Merge branch 'main' into docs

Core/CoreDraw.py

@@ -0,0 +1,199 @@
+ 
+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 agrigateFig():
+    
+    def __init__(self,data=[],needDraw=False ,subplot_titles=None):
+        self.data=data
+        self.ans=self.getAgrPlt()
+        if needDraw:
+            self.subplot_titles=subplot_titles
+            self.fig=coreDraw(self.ans,True,self.subplot_titles)
+        
+        
+    def getAgrPlt(self):
+        count=0
+        ans=[]
+        for i in self.data:
+            count=count+1
+            if type(i)==list:
+                for  g in i:
+                    for j in g.figDict:
+                        ans.append(j)
+                        ans[-1]['row']=count
+            else:
+                for j in i.figDict:
+                    ans.append(j)
+                    ans[-1]['row']=count
+        return ans
+
+
+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']
+        if 'colorType' in params.keys():
+            self.colorType=params['colorType']
+        
+
+
+
+
+class coreDraw():
+    def __init__(self, data=[],needShow=False):
+        self.data=self.getPlts(data)
+        self.needShow=needShow
+        self.ans=self.getAns()
+        
+        
+    
+
+    
+    
+    
+    def getBarColorList(self,l,colorType):
+        if colorType=='diffAbs':
+            ans=['green']
+            for i in range(1,len(l)):
+                if abs(l[i])>abs(l[i-1]):
+                    ans.append('green')
+                else:
+                    ans.append('red')
+        elif colorType=='diff':
+            ans=['green']
+            for i in range(1,len(l)):
+                if (l[i])>(l[i-1]):
+                    ans.append('green')
+                else:
+                    ans.append('red')
+        elif colorType=='normal':
+            ans=[]
+            for i in range(len(l)):
+                ans.append('gray')
+        return ans
+    
+    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)
+        fig.update_layout(barmode='relative')
+        
+        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
+                        )
+            elif i.vtype=='Bars':
+                for j in i.df.keys():
+                    if j!='date':
+                        try:
+                            colorType=i.colorType
+                        except:
+                            colorType='normal'
+                        colors=self.getBarColorList(i.df[j],colorType)
+                        fig.add_trace(go.Bar(x=i.df['date'], y=i.df[j],name=j,marker_color=colors))
+                
+        
+        
+        
+        ans=fig
+        if self.needShow:
+            plotly.offline.iplot(fig)
+        return ans

Core/CoreTraidMath.py

@@ -0,0 +1,200 @@
+ 
+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 agrigateFig():
+    
+    def __init__(self,data=[],needDraw=False ,subplot_titles=None):
+        self.data=data
+        self.ans=self.getAgrPlt()
+        if needDraw:
+            self.subplot_titles=subplot_titles
+            self.fig=coreDraw(self.ans,True,self.subplot_titles)
+        
+        
+    def getAgrPlt(self):
+        count=0
+        ans=[]
+        for i in self.data:
+            count=count+1
+            if type(i)==list:
+                for  g in i:
+                    for j in g.figDict:
+                        ans.append(j)
+                        ans[-1]['row']=count
+            else:
+                for j in i.figDict:
+                    ans.append(j)
+                    ans[-1]['row']=count
+        return ans
+
+
+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']
+        if 'colorType' in params.keys():
+            self.colorType=params['colorType']
+        
+
+
+
+
+class coreDraw():
+    def __init__(self, data=[],needShow=False,subplot_titles={}):
+        self.data=self.getPlts(data)
+        self.needShow=needShow
+        self.subplot_titles=subplot_titles
+        self.ans=self.getAns()
+        
+        
+    
+
+    
+    
+    
+    def getBarColorList(self,l,colorType):
+        if colorType=='diffAbs':
+            ans=['green']
+            for i in range(1,len(l)):
+                if abs(l[i])>abs(l[i-1]):
+                    ans.append('green')
+                else:
+                    ans.append('red')
+        elif colorType=='diff':
+            ans=['green']
+            for i in range(1,len(l)):
+                if (l[i])>(l[i-1]):
+                    ans.append('green')
+                else:
+                    ans.append('red')
+        elif colorType=='normal':
+            ans=[]
+            for i in range(len(l)):
+                ans.append('gray')
+        return ans
+    
+    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=0.1,
+            shared_yaxes=True,
+            #horizontal_spacing=0.02,
+            #column_widths=[]
+            subplot_titles=self.subplot_titles
+        )
+        
+        
+        fig.update_layout(xaxis_rangeslider_visible=False)
+        fig.update_layout(barmode='relative')
+        
+        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
+                        )
+            elif i.vtype=='Bars':
+                for j in i.df.keys():
+                    if j!='date':
+                        try:
+                            colorType=i.colorType
+                        except:
+                            colorType='normal'
+                        colors=self.getBarColorList(i.df[j],colorType)
+                        fig.add_trace(go.Bar(x=i.df['date'], y=i.df[j],name=j,marker_color=colors),row=i.row, col=i.col)
+                
+        
+        
+        
+        ans=fig
+        if self.needShow:
+            plotly.offline.iplot(fig)
+        return ans

