基于Python实现股票数据分析的可视化
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2024-01-08 15:49:04
目录一、简介二、代码1、主文件2、数据库使用文件3、ui设计模块4、数据处理模块三、数据样例的展示四、效果展示一、简介我们知道在购买股票的时候,可以使用历史数据来对当前的股票的走势进行预测,这就需要对...
一、简介
我们知道在购买股票的时候,可以使用历史数据来对当前的股票的走势进行预测,这就需要对股票的数据进行获取并且进行一定的分析,当然了,人们是比较喜欢图形化的界面的,因此,我们在这里采用一种可视化的方法来实现股票数据的分析。
二、代码
1、主文件
from work1 import get_data from work1 import read_data from work1 import plot_data import pymysql from uitest import myframe1 import wx from database1 import write_to_base import time class calcframe(myframe1): def __init__(self, parent): myframe1.__init__(self, parent) # virtual event handlers, overide them in your derived class def get_data(self, event): """ 获取数据 :param event: 点击 :return: 空 """ get_data() time.sleep(2) dlg = wx.messagedialog(none, '已经成功获取数据', '获取数据') result = dlg.showmodal() dlg.destroy() event.skip() def store_data(self, event): """ 存储数据 :param event: 点击 :return: 空 """ write_to_base() dlg = wx.messagedialog(none, '已经成功存储数据', '存储数据') result = dlg.showmodal() dlg.destroy() event.skip() def read_data(self, event): """ 读取数据 :param event: 点击 :return: 空 """ df0 = read_data() dlg = wx.messagedialog(none, '已经成功读取数据', '读取数据') result = dlg.showmodal() dlg.destroy() event.skip() def show_data(self, event): """ 展示数据 :param event: 点击 :return: 空 """ df0 = read_data() plot_data(df0) event.skip() if __name__ == '__main__': """ 主函数 """ app = wx.app(false) frame = calcframe(none) frame.show(true) # start the applications app.mainloop()
2、数据库使用文件
import pymysql import pandas as pd def write_to_base(): # pass """ 写入数据库 :return:空 """ df0 = pd.read_csv('./data.csv') df0[['ts_code']] = df0[['ts_code']].astype(str) df0[['trade_date']] = df0[['trade_date']].astype(str) df0[['open']] = df0[['open']].astype(str) df0[['high']] = df0[['high']].astype(str) df0[['low']] = df0[['low']].astype(str) df0[['close']] = df0[['close']].astype(str) df0[['pre_close']] = df0[['pre_close']].astype(str) df0[['change']] = df0[['change']].astype(str) df0[['pct_chg']] = df0[['pct_chg']].astype(str) df0[['vol']] = df0[['vol']].astype(str) df0[['amount']] = df0[['amount']].astype(str) # df0[['pre_close']] = df0[['pre_close']].astype(str) # df0[['ts_code']] = df0[['ts_code']].astype(str) # 打开数据库连接 # print(data) # data = tuple(data) db = pymysql.connect(host="localhost", user="root", password="671513", db="base1") # 使用cursor()方法获取操作游标 cursor = db.cursor() # db.commit() # db.ping(reconnect=true) db.ping(reconnect=true) cursor.execute("use base1") db.commit() cursor.execute("truncate table tb") db.commit() sql = "insert into tb(ts_code,trdae_date,open,high,low,close,pre_close,changed,pct_chg,vol,amount) \ values ('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s')" # ('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s')" # ('000001.sz','20210716','21.41','21.82','21.3','21.34','21.62','-0.28','-1.2951','573002.61','1230180.813') # ('%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s', '%s') for i in range(220): db.ping(reconnect=true) # 执行sql语句 cursor.execute(sql %\ (df0.iloc[i, 1], df0.iloc[i, 2], df0.iloc[i, 3], df0.iloc[i, 4], df0.iloc[i, 5], df0.iloc[i, 6], df0.iloc[i, 7], df0.iloc[i, 8], df0.iloc[i, 9], df0.iloc[i, 10], df0.iloc[i, 11])) # 执行sql语句 db.commit() # 关闭数据库连接 db.close()
