每日股市大盘自动复盘（基于聚宽量化投资平台）

直接上代码，代码里需要用到聚宽平台的jqdata，可在聚宽研究环境中运行。

import numpy as np
import datetime
import jqdata
from jqdata import *
import pandas as pd
import matplotlib.pyplot as plt
import json           #字符转换
matplotlib.rcParams['axes.unicode_minus']=False  #图像中负号显示不正常
import matplotlib.ticker as mtk###########可设定参数#############
# 设定复盘日期，-1为今天，-2为昨天，最多可看至十天前
trade_day = -1
##################################
#获取日期相关信息
def get_date(date,daynumb):startday = list(jqdata.get_trade_days(end_date=date,count=daynumb))[0]now1 = date.strftime("%Y-%m-%d %H:%M")now2 = datetime.datetime.strptime(now1, "%Y-%m-%d %H:%M")ne = now2 + datetime.timedelta(hours = 15) now3 = startday.strftime("%Y-%m-%d %H:%M")now4 = datetime.datetime.strptime(now3, "%Y-%m-%d %H:%M")ns = now4 + datetime.timedelta(hours = 9)npr = list(jqdata.get_trade_days(end_date=date,count=daynumb+1))[0]return npr,ns,ne#获取各指数日内日开盘价、收盘价、涨幅、振幅
def indexinfo(index,npr,ns,ne):Data_min = get_price(index,frequency='1m',start_date= ns, end_date= ne,fields=['open', 'close', 'high', 'low', 'volume'],fq='pre')Data_day = get_price(index,frequency='1d',end_date= today,fields=['open', 'close', 'high', 'low', 'volume','pre_close'],fq='pre',count = 1)open_price = Data_min['open'][0]close_price =  Data_min['close'][-1]rise = (Data_day['close'][0]/Data_day['pre_close'][0]-1)*100amplitude = ((Data_day['high'][0]-Data_day['low'][0])/Data_day['close'][0])*100return open_price,close_price,rise,amplitude# 指数文字报告
def index_report(today, indexlist, labels):print ("股票每日报告：",today,'\n')for i in range(len(indexlist)):open_price,close_price,rise,amplitude =indexinfo(indexlist[i],npr,ns1,ne1)if rise > 0:print("\033[0;31m %s \033[0m"%labels[i],end='') print("开盘报%s点，收盘%s点，上涨%.2f%%，振幅为%.2f%%；\n"%(open_price,close_price,rise,amplitude))else:print("\033[0;32m %s \033[0m"%labels[i],end='') print("开盘报%s点，收盘%s点，下跌%.2f%%，振幅为%.2f%%；\n"%(open_price,close_price,-rise,amplitude))
# 指数画图
def index_drawing(indexlist):fig1, ax4 = plt.subplots(figsize=(12,6))fig2, ax5 = plt.subplots(figsize=(12,3))scale_ls = np.array(range(len(indexlist)))+1def format_date(x, pos=None):        idx = np.clip(int(x+0.5), 0, Data.shape[0]-1)return Data['time'].iat[idx]for i in range(len(indexlist)):code0 = indexlist[i]Datap = get_price(code0,frequency='1d',end_date=npr,fields=['close'],skip_paused=False,fq='pre',count = 1)Data = get_price(code0,frequency='1m',start_date= ns1, end_date=ne1,fields=['open', 'close', 'high', 'low', 'volume'],fq='pre')Data['time']  = list(Data.index)xax = np.arange(Data.shape[0])val = ((Data['close']-Datap['close'][0])/Datap['close'][0])*100ax4.plot(xax, val, label= labels[i])if val[-1]<0:ax5.bar(i+1,val[-1],width =0.2,color = 'lightgreen')elif val[-1]>0:ax5.bar(i+1,val[-1],width =0.2,color = 'pink')xmajorLocator = mtk.MultipleLocator(30)    ax4.xaxis.set_major_locator(xmajorLocator)ax4.xaxis.set_major_formatter(mtk.FuncFormatter(format_date))   fmt='%.2f%%'  yticks = mtk.FormatStrFormatter(fmt)        ax4.yaxis.set_major_formatter(yticks)ax4.grid(True)fig1.autofmt_xdate()ax4.legend(loc='upper right',ncol=4)ax4.set_xlabel('时间',fontsize=15)ax4.set_ylabel('收益率',fontsize=15)ax4.set_title('各大指数每日复盘',fontsize=15);ax5.grid(True)plt.xticks(scale_ls,labels,fontsize=15)ax5.set_ylabel('收益率',fontsize=15)ax5.yaxis.set_major_formatter(yticks)plt.show()today = list(jqdata.get_trade_days(end_date=datetime.datetime.now().date(),count=10))[trade_day]
# 交易开盘、收盘等时间相关日期
npr,ns1,ne1 = get_date(today,1)
yesterday = list(jqdata.get_trade_days(end_date=datetime.datetime.now().date(),count=3))[0]
npr2,ns2,ne2 = get_date(yesterday,1)
#常用指数及代码
indexlist= ['000001.XSHG','399001.XSHE','000016.XSHG','000300.XSHG','000905.XSHG','000906.XSHG','399005.XSHE','399006.XSHE']
labels   = ['上证指数','深证成指','上证50', '沪深300', '中证500', '中证800','中小板','创业板指']

