diff --git a/chanlun.py b/chanlun.py new file mode 100644 index 0000000..3ba1f6f --- /dev/null +++ b/chanlun.py @@ -0,0 +1,1054 @@ +# -*- coding: utf-8 -*- +""" +Created on Mon Oct 12 13:32:43 2020 + +@author: Administrator +""" + +import sys +import pandas as pd +import numpy as np +from datetime import date +import os + + + +data_dir='/scratch/tmp/pudge/chan/data/' + + + +#end debug +#init from non-inclusion +def buy_sell(INDEX,data_dir,debug=1): + os.chdir(data_dir) + len_dir = os.listdir(data_dir) + + if date.fromtimestamp(os.path.getmtime(len_dir[INDEX]))= len(df): + print('skipped') + return ; + print('processing ' + len_dir[INDEX].split('_')[1].split('.')[0]) + i = 0 + while(True ): + if ( df['low'][i] <= df['low'][i+1] ) or (df['high'][i] <= df['high'][i+1]): + i = i + 1 + else : + break + df = df[i:].reset_index(drop=True) + + #REMOVE INCLUSION + while ( True ): + temp_len = len(df) + i=0 + while i<=len(df)-4: + if (df.iloc[i+2,0]>=df.iloc[i+1,0] and df.iloc[i+2,1]<=df.iloc[i+1,1]) or\ + (df.iloc[i+2,0]<=df.iloc[i+1,0] and df.iloc[i+2,1]>=df.iloc[i+1,1]): + if df.iloc[i+1,0]>df.iloc[i,0]: + df.iloc[i+2,0] = max(df.iloc[i+1:i+3,0]) + df.iloc[i+2,1] = max(df.iloc[i+1:i+3,1]) + df.drop(df.index[i+1],inplace=True) + + continue + else: + df.iloc[i+2,0] = min(df.iloc[i+1:i+3,0]) + df.iloc[i+2,1] = min(df.iloc[i+1:i+3,1]) + df.drop(df.index[i+1],inplace=True) + + continue + i = i + 1 + # print(len(df)) + if len(df)==temp_len: + break + + df= df.reset_index(drop=True) + #get difenxing and dingfenxing + ul=[0] + for i in range(len(df)-2): + if df.iloc[i+2,0] < df.iloc[i+1,0] and df.iloc[i,0] < df.iloc[i+1,0]: + ul = ul + [1] + continue + if df.iloc[i+2,0] > df.iloc[i+1,0] and df.iloc[i,0] > df.iloc[i+1,0]: + ul = ul + [-1]# difenxing -1 dingfenxing +1 + continue + else: + ul = ul + [0] + ul = ul + [0] + global df1 + df1 = pd.concat((df[['low','high']],pd.DataFrame(ul),df['datetime']),axis=1) + + i = 0 + + while df1.iloc[i,2] == 0 and i < len(df1)-2: + i = i + 1 + df1=df1[i:] + + i = 0 + while ( sum(abs(df1.iloc[i+1:i+4,2]))>0 or df1.iloc[i,2]==0) and i < len(df1)-2: + i = i + 1 + df1=df1[i:] + df1.rename(columns= {0:'od'},inplace=True) + #df1.columns=Index(['low', 'high', 'od', 'datetime'], dtype='object') + #df1.columns=Index(['low', 'high', 'od', 'datetime'], dtype='object') + #df1.columns=Index(['low', 'high', 'od', 'datetime'], dtype='object') + #df1.columns=Index(['low', 'high', 'od', 'datetime'], dtype='object') + if len(df1)<=60: + print('error!') + return ; + #remove those within 3 bars + df1=df1.reset_index(drop=True) + global od_list#od_list are the index of df1 whose corresponding point are fenxing extreme vertex + + od_list=[0] + judge(0,0,1) + + + #judge(27,34,-1) + + + + + + #generate seg + start = 0 + while start < len(od_list)-5: + if check_init_seg(od_list[start:start+4]): + break + else: + start = start + 1 + + lines = [] + + i = start + end = False + while i <= len(od_list)-4: + se = Seg(od_list[i:i+4]) + label = False + while label == False and i <= len(od_list)-6: + i = i + 2 + label,start = se.grow(od_list[i+2:i+4]) + if se.vertex[-1] > od_list[-3]: + end =True + + lines += [se.lines()] + break + if end: + break + i = np.where(np.array(od_list) == se.vertex[-1])[0][0] + #show datetime of the end of the segment + #print(df1.iloc[se.vertex[-1],3]) + lines += [se.lines()]#there are still remaining fewer than or equal to + #3 bies not considered in the last + #seg ,which is unfinished and named by tails + low_list=df1.iloc[se.vertex[-1]:,0] + high_list=df1.iloc[se.vertex[-1]:,1] + + low_extre=low_list.min() + high_extre=high_list.max() + if se.finished == True: + if lines[-1][0][1] < lines[-1][1][1] :#d==1 + lines += [ [(se.vertex[-1],lines[-1][1][1]),(low_list.idxmin(),low_extre)]] + else: + lines += [ [(se.vertex[-1],lines[-1][1][1]),(high_list.idxmax(),high_extre)]] + + else: + if lines[-1][0][1] < lines[-1][1][1] :#d==1 + if low_extre > lines[-1][0][1]: + lines[-1] = [ (lines[-1][0][0],lines[-1][0][1]),(high_list.idxmax(),high_extre)] + else: + if low_list.idxmin()-se.vertex[-1]>=10: + lines += [ [(se.vertex[-1],lines[-1][1][1]),(low_list.idxmin(),low_extre)]] + else: + + if high_extre < lines[-1][0][1]: + lines[-1] = [ (lines[-1][0][0],lines[-1][0][1]),(low_list.idxmin(),low_extre) ] + else: + if high_list.idxmax()-se.vertex[-1]>=10: + lines += [ [(se.vertex[-1],lines[-1][1][1]),(high_list.idxmax(),high_extre)]] + + #print(lines) + #tails is the unfinished seg,tails[4] is its direction + a,tails = get_pivot(lines) + pro_a= process_pivot(a) +# ============================================================================= +# if len(pro_a)>=4: +# if pro_a[-1].trend==-1 and pro_a[-2].trend==0 and pro_a[-3].trend==-1 and\ +# tails[4]==-1 and pro_a[-1].finished ==0 and df1.iloc[-1][0] next pivot gg + pro_a[-1].write_out('../buy1/'+len_dir[INDEX].split('_')[1].split('.')[0]+'_buy1.txt',tails) + + signal,interval = buy_point3_des(pro_a,tails) + if signal:#trend slow down, first pivot dd > next pivot gg + pro_a[-1].write_out('../buy3/'+len_dir[INDEX].split('_')[1].split('.')[0]+'_buy3.txt',tails) + signal,interval = buy_point23(pro_a,tails) + if signal:#trend slow down, first pivot dd > next pivot gg + pro_a[-1].write_out('../buy23/'+len_dir[INDEX].split('_')[1].split('.')[0]+'_buy23.txt',tails) + signal,interval = buy_point2(pro_a,tails) + if signal:#trend slow down, first pivot dd > next pivot gg + pro_a[-1].write_out('../buy2/'+len_dir[INDEX].split('_')[1].split('.')[0]+'_buy2.txt',tails) + signal,interval = sell_point1(pro_a,tails) + if signal:#trend slow down, first pivot dd > next pivot gg + pro_a[-1].write_out('../sell1/'+len_dir[INDEX].split('_')[1].split('.')[0]+'_sell1.txt',tails) + + signal,interval = sell_point3_ris(pro_a,tails) + if signal:#trend slow down, first pivot dd > next pivot gg + pro_a[-1].write_out('../sell3/'+len_dir[INDEX].split('_')[1].split('.')[0]+'_sell3.txt',tails) + signal,interval = sell_point2(pro_a,tails) + if signal:#trend slow down, first pivot dd > next pivot gg + pro_a[-1].write_out('../sell2/'+len_dir[INDEX].split('_')[1].split('.')[0]+'_sell2.txt',tails) + + +#end buy_sell + + +#utility +def same_d(a1,a2,b1,b2,a_sign): + #a1 low a2 high b1 low b2 high + if a_sign == 1: + return (a1 > b1 and a2 > b2) + else: + return (a1 < b1 and a2 < b2) +def new_extreme(a1,a2,b1,b2,a_sign): + #whether b has new extreme than a,true return true + if a_sign == 1: + return b2 >= a2 + else: + return a1 >= b1 + +def write_seg(temp_lines,file,buy_sign,interval): + if buy_sign==True: + f = open(file,'w') + f.write('seg-3:'+str(df1.iloc[temp_lines[-3][0][0],3])+' '+\ + str(df1.iloc[temp_lines[-3][0][0],1])+str(df1.iloc[temp_lines[-3][1][0],3])+' '+\ + str(df1.iloc[temp_lines[-3][1][0],0]) ) + f.write('\n') + f.write('seg-2:'+str(df1.iloc[temp_lines[-2][0][0],3])+' '+ + str(df1.iloc[temp_lines[-2][0][0],0])+str(df1.iloc[temp_lines[-2][1][0],3])+' '+ + str(df1.iloc[temp_lines[-2][1][0],1]) ) + f.write('\n') + f.write('seg-1:'+str(df1.iloc[temp_lines[-1][0][0],3])+' '+ + str(df1.iloc[temp_lines[-1][0][0],1])+str(df1.iloc[temp_lines[-1][1][0],3])+' '+ + str(df1.iloc[temp_lines[-1][1][0],0]) ) + f.write('cur_price:\n'+str(df1.iloc[-1,0])) + f.write('\n') + f.write('cur_time:\n'+str(df1.iloc[-1,3])) + f.write('\n') + if df1.iloc[temp_lines[-1][1][0],0]interval: + f.write('target_price:'+str(interval)) + else: + f.write('resist_price:'+str(interval)) + + f.close() +def exist_opposite(cur_i,d,pos): + #print("exist_opposite") + #print('e0'+str(cur_i+pos)) + #print('e1'+str(df1.iloc[cur_i+pos,0])) + return df1['od'].iloc[cur_i+pos]==-d and same_d(df1.iloc[cur_i,0],df1.iloc[cur_i,1],\ + df1.iloc[cur_i+pos,0],df1.iloc[cur_i+pos,1],d) + +def exist_new_extreme(cur_i,d,start,end): + j = start + while j <= end: + if new_extreme(df1.iloc[cur_i,0],df1.iloc[cur_i,1],df1.iloc[cur_i + j,0],df1.iloc[cur_i + j,1],d): + return cur_i + j,True + j = j + 1 + return cur_i,False + + + +def judge(prev_i,cur_i,d):#d the direction of fenxing to be confirmed, prev_i the previous confirmed + #d == df1['od][cur_i] should hold when finished and prev_i = cur_i is set + global od_list + + + + #print('start ' + str(cur_i)) + if cur_i + 4 >= len(df1)-1: + #print('finished') + + #stop() + return 0 + + if cur_i - prev_i < 4 or df1['od'].iloc[cur_i] != d: + cur_i = cur_i + 1 + #print(cur_i) + judge(prev_i,cur_i,d) + else:# at least 4 bars later and direction correct + # now df1['od'].iloc[cur_i] ==d and cur_i - prev_i >= 4 + + new_i,label1 = exist_new_extreme(cur_i,d,2,3) + if label1 == True: + cur_i = new_i + #print("f1") + judge(prev_i,cur_i,d) + else: + k = 4 + if cur_i + k + 1>= len(df1)-1: + #print ("finishe2!") + return 0 + + while not exist_opposite(cur_i,d,k): + #while True: + #kth >=4 later bar does not match opposite fenxing + new_i,label2 = exist_new_extreme(cur_i,d,k,k) + if label2 == True: + cur_i = new_i + judge(prev_i,cur_i,d) + return 0 + #print('f2') + else: + k = k + 1 + if cur_i + k >= len(df1)-1: + #print ("finishe4!") + return 0 + + #confirmed by existent opposite fenxing + prev_i = cur_i + cur_i = cur_i + k + od_list = od_list + [prev_i] + #print('added' + str(prev_i)) + #print('input ' + str(cur_i)) + #print('-d ' + str(d)) + judge(prev_i,cur_i,-d) + #print('post call judge' + str(cur_i)) +#end judge + + + +#utils for seg + + +def check_init_seg(start_l): + #return True successful False fail + d = -df1.iloc[start_l[0],2] + if not ((d == 1 or d == -1 )and(len(start_l)==4)): + print('initializing seg failed in check_init_seg!') + + if d == 1: + if df1.iloc[start_l[1],1] < df1.iloc[start_l[3],1] and \ + df1.iloc[start_l[0],0] < df1.iloc[start_l[2],0]:#valid + return True + else: + return False + else: + if df1.iloc[start_l[1],0] > df1.iloc[start_l[3],0] and \ + df1.iloc[start_l[0],1] > df1.iloc[start_l[2],1]:#valid + return True + else: + return False + +class Seg: + + # Initializer / Instance Attributes + #directino of a seg is the same as its first bi, + #direction of a bi is the negative of its starting fenxing: + #rising bi is +1 and falling bi is -1 + def __init__(self, start_l): + + self.start = start_l[0] + + if df1.iloc[start_l[0],2]==0: + print("error init!") + self.d = - df1.iloc[start_l[0],2] + + self.finished = False + self.vertex = start_l + self.gap = False + if self.d == 1: + self.cur_extreme = df1.iloc[start_l[3],1] + self.cur_extreme_pos = start_l[3] + self.prev_extreme = df1.iloc[start_l[1],1] + else: + self.cur_extreme = df1.iloc[start_l[3],0] + self.cur_extreme_pos = start_l[3] + self.prev_extreme = df1.iloc[start_l[1],0] + + def grow(self,new_l): + #len(new_l) == 2 + #two consecutive bis will be added + #new_d, direction of the first bi added + if 1 == self.d:#rising seg + if df1.iloc[new_l[1],1] >= self.cur_extreme:#new extreme + if df1.iloc[new_l[0],0] > self.prev_extreme: + self.gap = True + else: + self.gap = False + self.prev_extreme = self.cur_extreme + self.cur_extreme = df1.iloc[new_l[1],1] + self.cur_extreme_pos = new_l[1] + + else:# no new extreme two cases to finish + if (self.gap == False and df1.iloc[new_l[1],0] < df1.iloc[self.vertex[-1],0]) or \ + (self.gap == True and (df1.iloc[self.vertex[-1],1] < df1.iloc[self.vertex[-3],1] ) \ + and (df1.iloc[self.vertex[-2],0] < df1.iloc[self.vertex[-4],0] )): + self.finished = True + + self.vertex = [ i for i in self.vertex if i <= self.cur_extreme_pos] + #print("finished") + #print(self.vertex) + #print(self.getrange()) + return True,self.vertex[-1] + + + #seg continued + self.vertex = self.vertex + new_l + + return False,0 + + else: + if df1.iloc[new_l[1],0] <= self.cur_extreme:#new extreme + if df1.iloc[new_l[0],1] < self.prev_extreme: + self.gap = True + else: + self.gap = False + self.vertex = self.vertex + new_l + self.prev_extreme = self.cur_extreme + self.cur_extreme = df1.iloc[new_l[1],0] + self.cur_extreme_pos = new_l[1] + else:# no new extreme two cases to finish + if (self.gap == False and df1.iloc[new_l[1],1] > df1.iloc[self.vertex[-1],1]) or \ + (self.gap == True and (df1.iloc[self.vertex[-1],0] > df1.iloc[self.vertex[-3],0] ) \ + and (df1.iloc[self.vertex[-2],1] > df1.iloc[self.vertex[-4],1] )): + self.finished = True + + self.vertex = [ i for i in self.vertex if i <= self.cur_extreme_pos] + #print("finished") + #print(self.vertex) + #print(self.getrange()) + return True,self.vertex[-1] + + #seg continued + self.vertex = self.vertex + new_l + + return False,0 + def check_finish(self): + #two consecutive bis will be added + #new_d, direction of the first bi added + if len(self.vertex)-1 <= 5: + print("no need to check!") + return False + + def getrange(self): + if self.d == 1: + return [ df1.iloc[self.start,0],self.cur_extreme,self.d ] + else: + return [ df1.iloc[self.start,1],self.cur_extreme,self.d ] + def show(self): + print(self.vertex) + print( self.getrange() ) + print(df1.iloc[self.vertex[-1],3]) + def lines(self):#lines :d==1 ==> [(index in df1 of starting + #,low of line),(index of end ,high of line)], + #d==-1 ==>[(,high of line),(,low of line)] + return [(self.start,self.getrange()[0]),\ + (self.vertex[-1],self.getrange()[1])] + +#end class Seg +#each object of pivot is a pivot +#1min pivot +class Pivot1: + def __init__(self, lines,d):#lines a 3 element list of Seg.getlines() + + self.trend = -2 + self.level = 1 + self.enter_d = d# + self.aft_l_price = 0 + self.aft_l_time = '00'# time for third type buy or sell point + self.future_zd = -float('inf') + self.future_zg = float('inf') + if d == 1:#pivot=[zg,zd,dd,gg,start_time,end_time,d] d the direction of + #the seg pre-entering but not in pivot + if lines[3][1][1] <= lines[1][0][1]: #low of line i+3 < low of line i+1 + self.zg = min(lines[1][0][1],lines[3][0][1]) + self.zd = max(lines[3][1][1],lines[1][1][1]) + self.dd = lines[2][0][1] + self.gg = max(lines[1][0][1],lines[2][1][1]) + + else:#pivot=[zg,zd,dd,gg,start_time,end_time,start_seg_index,end_seg_index,d] d the seg pre-entering pivot + if lines[3][1][1] >= lines[1][0][1]: + self.zg = min(lines[1][1][1],lines[3][1][1]) + self.zd = max(lines[3][0][1],lines[1][0][1]) + self.dd = min(lines[2][1][1],lines[1][0][1]) + self.gg = lines[2][0][1] + + self.start_index = lines[1][0][0] + self.end_index = lines[2][1][0]# should be updated after growing + #lines[self.end_index] is the leaving seg + self.finished = 0 + self.enter_force = seg_force(lines[0]) + self.leave_force = seg_force(lines[3])# should be updated after growing + self.size = 3#should be updated + self.mean = 0.5*(self.zd + self.zg) + self.start_time = df1.iloc[self.start_index,3 ] + self.leave_start_time = df1.iloc[self.end_index,3 ]# should be updated after growing + self.leave_end_time = df1.iloc[lines[3][1][0],3 ] # should be updated after growing + self.leave_d = -d # should be updated after growing + self.leave_end_price = lines[3][1][1] # should be updated after growing + self.leave_start_price = lines[3][0][1] + self.prev2_force = seg_force(lines[1]) + self.prev1_force = seg_force(lines[2]) + self.prev2_end_price = lines[1][1][1] + #tail_price the leave seg's end price,if the seg + #is still not finished,its leave seg is the last seg within the pivot + + def grow(self,seg):#seg a Seg.getlines() + + self.prev2_force = self.prev1_force + self.prev1_force = self.leave_force + self.prev2_end_price = self.leave_start_price + if seg[1][1] > seg[0][1]:#d for the line is 1 + if (seg[1][1]>=self.zd and seg[0][1] <= self.zg) and (self.size <=28):#then the seg is + # added to the pivot + self.end_index = seg[0][0] + + self.size = self.size + 1 + self.dd = min(self.dd,seg[0][1]) + + self.leave_force = seg_force(seg) + self.leave_start_time = df1.iloc[self.end_index,3 ] + self.leave_end_time = df1.iloc[seg[1][0],3 ] + self.leave_d = 2*int(seg[1][1]>seg[0][1])-1 + self.leave_start_price = seg[0][1] + self.leave_end_price = seg[1][1] + + if self.size in [4,7,10,19,28]:#level expansion + self.future_zd = max(self.future_zd ,self.dd) + self.future_zg = min(self.future_zg ,self.gg) + + if self.size in [10,28]:#level expansion + self.level = self.level + 1 + self.zd = self.future_zd + self.zg = self.future_zg + self.future_zd = -float('inf') + self.future_zg = float('inf') + + else: + + if (seg[1][1]>=self.zd and seg[0][1] <= self.zg): + self.dd = min(self.dd,seg[0][1]) + self.finished = 0.5 + else: + self.finished = 1 + + + self.aft_l_price = seg[1][1] + self.aft_l_time = df1.iloc[seg[1][0],3] + #only when the seg is finished is the tail_price different from end_price + else:#d for the line is -1. falling line + if (seg[1][1]<=self.zg and seg[0][1] >= self.zd) and self.size<=28:#then the seg is + # added to the pivot + self.end_index = seg[0][0] + self.end_price = seg[0][1] + self.size = self.size + 1 + self.gg = max(self.gg,seg[0][1]) + + self.leave_force = seg_force(seg) + self.leave_start_time = df1.iloc[self.end_index,3 ] + self.leave_end_time = df1.iloc[seg[1][0],3 ] + self.leave_d = 2*int(seg[1][1]>seg[0][1])-1 + self.leave_start_price = seg[0][1] + self.leave_end_price = seg[1][1] + + if self.size in [4,7,10,19,28]:#level expansion + self.future_zd = max(self.future_zd ,self.dd) + self.future_zg = min(self.future_zg ,self.gg) + + if self.size in [10,28]:#level expansion + self.level = self.level + 1 + self.zd = self.future_zd + self.zg = self.future_zg + self.future_zd = -float('inf') + self.future_zg = float('inf') + else: + + + if (seg[1][1]<=self.zg and seg[0][1] >= self.zd) :#broke because it is too long + self.gg = max(self.gg,seg[0][1]) + self.finished = 0.5 + else: + self.finished = 1 + + + self.aft_l_price = seg[1][1] + self.aft_l_time = df1.iloc[seg[1][0],3] + + + def display(self): + print('enter_d:'+str(self.enter_d)) + print('zd:'+str(self.zd)) + print('zg:'+str(self.zg)) + print('dd:'+str(self.dd)) + print('gg:'+str(self.gg)) + print('start_index:'+str(self.start_index)) + print('end_index:'+str(self.end_index)) + print('start_time:'+str(self.start_time)) + + print('size:'+str(self.size)) + print('enter_force:'+str(self.enter_force)) + print('leave_force:'+str(self.leave_force)) + print('finished:'+str(self.finished)) + print('leave_start_time:'+str(self.leave_start_time)) + print('leave_end_time:'+str(self.leave_end_time)) + print('leave_d:'+str(self.leave_d)) + print('leave_start_price:'+str(self.leave_start_price)) + print('leave_end_price:'+str(self.leave_end_price)) + print('mean:'+str(self.mean)) + print('aft_l_price:'+str(self.aft_l_price)) + + def dis1(self): + print('trend:'+str(self.trend)) + print('level:'+str(self.level)) + print('enter_d:'+str(self.enter_d)) + print('zd:'+str(self.zd)) + print('zg:'+str(self.zg)) + print('dd:'+str(self.dd)) + print('gg:'+str(self.gg)) + print('leave_d:'+str(self.leave_d)) + print('start_time:'+str(self.start_time)) + print('leave_start_time:'+str(self.leave_start_time)) + print('\n') + + def write_out(self,filepath,extra=''): + f = open(filepath,'w') + f.write(' zd:' + str(self.zd)+' zg:'+str(self.zg) + + ' dd:' + str(self.dd)+' gg:'+str(self.gg) + + ' leave_d:' + str(self.leave_d)+ + ' prev2_leave_force:' +str(self.prev2_force)+ ' leave_force:' + str(self.leave_force)+ + '\n start_time:'+str(self.start_time)+ + ' leave_start_time:'+str(self.leave_start_time)+ + ' leave_end_time:'+str(self.leave_end_time)+ + ' prev2_end_price:'+str(self.prev2_end_price)+ + ' leave_end_price:'+str(self.leave_end_price)+ + '\n size: ' + str(self.size)+' finished: ' + str(self.finished) + ' trend:' + + str(self.trend) + ' level:' + + str(self.level)) + f.write('\n') + if extra!