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Cal_Stock_Identify.py
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'''
會在此用演算法計算選股的演算
並利用此驗證股票的價格
架構上Method , Decision , Identify 三個差不多
del 方法
https://python-reference.readthedocs.io/en/latest/docs/dunderdsc/delete.html
'''
import Get_Stock_Data_Save_Load as st_data
from time import time as clock
import math
score_Ratio = [0.4,0.3,0.2,0.05,0.05] #購買比例，選股演算結果排名後，依照比例購入
Method_Name = ['潛伏天數1d','潛伏天數2d','均勻獲利','潛伏天數1d_Exp','潛伏天數_ExpAbs']
Method_Format = [[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0],
[0,0,0,0,0]
]
def Index_Method_Name(name = ''):
return Method_Format[Method_Name.index(name)]
class Method():
global Method_Name
def __init__(self):
self.Paramater = []
self.InputData = []
self.EveryDayScore = []
self.Result = []
self.Ranks = []
self.History = []
self.Final_Score = 0
def __delete__(self, instance):
del self.Paramater
del self.InputData
del self.EveryDayScore
del self.Result
del self.Ranks
del self.History
del self.Final_Score
def Basic_Cal(self): #取得基本的漲跌資料，主要是以當天結算跟明天結算的比例差
self.EveryDayScore = st_data.List_1d_Score_Data
'''
self.EveryDayScore = []
for i in st_data.Stock_Data:
aa = i['Close']
bb = aa.shift(-1)
bb.fillna(method='pad', inplace=True)
cc = bb/aa
#print("EverySay Score",cc)
self.EveryDayScore.append(cc)
'''
def Get_Rank(self,data): #經過演算後取得排序，並依照購買比例算價格
self.Ranks = []
self.History = []
Current_Score = 1
#print("in Decisoin datas Length",len(data[0]))
#for i in data:
#print("Each_Length",len(i))
aa = [1, 1, 1, 1, 1]
for i in range(len(data[0])):
student_sort = sorted(data, key=lambda datas: datas[i] , reverse=True)
#print("student_sort",student_sort)
aa = [student_sort[0][0],student_sort[1][0],student_sort[2][0],student_sort[3][0],student_sort[4][0]]
#print(aa)
self.Ranks.append(aa)
#print("Total Rank_Length",len(self.Ranks))
#print("Total EveryDayScore_Length", len(self.EveryDayScore),len(self.EveryDayScore[0]))
#print("in Identify Rank Length",len(self.Ranks))
del self.Ranks[0] # Delect culoumun index
#del self.Ranks[0]
self.Result = []
#for i in self.InputData:
#print("InputData",len(i))
for i in range(len(self.Ranks)):
if i< 20:
Current_Score = 1
signal_history = [[1],[1],[1],[1],[1]]
setp_Score = 1
else:
setp_Score = 0
p_Score = 1
signal_history = []
#print(self.Ranks[i])
for j in range(5):
p_Score = self.InputData[self.Ranks[i][j]][i]
#p_Score *= (self.EveryDayScore[self.Ranks[i][j]][i]) * self.InputData[self.Ranks[i][j]][i]
setp_Score += p_Score * score_Ratio[j]
signal_history.append([st_data.Stock_index[self.Ranks[i][j]][1],"%.2f" %((p_Score-1)*100)])
#setp_Score += 1
'''
setp_Score = self.EveryDayScore[self.Ranks[i][0]][i]
'''
#signal_history.[setp_Score,signal_history]
#signal_history.append([setp_Score,signal_history])
Current_Score = Current_Score * setp_Score
self.Result.append(Current_Score)
self.History.append(["%.2f" %((setp_Score-1)*100),signal_history])
#print("Get Fineal Result",self.History)
def Get_Rank_Exp(self,data): #經過演算後取得排序，並依照購買比例算價格，但是使用expolation將之前的結果做衰竭
self.Ranks = []
self.History = []
Current_Score = 1
#print("in Decisoin datas Length",len(data[0]))
#for i in data:
#print("Each_Length",len(i))
aa = [1, 1, 1, 1, 1]
for i in range(len(data[0])):
student_sort = sorted(data, key=lambda datas: datas[i] , reverse=True)
#print("student_sort",student_sort)
aa = [student_sort[0][0],student_sort[1][0],student_sort[2][0],student_sort[3][0],student_sort[4][0]]
#print(aa)
self.Ranks.append(aa)