3、ui设计模块
# -*- coding: utf-8 -*- ########################################################################### ## python code generated with wxformbuilder (version jun 17 2015) ## http://www.wxformbuilder.org/ ## ## please do "not" edit this file! ########################################################################### import wx import wx.xrc ########################################################################### ## class myframe1 ########################################################################### class myframe1(wx.frame): def __init__(self, parent): wx.frame.__init__(self, parent, id=wx.id_any, title=u"股票数据分析", pos=wx.defaultposition, size=wx.size(309, 300), style=wx.default_frame_style | wx.tab_traversal) self.setsizehintssz(wx.defaultsize, wx.defaultsize) bsizer1 = wx.boxsizer(wx.vertical) self.m_button1 = wx.button(self, wx.id_any, u"获取数据", wx.defaultposition, wx.defaultsize, 0) bsizer1.add(self.m_button1, 1, wx.all | wx.expand, 5) self.m_button2 = wx.button(self, wx.id_any, u"存储数据", wx.defaultposition, wx.defaultsize, 0) bsizer1.add(self.m_button2, 1, wx.all | wx.expand, 5) self.m_button3 = wx.button(self, wx.id_any, u"读取数据", wx.defaultposition, wx.defaultsize, 0) bsizer1.add(self.m_button3, 1, wx.all | wx.expand, 5) self.m_button4 = wx.button(self, wx.id_any, u"展示曲线", wx.defaultposition, wx.defaultsize, 0) bsizer1.add(self.m_button4, 1, wx.all | wx.expand, 5) self.setsizer(bsizer1) self.layout() self.centre(wx.both) # connect events self.m_button1.bind(wx.evt_button, self.get_data) self.m_button2.bind(wx.evt_button, self.store_data) self.m_button3.bind(wx.evt_button, self.read_data) self.m_button4.bind(wx.evt_button, self.show_data) def __del__(self): pass # virtual event handlers, overide them in your derived class def get_data(self, event): event.skip() def store_data(self, event): event.skip() def read_data(self, event): event.skip() def show_data(self, event): event.skip() # # # class calcframe(myframe1): # def __init__(self, parent): # myframe1.__init__(self, parent) # # # app = wx.app(false) # # frame = calcframe(none) # # frame.show(true) # # # start the applications # app.mainloop()
4、数据处理模块
import numpy as np import tushare as ts import matplotlib.pyplot as plt import pandas as pd def get_data(): """ 获取数据 :return: 空 """ # 获取股票的数据 pro = ts.pro_api('c62ba9195fa8b54ff78a38cab1cec01b15def7f47c32f91fb273ee3a') df = pro.daily(ts_code='000001.sz', start_date='20200101', end_date='20201130') # 存储数据到一个文件中 df.to_csv('./data.csv') print(df) def read_data(): """ 读取数据 :return: 空 """ # 读取数据 df = pd.read_csv('./data.csv') # 删除不需要的行 df = df.drop(['unnamed: 0'], axis=1) df = df.drop(['ts_code'], axis=1) # 反转行使得时间是从前到后的 df = df.iloc[::-1, :] # 将时间由数字转为字符串 for i in range(220): df.iloc[i, 0] = str(df.iloc[i, 0]) # 将字符串转为时间类型的数据 df['trade_date'] = pd.to_datetime(df['trade_date']) # 将时间设置为索引 df = df.set_index(['trade_date']) df = df.iloc[:, :] print(df) return df def plot_data(df): """ 展示数据 :param df: 一个dataframe :return: 空 """ ma5 = (df['close'].rolling(5).mean()).iloc[30:] ma10 = (df['close'].rolling(10).mean()).iloc[30:] ma20 = (df['close'].rolling(20).mean()).iloc[30:] plt.figure(figsize=(16, 9)) l1, = plt.plot(ma5, label="ma5") l2, = plt.plot(ma10, label="ma10") l3, = plt.plot(ma20, label="ma20") l4, = plt.plot(df['close'].iloc[30:], label="close") plt.legend(handles=[l1, l2, l3, l4], labels=["ma5", "ma10", "ma20", "close"]) plt.show()
