小编smi*_*ith的帖子

我有 supertrend 实现的 python 代码。我正在使用 pandas 数据框。代码工作正常，但是随着数据帧长度的增加，超级趋势函数运行得越来越慢。我想知道是否可以在代码中更改任何内容来优化它并使其运行得更快，即使数据帧长度很大。

def trueRange(df):
    df['prevClose'] = df['close'].shift(1)
    df['high-low'] = df['high'] - df['low']
    df['high-pClose'] = abs(df['high'] - df['prevClose'])
    df['low-pClose'] = abs(df['low'] - df['prevClose'])
    tr = df[['high-low','high-pClose','low-pClose']].max(axis=1)
    
    return tr

def averageTrueRange(df, peroid=12):
    df['trueRange'] = trueRange(df)
    the_atr = df['trueRange'].rolling(peroid).mean()
    
    return the_atr
    

def superTrend(df, peroid=5, multipler=1.5):
    df['averageTrueRange'] = averageTrueRange(df, peroid=peroid)
    h2 = ((df['high'] + df['low']) / 2)
    df['Upperband'] = h2 + (multipler * df['averageTrueRange'])
    df['Lowerband'] = h2 - (multipler * df['averageTrueRange'])
    df['inUptrend'] = None

    for current in range(1,len(df.index)):
        prev …

我目前正在使用plotly，我有一个名为plotChart 的函数，它将数据帧作为输入并绘制烛台图。我试图找出一种方法将数据帧列表传递给函数plotChart，并使用绘图下拉菜单按股票名称显示输入列表上的选项。下拉菜单将包含数据框列表，当单击某个选项时，它将更新绘图中的图形是否可以执行此操作。下面是我必须绘制单个数据框的代码

def make_multi_plot(df):
    
    fig = make_subplots(rows=2, cols=2,
                        shared_xaxes=True,
                        vertical_spacing=0.03,
                        subplot_titles=('OHLC', 'Volume Profile'),
                        row_width=[0.2, 0.7])

    for s in df.name.unique():
        
        trace1 = go.Candlestick(
            x=df.loc[df.name.isin([s])].time,
            open=df.loc[df.name.isin([s])].open,
            high=df.loc[df.name.isin([s])].high,
            low=df.loc[df.name.isin([s])].low,
            close=df.loc[df.name.isin([s])].close,
            name = s)
        fig.append_trace(trace1,1,1)
        
        fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].BbandsMid, mode='lines',name='MidBollinger'),1,1)
        fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].BbandsUpp, mode='lines',name='UpperBollinger'),1,1)
        fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].BbandsLow, mode='lines',name='LowerBollinger'),1,1)
        fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].vwap, mode='lines',name='VWAP'),1,1)
        fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].STDEV_1, mode='lines',name='UPPERVWAP'),1,1)
        fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].STDEV_N1, mode='lines',name='LOWERVWAP'),1,1)
        fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].KcMid, mode='lines',name='KcMid'),1,1)
        fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].KcUpper, mode='lines',name='KcUpper'),1,1)
        fig.append_trace(go.Scatter(x=df.loc[df.name.isin([s])].time, y=df.loc[df.name.isin([s])].KcLow, mode='lines',name='KcLow'),1,1)
        

        trace2 = go.Bar(
                x=df.loc[df.name.isin([s])].time,
                y=df.loc[df.name.isin([s])].volume,
                name = s)
        fig.append_trace(trace2,2,1)
        # fig.update_layout(title_text=s)
        
        
        
    graph_cnt=len(fig.data)

        
    tr = 11
    symbol_cnt =len(df.name.unique())
    for g in range(tr, graph_cnt): …