#print("Total Rank_Length",len(self.Ranks))
#print("Total EveryDayScore_Length", len(self.EveryDayScore),len(self.EveryDayScore[0]))
#print("in Identify Rank Length",len(self.Ranks))
del self.Ranks[0] # Delect culoumun index
#del self.Ranks[0]
self.Result = []
#for i in self.InputData:
#print("InputData",len(i))
#print("expss_Start")
expss = math.exp(1)
#print("expss",expss)
Total_Length = len(self.Ranks)
#print("Total_Length",Total_Length)
for i in range(len(self.Ranks)):
if i< 20:
Current_Score = 1
signal_history = [[1],[1],[1],[1],[1]]
setp_Score = 1
else:
setp_Score = 0
p_Score = 1
signal_history = []
#print(self.Ranks[i])
for j in range(5):
p_Score = self.InputData[self.Ranks[i][j]][i]
#p_Score *= (self.EveryDayScore[self.Ranks[i][j]][i]) * self.InputData[self.Ranks[i][j]][i]
setp_Score += p_Score * score_Ratio[j]
signal_history.append([st_data.Stock_index[self.Ranks[i][j]][1],"%.2f" %((p_Score-1)*100)])
#setp_Score += 1
'''
setp_Score = self.EveryDayScore[self.Ranks[i][0]][i]
'''
#signal_history.[setp_Score,signal_history]
#signal_history.append([setp_Score,signal_history])
#aa = ((i+1)/Total_Length)*0.2+0.9#(math.exp(i/Total_Length)/5)+1
aa = math.exp(i/Total_Length)/math.exp(1)
if aa > 1.0:
aa = 1.0
setp_Score = ((setp_Score -1)*aa)+1
Current_Score = Current_Score * setp_Score
#print("Final aa",aa)
self.Result.append(Current_Score)
self.History.append(["%.2f" %((setp_Score-1)*100),signal_history])
#print("Get Fineal Result",self.History)
def Method_Cal(self,paramater,data):
self.Paramater = paramater[2]
self.InputData = data
self.dataTemp = []
start = clock()
if self.Paramater[0] == Method_Name[0]: # '潛伏天數_1d'
#計算潛伏天數 * 放大率
#self.Basic_Cal()
self.EveryDayScore = st_data.List_1d_Score_Data
#print("Every Days Data 1d", self.EveryDayScore)
Datas_Step1 = []
for i in range(len(self.EveryDayScore)): # Each
score = 1
MultiBase = 0
each_Step1_Data = [i]
for j in range(len(self.EveryDayScore[i])):
aa = self.EveryDayScore[i][j]
MultiBase = (MultiBase * self.Paramater[1][0] + self.EveryDayScore[i][j]) / (self.Paramater[1][0] +1)
if (abs((MultiBase -1)*100) < abs(self.Paramater[1][1])):
score += abs(self.Paramater[1][2])
else:
score /= abs(self.Paramater[1][2])
score = score * MultiBase
each_Step1_Data.append(score)
Datas_Step1.append(each_Step1_Data)
#print("in Decision Do Pre Datas",len(self.EveryDayScore[0]))
self.Get_Rank(Datas_Step1)
end = clock()
self.Final_Score = self.Result[-1]
'''
lan = int(len(self.Result) / 5)
a1 = self.Result[-(lan * 4): -(lan * 3)]
a2 = self.Result[-(lan * 3): -(lan * 2)]
a3 = self.Result[-(lan * 2): -(lan * 1)]
a4 = self.Result[-(lan * 1):]
b1 = (sum(a1) / len(a1)) - min(a1)
b2 = (sum(a2) / len(a2)) - min(a2)
b3 = (sum(a3) / len(a3)) - min(a3)
b4 = (sum(a4) / len(a4)) - min(a4)
li = [b1, b2, b3, b4]
av = sum(li) / len(li)
ag = 1 / ((max(li) - (av)) / av)
self.Final_Score = ag *ag * self.Result[-1]
'''
print("Identify Time cost",end-start)
return self.Result
if self.Paramater[0] == Method_Name[1]: # '潛伏天數_2d'
#計算潛伏天數 * 放大率
#self.Basic_Cal()
self.EveryDayScore = st_data.List_2d_Score_Data
#print("Every Days Data 2d",self.EveryDayScore)
Datas_Step1 = []
for i in range(len(self.EveryDayScore)): # Each
score = 1
MultiBase = 0
each_Step1_Data = [i]
for j in range(len(self.EveryDayScore[i])):
aa = self.EveryDayScore[i][j]
MultiBase = (MultiBase * self.Paramater[1][0] + self.EveryDayScore[i][j]) / (self.Paramater[1][0] +1)
if (abs((MultiBase -1)*100) < abs(self.Paramater[1][1])):
score += abs(self.Paramater[1][2])
