股票分析案例

使用tushare包获取某股票的历史行情数据

• pip install tushare

import tushare as ts
import pandas as pd

# 茅台的数据
maotai = ts.get_k_data(code='600519',start='1900-01-01')

maotai.head()

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# 存储本地
maotai.to_csv('./maotai.csv')

# 从本地读取数据
# index_col将哪一列['date']作为原数据的行索引
# parse_dates: 将哪些列的数据转换为date类型
# 将date的类型转成时间类型然后将其作为原数据的行索引
df = pd.read_csv('./maotai.csv', index_col='date', parse_dates=['date'])
df.drop(labels='Unnamed: 0',axis=1,inplace=True)  # 删除多余的一列 inplace表示,是否修改原数据

df.head()

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# 输出该股票所有收盘比开盘上涨3%以上的日期。
# (收盘-开盘)/开盘 > 0.03
(df['close'] - df['open']) / df['open'] > 0.03
# 将True对应的行数据取出
df.loc[(df['close'] - df['open']) / df['open'] > 0.03]
# 取行索引(时间)
df.loc[(df['close'] - df['open']) / df['open'] > 0.03].index

DatetimeIndex(['2001-08-27', '2001-08-28', '2001-09-10', '2001-12-21',
               '2002-01-18', '2002-01-31', '2003-01-14', '2003-10-29',
               '2004-01-05', '2004-01-14',
               ...
               '2019-03-01', '2019-03-18', '2019-04-10', '2019-04-16',
               '2019-05-10', '2019-05-15', '2019-06-11', '2019-06-20',
               '2019-09-12', '2019-09-18'],
              dtype='datetime64[ns]', name='date', length=303, freq=None)

# 输出该股票所有开盘比前日收盘跌幅超过2%的日期。
# (开盘-前日收盘)/前日收盘  < -0.02
# shift(1): 向下移动一行
(df['open'] - df['close'].shift(1)) / df['close'].shift(1) < -0.02
df.loc[(df['open'] - df['close'].shift(1)) / df['close'].shift(1) < -0.02]
df.loc[(df['open'] - df['close'].shift(1)) / df['close'].shift(1) < -0.02].index

DatetimeIndex(['2001-09-12', '2002-06-26', '2002-12-13', '2004-07-01',
               '2004-10-29', '2006-08-21', '2006-08-23', '2007-01-25',
               '2007-02-01', '2007-02-06', '2007-03-19', '2007-05-21',
               '2007-05-30', '2007-06-05', '2007-07-27', '2007-09-05',
               '2007-09-10', '2008-03-13', '2008-03-17', '2008-03-25',
               '2008-03-27', '2008-04-22', '2008-04-23', '2008-04-29',
               '2008-05-13', '2008-06-10', '2008-06-13', '2008-06-24',
               '2008-06-27', '2008-08-11', '2008-08-19', '2008-09-23',
               '2008-10-10', '2008-10-15', '2008-10-16', '2008-10-20',
               '2008-10-23', '2008-10-27', '2008-11-06', '2008-11-12',
               '2008-11-20', '2008-11-21', '2008-12-02', '2009-02-27',
               '2009-03-25', '2009-08-13', '2010-04-26', '2010-04-30',
               '2011-08-05', '2012-03-27', '2012-08-10', '2012-11-22',
               '2012-12-04', '2012-12-24', '2013-01-16', '2013-01-25',
               '2013-09-02', '2014-04-25', '2015-01-19', '2015-05-25',
               '2015-07-03', '2015-07-08', '2015-07-13', '2015-08-24',
               '2015-09-02', '2015-09-15', '2017-11-17', '2018-02-06',
               '2018-02-09', '2018-03-23', '2018-03-28', '2018-07-11',
               '2018-10-11', '2018-10-24', '2018-10-25', '2018-10-29',
               '2018-10-30', '2019-05-06', '2019-05-08'],
              dtype='datetime64[ns]', name='date', freq=None)

#假如我从2010年1月1日开始，每月第一个交易日买入1手股票，每年最后一个交易日卖出所有股票，到今天为止，我的收益如何？
df_2010 = df['2010':'2019']

• 基于开盘价进行股票的买卖
• 买股票的时机：
  • 每月的第一个交易日买入一手（100股）股票
  • 一个完整的年会买入12次股票共计1200股
• 卖股票的时机：
  • 每年的最后一个交易日卖出所有（1200股）的股票
  • 一共可以卖9次股票
• 注意：19年只可以买入不可以卖出，最后剩余的不能卖出的1000股股票是需要计算到总收益中
• 数据的重新取样resample()

# 买入股票花费的钱数
# df_2010.resample('M').first() 取出每月的第一天为索引的行
df_monthly = df_2010.resample('M').first()
cost_money = df_monthly['open'].sum() * 100
cost_money

3568986.0999999996

# 卖出股票收到多少钱
# df_2010.resample('A').last()取出每年的最后一天为索引的行
# 并且排除2019年
df_yearly = df_2010.resample('A').last()[:-1]
df_yearly

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last_price = df_2010['close'][-1]  # 昨天的收盘价

recv_monry = df_yearly['open'].sum() * 1200 + last_price * 1000
recv_monry - cost_money

590598.6999999997

原文地址：https://www.cnblogs.com/zyyhxbs/p/11693210.html