【数据分析与可视化】股票市场分析实战之历史趋势分析

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# 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

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start = datetime(2015,9,20)
# 获取阿里股票数据
alibaba = pdr.get_data_yahoo('BABA', start=start)
# 获取亚马逊数据
amazon = pdr.get_components_yahoo('AMZN')

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# 数据存储alibaba.to_csv('url')
# 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)

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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

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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

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# 闭盘走势 
alibaba['Adj Close'].plot(legend=True)

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<matplotlib.axes._subplots.AxesSubplot at 0x1a252e4ad0>

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

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<matplotlib.axes._subplots.AxesSubplot at 0x1a216b9790>

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# 闭盘走势-两家公司
alibaba['Adj Close'].plot()
amazon['Adj Close'].plot()

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<matplotlib.axes._subplots.AxesSubplot at 0x1a26641290>

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# 统计每日差值
alibaba['high-low'] = alibaba['High'] - alibaba['Low']
alibaba.head()

	Open	High	Low	Close	Adj Close	Volume	high-low
Date
2015-09-21	65.379997	66.400002	62.959999	63.900002	63.900002	22355100	3.440003
2015-09-22	62.939999	63.270000	61.580002	61.900002	61.900002	14897900	1.689998
2015-09-23	61.959999	62.299999	59.680000	60.000000	60.000000	22684600	2.619999
2015-09-24	59.419998	60.340000	58.209999	59.919998	59.919998	20645700	2.130001
2015-09-25	60.630001	60.840000	58.919998	59.240002	59.240002	17009100	1.920002

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

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<matplotlib.axes._subplots.AxesSubplot at 0x1a26668a90>

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# 每天的改变
alibaba['daily-return'] = alibaba['Adj Close'].pct_change()
alibaba.head()

	Open	High	Low	Close	Adj Close	Volume	high-low	daily-return
Date
2015-09-21	65.379997	66.400002	62.959999	63.900002	63.900002	22355100	3.440003	NaN
2015-09-22	62.939999	63.270000	61.580002	61.900002	61.900002	14897900	1.689998	-0.031299
2015-09-23	61.959999	62.299999	59.680000	60.000000	60.000000	22684600	2.619999	-0.030695
2015-09-24	59.419998	60.340000	58.209999	59.919998	59.919998	20645700	2.130001	-0.001333
2015-09-25	60.630001	60.840000	58.919998	59.240002	59.240002	17009100	1.920002	-0.011348

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alibaba['daily-return'].plot()

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<matplotlib.axes._subplots.AxesSubplot at 0x1a28c20410>

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# 美化图
alibaba['daily-return'].plot(figsize=(10,4),linestyle='--',marker='o')

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<matplotlib.axes._subplots.AxesSubplot at 0x1a29240a90>

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# 直方图
alibaba['daily-return'].plot(kind='hist')

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<matplotlib.axes._subplots.AxesSubplot at 0x1a2786f050>

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

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<matplotlib.axes._subplots.AxesSubplot at 0x1a28bf8710>

date volume

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