119 lines
3.3 KiB
Python
119 lines
3.3 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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init_notebook_mode()
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class CoreMath:
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def __init__(self, base_df, params={
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'dataType':'ohcl',
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'action': None,
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'actionOptions':{}
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}
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):
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self.base_df=base_df.reset_index(drop=True)
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self.params=params
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if self.params['dataType']=='ohcl':
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self.col=self.base_df[[self.params['actionOptions']['valueType']]]
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elif self.params['dataType']=='series':
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self.col=self.base_df
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self.ans=self.getAns()
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def getAns(self):
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ans=None
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if self.params['action']=='findExt':
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ans = self.getExtremumValue()
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if self.params['action']=='findMean':
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ans = self.getMeanValue()
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return ans
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def getExtremumValue(self):
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ans=None
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'''
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actionOptions:
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'extremumtype':
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'min'
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'max'
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'valueType':
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'open'
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'close'
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'high'
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'low'
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'''
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if self.params['actionOptions']['extremumtype']=='max':
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ans=max(self.col)
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if self.params['actionOptions']['extremumtype']=='min':
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ans=min(self.col)
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return ans
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def getMeanValue(self):
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'''
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actionOptions:
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'MeanType':
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'MA'
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'SMA'
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'EMA'
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'WMA'
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--'SMMA'
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'valueType':
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'open'
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'close'
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'high'
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'low'
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'window'
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'span'
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'weights'
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'''
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ans=None
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if self.params['actionOptions']['MeanType']=='MA':
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ans = self.col.mean()
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if self.params['actionOptions']['MeanType']=='SMA':
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#ans=np.convolve(self.col, np.ones(self.params['actionOptions']['window']), 'valid') / self.params['actionOptions']['window']
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ans=self.col.rolling(window=self.params['actionOptions']['window']).mean()
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if self.params['actionOptions']['MeanType']=='EMA':
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ans=self.col.ewm(span=elf.params['actionOptions']['span'], adjust=False).mean()
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if self.params['actionOptions']['MeanType']=='WMA':
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try:
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weights=self.params['actionOptions']['weights']
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except KeyError:
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weights=np.arange(1,self.params['actionOptions']['window']+1)
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ans=self.col.rolling(window=self.params['actionOptions']['window']).apply(lambda x: np.sum(weights*x) / weights.sum(), raw=False)
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return(ans)
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