##时效 实际回测约10秒钟/交易日，对应一年的回测约2500秒，也即40分钟。 ##待完善 现在的回测代码未考虑债券赎回的情况，比如：当晚发布公告，次日强制赎回，现在的代码是未考虑的，感兴趣的可以进行优化。比如：[核能转债(SH113026)赎回公告](https://xueqiu.com/S/SH113026/213330303) ##数据来源 从聚宽自带的三个表抓取双低数据： - 一 是可转债每日收盘价CONBOND_DAILY_PRICE - 二是可转债每日转股溢价=可转债收盘价-（100/转股价格）*正股收盘价），再除以（100/转股价格）*正股收盘价 得到溢价率，需要从CONBOND_CONVERT_PRICE_ADJUST抓取转股价，然后用Get_price得到股票价格，计算得到溢价率。 - 双低=转债价格+溢价率*100 ## 代码借鉴 [《可转债双低轮动回测2.0 年化高达101.00% 》](https://www.joinquant.com/view/community/detail/a020e88dfa9da317d9e51c655f2d6618?type=1) 作者：行于非道 和 [《可转债双低策略》](https://www.joinquant.com/view/community/detail/6bb33a631f5bc1d6a0b2e4eae8f1bcc0?page=1#212412) 作者：周喆颋 **构建双低函数，最后输出df_final为按照双低值升序排列的转债。** ## 双低函数 ``` # 可转债双低函数 def run_cvt_bond(date): run_date=date.strftime("%Y-%m-%d") # 获取指定日期可转债的收盘价 df1=bond.run_query(query( bond.CONBOND_DAILY_PRICE.date, bond.CONBOND_DAILY_PRICE.code, bond.CONBOND_DAILY_PRICE.name, bond.CONBOND_DAILY_PRICE.open, bond.CONBOND_DAILY_PRICE.close ).filter( bond.CONBOND_DAILY_PRICE.date==run_date, ).order_by(bond.CONBOND_DAILY_PRICE.code.asc() )) # 可转债代码列表 code_list=list(df1['code']) if df1.empty: df_final = pd.DataFrame() else: # 获取可转载对应的股票代码 df2=bond.run_query(query( bond.CONBOND_BASIC_INFO.code, bond.CONBOND_BASIC_INFO.company_code, bond.CONBOND_BASIC_INFO.convert_price ).filter(bond.CONBOND_BASIC_INFO.code.in_(code_list))) # 获取转债对应的股票清单（存在多只转债对应一只股票的情形） security_list=list(df2['company_code']) # 获取转载对应股票的价格 df3=get_price(security_list, start_date=run_date, end_date=run_date, frequency='daily', fields=['close'], skip_paused=False, fq='pre', panel=False) df3.rename(columns={'code':'company_code','close':'stk_close'}, inplace = True) # 定义转股价格函数，获取可转债在某日期的转股价格 def cb_cv_price(cb_code,stock_date): df=bond.run_query(query(bond.CONBOND_CONVERT_PRICE_ADJUST.code, bond.CONBOND_CONVERT_PRICE_ADJUST.adjust_date, bond.CONBOND_CONVERT_PRICE_ADJUST.new_convert_price, ).filter (bond.CONBOND_CONVERT_PRICE_ADJUST.code==cb_code) ) #可能依然是数据的问题，存在temp为空的情况 df_temp=df[df.adjust_date.map(lambda x:x.strftime("%Y-%m-%d")< =stock_date)] #如果CONBOND_CONVERT_PRICE_ADJUST表无法得到指定日期的转股价，则从原始info表抓取 if not df_temp.empty: cb_cv_price = df_temp.tail(1).reset_index(drop=True).loc[0, 'new_convert_price'] else: cb_cv_price = float(df2[df2['code']==cb_code]['convert_price']) return cb_cv_price cv_price_lst=[] # 获取转股价List for code in code_list: cv_price=cb_cv_price(code,run_date) cv_price_lst.append(cv_price) # 合并三张dataframe df_mrg=pd.merge(df1, df2, how='left', on=['code']) df_mrg=pd.merge(df_mrg,df3, how='left', on=['company_code']) df_mrg=df_mrg.assign(cv_price=cv_price_lst) # 计算双低值，并合并到现有df_mrg中 premium_ratio_lst=(df_mrg['close']-100/df_mrg['cv_price']*df_mrg['stk_close'])/ \ (100/df_mrg['cv_price']*df_mrg['stk_close'])*100 double_low_lst=df_mrg['close']+premium_ratio_lst df_final=df_mrg.assign(premium_ratio=premium_ratio_lst,double_low=double_low_lst) # 仅保留开盘价、收盘价都是正的可转债，部分转载在退市当天依然有数据 df_final=df_final[df_final['open']>0] df_final=df_final[df_final['close']>0] # 剔除双低为负数的转债 df_final=df_final[df_final['double_low']>0] df_final=df_final.sort_values(by='double_low') return df_final ``` ## 实盘回测函数 ``` from jqdata import bond import datetime import pandas as pd import matplotlib.pyplot as plt # 可转债交易部分 print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) # 初始资金 initial_cash=100000 # 当前持仓记录 stock = pd.DataFrame(data=None, index=None, columns=['code', 'qty', 'cost_price', 