import numpy as np
import pandas as pd
from stock.analysis.Common import Common

# https://hotorch.tistory.com/366

class MFI:

    common = None

    def __init__(self):
        self.common = Common()
        return

    def apply(self, df, period = 14):
        typical_price = (df.high + df.low + df.close) / 3
        money_flow = typical_price * df.volume

        positive_flow = []
        negative_flow = []

        for i in range(1, len(typical_price)):
            if typical_price[i] > typical_price[i-1]:
                positive_flow.append(money_flow[i-1])
                negative_flow.append(0)
            elif typical_price[i] < typical_price[i - 1]:
                positive_flow.append(0)
                negative_flow.append(money_flow[i - 1])
            else:
                positive_flow.append(0)
                negative_flow.append(0)

        positive_mf = []
        negative_mf = []

        for i in range(period-1, len(positive_flow)):
            positive_mf.append(sum(positive_flow[i+1-period: i+1]))
        for i in range(period - 1, len(negative_flow)):
            negative_mf.append(sum(negative_flow[i + 1 - period: i + 1]))

        mfi = list(100 * (np.array(positive_mf) / (np.array(positive_mf) + np.array(negative_mf))))
        mfi = list(np.repeat(np.nan, len(df)-len(mfi))) + mfi

        #df = df.assign(macd=macd, macds=macds, macdo=macdo).dropna()
        df = df.assign(mfi=mfi)

        return df

    def analyze(self, stock):

        df = pd.DataFrame()
        df = df.from_dict(stock['PRICE'])
        df = self.apply(df)

        for i in range(len(df.macd)):
            stock['PRICE'][i]['mfi'] = df.mfi.values[i]

        return