基于A股市场情绪的投资建议

最新推荐文章于 2024-09-23 18:04:04 发布
最新推荐文章于 2024-09-23 18:04:04 发布
阅读量496 收藏 1
点赞数 1
版权声明：本文为博主原创文章，遵循 CC 4.0 BY-SA 版权协议，转载请附上原文出处链接和本声明。
本文链接：https://blog.youkuaiyun.com/dafei178/article/details/125137950
版权

声名！！！

***情绪是检验市场的唯一标准***

***思路可以适配easytrader，可以作量化的特色指标，可以根据市场情绪进行交易***

Statement！！！

***Sentiment is the only test of the market***

***Thoughts can be adapted to easytrader, which can be used as a quantitative feature indicator and can be traded according to market sentiment***

日后会更新一个筛子，大伙保持持续关注！祝大伙在A股兴风作浪！！！

直接贴轮子！

若有报错或者问题可以联系wx： have1000000000000rmb

import pandas as pd
from bs4 import BeautifulSoup
from xml import etree
import json
import jsonpath
from fpdf import FPDF
import requests
import os
import time
import PySimpleGUI as sg
import matplotlib.pyplot as plt
import akshare as ak
from pyecharts import charts
from pyecharts import options as options
import tkinter as tk
root=tk.Tk()
root.title('情绪系统')
root.geometry('600x500')
menumode=tk.Menu(root)
rq_rank=tk.Menu(menumode)
menumode.add_cascade(label='情绪与市场热度',menu=rq_rank)
#A股情绪与市场热度
def rq_rank_china():
    '''
    A股情绪与市场热度
    '''
    url1='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=0.000957,1.600006,1.600313,0.000665,0.000625,0.000151,1.600354,0.002181,0.000595,0.002761,0.000025,1.601127,1.600127,0.000756,0.000868,1.600101,1.600268,0.300750,0.002594,0.002121&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654071182268'
    res1=requests.get(url1)
    text1=res1.text[28:len(res1.text)-2]
    json_text1=json.loads(text1)
    df1=pd.DataFrame(json_text1['data']['diff'])
    url2='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=0.000572,1.600297,0.000722,0.001318,0.000767,0.002561,0.002292,1.601633,0.002432,0.000523,1.600733,0.300059,0.002045,1.600158,0.002189,1.600062,0.000909,1.600960,1.600502,0.002241&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654071562097'
    res2=requests.get(url2)
    text2=res2.text[28:len(res2.text)-2]
    json_text2=json.loads(text2)
    df2=pd.DataFrame(json_text2['data']['diff'])
    url3='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=0.002183,1.601975,0.000716,1.601872,1.600058,1.600418,0.002613,0.002074,1.600550,1.601238,1.603069,0.002060,0.002066,0.002265,1.600026,1.600081,1.600283,0.300014,1.600284,0.002957&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654071777874'
    res3=requests.get(url3)
    text3=res3.text[28:len(res3.text)-2]
    json_text3=json.loads(text3)
    df3=pd.DataFrame(json_text3['data']['diff'])
    url4='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=0.000547,1.600546,0.000029,0.000521,0.002699,0.000524,0.002235,0.000009,1.603097,1.600096,1.605366,0.002400,0.000886,0.000735,1.600686,0.002466,0.000736,1.600238,0.002277,0.001319&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654071925167'
    res4=requests.get(url4)
    text4=res4.text[28:len(res4.text)-2]
    json_text4=json.loads(text4)
    df4=pd.DataFrame(json_text4['data']['diff'])
    url5='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=0.000547,1.600546,0.000029,0.000521,0.002699,0.000524,0.002235,0.000009,1.603097,1.600096,1.605366,0.002400,0.000886,0.000735,1.600686,0.002466,0.000736,1.600238,0.002277,0.001319&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654071925167'
    res5=requests.get(url5)
    text5=res5.text[28:len(res5.text)-2]
    json_text5=json.loads(text5)
    df5=pd.DataFrame(json_text5['data']['diff'])
    df=pd.concat([df1,df2,df3,df4,df5],ignore_index=True)
    columns=['-','last','increase','Quota','Code','Change in ranking from yesterday','name','-','-','-']
    df.columns=columns
    data=df[['name','last','increase','Quota','Code','Change in ranking from yesterday']]
    data.to_excel(r'C:\Users\Administrator\Desktop\建议.xlsx')
    print(data)

def rq_rank_hk():
    '''
   