index_report(today, indexlist, labels)
index_drawing(indexlist)

运行结果如下：
在这里插入图片描述

（下面这段代码是刚入门的时候写的。。。太烂啦有兴趣自己优化一下。。。）

#统计全A股信息
allstock = get_all_securities(types=['stock'])finalrep = []
rise = 0
fall = 0risestop = []
fallstop = []
risestopprice = []
fallstopprice = []
risestopamp = []
fallstopamp = []
risestoptype = []
fallstoptype = []a0 = 0
a1 = 0
a2 = 0
a3 = 0
a4 = 0
a5 = 0
a6 = 0
a7 = 0
a8 = 0
a9 = 0
fa0 = 0
fa1 = 0
fa2 = 0
fa3 = 0
fa4 = 0
fa5 = 0
fa6 = 0
fa7 = 0
fa8 = 0
fa9 = 0for i in range(len(allstock)):code = allstock.index[i]Datap = get_price(code,frequency='1d',end_date=npr,fields=['close'],skip_paused=False,fq='pre',count = 1)Data = get_price(code,frequency='1m',start_date= ns1, end_date= ne1,fields=['open', 'close', 'high', 'low', 'volume','high_limit','low_limit'],fq='pre')rep = (Data['close']-Datap['close'][0])/Datap['close'][0]if rep[-1] > 0:rise+=1if rep[-1] < 0:fall+=1if 0<rep[-1]<=0.01:a0+=1if 0.01<rep[-1]<=0.02:a1+=1if 0.02<rep[-1]<=0.03:a2+=1if 0.03<rep[-1]<=0.04:a3+=1if 0.04<rep[-1]<=0.05:a4+=1if 0.05<rep[-1]<=0.06:a5+=1if 0.06<rep[-1]<=0.07:a6+=1if 0.07<rep[-1]<=0.08:a7+=1if 0.08<rep[-1]<=0.09:a8+=1if 0.09<rep[-1]:a9+=1if -0.01<=rep[-1]<0:fa0+=1if -0.02<=rep[-1]<-0.01:fa1+=1if -0.03<=rep[-1]<-0.02:fa2+=1if -0.04<=rep[-1]<-0.03:fa3+=1if -0.05<=rep[-1]<-0.04:fa4+=1if -0.06<=rep[-1]<-0.05:fa5+=1if -0.07<=rep[-1]<-0.06:fa6+=1if -0.08<=rep[-1]<-0.07:fa7+=1if -0.09<=rep[-1]<-0.08:fa8+=1if rep[-1]<-0.09:fa9+=1if round(Data['close'][-1],2) == round(Datap['close'][0]*1.1,2):risestop.append(code)risestopprice.append(Data['close'][-1])x = round(Data['close'][-1]/Datap['close'][0],4)-1baix = '%.2f%%' % (x * 100)risestopamp.append(baix)count = 0for element in Data['close']:if element == Data['close'][-1]:count+=1if count == len(Data['close']):risestoptype.append(1)else:risestoptype.append(0)if round(Data['close'][-1],2) == round(Datap['close'][0]*0.9,2):fallstop.append(code)fallstopprice.append(Data['close'][-1])x = round(Data['close'][-1]/Datap['close'][0],4)-1baix = '%.2f%%' % (x * 100)fallstopamp.append(baix)count = 0for element in Data['close']:if element == Data['close'][-1]:count+=1if count == len(Data['close']):fallstoptype.append(1)else:fallstoptype.append(0)if get_extras('is_st', code, start_date=ns1, end_date=ne1).values:if round(Data['close'][-1],2) == round(Datap['close'][0]*1.05,2):risestop.append(code)risestopprice.append(Data['close'][-1])x = round(Data['close'][-1]/Datap['close'][0],4)-1baix = '%.2f%%' % (x * 100)risestopamp.append(baix)count = 0for element in Data['close']:if element == Data['close'][-1]:count+=1if count == len(Data['close']):risestoptype.append(1)else:risestoptype.append(0)if round(Data['close'][-1],2) == round(Datap['close'][0]*0.95,2):fallstop.append(code)fallstopprice.append(Data['close'][-1])x = round(Data['close'][-1]/Datap['close'][0],4)-1baix = '%.2f%%' % (x * 100)fallstopamp.append(baix)count = 0for element in Data['close']:if element == Data['close'][-1]:count+=1if count == len(Data['close']):fallstoptype.append(1)else:fallstoptype.append(0)finalrep.append(float('%.3f' % rep[-1]))risestopinfo = pd.DataFrame(columns=('股票代码', '股票名称', '收盘价','涨跌幅','涨停类型(1为一字涨停，0为自然涨停)'))
risestopinfo['股票代码'] = risestop
risestopinfo['股票名称'] = allstock.display_name[risestop].values
risestopinfo['收盘价'] = risestopprice
risestopinfo['涨跌幅'] = risestopamp
risestopinfo['涨停类型(1为一字涨停，0为自然涨停)'] = risestoptypefallstopinfo = pd.DataFrame(columns=('股票代码', '股票名称', '收盘价','涨跌幅','跌停类型(1为一字跌停，0为自然跌停)'))
fallstopinfo['股票代码'] = fallstop
fallstopinfo['股票名称'] = allstock.display_name[fallstop].values
fallstopinfo['收盘价'] = fallstopprice
fallstopinfo['涨跌幅'] = fallstopamp
fallstopinfo['跌停类型(1为一字跌停，0为自然跌停)'] = fallstoptype#统计一字跌停、自然跌停个数
zi = 0
yi = 0
for element in risestopinfo['涨停类型(1为一字涨停，0为自然涨停)']:if element == 0 :zi +=1if element == 1:yi += 1zi1 = 0
yi1 = 0
for element in fallstopinfo['跌停类型(1为一字跌停，0为自然跌停)']:if element == 0 :zi1 +=1if element == 1:yi1 += 1

