情节:如何将趋势线和平行线添加到时间序列数据中? [英] Plotly: How to add trendline and parallel lines to time series data?

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问题描述

我的目标是向交换指数添加5条拟合线,这是一个时间序列数据.

My goal is to add 5 fit lines to the exchange index, which is a time series data.

以下是我要实现的目标:

Below is what I want to achieve:

应该有一个标绘(经log2转换)的索引,一条最佳拟合线(以黄色显示)和其他4条平行线,它们分别覆盖该索引的95%,75%,25%和5%.图片中省略了x轴,但应该是日期.所以我的问题是如何使用Plotly添加这5行?

There should be a plotted (&log2 transformed) index, a best fit line (in yellow), and 4 other parallel lines where it covers 95%, 75%, 25% and 5% of the index respectively. The x-axis is omitted in the picture, but it should be dates. So my question is how to add these 5 lines using Plotly?

使用我当前的代码,我能够在没有5条平行线的情况下绘制索引,但是我无法将'trendline'参数传递给它.

With my current code, I was able to plot the index without the 5 parallel lines but I could not pass the 'trendline' argument to it.

我正在使用的代码如下:

The code I am using is like this:

figure = {'data': [{'x': log_hsi['Date'], 'y': log_hsi['Adj Close']}],
      'layout': {'xaxis': {'autorange': True},
                 'yaxis': {'range': [8, 11], 'autorange': False},
                 'title': 'Log transformed HSI'}}
iplot(figure)

我正在使用的数据框是这样的:(条目太多,因此我删除了其中的一部分)

The dataframe I am using is like this: (there are too many entries so I deleted some of it)

