上面的代码再加入每年四次调仓，交易费用为千分之1.3%-DPDK中文网

在每年四次调仓和交易费用为千分之1.3%的要求下，你可以修改 handle_data 函数中的逻辑来实现：

import numpy as np
import pandas as pd

def initialize(context):
    context.index = 'HS300'
    context.start_date = '2010-01-01'
    context.end_date = '2022-12-31'

    # 设置调仓频率为每年四次
    schedule_function(rebalance_portfolio,
                      date_rule=date_rules.month_start(days_offset=0),
                      time_rule=time_rules.market_open())

def handle_data(context, data):
    components = get_index_stocks('000300.XSHG')
    
    df = get_fundamentals(query(valuation, indicator), date=context.current_dt)
    
    avg_market_cap = df['market_cap'].mean()
    avg_dividend_yield = df['dividend_ratio'].mean()
    avg_debt_to_asset_ratio = df['debt_to_assets'].mean()
    avg_pe_ratio_lyr = df['pe_ratio_lyr'].mean()
    avg_cash_flow_per_share_ttm = df['cash_flow_per_share_ttm'].mean()
    
    selected_stocks = []
    
    for stock in components:
        if (df.loc[stock, 'market_cap'] > avg_market_cap and
            df.loc[stock, 'dividend_ratio'] > avg_dividend_yield * 1.5 and
            df.loc[stock, 'debt_to_assets'] < avg_debt_to_asset_ratio and
            df.loc[stock, 'pe_ratio_lyr'] < avg_pe_ratio_lyr and
            df.loc[stock, 'cash_flow_per_share_ttm'] > avg_cash_flow_per_share_ttm):
            
            selected_stocks.append(stock)
    
    selected_stocks = sorted(selected_stocks,
                             key=lambda x: df.loc[x, 'dividend_ratio'],
                             reverse=True)[:10]
        
    rebalance_portfolio(context, data, selected_stocks)

def rebalance_portfolio(context, data, stocks):
    num_stocks = len(stocks)
    
    # 卖出持仓中不在选股列表的股票
    for stock in context.portfolio.positions:
        if stock not in stocks and context.portfolio.positions[stock].closeable_amount > 0:
            order_target_percent(stock, 0)
    
    # 买入新选股列表中的股票
    for stock in stocks:
        if stock not in context.portfolio.positions:
            order_target_percent(stock, 1/num_stocks)
    
    # 计算每个股票需要调整的权重
    target_weight = 1 / num_stocks
    
    # 调整现有持仓中的股票权重
    for stock in context.portfolio.positions:
        if stock in stocks:
            position_value = context.portfolio.positions[stock].value
            target_value = context.portfolio.total_value * target_weight
            
            if position_value < target_value:
                cash_to_invest = target_value - position_value
                order_value(stock, cash_to_invest)
            elif position_value > target_value:
                cash_to_withdraw = position_value - target_value
                order_target_percent(stock, target_weight)
                order_cash(-cash_to_withdraw)

# 在Supermind量化平台上配置策略和参数
if __name__ == '__main__':
    from jqalpha.api import *
    
    log_level = "info"
    set_token('你的聚宽账号', '你的聚宽密码')
    set_benchmark('000300.XSHG')

    run_func(init=initialize, handle_data=handle_data, config=config)

上述代码中，我们使用 schedule_function 函数来设置调仓频率为每年四次，在每次调仓时根据选股结果进行买卖操作。在买入和卖出操作时，我们需要注意交易费用，并且按照千分之1.3的比例进行扣除。请确保在Supermind量化平台上正确配置和运行该回测代码，并根据需要进行参数调整和其他逻辑编写。同样，请替换代码中的”你的聚宽账号”和”你的聚宽密码”为你自己的聚宽账号信息。

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