181 lines
4.3 KiB
Python
181 lines
4.3 KiB
Python
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import pandas as pd
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import datetime
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import numpy as np
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import plotly as pl
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import plotly.graph_objs as go
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import matplotlib.pyplot as plt
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import math
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import scipy
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import random
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import statistics
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import datetime
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import matplotlib.dates as mdates
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import matplotlib.pyplot as plt
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import mplfinance as mpf
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import plotly
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#import plotly.plotly as py
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import plotly.graph_objs as go
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# these two lines allow your code to show up in a notebook
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from plotly.offline import init_notebook_mode, iplot
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from plotly.subplots import make_subplots
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import CoreTraidMath
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import CoreDraw
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init_notebook_mode()
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class ADXI:
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def __init__(self, base_df, options={
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'dataType':'ohcl',
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}, needShow=False,showOnlyIndex=True):
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self.base_df=base_df.reset_index(drop=True)
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self.options=options
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#self.norm_df=self.nornalize()
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#self.col=col
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#self.npCol=np.asarray(self.norm_df[self.col], dtype=np.float32)
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#self.options=options
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self.ans=self.getAns()
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if needShow:
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self.fig=self.pltShow(showOnlyIndex)
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def getDM(self):
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#m,p=self.getMP()
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dm_m=np.asarray([])
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dm_p=np.asarray([])
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tr=np.asarray([])
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for i in range(1,self.base_df.shape[0]):
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if (self.base_df['open'][i]>=
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self.base_df['open'][i-1]):
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dm_p=np.append(dm_p,self.base_df['open'][i]-self.base_df['open'][i-1])
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else:
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dm_p=np.append(dm_p,0)
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if (self.base_df['close'][i]<=self.base_df['close'][i-1]):
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dm_m=np.append(dm_m,self.base_df['close'][i-1]-self.base_df['close'][i])
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else:
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dm_m=np.append(dm_m,0)
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tr=np.append(tr,
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max(self.base_df['close'][i-1],self.base_df['high'][i])-
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min(self.base_df['close'][i-1],self.base_df['low'][i]))
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setattr(self,'dm_m',dm_m)
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setattr(self,'dm_p',dm_p)
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setattr(self,'tr',tr)
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return dm_m,dm_p
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def getEMA(self,Col):
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ser = pd.Series(Col, copy=False)
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op={'dataType':'series',
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'action':'findMean',
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'actionOptions':{'MeanType':'EMA','span':10}
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}
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ans=np.asarray(CoreTraidMath.CoreMath(ser,op).ans)
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#print(ans)
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#ans = np.asarray(ser.ewm(span=40,adjust=False).mean().to_list())
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#print(ans)
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#return(np.asarray(ser.ewm(span=40,adjust=False).mean().to_list()))
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return ans
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def getDI(self):
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dm,dp=self.getDM()
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dip=self.getEMA(dp/self.tr)
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dim=self.getEMA(dm/self.tr)
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return dim,dip
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def getAns(self):
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dim,dip=self.getDI()
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np.seterr(invalid='ignore')
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col=abs(np.true_divide((dim-dip),(dim+dip)))
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setattr(self,'col',col)
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adx=self.getEMA(col)
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ans={
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'DIM':dim*100,
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'DIP':dip*100,
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'ADX':adx*100
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}
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return ans
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def pltShow(self,showOnlyIndex):
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ans=None
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req=[]
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row=1
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if not showOnlyIndex:
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row=2
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req.append({
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'vtype':'OCHL',
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'df':self.base_df,
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'row':1,
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'col':1,
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'name':'OHCL'
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})
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req.append({
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'vtype':'Scatter',
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'df':pd.DataFrame(
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{'value':self.ans['ADX'],'date':self.base_df['date'].to_list()[1:]}) ,
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'row':row,
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'col':1,
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'name':'SenkouSpanB'
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})
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req.append({
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'vtype':'Scatter',
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'df':pd.DataFrame(
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{'value':self.ans['DIP'],'date':self.base_df['date'].to_list()[1:]}) ,
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'row':row,
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'col':1,
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'name':'+DI'
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})
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req.append({
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'vtype':'Scatter',
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'df':pd.DataFrame(
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{'value':self.ans['DIM'],'date':self.base_df['date'].to_list()[1:]}) ,
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'row':row,
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'col':1,
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'name':'-DI'
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})
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ans = CoreDraw.coreDraw(req,True)
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return ans
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