Core/Ind_ADX.py

@@ -0,0 +1,182 @@
+ 
+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
+
+
+import CoreTraidMath 
+import CoreDraw
+init_notebook_mode()
+
+class ADXI:
+    
+    def __init__(self, base_df, options={
+        'dataType':'ohcl',
+
+    }, needFig=False,showOnlyIndex=True,drawFig=False):
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+        #self.norm_df=self.nornalize()
+        #self.col=col
+        #self.npCol=np.asarray(self.norm_df[self.col], dtype=np.float32)
+        #self.options=options
+        self.ans=self.getAns()
+        if needFig:
+            self.fig=self.pltShow(showOnlyIndex,drawFig)
+        
+
+    def getDM(self):
+        
+        #m,p=self.getMP()
+        dm_m=np.asarray([])
+        dm_p=np.asarray([])
+        tr=np.asarray([])
+
+        
+        for i in range(1,self.base_df.shape[0]):
+            if (self.base_df['open'][i]>=
+                self.base_df['open'][i-1]):
+                dm_p=np.append(dm_p,self.base_df['open'][i]-self.base_df['open'][i-1])
+            else:
+                dm_p=np.append(dm_p,0)
+            if (self.base_df['close'][i]<=self.base_df['close'][i-1]):
+                dm_m=np.append(dm_m,self.base_df['close'][i-1]-self.base_df['close'][i])
+            else:
+                dm_m=np.append(dm_m,0)            
+
+            tr=np.append(tr,
+                         max(self.base_df['close'][i-1],self.base_df['high'][i])-
+                         min(self.base_df['close'][i-1],self.base_df['low'][i]))
+        
+        
+        setattr(self,'dm_m',dm_m)
+        setattr(self,'dm_p',dm_p)
+        setattr(self,'tr',tr)
+        
+        return dm_m,dm_p
+    
+    def getEMA(self,Col):
+        ser = pd.Series(Col, copy=False)
+        op={'dataType':'series',
+            'action':'findMean',
+            'actionOptions':{'MeanType':'EMA','span':10}
+        }        
+        ans=np.asarray(CoreTraidMath.CoreMath(ser,op).ans)
+        #print(ans)
+        #ans = np.asarray(ser.ewm(span=40,adjust=False).mean().to_list())
+        #print(ans)
+        #return(np.asarray(ser.ewm(span=40,adjust=False).mean().to_list()))
+        return ans
+    
+    def getDI(self):
+        dm,dp=self.getDM()
+        
+        dip=self.getEMA(dp/self.tr)
+        dim=self.getEMA(dm/self.tr)
+        
+        return dim,dip
+    
+    def getAns(self):
+
+        dim,dip=self.getDI()
+        np.seterr(invalid='ignore')
+        col=abs(np.true_divide((dim-dip),(dim+dip)))
+        setattr(self,'col',col)
+        
+        adx=self.getEMA(col)
+        
+        ans={
+            'DIM':dim*100,
+            'DIP':dip*100,
+            'ADX':adx*100
+        }
+        
+        
+        
+        return ans
+    
+    def pltShow(self,showOnlyIndex,drawFig):
+        ans=None
+        req=[]
+        
+        row=1
+        if not showOnlyIndex:
+            
+            row=2
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+            
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['ADX'],'date':self.base_df['date'].to_list()[1:]}) ,
+            'row':row,
+            'col':1,
+            'name':'SenkouSpanB'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['DIP'],'date':self.base_df['date'].to_list()[1:]}) ,
+            'row':row,
+            'col':1,
+            'name':'+DI'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['DIM'],'date':self.base_df['date'].to_list()[1:]}) ,
+            'row':row,
+            'col':1,
+            'name':'-DI'
+            
+        })
+        
+        self.figDict=req
+        
+
+                
+        ans = CoreDraw.coreDraw(req,drawFig)
+        
+        
+        return ans
+        
+    
+
+    
+
+
+
+
+        
+ 
+    