    '''
    url1='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=116.00700,116.01877,116.03690,116.01810,116.09988,116.00981,116.00175,116.02333,116.01211,116.01024,116.00719,116.01772,116.01919,116.00883,116.02238,116.02318,116.09618,116.00020,116.02899,116.03968&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654072693918'
    res1=requests.get(url1)
    text1=res1.text[28:len(res1.text)-2]
    json_text1=json.loads(text1)
    df1=pd.DataFrame(json_text1['data']['diff'])
    url2='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=116.09939,116.02359,116.00763,116.09992,116.02269,116.06185,116.02137,116.09868,116.02196,116.00493,116.06969,116.06030,116.09888,116.01088,116.03800,116.00941,116.02202,116.00316,116.02048,116.03606&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654072726971'
    res2=requests.get(url2)
    text2=res2.text[28:len(res2.text)-2]
    json_text2=json.loads(text2)
    df2=pd.DataFrame(json_text2['data']['diff'])
    url3='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=116.01171,116.03333,116.02007,116.01138,116.00285,116.06862,116.01093,116.00305,116.06158,116.00388,116.02015,116.00241,116.01951,116.00853,116.09999,116.06186,116.03669,116.00386,116.06618,116.01516&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654072760208'
    res3=requests.get(url3)
    text3=res3.text[28:len(res3.text)-2]
    json_text3=json.loads(text3)
    df3=pd.DataFrame(json_text3['data']['diff'])
    url4='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=116.01347,116.09926,116.02382,116.02380,116.06078,116.02338,116.01177,116.01776,116.01801,116.02331,116.02618,116.02197,116.03323,116.02150,116.00728,116.06160,116.02600,116.00914,116.00902,116.01918&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654072792119'
    res4=requests.get(url4)
    text4=res4.text[28:len(res4.text)-2]
    json_text4=json.loads(text4)
    df4=pd.DataFrame(json_text4['data']['diff'])
    url5='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=116.00460,116.09866,116.02423,116.01658,116.00881,116.00059,116.00189,116.00857,116.02020,116.06690,116.09626,116.01186,116.00489,116.09633,116.00836,116.01898,116.02607,116.01988,116.03996,116.02013&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654072820468'
    res5=requests.get(url5)
    text5=res5.text[28:len(res5.text)-2]
    json_text5=json.loads(text5)
    df5=pd.DataFrame(json_text5['data']['diff'])
    df=pd.concat([df1,df2,df3,df4,df5],ignore_index=True)
    columns=['-','last','increase','Quota','Code','-','name','TOP','low','-']
    df.columns=columns
    data=df[['name','last','increase','Quota','Code','TOP','low']]
    data.to_excel(r'C:\Users\Administrator\Desktop\建议.xlsx')
    print(data)

def rq_rank_us():
    '''
   
