这篇文章主要为大家展示了“如何使用Python实现股票数据分析的可视化”，内容简而易懂，条理清晰，希望能够帮助大家解决疑惑，下面让小编带领大家一起研究并学习一下“如何使用Python实现股票数据分析的可视化”这篇文章吧。
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。
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()
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()# -*- 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()
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(),ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount 0,000001.SZ,20201130,19.9,20.88,19.59,19.74,19.7,0.04,0.203,1581441.28,3213680.47 1,000001.SZ,20201127,20.0,20.0,19.38,19.7,19.5,0.2,1.0256,753773.74,1479430.635 2,000001.SZ,20201126,19.05,19.61,19.03,19.5,19.06,0.44,2.3085,639657.89,1240074.378 3,000001.SZ,20201125,19.48,19.7,19.05,19.06,19.36,-0.3,-1.5496,552585.01,1068352.014 4,000001.SZ,20201124,19.62,19.68,19.17,19.36,19.62,-0.26,-1.3252,678543.23,1313496.136 5,000001.SZ,20201123,18.85,19.62,18.8,19.62,18.86,0.76,4.0297,1165858.26,2252290.578 6,000001.SZ,20201120,18.83,18.99,18.52,18.86,18.85,0.01,0.0531,673919.22,1265262.915 7,000001.SZ,20201119,18.59,18.98,18.3,18.85,18.46,0.39,2.1127,1211740.62,2270476.474 8,000001.SZ,20201118,17.78,18.5,17.75,18.46,17.83,0.63,3.5334,1373400.72,2508632.642 9,000001.SZ,20201117,17.38,17.93,17.25,17.83,17.37,0.46,2.6482,852930.51,1509511.577 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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