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Core/CoreDraw.py

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+ 
+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 
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+
+import mplfinance as mpf
+
+import plotly
+#import plotly.plotly as py
+import plotly.graph_objs as go
+# these two lines allow your code to show up in a notebook
+from plotly.offline import init_notebook_mode, iplot
+from plotly.subplots import make_subplots
+init_notebook_mode()
+
+import CoreTraidMath 
+import plotly.express as px
+
+class corePlt():
+    def __init__(self, params={
+        'vtype':'',
+        'df':pd.DataFrame(),
+        'row':1,
+        'col':1,
+        'name':''
+    }):
+        self.vtype=params['vtype']
+        self.df=params['df']
+        self.row=params['row']
+        self.col=params['col']
+        self.name=params['name']
+        
+
+
+
+
+class coreDraw():
+    def __init__(self, data=[],needShow=False):
+        self.data=self.getPlts(data)
+        self.needShow=needShow
+        self.ans=self.getAns()
+        
+        
+    
+    
+    
+    def getPlts(self, data):
+        ans=None
+        
+        if type(data)==list:
+            ans=[]
+            for i in data:
+                ans.append(corePlt(i))
+        else:
+            ans=[corePlt(data)]
+        
+        
+        
+        
+        return ans
+    
+    def getAns(self):
+        '''
+        data list
+            vtype
+            df
+            row=1
+            col=1
+            name
+        
+        
+        
+        '''
+        
+        ans=None
+        
+        
+        
+       
+        maxRow=1
+        maxCol=1
+        for i in self.data:
+            if i.row > maxRow:
+                maxRow =i.row
+            if i.col > maxCol:
+                maxCol =i.col
+                
+        fig = make_subplots(
+            rows=maxRow,
+            cols=maxCol,
+            shared_xaxes=True,
+            vertical_spacing=0.02,
+            shared_yaxes=True,
+            horizontal_spacing=0.02,
+            #column_widths=[]
+        
+        )
+        
+        
+        fig.update_layout(xaxis_rangeslider_visible=False)
+        
+        
+        for i in self.data:
+            if i.vtype=='Scatter':
+                fig.add_trace(go.Scatter(x=i.df['date'],y=i.df['value'],name=i.name), row=i.row, col=i.col)
+            elif i.vtype=='OCHL':
+                fig.add_trace(go.Candlestick(
+                        x=i.df['date'],
+                        open=i.df['open'],
+                        high=i.df['high'],
+                        low=i.df['low'],
+                        close=i.df['close'],
+                        name=i.name),
+                              row=i.row, col=i.col
+                        )
+        
+        
+        
+        ans=fig
+        if self.needShow:
+            plotly.offline.iplot(fig)
+        return ans
+        
+        
+
+

Core/CoreTraidMath.py

@@ -0,0 +1,124 @@
+ 
+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 
+import matplotlib.dates as mdates
+import matplotlib.pyplot as plt
+
+import mplfinance as mpf
+
+import plotly
+#import plotly.plotly as py
+import plotly.graph_objs as go
+# these two lines allow your code to show up in a notebook
+from plotly.offline import init_notebook_mode, iplot
+from plotly.subplots import make_subplots
+init_notebook_mode()
+
+class CoreMath:
+    
+    def __init__(self, base_df, params={
+        'dataType':'ohcl',
+        'colName':{
+            'open':'open',
+            'close':'close',
+            'high':'high',
+            'low':'low',
+            'date':'date'
+        },
+        '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['colName'][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()
+        if self.params['action']=='findMean':
+            ans = self.getMeanValue()
+        
+            
+        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()
+        if self.params['actionOptions']['MeanType']=='EMA':
+            ans=self.col.ewm(span=elf.params['actionOptions']['span'], adjust=False).mean()
+        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)
+
+        
+        
+        return(ans)
+