数据获取

import pandas_datareader as pdralibaba = pdr.get_data_yahoo('BABA')alibaba.head()

结果：

alibaba.shape#(789, 6)alibaba.tail()

结果：

alibaba.describe()#汇总信息，基本信息

结果：

alibaba.info()#也是基本信息

结果：

<class 'pandas.core.frame.DataFrame'>DatetimeIndex: 789 entries, -09-19 to -11-03Data columns (total 6 columns):Open 789 non-null float64High 789 non-null float64Low789 non-null float64Close 789 non-null float64Adj Close 789 non-null float64Volume 789 non-null int64dtypes: float64(5), int64(1)memory usage: 43.1 KB

View Code

历史趋势分析

# 基本信息import numpy as npimport pandas as pdfrom pandas import Series, DataFrame# 股票数据的读取import pandas_datareader as pdr# 可视化import matplotlib.pyplot as pltimport seaborn as sns%matplotlib inline# timefrom datetime import datetime

start = datetime(,9,20)alibaba = pdr.get_data_yahoo('BABA', start=start)amazon = pdr.get_data_yahoo('AMZN', start=start)

alibaba.to_csv('../homework/BABA.csv')amazon.to_csv('../homework/AMZN.csv')

alibaba.head()

结果：

alibaba['Adj Close'].plot(legend=True)

结果：<matplotlib.axes._subplots.AxesSubplot at 0x11d910588>

alibaba['Volume'].plot(legend=True)

结果：<matplotlib.axes._subplots.AxesSubplot at 0x120ebcda0>

alibaba['Adj Close'].plot()amazon['Adj Close'].plot()

结果：<matplotlib.axes._subplots.AxesSubplot at 0x120ee9a90>

alibaba.head()

结果：

alibaba['high-low'] = alibaba['High'] - alibaba['Low']alibaba.head()

结果：

alibaba['high-low'].plot()

结果：<matplotlib.axes._subplots.AxesSubplot at 0x120fdcf98>

# daily return 每天变化情况alibaba['daily-return'] = alibaba['Adj Close'].pct_change() #.pct_change() 实现每天每天变化情况alibaba['daily-return'].plot(figsize=(10,4),linestyle='--',marker='o')#figsize图大小，marker每个数据点类型

结果：<matplotlib.axes._subplots.AxesSubplot at 0x121504d68>

alibaba['daily-return'].plot(kind='hist')#可以看出，基本上在-0.025到.025之间，所以股票价格稳定

结果：<matplotlib.axes._subplots.AxesSubplot at 0x12169c630>

sns.distplot(alibaba['daily-return'].dropna(),bins=100,color='purple')

结果：<matplotlib.axes._subplots.AxesSubplot at 0x121b15f98>

风险分析

# 基本信息import numpy as npimport pandas as pdfrom pandas import Series, DataFrame# 股票数据的读取import pandas_datareader as pdr# 可视化import matplotlib.pyplot as pltimport seaborn as sns%matplotlib inline# timefrom datetime import datetime

start = datetime(,1,1)company = ['AAPL','GOOG','MSFT','AMZN','FB']#5家公司top_tech_df = pdr.get_data_yahoo(company, start=start)['Adj Close']

top_tech_df.to_csv('../homework/top5.csv')

top_tech_df.head()

结果：

top_tech_dr = top_tech_df.pct_change()#股票数据每天的变化top_tech_dr.head()

结果：

top_tech_df.plot()#GOOG与AMZN曲线比较相似

结果：<matplotlib.axes._subplots.AxesSubplot at 0x122293fd0>

top_tech_df[['AAPL','FB','MSFT']].plot()#画出其中三列

结果：<matplotlib.axes._subplots.AxesSubplot at 0x1225960b8>

sns.jointplot('AMZN','GOOG', top_tech_dr, kind='scatter')#kind='scatter'表示散点图；看点是否在一条斜率为45度的直线上来观察两家股票的相关性，注意坐标大小代表的是上升或者下降

结果：<seaborn.axisgrid.JointGrid at 0x124dd0f98>

sns.jointplot('MSFT','FB', top_tech_dr, kind='scatter')

结果：<seaborn.axisgrid.JointGrid at 0x125928940>

sns.pairplot(top_tech_dr.dropna())#.dropna()表示去掉NaN

结果：<seaborn.axisgrid.PairGrid at 0x125fa27f0>

top_tech_dr['AAPL'].quantile(0.52)#结果：-0.0001447090809730694

top_tech_dr['MSFT'].quantile(0.05)#有（1-%5）的信心每天的变化最大为-0.021328913487084877 （最大亏损为%2）

结果：-0.021328913487084877

vips = pdr.get_data_yahoo('VIPS', start=start)['Adj Close']vips.plot()

结果：<matplotlib.axes._subplots.AxesSubplot at 0x127b0f7b8>

vips.pct_change().quantile(0.2)#-0.02181125767068397