Core/Ind_Alligator.py

@@ -0,0 +1,114 @@
+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 CoreDraw
+
+
+class Alligator:
+
+    def __init__(self, base_df,options={}, needFig=False,showOnlyIndex=True,drawFig=False):
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+        self.ans=self.getAns()
+        if needFig:
+            self.fig=self.pltShow(showOnlyIndex,drawFig)
+            
+            
+    def getMA(self,keyAns):
+        ans=None
+        op={'dataType':'ohcl',
+                'action':'findMean',
+                'actionOptions':{'MeanType':self.options[keyAns]['MeanType'],
+                                 'valueType':self.options['valueType'],
+                                 'window':self.options[keyAns]['window']}
+            }
+        ans=CoreTraidMath.CoreMath(self.base_df,op).ans
+        
+        return ans
+    
+    def getAns(self):
+        ans={'Jaw':{},
+             'Teeth':{},
+             'Lips':{}
+            }
+        
+        
+        for i in ans.keys():
+            ma=self.getMA(i)
+            ans[i]['y']=ma[:len(ma)-self.options[i]['shift']]
+            ans[i]['x']=self.base_df['date'][self.options[i]['window']+self.options[i]['shift']-1:]
+
+
+        return ans
+            
+            
+    def pltShow(self,showOnlyIndex,drawFig):
+        ans=None
+        req=[]
+        row=1
+        if not showOnlyIndex:
+            
+            #row=2
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['Jaw']['y'],'date':self.ans['Jaw']['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'Jaw'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['Teeth']['y'],'date':self.ans['Teeth']['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'Teeth'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['Lips']['y'],'date':self.ans['Lips']['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'Lips'
+            
+        })
+        
+        self.figDict=req
+        ans = CoreDraw.coreDraw(req,drawFig)
+        return ans

Core/Ind_DonchianChannel.py

@@ -0,0 +1,108 @@
+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 CoreDraw
+
+
+
+class IDC:
+
+    def __init__(self, base_df,options={}, needFig=False,showOnlyIndex=True,drawFig=False):
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+        self.ans=self.getAns()
+        if needFig:
+            self.fig=self.pltShow(showOnlyIndex,drawFig)
+
+    def getSMA(self,windowSMA):
+        return np.convolve(self.npCol, np.ones(int(windowSMA)), 'valid') / int(windowSMA)
+    
+    
+
+    
+    
+    def getAns(self):
+        ans={
+            'MaxExt':[],
+            'MinExt':[],
+            'x':[]
+            
+        }
+        opMin={'dataType':'ohcl',
+            'action':'findExt',
+            'actionOptions':{'extremumtype':'min','valueType':'low'}
+        }
+        opMax={'dataType':'ohcl',
+            'action':'findExt',
+            'actionOptions':{'extremumtype':'max','valueType':'high'}
+        }
+        
+        for i in range(self.options['window'],len(self.base_df)-self.options['shift']+1):
+            ans['MaxExt'].append(CoreTraidMath.CoreMath(self.base_df[i-self.options['window']:i],opMax).ans)
+            ans['x'].append(self.base_df['date'][i-1+self.options['shift']])
+            ans['MinExt'].append(CoreTraidMath.CoreMath(self.base_df[i-self.options['window']:i],opMin).ans)
+        return ans
+    
+    
+    def pltShow(self,showOnlyIndex,drawFig):
+        ans=None
+        req=[]
+        row=1
+        if not showOnlyIndex:
+            
+            #row=2
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['MaxExt'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'MaxExt'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['MinExt'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'MinExt'
+            
+        })
+                
+        
+        self.figDict=req
+        ans = CoreDraw.coreDraw(req,drawFig)
+        return ans