{'Date': {3654: Timestamp('2001-01-02 00:00:00'),
  3655: Timestamp('2001-01-03 00:00:00'),
  3656: Timestamp('2001-01-04 00:00:00'),
  3657: Timestamp('2001-01-05 00:00:00'),
  3658: Timestamp('2001-01-08 00:00:00'),
  3659: Timestamp('2001-01-09 00:00:00'),
  3660: Timestamp('2001-01-10 00:00:00'),
  3661: Timestamp('2001-01-11 00:00:00'),
  3662: Timestamp('2001-01-12 00:00:00'),
  3663: Timestamp('2001-01-15 00:00:00'),
  3664: Timestamp('2001-01-16 00:00:00'),
  3665: Timestamp('2001-01-17 00:00:00'),
  3666: Timestamp('2001-01-18 00:00:00'),
  3667: Timestamp('2001-01-19 00:00:00'),
  3668: Timestamp('2001-01-22 00:00:00'),
  3669: Timestamp('2001-01-23 00:00:00'),
  3673: Timestamp('2001-01-29 00:00:00'),
  3674: Timestamp('2001-01-30 00:00:00'),
  3675: Timestamp('2001-01-31 00:00:00'),
  3676: Timestamp('2001-02-01 00:00:00'),
  3677: Timestamp('2001-02-02 00:00:00'),
  3678: Timestamp('2001-02-05 00:00:00'),
  3679: Timestamp('2001-02-06 00:00:00'),
  3680: Timestamp('2001-02-07 00:00:00'),
  3681: Timestamp('2001-02-08 00:00:00'),
  3682: Timestamp('2001-02-09 00:00:00'),
  3683: Timestamp('2001-02-12 00:00:00'),
  3684: Timestamp('2001-02-13 00:00:00'),
  3685: Timestamp('2001-02-14 00:00:00'),
  3686: Timestamp('2001-02-15 00:00:00'),
  3687: Timestamp('2001-02-16 00:00:00'),
  3688: Timestamp('2001-02-19 00:00:00'),
  3689: Timestamp('2001-02-20 00:00:00'),
  3690: Timestamp('2001-02-21 00:00:00'),
  3691: Timestamp('2001-02-22 00:00:00'),
  3692: Timestamp('2001-02-23 00:00:00'),
  3693: Timestamp('2001-02-26 00:00:00'),
  3694: Timestamp('2001-02-27 00:00:00'),
  3695: Timestamp('2001-02-28 00:00:00'),
  3696: Timestamp('2001-03-01 00:00:00'),
  3697: Timestamp('2001-03-02 00:00:00'),
  3698: Timestamp('2001-03-05 00:00:00'),
  3699: Timestamp('2001-03-06 00:00:00'),
  3700: Timestamp('2001-03-07 00:00:00'),
  3701: Timestamp('2001-03-08 00:00:00'),
  3702: Timestamp('2001-03-09 00:00:00'),
  3703: Timestamp('2001-03-12 00:00:00'),
  3704: Timestamp('2001-03-13 00:00:00'),
  3705: Timestamp('2001-03-14 00:00:00'),
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  3726: Timestamp('2001-04-12 00:00:00'),
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  3748: Timestamp('2001-05-14 00:00:00'),
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  3751: Timestamp('2001-05-17 00:00:00'),
  3752: Timestamp('2001-05-18 00:00:00'),
  3753: Timestamp('2001-05-21 00:00:00'),
  3754: Timestamp('2001-05-22 00:00:00'),
  3755: Timestamp('2001-05-23 00:00:00'),
  3756: Timestamp('2001-05-24 00:00:00'),
  3757: Timestamp('2001-05-25 00:00:00'),
  3758: Timestamp('2001-05-28 00:00:00'),
  3759: Timestamp('2001-05-29 00:00:00'),
  3760: Timestamp('2001-05-30 00:00:00'),
  3761: Timestamp('2001-05-31 00:00:00'),
  3762: Timestamp('2001-06-01 00:00:00'),
  3763: Timestamp('2001-06-04 00:00:00'),
  3764: Timestamp('2001-06-05 00:00:00'),
  3765: Timestamp('2001-06-06 00:00:00'),
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  3769: Timestamp('2001-06-12 00:00:00'),
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  3771: Timestamp('2001-06-14 00:00:00'),
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  3775: Timestamp('2001-06-20 00:00:00'),
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  3785: Timestamp('2001-07-04 00:00:00'),
  3786: Timestamp('2001-07-05 00:00:00'),
  3788: Timestamp('2001-07-09 00:00:00'),
  3789: Timestamp('2001-07-10 00:00:00'),
  3790: Timestamp('2001-07-11 00:00:00'),
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  3792: Timestamp('2001-07-13 00:00:00'),
  3793: Timestamp('2001-07-16 00:00:00'),
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  3798: Timestamp('2001-07-23 00:00:00'),
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  3803: Timestamp('2001-07-30 00:00:00'),
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  3805: Timestamp('2001-08-01 00:00:00'),
  3806: Timestamp('2001-08-02 00:00:00'),
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  3808: Timestamp('2001-08-06 00:00:00'),
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  3811: Timestamp('2001-08-09 00:00:00'),
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  3814: Timestamp('2001-08-14 00:00:00'),
  3815: Timestamp('2001-08-15 00:00:00'),
  3816: Timestamp('2001-08-16 00:00:00'),
  3817: Timestamp('2001-08-17 00:00:00'),
  3818: Timestamp('2001-08-20 00:00:00'),
  3819: Timestamp('2001-08-21 00:00:00'),
  3820: Timestamp('2001-08-22 00:00:00'),
  3821: Timestamp('2001-08-23 00:00:00'),
  3822: Timestamp('2001-08-24 00:00:00'),
  3823: Timestamp('2001-08-27 00:00:00'),
  3824: Timestamp('2001-08-28 00:00:00'),
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  3826: Timestamp('2001-08-30 00:00:00'),
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  3846: Timestamp('2001-09-27 00:00:00'),
  3847: Timestamp('2001-09-28 00:00:00'),
  3850: Timestamp('2001-10-03 00:00:00'),
  3851: Timestamp('2001-10-04 00:00:00'),
  3852: Timestamp('2001-10-05 00:00:00'),
  3853: Timestamp('2001-10-08 00:00:00'),
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  3865: Timestamp('2001-10-24 00:00:00'),
  3866: Timestamp('2001-10-25 00:00:00'),
  3867: Timestamp('2001-10-26 00:00:00')},
 'Adj Close': {3654: 9.607097033393721,
  3655: 9.588062859608462,
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非常感谢所有花时间帮助您的人.

推荐答案

此答案直接关注如何将这些线(动态地)添加到绘图中.您如何计算这些行是另一回事.如果这些线实际上是平行的直线,则您需要计算的唯一棘手的部分是该线的起点或常数.线的斜率应等于 Adj Close 的线性趋势的斜率.

This answer focuses directly on how to add those lines (dynamically) to a plotly figure. How you calculate those lines is another matter. If the lines are in fact straight parallell lines, then the only tricky part you need to calculate are the starting points, or constants, of the line. The slope of the lines should be equal to the slope of the linear trend of your Adj Close.

