if 1: from oneclass.onedb import Onedb from oneclass.onedata import Onedata import os,math,sys,time,re,json,datetime,requests,warnings,logging,xlrd,traceback import numpy as np import pandas as pd import tushare as ts from sqlalchemy import create_engine import sqlalchemy #初始化数据库连接，使用pymysql模块 import pymysql import multiprocessing as mp if 1: ts.set_token('8f6e67ec8a48922bbb531b95687625d99ee263940a07561ae95f4648') # Tushare Pro pd.set_option('mode.chained_assignment', None) warnings.filterwarnings("ignore") durtime,durting1,FocusPeriod,uihandle,plock,showtype,havelistseq=0.001,0.5,0,0,0,1,1 stockcode,mysqlhost="300027.SS","119.45.95.223" xgresult,qsresult,allstocklist=pd.DataFrame(),pd.DataFrame(),pd.DataFrame() class Kdata(): '''K线类 技术分析评分机制''' def __init__(self,stockcode,showtype,havelistseq,parent=None): super().__init__() #没有父类,是否需要 self.allstocklist=pd.DataFrame() #不需要被重复更新的数据 self.init_parameter() #需要被重复更新的数据 self.onedb=Onedb() self.onedata=Onedata(1) '''自动运行''' def init_parameter(self): '''定义参数''' self.hp=0.0 self.ttlup=0.0 self.ttldown=0.0 self.mstatus=0 self.period = ['month', 'week', 'day', 'm30', 'm5', 'm1'] self.periodab = ['m', 'w', 'd', 't', 'f', 'o'] self.p2ab={'month':'m','week':'w','day':'d','m30':'t','m5':'f','m1':'o'} self.QSKJ = [100, 50, 20, 10, 5, 2] # 可以从config.ini设置 self.diffbyp ={"month":0.08,"week": 0.04,"day":0.02 ,"m30":0.01 ,"m5":0.005 ,"m1":0.0025 } self.space ={"month": 1,"week":0.2,"day":0.08,"m30":0.04,"m5":0.02,"m1":0.01} self.QS ={"month":"","week":"","day":"","m30":"","m5":"","m1":""} self.JG ={"month":"","week":"","day":"","m30":"","m5":"","m1":""} self.keypoint={"QS":0,"NDG":0,"NKL":0,"DD":0,"Kai":0,"GG":0,"Luo":0,"CHL1":0,"DDCGorGGCD1":0,"CHL":0,"DDCGorGGCD":0,"DDCGorGGCD1ma30":0,"DDCGorGGCDma30":0,"GGday":0,"DDday":0} self.kdata_m = pd.DataFrame() # K线数据库 self.kdata_w = pd.DataFrame() self.kdata_d = pd.DataFrame() self.kdata_t = pd.DataFrame() self.kdata_f = pd.DataFrame() self.kdata_o = pd.DataFrame() #趋势结果 - to Mysql self.QS_m = {} self.QS_w = {} self.QS_d = {} self.QS_t = {} self.QS_f = {} self.QS_o = {} #结构结果 - to Mysql self.JG_m = {} self.JG_w = {} self.JG_d = {} self.JG_t = {} self.JG_f = {} self.JG_o = {} #??? self.all_YLZC = {} self.PnS = {} #??? self.pns=pd.DataFrame() self.pnsttl=pd.DataFrame() self.newpnsttl=pd.DataFrame() self.refreshK_timeinter=10000 self.stockcode = stockcode # 股票代码 300027.S self.showtype=showtype # 显示什么内容 self.havelistseq=havelistseq self.sig=1 # 信号 self.havelist=pd.DataFrame() self.xgresult=pd.DataFrame() self.qsresult=pd.DataFrame() def run(self): self.whole_process() def whole_process(self): self.get_div() #取除权数据 self.get_K_all() #下载所有周期K线数据 self.cal_ma_all() #计算所有周期ma self.cal_qs_all() #计算所有周期的趋势和结构数据 def thread_it(self, func, *args): # '''将函数放入线程中执行''' #有两个参数 1，函数名称 func 2,函数变量 **args task = threading.Thread(target=func, args=args) # 创建线程 task.setDaemon(True) # 守护线程 task.start() def xg_all_mp(self): #计算所有股票 starttime=time.time() self.xgresult=pd.DataFrame() self.qsresult=pd.DataFrame() corelist=[0] pro = ts.pro_api() stocklist = pro.query('stock_basic', exchange='', list_status='L') stocklist = pd.concat([stocklist, pd.DataFrame([{'ts_code': "000001.SH", 'name': "上证综指"}])], ignore_index=True) stocklist = pd.concat([stocklist, pd.DataFrame([{'ts_code': "399006.SZ", 'name': "创业板指"}])], ignore_index=True) stocklist = pd.concat([stocklist, pd.DataFrame([{'ts_code': "399001.SZ", 'name': "深圳成指"}])], ignore_index=True) self.allstocklist=stocklist#[-1600:] print(self.allstocklist) #stocklist = pd.read_csv(r"./csv/2022.csv") stockqty=self.allstocklist.shape[0] qtyperjob=100 coreqty=math.ceil(stockqty/qtyperjob) for i in range(1,coreqty): corelist.append(i*100) print("XXXX corelist",corelist) threadqty=16 pool=mp.Pool(threadqty) qtyperthread=math.ceil(stockqty/threadqty) print("qtyperthread",qtyperthread) #corelist=[0,400,800,1200,1600,2000,2400,2800,3200,3600,4000,4400] result=pool.map(self.xg_all_for_mp,corelist) a,b=zip(*result) for i in range(len(a)): self.xgresult=pd.concat([self.xgresult, a[i]], ignore_index=True) self.qsresult=pd.concat([self.qsresult, b[i]], ignore_index=True) self.xgresult = self.xgresult.replace([np.inf, -np.inf], 0) self.qsresult = self.qsresult.replace([np.inf, -np.inf], 0) onedb=Onedb() onedb.del_table("xgall") onedb.del_table("qsall") onedb.into_table(self.xgresult,"xgall","stockcode",9) onedb.into_table(self.qsresult,"qsall","stockcode",9) print("TTL time:", time.time()-starttime) def xg_all_for_mp(self,n): xgresult=pd.DataFrame() qsresult=pd.DataFrame() stocklist = self.allstocklist stocklist=stocklist[n:n+100] print("stock qty =", stocklist.shape[0]) i=1 for index,row in stocklist.iterrows(): self.stockcode=row['ts_code'] print(i,self.stockcode) if self.stockcode[-3:]==".BJ": self.onedata=Onedata(0) else: self.onedata=Onedata(1) ''' self.get_div() try: self.get_K_D() #self.get_K_D_csv() except: continue if self.kdata_d.shape[0]==0: continue #self.kdata_d=self.wash_K_div(self.kdata_d) #self.get_K_MW() self.get_K_w() self.get_K_m() self.kdata_d=self.kdata_d[-300:] #缩减日K线数量以加速计算 try: self.kdata_t=self.get_K_TFO('m30') #得到30分钟数据 #self.get_K_T_csv() self.kdata_t=self.wash_K_div(self.kdata_t) except: continue try: self.kdata_f=self.get_K_TFO('m5') #得到5分钟数据 #self.get_K_F_csv() self.kdata_f=self.wash_K_div(self.kdata_f) except: continue try: self.kdata_o=self.get_K_TFO('m1') #得到1分钟数据 #self.get_K_O_csv() self.kdata_o=self.wash_K_div(self.kdata_o) except: continue ''' try: self.kdata_o,self.kdata_f,self.kdata_t,self.kdata_d,self.kdata_w,self.kdata_m=self.onedata.k(self.stockcode,"all") except: continue #only cal ma self.cal_ma_all() ''' self.kdata_m = self.cal_ma_K(self.kdata_m) self.kdata_w = self.cal_ma_K(self.kdata_w) self.kdata_d = self.cal_ma_K(self.kdata_d) self.kdata_t = self.cal_ma_K(self.kdata_t) self.kdata_f = self.cal_ma_K(self.kdata_f) self.kdata_o = self.cal_ma_K(self.kdata_o) ''' #only cal QS self.kdata_m,self.QS_m,self.JG_m = self.cal_qs(self.kdata_m, self.stockcode, 'month') self.kdata_w,self.QS_w,self.JG_w = self.cal_qs(self.kdata_w, self.stockcode, 'week') self.kdata_d,self.QS_d,self.JG_d = self.cal_qs(self.kdata_d, self.stockcode, 'day') self.kdata_t,self.QS_t,self.JG_t = self.cal_qs(self.kdata_t, self.stockcode, 'm30') self.kdata_f,self.QS_f,self.JG_f = self.cal_qs(self.kdata_f, self.stockcode, 'm5') self.kdata_o,self.QS_o,self.JG_o = self.cal_qs(self.kdata_o, self.stockcode, 'm1') print(self.QS['month'],self.QS['week'],self.QS['day'],self.QS['m30'],self.QS['m5'],self.QS['m1']) if 1: #try: tmp={'scode':self.stockcode[0:6],'stockcode':self.stockcode,'stockname':row['name'],'time':self.QS_t['reviseTime'],'c':self.QS_t['C'],\ 'QSm':self.QS['month'],'QSw':self.QS['week'],'QSd':self.QS['day'],'QSt':self.QS['m30'],'QSf':self.QS['m5'],'QSo':self.QS['m1'],\ 'ma30m':self.QS_m['ma30'],'ma30w':self.QS_w['ma30'],'ma30d':self.QS_d['ma30'],'ma30t':self.QS_t['ma30'],'ma30f':self.QS_f['ma30'],'ma30o':self.QS_o['ma30'],\ 'ma10m':self.QS_m['ma10'],'ma10w':self.QS_w['ma10'],'ma10d':self.QS_d['ma10'],'ma10t':self.QS_t['ma10'],'ma10f':self.QS_f['ma10'],'ma10o':self.QS_o['ma10'],\ 'spacePm':self.QS_m['spaceP'],'profitPm':self.QS_m['profitP'],'riskPm':self.QS_m['riskP'],\ 'NDGm':self.QS_m['NDG'],'NKLm':self.QS_m['NKL'],'DDm':self.QS_m['DD'],'Kaim':self.QS_m['Kai'],'GGm':self.QS_m['GG'],'Luom':self.QS_m['Luo'],\ 'CHL1m':self.QS_m['CHL1'],'DDCGorGGCD1m':self.QS_m['DDCGorGGCD1'], 'CHLm':self.QS_m['CHL'],'DDCGorGGCDm':self.QS_m['DDCGorGGCD'], 'spacePw':self.QS_w['spaceP'],'profitPw':self.QS_w['profitP'],'riskPw':self.QS_w['riskP'],\ 'NDGw':self.QS_w['NDG'],'NKLw':self.QS_w['NKL'],'DDw':self.QS_w['DD'],'Kaiw':self.QS_w['Kai'],'GGw':self.QS_w['GG'],'Luow':self.QS_w['Luo'],\ 'CHL1w':self.QS_w['CHL1'],'DDCGorGGCD1w':self.QS_w['DDCGorGGCD1'], 'CHLw':self.QS_w['CHL'],'DDCGorGGCDw':self.QS_m['DDCGorGGCD'], 'spacePd':self.QS_d['spaceP'],'profitPd':self.QS_d['profitP'],'riskPd':self.QS_d['riskP'],\ 'NDGd':self.QS_d['NDG'],'NKLd':self.QS_d['NKL'],'DDd':self.QS_d['DD'],'Kaid':self.QS_d['Kai'],'GGd':self.QS_d['GG'],'Luod':self.QS_d['Luo'],\ 'CHL1d':self.QS_d['CHL1'],'DDCGorGGCD1d':self.QS_d['DDCGorGGCD1'], 'CHLd':self.QS_d['CHL'],'DDCGorGGCDd':self.QS_d['DDCGorGGCD'], 'spacePt':self.QS_t['spaceP'],'profitPt':self.QS_t['profitP'],'riskPt':self.QS_t['riskP'],\ 'NDGt':self.QS_t['NDG'],'NKLt':self.QS_t['NKL'],'DDt':self.QS_t['DD'],'Kait':self.QS_t['Kai'],'GGt':self.QS_t['GG'],'Luot':self.QS_t['Luo'],\ 'CHL1t':self.QS_t['CHL1'],'DDCGorGGCD1t':self.QS_t['DDCGorGGCD1'], 'CHLt':self.QS_t['CHL'],'DDCGorGGCDt':self.QS_t['DDCGorGGCD'], 'spacePf':self.QS_f['spaceP'],'profitPf':self.QS_f['profitP'],'riskPf':self.QS_f['riskP'],\ 