# 集合竞价选股策略 本策略通过获取SHSE.000300沪深300的成份股数据并统计其30天内开盘价大于前收盘价的天数,并在该天数大于阈值10的时候加入股票池,随后对不在股票池的股票平仓并等权配置股票池的标的,每次交易间隔1个月. 回测数据为:SHSE.000300沪深300指数成份股 回测时间为:2016-04-05 到 2021-02-01 ```python import qteasy as qt import pandas as pd import numpy as np ``` ```python htypes = 'open, close' shares = qt.filter_stock_codes(index='000300.SH', date='20220131') print(shares[0:10]) dt = qt.get_history_data(htypes, shares=shares, asset_type='any', freq='m') one_share = shares[24] df = dt[one_share] ``` ['000001.SZ', '000002.SZ', '000063.SZ', '000066.SZ', '000069.SZ', '000100.SZ', '000157.SZ', '000166.SZ', '000301.SZ', '000333.SZ'] ## 第一种自定义策略设置方法，使用持仓数据和选股数据直接生成比例交易信号`PS`信号： 使用`GeneralStrategy`策略类，计算选股因子后，去掉所有小于零的因子，排序后提取排名前三十的股票 按以下逻辑生成交易信号： 1，检查当前持仓，如果持仓的股票未被选中，则全数卖出 2，检查当前持仓，如果新选中的股票没有持仓，则等权买入新增选中的股票 设置交易信号类型为`PS`，生成交易信号 由于生成交易信号需要用到持仓数据，因此不能使用批量生成模式，只能使用`stepwise`模式 ```python class GroupPS(qt.GeneralStg): def realize(self, h, r=None, t=None, pars=None): # 读取策略参数（开盘价大于收盘价的天数） if pars is None: n_day = self.pars[0] else: n_day = pars[0] # 从历史数据编码中读取四种历史数据的最新数值 opens = h[:, -30:, 0] # 从前一交易日起前30天内开盘价 closes = h[:, -31:-1, 1] # 从两个交易日前开始前30天内收盘价 # 从持仓数据中读取当前的持仓数量，并找到持仓股序号 own_amounts = t[:, 0] owned = np.where(own_amounts > 0)[0] # 所有持仓股的序号 not_owned = np.where(own_amounts == 0)[0] # 所有未持仓的股票序号 # 选股因子为开盘价大于收盘价的天数，使用astype将True/False结果改为1/0，便于加总 factors = ((opens - closes) > 0).astype('float') # 所有开盘价-收盘价>0的结果会被转化为1，其余结果转化为0，因此可以用sum得到开盘价大于收盘价的天数 factors = factors.sum(axis=1) # 选出开盘价大于收盘价天数大于十天的所有股票的序号 all_args = np.arange(len(factors)) selected = np.where(factors > n_day)[0] not_selected = np.setdiff1d(all_args, selected) # 计算选出的股票的数量 selected_count = len(selected) # 开始生成交易信号 signal = np.zeros_like(factors) # 如果持仓为正，且未被选中，生成全仓卖出交易信号 own_but_not_selected = np.intersect1d(owned, not_selected) signal[own_but_not_selected] = -1 # 在PS信号模式下 -1 代表全仓卖出 if selected_count == 0: # 如果选中的数量为0，则不需要生成买入信号，可以直接返回只有卖出的信号 return signal # 如果持仓为零，且被选中，生成全仓买入交易信号 selected_but_not_own = np.intersect1d(not_owned, selected) signal[selected_but_not_own] = 1. / selected_count # 在PS信号模式下，+1 代表全仓买进 （如果多只股票均同时全仓买进，则会根据资金总量平均分配资金） return signal ``` 创建一个`Operator`对象，并回测交易策略 ```python alpha = GroupPS(pars=(10,), par_count=1, par_types=['int'], par_range=[(3, 25)], name='GroupPS', description='本策略每隔1个月定时触发, 从SHSE.000300成份股中选择过去30天内开盘价大于前收盘价的天数大于10天的股票买入', data_types='open, close', strategy_run_freq='m', data_freq='d', window_length=32) op = qt.Operator(alpha, signal_type='PS') op.op_type = 