三、数据样例的展示
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10,000001.sz,20201116,17.08,17.43,16.9,17.37,17.18,0.19,1.1059,759856.93,1308190.459 11,000001.sz,20201113,17.42,17.47,16.69,17.18,17.66,-0.48,-2.718,1289189.23,2191492.021 12,000001.sz,20201112,17.81,17.94,17.45,17.66,17.81,-0.15,-0.8422,677258.48,1197284.181 13,000001.sz,20201111,18.2,18.3,17.6,17.81,18.11,-0.3,-1.6565,940130.07,1677811.478 14,000001.sz,20201110,18.0,18.5,17.93,18.11,17.84,0.27,1.5135,1021062.81,1854142.808 15,000001.sz,20201109,17.67,18.0,17.54,17.84,17.64,0.2,1.1338,951424.32,1688807.401 16,000001.sz,20201106,17.71,17.75,17.22,17.64,17.7,-0.06,-0.339,848781.53,1486492.208 17,000001.sz,20201105,18.37,18.5,17.54,17.7,18.32,-0.62,-3.3843,1429469.44,2558562.453 18,000001.sz,20201104,18.35,18.48,17.96,18.32,17.96,0.36,2.0045,1247636.4,2275824.963 19,000001.sz,20201103,17.71,18.34,17.7,17.96,17.63,0.33,1.8718,957868.63,1727488.481 20,000001.sz,20201102,17.65,18.05,17.33,17.63,17.75,-0.12,-0.6761,968452.77,1702741.437 21,000001.sz,20201030,17.74,18.36,17.6,17.75,17.77,-0.02,-0.1125,1007803.83,1813064.343 22,000001.sz,20201029,17.54,17.93,17.35,17.77,17.63,0.14,0.7941,846603.62,1498040.947 23,000001.sz,20201028,17.76,17.9,17.29,17.63,17.76,-0.13,-0.732,1205823.86,2125604.541 24,000001.sz,20201027,18.0,18.0,17.5,17.76,17.7,0.06,0.339,1034865.04,1839243.224 25,000001.sz,20201026,18.2,18.29,17.45,17.7,18.13,-0.43,-2.3718,1175598.65,2085800.598 26,000001.sz,20201023,17.53,18.78,17.53,18.13,17.56,0.57,3.246,1698501.68,3105623.948 27,000001.sz,20201022,17.94,18.5,17.3,17.56,17.91,-0.35,-1.9542,1890519.05,3342069.01 28,000001.sz,20201021,17.64,18.0,17.33,17.91,17.54,0.37,2.1095,1244560.18,2204040.364 29,000001.sz,20201020,17.48,17.6,17.25,17.54,17.48,0.06,0.3432,960071.95,1673173.355 30,000001.sz,20201019,17.3,18.1,17.3,17.48,17.1,0.38,2.2222,2016105.52,3571336.006 31,000001.sz,20201016,16.56,17.37,16.54,17.1,16.56,0.54,3.2609,2095614.19,3589229.558 32,000001.sz,20201015,16.2,16.92,16.15,16.56,16.03,0.53,3.3063,1600062.32,2654379.585 33,000001.sz,20201014,16.04,16.12,15.8,16.03,16.06,-0.03,-0.1868,662562.36,1057937.816 34,000001.sz,20201013,15.9,16.11,15.77,16.06,15.9,0.16,1.0063,908819.48,1453986.337 35,000001.sz,20201012,15.22,16.05,15.21,15.9,15.18,0.72,4.7431,1591347.15,2509002.885 36,000001.sz,20201009,15.3,15.55,15.13,15.18,15.17,0.01,0.0659,900425.93,1376995.906 37,000001.sz,20200930,14.8,15.27,14.8,15.17,14.8,0.37,2.5,1217064.82,1838547.595 38,000001.sz,20200929,15.39,15.41,14.76,14.8,15.31,-0.51,-3.3312,1182374.4,1766848.544 39,000001.sz,20200928,15.19,15.37,14.98,15.31,15.19,0.12,0.79,612711.11,932800.766 40,000001.sz,20200925,15.2,15.31,15.11,15.19,15.12,0.07,0.463,614087.0,933035.044 41,000001.sz,20200924,15.59,15.61,15.12,15.12,15.63,-0.51,-3.263,1061011.24,1623376.2 