'NDGf':self.QS_f['NDG'],'NKLf':self.QS_f['NKL'],'DDf':self.QS_f['DD'],'Kaif':self.QS_f['Kai'],'GGf':self.QS_f['GG'],'Luof':self.QS_f['Luo'],\ 'CHL1f':self.QS_f['CHL1'],'DDCGorGGCD1f':self.QS_f['DDCGorGGCD1'], 'CHLf':self.QS_f['CHL'],'DDCGorGGCDf':self.QS_f['DDCGorGGCD'], 'spacePo':self.QS_o['spaceP'],'profitPo':self.QS_o['profitP'],'riskPo':self.QS_o['riskP'],\ 'NDGo':self.QS_o['NDG'],'NKLo':self.QS_o['NKL'],'DDo':self.QS_o['DD'],'Kaio':self.QS_o['Kai'],'GGo':self.QS_o['GG'],'Luoo':self.QS_o['Luo'],\ 'CHL1o':self.QS_o['CHL1'],'DDCGorGGCD1o':self.QS_o['DDCGorGGCD1'], 'CHLo':self.QS_o['CHL'],'DDCGorGGCDo':self.QS_o['DDCGorGGCD'],\ 'ConfirmedJGm':self.JG_m['ConfirmedJG'],'CurrentJGm':self.JG_m['CurrentJG'],'JGdonem':self.JG_m['JGdone'],'QXm':self.JG_m['QX'],'JGQRm':self.JG_m['JGQR'],\ 'ConfirmedJGw':self.JG_w['ConfirmedJG'],'CurrentJGw':self.JG_w['CurrentJG'],'JGdonew':self.JG_w['JGdone'],'QXw':self.JG_w['QX'],'JGQRw':self.JG_w['JGQR'],\ 'ConfirmedJGd':self.JG_d['ConfirmedJG'],'CurrentJGd':self.JG_d['CurrentJG'],'JGdoned':self.JG_d['JGdone'],'QXd':self.JG_d['QX'],'JGQRd':self.JG_d['JGQR'],\ 'ConfirmedJGt':self.JG_t['ConfirmedJG'],'CurrentJGt':self.JG_t['CurrentJG'],'JGdonet':self.JG_t['JGdone'],'QXt':self.JG_t['QX'],'JGQRt':self.JG_t['JGQR'],\ 'ConfirmedJGf':self.JG_f['ConfirmedJG'],'CurrentJGf':self.JG_f['CurrentJG'],'JGdonef':self.JG_f['JGdone'],'QXf':self.JG_f['QX'],'JGQRf':self.JG_f['JGQR'],\ 'ConfirmedJGo':self.JG_o['ConfirmedJG'],'CurrentJGo':self.JG_o['CurrentJG'],'JGdoneo':self.JG_o['JGdone'],'QXo':self.JG_o['QX'],'JGQRo':self.JG_o['JGQR'],\ 'hp':self.hp,'ttlup':self.ttlup,'ttldown':self.ttldown,'mstatus':self.mstatus } #for key,value in tmp.items(): # if 1: #try: # if value>1000: # value=round(value) # tmp[key]=value #except: # print("error stockcode",self.stockcode) # pass xgresult=pd.concat([xgresult, tmp], ignore_index=True) #except: # pass qsresult=pd.concat([qsresult, self.QS_m], ignore_index=True) qsresult=pd.concat([qsresult, self.QS_w], ignore_index=True) qsresult=pd.concat([qsresult, self.QS_d], ignore_index=True) qsresult=pd.concat([qsresult, self.QS_t], ignore_index=True) qsresult=pd.concat([qsresult, self.QS_f], ignore_index=True) qsresult=pd.concat([qsresult, self.QS_o], ignore_index=True) i+=1 self.init_parameter() return xgresult,qsresult def xg_2022_mp(self): #计算2022中的股票 starttime=time.time() self.xgresult=pd.DataFrame() self.qsresult=pd.DataFrame() #result=pd.DataFrame() stocklist = pd.read_csv(r"./csv/2022.csv") print(f"former qty = {stocklist.shape[0]}") stocklist=stocklist.drop_duplicates(['stockcode']) print(f"former qty = {stocklist.shape[0]}") stockqty=stocklist.shape[0] threadqty=6 pool=mp.Pool(threadqty) qtyperthread=math.ceil(stockqty/threadqty) print("qtyperthread",qtyperthread) #corelist=[0,10,20,30,40,50] corelist=[0,30,60,90,120,150] print(corelist) #self.xgresult=self.xgresult.append(pool.map(self.xg_2022_for_mp,corelist)) result=pool.map(self.xg_2022_for_mp,corelist) a,b=zip(*result) for i in range(len(a)): self.xgresult=self.xgresult.append(a[i]) self.qsresult=self.qsresult.append(b[i]) self.xgresult = self.xgresult.replace([np.inf, -np.inf], 0) self.qsresult = self.qsresult.replace([np.inf, -np.inf], 0) onedb=Onedb() onedb.del_table("xg2022") onedb.del_table("qs2022") onedb.into_table(self.xgresult,"xg2022","stockcode",9) onedb.into_table(self.qsresult,"qs2022","stockcode",9) onedb.execute_sql("delete `xgall` from `xgall`,`xg2022` where `xgall`.`stockcode`=`xg2022`.`stockcode`") onedb.into_table(self.xgresult,"xgall","stockcode",9) #onedb.execute_sql("select * from xgall a left join xg2022 b on a.stockcode=b.stockcode where b.stockcode is NULL") #onedb.into_table(self.xgresult,"xgall","stockcode",9) #self.xgresult.to_csv(rf"./tmpout/xg2022.csv",encoding="utf_8_sig",index=True) #self.qsresult.to_csv(rf"./tmpout/qs2022.csv",encoding="utf_8_sig",index=True) #_=input("export here") #self.xg_2022_after_mp() print("TTL time:", time.time()-starttime) def xg_2022_for_mp(self,n): xgresult=pd.DataFrame() qsresult=pd.DataFrame() stocklist = pd.read_csv(r"./csv/2022.csv") stocklist=stocklist.drop_duplicates(['stockcode']) stocklist=stocklist[n:n+30] print("stock qty =", stocklist.shape[0]) for index,stock in stocklist.iterrows(): if len(stock['stockcode'])<9: if stock['stockcode'][1:2]=="3" or stock['stockcode'][1:2]=="0": stock['stockcode']=stock['stockcode'][1:7]+".SZ" elif stock['stockcode'][1:2]=="6": stock['stockcode']=stock['stockcode'][1:7]+".SH" elif stock['stockcode'][1:2]=="8" or stock['stockcode'][1:2]=="4": stock['stockcode']=stock['stockcode'][1:7]+".BJ" else: stock['stockcode']=stock['stockcode'][1:10] i=1 for index,row in stocklist.iterrows(): self.stockcode=row['stockcode'] print(i,self.stockcode) if self.stockcode[-3:]==".BJ": self.onedata=Onedata(0) else: self.onedata=Onedata(1) ''' self.get_div() try: self.get_K_D() except: continue if self.kdata_d.shape[0]==0: continue #self.kdata_d=self.wash_K_div(self.kdata_d) #self.get_K_MW() self.get_K_w() self.get_K_m() self.kdata_d=self.kdata_d[-300:] #缩减日K线数量以加速计算 self.kdata_t=self.get_K_TFO('m30') #得到30分钟数据 self.kdata_t=self.wash_K_div(self.kdata_t) self.kdata_f=self.get_K_TFO('m5') #得到5分钟数据 self.kdata_f=self.wash_K_div(self.kdata_f) self.kdata_o=self.get_K_TFO('m1') #得到1分钟数据 self.kdata_o=self.wash_K_div(self.kdata_o) ''' #''' try: self.kdata_o,self.kdata_f,self.kdata_t,self.kdata_d,self.kdata_w,self.kdata_m=self.onedata.k(self.stockcode,"all") except: continue #''' #only cal ma ''' self.kdata_m = self.cal_ma_K(self.kdata_m) self.kdata_w = self.cal_ma_K(self.kdata_w) self.kdata_d = self.cal_ma_K(self.kdata_d) self.kdata_t = self.cal_ma_K(self.kdata_t) self.kdata_f = self.cal_ma_K(self.kdata_f) self.kdata_o = self.cal_ma_K(self.kdata_o) ''' self.cal_ma_all() #only cal QS self.kdata_m,self.QS_m,self.JG_m = self.cal_qs(self.kdata_m, self.stockcode, 'month') self.kdata_w,self.QS_w,self.JG_w = self.cal_qs(self.kdata_w, self.stockcode, 'week') self.kdata_d,self.QS_d,self.JG_d = self.cal_qs(self.kdata_d, self.stockcode, 'day') self.kdata_t,self.QS_t,self.JG_t = self.cal_qs(self.kdata_t, self.stockcode, 'm30') self.kdata_f,self.QS_f,self.JG_f = self.cal_qs(self.kdata_f, self.stockcode, 'm5') self.kdata_o,self.QS_o,self.JG_o = self.cal_qs(self.kdata_o, self.stockcode, 'm1') #print("periodJG_m",self.JG_m,self.JG_w) print(self.QS['month'],self.QS['week'],self.QS['day'],self.QS['m30'],self.QS['m5'],self.QS['m1']) try: #[ttl:93]代码/名称/修改时间/趋势信号[6mwdtfo] #/趋势数据[84spaceP/profitP/riskP/NDG/NKL/DD/Kai/GG/Luo/CHL1/DDCGorGGCD/CHL/DDCGorGGCD/C[mwdtfo] #print("self.QS_m",self.stockcode,self.QS_m) tmp={'scode':self.stockcode[0:6],'stockcode':self.stockcode,'stockname':row['name'],'time':self.QS_t['reviseTime'],'c':self.QS_t['C'],\ 'QSm':self.QS['month'],'QSw':self.QS['week'],'QSd':self.QS['day'],'QSt':self.QS['m30'],'QSf':self.QS['m5'],'QSo':self.QS['m1'],\ 'ma30m':self.QS_m['ma30'],'ma30w':self.QS_w['ma30'],'ma30d':self.QS_d['ma30'],'ma30t':self.QS_t['ma30'],'ma30f':self.QS_f['ma30'],'ma30o':self.QS_o['ma30'],\ 'ma10m':self.QS_m['ma10'],'ma10w':self.QS_w['ma10'],'ma10d':self.QS_d['ma10'],'ma10t':self.QS_t['ma10'],'ma10f':self.QS_f['ma10'],'ma10o':self.QS_o['ma10'],\ 'spacePm':self.QS_m['spaceP'],'profitPm':self.QS_m['profitP'],'riskPm':self.QS_m['riskP'],\ 'NDGm':self.QS_m['NDG'],'NKLm':self.QS_m['NKL'],'DDm':self.QS_m['DD'],'Kaim':self.QS_m['Kai'],'GGm':self.QS_m['GG'],'Luom':self.QS_m['Luo'],\ 'CHL1m':self.QS_m['CHL1'],'DDCGorGGCD1m':self.QS_m['DDCGorGGCD1'], 'CHLm':self.QS_m['CHL'],'DDCGorGGCDm':self.QS_m['DDCGorGGCD'], 'spacePw':self.QS_w['spaceP'],'profitPw':self.QS_w['profitP'],'riskPw':self.QS_w['riskP'],\ 'NDGw':self.QS_w['NDG'],'NKLw':self.QS_w['NKL'],'DDw':self.QS_w['DD'],'Kaiw':self.QS_w['Kai'],'GGw':self.QS_w['GG'],'Luow':self.QS_w['Luo'],\ 'CHL1w':self.QS_w['CHL1'],'DDCGorGGCD1w':self.QS_w['DDCGorGGCD1'], 'CHLw':self.QS_w['CHL'],'DDCGorGGCDw':self.QS_m['DDCGorGGCD'], 'spacePd':self.QS_d['spaceP'],'profitPd':self.QS_d['profitP'],'riskPd':self.QS_d['riskP'],\ 'NDGd':self.QS_d['NDG'],'NKLd':self.QS_d['NKL'],'DDd':self.QS_d['DD'],'Kaid':self.QS_d['Kai'],'GGd':self.QS_d['GG'],'Luod':self.QS_d['Luo'],\ 'CHL1d':self.QS_d['CHL1'],'DDCGorGGCD1d':self.QS_d['DDCGorGGCD1'], 'CHLd':self.QS_d['CHL'],'DDCGorGGCDd':self.QS_d['DDCGorGGCD'], 'spacePt':self.QS_t['spaceP'],'profitPt':self.QS_t['profitP'],'riskPt':self.QS_t['riskP'],\ 'NDGt':self.QS_t['NDG'],'NKLt':self.QS_t['NKL'],'DDt':self.QS_t['DD'],'Kait':self.QS_t['Kai'],'GGt':self.QS_t['GG'],'Luot':self.QS_t['Luo'],\ 'CHL1t':self.QS_t['CHL1'],'DDCGorGGCD1t':self.QS_t['DDCGorGGCD1'], 'CHLt':self.QS_t['CHL'],'DDCGorGGCDt':self.QS_t['DDCGorGGCD'], 