    '''
    url1='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=105.AAPL,106.BABA,105.TSLA,105.JD,105.PDD,106.NIO,106.BEKE,106.XPEV,105.LI,106.EDU,106.SQM,105.AMZN,105.BILI,105.BIDU,106.ZTO,106.DIDI,105.FFIE,105.NTES,106.ALB,106.OXY&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654073086913'
    res1=requests.get(url1)
    text1=res1.text[28:len(res1.text)-2]
    json_text1=json.loads(text1)
    df1=pd.DataFrame(json_text1['data']['diff'])
    url2='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=105.NVDA,105.MSFT,105.IQ,105.NEGG,105.FB,106.PFE,106.JKS,105.NDAQ,105.TCOM,105.BGNE,106.MOS,106.MSC,106.PTR,105.GOOGL,106.TSM,105.KC,106.JPM,105.TXMD,106.ACH,105.SVA&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654073114477'
    res2=requests.get(url2)
    text2=res2.text[28:len(res2.text)-2]
    json_text2=json.loads(text2)
    df2=pd.DataFrame(json_text2['data']['diff'])
    url3='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=106.TAL,105.QCOM,106.DAO,106.KO,106.RBLX,106.BRK_A,106.HNP,106.AA,105.REDU,106.DQ,105.AMD,106.SNP,106.RLX,105.NFLX,105.GOOG,105.MOMO,105.SIGA,107.TKAT,105.AVGO,106.TME&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654073328523'
    res3=requests.get(url3)
    text3=res3.text[28:len(res3.text)-2]
    json_text3=json.loads(text3)
    df3=pd.DataFrame(json_text3['data']['diff'])
    url4='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=105.CPOP,106.HUYA,105.INTC,105.BZ,106.YMM,106.BTU,105.IMAB,106.COP,106.GME,106.MS,106.SWN,106.X,106.MRK,106.CVX,106.LAC,106.XOM,105.BNTX,106.BA,105.DADA,106.ATHM&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654073284004'
    res4=requests.get(url4)
    text4=res4.text[28:len(res4.text)-2]
    json_text4=json.loads(text4)
    df4=pd.DataFrame(json_text4['data']['diff'])
    url5='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=106.NKE,105.AIH,105.IFRX,106.TWTR,106.WMT,106.LTHM,106.VIPS,106.DDL,106.BAC,105.MRNA,106.BTCM,106.ZIM,106.HPQ,106.QD,106.CEA,106.JNJ,105.UPC,106.ZH,105.MU,105.FUTU&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654073308450'
    res5=requests.get(url5)
    text5=res5.text[28:len(res5.text)-2]
    json_text5=json.loads(text5)
    df5=pd.DataFrame(json_text5['data']['diff'])
    df=pd.concat([df1,df2,df3,df4,df5],ignore_index=True)
    columns=['-','last','increase','Quota','Code','-','name','TOP','low','-']
    df.columns=columns
    data=df[['name','last','increase','Quota','Code','TOP','low']]
    data.to_excel(r'C:\Users\Administrator\Desktop\建议.xlsx')
    print(data)
rq_rank.add_command(label='A股市场情绪与市场热度',command=rq_rank_china)
rq_rank.add_command(label='港股市场情绪与市场热度',command=rq_rank_hk)
rq_rank.add_command(label='美股市场情绪与市场热度',command=rq_rank_us)
rq_bs_rank=tk.Menu(menumode)
menumode.add_cascade(label='股票情绪',menu=rq_bs_rank)

def rq_bs_rank_china():
    '''

    '''
    url1='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=0.002998,1.688258,1.688329,1.688288,1.688367,1.605005,0.838030,1.688056,1.688511,0.002296,1.688345,1.688718,1.605299,1.603308,1.688197,1.600215,0.300731,0.300516,0.002575,0.300920&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654090163841'
    res1=requests.get(url1)
    text1=res1.text[28:len(res1.text)-2]
    json_text1=json.loads(text1)
    df1=pd.DataFrame(json_text1['data']['diff'])
    url2='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=1.600114,0.300705,0.000584,0.002651,1.603167,0.300795,1.600246,0.002970,0.002868,1.605266,0.300890,0.002819,0.300588,1.603700,1.688302,0.002829,0.300048,0.300610,0.002886,0.002918&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654090187425'
    res2=requests.get(url2)
    text2=res2.text[28:len(res2.text)-2]
    json_text2=json.loads(text2)
    df2=pd.DataFrame(json_text2['data']['diff'])
    url3='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=0.002674,1.688529,0.300580,0.002376,0.002491,1.688148,1.600847,0.003029,0.002147,0.002791,1.600386,0.300742,1.688772,1.688613,0.002785,0.000668,0.002813,0.002859,1.603315,1.688597&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654090210712'
    res3=requests.get(url3)
    text3=res3.text[28:len(res3.text)-2]
    json_text3=json.loads(text3)
    df3=pd.DataFrame(json_text3['data']['diff'])
    url4='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=1.688021,1.603016,0.300710,0.300990,1.688232,1.688277,1.600936,1.688601,0.300813,1.688567,0.002154,1.603987,0.300565,1.603926,1.688162,1.688626,0.002196,0.002264,1.605028,0.002940&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654090235331'
    res4=requests.get(url4)
    text4=res4.text[28:len(res4.text)-2]
    json_text4=json.loads(text4)
    df4=pd.DataFrame(json_text4['data']['diff'])
    url5='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=0.300911,0.300275,0.000576,1.603997,1.688266,1.688058,0.300370,0.002042,0.002598,0.300473,1.603680,0.300480,1.601107,1.603877,1.688676,0.002076,0.300196,1.688320,1.605336,0.300680&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654090268847'
    res5=requests.get(url5)
    text5=res5.text[28:len(res5.text)-2]
    json_text5=json.loads(text5)
    df5=pd.DataFrame(json_text5['data']['diff'])
    df=pd.concat([df1,df2,df3,df4,df5],ignore_index=True)
    columns=['-','last','increase','Quota','Code','Change in ranking from yesterday','name','-','-','-']
    df.columns=columns
    data=df[['name','last','increase','Quota','Code','Change in ranking from yesterday']]
    data.to_excel(r'C:\Users\Administrator\Desktop\建议.xlsx')
    print(data)

