Time series data in the data field of science is everywhere, in quantified financial sector is also very common, can be used to analyze price trends, forecast prices, exploring the price behavior.
Learn to time series data visualization, it can help us explore more intuitive time-series data, looking for potential law.
This article will use Python in matplotlib [1] library, and with examples to explain. matplotlib library is a ⼀ Use to create publication-quality graphs of tables ⾯ drawing package (2D graphics library), Python is the most basic visualization tools.
[Tools] Python 3
[Data] Tushare
[Note] attention is the example to explain the method, please be flexible.
01
Single time series
First of all, from the tushare.pro obtain Index daily market data and view the data type.
import tushare as ts
import pandas as pd
pd.set_option('expand_frame_repr', False) # 显示所有列
ts.set_token('your token')
pro = ts.pro_api()
df = pro.index_daily(ts_code='399300.SZ')[['trade_date', 'close']]
df.sort_values('trade_date', inplace=True)
df.reset_index(inplace=True, drop=True)
print(df.head())
trade_date close
0 20050104 982.794
1 20050105 992.564
2 20050106 983.174
3 20050107 983.958
4 20050110 993.879
print(df.dtypes)
trade_date object
close float64
dtype: object
Trading Time column 'trade_date' is not the time to type, and is not indexed, it needs to be transformed.
df['trade_date'] = pd.to_datetime(df['trade_date'])
df.set_index('trade_date', inplace=True)
print(df.head())
close
trade_date
2005-01-04 982.794
2005-01-05 992.564
2005-01-06 983.174
2005-01-07 983.958
2005-01-10 993.879
Next, the drawing can begin, we need to import matplotlib.pyplot [2], and then by setting set_xlabel () and set_xlabel () to tag the x-axis and y-axis.
import matplotlib.pyplot as plt
ax = df.plot(color='')
ax.set_xlabel('trade_date')
ax.set_ylabel('399300.SZ close')
plt.show()
matplotlib library has many built-in chart styles to choose from, by printing plt.style.available see what are the specific options, when applied directly call plt.style.use ( 'fivethirtyeight') can be.
print(plt.style.available)
['bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark-palette', 'seaborn-dark', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'seaborn', 'Solarize_Light2', 'tableau-colorblind10', '_classic_test']
plt.style.use('fivethirtyeight')
ax1 = df.plot()
ax1.set_title('FiveThirtyEight Style')
plt.show()
02
Set More details
上面画出的是一个很简单的折线图，其实可以在plot()里面通过设置不同参数的值，为图添加更多细节，使其更美观、清晰。
figsize(width, height)设置图的大小，linewidth设置线的宽度，fontsize设置字体大小。然后，调用set_title()方法设置标题。
ax = df.plot(color='blue', figsize=(8, 3), linewidth=2, fontsize=6)
ax.set_title('399300.SZ close from 2005-01-04 to 2019-07-04', fontsize=8)
plt.show()
如果想要看某一个子时间段内的折线变化情况，可以直接截取该时间段再作图即可，如df['2018-01-01': '2019-01-01']
df_subset_1 = df['2018-01-01':'2019-01-01']
ax = df_subset_1.plot(color='blue', fontsize=10)
plt.show()
如果想要突出图中的某一日期或者观察值，可以调用.axvline()和.axhline()方法添加垂直和水平参考线。
ax = df.plot(color='blue', fontsize=6)
ax.axvline('2019-01-01', color='red', linestyle='--')
ax.axhline(3000, color='green', linestyle='--')
plt.show()
也可以调用axvspan()的方法为一段时间添加阴影标注，其中alpha参数设置的是阴影的透明度，0代表完全透明，1代表全色。
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ax = df.plot(color='blue', fontsize=6)
ax.axvspan('2018-01-01', '2019-01-01', color='red', alpha=0.3)
ax.axhspan(2000, 3000, color='green', alpha=0.7)
plt.show()
03
移动平均时间序列
有时候，我们想要观察某个窗口期的移动平均值的变化趋势，可以通过调用窗口函数rolling来实现。下面实例中显示的是，以250天为窗口期的移动平均线close，以及与移动标准差的关系构建的上下两个通道线upper和lower。
ma = df.rolling(window=250).mean()
mstd = df.rolling(window=250).std()
ma['upper'] = ma['close'] + (mstd['close'] * 2)
ma['lower'] = ma['close'] - (mstd['close'] * 2)
ax = ma.plot(linewidth=0.8, fontsize=6)
ax.set_xlabel('trade_date', fontsize=8)
ax.set_ylabel('399300.SZ close from 2005-01-04 to 2019-07-04', fontsize=8)
ax.set_title('Rolling mean and variance of 399300.SZ cloe from 2005-01-04 to 2019-07-04', fontsize=10)
plt.show()
04
多个时间序列
If you want to visualize a plurality of time-series data, it can also be called directly plot () method . Our example from tushare.pro selected three stocks above the daily market data analysis.
# 获取数据
code_list = ['000001.SZ', '000002.SZ', '600000.SH']
data_list = []
for code in code_list:
print(code)
df = pro.daily(ts_code=code, start_date='20180101', end_date='20190101')[['trade_date', 'close']]
df.sort_values('trade_date', inplace=True)
df.rename(columns={'close': code}, inplace=True)
df.set_index('trade_date', inplace=True)
data_list.append(df)
df = pd.concat(data_list, axis=1)
print(df.head())
000001.SZ
000002.SZ
600000.SH
000001.SZ 000002.SZ 600000.SH
trade_date
20180102 13.70 32.56 12.72
20180103 13.33 32.33 12.66
20180104 13.25 33.12 12.66
20180105 13.30 34.76 12.69
20180108 12.96 35.99 12.68
# 画图
ax = df.plot(linewidth=2, fontsize=12)
ax.set_xlabel('trade_date')
ax.legend(fontsize=15)
plt.show()
Call .plot.area () method may generate time-series data in FIG area, cumulative total is displayed.
ax = df.plot.area(fontsize=12)
ax.set_xlabel('trade_date')
ax.legend(fontsize=15)
plt.show()
To show separately in different sub-graphs each time series, by setting the parameter subplots = True achieved. layout specified number of rows to be used, sharex and sharey for setting whether to share the row and column, the colormap = 'viridis' set a different color for each line.
df.plot(subplots=True,
layout=(2, 2),
sharex=False,
sharey=False,
colormap='viridis',
fontsize=7,
legend=False,
linewidth=0.3)
plt.show()
05
to sum up
This paper describes how to use the Python matplotlib library time-series data for some simple visualization, visualization comprising a single time series and disposed in the detail view, the visualization moving average time series and a plurality of time series.