Python-AttributeError: module ‘pandas‘ has no attribute ‘ewma‘报错解决方法

将

ewma60=pd.ewma(aapl_px,span=60)
改为

ewma60 = pd.DataFrame.ewm(aapl_px,span=60).mean()

ATR = pd.Series(TR_s.ewm(span=n, min_periods=n).mean())
    UpI = pd.Series(UpI)
    DoI = pd.Series(DoI)
    # PosDI = pd.Series(pd.ewma(UpI, span=n, min_periods=n - 1) / ATR)
    # NegDI = pd.Series(pd.ewma(DoI, span=n, min_periods=n - 1) / ATR)
    # ds = pd.Series(pd.ewma(abs(PosDI - NegDI) / (PosDI + NegDI), span=n_ADX, min_periods=n_ADX - 1), name=xnam)

    PosDI = pd.Series(UpI.ewm(span=n, min_periods=n - 1).mean() / ATR)
    NegDI = pd.Series(DoI.ewm(span=n, min_periods=n - 1).mean()/ ATR)
    ds = pd.Series(abs(PosDI - NegDI) / (PosDI + NegDI).ewm(span=n_ADX, min_periods=n_ADX - 1).mean(), name=xnam)

import logging
import pandas as pd
import numpy as np

log = logging.getLogger(__name__)

def moving_average(df, n):
    """计算股价的moving average.
    参考： https://blog.csdn.net/FrankieHello/article/details/85938381

    :param df: pandas.DataFrame
    :param n:
    :return: pandas.DataFrame
    """
    MA = pd.Series(df['close'].rolling(n, min_periods=n).mean(), name='MA_' + str(n))
    df = df.join(MA)
    return df

def exponential_moving_average(df, n):
    """
    计算股价的exponential moving average
    参考：https://www.cnblogs.com/wuliytTaotao/p/9479958.html
    :param df: pandas.DataFrame
    :param n:
    :return: pandas.DataFrame
    """
    EMA = pd.Series(df['close'].ewm(span=n, min_periods=n).mean(), name='EMA_' + str(n))
    df = df.join(EMA)
    return df

def momentum(df, n):
    """
    计算动力
    参考：http://www.waihuibang.com/fxschool/technical/54505.html
    :param df: pandas.DataFrame
    :param n:
    :return: pandas.DataFrame
    """
    M = pd.Series(df['close'].diff(n), name='Momentum_' + str(n))
    df = df.join(M)
    return df

def rate_of_change(df, n):
    """
    计算变化率
    参考：https://www.tradingview.com/wiki/Rate_of_Change_(ROC)
    :param df: pandas.DataFrame
    :param n:
    :return: pandas.DataFrame
    """
    M = df['close'].diff(n - 1)
    N = df['close'].shift(n - 1)
    ROC = pd.Series(M / N, name='ROC_' + str(n))
    df = df.join(ROC)
    return df

def average_true_range(df, n):
    """

    :param df: pandas.DataFrame
    :param n:
    :return: pandas.DataFrame
    """
    i = 0
    TR_l = [0]
    while i < df.index[-1]:
        TR = max(df.loc[i + 1, 'high'], df.loc[i, 'close']) - min(df.loc[i + 1, 'low'], df.loc[i, 'close'])
        TR_l.append(TR)
        i = i + 1
    TR_s = pd.Series(TR_l)
    ATR = pd.Series(TR_s.ewm(span=n, min_periods=n).mean(), name='ATR_' + str(n))
    df = df.join(ATR)
    return df

def bollinger_bands(df, n):
    """
    计算bolling bands
    :param df: pandas.DataFrame
    :param n:
    :return: pandas.DataFrame
    """
    MA = pd.Series(df['close'].rolling(n, min_periods=n).mean())
    MSD = pd.Series(df['close'].rolling(n, min_periods=n).std())
    b1 = 4 * MSD / MA
    B1 = pd.Series(b1, name='BollingerB_' + str(n))
    df = df.join(B1)
    b2 = (df['close'] - MA + 2 * MSD) / (4 * MSD)
    B2 = pd.Series(b2, name='Bollinger%b_' + str(n))
    df = df.join(B2)
    return df

def stochastic_oscillator_k(df):
    """
    :param df: pandas.DataFrame
    :return: pandas.DataFrame
    """
    SOk = pd.Series((df['close'] - df['low']) / (df['high'] - df['low']), name='SO%k')
    df = df.join(SOk)
    return df

def stochastic_oscillator_d(df, n):
    """
    :param df: pandas.DataFrame
    :param n:
    :return: pandas.DataFrame
    """
    SOk = pd.Series((df['close'] - df['low']) / (df['high'] - df['low']), name='SO%k')
    SOd = pd.Series(SOk.ewm(span=n, min_periods=n).mean(), name='SO%d_' + str(n))
    df = df.join(SOd)
    return df

def macd(df, n_fast, n_slow):
    """Calculate MACD, MACD Signal and MACD difference

    :param df: pandas.DataFrame
    :param n_fast:
    :param n_slow:
    :return: pandas.DataFrame
    """
    EMAfast = pd.Series(df['close'].ewm(span=n_fast, min_periods=n_slow).mean())
    EMAslow = pd.Series(df['close'].ewm(span=n_slow, min_periods=n_slow).mean())
    MACD = pd.Series(EMAfast - EMAslow, name='MACD_' + str(n_fast) + '_' + str(n_slow))
    MACDsign = pd.Series(MACD.ewm(span=9, min_periods=9).mean(), name='MACDsign_' + str(n_fast) + '_' + str(n_slow))
    MACDdiff = pd.Series(MACD - MACDsign, name='MACDdiff_' + str(n_fast) + '_' + str(n_slow))
    df = df.join(MACD)
    df = df.join(MACDsign)
    df = df.join(MACDdiff)
    return df

def ease_of_movement(df, n):
    """Ease of Movement for given data.

    :param df: pandas.DataFrame
    :param n:
    :return: pandas.DataFrame
    """
    EoM = (df['high'].diff(1) + df['low'].diff(1)) * (df['high'] - df['low']) / (2 * df['volume'])
    Eom_ma = pd.Series(EoM.rolling(n, min_periods=n).mean(), name='EoM_' + str(n))
    df = df.join(Eom_ma)
    return df

def standard_deviation(df, n):
    """计算标准差.

    :param df: pandas.DataFrame
    :param n:
    :return: pandas.DataFrame
    """
    df = df.join(pd.Series(df['close'].rolling(n, min_periods=n).std(), name='STD_' + str(n)))
    return df
————————————————
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原文链接：https://blog.csdn.net/weixin_38241876/article/details/110129990

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