def rq_bs_rank_hk():
    '''
 
    '''
    url1='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=116.01180,116.08249,116.01452,116.08239,116.00483,116.01855,116.08148,116.01059,116.08267,116.01663,116.02448,116.01429,116.00718,116.08350,116.02683,116.08613,116.08281,116.00118,116.00088,116.01632&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654087816027'
    res1=requests.get(url1)
    text1=res1.text[28:len(res1.text)-2]
    json_text1=json.loads(text1)
    df1=pd.DataFrame(json_text1['data']['diff'])
    url2='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=116.01699,116.01527,116.01752,116.00289,116.01319,116.00638,116.03395,116.08091,116.08201,116.08181,116.08519,116.01701,116.00062,116.06819,116.00865,116.08326,116.00650,116.01130,116.08430,116.08213&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654087842899'
    res2=requests.get(url2)
    text2=res2.text[28:len(res2.text)-2]
    json_text2=json.loads(text2)
    df2=pd.DataFrame(json_text2['data']['diff'])
    url3='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=116.01705,116.00574,116.01027,116.03638,116.00032,116.00105,116.01460,116.03869,116.01596,116.01780,116.08623,116.01707,116.00051,116.01271,116.00690,116.01062,116.00973,116.01980,116.08428,116.02108&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654087878145'
    res3=requests.get(url3)
    text3=res3.text[28:len(res3.text)-2]
    json_text3=json.loads(text3)
    df3=pd.DataFrame(json_text3['data']['diff'])
    url4='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=116.00157,116.09908,116.01680,116.01416,116.01031,116.01657,116.02111,116.02025,116.01232,116.00110,116.02188,116.00252,116.08297,116.00928,116.01977,116.00687,116.00030,116.08348,116.01329,116.01239&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654087907041'
    res4=requests.get(url4)
    text4=res4.text[28:len(res4.text)-2]
    json_text4=json.loads(text4)
    df4=pd.DataFrame(json_text4['data']['diff'])
    url5='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=116.00052,116.08179,116.03737,116.00174,116.00158,116.00184,116.00899,116.00033,116.08150,116.00239,116.08401,116.00114,116.00247,116.08147,116.01285,116.08229,116.06822,116.00240,116.08205,116.00897&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654087931250'
    res5=requests.get(url5)
    text5=res5.text[28:len(res5.text)-2]
    json_text5=json.loads(text5)
    df5=pd.DataFrame(json_text5['data']['diff'])
    df=pd.concat([df1,df2,df3,df4,df5],ignore_index=True)
    columns=['-','last','increase','Quota','Code','-','name','TOP','low','-']
    df.columns=columns
    data=df[['name','last','increase','Quota','Code','TOP','low']]
    data.to_excel(r'C:\Users\Administrator\Desktop\建议.xlsx')
    print(data)

def rq_bs_rank_us():
    '''
    
