moved things to a new place !

notebooks/autogen/decisionManager.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# In[4]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+
+import pickle
+from signals import *
+from dealManager import *
+from trandeVoter import *
+from riskManager import riskManager
+
+
+# In[5]:
+
+
+df_candle = pd.read_csv("../data/EURUSD_price_candlestick.csv")
+df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
+df_candle
+
+
+# In[6]:
+
+
+class decsionManager():
+
+    
+    def __init__(self,name):
+        self.name = name
+        self.RM = riskManager()
+        self.DM = DealManager()
+        self.TV = trandeVoter(name)
+        self.SA = signalAgrigator()
+        pass
+    
+    #вытащенный из signalAgrigator метод теста для сигналов
+    def getSignalTest(self,data: pd.DataFrame(),reqSig: dict, batchSize=30, dataType='candel') -> dict:
+        
+        self.SA.mode = 'retroFast'
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=30
+        )
+        ans = t.SA.getAns(data)
+        return ans
+    
+    #метод для генерации матрицы вероятностей.
+    def generateMatrixProbability(self, 
+                                  data: pd.DataFrame(),
+                                  reqSig: dict, 
+                                  target: str, 
+                                  batchSize=30, 
+                                  #dataType='candel'
+                                 ):
+        data=data.reset_index(drop=True)
+        
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=batchSize
+        )
+        self.TV.createMatrixAmounts(reqSig.keys())
+        for i in range(data.shape[0]-batchSize-1):
+            sigAns=self.SA.getAns(data[i:i+batchSize])
+            rightAns=self.getRetroStepAns(data[target][i],data[target][i+1])
+            self.TV.setDecisionBySignals(self.KostilEbaniy(sigAns),rightAns)
+        self.TV.generateMatrixProbability()
+    
+    #без коментариев блять
+    def KostilEbaniy(self,d):
+        ans={}
+        for i in d.keys():
+            if d[i] == 0:
+                ans[i] = 'none'
+            elif d[i] == 1:
+                ans[i] = 'up'
+            elif d[i] == -1:
+                ans[i] = 'down'
+        return ans
+    
+    #тож понятная хуита
+    def getRetroStepAns(self, value1,value2):
+        
+        if value1 == value2:
+            ans = 'none'
+        elif value1 < value2:
+            ans = 'up'
+        else:
+            ans = 'down'
+        return ans
+        
+    #метод для онлай получения решения по сигналу
+    def getSignal(self,data: pd.DataFrame(),reqSig: dict, dataType='candel') -> dict:
+        data=data.reset_index(drop=True)
+        self.SA.mode = 'online'
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=30
+        )
+        ans = t.SA.getAns(data)
+        return ans        
+    
+    
+    #Создание сигналов. Вызывать перед getOnlineAns
+    def crateSignals(self,data: pd.DataFrame(),reqSig: dict, dataType='candel'):
+        data=data.reset_index(drop=True)
+        self.SA.mode = 'online'
+        t.SA.createSingnalInstances(
+            data = data,
+            dictAgrigSignal = reqSig,
+            dataType='candel',
+            batchSize=30
+        )
+        
+    
+    def getOnlineAns(self,data: pd.DataFrame(),price):
+        sigAns = self.SA.getAns(data)
+        prob = self.TV.getDecisionBySignals(sigAns)
+        ans = self.RM.getDecision(sigAns,prob,price)
+        return ans
+        
+        
+        
+        
+
+
+# In[ ]:
+
+
+
+
+
+# In[7]:
+
+
+t= decsionManager('TEST')
+
+
+# In[8]:
+
+
+t.__dict__
+
+
+# In[9]:
+
+
+reqSig={
+    'BB1':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':15
+    },
+    'BB2':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':20
+    }
+}
+
+
+# In[10]:
+
+
+reqSig.keys()
+
+
+# In[11]:
+
+
+t.SA.__dict__
+
+
+# In[12]:
+
+
+t.generateMatrixProbability(df_candle[:10000],reqSig,'close',40)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
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+
+
+
+
+# In[ ]:
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+
+
+# In[ ]:
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+
+# In[ ]:
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+
+
+# In[ ]:
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+
+
+
+# In[13]:
+
+
+mop = t.TV.matrixProbability
+mop
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[14]:
+
+
+t.getSignal(df_candle[:10000],reqSig)
+
+
+# In[15]:
+
+
+t.getSignalTest(df_candle[:10000],reqSig,40)
+
+
+# In[ ]:
+
+
+
+
+
+# In[16]:
+
+
+t.SA.createSingnalInstances(
+    data = df_candle[:10000],
+    dictAgrigSignal = reqSig,
+    dataType='candel',
+    batchSize=30
+)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[17]:
+
+
+reqSig={
+    'BB1':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':15
+    },
+    'BB2':{
+        'className':signal_BB,
+        'indParams':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5},
+        'signalParams':{'source':'close','target':'close'},
+        'batchSize':20
+    }
+}
+
+
+# In[18]:
+
+
+t=decsionManager(reqSig)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+import pickle
+
+
+# In[ ]:
+
+
+dictionary_data = {"a": 1, "b": 2}
+
+
+a_file = open("data.pkl", "wb")
+
+pickle.dump(reqSig, a_file)
+
+a_file.close()
+
+
+a_file = open("data.pkl", "rb")
+
+output = pickle.load(a_file)
+
+print(output)
+
+
+
+a_file.close()
+
+
+# In[ ]:
+
+
+
+