features.py 文件源码

def get_power_adjusted_price(books, n=10, power=2):
    '''
    Returns the percent change of an average of order prices weighted by inverse
    distance-wieghted volume for each data point in DataFrame of book data
    '''

    def calc_adjusted_price(book):
        def calc(x):
            return 0 if x.price-book.mid==0 else x.amount*(.5*book.width/(x.price-book.mid))**power
        bid_inv = 1/book.bids.iloc[:n].apply(calc, axis=1)
        ask_inv = 1/book.asks.iloc[:n].apply(calc, axis=1)
        bid_price = book.bids.price.iloc[:n]
        ask_price = book.asks.price.iloc[:n]
        sum_numerator = (bid_price*bid_inv + ask_price*ask_inv).sum()
        sum_denominator = (bid_inv + ask_inv).sum()
        # if np.isnan(sum_numerator) or np.isinf(sum_numerator) or sum_numerator == 0.0 or np.isnan(sum_denominator) or np.isinf(sum_denominator) or sum_denominator == 0.0:
        #     return 0
        quotient = sum_numerator / sum_denominator
        # if quotient < 0.0:
        #     return 0
        return quotient
    adjusted = books.apply(calc_adjusted_price, axis=1)
    return (adjusted/books.mid).apply(log).fillna(0)