    '''
    url1='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=105.AAPL,106.BABA,105.TSLA,105.JD,105.LI,106.NIO,106.XPEV,105.PDD,106.BEKE,106.SQM,105.BIDU,106.EDU,105.AMZN,105.BILI,105.FFIE,105.NEGG,106.ZTO,105.NTES,106.ALB,106.DIDI&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654088423625'
    res1=requests.get(url1)
    text1=res1.text[28:len(res1.text)-2]
    json_text1=json.loads(text1)
    df1=pd.DataFrame(json_text1['data']['diff'])
    url2='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=105.NVDA,105.MSFT,105.FB,106.OXY,105.SVA,105.TCOM,106.PFE,106.JKS,105.IQ,106.PTR,105.GOOGL,105.BGNE,106.MOS,105.NDAQ,106.TSM,106.JPM,106.KO,106.TAL,105.AMD,106.MSC&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654088449840'
    res2=requests.get(url2)
    text2=res2.text[28:len(res2.text)-2]
    json_text2=json.loads(text2)
    df2=pd.DataFrame(json_text2['data']['diff'])
    url3='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=105.KC,106.RLX,105.GOOG,107.TKAT,105.TXMD,105.WB,106.ACH,106.DQ,106.COP,106.AA,105.SIGA,105.QCOM,106.RBLX,106.SWN,106.CVX,106.BRK_A,105.NFLX,106.XOM,106.TME,105.BNTX&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654088479035'
    res3=requests.get(url3)
    text3=res3.text[28:len(res3.text)-2]
    json_text3=json.loads(text3)
    df3=pd.DataFrame(json_text3['data']['diff'])
    url4='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=106.GME,106.CEA,106.LAC,106.LTHM,106.SNP,106.BA,106.BTU,105.IMAB,106.YMM,105.INTC,106.HNP,105.AVGO,105.MOMO,105.MRNA,105.REDU,106.TWTR,106.DAO,106.MRK,106.WMT,105.BZ&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654088513403'
    res4=requests.get(url4)
    text4=res4.text[28:len(res4.text)-2]
    json_text4=json.loads(text4)
    df4=pd.DataFrame(json_text4['data']['diff'])
    url5='https://push2.eastmoney.com/api/qt/ulist.np/get?fltt=2&np=3&ut=a79f54e3d4c8d44e494efb8f748db291&invt=2&secids=105.COST,106.ATHM,106.NKE,106.GS,106.BAC,105.IFRX,106.DDL,105.CPOP,106.CRM,105.MU,106.JNJ,105.AIH,106.X,105.DADA,106.BTCM,106.HUYA,106.VIPS,106.ZIM,105.MF,106.FMC&fields=f1,f2,f3,f4,f12,f13,f14,f152,f15,f16&cb=qa_wap_jsonpCB1654088534731'
    res5=requests.get(url5)
    text5=res5.text[28:len(res5.text)-2]
    json_text5=json.loads(text5)
    df5=pd.DataFrame(json_text5['data']['diff'])
    df=pd.concat([df1,df2,df3,df4,df5],ignore_index=True)
    columns=['-','last','increase','Quota','Code','-','name','TOP','low','-']
    df.columns=columns
    data=df[['name','last','increase','Quota','Code','TOP','low']]
    data.to_excel(r'C:\Users\Administrator\Desktop\建议.xlsx')
    print(data)
rq_bs_rank.add_command(label='A股情绪位次',command=rq_bs_rank_china)
rq_bs_rank.add_command(label='港股情绪位次',command=rq_bs_rank_hk)
rq_bs_rank.add_command(label='美股情绪位次',command=rq_bs_rank_us)
ths_rq_rank=tk.Menu(menumode)
menumode.add_cascade(label='情绪与市场热度',menu=ths_rq_rank)

def ths_wc_rank_stock():
    '''

    '''
    date=sg.popup_get_text('输入时间比如20220605')
    df=ak.stock_hot_rank_wc(date=date)
    df.to_excel(r'C:\Users\Administrator\Desktop\建议.xlsx')
    print(df)
ths_rq_rank.add_command(label='情绪与市场热度',command=ths_wc_rank_stock)
root['menu']=menumode
root.mainloop()

