Python实现股票数据分析的可视化
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Python实现股票数据分析的可视化
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
,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,20201110,18.0,18.5,17.93,18.11,17.84,0.27Python实现股票数据分析的可视化