# Alpha选股交易策略 本策略每隔1个月定时触发计算SHSE.000300成份股的过去的EV/EBITDA并选取EV/EBITDA大于0的股票，随后平掉排名EV/EBITDA不在最小的30的股票持仓并等权购买EV/EBITDA最小排名在前30的股票 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 pdd import numpy as np ``` 在选股之前，需要检查需要的历史数据 EV/EBITDA数据并不直接存在于qteasy定义的数据类型中，需要通过几个数据组合计算出来 EV/EBITDA = (Market Capitalization + Total Debt - Total Cash) / EBITDA 上面几个数据分别代表总市值、总负债、总现金及现金等价物，这些数据需要从`qteasy`内置的数据类型中分别提取，并使用上面的公式计算后，作为选股因子。排除掉小于0的因子后，将所有选股因子从小到大排列，选出因子排在最前的30支股票，将手中的全部资金平均分配投入到所有选中的股票中持有一个月，直到下一次选股为止。 我们可以通过下面的方法检查数据检查结果。 ```python htypes = 'total_mv, total_liab, c_cash_equ_end_period, ebitda' 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] df['ev_to_ebitda'] = (df.total_mv + df.total_liab - df.c_cash_equ_end_period) / df.ebitda ``` 输出结果如下 ['000001.SZ', '000002.SZ', '000063.SZ', '000066.SZ', '000069.SZ', '000100.SZ', '000157.SZ', '000166.SZ', '000301.SZ', '000333.SZ'] 有了上面的数据定义，我们就可以创建这样的选股策略了。 实际上，我们可以使用好几种不同的方法实现同样的选股策略： ## 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，如果持仓的股票不在选中的30个中间，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 策略的定义如下 ```python class AlphaPS(qt.GeneralStg): def realize(self, h, r=None, t=None, pars=None): # 从历史数据编码中读取四种历史数据的最新数值 total_mv = h[:, -1, 0] # 总市值 total_liab = h[:, -1, 1] # 总负债 cash_equ = h[:, -1, 2] # 现金及现金等价物总额 ebitda = h[:, -1, 3] # ebitda，息税折旧摊销前利润 # 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EV/EBIDTA，使用下面公式计算 factors = (total_mv + total_liab - cash_equ) / ebitda # 处理交易信号，将所有小于0的因子变为NaN factors = np.where(factors < 0, np.nan, factors) # 选出数值最小的30个股票的序号 arg_partitioned = factors.argpartition(30) selected = arg_partitioned[:30] # 被选中的30个股票的序号 not_selected = arg_partitioned[30:] # 未被选中的其他股票的序号（包括因子为NaN的股票） # 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 # 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] = 0.0333 # 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，并运行这个策略。 由于策略使用了当前持仓数据，因此只能使用`stepwise`模式回测 ```python alpha = AlphaPS(pars=(), par_count=0, par_types=[], par_range=[], name='AlphaPS', description='本策略每隔1个月定时触发计算SHSE.000300成份股的过去的EV/EBITDA并选取EV/EBITDA大于0的股票', data_types='total_mv, total_liab, c_cash_equ_end_period, ebitda', strategy_run_freq='m', data_freq='d', window_length=100) op = qt.Operator(alpha, signal_type='PS') op.op_type = 'stepwise' op.run(mode=1, asset_type='E', asset_pool=shares, trade_batch_size=100, sell_batch_size=1, trade_log=True) ``` 输出结果如下： ==================================== | | | BACK TESTING RESULT | | | ==================================== qteasy running mode: 1 - History back testing time consumption for operate signal creation: 0.0ms time consumption for operation back looping: 6s 192.6ms 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 000301.SZ 1 1 2 10.3% 0.0% 89.7% 000786.SZ 2 2 4 27.5% 0.0% 72.5% 000895.SZ 1 2 3 66.4% 0.0% 33.6% 002001.SZ 2 2 4 56.9% 0.0% 43.1% 002007.SZ 0 1 1 68.3% 0.0% 31.7% 002027.SZ 2 2 4 41.3% 0.0% 58.7% 002032.SZ 2 2 4 6.9% 0.0% 93.1% 002044.SZ 1 1 2 1.8% 0.0% 98.2% 002049.SZ 1 1 2 5.1% 0.0% 94.9% 002050.SZ 4 4 8 13.8% 0.0% 86.2% ... ... ... ... ... ... ... 