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  • 算数平均值/加权平均值

    算数平均值

    S = [s1, s2, ..., sn]

    样本中的每个值都是真值与误差的和。

    算数平均值:
    m = (s1 + s2 + ... + sn) / n

    算数平均值表示对真值的无偏估计。

    np.mean(array)
    array.mean()

    案例:计算收盘价的算术平均值。

    #算数平均值
    import numpy as np
    import matplotlib.pyplot as mp
    import datetime as dt
    import matplotlib.dates as md
    
    
    def dmy2ymd(dmy):
      """
      把日月年转年月日
      :param day:
      :return:
      """
      dmy = str(dmy, encoding='utf-8')
      t = dt.datetime.strptime(dmy, '%d-%m-%Y')
      s = t.date().strftime('%Y-%m-%d')
      return s
    
    
    dates, opening_prices, 
    highest_prices, lowest_prices, 
    closing_prices = 
      np.loadtxt('aapl.csv',
                 delimiter=',',
                 usecols=(1, 3, 4, 5, 6),
                 unpack=True,
                 dtype='M8[D],f8,f8,f8,f8',
                 converters={1: dmy2ymd})  # 日月年转年月日
    
    # 绘制收盘价的折现图
    mp.figure('APPL', facecolor='lightgray')
    mp.title('APPL', fontsize=18)
    mp.xlabel('Date', fontsize=14)
    mp.ylabel('Price', fontsize=14)
    mp.grid(linestyle=":")
    
    # 设置刻度定位器
    # 每周一一个主刻度,一天一个次刻度
    
    ax = mp.gca()
    ma_loc = md.WeekdayLocator(byweekday=md.MO)
    ax.xaxis.set_major_locator(ma_loc)
    ax.xaxis.set_major_formatter(md.DateFormatter('%Y-%m-%d'))
    ax.xaxis.set_minor_locator(md.DayLocator())
    # 修改dates的dtype为md.datetime.datetiem
    dates = dates.astype(md.datetime.datetime)
    mp.plot(dates, closing_prices,
            color='dodgerblue',
            linewidth=2,
            linestyle='--',
            alpha=0.8,
            label='APPL Closing Price')
    #计算收盘价的均值
    mean = np.mean(closing_prices)
    # mean = closing_prices.mean()
    print(mean)
    mp.hlines(mean,dates[0],dates[-1],colors='orangered',label='mean')
    mp.legend()
    mp.gcf().autofmt_xdate()
    mp.show()

    加权平均值

    样本:S = [s1, s2, ..., sn]

    权重:W = [w1, w2, ..., wn]

    加权平均值:a = (s1w1+s2w2+...+snwn)/(w1+w2+...+wn)

    np.average(closing_prices, weights=volumes)

    VWAP - 成交量加权平均价格(成交量体现了市场对当前交易价格的认可度,成交量加权平均价格将会更接近这支股票的真实价值)

     

    import numpy as np
    closing_prices, volumes = np.loadtxt(
        '../../data/aapl.csv', delimiter=',',
        usecols=(6, 7), unpack=True)
    vwap, wsum = 0, 0
    for closing_price, volume in zip(
            closing_prices, volumes):
        vwap += closing_price * volume
        wsum += volume
    vwap /= wsum
    print(vwap)
    vwap = np.average(closing_prices, weights=volumes)
    print(vwap)

    TWAP - 时间加权平均价格(时间越晚权重越高,参考意义越大)

    import datetime as dt
    import numpy as np
    
    def dmy2days(dmy):
        dmy = str(dmy, encoding='utf-8')
        date = dt.datetime.strptime(dmy, '%d-%m-%Y').date()
        days = (date - dt.date.min).days
        return days
    
    days, closing_prices = np.loadtxt(
        '../../data/aapl.csv', delimiter=',',
        usecols=(1, 6), unpack=True,
        converters={1: dmy2days})
    twap = np.average(closing_prices, weights=days)
    print(twap)
    # 加权平均值
    import numpy as np
    import matplotlib.pyplot as mp
    import datetime as dt
    import matplotlib.dates as md
    
    
    def dmy2ymd(dmy):
      """
      把日月年转年月日
      :param day:
      :return:
      """
      dmy = str(dmy, encoding='utf-8')
      t = dt.datetime.strptime(dmy, '%d-%m-%Y')
      s = t.date().strftime('%Y-%m-%d')
      return s
    
    
    dates, opening_prices, 
    highest_prices, lowest_prices, 
    closing_prices ,volumes= 
      np.loadtxt('aapl.csv',
                 delimiter=',',
                 usecols=(1, 3, 4, 5, 6,7),
                 unpack=True,
                 dtype='M8[D],f8,f8,f8,f8,f8',
                 converters={1: dmy2ymd})  # 日月年转年月日
    print(dates)
    # 绘制收盘价的折现图
    mp.figure('APPL', facecolor='lightgray')
    mp.title('APPL', fontsize=18)
    mp.xlabel('Date', fontsize=14)
    mp.ylabel('Price', fontsize=14)
    mp.grid(linestyle=":")
    
    # 设置刻度定位器
    # 每周一一个主刻度,一天一个次刻度
    
    ax = mp.gca()
    ma_loc = md.WeekdayLocator(byweekday=md.MO)
    ax.xaxis.set_major_locator(ma_loc)
    ax.xaxis.set_major_formatter(md.DateFormatter('%Y-%m-%d'))
    ax.xaxis.set_minor_locator(md.DayLocator())
    # 修改dates的dtype为md.datetime.datetiem
    dates = dates.astype(md.datetime.datetime)
    mp.plot(dates, closing_prices,
                      color='dodgerblue',
                      linewidth=2,
                      linestyle='--',
                      alpha=0.8,
                      label='APPL Closing Price')
    # 计算收盘价的均值
    mean = np.mean(closing_prices)
    # mean = closing_prices.mean()
    mp.hlines(mean, dates[0], dates[-1], colors='orangered',
              label='mean')
    # VWP成交量加权平均值
    vwap = np.average(closing_prices, weights=volumes)
    mp.hlines(vwap, dates[0], dates[-1], color='blue', label='VWAP')
    #TWAP事件加权平均价格
    w = np.linspace(1,4,30)
    twap = np.average(closing_prices,weights=w)
    mp.hlines(twap,dates[0],dates[-1],color='green',label='TWAP')
    mp.legend()
    mp.gcf().autofmt_xdate()
    mp.show()

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  • 原文地址:https://www.cnblogs.com/maplethefox/p/11457290.html
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