361 lines
6.6 KiB
Python
361 lines
6.6 KiB
Python
#!/usr/bin/env python
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# coding: utf-8
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# In[1]:
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import pandas as pd
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import datetime
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import numpy as np
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import random
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from signals import * #потом удалить
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# In[2]:
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class trandeVoter():
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def __init__(self,name):
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self.name = name # просто имя
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self.trandeValuesList = ['up','none','down'] #словарь трегдов
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self.matrixAmounts = None # матрица сумм
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self.keysMatrixAmounts = None #ключи матрицы сумм, техническое поле
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self.matrixProbability = None # матрица вероятностей
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#функция которая создает df с заданным набором колонок и индексов. индексы - уникальные соотношения
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def createDFbyNames(self, namesIndex, namesColoms,defaultValue=0.0):
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df = pd.DataFrame(dict.fromkeys(namesColoms, [defaultValue]*pow(3,len(namesIndex))),
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index=pd.MultiIndex.from_product([self.trandeValuesList]*len(namesIndex), names=namesIndex)
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#,columns=namesColoms
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)
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return(df)
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#создание матрицы сумм с дефолтным значением
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def createMatrixAmounts(self,namesIndex: list) -> pd.DataFrame():
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self.matrixAmounts = self.createDFbyNames(namesIndex,self.trandeValuesList,0)
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self.keysMatrixAmounts = self.matrixAmounts.to_dict('tight')['index_names']
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self.createMatrixProbability(namesIndex)
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return(self.matrixAmounts)
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#создание матрицы вероятностей с дефолтным значением
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def createMatrixProbability(self,namesIndex: list) -> pd.DataFrame():
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self.matrixProbability = self.createDFbyNames(namesIndex,self.trandeValuesList)
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return(self.matrixProbability)
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#установка значений в матрицы сумм. signalDecisions - значения индикаторов key:value; trande - реальное значение
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def setDecisionBySignals(self,signalDecisions: dict,trande: str) -> None:
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buff=[]
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for i in self.keysMatrixAmounts:
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buff.append(signalDecisions[i])
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self.matrixAmounts.loc[tuple(buff),trande] += 1
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#заполнение матрицы вероятностей вычисляемыми значениями из матрицы сумм
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def generateMatrixProbability(self) -> None:
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for i in range(self.matrixAmounts.shape[0]):
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rowSum=sum(self.matrixAmounts.iloc[i])
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self.matrixProbability.iloc[i]['up'] = (self.matrixAmounts.iloc[i]['up'] / rowSum)
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self.matrixProbability.iloc[i]['none'] = self.matrixAmounts.iloc[i]['none'] / rowSum
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self.matrixProbability.iloc[i]['down'] = self.matrixAmounts.iloc[i]['down'] / rowSum
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#получение рещения из матрицы вероятностей по заданным значениям сигналов
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def getDecisionBySignals(self,signalDecisions: dict) -> dict:
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ans = {}
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spliceSearch =self.matrixProbability.xs(tuple(signalDecisions.values()),
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level=list(signalDecisions.keys())
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)
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ans['probability'] = spliceSearch.to_dict('records')[0]
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ans['trande'] = spliceSearch.iloc[0].idxmax()
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return ans
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#получение матриц вероятностей и суммы в видей словарей
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def getMatrixDict(self) -> dict:
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ans={}
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ans['amounts'] = self.matrixAmounts.to_dict('tight')
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ans['probability'] = self.matrixProbability.to_dict('tight')
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return ans
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#установка матриц вероятностей и суммы в видей словарей
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def setMatrixDict(self,matrixDict: dict) -> dict:
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if matrixDict['amounts'] != None:
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self.matrixAmounts = pd.DataFrame.from_dict(y['amounts'], orient='tight')
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if matrixDict['probability'] != None:
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self.matrixProbability = pd.DataFrame.from_dict(y['probability'], orient='tight')
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# In[3]:
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reqSig={
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'BB1':{
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'className':signal_BB,
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'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
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'signalParams':{'source':'close','target':'close'},
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'batchSize':15
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},
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'BB2':{
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'className':signal_BB,
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'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
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'signalParams':{'source':'close','target':'close'},
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'batchSize':20
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}
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}
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# In[4]:
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reqDS={'BB1':'up','BB2':'none'}
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# In[7]:
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reqCreate=list(reqSig.keys())
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reqCreate
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# In[8]:
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t=trandeVoter('piu')
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o=t.createMatrixAmounts(['BB1', 'BB2'])
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o
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# In[9]:
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for i in range(100000):
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t.setDecisionBySignals({'BB1':random.choice(['up','down','none']),
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'BB2':random.choice(['up','down','none'])},
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random.choice(['up','down','none']))
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# In[10]:
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t.matrixAmounts
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# In[11]:
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t.generateMatrixProbability()
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# In[577]:
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t.matrixProbability
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# In[14]:
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t.setMatrixDict(y)
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# In[15]:
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t.getDecisionBySignals(reqDS)
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# In[ ]:
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# In[13]:
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y = t.getMatrixDict()
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y
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# In[16]:
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ddf = pd.DataFrame.from_dict(y['amounts'], orient='tight')
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ddf
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# In[17]:
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t.matrixProbability.iloc[0]['up'] = (t.matrixProbability.iloc[0]['up'] / (sum(t.matrixProbability.iloc[0])))
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t.matrixProbability
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# In[18]:
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t.matrixProbability['trandе']
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# In[19]:
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random.choice(['up','down','none'])
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# In[20]:
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t.setDecisionBySignals(reqDS,'up')
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# In[21]:
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#t.matrixAmounts.at(bbb,'up')
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t.matrixAmounts.iloc[0]
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# In[22]:
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for i in t.matrixAmounts.iloc[0]:
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print (i)
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# In[23]:
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(t.matrixAmounts.iloc[0]).idxmax()
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# In[24]:
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t.matrixAmounts
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# In[ ]:
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# In[25]:
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o.xs(('up','down'), level=['BB1','BB2'])['up'].iloc[0]
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#oldValue = o.xs(('up','down'), level=['BB1','BB2'])['up']
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#o=o.replace(oldValue,oldValue.iloc[0]+1)
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#o.xs(('up','down'), level=['BB1','BB2'])
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# In[26]:
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o.xs(('up','down'), level=['BB1','BB2'], drop_level=False)#.iloc[0].loc['up']=2#.at['up']=4
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# In[27]:
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o.xs(('up','down'), level=['BB1','BB2']).iloc[0].at['up']
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# In[28]:
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o.loc['up'].loc['down']
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# In[29]:
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bbb=tuple(['up','down'])
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bbb
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# In[30]:
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o.loc[bbb,]
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# In[31]:
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o.at[bbb, 'up']+=1
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o
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# In[32]:
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o.loc[bbb]
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# In[33]:
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dict(zip(['a','b','c'], [1,2,3]))
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# In[ ]:
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