我已经提出了一个建议,该建议通过使用statsmodels从源数据中计算一些必要的参数来构建给定的行数.

I've put togehter a suggestion that builds a given number of lines by calculating some necessary parameters from your source data using statsmodels.

mod = sm.OLS(df['Adj Close'],sm.add_constant(df.ix)).fit()
const = mod.params[0]
trend = mod.params[1]

然后我指定了一个起点列表调整(模型常量),如下所示:

And then I've specified a list of adjustments to the starting points (model constant) like this:

extra_lines = [-0.2,-0.1,0, 0.1,0.2] # add or  remove as you please

之所以将 df.ix 包含为连续索引,是因为原始索引似乎在其中有些跳跃.

The reason why df.ix is included as a continuous index because your original index seemed to have some jumps in it.

然后我继续使用以下方法将调整后的趋势线添加到 fig :

I then proceed to add adjusted trendlines to a fig using:

for i, m in enumerate(model):
    df[m['Line']]=[(m['const']) + (trend*i) for i,v in enumerate(df.index)]
    fig.add_traces(go.Scatter(x=df.ix, y=df[m['Line']]))

以下是根据您的示例数据得出的结果:

Here's the result based on your sample data:

import plotly.graph_objects as go
import statsmodels.api as sm
import pandas as pd
import numpy as np
import datetime
from pandas import Timestamp