Core/Ind_Envelopes.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()
+
+import CoreTraidMath 
+import CoreDraw
+
+class Envelopes:
+
+    def __init__(self, base_df,options={}, needFig=False,showOnlyIndex=True,drawFig=False):
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+        self.ans=self.getAns()
+        if needFig:
+            self.fig=self.pltShow(showOnlyIndex,drawFig)
+            
+            
+    
+    def getAns(self):
+        ans={
+                'MainEnv':[],
+                'PlusEnv':[],
+                'MinusEnv':[],
+                'x':[]
+        }
+        dictResp={}
+        dictResp['MeanType']=self.options['MeanType']
+        dictResp['valueType']=self.options['valueType']
+        
+        try:
+            dictResp['window'] = self.options['window']
+            dictResp['span'] = self.options['window']
+        except:
+            pass
+        
+    
+        
+        
+        op={'dataType':'ohcl',
+            'action':'findMean',
+            'actionOptions':dictResp
+        }
+        if dictResp['MeanType']=='SMA':
+
+            y=CoreTraidMath.CoreMath(self.base_df,op).ans
+            ans['MainEnv']=y[:len(y)-self.options['shift']]
+            ans['PlusEnv']=ans['MainEnv']*(1+self.options['kProc']/100)
+            ans['MinusEnv']=ans['MainEnv']*(1-self.options['kProc']/100)
+            ans['x']=self.base_df['date'][self.options['window']-1+self.options['shift']:]
+        
+        
+        
+        
+        
+        return ans
+    
+    def pltShow(self,showOnlyIndex,drawFig):
+        ans=None
+        req=[]
+        row=1
+        if not showOnlyIndex:
+            
+            #row=2
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['MainEnv'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'MainEnv'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['PlusEnv'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'PlusEnv'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['MinusEnv'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'MinusEnv'
+            
+        })
+        
+        self.figDict=req
+        ans = CoreDraw.coreDraw(req,drawFig)
+        return ans

Core/Ind_Gator.py

@@ -0,0 +1,108 @@
+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 CoreDraw
+import Ind_Alligator
+
+class Gator:
+
+    def __init__(self, base_df,options={}, needFig=False,showOnlyIndex=True,drawFig=False):
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+        self.ans=self.getAns()
+        if needFig:
+            self.fig=self.pltShow(showOnlyIndex,drawFig)
+            
+            
+    def getAns(self):
+        
+        req=Ind_Alligator.Alligator(self.base_df,self.options).ans
+        #self.req=req
+        '''
+        ans={'Jaw-Teeth':{'y':[],'x':[]},
+             'Teeth-Lips':{'y':[],'x':[]},
+            }
+        JawTeethIter=self.options['Jaw']['window']+self.options['Jaw']['shift']-self.options['Teeth']['window']-self.options['Teeth']['shift']
+        TeethLipsIter=self.options['Teeth']['window']+self.options['Teeth']['shift']-self.options['Lips']['window']-self.options['Lips']['shift']
+        
+        #print(TeethLipsIter)
+        for i in range(len(req['Jaw']['y'])):
+            ans['Jaw-Teeth']['y'].append(abs(req['Jaw']['y'][i]-req['Teeth']['y'][JawTeethIter+i]))
+        ans['Jaw-Teeth']['x']=req['Jaw']['x']
+        for i in range(len(req['Teeth']['y'])):
+            ans['Teeth-Lips']['y'].append(-abs(req['Teeth']['y'][i]-req['Lips']['y'][TeethLipsIter+i]))
+        ans['Teeth-Lips']['x']=req['Teeth']['x']
+        '''
+        ans={'Jaw-Teeth':[],
+             'Teeth-Lips':[],
+             'x':[]
+        }
+        JawTeethIter=self.options['Jaw']['window']+self.options['Jaw']['shift']-self.options['Teeth']['window']-self.options['Teeth']['shift']
+        TeethLipsIter=self.options['Teeth']['window']+self.options['Teeth']['shift']-self.options['Lips']['window']-self.options['Lips']['shift']
+        for i in range(len(req['Jaw']['y'])):
+            ans['Jaw-Teeth'].append(abs(req['Jaw']['y'][i]-req['Teeth']['y'][JawTeethIter+i]))        
+            ans['Teeth-Lips'].append(-abs(req['Teeth']['y'][JawTeethIter+i]-req['Lips']['y'][JawTeethIter+TeethLipsIter+i]))
+        ans['x']=req['Jaw']['x']
+        
+        return ans
+            
+            
+    def pltShow(self,showOnlyIndex,drawFig):
+        ans=None
+        req=[]
+        row=1
+        if not showOnlyIndex:
+            
+            row=2
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+            
+
+        req.append({
+            'vtype':'Bars',
+            'df':pd.DataFrame(
+                {'Jaw-Teeth':self.ans['Jaw-Teeth'],
+                 'Teeth-Lips':self.ans['Teeth-Lips'],
+                 'date':self.ans['x'].to_list()}
+            ) ,
+            'row':row,
+            'col':1,
+            'name':'Gator',
+            'colorType':'diffAbs'
+            
+        })
+
+        
+        self.figDict=req
+        ans = CoreDraw.coreDraw(req,drawFig)
+        return ans