有效前沿在A股市场的应用：量化价值投资案例研究
AI天才研究院
04-20 874
本研究的目的在于探索有效前沿理论在A股市场量化价值投资中的实际应用。通过对历史数据的分析和建模，构建基于有效前沿的投资组合，以实现风险调整后的收益最大化。研究范围涵盖了A股市场的主要板块，选取了具有代表性的股票进行分析和组合构建。旨在为投资者提供一种科学、系统的量化投资方法，帮助他们在复杂多变的A股市场中做出更明智的投资决策。本文首先介绍有效前沿的背景知识，包括其基本概念、在投资领域的重要性等。然后详细阐述有效前沿的核心概念与联系，通过图表和流程图进行直观展示。
东方财富网 数据访问接口及字段说明
雨中山草
08-12 8177
东方财富网 数据访问接口和字段说明
股票接口
飞鱼计划
03-25 2491
同花顺 http://stockpage.10jqka.com.cn/1A0001/quote/header/
股票K线5,15,30,60分钟数据接口
热门推荐
我就是我不一样的美男子！
01-08 3万+
1. 新浪接口 https://quotes.sina.cn/cn/api/json_v2.php/CN_MarketDataService.getKLineData?symbol=sh000300&scale=30&ma=no&datalen=1023 symnol = 股票代码 scale = 5,15,30,60 datalen = 获取数据长度，最大1023 ...
python爬虫之入门级实战实例（东方财富人气top100、汉服荟视频下载）
tiebanggg的博客
03-07 8873
文章目录前言一、东方财富人气top1001.需求说明2.数据爬取①首页数据② 实时趋势（排名）③历史趋势（排名）二、汉服荟小姐姐主页的视频爬取1.需求说明2. 数据爬取总结 前言 最近时间排不过来（在和大佬学习研究JS），所以本次更新内容较为简单，有两个站进行讲解示例。文章写的不好，py写的也不好，请大佬们看到的飘过~见笑了见笑了。本项目仅用于交流学习，若侵犯到贵公司权益请联系邮箱229456906@qq.com第一时间删除。读者请切忌用于一切非法途径，否则后果自行承担！ 项目一（东方财富人气top10
Mac 版本看基软件（摸鱼专用）
岁月无锋,抽走留痕
03-16 1667
Mac 版本看基软件（摸鱼专用） 前段时间基金市场比较火热，市场上的基金软件在盘中都只能给出一个当前基金的估算涨幅，没有给出今日自己的基金能收益（kuisun????）多少，本人又是个急性子，于是自己就想办法弄出来这样一个软件；先看图 数据来源 数据来源于这里，而且本人也是第一次开发MacOS 软件，也是参考了这位大佬的代码，这里非常感谢大佬 主页代码 主要功能就是首页展示的模块，具体代码如下 - (void)viewDidLoad { [super viewDidLoad]; // D
A股量化风格报告：市场情绪趋弱，价值蓝筹风格或持续-0505-广发证券-22页.pdf
07-26
本报告为A股量化风格分析，重点关注了市场情绪的变化以及对价值蓝筹风格的持续性预测。报告指出，四月份以来，尽管经济数据有所好转，如社会融资规模（社融）和GDP同比增长超出预期，但CPI（消费者价格指数）的上涨...
2020年1月A股市场分析及投资建议：初春策略更新
该报告对A股市场在当月的走势和投资配置提出了专业的观点和建议。根据标题和描述，我们可以推断报告重点可能包括以下几个方面的知识点： 1. A股市场的总体走势分析：报告可能会从宏观经济、市场情绪、政策导向等多...
下半年A股投资策略：长征-0709-申万宏源-119页.pdf
07-24
2019年下半年的中国A股市场面临着诸多挑战与机遇，而申万宏源发布的《下半年A股投资策略：长征》报告为我们提供了一个深入分析和全面展望的视角。这份报告详细地梳理了即将到来的市场行情，展望了中长期的投资方向，...
如何通过python多线程抓取所有东方财富股票信息
weixin_42430074的博客
07-08 1万+
因为本人暑假想搞一个股票筛选软件自己玩玩，所有开始学习python语言，算是边学边应用吧，有错的地方请大佬不吝啬指导 1.首页去东方财富股票列表展示按F12查看Network请求的数据 可以找到这串请求 http://64.push2.eastmoney.com/api/qt/clist/get?cb=jQuery1124001651461580406255_1561992580766&am...
爬虫学习笔记（用python爬取东方财富网实验）