else:
score /= abs(self.Paramater[1][2])
score = score * MultiBase
each_Step1_Data.append(score)
Datas_Step1.append(each_Step1_Data)
#print("in Decision Do Pre Datas",len(self.EveryDayScore[0]))
self.Get_Rank(Datas_Step1)
end = clock()
self.Final_Score = self.Result[-1]
print("Identify Time cost",end-start)
return self.Result
if self.Paramater[0] == Method_Name[2]: # '均勻獲利'
# 計算潛伏天數 * 放大率
# self.Basic_Cal()
self.EveryDayScore = st_data.List_1d_Score_Data
# print("Every Days Data 1d", self.EveryDayScore)
Datas_Step1 = []
for i in range(len(self.EveryDayScore)): # Each
score = 1
MultiBase = 0
each_Step1_Data = [i]
for j in range(len(self.EveryDayScore[i])):
aa = self.EveryDayScore[i][j]
MultiBase = (MultiBase * self.Paramater[1][0] + self.EveryDayScore[i][j]) / (
self.Paramater[1][0] + 1)
if (abs((MultiBase - 1) * 100) < abs(self.Paramater[1][1])):
score += abs(self.Paramater[1][2])
else:
score /= abs(self.Paramater[1][2])
score = score * MultiBase
each_Step1_Data.append(score)
Datas_Step1.append(each_Step1_Data)
# print("in Decision Do Pre Datas",len(self.EveryDayScore[0]))
self.Get_Rank(Datas_Step1)
end = clock()
'''
self.Final_Score = self.Result[-1]
'''
lan = int(len(self.Result) / 5)
a1 = self.Result[-(lan * 4): -(lan * 3)]
a2 = self.Result[-(lan * 3): -(lan * 2)]
a3 = self.Result[-(lan * 2): -(lan * 1)]
a4 = self.Result[-(lan * 1):]
b1 = (sum(a1) / len(a1)) - min(a1)
b2 = (sum(a2) / len(a2)) - min(a2)
b3 = (sum(a3) / len(a3)) - min(a3)
b4 = (sum(a4) / len(a4)) - min(a4)
li = [b1, b2, b3, b4]
av = sum(li) / len(li)
ag = 1 / ((max(li) - (av)) / av)
self.Final_Score = ag *ag * self.Result[-1]
print("Identify Time cost", end - start)
return self.Result
if self.Paramater[0] == Method_Name[3]: # '潛伏天數_1d_Exp'
#計算潛伏天數 * 放大率
#self.Basic_Cal()
self.EveryDayScore = st_data.List_1d_Score_Data
#print("Every Days Data 1d", self.EveryDayScore)
Datas_Step1 = []
for i in range(len(self.EveryDayScore)): # Each
score = 1
MultiBase = 0
each_Step1_Data = [i]
for j in range(len(self.EveryDayScore[i])):
aa = self.EveryDayScore[i][j]
MultiBase = (MultiBase * self.Paramater[1][0] + self.EveryDayScore[i][j]) / (self.Paramater[1][0] +1)
if (abs((MultiBase -1)*100) < abs(self.Paramater[1][1])):
score += abs(self.Paramater[1][2])
else:
score /= abs(self.Paramater[1][2])
score = score * MultiBase
each_Step1_Data.append(score)
Datas_Step1.append(each_Step1_Data)
#print("in Decision Do Pre Datas",len(self.EveryDayScore[0]))
self.Get_Rank_Exp(Datas_Step1)
end = clock()
self.Final_Score = self.Result[-1]
#print("Identify Time cost",end-start)
return self.Result
if self.Paramater[0] == Method_Name[4]: # '潛伏天數_1d_Exp_Abs'
#計算潛伏天數 * 放大率
#self.Basic_Cal()
self.EveryDayScore = st_data.List_1d_Score_Data
#print("Every Days Data 1d", self.EveryDayScore)
Datas_Step1 = []
for i in range(len(self.EveryDayScore)): # Each
score = 1
MultiBase = 0
each_Step1_Data = [i]
for j in range(len(self.EveryDayScore[i])):
aa = abs(self.EveryDayScore[i][j]-1) + 1
MultiBase = (MultiBase * self.Paramater[1][0] + aa) / (self.Paramater[1][0] +1)
if (abs((MultiBase -1)*100) < abs(self.Paramater[1][1])):
score += abs(self.Paramater[1][2])
else:
score /= abs(self.Paramater[1][2])
score = score * MultiBase
each_Step1_Data.append(score)
Datas_Step1.append(each_Step1_Data)
#print("in Decision Do Pre Datas",len(self.EveryDayScore[0]))
self.Get_Rank_Exp(Datas_Step1)
end = clock()
self.Final_Score = self.Result[-1]
print("Identify Time cost",end-start)
return self.Result