market_trade/core/CoreTraidMath.py

@@ -1,138 +0,0 @@
- 
-import pandas as pd
-import datetime
-import numpy as np
-import plotly as pl
-import plotly.graph_objs as go
-import matplotlib.pyplot as plt
-import math
-import scipy
-import random
-import statistics 
-
-
-import datetime 
-
-
-
-
-
-class CoreMath:
-    
-    def __init__(self, base_df, params={
-        'dataType':'ohcl',
-        'action': None,
-        'actionOptions':{}
-    }
-                ):
-    
-        self.base_df=base_df.reset_index(drop=True)
-        self.params=params
-        if self.params['dataType']=='ohcl':
-            self.col=self.base_df[self.params['actionOptions']['valueType']]
-        elif self.params['dataType']=='series':
-            self.col=self.base_df
-        self.ans=self.getAns()
-        
-    
-    def getAns(self):
-        ans=None
-        
-        if self.params['action']=='findExt':
-            ans = self.getExtremumValue()
-        elif self.params['action']=='findMean':
-            ans = self.getMeanValue()
-        elif self.params['action']=='findSTD':
-            ans=self.getSTD()
-        
-            
-        return ans
-    
-    
-    def getExtremumValue(self):
-        ans=None
-        '''
-        actionOptions:
-            'extremumtype':
-                'min'
-                'max'
-            'valueType':
-                'open'
-                'close'
-                'high'
-                'low'
-        '''
-        if self.params['actionOptions']['extremumtype']=='max':
-            ans=max(self.col)
-            
-        if self.params['actionOptions']['extremumtype']=='min':
-            ans=min(self.col)
-
-            
-        return ans
-     
-        
-    def getMeanValue(self):
-        '''
-        actionOptions:
-            'MeanType':
-                'MA'
-                'SMA'
-                'EMA'
-                'WMA'
-                --'SMMA'
-            'valueType':
-                'open'
-                'close'
-                'high'
-                'low'
-            'window'
-            'span'
-            'weights'
-        '''
-        ans=None
-        if self.params['actionOptions']['MeanType']=='MA':
-            ans = self.col.mean()
-        if self.params['actionOptions']['MeanType']=='SMA':
-            ans=np.convolve(self.col, np.ones(self.params['actionOptions']['window']), 'valid') / self.params['actionOptions']['window']
-            #ans=self.col.rolling(window=self.params['actionOptions']['window']).mean().to_list()
-            
-        if self.params['actionOptions']['MeanType']=='EMA':
-            ans=self.col.ewm(span=self.params['actionOptions']['span'], adjust=False).mean().to_list()
-        if self.params['actionOptions']['MeanType']=='WMA':
-            try:
-                weights=self.params['actionOptions']['weights']
-            except KeyError:
-                weights=np.arange(1,self.params['actionOptions']['window']+1)
-            ans=self.col.rolling(window=self.params['actionOptions']['window']).apply(lambda x: np.sum(weights*x) / weights.sum(), raw=False).to_list()
-
-        
-        
-        return(ans)
-    
-    def getSTD(self):
-        '''
-        actionOptions:
-            window
-        
-        
-        
-        
-        
-        '''
-        
-        ans=None
-
-
-        try:
-            window=self.params['actionOptions']['window']
-            ans=np.asarray([])
-            for i in range(len(self.col)-window+1):
-                ans=np.append(ans,np.std(self.col[i:i+window], ddof=1))
-            
-        except:
-            #window = len(self.col)
-            ans=np.std(self.col, ddof=1)
-
-        return ans
-