'spacePf':self.QS_f['spaceP'],'profitPf':self.QS_f['profitP'],'riskPf':self.QS_f['riskP'],\ 'NDGf':self.QS_f['NDG'],'NKLf':self.QS_f['NKL'],'DDf':self.QS_f['DD'],'Kaif':self.QS_f['Kai'],'GGf':self.QS_f['GG'],'Luof':self.QS_f['Luo'],\ 'CHL1f':self.QS_f['CHL1'],'DDCGorGGCD1f':self.QS_f['DDCGorGGCD1'], 'CHLf':self.QS_f['CHL'],'DDCGorGGCDf':self.QS_f['DDCGorGGCD'], 'spacePo':self.QS_o['spaceP'],'profitPo':self.QS_o['profitP'],'riskPo':self.QS_o['riskP'],\ 'NDGo':self.QS_o['NDG'],'NKLo':self.QS_o['NKL'],'DDo':self.QS_o['DD'],'Kaio':self.QS_o['Kai'],'GGo':self.QS_o['GG'],'Luoo':self.QS_o['Luo'],\ 'CHL1o':self.QS_o['CHL1'],'DDCGorGGCD1o':self.QS_o['DDCGorGGCD1'], 'CHLo':self.QS_o['CHL'],'DDCGorGGCDo':self.QS_o['DDCGorGGCD'],\ 'ConfirmedJGm':self.JG_m['ConfirmedJG'],'CurrentJGm':self.JG_m['CurrentJG'],'JGdonem':self.JG_m['JGdone'],'QXm':self.JG_m['QX'],'JGQRm':self.JG_m['JGQR'],\ 'ConfirmedJGw':self.JG_w['ConfirmedJG'],'CurrentJGw':self.JG_w['CurrentJG'],'JGdonew':self.JG_w['JGdone'],'QXw':self.JG_w['QX'],'JGQRw':self.JG_w['JGQR'],\ 'ConfirmedJGd':self.JG_d['ConfirmedJG'],'CurrentJGd':self.JG_d['CurrentJG'],'JGdoned':self.JG_d['JGdone'],'QXd':self.JG_d['QX'],'JGQRd':self.JG_d['JGQR'],\ 'ConfirmedJGt':self.JG_t['ConfirmedJG'],'CurrentJGt':self.JG_t['CurrentJG'],'JGdonet':self.JG_t['JGdone'],'QXt':self.JG_t['QX'],'JGQRt':self.JG_t['JGQR'],\ 'ConfirmedJGf':self.JG_f['ConfirmedJG'],'CurrentJGf':self.JG_f['CurrentJG'],'JGdonef':self.JG_f['JGdone'],'QXf':self.JG_f['QX'],'JGQRf':self.JG_f['JGQR'],\ 'ConfirmedJGo':self.JG_o['ConfirmedJG'],'CurrentJGo':self.JG_o['CurrentJG'],'JGdoneo':self.JG_o['JGdone'],'QXo':self.JG_o['QX'],'JGQRo':self.JG_o['JGQR'],\ 'hp':self.hp,'ttlup':self.ttlup,'ttldown':self.ttldown,'mstatus':self.mstatus } for key,value in tmp.items(): try: if value>1000: value=round(value) tmp[key]=value except: pass #print("tmp",tmp) xgresult=xgresult.append(tmp,ignore_index=True) except: pass qsresult=qsresult.append(self.QS_m,ignore_index=True) qsresult=qsresult.append(self.QS_w,ignore_index=True) qsresult=qsresult.append(self.QS_d,ignore_index=True) qsresult=qsresult.append(self.QS_t,ignore_index=True) qsresult=qsresult.append(self.QS_f,ignore_index=True) qsresult=qsresult.append(self.QS_o,ignore_index=True) #print("qsresult",qsresult) i+=1 self.init_parameter() #print("xgresult inside mp",xgresult) return xgresult,qsresult #xgresult.to_cs v(rf"./tmpout/2022result.csv",encoding="utf_8_sig",index=True) def xg_2022_after_mp(self): #print("self.xgresult after mp",self.xgresult) newlist=pd.DataFrame() newlistt=pd.DataFrame() for key,stock in self.xgresult.iterrows(): if "-1abc" in stock.day or "-1abc" in stock.m30: newlist=pd.concat([newlist, pd.DataFrame([stock])], ignore_index=True) if "-1a" in stock.m30 or "-2" in stock.m30: newlistt=pd.concat([newlistt, pd.DataFrame([stock])], ignore_index=True) print("day -1abc and m30 -1abc") print(newlist) print("m30 -1a or m30 -2:") print(newlistt) def xg_havelist(self): global xgresult mycode=pd.DataFrame() starttime=time.time() print(self.havelist) stocklist=self.havelist for index,stock in stocklist.iterrows(): if stock['code'][:1]=="3" or stock['code'][:1]=="0": stock['code']=stock['code'][:6]+".SZ" mycode=mycode.append(stock) if stock['code'][:1]=="6": stock['code']=stock['code'][:6]+".SH" mycode=mycode.append(stock) if stock['code'][:1]=="8" or stock['code'][:1]=="4": stock['code']=stock['code'][:6]+".BJ" mycode=mycode.append(stock) print(mycode) for index,row in mycode.iterrows(): self.stockcode=row['code'] self.run() print("in xg_havelist") print(self.newpnsttl) self.init_parameter() print("TTL time:", time.time()-starttime) def export(self): # 输出所有K线数据(除权后的)到kdata/文件夹下 self.kdata_m.to_csv(rf"./kdata/kdata_m.csv", encoding="utf_8_sig", index=True) self.kdata_w.to_csv(rf"./kdata/kdata_w.csv", encoding="utf_8_sig", index=True) self.kdata_d.to_csv(rf"./kdata/kdata_d.csv", encoding="utf_8_sig", index=True) self.kdata_t.to_csv(rf"./kdata/kdata_t.csv", encoding="utf_8_sig", index=True) self.kdata_f.to_csv(rf"./kdata/kdata_f.csv", encoding="utf_8_sig", index=True) self.kdata_o.to_csv(rf"./kdata/kdata_o.csv", encoding="utf_8_sig", index=True) logging.info("fake export K data done!") def show_log(self): logging.info("fake show_log done!") def get_div_fromdb(self): #从onedb取除权数据 并 存放到 ./csv/divdata.csv #每天早上执行一次,当天就不用重复执行 #是否需要重新索引和重新排序??? #应该是在读取SPLIT.PWR并存放到db前,做索引和排序的工作 print("start get_divdata_fromdb") onedb=Onedb() mydiv = onedb.From_table("select * from divdata") mydiv.to_csv(rf"./csv/divdata.csv",encoding="utf_8_sig",index=False) print("end get_divdata_fromdb") def get_div(self): #从csv/divdata.csv读取当前分析股票的除权数据 self.divpd = pd.read_csv(r"./csv/divdata.csv") self.divpd = self.divpd[self.divpd['code'] == self.stockcode] self.divpd['date'] = pd.to_datetime(self.divpd['date']) self.divpd = self.divpd.set_index('date') self.divpd = self.divpd.sort_index(ascending=False) def wash_K_div(self,kdata): #使用除权数据清洗K线 #self.divpd['date'] = pd.to_datetime(self.divpd['date']) #self.divpd = self.divpd.set_index('date') #self.divpd = self.divpd.sort_index(ascending=False) #self.divpd中有没有数据 for key, val in self.divpd.iterrows(): date = key - datetime.timedelta(minutes=1) if date < kdata.index[0]: break for field in kdata.columns.values: if field != 'volume' and field != 'amount': # open close high low vol kdata.loc[:date, field] -= val.bonus / 10 kdata.loc[:date, field] += val.price * (val.rationed / 10) kdata.loc[:date:, field] /= 1 + val.present / 10 + val.rationed / 10 return kdata def get_K_all(self): #多线程一次性获取6个周期的K线数据，并存放到6个dataframe中 self.get_K_D() self.get_K_w() self.get_K_m() #self.kdata_d=self.wash_K_div(self.kdata_d) #self.get_K_MW() self.kdata_d=self.kdata_d[-300:] #缩减日K线数量以加速计算 self.kdata_t=self.get_K_TFO('m30') #得到30分钟数据 self.kdata_t=self.wash_K_div(self.kdata_t) self.kdata_f=self.get_K_TFO('m5') #得到5分钟数据 self.kdata_f=self.wash_K_div(self.kdata_f) self.kdata_o=self.get_K_TFO('m1') #得到1分钟数据 self.kdata_o=self.wash_K_div(self.kdata_o) def get_K_D(self): my_k=0 urlstockcode = self.stockcode[-2:].lower() + self.stockcode[:6] # http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param=sz300027,month,1990-12-01,,10240,qfq #url = rf'http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={urlstockcode},day,1990-12-01,,10240,bfq' url = rf'http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={urlstockcode},day,1990-12-01,,2000,qfq' originaltxt = self.gethtml(url) goodtxt = re.sub(',{(.+?)}]', ']', originaltxt) my_json = json.loads(goodtxt) #my_k = my_json['data'][urlstockcode]['day'] if not(urlstockcode=="sz399001" or urlstockcode=="sz399006" or urlstockcode=="sh000001"): my_k = my_json['data'][urlstockcode]['qfqday'] else: my_k = my_json['data'][urlstockcode]['day'] arrayk = np.array(my_k) self.kdata_d = pd.DataFrame(arrayk, columns=['date', 'open', 'close', 'high', 'low', 'vol']) # ???ul1 self.kdata_d[['open', 'close', 'high', 'low']] = self.kdata_d[['open', 'close', 'high', 'low']].apply(pd.to_numeric).round(2) self.kdata_d['vol'] = self.kdata_d['vol'].apply(pd.to_numeric).astype(int) # vol 转换为 整型 self.kdata_d['date'] = pd.to_datetime(self.kdata_d['date']) self.kdata_d.set_index(["date"], inplace=True) # set date as index self.kdata_d['vol'] = self.kdata_d['vol'].map(lambda x: x / 1000).round(3) # 成交量/1000,避免溢出？？？有没有问题 def get_K_w(self): urlstockcode = self.stockcode[-2:].lower() + self.stockcode[:6] # http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param=sz300027,month,1990-12-01,,10240,qfq #url = rf'http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={urlstockcode},day,1990-12-01,,10240,bfq' url = rf'http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={urlstockcode},week,1990-12-01,,2000,qfq' originaltxt = self.gethtml(url) goodtxt = re.sub(',{(.+?)}]', ']', originaltxt) #print("week goodtxt",goodtxt) my_json = json.loads(goodtxt) if not(urlstockcode=="sz399001" or urlstockcode=="sz399006" or urlstockcode=="sh000001"): my_k = my_json['data'][urlstockcode]['qfqweek'] else: my_k = my_json['data'][urlstockcode]['week'] arrayk = np.array(my_k) self.kdata_w = pd.DataFrame(arrayk, columns=['date', 'open', 'close', 'high', 'low', 'vol']) # ???ul1 self.kdata_w[['open', 'close', 'high', 'low']] = self.kdata_w[['open', 'close', 'high', 'low']].apply(pd.to_numeric).round(2) self.kdata_w['vol'] = self.kdata_w['vol'].apply(pd.to_numeric).astype(int) # vol 转换为 整型 self.kdata_w['date'] = pd.to_datetime(self.kdata_w['date']) self.kdata_w.set_index(["date"], inplace=True) # set date as index self.kdata_w['vol'] = self.kdata_w['vol'].map(lambda x: x / 1000).round(3) # 成交量/1000,避免溢出？？？