import pandas as pd
import numpy as np

df = pd.read_csv('../datas.csv', sep=',')

def tmf(df, n=21, fillna = False):
    high = df['High']
    low = df['Low']
    prev_close = df['Adj Close'].shift(1)
    prev_close = prev_close.fillna(df['Low'][0])
    close = df['Adj Close']
    volume = df['Volume']

    trh = abs(np.maximum(prev_close,high))
    trl = abs(np.minimum(prev_close,low))
    TR = trh-trl
    mfv = (((close - trl)/TR)*2) - 1
    mfv = mfv.fillna(0.0)
    ad = mfv * volume

    tmf = (ad.ewm(span=((n*2)-1),min_periods=0).mean() / volume.ewm(span=((n*2)-1),min_periods=0).mean())
    if fillna:
        tmf = tmf.replace([np.inf, -np.inf], np.nan).fillna(0)
    return tmf

def cmf(df, n=21, fillna = False):
    high = df['High']
    low = df['Low']
    close = df['Adj Close']
    volume = df['Volume']

    mfv = ((close - low - (high - close))) / (high - low)
    mfv = mfv.fillna(0.0)
    mfv *= volume
    cmf = (mfv.rolling(n, min_periods=0).sum()
    / volume.rolling(n, min_periods=0).sum()
    )
    if fillna:
        cmf = cmf.replace([np.inf, -np.inf], np.nan).fillna(0)
    return cmf


def compare(a, b):
    if a.all() == b.all():
        return True
    else:
        return False
print(compare (cmf(df), tmf(df)))