python获取股票数据，从csv,从postgresql数据库，从tushare

痛定思痛。 2021-07-26 23:10 408阅读 0赞

# -*- coding: utf-8 -*-
    import tushare as ts
    import matplotlib.pyplot as plt
    import time
    import psycopg2
    import os
    from sqlalchemy import create_engine
    from io import StringIO
    import mpl_finance
    from matplotlib.pylab import date2num
    import pandas as pd
    import datetime
    # % matplotlib inline
    
    ip = "127.0.0.1"
    pwd = "12345678"
    db = "finance"
    user = "postgres"
    port = "5432"
    def get_stock():
        # 设置历史数据区间
        date1 = (2014, 12, 1)  # 起始日期，格式：(年，月，日)元组
        date2 = (2016, 12, 1)  # 结束日期，格式：(年，月，日)元组
        ts.set_token('8194e7012b78acccca78de7e6c5597056ec15fba0202edcd9a352ff1')
        # print(tushare.__version__)
        # 获取pro_api
        pro = ts.pro_api()
        # 获取数据，以公司名单为例
        # df = pro.stock_basic()
        # df= pro.get_tick_data('002415',date='2017-05-02')
        # data = pro.get_h_data('002415',start='2017-05-02', end='2018-05-02')
        # ktype：string数据类型，D=日k线 W=周 M=月 5=5分钟 15=15分钟 30=30分钟 60=60分钟，默认为D
        data = pro.get_k_data('002415', start='2017-05-02', end='2018-05-02',ktype='D')
        data1 = pd.DataFrame(data)
        output = StringIO()
        data1.to_csv(output, sep='\t', index=False, header=False)
        # quotes = ts.get_tick_data('601558.ss', '2016-05-02')
        print(str(data1));
    def get_data(file_name):
        da=pd.read_csv(filepath_or_buffer=file_name,sep='\t')
        datas=pd.DataFrame(da)
        # print(datas)
        return datas
    def plot(quotes):
        # 创建一个子图
        fig, ax = plt.subplots(facecolor=(0.5, 0.5, 0.5))
        fig.subplots_adjust(bottom=0.2)
        # 设置X轴刻度为日期时间
        ax.xaxis_date()
        # X轴刻度文字倾斜45度
        plt.xticks(rotation=45)
        plt.title("代码：601558")
        plt.xlabel("时间")
        plt.ylabel("价（元）")
        # mpf.candlestick_ohlc(ax, quotes, width=1.2, colorup='r', colordown='green')
        plt.grid(True)
    
    def fake_data():
        # df1 = DataFrame(np.random.rand(40).reshape(8, 5), columns=['A', 'B', 'C', 'D', 'E'])
        # df1 = DataFrame(np.random.randint(low=0,high=100,size=[10,12]), columns=['code','data', 'open', 'high', 'close', 'low','volume','amount','turnoverratio','A','B','C'])
    
        # df1.to_csv(path_or_buf='data.csv')
        df1=pd.read_csv(filepath_or_buffer='data.csv',sep='\t')
        # print(df1['code'][0])
        return df1
    def plot2():
        # matlabplot画图
        # plt.plot_date(df1['data'],df1['open'],'b-',label='open')
    