603806.SH 3 3 6 62.1% 0.0% 37.9% 603899.SH 3 3 6 38.1% 0.0% 61.9% 000408.SZ 2 3 5 35.5% 0.0% 64.5% 002648.SZ 1 1 2 5.2% 0.0% 94.8% 002920.SZ 1 1 2 5.1% 0.0% 94.9% 300223.SZ 1 1 2 5.2% 0.0% 94.8% 300496.SZ 1 1 2 10.5% 0.0% 89.5% 600219.SH 0 1 1 6.1% 0.0% 93.9% 603185.SH 1 1 2 5.2% 0.0% 94.8% 688005.SH 1 1 2 3.5% 0.0% 96.5% Total operation fee: ¥ 750.28 total investment amount: ¥ 100,000.00 final value: ¥ 182,142.87 Total return: 82.14% Avg Yearly return: 13.22% Skewness: -0.36 Kurtosis: 2.40 Benchmark return: 65.96% Benchmark Yearly return: 11.06% ------strategy loop_results indicators------ alpha: 0.035 Beta: 1.175 Sharp ratio: 1.244 Info ratio: 0.005 250 day volatility: 0.113 Max drawdown: 19.03% peak / valley: 2018-05-22 / 2019-01-03 recovered on: 2019-04-01 ===========END OF REPORT============= ![png](img/output_5_1_2.png) ## 第二种自定义策略设置方法，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 AlphaPT(qt.GeneralStg): def realize(self, h, r=None, t=None, pars=None): # 从历史数据编码中读取四种历史数据的最新数值 total_mv = h[:, -1, 0] # 总市值 total_liab = h[:, -1, 1] # 总负债 cash_equ = h[:, -1, 2] # 现金及现金等价物总额 ebitda = h[:, -1, 3] # ebitda，息税折旧摊销前利润 # 选股因子为EV/EBIDTA，使用下面公式计算 factors = (total_mv + total_liab - cash_equ) / ebitda # 处理交易信号，将所有小于0的因子变为NaN factors = np.where(factors < 0, np.nan, factors) # 选出数值最小的30个股票的序号 arg_partitioned = factors.argpartition(30) selected = arg_partitioned[:30] # 被选中的30个股票的序号 not_selected = arg_partitioned[30:] # 未被选中的其他股票的序号（包括因子为NaN的股票） # 开始生成PT交易信号 signal = np.zeros_like(factors) # 所有被选中的股票的持仓目标被设置为0.03，表示持有3.3% signal[selected] = 0.0333 # 其余未选中的所有股票持仓目标在PT信号模式下被设置为0，代表目标仓位为0 signal[not_selected] = 0 return signal ``` 生成一个Operator对象，运行回测 ```python alpha = AlphaPT(pars=(), par_count=0, par_types=[], par_range=[], name='AlphaSel', description='本策略每隔1个月定时触发计算SHSE.000300成份股的过去的EV/EBITDA并选取EV/EBITDA大于0的股票', data_types='total_mv, total_liab, c_cash_equ_end_period, ebitda', strategy_run_freq='m', data_freq='d', window_length=100) op = qt.Operator(alpha, signal_type='PT') res = op.run(mode=1, asset_type='E', asset_pool=shares, PT_buy_threshold=0.00, # 如果设置PBT=0.00，PST=0.03，最终收益会达到30万元 PT_sell_threshold=0.00, trade_batch_size=100, sell_batch_size=1, maximize_cash_usage=True, trade_log=True ) ``` 回测结果如下： ==================================== | | | BACK TESTING RESULT | | | ==================================== qteasy running mode: 1 - History back testing time consumption for operate signal creation: 7.2ms time