Core/Ind_Ishimoku.py

@@ -0,0 +1,188 @@
+ 
+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
+import CoreDraw
+init_notebook_mode()
+import CoreTraidMath 
+import plotly.express as px
+
+
+class Ishimoku:
+    
+    def __init__(self, base_df, options={
+        'dataType':'ohcl',
+        'short':9,
+        'middle':26,
+        'long':52,
+        'backstep':26,
+        'forwardstep':26
+        },needFig=False,showOnlyIndex=True,drawFig=False
+                ):
+        
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+
+        self.ans=self.getAns()
+        if needFig:
+            self.fig=self.pltShow(showOnlyIndex,drawFig)
+        
+        
+    def getTankenSen(self):
+        y=np.asarray([])
+        x=np.asarray([])
+        for i in range(self.options['short'],self.base_df.shape[0]):
+            maxValue=max(self.base_df['high'][i-self.options['short']:i])
+            minValue=min(self.base_df['low'][i-self.options['short']:i])
+            y=np.append(y,(maxValue+minValue)*0.5)
+            x=np.append(x,self.base_df['date'][i])
+            #ts.append(max(self.base_df[self.options['colName']['high']][i-self.options['short']:i]))
+        ans={'y':y,'x':x}
+        return(ans)
+    
+    def getKijunSen(self):
+        y=np.asarray([])
+        x=np.asarray([])
+        for i in range(self.options['middle'],self.base_df.shape[0]):
+            maxValue=max(self.base_df['high'][i-self.options['middle']:i])
+            minValue=min(self.base_df['low'][i-self.options['middle']:i])
+            y=np.append(y,(maxValue+minValue)*0.5)
+            x=np.append(x,self.base_df['date'][i])
+            #ts.append(max(self.base_df[self.options['colName']['high']][i-self.options['short']:i]))
+        ans={'y':y,'x':x}
+        return(ans)
+    
+    def getChinkoSpan(self):
+        y=np.asarray(self.base_df['close'][self.options['backstep']:])
+        x=np.asarray(self.base_df['date'][:self.base_df.shape[0]-self.options['backstep']])
+        ans={'y':y,'x':x}
+        return(ans)    
+    
+    def getSenkouSpanA(self, data):
+        y=np.asarray([])
+        x=np.asarray([])
+        shift=len(data['TankenSen']['y'])-len(data['KijunSen']['y'])
+        for i in range(len(data['KijunSen']['x'])-self.options['forwardstep']):
+            y=np.append(y,(data['KijunSen']['y'][i]+data['TankenSen']['y'][i+shift])*0.5)
+            x=np.append(x,data['KijunSen']['x'][i+self.options['forwardstep']])
+            
+        ans={'y':y,'x':x}
+        return(ans)    
+    
+    def getSenkouSpanB(self):
+        y=np.asarray([])
+        x=np.asarray([])
+        for i in range(self.options['long'],self.base_df.shape[0]-self.options['forwardstep']):
+            maxValue=max(self.base_df['high'][i-self.options['long']:i])
+            minValue=min(self.base_df['low'][i-self.options['long']:i])
+            y=np.append(y,(maxValue+minValue)*0.5)
+            x=np.append(x,self.base_df['date'][i+self.options['forwardstep']])
+            #ts.append(max(self.base_df[self.options['colName']['high']][i-sel        
+        
+        ans={'y':y,'x':x}
+        return(ans)    
+    
+    
+    
+    
+    
+    
+    def getAns(self):
+        ans={}
+        ans['TankenSen']=self.getTankenSen()
+        ans['KijunSen']=self.getKijunSen()
+        ans['ChinkoSpan']=self.getChinkoSpan()
+        ans['SenkouSpanA']=self.getSenkouSpanA(ans)
+        ans['SenkouSpanB']=self.getSenkouSpanB()
+        #print(ans)
+        return(ans)
+    
+    
+    def pltShow(self,showOnlyIndex,drawFig):
+        ans=None
+        req=[]
+        
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['TankenSen']['y'],'date':self.ans['TankenSen']['x']}) ,
+            'row':1,
+            'col':1,
+            'name':'TankenSen'
+            
+        })
+        
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['KijunSen']['y'],'date':self.ans['KijunSen']['x']}) ,
+            'row':1,
+            'col':1,
+            'name':'KijunSen'
+            
+        })        
+
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['ChinkoSpan']['y'],'date':self.ans['ChinkoSpan']['x']}) ,
+            'row':1,
+            'col':1,
+            'name':'ChinkoSpan'
+            
+        })        
+
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['SenkouSpanA']['y'],'date':self.ans['SenkouSpanA']['x']}) ,
+            'row':1,
+            'col':1,
+            'name':'SenkouSpanA'
+            
+        }) 
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['SenkouSpanB']['y'],'date':self.ans['SenkouSpanB']['x']}) ,
+            'row':1,
+            'col':1,
+            'name':'SenkouSpanB'
+            
+        })
+        
+        
+        if not showOnlyIndex:
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+        self.figDict=req
+        ans = CoreDraw.coreDraw(req,drawFig)
+        #print(ans)
+        return ans

