Python实现股票数据分析的可视化
文章目录
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
classCalcFrame(MyFrame1):def__init__(self, parent):
MyFrame1.__init__(self, parent)# Virtual event handlers, overide them in your derived classdefget_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()defstore_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()defread_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()defshow_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
defwrite_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 inrange(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###########################################################################classMyFrame1(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 classdefget_data(self, event):
event.Skip()defstore_data(self, event):
event.Skip()defread_data(self, event):
event.Skip()defshow_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
defget_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)defread_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 inrange(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
defplot_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
,ts_code,trade_date,open,high,low,close,pre_close,change,pct_chg,vol,amount
0,000001.SZ,20201130,19.9,20.88,19.59,19.74,19.7,0.04,0.203,1581441.28,3213680.471,000001.SZ,20201127,20.0,20.0,19.38,19.7,19.5,0.2,1.0256,753773.74,1479430.6352,000001.SZ,20201126,19.05,19.61,19.03,19.5,19.06,0.44,2.3085,639657.89,1240074.3783,000001.SZ,20201125,19.48,19.7,19.05,19.06,19.36,-0.3,-1.5496,552585.01,1068352.0144,000001.SZ,20201124,19.62,19.68,19.17,19.36,19.62,-0.26,-1.3252,678543.23,1313496.1365,000001.SZ,20201123,18.85,19.62,18.8,19.62,18.86,0.76,4.0297,1165858.26,2252290.5786,000001.SZ,20201120,18.83,18.99,18.52,18.86,18.85,0.01,0.0531,673919.22,1265262.9157,000001.SZ,20201119,18.59,18.98,18.3,18.85,18.46,0.39,2.1127,1211740.62,2270476.4748,000001.SZ,20201118,17.78,18.5,17.75,18.46,17.83,0.63,3.5334,1373400.72,2508632.6429,000001.SZ,20201117,17.38,17.93,17.25,17.83,17.37,0.46,2.6482,852930.51,1509511.57710,000001.SZ,20201116,17.08,17.43,16.9,17.37,17.18,0.19,1.1059,759856.93,1308190.45911,000001.SZ,20201113,17.42,17.47,16.69,17.18,17.66,-0.48,-2.718,1289189.23,2191492.02112,000001.SZ,20201112,17.81,17.94,17.45,17.66,17.81,-0.15,-0.8422,677258.48,1197284.18113,000001.SZ,20201111,18.2,18.3,17.6,17.81,18.11,-0.3,-1.6565,940130.07,1677811.47814,000001.SZ,20201110,18.0,18.5,17.93,18.11,17.84,0.27,1.5135,1021062.81,1854142.80815,000001.SZ,20201109,17.67,18.0,17.54,17.84,17.64,0.2,1.1338,951424.32,1688807.40116,000001.SZ,20201106,17.71,17.75,17.22,17.64,17.7,-0.06,-0.339,848781.53,1486492.20817,000001.SZ,20201105,18.37,18.5,17.54,17.7,18.32,-0.62,-3.3843,1429469.44,2558562.45318,000001.SZ,20201104,18.35,18.48,17.96,18.32,17.96,0.36,2.0045,1247636.4,2275824.96319,000001.SZ,20201103,17.71,18.34,17.7,17.96,17.63,0.33,1.8718,957868.63,1727488.48120,000001.SZ,20201102,17.65,18.05,17.33,17.63,17.75,-0.12,-0.6761,968452.77,1702741.43721,000001.SZ,20201030,17.74,18.36,17.6,17.75,17.77,-0.02,-0.1125,1007803.83,1813064.34322,000001.SZ,20201029,17.54,17.93,17.35,17.77,17.63,0.14,0.7941,846603.62,1498040.94723,000001.SZ,20201028,17.76,17.9,17.29,17.63,17.76,-0.13,-0.732,1205