='': + f.write('tails:') + f.write(str(extra)) + f.write('\n') + f.write('now') + f.write(str(df1.iloc[-1])) + f.close() + return + + +#ebd class Pivot +def seg_force(seg): + return 1000*abs(seg[1][1]/seg[0][1]-1)/(seg[1][0]-seg[0][0]) + + +def get_pivot(lines): + Pivot1_array = [] + i = 0 + + while i < len(lines): + #print(i) + d = 2 * int( lines[i][0][1] < lines[i][1][1] ) - 1 + if i < len(lines)-3: + if d == 1:#pivot=[zg,zd,dd,gg,start_time,end_time,d] d the direction of + #the seg pre-entering but not in pivot + if lines[i+3][1][1] <= lines[i+1][0][1]: #low of line i+3 < low of line i+1 + pivot = Pivot1(lines[i:i+4],d) + i_j = 1 + while i + i_j < len(lines)-3 and pivot.finished == 0: + pivot.grow(lines[i + i_j + 3]) + i_j = i_j +1 + + + i = i + pivot.size + Pivot1_array = Pivot1_array + [pivot] + continue + else: + i = i + 1 + + else:#pivot=[zg,zd,dd,gg,start_time,end_time,start_seg_index,end_seg_index,d] d the seg pre-entering pivot + if lines[i+3][1][1] >= lines[i+1][0][1]: + pivot = Pivot1(lines[i:i+4],d) + i_j = 1 + while i + i_j < len(lines)-3 and pivot.finished == 0: + pivot.grow(lines[i + i_j + 3]) + i_j = i_j +1 + i = i + pivot.size + + Pivot1_array = Pivot1_array + [pivot] + continue + else: + i = i + 1 + + else: + i = i + 1 + + + #pivot [zd,zg,dd,gg] zd and zg may not be valid after expansion + # the second para returned is the tails,or the last unconfirmed seg,with tails[4] its d + return Pivot1_array , [df1.iloc[lines[-1][0][0],3],lines[-1][0][1],\ + df1.iloc[lines[-1][1][0],3],lines[-1][1][1],2*int(lines[-1][1][1]>lines[-1][0][1])-1] + + + + +#same hierachy decomposition +#def process_pivot(pivot) : +# i = 0 +# while i < len(pivot)-1: +# if min(pivot[i][2:4]) <= max(pivot[i+1][2:4]) and\ +# max(pivot[i][2:4]) >= min(pivot[i+1][2:4]): +# pivot[i+1][2] = min(pivot[i][2],pivot[i+1][2]) +# pivot[i+1][3] = max(pivot[i][3],pivot[i+1][3]) +# pivot[i+1][4] =pivot[i][4] +# pivot[i+1][5] = pivot[i+1][5] +# del pivot[i] +# else: +# i = i + 1 +# return pivot + +def process_pivot(pivot): + for i in range(0,len(pivot)-1): + if pivot[i ].level==1 and pivot[i+1].level==1: + if pivot[i].dd > pivot[i+1].gg: + pivot[i+1].trend=-1 + else: + if pivot[i].gg < pivot[i+1].dd: + pivot[i+1].trend=1 + else: + pivot[i+1].trend=0 + else: + if pivot[i ].gg> pivot[i +1].gg and pivot[i ].dd> pivot[i +1].dd: + pivot[i+1].trend=-1 + else: + if pivot[i ].gg < pivot[i +1].gg and pivot[i ].dd < pivot[i +1].dd: + pivot[i+1].trend=1 + else: + pivot[i+1].trend=0 + return pivot + + + + + +def buy_point1(pro_pivot,tails,num_pivot=2): + if len(pro_pivot)<=3 or tails[4]==1 or pro_pivot[-1].size>=8 or pro_pivot[-1].finished!=0 \ + or df1.iloc[-1][0]/pro_pivot[-1].leave_end_price -1>0 or \ + df1.iloc[-1][0] > tails[3]: + return False,0 + else:#two pivot descending + #no slow down + if ( pro_pivot[-1].prev2_end_price >pro_pivot[-1].leave_end_price ) and \ + (pro_pivot[-1].leave_start_time==tails[0]) and\ + df1.iloc[-1][0] < pro_pivot[-1].dd and \ + 1.2*pro_pivot[-1].leave_force pro_pivot[-1].leave_end_price ): + return True,pro_pivot[-1].dd#target price + else: + return False,0 +# ============================================================================= +# if pro_pivot[-1].finished == 1 or pro_pivot[-1].leave_d!=-1 or \ +# pro_pivot[-1].finished == 0.5: +# return False,0 +# if num_pivot == 2: +# if pro_pivot[-2].enter_d==-1 and pro_pivot[-1].gg < pro_pivot[-2].dd \ +# and pro_pivot[-1].enter_d==-1 and tails[0]==pro_pivot[-1].leave_start_time \ +# and tails[3] <= pro_pivot[-1].dd:#tails[0] tail seg start_time +# return True,pro_pivot[-1].zd +# else: +# return False,0 +# if num_pivot == 3: +# if pro_pivot[-3].enter_d==-1 and pro_pivot[-2].gg < pro_pivot[-3].dd and \ +# pro_pivot[-2].enter_d==-1 and pro_pivot[-1].gg < pro_pivot[-2].dd and\ +# pro_pivot[-1].enter_d==-1 and \ +# pro_pivot[-1].leave_force<\ +# pro_pivot[-1].enter_force and tails[0]==pro_pivot[-1].leave_start_time \ +# and tails[3] <= pro_pivot[-1].dd: +# return True,pro_pivot[-1].zd +# else: +# return False,0 +# ============================================================================= + +def buy_point2(pro_pivot,tails,num_pivot=2): + if len(pro_pivot)<=3 or tails[4]==1 or pro_pivot[-1].size>=8 or pro_pivot[-1].finished!