python根据历史大事建立跨组合，并计算执行价格。然后建立宽跨组合，并计算跨距

建立跨组合和宽跨组合需要使用量化交易的框架和工具，比如pandas、numpy、matplotlib等库，以及聚宽提供的数据和API接口。

以下是一个简单的示例代码，演示如何使用聚宽API获取历史数据、计算跨组合和宽跨组合的执行价格和跨距：

import jqdatasdk as jq
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# 登录聚宽账号
jq.auth('username', 'password')

# 获取历史数据
start_date = '2010-01-01'
end_date = '2021-12-31'
data = jq.get_price(['000001.XSHE', '000002.XSHE', '000003.XSHE'], start_date=start_date, end_date=end_date, frequency='daily', fields=['open', 'close', 'high', 'low', 'volume'])

# 计算跨组合执行价格
data['price_diff'] = data['close']['000001.XSHE'] - data['close']['000002.XSHE']
mean_price_diff = np.mean(data['price_diff'])
std_price_diff = np.std(data['price_diff'])
buy_price = data['close']['000001.XSHE'] - mean_price_diff + 0.5 * std_price_diff
sell_price = data['close']['000002.XSHE'] + mean_price_diff + 0.5 * std_price_diff

# 计算宽跨组合跨距
long_amount = 1000
short_amount = 500
long_price = data['close']['000001.XSHE'] - mean_price_diff + 0.5 * std_price_diff
short_price = data['close']['000002.XSHE'] + mean_price_diff + 0.5 * std_price_diff
long_value = long_amount * long_price
short_value = short_amount * short_price
spread = long_value - short_value
spread_pct = spread / ((long_value + short_value) / 2)

# 可视化结果
plt.figure(figsize=(10, 5))
plt.plot(data['price_diff'])
plt.axhline(mean_price_diff, color='r')
plt.axhline(mean_price_diff + 0.5 * std_price_diff, color='g')
plt.axhline(mean_price_diff - 0.5 * std_price_diff, color='g')
plt.title('Price difference between 000001.XSHE and 000002.XSHE')
plt.xlabel('Date')
plt.ylabel('Price difference')
plt.show()

print('Buy price:', buy_price)
print('Sell price:', sell_price)
print('Spread:', spread)
print('Spread percentage:', spread_pct)

请注意，上述代码仅提供了一个简单的示例，实际的跨组合和宽跨组合交易策略需要更加复杂和完善的模型和算法，需要根据具体情况进行优化和调整。同时，在实际交易中还需要考虑风险管理、资金管理等因素，以确保交易的稳定和可持续性。