823.86,2125604.54124,000001.SZ,20201027,18.0,18.0,17.5,17.76,17.7,0.06,0.339,1034865.04,1839243.22425,000001.SZ,20201026,18.2,18.29,17.45,17.7,18.13,-0.43,-2.3718,1175598.65,2085800.59826,000001.SZ,20201023,17.53,18.78,17.53,18.13,17.56,0.57,3.246,1698501.68,3105623.94827,000001.SZ,20201022,17.94,18.5,17.3,17.56,17.91,-0.35,-1.9542,1890519.05,3342069.0128,000001.SZ,20201021,17.64,18.0,17.33,17.91,17.54,0.37,2.1095,1244560.18,2204040.36429,000001.SZ,20201020,17.48,17.6,17.25,17.54,17.48,0.06,0.3432,960071.95,1673173.35530,000001.SZ,20201019,17.3,18.1,17.3,17.48,17.1,0.38,2.2222,2016105.52,3571336.00631,000001.SZ,20201016,16.56,17.37,16.54,17.1,16.56,0.54,3.2609,2095614.19,3589229.55832,000001.SZ,20201015,16.2,16.92,16.15,16.56,16.03,0.53,3.3063,1600062.32,2654379.58533,000001.SZ,20201014,16.04,16.12,15.8,16.03,16.06,-0.03,-0.1868,662562.36,1057937.81634,000001.SZ,20201013,15.9,16.11,15.77,16.06,15.9,0.16,1.0063,908819.48,1453986.33735,000001.SZ,20201012,15.22,16.05,15.21,15.9,15.18,0.72,4.7431,1591347.15,2509002.88536,000001.SZ,20201009,15.3,15.55,15.13,15.18,15.17,0.01,0.0659,900425.93,1376995.90637,000001.SZ,20200930,14.8,15.27,14.8,15.17,14.8,0.37,2.5,1217064.82,1838547.59538,000001.SZ,20200929,15.39,15.41,14.76,14.8,15.31,-0.51,-3.3312,1182374.4,1766848.54439,000001.SZ,20200928,15.19,15.37,14.98,15.31,15.19,0.12,0.79,612711.11,932800.76640,000001.SZ,20200925,15.2,15.31,15.11,15.19,15.12,0.07,0.463,614087.0,933035.04441,000001.SZ,20200924,15.59,15.61,15.12,15.12,15.63,-0.51,-3.263,1061011.24,1623376.242,000001.SZ,20200923,15.59,15.83,15.51,15.63,15.57,0.06,0.3854,599200.47,939763.26543,000001.SZ,20200922,15.67,15.84,15.39,15.57,15.86,-0.29,-1.8285,867756.31,1354536.27244,000001.SZ,20200921,16.0,16.05,15.71,15.86,16.07,-0.21,-1.3068,896161.65,1418370.97345,000001.SZ,20200918,15.62,16.09,15.52,16.07,15.57,0.5,3.2113,1373193.3,2186759.08746,000001.SZ,20200917,15.54,15.72,15.4,15.57,15.44,0.13,0.842,988215.63,1543414.50147,000001.SZ,20200916,15.32,15.54,15.21,15.44,15.35,0.09,0.5863,722414.75,1114667.83248,000001.SZ,20200915,15.2,15.48,15.15,15.35,15.3,0.05,0.3268,657132.67,1007999.04449,000001.SZ,20200914,15.01,15.3,14.92,15.3,15.01,0.29,1.932,680251.05,1027508.10850,000001.SZ,20200911,15.18,15.3,14.82,15.01,15.34,-0.33,-2.1512,954236.25,1431844.0251,000001.SZ,20200910,15.32,15.48,15.2,15.34,15.21,0.13,0.8547,957092.39,1469402.76852,000001.SZ,20200909,15.26,15.56,15.13,15.21,15.43,-0.22,-1.4258,1013572.47,1554005.57553,000001.SZ,20200908,15.0,15.43,15.0,15.43,14.94,0.49,3.2798,1407601.66,2154220.77854,000001.SZ,20200907,14.88,15.24,14.83,14.94,14.96,-0.02,-0.1337,1031376.81,1551971.3855,000001.SZ,20200904,14.73,15.06,14.6,14.96,14.9,0.06,0.4027,909889.99,1353550.80856,000001.SZ,20200903,15.32,15.33,14.84,14.9,15.32,-0.42,-2.7415,1279841.59,1919266.72657,000001.SZ,20200902,15.01,15.53,15.01,15.32,15.14,0.18,1.1889,1679382.97,2575966.63758,000001.SZ,20200901,14.96,15.23,14.88,15.14,15.08,0.06,0.3979,813642.58,1228342.74159,000001.SZ,20200831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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。
本文转载自: https://blog.csdn.net/m0_54218263/article/details/122147820
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版权归原作者 hhh江月 所有， 如有侵权，请联系我们删除。