#沪深两市十日成交额及成交量
tenday = list(jqdata.get_trade_days(end_date=datetime.datetime.now().date(),count=11))data = get_price('000001.XSHG', start_date=tenday[0], end_date=tenday[-2], frequency='daily', fields=['volume','money'])
data2 = get_price('399001.XSHE', start_date=tenday[0], end_date=tenday[-2], frequency='daily', fields=['volume','money'])
dvolume = data['volume']
dmoney = data['money']
dvolume2 = data2['volume']
dmoney2 = data2['money']
bar_width = 0.35xax = np.arange(data.shape[0])fig = plt.figure(figsize=(12,6))ax1 = fig.add_subplot(111)
ax1.bar(xax+(bar_width)/2, dvolume/100000000,bar_width,color = 'lightgreen',label="沪市")
ax1.bar(xax-(bar_width)/2, dvolume2/100000000,bar_width,color = 'pink',label="深市")
ax1.set_ylabel("成交量（亿股）",fontsize=15)
ax1.set_title("沪深两市十日成交额及成交量",fontsize=15)
ax1.set_xlabel("日期",fontsize=15)
ax1.legend(loc='upper right')ax2 = ax1.twinx()
ax2.plot(xax, dmoney/100000000,color='green',marker='o')
ax2.plot(xax, dmoney2/100000000,color='red',marker='o')
ax2.set_ylim([0, 3000])
ax2.set_ylabel("成交额（亿元）",fontsize=15)fig.autofmt_xdate()
plt.xticks(xax,data.index.strftime("%Y-%m-%d"),fontsize=15)
plt.show()#沪深两市中，涨跌股票
drawlist=[]
drawlistlabels = ['上涨','下跌']
colorlabel = ['pink','lightgreen']
drawlist.append(rise)
drawlist.append(fall)
plt.pie(drawlist,autopct='%1.1f%%',labels=drawlistlabels,colors= colorlabel,startangle=50,explode=[0.05,0.05],shadow = True)
plt.axis('equal')
plt.title('沪深两市,股票涨跌比',fontsize=15)
plt.show()#个股涨跌数量统计
liststock = [fa9,fa8,fa7,fa6,fa5,fa4,fa3,fa2,fa1,fa0,a0,a1,a2,a3,a4,a5,a6,a7,a8,a9]
liststockx = []
zdf = ['<-9%','-9%~-8%','-8%~-7%','-7%~-6%','-6%~-5%','-5%~-4%','-4%~-3%','-3%~-2%','-2%~-1%','-1%~0','0~1%','1%~2%','2%~3%','3%~4%','4%~5%','5%~6%','6%~7%','7%~8%','8%~9%','>9%']
for i in range(len(liststock)):liststockx.append(i)
fig, ax2 = plt.subplots(figsize=(12,6))for i in range(0,10):ax2.bar(liststockx[i], liststock[i],bar_width,color = 'lightgreen')ax2.text(liststockx[i]-0.1, liststock[i]+10, liststock[i])for i in range(10,20):ax2.bar(liststockx[i], liststock[i],bar_width,color = 'pink')ax2.text(liststockx[i]-0.1, liststock[i]+10, liststock[i])fig.autofmt_xdate()
ax2.set_ylabel("股票数量",fontsize=15)
ax2.set_title("个股涨跌数量统计",fontsize=15)
ax2.set_xlabel("涨跌幅",fontsize=15)
plt.xticks(liststockx,zdf,fontsize=15)
plt.show()

运行结果如下：
在这里插入图片描述

from collections import Counter# 展示股票池行业分布
def get_stock_industry_distribution(stock_pool, rise=True):hy_name_list = []for i in range(len(stock_pool)):try:hy_name = get_industry(stock_pool[i])[stock_pool[i]]['jq_l1']['industry_name']hy_name_list.append(hy_name)except:passprint(hy_name_list)result=Counter(hy_name_list)result = np.array(sorted(result.items(), key=lambda d:d[0]))fig = plt.figure(figsize=(12,6))plt.pie(result[:,1],autopct='%1.1f%%',labels=result[:,0])plt.axis('equal')plt.show()print ("沪深两市中，股票涨停%d只，跌停%d只。"%(len(risestop),len(fallstop)))
print ("涨停股票中，一字涨停%d只，自然涨停%d只。\n跌停股票中，一字跌停%d只，自然跌停%d只。"%(yi,zi,yi1,zi1))print('\n')
print('\033[0;31m涨停股票\033[0m',end='')
print('行业分布：')
get_stock_industry_distribution(risestopinfo['股票代码'])
print('\033[0;32m跌停股票\033[0m',end='')
print('行业分布：')
get_stock_industry_distribution(fallstopinfo['股票代码'])