Core/Ind_LRI.py

@@ -0,0 +1,104 @@
+ 
+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 CoreDraw
+
+
+class LRI:
+    def __init__(self, base_df,options={}, needFig=False,showOnlyIndex=True,drawFig=False):
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+        self.col=self.base_df[self.options['valueType']]
+        self.ans=self.getAns()
+        if needFig:
+            self.fig=self.pltShow(showOnlyIndex,drawFig)
+            
+    def getAns(self):
+        ans=None
+        
+        l=np.asarray(list(range(len(self.col))))
+        k,b=np.polyfit(l,self.col,1)
+        setattr(self,'k',k)
+        setattr(self,'b',b)
+        b1=b+self.options['k']*pow(1-k*k,0.5)
+        b2=b-self.options['k']*pow(1-k*k,0.5)
+        ans={
+            'LRI':l*k+b,
+            'LRI+':l*k+b1,
+            'LRI-':l*k+b2,
+            'x':self.base_df['date']
+        }
+
+
+        
+        return ans
+    def pltShow(self,showOnlyIndex,drawFig):
+        ans=None
+        req=[]
+        row=1
+        if not showOnlyIndex:
+            
+            #row=2
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['LRI'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'LRI'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['LRI+'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'LRI+'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['LRI-'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'LRI-'
+            
+        })
+        self.figDict=req
+        ans = CoreDraw.coreDraw(req,drawFig)
+        return ans

Core/Ind_STD.py

@@ -0,0 +1,88 @@
+ 
+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 CoreDraw
+
+class ISTD:
+    def __init__(self, base_df,options={}, needFig=False,showOnlyIndex=True,drawFig=False):
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+        self.ans=self.getAns()
+        if needFig:
+            self.fig=self.pltShow(showOnlyIndex,drawFig)
+            
+    def getAns(self):
+        ans=None
+        try:
+            op={'dataType':'ohcl',
+                    'action':'findSTD',
+                    'actionOptions':{
+                                        'valueType':self.options['valueType'],
+                                        'window':self.options['window']
+                                    }
+                }
+            x=self.base_df['date'][self.options['window']-1:].to_list()
+        except:
+            op={'dataType':'ohcl',
+                    'action':'findSTD',
+                    'actionOptions':{'valueType':self.options['valueType']}
+                }
+            x=self.base_df['date'].to_list()
+        y= CoreTraidMath.CoreMath(self.base_df,op).ans
+        ans={'y':y,'x':x}
+
+        
+        
+        return ans
+    def pltShow(self,showOnlyIndex,drawFig):
+        ans=None
+        req=[]
+        row=1
+        if not showOnlyIndex:
+            
+            row=2
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['y'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'ISTD'
+            
+        })
+        self.figDict=req
+        ans = CoreDraw.coreDraw(req,drawFig)
+        return ans