=0 \ + or df1.iloc[-1][0]/pro_pivot[-1].leave_end_price -1>0 or \ + df1.iloc[-1][0] > tails[3]: + return False,0 + else:#two pivot descending + #no slow down + if ( pro_pivot[-1].prev2_end_price 0.51*(pro_pivot[-1].zd+pro_pivot[-1].zg) : + return True,pro_pivot[-1].prev2_end_price#support price + else: + return False,0 + + + +def buy_point3_des(pro_pivot,tails): + if len(pro_pivot)<=2 or(tails[4]==1) or (pro_pivot[-1].finished!=1) or \ + pro_pivot[-1].level > 1 or df1.iloc[-1][0]/pro_pivot[-1].leave_end_price -1>0 or \ + df1.iloc[-1][0] > tails[3]: + return False,0 + else:#two pivot descending + if df1.iloc[-1][0] <0.98*pro_pivot[-1].leave_end_price and df1.iloc[-1][0] >1.02*pro_pivot[-1].zg and \ + pro_pivot[-1].aft_l_price >1.02*pro_pivot[-1].zg and \ + tails[0] == pro_pivot[-1].leave_end_time and \ + pro_pivot[-1].leave_force > pro_pivot[-1].prev2_force and\ + pro_pivot[-1].leave_end_price > pro_pivot[-1].prev2_end_price: + return True,pro_pivot[-1].zg#support price + else: + return False,0 + #no slow down +# ============================================================================= +# if not np.mean( pro_pivot[-3][0:2]) / np.mean(pro_pivot[-2][0:2]) > \ +# np.mean(pro_pivot[-2][0:2]) / np.mean(pro_pivot[-1][0:2]): +# return False,0 +# if num_pivot == 3: +# if pro_pivot[-4][2]>pro_pivot[-3][2] and pro_pivot[-3][2]>pro_pivot[-2][2] \ +# : +# return True,pro_pivot[-1][0] +# else: +# return False,0 +# if num_pivot == 2: +# if pro_pivot[-3][2]>pro_pivot[-2][2] and pro_pivot[-2][2]>pro_pivot[-1][2] \ +# : +# return True,pro_pivot[-1][0] +# else: +# return False,0 +# ============================================================================= +def buy_point23(pro_pivot,tails): + if len(pro_pivot)<=3 or pro_pivot[-1].finished!=1 or \ + pro_pivot[-1].level > 1 or df1.iloc[-1][0]/pro_pivot[-1].leave_end_price -1>0 or \ + df1.iloc[-1][0] > tails[3]: + return False,0 + else:#two pivot descending + #no slow down + if df1.iloc[-1][0] <0.98*pro_pivot[-1].leave_end_price and df1.iloc[-1][0] >1.01*pro_pivot[-1].zg and pro_pivot[-1].trend==-1 \ + and tails[3] >1.01*\ + pro_pivot[-1].zg and tails[0] == pro_pivot[-1].leave_end_time and \ + pro_pivot[-1].leave_start_price ==pro_pivot[-1].dd: + return True,pro_pivot[-1].zg# support price + else: + return False,0 + + +def sell_point1(pro_pivot,tails,num_pivot=2): + if len(pro_pivot)<=3 or tails[4]==-1 or pro_pivot[-1].size>=8 or pro_pivot[-1].finished!=0\ + or df1.iloc[-1][1]/pro_pivot[-1].leave_end_price -1<0 or \ + df1.iloc[-1][0] < tails[3]: + return False,0 + else:#two pivot descending + #no slow down + if ( pro_pivot[-1].prev2_end_price pro_pivot[-1].zg and \ + 1.2*pro_pivot[-1].leave_force =8 or pro_pivot[-1].finished!=0\ + or df1.iloc[-1][1]/pro_pivot[-1].leave_end_price -1<0 or \ + df1.iloc[-1][0] < tails[3]: + return False,0 + else:#two pivot descending + #no slow down + if ( pro_pivot[-1].prev2_end_price >pro_pivot[-1].leave_end_price ) and \ + (pro_pivot[-1].leave_start_time==tails[0]) and\ + df1.iloc[-1][0] > 0.51*(pro_pivot[-1].zd+pro_pivot[-1].zg) and \ + pro_pivot[-1].prev2_end_price==pro_pivot[-1].gg: + return True,pro_pivot[-1].zg #buyback and support price + else: + return False,0 + +def sell_point3_ris(pro_pivot,tails,num_pivot=2): + if len(pro_pivot)<=3 or tails[4]==-1 or pro_pivot[-1].size>=8 or pro_pivot[-1].finished!=1 \ + or \ + df1.iloc[-1][0] < tails[3]: + return False,0 + else:#two pivot descending + #no slow down + if ( 1.02*pro_pivot[-1].leave_end_price < df1.iloc[-1][0] ) and \ + (pro_pivot[-1].leave_end_time==tails[0]) and \ + pro_pivot[-1].leave_force>pro_pivot[-1].prev2_force\ + and df1.iloc[-1][1]=df.iloc[i+1,0] and df.iloc[i+2,1]<=df.iloc[i+1,1]) or\ + (df.iloc[i+2,0]<=df.iloc[i+1,0] and df.iloc[i+2,1]>=df.iloc[i+1,1]): + if df.iloc[i+1,0]>df.iloc[i,0]: + df.iloc[i+2,0] = max(df.iloc[i+1:i+3,0]) + df.iloc[i+2,1] = max(df.iloc[i+1:i+3,1]) + df.drop(df.index[i+1],inplace=True) + + continue + else: + df.iloc[i+2,0] = min(df.iloc[i+1:i+3,0]) + df.iloc[i+2,1] = min(df.iloc[i+1:i+3,1]) + df.drop(df.index[i+1],inplace=True) + + continue + i = i + 1 + # print(len(df)) + if len(df)==temp_len: + break + + df= df.reset_index(drop=True) + #get difenxing and dingfenxing + ul=[0] + for i in range(len(df)-2): + if df.iloc[i+2,0] < df.iloc[i+1,0] and df.iloc[i,0] < df.iloc[i+1,0]: + ul = ul + [1] + continue + if df.iloc[i+2,0] > df.iloc[i+1,0] and df.iloc[i,0] > df.iloc[i+1,0]: + ul = ul + [-1]# difenxing -1 dingfenxing +1 + continue + else: + ul = ul + [0] + ul = ul + [0] + global df1 + df1 = pd.concat((df[['low','high']],pd.DataFrame(ul),df['datetime']),axis=1) + + i = 0 + + while df1.iloc[i,2] == 0 and i < len(df1)-2: + i = i + 1 + df1=df1[i:] + + i = 0 + while ( sum(abs(df1.iloc[i+1:i+4,2]))>0 or df1.iloc[i,2]==0) and i < len(df1)-2: + i = i + 1 + df1=df1[i:] + df1.rename(columns= {0:'od'},inplace=True) + #df1.columns=Index(['low', 'high', 'od', 'datetime'], dtype='object') + if len(df1)<=60: + print('error!') + return ; + #remove those within 3 bars + df1=df1.reset_index(drop=True) + global od_list#od_list are the index of df1 whose corresponding point are fenxing extreme vertex + + od_list=[0] + judge(0,0,1) + #od_list are the index of df1 whose corresponding point are fenxing extreme vertex + #od_list are the index of df1 whose corresponding point are fenxing extreme vertex + #od_list are the index of df1 whose corresponding point are fenxing extreme vertex + #od_list are the index of df1 whose corresponding point are fenxing extreme vertex + #od_list are the index of df1 whose corresponding point are fenxing extreme vertex + #od_list are the index of df1 whose corresponding point are fenxing extreme vertex + + + #generate seg + start = 0 + while start < len(od_list)-5: + if check_init_seg(od_list[start:start+4]): + break + else: + start = start + 1 + + lines = [] + + i = start + end = False + while i <= len(od_list)-4: + se = Seg(od_list[i:i+4]) + label = False + while label == False and i <= len(od_list)-6: + i = i + 2 + label,start = se.grow(od_list[i+2:i+4]) + if se.vertex[-1] > od_list[-3]: + end =True + + lines += [se.lines()] + break + if end: + break + i = np.where(np.array(od_list) == se.vertex[-1])[0][0] + #show datetime of the end of the segment + #print(df1.iloc[se.vertex[-1],3]) + lines += [se.lines()]#there are still remaining fewer than or equal to + #3 bies not considered in the last + #seg ,which is unfinished and named by tails + low_list=df1.iloc[se.vertex[-1]:,0] + high_list=df1.iloc[se.vertex[-1]:,1] + + low_extre=low_list.min() + high_extre=high_list.max() + if se.finished == True: + if lines[-1][0][1] < lines[-1][1][1] :#d==1 + lines += [ [(se.vertex[-1],lines[-1][1][1]),(low_list.idxmin(),low_extre)]] + else: + lines += [ [(se.vertex[-1],lines[-1][1][1]),(high_list.idxmax(),high_extre)]] + + else: + if lines[-1][0][1] < lines[-1][1][1] :#d==1 + if low_extre > lines[-1][0][1]: + lines[-1] = [ (lines[-1][0][0],lines[-1][0][1]),(high_list.idxmax(),high_extre)] + else: + if low_list.idxmin()-se.vertex[-1]>=10: + lines += [ [(se.vertex[-1],lines[-1][1][1]),(low_list.idxmin(),low_extre)]] + else: + + if high_extre < lines[-1][0][1]: + lines[-1] = [ (lines[-1][0][0],lines[-1][0][1]),(low_list.idxmin(),low_extre) ] + else: + if high_list.idxmax()-se.vertex[-1]>=10: + lines += [ [(se.vertex[-1],lines[-1][1][1]),(high_list.idxmax(),high_extre)]] + + #print(lines) + #tails is the unfinished seg,tails[4] is its direction + #lines are the segment,lines[0] is the first segment's start_index,start_price,end_index,end_price + a,tails = get_pivot(lines) + pro_a= process_pivot(a) + for i in range(len(pro_a)): + print('pivot {}\'s info:'.format(i)) + pro_a[i].display() + print('these index are for df1,not df!,ex:pro_a[0].leave_end_time to obtain the leave end time') + + + +if __name__=="__main__": + main() diff --git a/sh.csv b/sh.csv new file mode 100644 index 0000000..1440772 --- /dev/null +++ b/sh.csv @@ -0,0 +1,3824 @@ +Date,open,high,low,close 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