        # plt.plot_date(df1['data'], df1['low'], 'r--', label='low')
        # plt.gcf().autofmt_xdate()
        # plt.show()
        # pandos画图
        # df1['low'].plot(kind='bar',color='green')
        # df1['open'].plot(kind='bar')
        df2 = pd.DataFrame(df1, columns=['low', 'open'])
        df2.plot()
        plt.show()
    def df_test():
        da=pd.read_csv(filepath_or_buffer='data.csv',sep='\t')
        print(da)
        datas=pd.DataFrame(da)
        # 第2行第2列
        ds =datas.ix[[1]].values[0][1]
        ds = datas.loc[1, 'open']
        # 名为“open"的列
        ds = datas["open"]
        # 选取open列大于4的
        ds = datas[datas["open"]>4]
        # 2-4行，"date","open"列
        ds = datas.loc[1:3,["date","open"]]
        # 第2行
        ds = datas.ix[[1]]
        ds = datas[1:2]
        # list转为str
        # (",".join(str(i) for i in params)
        print(ds)
    def plot_k():
        datas=get_data()
        quotes=[]
        for i in range(80):
            if i == 0:
                sdate = str(datas.loc[i, 'date'])  # 注意：loc返回数值，iloc返回dataframe
                sdate_change_format = sdate[0:4] + '-' + sdate[4:6] + '-' + sdate[6:]
                sdate_num = date2num(datetime.datetime.strptime(sdate, '%Y-%m-%d'))  # 日期需要特定形式，这里进行转换
                sdate_plt = sdate_num
            else:
                sdate_plt = sdate_num + i
    
            sopen = datas.loc[i, 'open']
            shigh = datas.loc[i, 'high']
            slow = datas.loc[i, 'low']
            sclose = datas.loc[i, 'close']
            data_row = (sdate_plt, sopen, sclose, shigh, slow)
            quotes.append(data_row)
        print(quotes)
        fig, ax = plt.subplots(facecolor=(0, 0.3, 0.5), figsize=(12, 8))
        fig.subplots_adjust(bottom=0.1)
        ax.xaxis_date()
        plt.xticks(rotation=45)  # 日期显示的旋转角度
        plt.title('600000')
        plt.xlabel('time')
        plt.ylabel('price')
        mpf.candlestick_ohlc(ax, quotes, width=0.7, colorup='red', colordown='green')  # 上涨为红色K线，下跌为绿色，K线宽度为0.7
        plt.grid(True)
        plt.show()
    def plot_k2():
        datas=get_data()
        quotes=[]
        for i in range(14):
            sdate_plt = i+733920
            sopen = datas.loc[i, 'open']
            shigh = datas.loc[i, 'high']
            slow = datas.loc[i, 'low']
            sclose = datas.loc[i, 'close']
            data_row = (sdate_plt, sopen, sclose, shigh, slow)
            quotes.append(data_row)
        print(quotes)
        fig, ax = plt.subplots(figsize=(10, 5))
        mpl_finance.candlestick_ochl(
            ax=ax,
            quotes=quotes,
            width=0.7,
            colorup='r',
            colordown='g',
            alpha=0.7)
        ax.xaxis_date()
        plt.xticks(rotation=30);
        plt.show()
    def deco(func):
        def wrapper(*args,**kwargs):
            startTime = time.time()
            func(*args,**kwargs)
            endTime = time.time()
            ms=(endTime-startTime) * 1000
            print("runtime is %d ms" %ms)
    @deco
    def dataTest():
        da=pd.read_csv('data.csv')
        datas=pd.DataFrame(da)
        print(datas.loc[2, 'open'])
    def operate_pg():
        sql = '''insert into day_line(code,data) values ('601558','2017-05-02');
            '''
        pgUtil.executeInsert(ip, pwd, db, user, sql)
    def insertMany(data):
        sql = '''insert into day_line(date,open,close,high,low,volume,code) values (%s,%f,%f,%f,%f,%f,%s);
                '''
        datas = pd.DataFrame(data)
        params=datas.loc[1].tolist()
        paramList=[]
        for i in range(datas.__len__()):
            paramList.append((datas.loc[i, 'date'],float(datas.loc[i, 'open']),datas.loc[i, 'close'],datas.loc[i, 'high']
                             ,datas.loc[i, 'low'],datas.loc[i, 'volume'],datas.loc[i, 'code']))
        print(paramList)
        pgUtil.executeMany(ip, pwd, db, sql,user, paramList)
    
    if __name__=='__main__':
        data=get_data('data/002236.csv')
        # insert_pg(data)
        # get_stock()
        # df1 = fake_data()
        # df_test()
        # plot_k()
        # plot_k2()
        # dataTest()
        # operate_pg()
    
        insertMany(data)