'stepwise' op.set_parameter(0, (20,)) op.run(mode=1, asset_type='E', asset_pool=shares, trade_batch_size=100, sell_batch_size=1, trade_log=True) print() ``` 运行结果如下： ==================================== | | | BACK TESTING RESULT | | | ==================================== qteasy running mode: 1 - History back testing time consumption for operate signal creation: 0.0ms time consumption for operation back looping: 12s 766.3ms investment starts on 2016-04-05 00:00:00 ends on 2021-02-01 00:00:00 Total looped periods: 4.8 years. -------------operation summary:------------ Only non-empty shares are displayed, call "loop_result["oper_count"]" for complete operation summary Sell Cnt Buy Cnt Total Long pct Short pct Empty pct 000001.SZ 1 1 2 1.7% 0.0% 98.3% 000069.SZ 2 2 4 3.4% 0.0% 96.6% 000301.SZ 3 3 6 13.6% 0.0% 86.4% 000333.SZ 1 1 2 1.7% 0.0% 98.3% 000338.SZ 1 1 2 3.5% 0.0% 96.5% 000596.SZ 1 1 2 1.8% 0.0% 98.2% 000651.SZ 1 1 2 1.7% 0.0% 98.3% 000776.SZ 1 1 2 1.7% 0.0% 98.3% 000786.SZ 1 1 2 1.7% 0.0% 98.3% 000800.SZ 2 2 4 5.4% 0.0% 94.6% ... ... ... ... ... ... ... 603806.SH 2 2 4 3.6% 0.0% 96.4% 603939.SH 0 1 1 3.7% 0.0% 96.3% 688599.SH 0 1 1 3.7% 0.0% 96.3% 000408.SZ 1 1 2 1.7% 0.0% 98.3% 002648.SZ 2 2 4 3.4% 0.0% 96.6% 300751.SZ 1 1 2 1.7% 0.0% 98.3% 688065.SH 1 1 2 1.7% 0.0% 98.3% 600674.SH 2 2 4 3.7% 0.0% 96.3% 600803.SH 1 1 2 1.7% 0.0% 98.3% 601615.SH 1 1 2 1.7% 0.0% 98.3% Total operation fee: ¥ 1,290.37 total investment amount: ¥ 100,000.00 final value: ¥ 216,271.60 Total return: 116.27% Avg Yearly return: 17.32% Skewness: 0.29 Kurtosis: 7.52 Benchmark return: 65.96% Benchmark Yearly return: 11.06% ------strategy loop_results indicators------ alpha: 0.115 Beta: 0.525 Sharp ratio: 0.956 Info ratio: 0.017 250 day volatility: 0.149 Max drawdown: 18.93% peak / valley: 2018-05-25 / 2018-09-11 recovered on: 2019-04-18 ===========END OF REPORT============= ![png](img/output_4_1_2.png) ## 第二种自定义策略设置方，使用PT交易信号设置持仓目标： 在完成选股因子的计算之后，直接设置每个股票的持仓目标，这样就不需要使用知道持仓数据，直接输出持仓目标信号 ，在回测过程中根据实际持仓量生成交易信号。 ```python class GroupPT(qt.GeneralStg): def realize(self, h, r=None, t=None, pars=None): # 读取策略参数（开盘价大于收盘价的天数） if pars is None: n_day = self.pars[0] else: n_day = pars[0] # 从历史数据编码中读取四种历史数据的最新数值 opens = h[:, -30:, 0] # 从前一交易日起前30天内开盘价 closes = h[:, -31:-1, 1] # 从两个交易日前开始前30天内收盘价 # 选股因子为开盘价大于收盘价的天数，使用astype将True/False结果改为1/0，便于加总 factors = ((opens - closes) > 0).astype('float') # 所有开盘价-收盘价>0的结果会被转化为1，其余结果转化为0，因此可以用sum得到开盘价大于收盘价的天数 factors = factors.sum(axis=1) # 选出开盘价大于收盘价天数大于十天的所有股票的序号 all_args = np.arange(len(factors)) selected = np.where(factors > n_day)[0] not_selected = np.setdiff1d(all_args, selected) # 计算选出的股票的数量 selected_count = len(selected) # 开始生成交易信号 signal = np.zeros_like(factors) if selected_count == 0: return signal # 所有被选中的股票均设置为正持仓目标 signal[selected] = 1. / selected_count # 未被选中的股票持仓目标被设置为0 signal[not_selected] = 0 return signal ``` 创建一个Operator对象，开始回测交易策略 ```python alpha = GroupPT(pars=(10,), par_count=1, par_types=['int'], par_range=[(3, 25)], name='GroupPS', description='本策略每隔1个月定时触发, 从SHSE.000300成份股中选择过去30天内开盘价大于前收盘价的天数大于10天的股票买入', data_types='open, close', strategy_run_freq='m', data_freq='d', window_length=32) op = qt.Operator(alpha, signal_type='PT') op.op_type = 'batch' op.set_parameter(0, (20,)) op.run(mode=1, asset_type='E', asset_pool=shares, trade_batch_size=100, sell_batch_size=1, trade_log=True) print() ``` 交易回测结果如下： ==================================== | | | BACK TESTING RESULT | | | ==================================== qteasy running mode: 1 - History back testing time consumption for operate signal creation: 399.1ms time consumption for operation back looping: 8s 621.5ms investment starts on 2016-04-05 00:00:00 ends on 2021-02-01 00:00:00 Total looped periods: 4.8 years. -------------operation summary:------------ Only non-empty shares are displayed, call "loop_result["oper_count"]" for complete operation summary Sell Cnt Buy Cnt Total Long pct Short pct Empty pct 000001.SZ 1 1 2 1.7% 0.0% 98.3% 000069.SZ 2 2 4 3.4% 0.0% 96.6% 000301.SZ 6 5 11 13.6% 0.0% 86.4% 000338.SZ 1 1 2 1.7% 0.0% 98.3% 000596.SZ 1 1 2 1.8% 0.0% 98.2% 000625.SZ 1 1 2 1.7% 0.0% 98.3% 000661.SZ 1 1 2 1.7% 0.0% 98.3% 000776.SZ 1 1 2 1.7% 0.0% 98.3% 000786.SZ 1 1 2 1.7% 0.0% 98.3% 000800.SZ 2 3 5 5.4% 0.0% 94.6% ... ... ... ... ... ... ... 603806.SH 2 2 4 3.6% 0.0% 96.4% 603939.SH 0 2 2 3.7% 0.0% 96.3% 688599.SH 0 2 2 3.7% 0.0% 96.3% 000408.SZ 1 1 2 1.7% 0.0% 98.3% 002648.SZ 1 1 2 1.7% 0.0% 98.3% 300751.SZ 1 1 2 1.7% 0.0% 98.3% 688065.SH 1 1 2 1.7% 0.0% 98.3% 600674.SH 2 2 4 3.7% 0.0% 96.3% 600803.SH 1 1 2 1.7% 0.0% 98.3% 601615.SH 1 1 2 1.7% 0.0% 98.3% Total operation fee: ¥ 1,375.35 total investment amount: ¥ 100,000.00 final value: ¥ 216,215.03 Total return: 116.22% Avg Yearly return: 17.31% Skewness: 0.23 Kurtosis: 6.63 Benchmark return: 65.96% Benchmark Yearly return: 11.06% ------strategy loop_results indicators------ alpha: 0.110 Beta: 0.542 Sharp ratio: 1.015 Info ratio: 0.017 250 day volatility: 0.139 Max drawdown: 18.58% peak / valley: 2016-04-14 / 2017-01-16 recovered on: 2017-09-27 ===========END OF REPORT============= ![png](img/output_6_1.png)