moved things to a new place !

notebooks/autogen/indicators_v2.py

@@ -0,0 +1,194 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+# In[8]:
+
+
+import pandas as pd
+import datetime
+import numpy as np
+
+import CoreTraidMath 
+
+
+# In[9]:
+
+
+df_candle = pd.read_csv(r"../data/EURUSD_price_candlestick.csv")
+df_candle.rename(columns={'timestamp': 'date'}, inplace=True)
+df_candle
+
+
+# In[10]:
+
+
+class coreIndicator():
+    
+    def __init__(self,options: dict, dataType: str = None, predictType: str = None, name: str = None):
+        self.options = options
+        self.dataType = dataType #ochl
+        self.predictType = predictType #trend
+        
+    
+    def getAns(self, data: pd.DataFrame() ):
+        return "ERROR"
+
+
+# In[11]:
+
+
+class ind_BB(coreIndicator):
+    """
+    options
+        MeanType -> SMA
+        window -> int
+        valueType -> str: low, high, open, close
+        kDev -> float
+    
+    """
+    
+    def __init__(self,options: dict,name = None):
+        super().__init__(
+            options = options,
+            dataType = 'ochl',
+            predictType = 'trend',
+            name = name
+        )
+    
+    def getAns(self, data: pd.DataFrame()):
+        data=data.reset_index(drop=True)
+        ans={}
+        opMA={'dataType':'ohcl',
+            'action':'findMean',
+            'actionOptions':{
+                'MeanType':self.options['MeanType'],
+                'valueType':self.options['valueType'],
+                'window':self.options['window']
+            }
+        }
+        ans['BB']=CoreTraidMath.CoreMath(data,opMA).ans
+        opSTD={'dataType':'ohcl',
+            'action':'findSTD',
+            'actionOptions':{'valueType':self.options['valueType'],'window':self.options['window']}
+        }
+        ans['STD']=CoreTraidMath.CoreMath(data,opSTD).ans
+        ans['pSTD']=ans['BB']+ans['STD']*self.options['kDev']
+        ans['mSTD']=ans['BB']-ans['STD']*self.options['kDev']
+        ans['x']=np.array(data['date'][self.options['window']-1:].to_list())
+        self.ans= ans
+        return ans
+
+
+# In[12]:
+
+
+class indicatorsAgrigator:
+    
+    def __init__ (self,indDict={}):
+        self.indDict = indDict
+        self.indInst = {}
+        self.ans={}
+        self.createIndicatorsInstance()
+    
+    def createIndicatorsInstance(self):
+        for i in self.indDict.keys():
+            self.indInst[i]=self.indDict[i]['className'](self.indDict[i]['params'])
+            
+    def getAns(self,dataDict={}):
+        ans={}
+        for i in dataDict.keys():
+            ans[i] = self.indInst[i].getAns(dataDict[i])
+        return ans
+
+
+# In[13]:
+
+
+indicators = {
+    'ind_BB':{
+        'className':ind_BB,
+        'params':{'MeanType':'SMA','window':15,'valueType':'close','kDev':2.5}
+    }
+}
+dataDic={
+    'ind_BB':df_candle[:1000]
+}
+
+
+# In[ ]:
+
+
+
+
+
+# In[14]:
+
+
+ia= indicatorsAgrigator(indicators)
+
+
+# In[15]:
+
+
+ia.__dict__
+
+
+# In[16]:
+
+
+ia.indInst['ind_BB'].__dict__
+
+
+# In[17]:
+
+
+ia.getAns(dataDict=dataDic)
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+
+
+# In[18]:
+
+
+op = {'MeanType':'SMA','window':5,'valueType':'low','kDev':2}
+
+
+# In[19]:
+
+
+t =  ind_BB(op)
+
+
+# In[20]:
+
+
+t.getAns(df_candle[:100])
+
+
+# In[21]:
+
+
+t.__dict__
+
+
+# In[ ]:
+
+
+
+
+
+# In[ ]:
+
+
+
+