python基础学习笔记（五）tushare

﹏ヽ暗。殇╰゛Y 2022-04-05 06:14 188阅读 0赞

## 官网 ##

http://www.tushare.org

## 安装 ##

pip install tushare

## 引用 ##

import tushare

tushare.get\_k\_data('601318','1991-01-01') \# 取K线历史数据

tushare.trade\_cal() \# 获取交易日信息

## 例子 ##

1.获取某股票历史行情信息

import numpy as np
    
    import pandas as pd
    
    import matplotlib.pyplot ps plt
    
    import tushare as ts
    
    df = ts.get_k_data("600519",start="1988-01-01")
    
    df.to_csv("600519.csv")
    
    df = pd.read_csv("600519.csv",index_col='date',parse_dates=['date'])[['open','close','high','low']]
    
    df

2.输出改股票所有收盘比开盘上涨3%以上的日期

df[(df['close']-df['open'])/df['open']>=0.03].index

3.输出改股票所有开盘比前日收盘幅度超过2%的日期

df[(df['open']-df['close'].shift(1))/df['close'].shift(1)<=-0.02].index

4.加入从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，至今为止收益如何

price_last = df['open'][-1] #最后一条股票的价格
    
    df = df['2001-09':'2018-12'] #剔除首尾数据
    
    df_monthly = df.resample("M").first()#每月第一个交易日
    
    df_yearly = df.resample("A").last().[:-1]#每年最后一个交易日
    
    cost_monthly = 0
    
    hold = 0
    
    for year in range(2001,2018):
    
        cost_money +=df_monthly[str(year)][open].sum()*100 #每个月买100股
    
        hold += len(df_monthly[str(year)]['open']) * 100
    
         if year != 2017:
    
            cost_money -= df_year[str(year)]['open'][0]*hold #每年最后一个交易日卖出
    
           hold=0
    
           print(cost_money)
    
    cost_money -= hold * price_last
    
    print(-cost_money)

5.获取股票数据计算该骨片的5日均线和30日均线

import numpy as np
    
    import pandas as pd
    
    import matplotlib.pyplot ps plt
    
    import tushare as ts
    
    df = pd.read_csv("601318.csv",index_col='date',parse_dates=['date'])['open','close','low','high']
    
    df['ma5'] = np.nan #新加列
    df['ma30'] = np.nan
    #循环写法
    for i in range(4,len(df)): #从第5行开始到最后
        df.loc[df.index[i],'ma5'] = df['close'][i-4:i+1].mean() #每5个求平均并赋值
    
    for i in range(29,len(df)):
        df.loc[df.index[i],'ma30'] = df['close'][i-29:i+1].mean()
    
    #rolling写法,滚动数据取值
    df['ma5'] = df['close'].rolling(5).mean()
    df['ma30'] = df['close'].rolling(30).mean()

6.使用matplotlib包绘制两条均线

df[['close','ma5','ma30']].plot()
    
    plt.show()
    
    df.loc['2012',['close','ma5','ma30']].plot() #只画一年
    
    plt.show()

7.输出所有的金叉日期和死叉日期

df = df.dropna()
    df = df['2010-01-01':] #截取数据
    golden_cross = []
    death_cross = []
    #循环写法
    for i in range(30,len(df)):#按天循环,从长期均线有值的数据开始
        #今天的ma5>=ma30并且昨天的ma5小于ma30
        if df['ma5'][i] >=df['ma30'][i] and df['ma5'][i-1]<df['ma30'][i-1]:
            golden_cross.append(df.index[i].to_pydatetime())#保存金叉数据
        #今天的ma5<=ma30并且昨天的ma5大于ma30
        if df['ma5'][i] <=df['ma30'][i] and df['ma5'][i-1]>df['ma30'][i-1]：
            death_cross.append(df.index[i].to_pydatetime())#保存死叉数据
    
    #shift写法，不使用循环
    str1 = df['ma5'] < df['ma30']
    str2 = df['ma5'] >= df['ma30']
    
    death_cross = df[str1 & str2.shift(1)].index
    golden_cross = df[~(str1 | str2.shift(1))].index

8.如果从2010年开始，初始资金10万，金叉买入死叉卖出，到尽头为止的收入是多少

first_money = 100000 #初始资金
    money = first_money
    hold = 0 #持有股份数量
    #日期排序
    sr1 = pd.Series(1,index=golden_cross)
    sr2 = pd.Series(0,index=death_cross)
    sr = sr1.append(sr2).sort_index()
    
    for i in range(0,len(sr)):
        p = df['open'][sr.index[i]] #当天的开盘价格
        #金叉
        if sr.iloc[i] == 1:
            buy = (money // (100*p)) #整除（按手计算）能买多少
            hold += buy * 100
            money -= buy*100*p
        else:
            money += hold * p
            hold = 0
    
    p = df['open'][-1] #最后一天开盘价
    now_money = hold * p + money
    print(now_money - first_money)

# #

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