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]
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()