有没有问题 def get_K_m(self): urlstockcode = self.stockcode[-2:].lower() + self.stockcode[:6] # http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param=sz300027,month,1990-12-01,,10240,qfq #url = rf'http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={urlstockcode},day,1990-12-01,,10240,bfq' #https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param=bj830799,day,1990-12-01,,2000,qfq url = rf'http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={urlstockcode},month,1990-12-01,,2000,qfq' originaltxt = self.gethtml(url) goodtxt = re.sub(',{(.+?)}]', ']', originaltxt) my_json = json.loads(goodtxt) if not(urlstockcode=="sz399001" or urlstockcode=="sz399006" or urlstockcode=="sh000001"): my_k = my_json['data'][urlstockcode]['qfqmonth'] else: my_k = my_json['data'][urlstockcode]['month'] arrayk = np.array(my_k) self.kdata_m = pd.DataFrame(arrayk, columns=['date', 'open', 'close', 'high', 'low', 'vol']) # ???ul1 self.kdata_m[['open', 'close', 'high', 'low']] = self.kdata_m[['open', 'close', 'high', 'low']].apply(pd.to_numeric).round(2) self.kdata_m['vol'] = self.kdata_m['vol'].apply(pd.to_numeric).astype(int) # vol 转换为 整型 self.kdata_m['date'] = pd.to_datetime(self.kdata_m['date']) self.kdata_m.set_index(["date"], inplace=True) # set date as index self.kdata_m['vol'] = self.kdata_m['vol'].map(lambda x: x / 1000).round(3) # 成交量/1000,避免溢出？？？有没有问题 def get_K_D_csv(self): urlstockcode = self.stockcode[-2:].lower() + self.stockcode[:6] self.kdata_d=pd.read_csv(f"kdata/{self.stockcode[:6]}_d.csv")#,index_col=0) self.kdata_d['date'] = pd.to_datetime(self.kdata_d['date']) self.kdata_d.set_index(["date"], inplace=True) # set date as index def get_K_T_csv(self): urlstockcode = self.stockcode[-2:].lower() + self.stockcode[:6] self.kdata_t=pd.read_csv(f"kdata/{self.stockcode[:6]}_t.csv")#,index_col=0) self.kdata_t['date'] = pd.to_datetime(self.kdata_t['date']) self.kdata_t.set_index(["date"], inplace=True) # set date as index def get_K_F_csv(self): urlstockcode = self.stockcode[-2:].lower() + self.stockcode[:6] self.kdata_f=pd.read_csv(f"kdata/{self.stockcode[:6]}_f.csv")#,index_col=0) self.kdata_f['date'] = pd.to_datetime(self.kdata_f['date']) self.kdata_f.set_index(["date"], inplace=True) # set date as index def get_K_O_csv(self): urlstockcode = self.stockcode[-2:].lower() + self.stockcode[:6] self.kdata_o=pd.read_csv(f"kdata/{self.stockcode[:6]}_o.csv")#,index_col=0) self.kdata_o['date'] = pd.to_datetime(self.kdata_o['date']) self.kdata_o.set_index(["date"], inplace=True) # set date as index def get_K_MW(self): mvol = self.kdata_d['vol'].resample('M').sum().round(2) mopen = self.kdata_d['open'].resample('M').first().round(2) mclose = self.kdata_d['close'].resample('M').last().round(2) mhigh = self.kdata_d['high'].resample('M').max().round(2) mlow = self.kdata_d['low'].resample('M').min().round(2) self.kdata_m = pd.concat([mopen, mclose, mhigh, mlow, mvol], axis=1) self.kdata_m = self.kdata_m.dropna(axis=0, how='any') hhh=self.kdata_d['high'].max().round(2) lll=self.kdata_d['low'].min().round(2) ttlk=self.kdata_d.shape[0] cp=self.kdata_d['close'][ttlk-1].round(2) try: #把历史最高价与历史最低价的差等分10份,现在处在第几份 self.hp=(cp-lll)/((hhh-lll)/100) self.hp=self.hp.round(1) #print("hhh,lll,ttlk,self.hp",hhh,lll,ttlk,self.hp) except: print("calculate self.hp error") wvol = self.kdata_d['vol'].resample('W-FRI').sum().round(2) wopen = self.kdata_d['open'].resample('W-FRI').first().round(2) wclose = self.kdata_d['close'].resample('W-FRI').last().round(2) whigh = self.kdata_d['high'].resample('W-FRI').max().round(2) wlow = self.kdata_d['low'].resample('W-FRI').min().round(2) self.kdata_w = pd.concat([wopen, wclose, whigh, wlow, wvol], axis=1) self.kdata_w = self.kdata_w.dropna(axis=0, how='any') def gethtml(self,url): ''' 最多读取10次，每次等待3秒钟 如果超过3次没有抓到K线数据，就把 i+url 输出到htmlerror.txt文件中 每次程序开始运行的时候，清空 htmlerror.txt 文件 ''' i = 0 while i < 10: try: html = requests.get(url, timeout=3).text return html except requests.exceptions.RequestException: i += 1 if i==10: #10次都没有取得数据 print(f"尝试过{i}次后，依然没有通过{url}得到数据") if i > 1 and i!=10: #self.string2txt(1, str(i) + url) print(f"{i} 次通过{url} 没有得到数据") def get_K_TFO(self,period): urlstockcode = self.stockcode[-2:].lower() + self.stockcode[:6] url = rf'http://ifzq.gtimg.cn/appstock/app/kline/mkline?param={urlstockcode},{period},,320&_var=m1_today&r=0.260880015116' # vol后面多了两列 {} 和 类似成交量的一个数字, 用 un1 和 un2替代后，再删除 originaltxt = self.gethtml(url) goodtxt = re.sub(',{}(.+?)]', ']', originaltxt) goodtxt = goodtxt[9:] #logging.info(goodtxt) my_json = json.loads(goodtxt) my_k = my_json['data'][urlstockcode][period] arrayk = np.array(my_k) kdata = pd.DataFrame(arrayk, columns=['date', 'open', 'close', 'high', 'low', 'vol']) # ???ul1 kdata[['open', 'close', 'high', 'low']] = kdata[['open', 'close', 'high', 'low']].apply( pd.to_numeric).round(2) kdata['vol'] = kdata['vol'].apply(pd.to_numeric).astype(int) # vol 转换为 整型 kdata['date'] = pd.to_datetime(kdata['date']) kdata.set_index(["date"], inplace=True) # set date as index kdata['vol'] = kdata['vol'].map(lambda x: x / 1000).round(3) # 成交量/1000,避免溢出？？？有没有问题 #logging.info("kdata",kdata) return(kdata) def cal_ma_all(self): self.kdata_m = self.cal_ma_K(self.kdata_m) self.kdata_w = self.cal_ma_K(self.kdata_w) self.kdata_d = self.cal_ma_K(self.kdata_d) self.kdata_t = self.cal_ma_K(self.kdata_t) self.kdata_f = self.cal_ma_K(self.kdata_f) self.kdata_o = self.cal_ma_K(self.kdata_o) #如果月K线多于60根, 且股价在月线ma30上以的时间超过80%,则可以认为是有价值投资的股票 ttlkm=self.kdata_m.shape[0] if ttlkm>60: df=self.kdata_m #print("month_kdata:",self.kdata_m[:10]) self.ttlup =int(df[df['low'] >=df['ma30']].shape[0]/ttlkm*100) self.ttldown=int(df[(df['high']+df['low'])/2=df['ma30'][ttlkm-5] or df['high'][ttlkm-1] >=df['ma30'][ttlkm-1]: self.mstatus=1 else: self.mstatus=2 ''' if df['high'][ttlkm-1]<=df['ma30'][ttlkm-1]: self.mstatus=3 if df['low'][ttlkm-1]df['ma30'][ttlkm-1]: self.mstatus=2 ''' def cal_ma_K(self,kdata): kdata['ma5'] = kdata['close'].rolling(5).mean().round(3) kdata['ma10'] = kdata['close'].rolling(10).mean().round(3) kdata['ma30'] = kdata['close'].rolling(30).mean().round(3) kdata = kdata.fillna(method='bfill') # bfill由后向前填充 ffill由前向后填充 ''' ma5 = kdata["close"].rolling(5).mean() ma10 = kdata["close"].rolling(10).mean() ma30 = kdata["close"].rolling(30).mean() kdata.insert(5, 'ma5', ma5) kdata.insert(6, 'ma10', ma10) kdata.insert(7, 'ma30', ma30) kdata = kdata.fillna(method='bfill') # bfill由后向前填充 ffill由前向后填充 kdata['ma5'] = kdata['ma5'].round(3) # 保留4位是为了计算的准确性？？？ kdata['ma10'] = kdata['ma10'].round(3) # 最大的计算是除权 kdata['ma30'] = kdata['ma30'].round(3) # 除权与均线无关，且是除权后算均线 ''' return kdata def Merge(self, dict1, dict2): res = {**dict1, **dict2} return res def get_havelist(self): onedb=Onedb() #self.havelist = self.From_table("select * from havelist") self.havelist = onedb.From_table("select * from havelist") self.havelist.index = self.havelist.index + 1 self.havelist=self.havelist[['代码','成本','数量','名称']] self.havelist.columns=['code','cost','qty','name'] self.havelist['qty']=self.havelist['qty']/100 self.havelist['qty']=self.havelist['qty'].round(0) insertx=3 ''' self.havelist.insert(insertx,"%",0.00) self.havelist.insert(insertx+1,"S1Q",0) self.havelist.insert(insertx+2, "S1", 0.00) self.havelist.insert(insertx+3, "B1", 0.00) self.havelist.insert(insertx+4, "B1Q", 0) self.havelist.insert(insertx+5, "amt", self.havelist['cost']*self.havelist['qty']/100) ''' self.havelist["%"]=0.00 self.havelist["S1Q"]=0 self.havelist["S1"]=0.00 self.havelist["B1"]=0.00 self.havelist["B1Q"]=0 self.havelist["amt"]=self.havelist['cost']*self.havelist['qty']/100 self.havelist['amt']=self.havelist['amt'].round(2) #self.havelist=self.havelist.set_index['amt'] self.havelist=self.havelist.sort_values(axis=0,by='amt',ascending=False,ignore_index=True) self.havelist.index=self.havelist.index+1 #self.havelist=self.havelist.reset_index(drop=True) ''' self.havelist.insert(insertx+6,'Q',0.00) self.havelist.insert(insertx+7,'+',0.00) self.havelist.insert(insertx+8,'-',0.00) self.havelist.insert(insertx+9,'sn',self.havelist.index) ''' self.havelist['Q']=0.00 self.havelist['+']=0.00 self.havelist['-']=0.00 self.havelist['sn']=self.havelist.index self.get_hq() def get_hq(self): seq="http://qt.gtimg.cn/q=" for key,have in self.havelist.iterrows(): if have.code[:1]=="6": seq=seq+"sh"+have.code+"," else: seq=seq+"sz"+have.code+"," html=requests.get(seq,timeout=3).text sp=html.split(";") for spn in sp: if len(spn)>10: spnn=spn.split("~") #print("spnn",i,spnn[3],spnn[2],spnn[20],spnn[19],spnn[9],spnn[10],spnn[32]) ttl=self.havelist.shape[0] for j in range(1,ttl+1): if self.havelist['code'][j]==spnn[2]: self.havelist['%'][j]=spnn[32] self.havelist['S1Q'][j]=int(spnn[20]) self.havelist['S1'][j]=spnn[19] self.havelist['B1'][j]=spnn[9] self.havelist['B1Q'][j]=int(spnn[10]) self.havelist['Q'][j]=spnn[49] self.havelist['+'][j]=spnn[47] self.havelist['-'][j]=spnn[48] def