df = pd.DataFrame({'Date': {3762: Timestamp('2001-06-01 00:00:00'),
  3763: Timestamp('2001-06-04 00:00:00'),
  3764: Timestamp('2001-06-05 00:00:00'),
  3765: Timestamp('2001-06-06 00:00:00'),
  3766: Timestamp('2001-06-07 00:00:00'),
  3767: Timestamp('2001-06-08 00:00:00'),
  3768: Timestamp('2001-06-11 00:00:00'),
  3769: Timestamp('2001-06-12 00:00:00'),
  3770: Timestamp('2001-06-13 00:00:00'),
  3771: Timestamp('2001-06-14 00:00:00'),
  3772: Timestamp('2001-06-15 00:00:00'),
  3773: Timestamp('2001-06-18 00:00:00'),
  3774: Timestamp('2001-06-19 00:00:00'),
  3775: Timestamp('2001-06-20 00:00:00'),
  3776: Timestamp('2001-06-21 00:00:00'),
  3777: Timestamp('2001-06-22 00:00:00'),
  3779: Timestamp('2001-06-26 00:00:00'),
  3780: Timestamp('2001-06-27 00:00:00'),
  3781: Timestamp('2001-06-28 00:00:00'),
  3782: Timestamp('2001-06-29 00:00:00'),
  3784: Timestamp('2001-07-03 00:00:00'),
  3785: Timestamp('2001-07-04 00:00:00'),
  3786: Timestamp('2001-07-05 00:00:00'),
  3788: Timestamp('2001-07-09 00:00:00'),
  3789: Timestamp('2001-07-10 00:00:00'),
  3790: Timestamp('2001-07-11 00:00:00'),
  3791: Timestamp('2001-07-12 00:00:00'),
  3792: Timestamp('2001-07-13 00:00:00'),
  3793: Timestamp('2001-07-16 00:00:00'),
  3794: Timestamp('2001-07-17 00:00:00'),
  3795: Timestamp('2001-07-18 00:00:00'),
  3796: Timestamp('2001-07-19 00:00:00'),
  3797: Timestamp('2001-07-20 00:00:00'),
  3798: Timestamp('2001-07-23 00:00:00'),
  3799: Timestamp('2001-07-24 00:00:00'),
  3801: Timestamp('2001-07-26 00:00:00'),
  3802: Timestamp('2001-07-27 00:00:00'),
  3803: Timestamp('2001-07-30 00:00:00'),
  3804: Timestamp('2001-07-31 00:00:00'),
  3805: Timestamp('2001-08-01 00:00:00'),
  3806: Timestamp('2001-08-02 00:00:00'),
  3807: Timestamp('2001-08-03 00:00:00'),
  3808: Timestamp('2001-08-06 00:00:00'),
  3809: Timestamp('2001-08-07 00:00:00'),
  3810: Timestamp('2001-08-08 00:00:00'),
  3811: Timestamp('2001-08-09 00:00:00'),
  3812: Timestamp('2001-08-10 00:00:00'),
  3813: Timestamp('2001-08-13 00:00:00'),
  3814: Timestamp('2001-08-14 00:00:00'),
  3815: Timestamp('2001-08-15 00:00:00'),
  3816: Timestamp('2001-08-16 00:00:00'),
  3817: Timestamp('2001-08-17 00:00:00'),
  3818: Timestamp('2001-08-20 00:00:00'),
  3819: Timestamp('2001-08-21 00:00:00'),
  3820: Timestamp('2001-08-22 00:00:00'),
  3821: Timestamp('2001-08-23 00:00:00'),
  3822: Timestamp('2001-08-24 00:00:00'),
  3823: Timestamp('2001-08-27 00:00:00'),
  3824: Timestamp('2001-08-28 00:00:00'),
  3825: Timestamp('2001-08-29 00:00:00'),
  3826: Timestamp('2001-08-30 00:00:00'),
  3827: Timestamp('2001-08-31 00:00:00'),
  3828: Timestamp('2001-09-03 00:00:00'),
  3829: Timestamp('2001-09-04 00:00:00'),
  3830: Timestamp('2001-09-05 00:00:00'),
  3831: Timestamp('2001-09-06 00:00:00'),
  3832: Timestamp('2001-09-07 00:00:00'),
  3833: Timestamp('2001-09-10 00:00:00'),
  3834: Timestamp('2001-09-11 00:00:00'),
  3835: Timestamp('2001-09-12 00:00:00'),
  3836: Timestamp('2001-09-13 00:00:00'),
  3837: Timestamp('2001-09-14 00:00:00'),
  3838: Timestamp('2001-09-17 00:00:00'),
  3839: Timestamp('2001-09-18 00:00:00'),
  3840: Timestamp('2001-09-19 00:00:00'),
  3841: Timestamp('2001-09-20 00:00:00'),
  3842: Timestamp('2001-09-21 00:00:00'),
  3843: Timestamp('2001-09-24 00:00:00'),
  3844: Timestamp('2001-09-25 00:00:00'),
  3845: Timestamp('2001-09-26 00:00:00'),
  3846: Timestamp('2001-09-27 00:00:00'),
  3847: Timestamp('2001-09-28 00:00:00'),
  3850: Timestamp('2001-10-03 00:00:00'),
  3851: Timestamp('2001-10-04 00:00:00'),
  3852: Timestamp('2001-10-05 00:00:00'),
  3853: Timestamp('2001-10-08 00:00:00'),
  3854: Timestamp('2001-10-09 00:00:00'),
  3855: Timestamp('2001-10-10 00:00:00'),
  3856: Timestamp('2001-10-11 00:00:00'),
  3857: Timestamp('2001-10-12 00:00:00'),
  3858: Timestamp('2001-10-15 00:00:00'),
  3859: Timestamp('2001-10-16 00:00:00'),
  3860: Timestamp('2001-10-17 00:00:00'),
  3861: Timestamp('2001-10-18 00:00:00'),
  3862: Timestamp('2001-10-19 00:00:00'),
  3863: Timestamp('2001-10-22 00:00:00'),
  3864: Timestamp('2001-10-23 00:00:00'),
  3865: Timestamp('2001-10-24 00:00:00'),
  3866: Timestamp('2001-10-25 00:00:00'),
  3867: Timestamp('2001-10-26 00:00:00')},
 'Adj Close': {3762: 9.483521300451965,
  3763: 9.488539389609842,
  3764: 9.506873417520655,
  3765: 9.516059526271494,
  3766: 9.52540142267562,
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  3784: 9.486816137667164,