Core/Ind_Stochastic.py

@@ -0,0 +1,147 @@
+ 
+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 CoreDraw
+
+class Stochastic:
+    
+    def __init__(self, base_df, options={
+        'dataType':'ohcl',
+        'window':14,
+        'windowSMA':5
+    }, needFig=False,showOnlyIndex=True,drawFig=False
+                ):
+    
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+
+        self.ans=self.getAns()
+        if needFig:
+            self.fig=self.pltShow(showOnlyIndex,drawFig)
+        
+    def getKn(self):
+        ans={}
+        y=np.asarray([])
+        x=np.asarray([])
+        for i in range(self.options['window'],self.base_df.shape[0]):
+            minValue=min(self.base_df['low'][i-self.options['window']:i])
+            maxValue=max(self.base_df['high'][i-self.options['window']:i])
+            
+            y=np.append(y,(self.base_df['close'][i-1]-minValue)/(maxValue-minValue))
+            x=np.append(x,self.base_df['date'][i-1])
+            #print(i,minValue,maxValue,self.base_df[self.options['colName']['close']][i],y[-1])
+        ans['y'],ans['x']=y,x
+        
+        
+        
+        return ans
+        
+        
+    def getSMA(self,col):
+        ans=None
+        ser = pd.Series(col, copy=False)
+        op={'dataType':'series',
+            'action':'findMean',
+            'actionOptions':{'MeanType':'SMA','window':self.options['windowSMA']}
+        }        
+        ans=np.asarray(CoreTraidMath.CoreMath(ser,op).ans)        
+        return ans
+        #return np.convolve(col, np.ones(self.options['windowSMA']), 'valid') /self.options['windowSMA']
+        
+        
+        
+    
+    def getDn(self,col):
+        ans={}
+        y=np.asarray([])
+        x=np.asarray([])
+        for i in range(self.options['windowSMA'],len(col['y'])):
+            y=np.append(y, self.getSMA(col['y'][i-self.options['windowSMA']:i]))
+            x=np.append(x,col['x'][i])
+        
+        ans['y'],ans['x']=y,x
+        return ans
+    
+    def getAns(self):
+        ans={}
+        ans['Kn']=self.getKn()
+        ans['Dn']=self.getDn(ans['Kn'])
+
+        #print(ans)
+        return(ans)
+    
+    
+    
+    def pltShow(self,showOnlyIndex,drawFig):
+        ans=None
+        
+        
+        
+        req=[]
+        
+        row=1
+        if not showOnlyIndex:
+            
+            row=2
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+            
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['Kn']['y'],'date':self.ans['Kn']['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'Kn'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['Dn']['y'],'date':self.ans['Dn']['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'Dn'
+            
+        })
+        
+        self.figDict=req
+
+        
+
+                
+        ans = CoreDraw.coreDraw(req,drawFig)
+        
+        return ans
+    

Core/Ind_bollingerBands.py

@@ -0,0 +1,109 @@
+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 CoreDraw
+
+
+class BB:
+
+    def __init__(self, base_df,options={}, needFig=False,showOnlyIndex=True,drawFig=False):
+        self.base_df=base_df.reset_index(drop=True)
+        self.options=options
+        self.ans=self.getAns()
+        if needFig:
+            self.fig=self.pltShow(showOnlyIndex,drawFig)
+            
+            
+    
+    def getAns(self):
+        ans={}
+        
+        opMA={'dataType':'ohcl',
+            'action':'findMean',
+            'actionOptions':{
+                'MeanType':self.options['MeanType'],
+                'valueType':self.options['valueType'],
+                'window':self.options['window']
+            }
+        }
+        ans['BB']=CoreTraidMath.CoreMath(self.base_df,opMA).ans
+        opSTD={'dataType':'ohcl',
+            'action':'findSTD',
+            'actionOptions':{'valueType':self.options['valueType'],'window':self.options['window']}
+        }
+        ans['STD']=CoreTraidMath.CoreMath(self.base_df,opSTD).ans
+        ans['pSTD']=ans['BB']+ans['STD']*self.options['kDev']
+        ans['mSTD']=ans['BB']-ans['STD']*self.options['kDev']
+        ans['x']=np.array(self.base_df['date'][self.options['window']-1:].to_list())
+        return ans
+    
+    def pltShow(self,showOnlyIndex,drawFig):
+        ans=None
+        req=[]
+        row=1
+        if not showOnlyIndex:
+            
+            #row=2
+            req.append({
+                'vtype':'OCHL',
+                'df':self.base_df,
+                'row':1,
+                'col':1,
+                'name':'OHCL'
+
+            })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['BB'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'BB'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['pSTD'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'pSTD'
+            
+        })
+        req.append({
+            'vtype':'Scatter',
+            'df':pd.DataFrame(
+                {'value':self.ans['mSTD'],'date':self.ans['x']}) ,
+            'row':row,
+            'col':1,
+            'name':'mSTD'
+            
+        })
+        
+        self.figDict=req
+        ans = CoreDraw.coreDraw(req,drawFig)
+        return ans

Core/readme.txt

@@ -0,0 +1,2 @@
+
+Ind_Envelopes - SMA only

Docs/readme.txt

@@ -0,0 +1 @@
+

Docs/с4.drawio

@@ -0,0 +1 @@
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News/InvestCal.py

