technical_indicators.py 文件源码

def ichimoku(price_objs):
    """
    computes the ichimoku cloud for price_objs
    """

    dates = [pd.to_datetime(str(obj.created_on)) for obj in price_objs]
    prices = [obj.price for obj in price_objs]

    d = {'date': dates,
         'price': prices}
    _prices = pd.DataFrame(d)

    # Tenkan-sen (Conversion Line): (9-period high + 9-period low)/2))
    period9_high = pd.rolling_max(_prices['price'], window=9)
    period9_low = pd.rolling_min(_prices['price'], window=9)
    tenkan_sen = (period9_high + period9_low) / 2

    # Kijun-sen (Base Line): (26-period high + 26-period low)/2))
    period26_high = pd.rolling_max(_prices['price'], window=26)
    period26_low = pd.rolling_min(_prices['price'], window=26)
    kijun_sen = (period26_high + period26_low) / 2

    # Senkou Span A (Leading Span A): (Conversion Line + Base Line)/2))
    senkou_span_a = ((tenkan_sen + kijun_sen) / 2).shift(26)

    # Senkou Span B (Leading Span B): (52-period high + 52-period low)/2))
    period52_high = pd.rolling_max(_prices['price'], window=52)
    period52_low = pd.rolling_min(_prices['price'], window=52)
    senkou_span_b = ((period52_high + period52_low) / 2).shift(26)

    # The most current closing price plotted 22 time periods behind (optional)
    chikou_span = _prices.shift(-22)  # 22 according to investopedia

    return {
        'tenkan_sen': tenkan_sen,
        'kijun_sen': kijun_sen,
        'senkou_span_a': senkou_span_a,
        'senkou_span_b': senkou_span_b,
        'chikou_span': chikou_span,
    }