'amount', 'price']) # 交易记录 trade_log = pd.DataFrame(data=None, index=None, columns=['date', 'code', 'qty', 'price', 'amount', 'profit','cash']) # 每日市值记录 stock_log = pd.DataFrame(data=None, index=None, columns=['date', 'stock_amount', 'cash' , 'profit']) # 存在不少数据问题，暂时归集到这里 debug_log = pd.DataFrame(data=None, index=None, columns=['date', 'code', 'qty', 'price', 'amount', 'profit','cash']) # 交易起止日期 begin = pd.to_datetime('20220101') end = pd.to_datetime('20220418') # 可用资金 cash = initial_cash # 自然日天数 days = 0 print('开始日期:', begin.strftime("%Y-%m-%d")) print('结束日期:', end.strftime("%Y-%m-%d")) # 目标持仓转债数量 hold_num=10 # 初始赋值 run_date=begin while run_date < = end: print('正在计算:',run_date.strftime("%Y-%m-%d") , end = '' ) print('\r',end='') days = days+1 # 节假日无可转债数据 # 获取前一天转载的双低清单 cvt_bond_list = run_cvt_bond(run_date) hold_df = cvt_bond_list[0:9] if cvt_bond_list.empty: # 当日非交易日，日期加一天 run_date=run_date + datetime.timedelta(days=1) # 跳出今日交易 continue # 开盘卖出非目标可转债 for code in stock['code']: if code not in list(hold_df['code']): stock_code = code cost_price = float(stock[stock['code']==code]['cost_price']) stock_qty = float(stock[stock['code']==code]['qty']) # 转债开盘价卖出 # 卖出价要在大的可转债表里面查询 sell_temp = cvt_bond_list[cvt_bond_list['code']==code] if not sell_temp.empty: sell_price = float(cvt_bond_list[cvt_bond_list['code']==code]['open']) else: sell_price = cost_price debug_log.loc[len(debug_log)] =[run_date, stock_code, stock_qty * -1, sell_price, amount * 1, profit ,cash] amount = sell_price * stock_qty profit = (sell_price - cost_price)* stock_qty cash = cash + amount # 删除已经卖出的债券 stock = stock.drop(stock[stock['code'] == code].index) # 重置持仓索引 stock = stock.reset_index(drop=True) trade_log.loc[len(trade_log)] = [run_date, stock_code, stock_qty * -1, sell_price, amount * 1, profit ,cash] # 拟加仓的可转债数量 buy_num=hold_num-len(stock) # 拟购买转债的金额 if buy_num>0: buy_cash= cash/buy_num # print(buy_cash) else: buy_cash=0 # 买入目标可转债 for code in hold_df['code']: if code not in list(stock['code']): stock_code = code # 转债开盘价买入 cost_price = float(hold_df[hold_df['code']==code]['open']) stock_qty = round(buy_cash/cost_price/10,0)*10 sell_price = cost_price amount = cost_price * stock_qty profit = 0 cash = cash - amount stock.loc[len(stock)]=[stock_code,stock_qty,cost_price,amount,sell_price] # 添加交易记录 trade_log.loc[len(trade_log)] = [run_date, stock_code, stock_qty * 1, sell_price, amount * (-1), profit ,cash] # print(stock) # 记录每日持仓市值和收益，方便画图 sum_amount = round(stock['amount'].sum(), 2) stock_log.loc[len(stock_log)] = [run_date, sum_amount, cash , float(sum_amount + cash)] run_date = run_date + datetime.timedelta(days=1) # 交易循环结束 # 记录交易日志 trade_log.to_csv("trade_log.csv") stock_log.to_csv("stock_log.csv") debug_log.to_csv("debug_log.csv") stock.to_csv("stock.csv") print('==============代码运行结束===============') print(time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())) # 计算最终持仓市值 last_amount = round(stock['amount'].sum(), 2) profit = round(cash + last_amount, 2) print('总天数:', days) print('初始资产:', initial_cash) print('最终资产:', profit) print('收益率:%.2f%%' % ((profit - initial_cash) / initial_cash * 100)) yy = days / 365 year_profit = ((profit / initial_cash) ** (1 / yy) - 1) * 100 print('年化:%.2f%%' % year_profit) #设置画布大小,默认画布序号为零 plt.figure(figsize=(16,8)) plt.plot(stock_log['date'], stock_log['profit']) plt.show() ``` 经回测，2022年1月1日至2022年4月19日，收益率为-14%。 感兴趣的可以进行进一步优化。