get_hq_all(self): pro = ts.pro_api() stocklist = pro.query('stock_basic', exchange='', list_status='L') stocklist.index = stocklist.index + 1 #stocklist=stocklist[(stocklist['list_date']<"20180101")] stocklist=stocklist[:800] stocklist['close']=0.00 stocklist['percent']=0.00 stocklist['highmax']=0.00 stocklist['lowmax']=0.00 seq="http://qt.gtimg.cn/q=" for key,stock in stocklist.iterrows(): if stock.ts_code[:1]=="6": seq=seq+"sh"+stock.ts_code[:6]+"," else: seq=seq+"sz"+stock.ts_code[:6]+"," html=requests.get(seq,timeout=3).text sp=html.split(";") for spn in sp: if len(spn)>10: spnn=spn.split("~") ttl=stocklist.shape[0] for j in range(1,ttl+1): if (stocklist['ts_code'][j][:6]==spnn[2]): stocklist['close'][j]=spnn[3] stocklist['percent'][j]=spnn[32] stocklist['highmax'][j]=spnn[47] stocklist['lowmax'][j]=spnn[48] print(stocklist) def show(self,showtype): #os.system("clear") if showtype==1: self.get_havelist() print(self.havelist) if showtype==2: #select stock self.get_havelist() self.stockcode=self.havelist.iloc[havelistseq-1,0] if self.stockcode[:1]=='6': self.stockcode=self.stockcode+".SH" elif self.stockcode[:1]=='8' or self.stockcode[:1]=='4': self.stockcode=self.stockcode+".BJ" else: self.stockcode = self.stockcode + ".SZ" self.run() print("\033[0;31;40m\tstockcode:\033[0m", self.stockcode) print("seq stockcode",self.stockcode) if showtype==3: #keyin stock self.run() def cal_qs_KaiLuo(self,kdata,ttlk): #找到可能的Kai 和 Luo两种信号 # 从第31根K线开始，到ttlk-10,计算MA30上的起涨点和起跌点 +1代表起跌点 -1代表起涨点 kdata['kkll']=0 kdata['kkllx']=0 kdata['kkllv']=0.0 kdata['kkllxv']=0.0 ''' kdata.insert(8, 'kkll', 0) # -1起涨点 +1起跌点 kdata.insert(9, 'kkllv', 0.00) # 起涨价和起跌价 kdata.insert(10, 'kkllx', 0) # 最高价点+2 和 最低价点-2 kdata.insert(11, 'kkllxv', 0.00) # 最高价 和 最低价 ''' for i in range(30, ttlk - 10): # bcheckttl = 0; # 如果是V型，即可能的起涨点 if kdata['ma30'][i] <= kdata['ma30'][i - 1] and kdata['ma30'][i] < kdata['ma30'][i + 1]: # 如果后面9根K线中，有6根K线的重心（H+L）/2在MA30以上，并且MA30持续向上，则确定为一个起涨点 for k in range(9): if (kdata['high'][i + k] + kdata['low'][i + k]) / 2 > kdata['ma30'][i + k] \ and kdata['ma30'][i + k] < kdata['ma30'][i + k + 1]: bcheckttl = bcheckttl + 1 if bcheckttl >= 6: kdata['kkll'][i] = -1 # 起涨点 V 型 tmpk=kdata[i+4:i+9] if tmpk[(tmpk["low"]>tmpk["ma30"])].shape[0]>=4: kdata['kkll'][i]=-1 # 如果是A型，即可能的起跌点 elif kdata['ma30'][i] >= kdata['ma30'][i - 1] and kdata['ma30'][i] > kdata['ma30'][i + 1]: # 如果后面9根K线中，有6极K线的重心在MA30以下，并且MA30持续向下，则确定为一个起跌点 for k in range(9): if (kdata['high'][i + k] + kdata['low'][i + k]) / 2 < kdata['ma30'][i + k] \ and kdata['ma30'][i + k] > kdata['ma30'][i + k + 1]: bcheckttl = bcheckttl + 1 if bcheckttl >= 6: kdata['kkll'][i] = 1 # 起跌点 A 型 tmpk=kdata[i+4:i+9] if tmpk[(tmpk["high"]=4: kdata['kkll'][i]=1 #data_df = data_df[(data_df["risk"] >=0) & (data_df["time"] <60)] # 最后10根K线，从后向前找，只要找到最近的第一次 MA30出现V型 或是 A型 就直接标注并退出 for i in range(2, 10): # 算最后的几根K线，看有没有趋势 j = ttlk - i if (kdata['ma30'][j] <= kdata['ma30'][j - 1] and kdata['ma30'][j] < kdata['ma30'][j + 1] and kdata['high'][j + 1] >kdata['ma30'][j + 1]): kdata['kkll'][j] = -1 break elif (kdata['ma30'][j] >= kdata['ma30'][j - 1] and kdata['ma30'][j] > kdata['ma30'][j + 1] and kdata['low'][j + 1] kdata['high'][i]: # 起跌点找重复1中的H的最高值 kdata['kkll'][i] = 0 i = j else: kdata['kkll'][j] = 0 elif kdata['kkll'][i] == -1: # !!!!!!! for j in range(i + 1, ttlk): if kdata['kkll'][j] == 1: i = j - 1 # !!!!! break if kdata['kkll'][j] == -1: if kdata['low'][j] < kdata['low'][i]: # 起涨点找重复-1中L的最低值 kdata['kkll'][i] = 0 i = j else: kdata['kkll'][j] = 0 return kdata def cal_qs_GGDD(self,kdata,ttlk): # ================================================================================= # 从后向前找，如果碰到-1，则找出与下一个+1之间的最低点，作为-2（最低价） # 如果碰到+1，则找出与下一个-1之间的最高点，作为+2（最高价） for i in range(ttlk - 1, 30, -1): if kdata['kkll'][i] == -1: # 如果碰到-1 LL = kdata['low'][i] disLL = 0 # logging.info(data.iloc[i].name) for j in range(i - 1, -1, -1): # logging.info(data.iloc[j].name,j) if kdata['kkll'][j] == 1 or j == 0: # 定义找-2的区间，从-1到（要么找到下一个+1，要么找到第一根K线） for k in range(i - 1, j, -1): if kdata['low'][k] <= LL: disLL = i - k LL = kdata['low'][k] kdata['kkllx'][i - disLL] = -2 kdata['kkllxv'][i - disLL] = LL break if kdata['kkll'][i] == 1: # 如果碰到+1 HH = kdata['high'][i] disHH = 0 for j in range(i - 1, -1, -1): if kdata['kkll'][j] == -1 or j == 0: # 定义找+2的区间，从+1到（要么找到下一个-1，要么找到第一根K线） for k in range(i - 1, j, -1): if kdata['high'][k] >= HH: disHH = i - k HH = kdata['high'][k] kdata['kkllx'][i - disHH] = 2 kdata['kkllxv'][i - disHH] = HH break return kdata def cal_qs_kkllv(self,kdata,ttlk): # range可以带三个参数，但是这个地方只带了一个？？？ for i in range(ttlk): ### 计算平均价 kai/luo 1和-1这两个位置的，因为大智慧计算太慢 ### if kdata['kkllx'][i] == 2: TTLa = 0 TTLv = 0 for j in range(i, ttlk): #TTLa= TTLa+ kdata['amt'][j] #kdata['close'][j] * kdata['vol'][j] TTLa = TTLa + kdata['close'][j] * kdata['vol'][j] TTLv = TTLv + kdata['vol'][j] if kdata['kkll'][j] == 1: i = j if TTLv == 0: # 如果TTLv==0，则直接提示并将kkllv置零，避免/0错误！！！ #logging.info(stockcode, period, "TTLv=0") kdata['kkllv'][j] = 0 else: kdata['kkllv'][j] = TTLa / TTLv break if kdata['kkllx'][i] == -2: TTLa = 0 TTLv = 0 for j in range(i, ttlk): #TTLa = TTLa+ kdata['amt'][j] #kdata['close'][j] * kdata['vol'][j] TTLa = TTLa + kdata['close'][j] * kdata['vol'][j] TTLv = TTLv + kdata['vol'][j] if kdata['kkll'][j] == -1: i = j if TTLv == 0: #logging.info(stockcode, period, "TTLv=0") kdata['kkllv'][j] = 0 else: kdata['kkllv'][j] = TTLa / TTLv break kdata['kkll'] = kdata['kkll'].round(0) kdata['kkllx'] = kdata['kkllx'].round(0) kdata['kkllv'] = kdata['kkllv'].round(2) kdata['kkllxv'] = kdata['kkllxv'].round(2) return kdata def cal_qs_QSmodule_NDGNKL(self,kdata,ttlk): #计算11点趋势模型中的NKL和NDG NDG=NKL=0 DDday=GGday=0 for i in range(ttlk - 1, -1, -1): # 即最近一个最值点如果是-2（下跌趋势中），则NKL=NLuo NDG=NGG if kdata['kkllx'][i] == -2: # DD DDday = i # !!!!! for j in range(i - 1, -1, -1): if kdata['kkll'][j] == -1: NKL = kdata['kkllv'][j] if kdata['kkllx'][j] == -2: NDG = kdata['kkllxv'][j] break break elif kdata['kkllx'][i] == 2: # GG GGday = i # !!!!! for j in range(i - 1, -1, -1): if kdata['kkll'][j] == 1: NKL = kdata['kkllv'][j] if kdata['kkllx'][j] == 2: NDG = kdata['kkllxv'][j] break break self.keypoint['NDG']=NDG self.keypoint['NKL']=NKL self.keypoint['GGday']=GGday self.keypoint['DDday']=DDday return kdata def cal_qs_QSmodule_QSDDKaiGGLuo(self,kdata,ttlk): DD=Kai=GG=Luo=Kaiday=Luoday=0 for i in range(ttlk - 1, -1, -1): if kdata['kkllx'][i] == -2: # DD #kdata.insert(12, 'DD', kdata['kkllxv'][i]) # 所有的DD都设置为最后一个DD kdata['DD']=kdata['kkllxv'][i] # 所有的DD都设置为最后一个DD DD = kdata['kkllxv'][i] break # 只找最后一个 added = kdata.shape[1] # 如果没有DD，则 加一列 DD if added == 12: #kdata.insert(12, 'DD', 0) # 如果趋势不全，找不到-2，则所有DD为0 XXXXXXXX kdata['DD']=0 # 如果趋势不全，找不到-2，则所有DD为0 XXXXXXXX for i in range(ttlk - 1, -1, -1): if kdata['kkll'][i] == -1: # Kai #kdata.insert(13, 'Kai', kdata['kkllv'][i]) kdata['Kai']=kdata['kkllv'][i] Kai = kdata['kkllv'][i] break Kaiday = ttlk - i # 和Luoday比较大小，Kaiday小，是上涨趋势，Kaiday大，是下跌趋势 added = kdata.shape[1] if added == 13: #kdata.insert(13, 'Kai', 0) # 如果趋势不全，找不到-1 kdata['Kai']=0 for i in range(ttlk - 1, -1, -1): if kdata['kkllx'][i] == 2: # GG #kdata.insert(14, 'GG', kdata['kkllxv'][i]) kdata['GG']=kdata['kkllxv'][i] GG = kdata['kkllxv'][i] break added = kdata.shape[1] if added == 14: #kdata.insert(14, 'GG', 0) kdata['GG']=0 for i in range(ttlk - 1, -1, -1): if kdata['kkll'][i] == 1: # Luo #kdata.insert(15, 'Luo', kdata['kkllv'][i]) kdata['Luo']=kdata['kkllv'][i] Luo = kdata['kkllv'][i] break Luoday = ttlk - i added = kdata.shape[1] if added == 15: #kdata.insert(15, 'Luo', 0) kdata['Luo']=0 if Kaiday >= Luoday: QS = -1 else: QS = 1 self.keypoint['QS']=QS self.keypoint['DD']=DD self.keypoint['Kai']=Kai self.keypoint['GG']=GG self.keypoint['Luo']=Luo return kdata def cal_qs_QSmodule_CHL1andCHL(self,stockcode,kdata,ttlk,period): NDG=self.keypoint['NDG'] NKL=self.keypoint['NKL'] DD=self.keypoint['DD'] Kai=self.keypoint['Kai'] GG=self.keypoint['GG'] Luo=self.keypoint['Luo'] QS=self.keypoint['QS'] # 周期 方向 指导建议 空间[起涨 起跌] [收益 % 风险 % 益险 %][最低 最高] 当前极值 periodsuggestion = " " # 操盘建议 profitP = 0 # 收益% LUO/C () riskP = 0 # 风险% C/Kai (if Kai<>0, C/Kai else 100) PLP = 0 # 收益风险率 profitP/riskP () CHL = 0.0 CHLP = 0 # DDCGorGGCD = 0.0 DDCGorGGCDma30 = 0.0 closeprice = kdata['close'][-1] CHL1=0 DDCGorGGCD1=0 DDCGorGGCD1ma30=0 DDCGorGGCD1location=0 CHL1location=0 HHHlocation = 0 LLLlocation = 0 # 从后向前找趋势极值 #2-28-2022 #WWW服务器只抓数据,不运算,是否会加快计算进度[每天收盘做一次] #DDCGorGGCD1是CHL向[ 前 ]再找一个HHH或LLL碰Ma30的位置 #CHL1在DDCGorGGCD[ 前 ]与CHL的性质相同的最高价或最低价 #CHL与CHL1相比较,用来判断有没有创新高或创新低 #QStask1: 完成趋势空间 [CHL完成? CHL1完成?] #QStask2: 完成趋势结构 [如果CHL与CHL1之前,有一个H或L碰到Ma30,则完成趋势结构] #QStask3: 创新,创新低或创新高 [+1 创新高 -1创新低 还是1就代表创新] #QStask4: 趋势转折点 [本趋势转折点,即下一个MA30会让生命线变红] #QStask5: 趋势启动 [L-2级别连续放量大涨,还是只针对日线级别] for i in range(ttlk - 1, -1, -1): if i == ttlk - 1: HHH = kdata['high'][i] LLL = kdata['low'][i] HHHlocation = i LLLlocation = i if kdata['high'][i] > HHH: #如果==则记录后一个高点 HHH = kdata['high'][i] HHHlocation = i if kdata['low'][i] < LLL: #如果==则记录后一个低点 LLL = kdata['low'][i] LLLlocation = i if kdata['kkll'][i] == 1: # Luo =>[[[[下跌趋势中...]]]] CHL = LLL # 下跌趋势中CHL取最低价 CHLlocation=LLLlocation #1 Luo的位置 [Luo | CHL1 DDCGorGGCD1 CHL DDCGorGGCD C] Luolocation=i if Kai != 0: CHLP = (CHL / Kai * 100 - 100).round(1) # 判断下跌趋势极值离完成任务还有多远，负数代表超额完成下跌任务 else: CHLP = 0 # 999代表趋势不全，即Kai=0, 直接除会程序报错 #1 [Luo] DDCGorGGCD1 and DDCGorGGCD1ma30 for k in range(CHLlocation-1,Luolocation,-1): #从CHL向前找一个[完成止跌结构]的"伪"高点,这个高点不是DDCGorGGCD1 if kdata['high'][k]>kdata['ma30'][k]: #如果找到,再向前到Luo后,找CHL1 CHL1=kdata['low'][k] #如果没有找到,就不用DDCGorGGCD1和CHL这两个参考点 CHL1location=k for m in range(k,Luolocation,-1): #即没有完成止跌结构,就不用比是否创新低 if kdata['low'][m]DDCGorGGCD1: DDCGorGGCD1=kdata['high'][m] DDCGorGGCD1ma30=kdata['ma30'][m] DDCGorGGCD1location=m break #只要找到一个high>ma30的伪"高点"就向前找最低点CHL1,再向CHL找DDCGorGGCD1 for k in range(ttlk - 1, CHLlocation, -1): # 如果找到的是下跌趋势中的最低值，则找C与趋势最低值之间的最高价 if k == ttlk - 1: DDCGorGGCD = kdata['high'][k] DDCGorGGCDma30 = kdata['ma30'][k] if kdata['high'][k] > DDCGorGGCD: DDCGorGGCD = kdata['high'][k] DDCGorGGCDma30 = kdata['ma30'][k] if DDCGorGGCD == 0: DDCGorGGCD = kdata['high'][ttlk - 1] DDCGorGGCDma30 = kdata['ma30'][ttlk - 1] break if kdata['kkll'][i] == -1: # Kai =>[[[[...上涨趋势中...]]] CHL = HHH # 上涨趋势中CHL取最高价 CHLlocation=HHHlocation #1 Kai的位置 Kailocation=i #1 end CHLP = (Luo / CHL * 100 - 100).round(1) # 判断上涨趋势极值离完成任务还有多远，负数代表超额完成上涨任务 #1 [如果创新低后再一次形成DDCGorGGCD]找DDCGorDDCG1 # 上涨趋势中形成的最高CHL向前找DDCGorGGCD1的前提是 # 下跌趋势中形成的最低CHL向前找DDCGorGGCD1的前提是向上碰Ma30(完成止跌结构,否则没有DDCGorGGCD1也就没有CHL for k in range(CHLlocation-1,Kailocation,-1): #从CHL(最高)向前找一个[完成止跌结构]的"伪"低点,这个低点不是DDCGorGGCD1 if kdata['low'][k]CHL1: CHL1=kdata['high'][m] CHL1location=m DDCGorGGCD1=kdata['low'][CHL1location] for m in range(CHL1location,CHLlocation,1): if kdata['low'][m] GG * 1.01 or closeprice < DD * 0.99: outofrange = 1 else: outofrange = 0 if GG == 0 or DD == 0 or Kai == 0 or Luo == 0: QSabnormal = 1 else: QSabnormal = 0 if Kai != 0: spaceP = (Luo / Kai * 100 - 100).round(1) else: spaceP = 0 revisetime = datetime.datetime.strftime(datetime.datetime.now(), '%m-%d %H:%M') try: CHL=CHL.round(2) CHL1=CHL1.round(2) except: pass periodQS = {'code': stockcode + period, 'stockcode': stockcode, 'period': period, 'C': closeprice, # data['close'][-1], 'QS': QS, 'DD': DD, 'Kai': Kai, 'GG': GG, 'Luo': Luo, 'profitP': profitP, 'riskP': riskP, 'PLP': PLP, 'CHL': CHL, 'CHLP': CHLP, 'spaceP': spaceP, 'outofrange': outofrange, 'QSabnormal': DDCGorGGCDma30, # QSabnormal, 'ma5': kdata['ma5'][-1].round(2), 'ma10': kdata['ma10'][-1].round(2), 'ma30': kdata['ma30'][-1].round(2), 'ma5P': (closeprice / kdata['ma5'][-1] * 100 - 100).round(2), 'ma10P': (closeprice / kdata['ma10'][-1] * 100 - 100).round(2), 'ma30P': (closeprice / kdata['ma30'][-1] * 100 - 100).round(2), 'periodSugg': periodsuggestion, 'DDCGorGGCD': DDCGorGGCD, 'reviseTime': revisetime, 'NDG' : NDG, 'NKL' : NKL, 'CHL1' : CHL1, 'DDCGorGGCD1': DDCGorGGCD1 } periodQStime={ 'code' :stockcode+period, 'stockcode' :stockcode, 'period' :period, #'DDtime' : DDtime, #'Kaitime' : Kaitime, #'GGtime' : GGtime, #'Luotime' : Luotime, #'CHLtime' : CHLtime, #'DDCGorGGCDtime': DDCGorGGCDtime, #'NDGtime' : NDGtime, #'NKLtime' : NKLtime 'reviseTime': revisetime } self.keypoint['CHL1']=CHL1 self.keypoint['DDCGorGGCD1']=DDCGorGGCD1 self.keypoint['CHL']=CHL self.keypoint['DDCGorGGCD']=DDCGorGGCD self.keypoint['DDCGorGGCD1ma30']=DDCGorGGCD1ma30 self.keypoint['DDCGorGGCDma30']=DDCGorGGCDma30 return kdata,periodQS #,GGday,DDday def cal_qs_QSmessage(self,kdata,ttlk,period): NDG=self.keypoint['NDG'] NKL=self.keypoint['NKL'] DD=self.keypoint['DD'] Kai=self.keypoint['Kai'] GG=self.keypoint['GG'] Luo=self.keypoint['Luo'] QS=self.keypoint['QS'] CHL1=self.keypoint['CHL1'] CHL=self.keypoint['CHL'] DDCGorGGCD1=self.keypoint['DDCGorGGCD1'] DDCGorGGCD1ma30=self.keypoint['DDCGorGGCD1ma30'] DDCGorGGCD=self.keypoint['DDCGorGGCD'] DDCGorGGCDma30=self.keypoint['DDCGorGGCDma30'] #如果趋势空间合理 + 收盘价在趋势空间[DD,GG]以内,当前趋势就有价值 closeprice=kdata['close'][ttlk-1] goodspace=False insidespace=False self.QS[period]="" #avoid nan error #self.space={"month": 1,"week":0.2,"day":0.08,"m30":0.04,"m5":0.02,"m1":0.01} #趋势空间 月线100% 周线20% 日线8% 30分钟4% 5分钟2% 1分钟1% #self.diffbyp ={"month":0.08,"week": 0.04,"day":0.02 ,"m30":0.01 ,"m5":0.005 ,"m1":0.0025 } #容错率 月线 8% 周线 4% 日线2% 30分钟1% 5分钟0.5% 1分钟0.25% if Kai!=0: if Luo/Kai>(1+self.space[period]): goodspace=True #下跌趋势中，如果CHL和CHL1中有一个是0，那么minchl就是0，则趋势就自动变为-2，因为minchl不可能大于DD #上涨趋势中，不存在这个问题 minchl=min(CHL,CHL1) maxchl=max(CHL,CHL1) if CHL==0 or CHL1==0: minchl=maxchl #if closeprice>DD*(1-self.diffbyp[period]) and closepriceDD*(1-self.diffbyp[period]) and maxchlGG*(1+self.diffbyp[period]): self.QS[period]+="+0" #盘整 else: if closepriceDDCGorGGCDma30 and DDCGorGGCD*DDCGorGGCDma30!=0: DoGfinishJG=True if DDCGorGGCD1>DDCGorGGCD1ma30 and DDCGorGGCD1*DDCGorGGCD1ma30!=0: DoG1finishJG=True if DoGfinishJG or DoG1finishJG: self.QS[period]+="b" #1 计算QStask3 #下跌趋势中是完成趋势任务后创新低 if CHL1>0 and CHL=tmpk['ma30'][28]: self.QS[period]+="d" except: pass elif QS==1: #上涨趋势 #计算本级别趋势是否有意义[上涨趋势] #如果趋势空间合理 + 收盘价在趋势空间[DD,GG]以内,当前趋势就有价值 #计算QStask1 #CHL或CHL1中小的那个完成了趋势空间,就行 if min(CHL,CHL1)>Luo*(1-self.diffbyp[period]): self.QS[period]+="a" #计算QStask2 #如果DDCGorGGCD 上碰Ma30,完成止跌结构 #如果DDCGorGGCD1 上碰Ma30,完成止跌结构 DoGfinishJG=False DoG1finishJG=False if DDCGorGGCD0 and CHL>CHL1 and CHL1>Luo*(1-self.diffbyp[period]): #print(period,"CHL,CHL1",CHL1,CHL) self.QS[period]+="c" #下跌趋势中的趋势突破是下一根K线让生命线向上 tmpk=kdata[-29:] newma30=tmpk['close'].sum()+kdata['close'][ttlk-1] newma30=round(newma30/30,4) try: if newma30<=tmpk['ma30'][28]: self.QS[period]+="d" except: pass #如果30天内有连续两个涨停,则e df=self.kdata_d[-30:] #df['zdf']=(df['close']-df['close'].shift(1))/df['close'].shift(1) try: for i in range(2,30,1): #连续两天上涨超过9%则为+e,连续两天下跌超过9%则为-e, 同时出现+e和-e看先后顺序 if df.close[i]/df.close[i-1]>1.08 and df.close[i-1]/df.close[i-2]>1.08: self.QS[period]+="+e" break for i in range(2,30,1): #连续两天上涨超过9%则为+e,连续两天下跌超过9%则为-e, 同时出现+e和-e看先后顺序 if df.close[i]/df.close[i-1]<0.92 and df.close[i-1]/df.close[i-2]<0.92: self.QS[period]+="-e" break except: pass def cal_jg_JGmodule(self,kdata,ttlk,period,periodQS): # 计算结构 # ==================================================== ''' DD -2 Kai -1 GG 2 Luo 1 从后向前找，如果第一个找到-2，即当前在上涨趋势中 1，下跌趋势中找底分型， 2，两个底分型之间找最高点（或碰MA10或MA30的点） ''' CHL =periodQS['CHL'] Kai =periodQS['Kai'] Luo =periodQS['Luo'] QS =periodQS['QS'] GGday=self.keypoint['GGday'] DDday=self.keypoint['DDday'] revisetime =periodQS['reviseTime'] ''' kdata.insert(16, 'JGVFX', 0.00) # 结构中的V分型 kdata.insert(17, 'JGAFX', 0.00) # 结构中的A分型 kdata.insert(18, 'JG', ' ') # 结构 kdata.insert(19, 'JGV', 0.00) # 结构特征价格 ''' kdata['JGVFX']=0.00 # 结构中的V分型 kdata['JGAFX']=0.00 # 结构中的A分型 kdata['JG']=' ' # 结构 kdata['JGV']=0.00 # 结构特征价格 JGdone = 0 # 完成止跌结构 #JGdone KJdone = 0 # 完成下跌空间 #KJdone JGQR = 0 # 结构强弱 #JGQR 1代表强(创新高/不创新低) -1代表弱(不创新高/创新低) QX = 0 # 情绪, 代替以前的KongHuang和KangFen两个指标,完成止跌结构后创新低制造恐慌 ConfirmedJG = ' ' #当前能确认的结构??? CurrentJG = ' ' #最后一根K线可能的结构 CurrentJGV = 0 #当前结构value minlpercent =0 minllocation = 0 maxhpercent=0 maxhlocation=0 for i in range(ttlk - 1, -1, -1): # 0<=i<=ttlk-1 # 下跌趋势中上结构判断 if kdata['kkllx'][i] == 2: # 从最高点开始找L离MA30最远的那根K线，开始找第一个底 minlpercent = kdata['low'][i] /kdata['ma30'][i] minllocation = i for j in range(i ,ttlk -1 ,1): minlpercentnew =kdata['low'][j] /kdata['ma30'][j] if minlpercentnew kdata['low'][k]: j=k # 如果在找到顶分型找到更低的底，则 break if lowhighcurrentHigh: currentHigh=kdata['high'][k] currentHighLocation=k if kdata['JGVFX'][k]==-1: j=k break if k!