EddyPaul的博客
10-20 8663
参考文章以及视频：(11条消息) 爬虫实战 | 爬取东方财富网股票数据_简说Python的博客-优快云博客、手把手教你从东方财富网上获取股票数据_哔哩哔哩_bilibili、【Python爬虫案例】如何用Python爬取股市数据，并进行数据可视化_哔哩哔哩_bilibili、python爬虫爬取豆瓣网评分最高的250部电影_哔哩哔哩_bilibili 分为3个步骤： 1. 爬取网页 2.逐一解析数据 3. 保存网页 1. 爬取网页 打开网站，找到需要的数据。 行情中心：国内快捷全面的股票、基金、
东方财富获取人气/人气飙升数据
最新发布
weixin_42661541的博客
09-23 1217
【代码】东方财富获取人气/人气飙升数据。
python爬虫 | 异步加载网页的链接问题 | 小白速成篇
mldl_的博客
06-16 2145
今天刚学，都是个人理解，不准确或者错误的地方，跪求大佬轻喷，┭┮﹏┭┮ 好像写的很乱，大概就是，跟着我说的做一遍，应该会有一个基本的了解~~ 前言：     python课最后的实验报告是要爬取一个异步加载网页的数据，然后，很多人就懵了，点下一页，URL不变。对从豆瓣 top250 开始学爬虫的小白及其不友好，骂骂咧咧的打开B站，这代码真白，呸，这代码真妙。     我看了大佬的视频，大概要几个小时可以全部看完，不喜欢看文字.
python爬虫实现股票数据存储_Python 爬虫 | 股票数据的获取
weixin_39911998的博客
02-04 799
最近股市有了一股牛市的味道，我也来蹭波热度，这节我们将爬取股票数据，股票数据在网上很容易找到，这里以某方财富网为例目标：爬取股票数据并存储进数据库中(这里以MongoDB为例)目标网站(base64编码)：aHR0cDovL3F1b3RlLmVhc3Rtb25leS5jb20vY2VudGVyL2dyaWRsaXN0Lmh0bWwjaHNfYV9ib2FyZA==目录：1、环境2、分析网站3、实现...
用python爬取股票网
小胖墩
12-27 1833
找到对应的json文件 http://50.push2.eastmoney.com/api/qt/clist/get?cb=jQuery1124002312725213714928_1577232380831&pn=’+str(i)+’&pz=20&po=1&np=1&ut=bd1d9ddb04089700cf9c27f6f7426281&fltt=...
量化投资—数据篇
一点一滴编程路
01-15 3572
系列文章目录 1：量化投资—数据篇 文章目录 系列文章目录 前言 一、pandas是什么？ 二、使用步骤 1.引入库 2.读入数据 总结 前言 做量化，首先准备数据，日线，分时，资金，财务等。目前市面上的库，比如tushare，但是这哥们是收费的，流量也有限制，使用起来很不便。 一、常用的金融量化数据 1.tushare 优点：数据稳定，免费开源。 缺点：部分关键数据需要积分，积分不容易获取。 2.joinquant ...
QTWebkit中的webkit/qt/api文档
weixin_30920597的博客
03-05 209
QTWebkit中的webkit/qt/api文档 概述 该目录下的文件均以前前缀qweb开头，主要用于支持前端的实现。核心的类为：qwebview、qwebpage、qwebframe等三个类。这三者间的关系由图描述。（注：图来自QTWebkit自带文档） 图1 qwebview,qwebpage,qwebframe三者关系 View是一个逻辑视图，真正的工作分别由page和f...
评论
添加红包

请填写红包祝福语或标题

个

红包个数最小为10个

元

红包金额最低5元

当前余额3.43元 前往充值 >
需支付：10.00元
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

大飞178

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付：¥1
获取中
扫码支付

您的余额不足，请更换扫码支付或充值

打赏作者

实付元
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明：

1.余额是钱包充值的虚拟货币，按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载，可以购买VIP、付费专栏及课程。

余额充值