:00,40
2020/01/06 08:00:00,50"
tb=readtimearray(IOBuffer(s),format="yyyy/mm/dd HH:MM:SS")
plot(tb.Volume)

标签： junit

Answer 1

方法一：我找到了一个可行但并不完美的方法。例如，按体积按小时绘制

using DataFrames,Statistics,Dates
df = DataFrame(ta)
df.ms = Date.value.(df.timestamp)
df.hour = df.ms
df.hour = df.ms .÷ (60*60*1000)
df2 = aggregate(df[:, [:hour, :Volume]], :hour, sum)
df2.timestamp = convert.(DateTime, Dates.Millisecond.(df2.hour.*(60*60*1000)))
tb=TimeArray(df2[:,[:timestamp,:Volume_sum]], timestamp=:timestamp)
plot(tb)

tb的内容

4×1 TimeArray{Float64,1,DateTime,Array{Float64,1}} 2020-01-05T16:00:00 to 2020-01-06T08:00:00
│                     │ Volume_sum │
├─────────────────────┼────────────┤
│ 2020-01-05T16:00:00 │ 50.0       │
│ 2020-01-05T17:00:00 │ 10.0       │
│ 2020-01-05T18:00:00 │ 40.0       │
│ 2020-01-06T08:00:00 │ 50.0       │

floor方法2：函数似乎有更简单的方法

df.hour2 = floor.(df.timestamp, Dates.Hour(1))
df2 = aggregate(df[:, [:hour2, :Volume]], :hour2, sum)
tb=TimeArray(df2[:,[:hour2,:Volume_sum]], timestamp=:hour2)

方法 3：只使用collapse第二种形式的语法

using Statistics
tb1 = collapse(ta[:, :Open],   hour,  first, first) 
tb2 = collapse(ta[:, :High],   hour,  first, maximum)         
tb3 = collapse(ta[:, :Low],    hour,  first, minimum)
tb4 = collapse(ta[:, :Close],  hour,  first, last) 
tb5 = collapse(ta[:, :Volume], hour,  first, sum)
tb = merge(tb1, tb2, tb3, tb4, tb5)