本文参考： + 华泰证券《指数增强方法汇总及实例——量化多因子指数增强策略实证》 + 天风证券《基于自适应风险控制的指数增强策略》 配合分析的回测模块比较简单可以直接粘贴这里的代码节约积分,也可以到我的github中[下载](https://github.com/hugo2046/Quantitative-analysis/tree/master/多因子指数增强) 其中特质波动率的构造见[【复现】东吴证券-特质波动率因子](https://www.joinquant.com/view/community/detail/6e4ddf0a1cf3bb17367b463cefe3b5e4) ``` ''' Author: Hugo Date: 2020-06-10 20:52:43 LastEditTime: 2020-11-02 10:08:02 LastEditors: Please set LastEditors Description: 读取指数增强研究文档生成的result_df文件进行下单 ''' from jqdata import * import pandas as pd import numpy as np from six import BytesIO # 文件读取 enable_profile() # 开启性能分析 def initialize(context): set_params() set_variables() set_backtest() run_monthly(Trade, -1, time='open', reference_security='000300.XSHG') def set_params(): g.result_df = pd.read_csv( BytesIO(read_file('result_df.csv')), index_col=[0],) def set_variables(): pass def set_backtest(): set_option("avoid_future_data", True) # 避免数据 set_option("use_real_price", True) # 真实价格交易 set_benchmark('000300.XSHG') # 设置基准 #log.set_level("order", "debuge") log.set_level('order', 'error') # 每日盘前运行 def before_trading_start(context): # 手续费设置 # 将滑点设置为0 set_slippage(FixedSlippage(0)) # 根据不同的时间段设置手续费 dt = context.current_dt if dt > datetime.datetime(2013, 1, 1): set_commission(PerTrade(buy_cost=0.0003, sell_cost=0.0013, min_cost=5)) elif dt > datetime.datetime(2011, 1, 1): set_commission(PerTrade(buy_cost=0.001, sell_cost=0.002, min_cost=5)) elif dt > datetime.datetime(2009, 1, 1): set_commission(PerTrade(buy_cost=0.002, sell_cost=0.003, min_cost=5)) else: set_commission(PerTrade(buy_cost=0.003, sell_cost=0.004, min_cost=5)) def Trade(context): bar_time = context.current_dt.strftime('%Y-%m-%d') log.info('%s启动' % bar_time) if bar_time in g.result_df.index: print('存在') target_slice = g.result_df.loc[bar_time] BuyStock(context, target_slice) def BuyStock(context, target_slice: pd.DataFrame): order_dict = target_slice.set_index('code')['w'].to_dict() for hold in context.portfolio.long_positions: if hold not in order_dict: order_target(hold, 0) totalasset = context.portfolio.total_value for buy_stock, pre in order_dict.items(): order_target_value(buy_stock, pre * totalasset) ```