@@ -0,0 +1,123 @@
+from selenium import webdriver
+import time
+import pandas as pd
+import requests 
+from bs4 import BeautifulSoup 
+import nest_asyncio
+import asyncio
+import requests_html as rh
+import datetime 
+import pandas as pd
+import json
+
+
+def f_write(driver):
+    with open(r'C:\\BinOptions\\exemple_pars_tomorrow.txt', 'w',encoding="utf-8") as f:
+        f.write(driver.page_source)
+
+
+
+def investingPars(path_file=None,dateDict=None):
+    ans=None
+    op=webdriver.FirefoxOptions()
+    #op.add_argument("--headless")
+    url='https://ru.investing.com/economic-calendar/'
+    #driver = webdriver.Firefox(executable_path='C:\\Users\\Redsandy\\Downloads\\geckodriver.exe')
+    EXE_PATH = r'C:\\Users\\Redsandy\\Downloads\\geckodriver.exe'
+    driver = webdriver.Firefox(executable_path=EXE_PATH,options=op)
+    try:
+        driver.get(url=url)
+
+        #time.sleep(5)
+        #driver.find_element_by_id('timeFrame_tomorrow').click()
+        #time.sleep(5)
+        #f_write(driver)
+        if dateDict!=None:
+            driver.find_element_by_id('datePickerToggleBtn').click()
+            driver.find_element_by_id("startDate").clear()
+            driver.find_element_by_id("startDate").send_keys(dateDict['startDate'])
+            driver.find_element_by_id("endDate").clear()
+            driver.find_element_by_id("endDate").send_keys(dateDict['endDate'])
+            #driver.find_element_by_id("startDate").send_keys("31/03/2022")
+            driver.find_element_by_id('applyBtn').click()
+        time.sleep(2)
+        driver.execute_script("window.scrollTo(0,document.body.scrollHeight)")
+        time.sleep(2)
+        f_write(driver)
+        page_source=driver.page_source       
+        
+    except Exception as ex:
+        print(ex)
+    finally:
+        driver.close()
+        driver.quit()
+
+        
+    path_file_csv=path_file
+    ans=getDF(page_source,path_file_csv)
+    return ans
+
+
+
+def getTime(strok):
+    ans=None
+    if len(strok.text.split())==2:
+        ans = strok['title'].split()[-1]
+    else:
+        ans = strok.text
+    return ans
+def getValat(strok):
+    ans=None
+    inc=0
+    for i in strok.find_all('i'):
+        if i['class'][0]=='grayFullBullishIcon':
+            inc=inc+1
+    ans= str(inc)+"/3"
+    return(ans)
+def getUrl(strok):
+    ans=None
+    baseUrl='https://ru.investing.com'
+    ans=baseUrl+strok.find_all('a', href=True)[0]['href']
+    return ans
+def getDF(doc,path_file):
+    ans=None
+    
+    soup = BeautifulSoup(doc, 'html.parser')
+    tabl=soup.find("table", {"id": "economicCalendarData"}).find('tbody')
+    tdList=[]
+    buff=[]
+    for stats in tabl.find_all('tr'):
+        for row in stats.find_all('td'):
+            buff.append(row)
+        tdList.append(buff)
+        buff=[]
+        col_names=['Время', 'Валюта', 'Важность','Событие','URL', 'Факт.','Прогноз','Пред.','Date']
+        df= pd.DataFrame(columns=col_names)
+
+
+        for i in range(0, len(tdList)):
+            try:
+                if tdList[i][0]["class"][0]=='theDay':
+                    colDate = tdList[i][0].text[:-3]
+                else:
+                    newRow={
+                        col_names[0]:getTime(tdList[i][0]),
+                        col_names[1]:tdList[i][1].text.strip(),
+                        col_names[2]:getValat(tdList[i][2]),
+                        col_names[3]:tdList[i][3].text.strip(),
+                        col_names[4]:getUrl(tdList[i][3]),
+                        col_names[5]:tdList[i][4].text.strip(),
+                        col_names[6]:tdList[i][5].text.strip(),
+                        col_names[7]:tdList[i][6].text.strip(),
+                        col_names[8]:colDate
+                    }
+                    df=df.append(newRow, ignore_index=True)
+            except:
+                pass
+    if path_file!=None:
+        df.to_csv(path_file, index=False, encoding='utf-8-sig')
+    
+    
+    
+    return df
+

News/readme.txt

@@ -0,0 +1,9 @@
+dateDict={
+    'startDate':'30/03/2022',
+    'endDate':'01/04/2022'
+
+}
+
+p='C:\\BinOptions\\df_tomorrow.csv'
+InvestCal.investingPars(p,dateDict)
+or InvestCal.investingPars()

Untitled Diagram.drawio

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