基于Python實(shí)現(xiàn)股票數(shù)據(jù)分析的可視化
更新時間：2021年12月31日 10:26:20 作者：hhh江月
在購買股票的時候，可以使用歷史數(shù)據(jù)來對當(dāng)前的股票的走勢進(jìn)行預(yù)測，這就需要對股票的數(shù)據(jù)進(jìn)行獲取并且進(jìn)行一定的分析。本文將介紹如何通過Python實(shí)現(xiàn)股票數(shù)據(jù)分析的可視化，需要的可以參考一下
一、簡介
我們知道在購買股票的時候，可以使用歷史數(shù)據(jù)來對當(dāng)前的股票的走勢進(jìn)行預(yù)測，這就需要對股票的數(shù)據(jù)進(jìn)行獲取并且進(jìn)行一定的分析，當(dāng)然了，人們是比較喜歡圖形化的界面的，因此，我們在這里采用一種可視化的方法來實(shí)現(xiàn)股票數(shù)據(jù)的分析。
二、代碼
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):
"""
獲取數(shù)據(jù)
:param event: 點(diǎn)擊
:return: 空
"""
get_data()
time.sleep(2)
dlg = wx.MessageDialog(None, '已經(jīng)成功獲取數(shù)據(jù)', '獲取數(shù)據(jù)')
result = dlg.ShowModal()
dlg.Destroy()
event.Skip()
def store_data(self, event):
"""
存儲數(shù)據(jù)
:param event: 點(diǎn)擊
:return: 空
"""
write_to_base()
dlg = wx.MessageDialog(None, '已經(jīng)成功存儲數(shù)據(jù)', '存儲數(shù)據(jù)')
result = dlg.ShowModal()
dlg.Destroy()
event.Skip()
def read_data(self, event):
"""
讀取數(shù)據(jù)
:param event: 點(diǎn)擊
:return: 空
"""
df0 = read_data()
dlg = wx.MessageDialog(None, '已經(jīng)成功讀取數(shù)據(jù)', '讀取數(shù)據(jù)')
result = dlg.ShowModal()
dlg.Destroy()
event.Skip()
def show_data(self, event):
"""
展示數(shù)據(jù)
:param event: 點(diǎn)擊
:return: 空
"""
df0 = read_data()
plot_data(df0)
event.Skip()
if __name__ == '__main__':
"""
主函數(shù)
"""
app = wx.App(False)
frame = CalcFrame(None)
frame.Show(True)
# start the applications
app.MainLoop()
2、數(shù)據(jù)庫使用文件
import pymysql
import pandas as pd
def write_to_base():
# pass
"""
寫入數(shù)據(jù)庫
: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)
# 打開數(shù)據(jù)庫連接
# print(data)
# data = tuple(data)
db = pymysql.connect(host="localhost",
user="root",
password="671513",
db="base1")
# 使用cursor()方法獲取操作游標(biāo)
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)
# 執(zhí)行sql語句
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]))
# 執(zhí)行sql語句
db.commit()
# 關(guān)閉數(shù)據(jù)庫連接
db.close()
3、ui設(shè)計(jì)模塊
# -*- 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"股票數(shù)據(jù)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"獲取數(shù)據(jù)", 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"存儲數(shù)據(jù)", 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"讀取數(shù)據(jù)", 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"展示曲線", 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、數(shù)據(jù)處理模塊
import numpy as np
import tushare as ts
import matplotlib.pyplot as plt
import pandas as pd
def get_data():
"""
獲取數(shù)據(jù)
:return: 空
"""
# 獲取股票的數(shù)據(jù)
pro = ts.pro_api('c62ba9195fa8b54ff78a38cab1cec01b15def7f47c32f91fb273ee3a')
df = pro.daily(ts_code='000001.SZ', start_date='20200101', end_date='20201130')
# 存儲數(shù)據(jù)到一個文件中
df.to_csv('./data.csv')
print(df)
def read_data():
"""
讀取數(shù)據(jù)
:return: 空
"""
# 讀取數(shù)據(jù)
df = pd.read_csv('./data.csv')
# 刪除不需要的行
df = df.drop(['Unnamed: 0'], axis=1)
df = df.drop(['ts_code'], axis=1)
# 反轉(zhuǎn)行使得時間是從前到后的
df = df.iloc[::-1, :]
# 將時間由數(shù)字轉(zhuǎn)為字符串
for i in range(220):
df.iloc[i, 0] = str(df.iloc[i, 0])
# 將字符串轉(zhuǎn)為時間類型的數(shù)據(jù)
df['trade_date'] = pd.to_datetime(df['trade_date'])
# 將時間設(shè)置為索引
df = df.set_index(['trade_date'])
df = df.iloc[:, :]
print(df)
return df
def plot_data(df):
"""
展示數(shù)據(jù)
:param df: 一個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、數(shù)據(jù)樣例的展示
,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,20201110,18.0,18.5,17.93,18.11,17.84,0.27,1.5135,1021062.81,1854142.808 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 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208,000001.SZ,20200117,16.38,16.55,16.35,16.39,16.33,0.06,0.3674,605436.69,995909.007 209,000001.SZ,20200116,16.52,16.57,16.2,16.33,16.52,-0.19,-1.1501,1028104.67,1678888.507 210,000001.SZ,20200115,16.79,16.86,16.45,16.52,16.76,-0.24,-1.432,859439.12,1424889.228 211,000001.SZ,20200114,16.99,17.27,16.76,16.76,16.99,-0.23,-1.3537,1304493.66,2217608.852 212,000001.SZ,20200113,16.75,17.03,16.61,16.99,16.69,0.3,1.7975,872133.36,1468271.683 213,000001.SZ,20200110,16.79,16.81,16.52,16.69,16.79,-0.1,-0.5956,585548.45,975154.818 214,000001.SZ,20200109,16.81,16.93,16.53,16.79,16.66,0.13,0.7803,1031636.65,1725326.806 215,000001.SZ,20200108,17.0,17.05,16.63,16.66,17.15,-0.49,-2.8571,847824.12,1423608.811 216,000001.SZ,20200107,17.13,17.28,16.95,17.15,17.07,0.08,0.4687,728607.56,1247047.135 217,000001.SZ,20200106,17.01,17.34,16.91,17.07,17.18,-0.11,-0.6403,862083.5,1477930.193 218,000001.SZ,20200103,16.94,17.31,16.92,17.18,16.87,0.31,1.8376,1116194.81,1914495.474 219,000001.SZ,20200102,16.65,16.95,16.55,16.87,16.45,0.42,2.5532,1530231.87,2571196.482
四、效果展示
我們采用視頻的形式來進(jìn)行效果的展示；
https://www.bilibili.com/video/BV1RF411q7g2?spm_id_from=333.999.0.0
股票數(shù)據(jù)分析的實(shí)現(xiàn)
以上就是我實(shí)現(xiàn)的股票數(shù)據(jù)分析的可視化的處理的結(jié)果，謝謝大家的閱讀與支持啦。?
到此這篇關(guān)于基于Python實(shí)現(xiàn)股票數(shù)據(jù)分析的可視化的文章就介紹到這了,更多相關(guān)Python股票數(shù)據(jù)分析可視化內(nèi)容請搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！
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