【数据分析可视化】股票市场分析实战之风险分析
# 基本信息
import numpy as np
import pandas as pd
from pandas import Series, DataFrame
# 股票数据读取
import pandas_datareader as pdr
# 可视化
import matplotlib.pyplot as plt
import seaborn as sns
#%matplotlib inline
# time
from datetime import datetime# 读取本地数据
alibaba = pd.read_csv('/Users/bennyrhys/Desktop/数据分析可视化-数据集/homework/BABA.csv',index_col=0)
amazon = pd.read_csv('/Users/bennyrhys/Desktop/数据分析可视化-数据集/homework/AMZN.csv',index_col=0)alibaba.head()Open | High | Low | Close | Adj Close | Volume | |
|---|---|---|---|---|---|---|
Date | ||||||
2015-09-21 | 65.379997 | 66.400002 | 62.959999 | 63.900002 | 63.900002 | 22355100 |
2015-09-22 | 62.939999 | 63.270000 | 61.580002 | 61.900002 | 61.900002 | 14897900 |
2015-09-23 | 61.959999 | 62.299999 | 59.680000 | 60.000000 | 60.000000 | 22684600 |
2015-09-24 | 59.419998 | 60.340000 | 58.209999 | 59.919998 | 59.919998 | 20645700 |
2015-09-25 | 60.630001 | 60.840000 | 58.919998 | 59.240002 | 59.240002 | 17009100 |
amazon.head()Open | High | Low | Close | Adj Close | Volume | |
|---|---|---|---|---|---|---|
Date | ||||||
2015-09-21 | 544.330017 | 549.780029 | 539.590027 | 548.390015 | 548.390015 | 3283300 |
2015-09-22 | 539.710022 | 543.549988 | 532.659973 | 538.400024 | 538.400024 | 3841700 |
2015-09-23 | 538.299988 | 541.210022 | 534.000000 | 536.070007 | 536.070007 | 2237600 |
2015-09-24 | 530.549988 | 534.559998 | 522.869995 | 533.750000 | 533.750000 | 3501000 |
2015-09-25 | 542.570007 | 542.799988 | 521.400024 | 524.250000 | 524.250000 | 4031000 |
start = datetime(2015,1,1)
company = ['AAPL','GOOG','MSFT','AMZN','FB']
top_tech_df = pdr.get_data_yahoo(company, start=start)['Adj Close']/Users/bennyrhys/opt/anaconda3/lib/python3.7/site-packages/pandas_datareader/base.py:270: SymbolWarning: Failed to read symbol: 'AAPL', replacing with NaN.
warnings.warn(msg.format(sym), SymbolWarning)top_tech_df.head()Symbols | GOOG | MSFT | AMZN | FB | AAPL |
|---|---|---|---|---|---|
Date | |||||
2014-12-31 | 524.958740 | 41.587284 | 310.350006 | 78.019997 | NaN |
2015-01-02 | 523.373108 | 41.864841 | 308.519989 | 78.449997 | NaN |
2015-01-05 | 512.463013 | 41.479866 | 302.190002 | 77.190002 | NaN |
2015-01-06 | 500.585632 | 40.871037 | 295.290009 | 76.150002 | NaN |
2015-01-07 | 499.727997 | 41.390320 | 298.420013 | 76.150002 | NaN |
# 读取本地数据
top_tech_df = pd.read_csv('/Users/bennyrhys/Desktop/数据分析可视化-数据集/homework/top5.csv',index_col=0)
top_tech_df.head()AAPL | AMZN | FB | GOOG | MSFT | |
|---|---|---|---|---|---|
Date | |||||
2017-11-03 | 172.500000 | 1111.599976 | 178.919998 | 1032.479980 | 84.139999 |
2017-11-02 | 168.110001 | 1094.219971 | 178.919998 | 1025.579956 | 84.050003 |
2017-11-01 | 166.889999 | 1103.680054 | 182.660004 | 1025.500000 | 83.180000 |
2017-10-31 | 169.039993 | 1105.280029 | 180.059998 | 1016.640015 | 83.180000 |
2017-10-30 | 166.720001 | 1110.849976 | 179.869995 | 1017.109985 | 83.889999 |
# 每日差距
top_tech_dr = top_tech_df.pct_change()
top_tech_dr.head()AAPL | AMZN | FB | GOOG | MSFT | |
|---|---|---|---|---|---|
Date | |||||
2017-11-03 | NaN | NaN | NaN | NaN | NaN |
2017-11-02 | -0.025449 | -0.015635 | 0.000000 | -0.006683 | -0.001070 |
2017-11-01 | -0.007257 | 0.008646 | 0.020903 | -0.000078 | -0.010351 |
2017-10-31 | 0.012883 | 0.001450 | -0.014234 | -0.008640 | 0.000000 |
2017-10-30 | -0.013725 | 0.005039 | -0.001055 | 0.000462 | 0.008536 |
# 画全部
top_tech_df.plot()<matplotlib.axes._subplots.AxesSubplot at 0x1a2c7be8d0># 画局部
top_tech_df[['AAPL','FB','MSFT']].plot()<matplotlib.axes._subplots.AxesSubplot at 0x1a2c8506d0># 散点图
sns.jointplot('GOOG', 'GOOG', top_tech_dr, kind='scatter')<seaborn.axisgrid.JointGrid at 0x1a27942b90># 对比谷歌亚马逊(具有相关性 45度)
sns.jointplot('AMZN', 'GOOG', top_tech_dr, kind='scatter')<seaborn.axisgrid.JointGrid at 0x1a2794aa90># 相关性相对小（上移 且 宽）
sns.jointplot('MSFT', 'FB', top_tech_dr, kind='scatter')<seaborn.axisgrid.JointGrid at 0x1a2d45af10># 矩阵散点图(可以看到谷歌和亚马逊最接近)
sns.pairplot(top_tech_dr.dropna())<seaborn.axisgrid.PairGrid at 0x1a2d694710># 分位数（涉及数学比较多）
# 可以理解为误差百分比,48%-0.0001447090809730694
top_tech_dr['AAPL'].quantile(0.52)-0.0001447090809730694top_tech_dr['AAPL'].quantile(0.05)-0.022946394303717855# 唯品会的趋势
vips = pdr.get_data_yahoo('VIPS', start=start)['Adj Close']
vips.plot()<matplotlib.axes._subplots.AxesSubplot at 0x1a32a77e50>vips.pct_change().quantile(0.2)-0.023114020115947723腾讯云开发者
