用PYTHON写一个基于tushare数据的中证1000指数的VAR和CVAR的风险预警模型

首先，需要安装tushare库，并导入所需的库和模块：

import tushare as ts
import numpy as np
import pandas as pd
from scipy.stats import norm
import matplotlib.pyplot as plt

然后，获取中证1000指数的历史数据：

index_data = ts.get_k_data('000852', start='2010-01-01', end='2021-12-31')
index_data.set_index('date', inplace=True)
index_data.sort_index(inplace=True)
index_data.head()

接下来，计算日收益率：

returns = np.log(index_data['close'] / index_data['close'].shift(1))
returns.dropna(inplace=True)

计算历史VAR和CVAR：

alpha = 0.05 # 设置置信水平为0.05
var = norm.ppf(alpha) * returns.std()
cvar = returns[returns <= -var].mean()

接下来，定义风险预警函数：

def risk_warning():
    last_price = ts.get_realtime_quotes('000852')['price'].iloc[0] # 获取最新价格
    last_return = np.log(float(last_price) / index_data['close'].iloc[-1])
    var_warning = index_data['close'].iloc[-1] * (1 - np.exp(-var * np.sqrt(1))) # VAR风险预警价位
    cvar_warning = index_data['close'].iloc[-1] * (1 - np.exp(-cvar * np.sqrt(1))) # CVAR风险预警价位
    if float(last_price) <= var_warning:
        print('VAR风险预警！最新价格为%s，VAR风险预警价位为%s。' % (last_price, round(var_warning, 2)))
    elif float(last_price) <= cvar_warning:
        print('CVAR风险预警！最新价格为%s，CVAR风险预警价位为%s。' % (last_price, round(cvar_warning, 2)))
    else:
        print('风险正常。最新价格为%s，VAR风险预警价位为%s，CVAR风险预警价位为%s。' % (last_price, round(var_warning, 2), round(cvar_warning, 2)))

最后，调用函数进行风险预警：

risk_warning()

完整代码如下：

import tushare as ts
import numpy as np
import pandas as pd
from scipy.stats import norm
import matplotlib.pyplot as plt

index_data = ts.get_k_data('000852', start='2010-01-01', end='2021-12-31')
index_data.set_index('date', inplace=True)
index_data.sort_index(inplace=True)

returns = np.log(index_data['close'] / index_data['close'].shift(1))
returns.dropna(inplace=True)

alpha = 0.05 # 设置置信水平为0.05
var = norm.ppf(alpha) * returns.std()
cvar = returns[returns <= -var].mean()

def risk_warning():
    last_price = ts.get_realtime_quotes('000852')['price'].iloc[0] # 获取最新价格
    last_return = np.log(float(last_price) / index_data['close'].iloc[-1])
    var_warning = index_data['close'].iloc[-1] * (1 - np.exp(-var * np.sqrt(1))) # VAR风险预警价位
    cvar_warning = index_data['close'].iloc[-1] * (1 - np.exp(-cvar * np.sqrt(1))) # CVAR风险预警价位
    if float(last_price) <= var_warning:
        print('VAR风险预警！最新价格为%s，VAR风险预警价位为%s。' % (last_price, round(var_warning, 2)))
    elif float(last_price) <= cvar_warning:
        print('CVAR风险预警！最新价格为%s，CVAR风险预警价位为%s。' % (last_price, round(cvar_warning, 2)))
    else:
        print('风险正常。最新价格为%s，VAR风险预警价位为%s，CVAR风险预警价位为%s。' % (last_price, round(var_warning, 2), round(cvar_warning, 2)))

risk_warning()