Function plot_ichimoku

plot/data_plot.py:295–333

df为dataframe数据，包含'open','high','low','close','volume‘列，索引为时间格式 t:Tenkan-sen：转折线计算周期，默认9个交易日 k：Kijun-sen：基准线计算周期，默认26 l:Chikou Span：滞后跨度或延迟线计算周期，默认30天 s:Senkou Span B：计算前导跨度B或先行上线B，默认52个交易日

(df, t=9, k=26, l=30, s=52)

# ichimoku云图
def plot_ichimoku(df, t=9, k=26, l=30, s=52):
    '''df为dataframe数据，包含'open','high','low','close','volume‘列，索引为时间格式
    t:Tenkan-sen：转折线计算周期，默认9个交易日
    k：Kijun-sen：基准线计算周期，默认26
    l:Chikou Span：滞后跨度或延迟线计算周期，默认30天
    s:Senkou Span B：计算前导跨度B或先行上线B，默认52个交易日
    '''
    data = df.copy()

    # 计算转换线
    high_1 = data['high'].rolling(t).max()
    low_1 = data['low'].rolling(t).min()
    # Tenkan-sen：转换线
    data['conversion_line'] = (high_1 + low_1) / 2
    # 计算基准线
    high_2 = data['high'].rolling(k).max()
    low_2 = data['low'].rolling(k).min()
    data['base_line'] = (high_2 + low_2) / 2
    # 计算前导跨度A
    data['lead_span_A'] = ((data.conversion_line + data.base_line) / 2).shift(l)
    # 计算前导跨度A
    high_3 = data['high'].rolling(s).max()
    low_3 = data['high'].rolling(s).min()
    data['lead_span_B'] = ((high_3 + low_3) / 2).shift(s)
    # 滞后跨度
    data['lagging_span'] = data['close'].shift(-l)
    # 删除缺失值
    data.dropna(inplace=True)
    fig, ax = plt.subplots(1, 1, sharex=True, figsize=(15, 7))
    ax.plot(data.index, data['close'], linewidth=2, label='收盘价')
    ax.plot(data.index, data['lead_span_A'], label='前导跨度A', color='k')
    ax.plot(data.index, data['lead_span_B'], label='前导跨度B', color='y')
    ax.fill_between(data.index, data['lead_span_A'], data['lead_span_B'],
                    where=data['lead_span_A'] >= data['lead_span_B'], color='lightcoral')
    ax.fill_between(data.index, data['lead_span_A'], data['lead_span_B'],
                    where=data['lead_span_A'] < data['lead_span_B'], color='lightgreen')
    plt.legend(loc=0)
    plt.grid()
    plt.show()