=ttlk-1: kdata['JGAFX'][currentHighLocation]=1 # 删除下跌趋势中无效A分型信号和左右两边较高的那个V分型信号 # 如果要删除的那个V是起始的6，则必须保留(当前是保留 因为其比例意义） # （或者换6到新的底） # 如果移动6，则minllocation要跟着一起改 #删除碰不到MA10或MA30的A分型信号 for j in range(minllocation,ttlk-1,1): if kdata['JGAFX'][j]==1: high2ma10 =abs(kdata['ma10'][j]-kdata['high'][j]) high2ma5=abs(kdata['ma5'][j]-kdata['high' ] [j]) if kdata['high'][j]< kdata['ma10'][j] \ and high2ma5/2kdata['ma30'][j] or high2ma30normalspaceP[period]!!! KJdone=1 # 需要参考对应级别的正常趋势空间 ZDJG=1 # 完成止跌结构 #10%以内都算完成趋势空间XXX CHLkdata['ma10'][j] or high2ma10kdata['high'][j]: CurrentJG = '7' CurrentJGV = kdata['high'][ttlk - 1] else: CurrentJG ='6' CurrentJGV=kdata['low'][ttlk-1] if lastFXis == '7': if kdata['low'][ttlk-1]kdata['high'][j]: CurrentJG = '9' CurrentJGV = kdata['high'][ttlk - 1] else: CurrentJG ='8' CurrentJGV=kdata['low'][ttlk-1] if lastFXis == '9': if kdata['low'][ttlk-1]kdata['high'][j]: CurrentJG ='01' #'1?' CurrentJGV = kdata['high'][ttlk - 1] else: CurrentJG ='0' CurrentJGV=kdata['low'][ttlk-1] kdata['JG'][ttlk-1]=CurrentJG kdata['JGV'][ttlk-1] = CurrentJGV ConfirmedJG=lastFXis break #完成结构9后是否创新低 //KongHuang? for j in range(GGday,ttlk-1,1): if kdata['JG'][j]=='9': lowbefore =kdata.iloc[GGday:j, 3].min() # 3 'low' lowafter = kdata.iloc[j:ttlk-1, 3].min() # 2 high 1 c losee 0 open if lowafterkdata ['low'][k]: JGQR=1 # 结构强弱 //后面一个V比前面一个高, 则下跌结构强 break else: JGQR=-1 #后面一个V比前面一个低,则下跌结构弱 break break # 只找最近的两个下跌的结构，比较强弱 break # 计算完下跌趋势的结构后退出程序（不用再向前找了） # 上涨趋势中上结构判断 if kdata['kkllx'][i] == -2: # 从最高点开始找L离MA30最远的那根K线，开始找第一个底 #logging.info(f"{period}是下跌趋势") maxhpercent = kdata['high'][i] /kdata['ma30'][i] maxhlocation = i for j in range(i ,ttlk -1 ,1): maxhpercentnew =kdata['high'][j] /kdata['ma30'][j] if maxhpercentnew > maxhpercent: maxhlocation = j maxhpercent =maxhpercentnew kdata['JGAFX'][maxhlocation ] =1 # 最远点为顶分型 kdata['JG'][maxhlocation] = "1" # 最远点为结构1 kdata['JGV'][maxhlocation] = kdata['high'][maxhlocation] if maxhlocation == ttlk - 1: break # 如果6是最后一根K线，则不计算其它结构只有6 # 不同的结构算法，可能的计算起点不同，但是下面的计算都是一致的 # 从1+1开始找下跌趋势中的A分型 for j in range(maxhlocation +1, ttlk -1 ,1): if kdata['high'][j] >kdata['high'][j -1]: highlow =kdata['low'][j] highhigh =kdata['high'][j] for k in range(j +1 ,ttlk -1 ,1): if highhigh kdata['low'][k]: kdata['JGAFX'][j] = 1 j=k break # 找到一个高顶 # 1开始找上涨趋势中的V分型 k=0 for j in range(maxhlocation,ttlk-1,1): if kdata ['JGAFX'][j]==1: currentLow=kdata['low'][j] currentLowLocation=j for k in range(j+1,ttlk-1,1): if kdata['low'][k] kdata['ma10'][j] \ and low2ma5/2>low2ma10: #？？？high超过ma10或high离ma5的距离是到ma10距离的2倍 #如果下跌过程中的A分型不满足79的结构条件，则删除这个A分型 kdata['JGVFX'][j] = 0 # 2 for debug #找A分型的 下一个 A分型 （可能没有） comp_OK=0 for k in range(j,ttlk-1,1): if kdata ['JGAFX'][k]==1: nextAFX=k #如果后面没有V分型，则nextVFX=? comp_OK=comp_OK+1 #后面有个V break #找A分型的 上一个 A分型 （必定有，至少有6） for k in range(j,-1,-1): if kdata['JGAFX'][k]==1: lastAFX=k comp_OK=comp_OK+1 #前面有个V break if comp_OK==2: #确保有两个V分型，再比较，否则程序报错 logging.info(f"lastAFX is {lastAFX} ||| nextAFX is {nextAFX}") if kdata['high'][nextAFX]>=kdata['high'][lastAFX]: #如果下一个low更低 if kdata['JG'][lastAFX]!="1": #上一个不是6，就把上一个V分型信号删除 #try: #任何一个A分型前都能找到至少一个V分型信号（6） kdata['JGAFX'][lastAFX]=0 #这个地方应该不会出错 lastVFX下标一定能找到 else: kdata['JGAFX'][nextAFX] =0 #???A后是否必定有nextVFX else: kdata['JGAFX'][nextAFX]=0 #???删除下一个V分型信号 # 计算其它结构 # have 7 have8 # lastFXis='6' # lastVFXis='6' # lastAFXis='0' # 从6后面的第一根K线开始找分型 # 判断 9 和 是否完成下跌空间指标 XDKJ for j in range(maxhlocation+1,ttlk-1,1): if kdata['JGVFX'][j]!=0: low2ma30 = abs(kdata ['ma30'][j] - kdata['low'][j]) low2ma10 = abs(kdata['ma10'][j] - kdata['low'][j]) low2ma5 = abs(kdata['ma5'][j] - kdata['low'][j]) if kdata['low'][j]Luo/1.1: # and spaceP>normalspaceP[period]!!! XDKJ=1 # 需要参考对应级别的正常趋势空间 ZDJG=1 # 完成止跌结构 #10%以内都算完成趋势空间XXX CHLkdata['high'][k]: kdata['JGAFX'][k]=0 else: kdata['JGAFX'][j]=0 else: kdata['JGAFX'][k] = 0 # 定义最后一根K线的结构 #logging.info(f"{period},{kdata}") for j in range(ttlk-1, maxhlocation-1, -1): lastFXis=kdata['JG'][j] if lastFXis!=' ': if lastFXis == '1': if kdata['low'][ttlk-1]kdata['high'][j]: CurrentJG = '3' CurrentJGV = kdata['high'][ttlk - 1] else: CurrentJG ='2' CurrentJGV=kdata['low'][ttlk-1] if lastFXis == '3': if kdata['low'][ttlk-1]kdata['high'][j]: CurrentJG = '5' CurrentJGV = kdata['low'][ttlk - 1] else: CurrentJG ='4' CurrentJGV=kdata['low'][ttlk-1] if lastFXis == '5': if kdata['low'][ttlk-1]highbefore: QX=1 #比较下跌结构强弱 for j in range(ttlk-2,maxhlocation-1,-1): if kdata['JG'][j] == '1' or kdata['JG'][j] == '3' or kdata[ 'JG'][j] == '5': for k in range(j-1 , maxhlocation-1 , -1 ): if kdata['JG'][k] == '1' or kdata['JG'][k]=='3' or kdata['JG'][k]=='5': if kdata['high'][j] {kdata['low'][j]}") break break # 只找最近的两个下跌的结构，比较强弱 break # 计算完下跌趋势的结构后退出程序（不用再向前找了） periodJG={ #'code' : stockcode+period, #'stockcode' : stockcode, #'period' : period, #'QS' : QS, # 趋势方向 'ConfirmedJG' : ConfirmedJG,# 确认的结构 'CurrentJG' : CurrentJG, # 当前（可能）结构'XDKJ' : XDKJ, # 完成下跌空间？ 'JGdone' : JGdone, #完成结构 有9或4且又没有大幅创新高或创新低 与ma30的相对距离判断 #'KJDone' : KJdone, #完成空间 #这个应该是在趋势计算中判断 #'ZDJG' : ZDJG, # 形成止跌结构？ 'QX' : QX, # 制造恐慌？ 'JGQR' : JGQR, # 结构强弱 #'XDKJ' : XDKJ, # 完成下跌空间 #'reviseTime' : revisetime } #kdata.to_csv(rf"./tmpout/{period}.csv", encoding="utf_8_sig", index=True) return kdata, periodJG def topns(self,module,pns,reason,price,period): tmppns={'module':module,'pns':pns,'reason':reason,'price':price,'per':0,'period':period} self.pns=pd.concat([self.pns, pd.DataFrame([tmppns])], ignore_index=True) def cal_qsjg_pns(self,periodQS,periodJG,period): self.topns("QS","P","GG",periodQS['GG'],self.p2ab[period]) self.topns("QS","P","Luo",periodQS['Luo'],self.p2ab[period]) self.topns("QS","S","Kai",periodQS['Kai'],self.p2ab[period]) self.topns("QS","S","DD",periodQS['DD'],self.p2ab[period]) tmpqs=self.QS[period] #print(f"period:{period}") if ("-1" in tmpqs or "-2" in tmpqs or "0" in tmpqs) and period!="m1": self.topns("JG","P","ma30",periodQS['ma30'],self.p2ab[period]) if int(periodJG['CurrentJG'])<=7 and periodJG['CurrentJG']!="0" and period!="m1" and period!="m5": self.topns("JG","P","ma10",periodQS['ma10'],self.p2ab[period]) if ("+1" in tmpqs or "+2" in tmpqs) and period!="m1": self.topns("JG","S","ma30",periodQS['ma30'],self.p2ab[period]) if int(periodJG['CurrentJG'])<=2 and period!="m1" and period!="m5": self.topns("JG","S","ma10",periodQS['ma10'],self.p2ab[period]) def cal_qs_pnsall(self): if self.QS_m['Kai']!=0: if self.QS_m['Luo']/self.QS_m['Kai']-1>=self.space['month']: self.PnS=\ {'mGG': self.QS_m['GG'],'mLuo':self.QS_m['Luo'],'mDD': self.QS_m['DD'],'mKai':self.QS_m['Kai']\ ,'mma5':self.QS_m['ma5'],'mma10':self.QS_m['ma10'],'mma30':self.QS_m['ma30'] } if self.QS_w['Kai']!=0: if self.QS_w['Luo']/self.QS_w['Kai']-1>=self.space['week']: self.PnS=self.Merge(self.PnS,\ {'wGG': self.QS_w['GG'],'wLuo':self.QS_w['Luo'],'wDD': self.QS_w['DD'],'wKai':self.QS_w['Kai']\ ,'wma5':self.QS_w['ma5'],'wma10':self.QS_w['ma10'],'wma30':self.QS_w['ma30'] }) if self.QS_d['Kai']!=0: if self.QS_d['Luo']/self.QS_d['Kai']-1>=self.space['day']: self.PnS=self.Merge(self.PnS,\ {'dGG': self.QS_d['GG'],'dLuo':self.QS_d['Luo'],'dDD': self.QS_d['DD'],'dKai':self.QS_d['Kai']\ ,'dma5':self.QS_d['ma5'],'dma10':self.QS_d['ma10'],'dma30':self.QS_d['ma30'] }) if self.QS_t['Kai']!=0: if self.QS_t['Luo']/self.QS_t['Kai']-1>=self.space['m30']: self.PnS=self.Merge(self.PnS,\ {'tGG': self.QS_t['GG'],'tLuo':self.QS_t['Luo'],'tDD': self.QS_t['DD'],'tKai':self.QS_t['Kai']\ ,'tma5':self.QS_t['ma5'],'tma10':self.QS_t['ma10'],'tma30':self.QS_t['ma30'] }) if self.QS_f['Kai']!=0: if self.QS_f['Luo']/self.QS_f['Kai']-1>=self.space['m5']: self.PnS=self.Merge(self.PnS,\ {'fGG': self.QS_f['GG'],'fLuo':self.QS_f['Luo'],'fDD': self.QS_f['DD'],'fKai':self.QS_f['Kai']\ ,'fma5':self.QS_f['ma5'],'fma10':self.QS_f['ma10'],'fma30':self.QS_f['ma30'] }) if self.QS_o['Kai']!=0: if self.QS_o['Luo']/self.QS_o['Kai']-1>=self.space['m1']: self.PnS=self.Merge(self.PnS,\ {'oGG': self.QS_o['GG'],'oLuo':self.QS_o['Luo'],'oDD': self.QS_o['DD'],'oKai':self.QS_o['Kai']\ ,'oma5':self.QS_o['ma5'],'oma10':self.QS_o['ma10'],'oma30':self.QS_o['ma30'] }) self.PnS = self.Merge(self.PnS, \ {'C':self.QS_m['C'],'H':self.kdata_d['high'][(self.kdata_d.shape[0]-1)],'L':self.kdata_d['low'][(self.kdata_d.shape[0]-1)] }) tmppd=pd.DataFrame.from_dict(self.PnS,orient='index',columns=['value']) tmppd=tmppd.reset_index().rename(columns={'index':'name'}) #tmppd.insert(2, 'pct', 0.00) # -1起涨点 +1起跌点 tmppd['pct']=0.00 # -1起涨点 +1起跌点 tmppd['pct'] = tmppd['value'].map(lambda x: (x / self.QS_m['C']-1)*100) tmppd['pct']=tmppd['pct'].round(2) tmppd=tmppd.sort_values(by="pct", ascending=False) tmppd=tmppd.reset_index(drop=True) tmppd.index=tmppd.index+1 self.PnSpd=tmppd def cal_qs_pnsbyperiod(self): #数据 - > 趋势分析 -> 结构分析 ->pns -> BSZC #print("by period self.QS_m:",self.QS_m) #print("by period self.JG_m:",self.JG_m) for i in self.periodab: #print(i) pass pass def cal_pns(self): newpns=self.pns #月线pns if "0" in self.QS['month']:newpns=newpns[(newpns['period']!="m")] if "0" in self.QS['week']:newpns=newpns[(newpns['period']!="w")] if "0" in self.QS['day']:newpns=newpns[(newpns['period']!="d")] if "0" in self.QS['m30']:newpns=newpns[(newpns['period']!="t")] if "0" in self.QS['m5']:newpns=newpns[(newpns['period']!="f")] if "0" in self.QS['m1']:newpns=newpns[(newpns['period']!