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()