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  3786: 9.472664671722018,
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  3790: 9.435713467289014,
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  3792: 9.442466660552066,
  3793: 9.443397047352386,
  3794: 9.433103851072097,
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  3796: 9.41571256910222,
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  3799: 9.4103462690634,
  3801: 9.39597479458201,
  3802: 9.407728679911855,
  3803: 9.399857656975392,
  3804: 9.418710567070383,
  3805: 9.431781694039891,
  3806: 9.430789907045172,
  3807: 9.414837561626188,
  3808: 9.404986466190781,
  3809: 9.39326095182,
  3810: 9.389156606132271,
  3811: 9.368776387849374,
  3812: 9.372953110523751,
  3813: 9.366855970805329,
  3814: 9.391912461823267,
  3815: 9.404395312850555,
  3816: 9.378600227328686,
  3817: 9.37201776092802,
  3818: 9.34650456280641,
  3819: 9.344901824694107,
  3820: 9.32264802844274,
  3821: 9.33656588127212,
  3822: 9.315627867418097,
  3823: 9.326764237890817,
  3824: 9.332604930413563,
  3825: 9.327448527151956,
  3826: 9.333940224481115,
  3827: 9.313842403932533,
  3828: 9.29676020844021,
  3829: 9.318015638210596,
  3830: 9.300468022736998,
  3831: 9.27465889826041,
  3832: 9.248040717937537,
  3833: 9.246317398619535,
  3834: 9.25122895807117,
  3835: 9.158375285355174,
  3836: 9.166305927329747,
  3837: 9.175277821947487,
  3838: 9.13984812080253,
  3839: 9.1386188229253,
  3840: 9.165149513582218,
  3841: 9.139701196323891,
  3842: 9.097641909876808,
  3843: 9.13610162204065,
  3844: 9.128051597198034,
  3845: 9.145455124069166,
  3846: 9.169600669798987,
  3847: 9.205398199033475,
  3850: 9.200001069931528,
  3851: 9.238576907009563,
  3852: 9.237700631328401,
  3853: 9.207118194132338,
  3854: 9.245604198507314,
  3855: 9.23972830855306,
  3856: 9.26128158783136,
  3857: 9.237384352858927,
  3858: 9.223314822990815,
  3859: 9.225080227987517,
  3860: 9.236087021069979,
  3861: 9.198329565352042,
  3862: 9.192770913389573,
  3863: 9.189886616720194,
  3864: 9.23208619279342,
  3865: 9.23439472833901,
  3866: 9.23439472833901,
  3867: 9.250016773018734},
 'Volume': {3762: 0.0,
  3763: 0.0,
  3764: 0.0,
  3765: 0.0,
  3766: 0.0,
  3767: 0.0,
  3768: 0.0,
  3769: 0.0,
  3770: 0.0,
  3771: 0.0,
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  3773: 0.0,
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  3780: 0.0,
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  3786: 0.0,
  3788: 257038800.0,
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  3798: 166057200.0,
  3799: 139913800.0,
  3801: 221039000.0,
  3802: 124388600.0,
  3803: 153086200.0,
  3804: 227109800.0,
  3805: 243126000.0,
  3806: 194471600.0,
  3807: 168728800.0,
  3808: 141753200.0,
  3809: 208445200.0,
  3810: 178200800.0,
  3811: 231948800.0,
  3812: 148634200.0,
  3813: 137231600.0,
  3814: 172713800.0,
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  3829: 303746000.0,
  3830: 206642600.0,
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  3843: 442181500.0,
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  3845: 390860600.0,
  3846: 296667600.0,
  3847: 320775600.0,
  3850: 333197400.0,
  3851: 358779000.0,
  3852: 576213400.0,
  3853: 511535600.0,
  3854: 409534200.0,
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  3856: 398527200.0,
  3857: 388528000.0,
  3858: 275161200.0,
  3859: 192816600.0,
  3860: 414838800.0,
  3861: 365696000.0,
  3862: 297211400.0,
  3863: 236566000.0,
  3864: 344018800.0,
  3865: 287418600.0,
  3866: 0.0,
  3867: 346798600.0}})

# line parameters using statsmodels
df['Date'] = pd.to_datetime(df['Date'])
df['ix']=np.arange(0, len(df))
mod = sm.OLS(df['Adj Close'],sm.add_constant(df.ix)).fit()
const = mod.params[0]
trend = mod.params[1]

# dict that stores adjusted constants (starting points)
extra_lines = [-0.2,-0.1,0, 0.1,0.2] # add or  remove as you please
model = [{'Line': 'Line_'+str(i+1), 'value': k, 'const': const+k} for i, k in enumerate(extra_lines)]

# plotly
fig = go.Figure(go.Scatter(x=df.ix, y=df['Adj Close']))
for i, m in enumerate(model):
    df[m['Line']]=[(m['const']) + (trend*i) for i,v in enumerate(df.index)]
    fig.add_traces(go.Scatter(x=df.ix, y=df[m['Line']]))
    
fig.show()

这篇关于情节:如何将趋势线和平行线添加到时间序列数据中?的文章就介绍到这了,希望我们推荐的答案对大家有所帮助,也希望大家多多支持IT屋!

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