="o")] newhavelist=self.havelist[(self.havelist['code']==self.stockcode[:6])] newhavelist=newhavelist.reset_index(drop=True) maxprice=newhavelist['+'][0] minprice=newhavelist['-'][0] newpns=newpns[(newpns['price']minprice)] newpns=newpns.reset_index(drop=True) print(newpns) #self.pns=newpns pnsm=self.pns[(self.pns['period']=="m") | (self.pns['module']=="")] pnsm=pnsm[['reason','price','per']] pnsm=pnsm.reset_index(drop=True) #周线pns try: pnsw=self.pns[(self.pns['period'] =="m") | (self.pns['period']=="w") | (self.pns['module']=="")] pnsw=pnsw.reset_index(drop=True) try: startx=pnsw[pnsw['reason']=="GG_w"].index[0] except: startx=100 startx1=pnsw[pnsw['reason']=="H_"].index[0] startx=min(startx,startx1) try: endx=pnsw[pnsw['reason']=="DD_w"].index[0] except: endx=0 endx1=pnsw[pnsw['reason']=="L_"].index[0] endx=max(endx,endx1) pnsw=pnsw[startx:endx+1] pnsw=pnsw[['reason','price','per']] pnsw=pnsw.reset_index(drop=True) except: pnsw=pd.DataFrame() #日线pns try: pnsd=self.pns[(self.pns['period'] =="m") | (self.pns['period']=="w") | (self.pns['period']=="d") | (self.pns['module']=="")] pnsd=pnsd.reset_index(drop=True) try: startx=pnsd[pnsd['reason']=="GG_d"].index[0] except: startx=100 startx1=pnsd[pnsd['reason']=="H_"].index[0] startx=min(startx,startx1) try: endx=pnsd[pnsd['reason']=="DD_d"].index[0] except: endx=0 endx1=pnsd[pnsd['reason']=="L_"].index[0] endx=max(endx,endx1) pnsd=pnsd[startx:endx+1] pnsd=pnsd[['reason','price','per']] pnsd=pnsd.reset_index(drop=True) except: pnsd=pd.DataFrame() #30分钟pns try: pnst=self.pns[(self.pns['period'] =="m") | (self.pns['period']=="w") | (self.pns['period']=="d") | (self.pns['period']=="t") | (self.pns['module']=="")] pnst=pnst.reset_index(drop=True) try: startx=pnst[pnst['reason']=="GG_t"].index[0] except: startx=100 startx1=pnst[pnst['reason']=="H_"].index[0] startx=min(startx,startx1) try: endx=pnst[pnst['reason']=="DD_t"].index[0] except: endx=0 endx1=pnst[pnst['reason']=="L_"].index[0] endx=max(endx,endx1) pnst=pnst[startx:endx+1] pnst=pnst[['reason','price','per']] pnst=pnst.reset_index(drop=True) except: pnst=pd.DataFrame() #5分钟pns try: pnsf=self.pns[(self.pns['period'] =="m") | (self.pns['period']=="w") | (self.pns['period']=="d") | (self.pns['period']=="t") | (self.pns['period']=="f") | (self.pns['module']=="")] pnsf=pnsf.reset_index(drop=True) try: startx=pnsf[pnsf['reason']=="GG_f"].index[0] except: startx=100 startx1=pnsf[pnsf['reason']=="H_"].index[0] startx=min(startx,startx1) try: endx=pnsf[pnsf['reason']=="DD_f"].index[0] except: endx=0 endx1=pnsf[pnsf['reason']=="L_"].index[0] endx=max(endx,endx1) pnsf=pnsf[startx:endx+1] pnsf=pnsf[['reason','price','per']] pnsf=pnsf.reset_index(drop=True) except: pass #1分钟pns try: pnso=self.pns[(self.pns['period'] =="m") | (self.pns['period']=="w") | (self.pns['period']=="d") | (self.pns['period']=="t") | (self.pns['period']=="f") | (self.pns['period']=="o") | (self.pns['module']=="")] pnso=pnso.reset_index(drop=True) try: startx=pnso[pnso['reason']=="GG_o"].index[0] except: startx=100 startx1=pnso[pnso['reason']=="H_"].index[0] startx=min(startx,startx1) try: endx=pnso[pnso['reason']=="DD_o"].index[0] except: endx=0 endx1=pnso[pnso['reason']=="L_"].index[0] endx=max(endx,endx1) pnso=pnso[startx:endx+1] pnso=pnso[['reason','price','per']] pnso=pnso.reset_index(drop=True) except: pass pnsttl=pd.concat([pnsm,pnsw,pnsd,pnst,pnsf,pnso],axis=1) pnsttl=pnsttl.fillna("") newpnsm=pnsm[(pnsm['reason']=="fake")] newpnsw=newpnsm newpnsd=newpnsm newpnst=newpnsm newpnsf=newpnsm newpnso=newpnsm if "0" not in self.QS['month']: newpnsm=pnsm[(pnsm['price']minprice)] if "0" not in self.QS['week']: newpnsw=pnsw[(pnsw['price']minprice)] if "0" not in self.QS['day']: newpnsd=pnsd[(pnsd['price']minprice)] if "0" not in self.QS['m30']: newpnst=pnst[(pnst['price']minprice)] if "0" not in self.QS['m5']: newpnsf=pnsf[(pnsf['price']minprice)] if "0" not in self.QS['m1']: newpnso=pnso[(pnso['price']minprice)] newpnsttl=pd.concat([newpnsm,newpnsw,newpnsd,newpnst,newpnsf,newpnso],axis=1) newpnsttl=newpnsttl.fillna("") self.get_havelist() #os.system("clear") print(self.havelist) #print(self.QS) tmpstr=f"--M:{self.QS['month']}-------------W:{self.QS['week']}-------------D:{self.QS['day']}-------------T:{self.QS['m30']}--------------F:{self.QS['m5']}-------------O:{self.QS['m1']}----------" #print(f"-------------------M:{self.QS['month']}-------------W:{self.QS['week']}-------------D:{self.QS['day']}-------------T:{self.QS['m30']}--------------F:{self.QS['m5']}-------------O:{self.QS['m1']}----------") print(f"\033[0;31;40m\t {tmpstr} \033[0m") print(pnsttl) print(f"\033[0;31;40m\t {tmpstr} \033[0m") print(newpnsttl) print(f"\033[0;31;40m\t {tmpstr} \033[0m") #print(f"-------------------M:{self.QS['month']}-------------W:{self.QS['week']}-------------D:{self.QS['day']}-------------T:{self.QS['m30']}--------------F:{self.QS['m5']}-------------O:{self.QS['m1']}----------") #print("-------------------M------------------W------------------D------------------T-------------------F------------------O----------") self.pnsttl=pnsttl self.newpnsttl=newpnsttl def cal_qs(self,kdata, stockcode, period): #periodQS={} closeprice=kdata['close'][-1] ''' periodQS = {'code': stockcode + period, 'stockcode': stockcode, 'period': period, 'C': closeprice, # data['close'][-1], 'QS': 0, 'DD': 0, 'Kai': 0, 'GG': 0, 'Luo': 0, 'profitP': 0, 'riskP': 0, 'PLP': 0, 'CHL': 0, 'CHLP': 0, 'spaceP': 0, 'outofrange': 0, 'QSabnormal': 0, # QSabnormal, 'ma5': kdata['ma5'][-1].round(2), 'ma10': kdata['ma10'][-1].round(2), 'ma30': kdata['ma30'][-1].round(2), 'ma5P': (closeprice / kdata['ma5'][-1] * 100 - 100).round(2), 'ma10P': (closeprice / kdata['ma10'][-1] * 100 - 100).round(2), 'ma30P': (closeprice / kdata['ma30'][-1] * 100 - 100).round(2), 'periodSugg': 0, 'DDCGorGGCD': 0, 'reviseTime': "nodata"+period, 'NDG' : 0, 'NKL' : 0, 'CHL1' : 0, 'DDCGorGGCD1': 0 } ''' periodQS = {'code': stockcode + period, 'stockcode': stockcode, 'period': period, 'C': 99,#closeprice, # data['close'][-1], 'QS': 99, 'DD': 99, 'Kai': 99, 'GG': 99, 'Luo': 99, 'profitP': 99, 'riskP': 99, 'PLP': 99, 'CHL': 99, 'CHLP': 99, 'spaceP': 99, 'outofrange': 99, 'QSabnormal': 99, # QSabnormal, 'ma5': 99,#kdata['ma5'][-1].round(2), 'ma10': 99,#kdata['ma10'][-1].round(2), 'ma30': 99,#kdata['ma30'][-1].round(2), 'ma5P': 99,#(closeprice / kdata['ma5'][-1] * 100 - 100).round(2), 'ma10P': 99,#(closeprice / kdata['ma10'][-1] * 100 - 100).round(2), 'ma30P': 99,#(closeprice / kdata['ma30'][-1] * 100 - 100).round(2), 'periodSugg': 99, 'DDCGorGGCD': 99, 'reviseTime': 99,#"nodata"+period, 'NDG' : 99, 'NKL' : 99, 'CHL1' : 99, 'DDCGorGGCD1': 99 } #periodJG={} periodJG={ 'ConfirmedJG' : 99,# 确认的结构 'CurrentJG' : 99, # 当前（可能）结构'XDKJ' : XDKJ, # 完成下跌空间？ 'JGdone' : 99, #完成结构 有9或4且又没有大幅创新高或创新低 与ma30的相对距离判断 'QX' : 99, # 制造恐慌？ 'JGQR' : 99 # 结构强弱 } ttlk = kdata.shape[0] if ttlk<50: return kdata,periodQS,periodJG kdata=self.cal_qs_KaiLuo(kdata,ttlk) #+1 -1 [Luo 和 Kai] kdata=self.cal_qs_GGDD(kdata,ttlk) #+2 -2 [GG 和 DD] kdata=self.cal_qs_kkllv(kdata,ttlk) #计算+1 和 -1的数值 #趋势模型 kdata=self.cal_qs_QSmodule_NDGNKL(kdata,ttlk) kdata=self.cal_qs_QSmodule_QSDDKaiGGLuo(kdata,ttlk) kdata,periodQS=self.cal_qs_QSmodule_CHL1andCHL(stockcode,kdata,ttlk,period) #趋势信号 self.cal_qs_QSmessage(kdata,ttlk,period) #结构模型 kdata,periodJG=self.cal_jg_JGmodule(kdata,ttlk,period,periodQS) self.cal_qsjg_pns(periodQS,periodJG,period) return kdata, periodQS, periodJG def cal_qs_all(self): self.kdata_m,self.QS_m,self.JG_m = self.cal_qs(self.kdata_m, self.stockcode, 'month') self.kdata_w,self.QS_w,self.JG_w = self.cal_qs(self.kdata_w, self.stockcode, 'week') self.kdata_d,self.QS_d,self.JG_d = self.cal_qs(self.kdata_d, self.stockcode, 'day') self.kdata_t,self.QS_t,self.JG_t = self.cal_qs(self.kdata_t, self.stockcode, 'm30') self.kdata_f,self.QS_f,self.JG_f = self.cal_qs(self.kdata_f, self.stockcode, 'm5') self.kdata_o,self.QS_o,self.JG_o = self.cal_qs(self.kdata_o, self.stockcode, 'm1') self.cal_qs_pnsall() #所有级别的ps放在一起 self.cal_qs_pnsbyperiod() #单独计算每个级别的ps self.topns("","","H",self.kdata_d['high'][(self.kdata_d.shape[0]-1)],"") self.topns("","","L",self.kdata_d['low'][(self.kdata_d.shape[0]-1)],"") self.topns("","","C",self.QS_m['C'],"") self.pns['per']=round((self.pns['price']/self.QS_m['C']-1)*100,1) #print("all",self.pns) pns1=self.pns[(self.pns['per']>0) & (self.pns['pns']=="S")] self.pns=pd.concat([self.pns, pns1]).drop_duplicates(keep=False) pns1=self.pns[(self.pns['per']<0) & (self.pns['pns']=="P")] self.pns=pd.concat([self.pns, pns1]).drop_duplicates(keep=False) self.pns=self.pns.sort_values(by=['per'],ascending=False) self.pns['reason']=self.pns['reason']+"_"+self.pns['period'] self.cal_pns()