pandas_计算夏普比率

数据为收盘价，求夏普比率

概念说明：

夏普比率：(return-Rf)/vol
return为年化收益；Rf为无风险利率一般使用 三个月的短期国债 或 三个月的银行存款利率 (0.011)； vol为年化收益波动率

Rf本文取三个月银行存款
http://www.pbc.gov.cn/zhengcehuobisi/125207/125213/125440/125838/125888/2968982/index.html

使用：夏普比率越大越好 

代码：

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
df_aapl = pd.read_csv('AAPL.csv',encoding='utf-8')
df_aapl.head()

df_aapl['ret'] = df_aapl['Close'].pct_change()
# 1. 年化收益率
# 1.1 日平均收益率
r_daily_mean = ((1+df_aapl['ret']).prod())**(1/df_aapl.shape[0])-1
r_daily_mean
# out: 0.0006941719590121131

def annualize_rets(returns,n_periods):
    '''
    给定一系列的收益率和期数，算出年化收益率
    '''
    # 每一期的平均收益
    r_periodic_mean = ((1+returns).prod())**(1/returns.shape[0])-1
    return (1+r_periodic_mean)**n_periods-1
annualize_rets(df_aapl['ret'],252)
# out: 0.19109213356890775

# 2. 年化波动率
# 2.1 年化方差
annual_var = df_aapl['ret'].var()*252
annual_var
# out: 0.20305560031619677
# 2.2 年化波动率，标准差
annual_std = df_aapl['ret'].std()*np.sqrt(252)
annual_std
# out: 0.45061691081915334
def annualize_std(returns,n_periods):
    '''
    给定一系列的收益率，算出年化的标准差
    '''
    return returns.std()*np.sqrt(n_periods)
annualize_std(df_aapl['ret'],252)
# out: 0.45061691081915334

# 3. 计算夏普比率
sharp_ratio = (annual_rets-0.011)/annual_std
sharp_ratio
# out: 0.3996568465251948
def annual_sharpe_ratio(returns,n_periods,risk_free_rate):
    '''
    给定一系列的收益率，计算年化的夏普比率
    '''
    annual_r = annualize_rets(returns,n_periods)
    annual_v = annualize_std(returns,n_periods)
    return (annual_r-risk_free_rate)/annual_v
annual_sharpe_ratio(df_aapl['ret'],252,0.011)
# out: 0.3996568465251948