使用相邻行计算 Pandas Dataframe 中的列而不遍历每一行

Question

我想看看是否有一种方法可以在不遍历每一行的情况下使用类似于移动平均线的东西来计算数据框中的列。当前工作代码：

def create_candles(ticks, instrument, time_slice):
    candlesticks = ticks.price.resample(time_slice, base=00).ohlc().bfill()
    volume = ticks.amount.resample(time_slice, base=00).sum()
    candlesticks['volume'] = volume
    candlesticks['instrument'] = instrument
    candlesticks['ttr'] = 0
    # candlesticks['vr_7'] = 0
    candlesticks['vr_10'] = 0
    candlesticks = calculate_indicators(candlesticks, instrument, time_slice)

    return candlesticks


def calculate_indicators(candlesticks, instrument):
    candlesticks.sort_index(inplace=True)
    # candlesticks['rsi_14'] = talib.RSI(candlesticks.close, timeperiod=14)
    candlesticks['lr_50'] = talib.LINEARREG(candlesticks.close, timeperiod=50)
    # candlesticks['lr_150'] = talib.LINEARREG(candlesticks.close, timeperiod=150)
    # candlesticks['ema_55'] = talib.EMA(candlesticks.close, timeperiod=55)
    # candlesticks['ema_28'] = talib.EMA(candlesticks.close, timeperiod=28)
    # candlesticks['ema_18'] = talib.EMA(candlesticks.close, timeperiod=18)
    # candlesticks['ema_9'] = talib.EMA(candlesticks.close, timeperiod=9)
    # candlesticks['wma_21'] = talib.WMA(candlesticks.close, timeperiod=21)
    # candlesticks['wma_12'] = talib.WMA(candlesticks.close, timeperiod=12)
    # candlesticks['wma_11'] = talib.WMA(candlesticks.close, timeperiod=11)
    # candlesticks['wma_5'] = talib.WMA(candlesticks.close, timeperiod=5)
    candlesticks['cmo_9'] = talib.CMO(candlesticks.close, timeperiod=9)

    for row in candlesticks.itertuples():
        current_index = candlesticks.index.get_loc(row.Index)
        if current_index >= 1:
            previous_close = candlesticks.iloc[current_index - 1, candlesticks.columns.get_loc('close')]
            candlesticks.iloc[current_index, candlesticks.columns.get_loc('ttr')] = max(
                row.high - row.low,
                abs(row.high - previous_close),
                abs(row.low - previous_close))

        if current_index > 10:
            candlesticks.iloc[current_index, candlesticks.columns.get_loc('vr_10')] = candlesticks.iloc[current_index, candlesticks.columns.get_loc('ttr')] / (
                max(candlesticks.high[current_index - 9: current_index].max(), candlesticks.close[current_index - 11]) -
                min(candlesticks.low[current_index - 9: current_index].min(), candlesticks.close[current_index - 11]))

    candlesticks['timestamp'] = pd.to_datetime(candlesticks.index)
    candlesticks['instrument'] = instrument
    candlesticks.fillna(0, inplace=True)
    return candlesticks

在迭代中，我正在计算真实范围 ('TTR')，然后是波动率 ('VR_10')

除第一行外，DF 中的每一行都会计算 TTR。它使用上一行的收盘价列和当前行的最高价和最低价列。

VR_10除前10行以外的每一行都计算。它使用前9行的高低列和第10行的收盘价。

编辑 2 我已经尝试了很多方法来在这个问题中添加基于文本的数据框，但似乎没有适合我的框架宽度的解决方案。除了列 TTR 和 VR_10 在输入中全为 0，并且在输出中具有非零值之外，输入和输出数据帧没有区别。一个例子是这个数据框：

有没有办法不用迭代就可以做到这一点？

Answer 1

在 Andreas 的推动下使用滚动，我得出了一个答案：首先，我必须找出如何使用多列滚动。发现here。我做了一个修改，因为我需要向上卷，而不是向下

def roll(df, w, **kwargs):
    df.sort_values(by='timestamp', ascending=0, inplace=True)
    v = df.values
    d0, d1 = v.shape
    s0, s1 = v.strides

    a = stride(v, (d0 - (w - 1), w, d1), (s0, s0, s1))

    rolled_df = pd.concat({
        row: pd.DataFrame(values, columns=df.columns)
        for row, values in zip(df.index, a)
    })

    return rolled_df.groupby(level=0, **kwargs)

之后，我创建了 2 个函数：

def calculate_vr(window):
    return window.iloc[0].ttr / (max(window.high[1:9].max(), window.iloc[10].close) - min(window.low[1:9].min(), window.iloc[10].close))


def calculate_ttr(window):
    return max(window.iloc[0].high - window.iloc[0].low, abs(window.iloc[0].high - window.iloc[1].close), abs(window.iloc[0].low - window.iloc[1].close))

并像这样调用这些函数：

    candlesticks['ttr'] = roll(candlesticks, 3).apply(calculate_ttr)
    candlesticks['vr_10'] = roll(candlesticks, 11).apply(calculate_vr)

向两种方式添加了计时器，这种方式比迭代慢大约 3 倍。

使用相邻行计算 Pandas Dataframe 中的列而不遍历每一行

Calculate column in Pandas Dataframe using adjacent rows without iterating through each row

python

pandas

ta-lib