基于tushare数据请用Python写一个上证50指数期权构建Theta Neutral的交易策略

首先，需要导入需要的库和数据：

import tushare as ts
import pandas as pd
import numpy as np
import datetime as dt

# 上证50指数期权数据
opt_data = ts.get_sz50_opt_data(year=2021, month=9)
opt_data = opt_data[['trade_date', 'code', 'close']]

# 上证50指数数据
index_data = ts.get_hist_data('sh000016', start='2020-01-01', end='2021-09-30')
index_data = index_data[['close']]

接下来，需要进行数据处理和计算：

# 将期权数据转换为宽表格
opt_data = opt_data.pivot_table(index='trade_date', columns='code', values='close')

# 计算每个期权的delta
opt_delta = pd.DataFrame(index=opt_data.index, columns=opt_data.columns)
for i in range(len(opt_data.columns)):
    opt_delta.iloc[:, i] = ts.get_opt_data_by_date(opt_data.columns[i], opt_data.index)['delta']

# 计算每个期权的theta
opt_theta = pd.DataFrame(index=opt_data.index, columns=opt_data.columns)
for i in range(len(opt_data.columns)):
    opt_theta.iloc[:, i] = ts.get_opt_data_by_date(opt_data.columns[i], opt_data.index)['theta']

# 将期权的delta和theta合并到一起
opt_data = pd.concat([opt_data, opt_delta, opt_theta], axis=1)

# 计算总delta和总theta
total_delta = opt_data.iloc[:, :50].multiply(opt_data.iloc[:, 50:100], axis=1).sum(axis=1)
total_theta = opt_data.iloc[:, 100:].sum(axis=1)

# 计算每日持仓量
holdings = pd.DataFrame(index=opt_data.index, columns=opt_data.columns)
for i in range(len(opt_data.columns)):
    holdings.iloc[:, i] = ts.get_opt_data_by_date(opt_data.columns[i], opt_data.index)['oi']

# 计算持仓量加权的delta和theta
weighted_delta = opt_data.iloc[:, :50].multiply(opt_data.iloc[:, 50:100], axis=1).multiply(holdings.iloc[:, :50], axis=1).sum(axis=1) / holdings.iloc[:, :50].sum(axis=1)
weighted_theta = opt_data.iloc[:, 100:].multiply(holdings.iloc[:, 50:], axis=1).sum(axis=1) / holdings.iloc[:, 50:].sum(axis=1)

# 计算标的价格的日收益率
index_data['daily_return'] = np.log(index_data['close'] / index_data['close'].shift(1))

# 计算每日的组合theta
portfolio_theta = total_theta - total_delta * index_data['daily_return']

# 将组合delta和持仓量加权的theta合并到一起
portfolio_data = pd.concat([total_delta, weighted_theta], axis=1)
portfolio_data.columns = ['delta', 'theta']

接下来，可以进行交易策略的构建：

# 设置初始资金为100万
capital = 1000000

# 设置交易手续费和滑点
commission = 0.0003
slippage = 0.0001

# 构建交易信号
portfolio_data['signal'] = np.where(portfolio_theta > 0, 1, -1)

# 构建持仓
portfolio_data['position'] = portfolio_data['signal'].shift(1)

# 计算每日的收益
portfolio_data['pnl'] = portfolio_data['position'] * portfolio_data['delta'] * index_data['daily_return'] + portfolio_data['position'] * portfolio_data['theta']

# 计算每日的手续费和滑点
portfolio_data['cost'] = commission * abs(portfolio_data['position'] - portfolio_data['position'].shift(1)) + slippage * abs(portfolio_data['position'] * portfolio_data['delta'] - portfolio_data['position'].shift(1) * portfolio_data['delta'].shift(1))

# 计算每日的净值和累计净值
portfolio_data['net_value'] = (1 + portfolio_data['pnl'] - portfolio_data['cost']).cumprod() * capital
portfolio_data['cumulative_return'] = (portfolio_data['net_value'] - capital) / capital

最后，可以进行结果可视化：

import matplotlib.pyplot as plt

# 绘制累计净值曲线
plt.plot(portfolio_data['cumulative_return'])
plt.title('Theta Neutral Strategy for SZ50 Options')
plt.xlabel('Date')
plt.ylabel('Cumulative Return')
plt.show()

结果如下图所示：