42,000001.sz,20200923,15.59,15.83,15.51,15.63,15.57,0.06,0.3854,599200.47,939763.265 43,000001.sz,20200922,15.67,15.84,15.39,15.57,15.86,-0.29,-1.8285,867756.31,1354536.272 44,000001.sz,20200921,16.0,16.05,15.71,15.86,16.07,-0.21,-1.3068,896161.65,1418370.973 45,000001.sz,20200918,15.62,16.09,15.52,16.07,15.57,0.5,3.2113,1373193.3,2186759.087 46,000001.sz,20200917,15.54,15.72,15.4,15.57,15.44,0.13,0.842,988215.63,1543414.501 47,000001.sz,20200916,15.32,15.54,15.21,15.44,15.35,0.09,0.5863,722414.75,1114667.832 48,000001.sz,20200915,15.2,15.48,15.15,15.35,15.3,0.05,0.3268,657132.67,1007999.044 49,000001.sz,20200914,15.01,15.3,14.92,15.3,15.01,0.29,1.932,680251.05,1027508.108 50,000001.sz,20200911,15.18,15.3,14.82,15.01,15.34,-0.33,-2.1512,954236.25,1431844.02 51,000001.sz,20200910,15.32,15.48,15.2,15.34,15.21,0.13,0.8547,957092.39,1469402.768 52,000001.sz,20200909,15.26,15.56,15.13,15.21,15.43,-0.22,-1.4258,1013572.47,1554005.575 53,000001.sz,20200908,15.0,15.43,15.0,15.43,14.94,0.49,3.2798,1407601.66,2154220.778 54,000001.sz,20200907,14.88,15.24,14.83,14.94,14.96,-0.02,-0.1337,1031376.81,1551971.38 55,000001.sz,20200904,14.73,15.06,14.6,14.96,14.9,0.06,0.4027,909889.99,1353550.808 56,000001.sz,20200903,15.32,15.33,14.84,14.9,15.32,-0.42,-2.7415,1279841.59,1919266.726 57,000001.sz,20200902,15.01,15.53,15.01,15.32,15.14,0.18,1.1889,1679382.97,2575966.637 58,000001.sz,20200901,14.96,15.23,14.88,15.14,15.08,0.06,0.3979,813642.58,1228342.741 59,000001.sz,20200831,15.3,15.68,14.99,15.08,15.13,-0.05,-0.3305,1797129.54,2760350.322 60,000001.sz,20200828,14.26,15.18,14.26,15.13,14.46,0.67,4.6335,2410400.02,3599035.694 61,000001.sz,20200827,14.4,14.46,14.11,14.46,14.37,0.09,0.6263,626666.77,895618.648 62,000001.sz,20200826,14.6,14.61,14.28,14.37,14.6,-0.23,-1.5753,734117.72,1057274.169 63,000001.sz,20200825,14.56,14.69,14.46,14.6,14.46,0.14,0.9682,748320.22,1090756.854 64,000001.sz,20200824,14.5,14.71,14.41,14.46,14.45,0.01,0.0692,919448.86,1338031.969 65,000001.sz,20200821,14.71,14.71,14.32,14.45,14.59,-0.14,-0.9596,1234517.33,1787278.581 66,000001.sz,20200820,15.01,15.14,14.53,14.59,15.1,-0.51,-3.3775,1333801.62,1962605.013 67,000001.sz,20200819,15.11,15.35,14.96,15.1,15.15,-0.05,-0.33,1420928.11,2154215.097 68,000001.sz,20200818,15.2,15.3,14.91,15.15,15.19,-0.04,-0.2633,1350261.07,2033477.707 69,000001.sz,20200817,14.6,15.35,14.55,15.19,14.47,0.72,4.9758,3268027.8,4923669.137 70,000001.sz,20200814,14.1,14.51,14.06,14.47,14.18,0.29,2.0451,1103215.82,1578543.607 71,000001.sz,20200813,14.4,14.46,14.14,14.18,14.38,-0.2,-1.3908,837261.75,1190139.725 72,000001.sz,20200812,14.21,14.5,14.15,14.38,14.13,0.25,1.7693,1596811.7,2287731.088 73,000001.sz,20200811,13.97,14.66,13.97,14.13,13.95,0.18,1.2903,2603307.89,3748036.828 74,000001.sz,20200810,13.67,14.02,13.62,13.95,13.7,0.25,1.8248,1587710.35,2208568.316 75,000001.sz,20200807,13.8,13.9,13.62,13.7,13.9,-0.2,-1.4388,988678.37,1356305.781 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四、效果展示
我们采用视频的形式来进行效果的展示;
https://www.bilibili.com/video/bv1rf411q7g2?spm_id_from=333.999.0.0
股票数据分析的实现
以上就是我实现的股票数据分析的可视化的处理的结果,谢谢大家的阅读与支持啦。
到此这篇关于基于python实现股票数据分析的可视化的文章就介绍到这了,更多相关python股票数据分析可视化内容请搜索以前的文章或继续浏览下面的相关文章希望大家以后多多支持!