能给出具体python代码吗

当然可以，以下是一个简单的Python代码示例，可以用于计算先按市值再按盈利价格比序贯排序的资产组合月平均收益率：

import pandas as pd
import numpy as np

# 读取数据
df_returns = pd.read_csv('returns.csv')
df_marketcap = pd.read_csv('marketcap.csv')
df_pe = pd.read_csv('pe.csv')

# 合并数据
df = pd.merge(df_returns, df_marketcap, on=['code', 'date'])
df = pd.merge(df, df_pe, on=['code', 'date'])

# 按市值排序并分组
df['marketcap_group'] = pd.qcut(df['marketcap'], 5, labels=False)

# 按盈利价格比排序并分组
df['pe_group'] = pd.qcut(df['pe'], 5, labels=False)
df['pe_rank'] = df.groupby(['date'])['pe'].rank(ascending=False)

# 计算组合收益率
df['group'] = df['marketcap_group'] * 5 + df['pe_group'] + 1
df['group_return'] = df.groupby(['date', 'group'])['returns'].transform('mean')

# 计算组合年化收益率和波动率
df_group = df.groupby(['group'])[['group_return']].mean()
df_group['group_annual_return'] = (1 + df_group['group_return']) ** 12 - 1
df_group['group_std'] = df.groupby(['group'])[['group_return']].std() * np.sqrt(12)

# 输出结果
print(df_group)

以上代码仅供参考，具体实现过程中还需要根据数据的格式和质量进行调整和改进。同时，还需要进行数据清洗、异常值处理、缺失值处理等预处理操作，以保证结果的可靠性和有效性。