From 785daedb36340240f2cb8e5c56c59cf41ab20ebd Mon Sep 17 00:00:00 2001
From: Redsandy <34872843+Redsandyg@users.noreply.github.com>
Date: Fri, 4 Mar 2022 16:37:47 +0300
Subject: [PATCH] Update CoreTraidMath.py

---
 Core/CoreTraidMath.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/Core/CoreTraidMath.py b/Core/CoreTraidMath.py
index 71e5e7b..24a0e60 100644
--- a/Core/CoreTraidMath.py
+++ b/Core/CoreTraidMath.py
@@ -37,7 +37,7 @@ class CoreMath:
         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']]]
+            self.col=self.base_df[self.params['actionOptions']['valueType']]
         elif self.params['dataType']=='series':
             self.col=self.base_df
         self.ans=self.getAns()
@@ -101,18 +101,17 @@ class CoreMath:
             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()
+            ans=self.col.rolling(window=self.params['actionOptions']['window']).mean().to_list()
         if self.params['actionOptions']['MeanType']=='EMA':
-            ans=self.col.ewm(span=elf.params['actionOptions']['span'], adjust=False).mean()
+            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)
+            ans=self.col.rolling(window=self.params['actionOptions']['window']).apply(lambda x: np.sum(weights*x) / weights.sum(), raw=False).to_list()
 
         
         
         return(ans)
         
-