各种股票收益率(returns)的计算方法
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各种股票收益率(returns)的计算方法#
Fig. 16 股票收益率#
前言
我们一般对股票的理解一般只停留于各大交易软件如东方财富、通达信等所提供的基本信息。 股票的日线图除了告诉我们股价的走势之外，股民基本上将其用于MACD、KDJ等技术分析上。 事实上， 我们可以利用股票的历史价格可以算出股票的走势是否正态分布、 回报率(Returns)、波动率(Volatility)、偏度(skewness) 和 峰度(kurtosis)。
本文将介绍收益率的计算方法。
实现过程#
import tushare as ts
import pandas as pd
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
plt.style.use('seaborn-white')
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
pro = ts.pro_api('xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx') #这里需要填写你注册好的Tushare的TOKEN凭证
通过调用tushare获取股票600377（宁沪高速）的股票数据，这里不设置日期，那么默认获取Tushare提供的历史数据。
ticker_data = pro.daily(ts_code='600377.SH')
print('数据数目：',len(ticker_data))
ticker_data.head(5)
数据数目： 5000
| ts_code | trade_date | open | high | low | close | pre_close | change | pct_chg | vol | amount | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 600377.SH | 20220609 | 8.23 | 8.28 | 8.19 | 8.23 | 8.25 | -0.02 | -0.2424 | 52379.64 | 43138.404 |
| 1 | 600377.SH | 20220608 | 8.22 | 8.27 | 8.15 | 8.25 | 8.19 | 0.06 | 0.7326 | 65288.11 | 53543.805 |
| 2 | 600377.SH | 20220607 | 8.13 | 8.21 | 8.08 | 8.19 | 8.13 | 0.06 | 0.7380 | 81555.11 | 66554.661 |
| 3 | 600377.SH | 20220606 | 8.36 | 8.39 | 8.11 | 8.13 | 8.37 | -0.24 | -2.8674 | 118437.62 | 96760.291 |
| 4 | 600377.SH | 20220602 | 8.41 | 8.41 | 8.33 | 8.37 | 8.41 | -0.04 | -0.4756 | 49257.52 | 41240.142 |
从上面可以看出，序号并不是以时间作为单位的。那么我们首先需要将trade_date转为datetime格式，然后设置为序号以便于画图。
ticker_data['trade_date'] = pd.to_datetime(ticker_data['trade_date'],format='%Y%m%d')
ticker_data.set_index('trade_date', inplace=True)
returns = ticker_data["close"].pct_change().dropna()
plt.figure(figsize=(15, 5))
plt.title("股票代码:600377 - 宁沪高速", weight='bold')
ticker_data['close'].plot()
<AxesSubplot:title={'center':'股票代码:600377 - 宁沪高速'}, xlabel='trade_date'>
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 32929 (\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 31080 (\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20195 (\N{CJK UNIFIED IDEOGRAPH-4EE3}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 30721 (\N{CJK UNIFIED IDEOGRAPH-7801}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 23425 (\N{CJK UNIFIED IDEOGRAPH-5B81}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 27818 (\N{CJK UNIFIED IDEOGRAPH-6CAA}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 39640 (\N{CJK UNIFIED IDEOGRAPH-9AD8}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 36895 (\N{CJK UNIFIED IDEOGRAPH-901F}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
下面将画出每日收盘价的百分比变化图：
plt.figure(figsize=(15, 5))
ticker_data["close"].pct_change().plot()
plt.title("股票代码:600377 - 宁沪高速", weight='bold');
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 32929 (\N{CJK UNIFIED IDEOGRAPH-80A1}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 31080 (\N{CJK UNIFIED IDEOGRAPH-7968}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 20195 (\N{CJK UNIFIED IDEOGRAPH-4EE3}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 30721 (\N{CJK UNIFIED IDEOGRAPH-7801}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 23425 (\N{CJK UNIFIED IDEOGRAPH-5B81}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 27818 (\N{CJK UNIFIED IDEOGRAPH-6CAA}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 39640 (\N{CJK UNIFIED IDEOGRAPH-9AD8}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/IPython/core/pylabtools.py:151: UserWarning: Glyph 36895 (\N{CJK UNIFIED IDEOGRAPH-901F}) missing from current font.
fig.canvas.print_figure(bytes_io, **kw)
从上面可以看出，由于A股市场的每日涨跌幅限制，可以看到宁沪高速最大的涨跌幅为10%， 但其中有一个数据是超出10%（介于2016年至2017年坐标轴之间），估计是高开涨停。上图也可以理解为股票收益的波动图.
普通累计收益#
plt.figure(figsize=(15, 5))
rets_add_one = returns + 1
cumulative_rets = rets_add_one.cumprod()-1
cumulative_rets.plot()
<AxesSubplot:xlabel='trade_date'>
print('累计收益：',cumulative_rets[-1])
累计收益： 0.14216281895503435
对数累计收益(log returns)#
log_ret = np.log(returns+1)
cumulative_rets = log_ret.cumsum()
plt.figure(figsize=(15, 5))
plt.plot(cumulative_rets)
plt.show()
print('累计收益：',cumulative_rets[-1])
累计收益： 0.1329236745869773