consumption for operation back looping: 6s 308.5ms 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 000301.SZ 1 1 2 10.3% 0.0% 89.7% 000786.SZ 2 3 5 27.5% 0.0% 72.5% 000895.SZ 1 1 2 68.7% 0.0% 31.3% 002001.SZ 2 2 4 57.5% 0.0% 42.5% 002007.SZ 0 1 1 68.3% 0.0% 31.7% 002027.SZ 6 7 13 41.3% 0.0% 58.7% 002032.SZ 3 1 4 7.5% 0.0% 92.5% 002044.SZ 1 1 2 1.8% 0.0% 98.2% 002049.SZ 1 1 2 5.1% 0.0% 94.9% 002050.SZ 4 4 8 13.8% 0.0% 86.2% ... ... ... ... ... ... ... 603806.SH 5 3 8 62.1% 0.0% 37.9% 603899.SH 2 3 5 36.3% 0.0% 63.7% 000408.SZ 3 5 8 35.5% 0.0% 64.5% 002648.SZ 1 1 2 5.2% 0.0% 94.8% 002920.SZ 1 1 2 5.1% 0.0% 94.9% 300223.SZ 1 2 3 5.2% 0.0% 94.8% 300496.SZ 1 1 2 10.5% 0.0% 89.5% 600219.SH 1 1 2 6.1% 0.0% 93.9% 603185.SH 1 1 2 5.2% 0.0% 94.8% 688005.SH 1 2 3 5.2% 0.0% 94.8% Total operation fee: ¥ 985.25 total investment amount: ¥ 100,000.00 final value: ¥ 189,723.44 Total return: 89.72% Avg Yearly return: 14.18% Skewness: -0.41 Kurtosis: 2.87 Benchmark return: 65.96% Benchmark Yearly return: 11.06% ------strategy loop_results indicators------ alpha: 0.044 Beta: 1.134 Sharp ratio: 1.284 Info ratio: 0.011 250 day volatility: 0.120 Max drawdown: 20.95% peak / valley: 2018-05-22 / 2019-01-03 recovered on: 2019-09-09 ===========END OF REPORT============= ![png](img/output_7_1.png) ## 第三种自定义策略设置方法： 使用`FactorSorter`策略类，直接生成交易策略的选股因子，再根据 `FactorSorter`策略的选股参数实现选股，本质上与第二种方式相同，但是 可以大大减少代码量 设置交易信号类型为`PT`，生成持仓目标，自动生成交易信号 ```python class AlphaFac(qt.FactorSorter): # 注意这里使用FactorSorter策略类 def realize(self, h, r=None, t=None, pars=None): # 从历史数据编码中读取四种历史数据的最新数值 total_mv = h[:, -1, 0] # 总市值 total_liab = h[:, -1, 1] # 总负债 cash_equ = h[:, -1, 2] # 现金及现金等价物总额 ebitda = h[:, -1, 3] # ebitda，息税折旧摊销前利润 # 选股因子为EV/EBIDTA，使用下面公式计算 factor = (total_mv + total_liab - cash_equ) / ebitda # 对比第二种策略定义方法，使用FactorSorter策略类可以直接处理选股因子 # 从而大大降低代码量 # 由于使用因子排序选股策略，因此直接返回选股因子即可，策略会自动根据设置条件选股 return factor ``` 同样创建一个`Operator`对象，回测交易策略 注意这个交易策略需要更多的参数用于因子选股： - `max_sel_count=30` 设置选股数量，最多选出30个股票 - `condition='greater'` 设置筛选条件，仅筛选因子大于`ubound`的股票 - `ubound=0.0` 设置筛选条件，仅筛选因子大于0的股票 - `weighting='even'` 设置股票权重，所有选中的股票平均分配权重 - `sort_ascending=True` 设置排序方式，因子从小到大排序选择头30名 ```python alpha = AlphaFac(pars=(), par_count=0, par_types=[], par_range=[], name='AlphaSel', description='本策略每隔1个月定时触发计算SHSE.000300成份股的过去的EV/EBITDA并选取EV/EBITDA大于0的股票', data_types='total_mv, total_liab, c_cash_equ_end_period, ebitda', strategy_run_freq='m', data_freq='d', window_length=100, max_sel_count=30, # 设置选股数量，最多选出30个股票 condition='greater', # 设置筛选条件，仅筛选因子大于ubound的股票 ubound=0.0, # 设置筛选条件，仅筛选因子大于0的股票 weighting='even', # 设置股票权重，所有选中的股票平均分配权重 sort_ascending=True) # 设置排序方式，因子从小到大排序选择头30名 op = qt.Operator(alpha, signal_type='PT') res = op.run(mode=1, asset_type='E', asset_pool=shares, PT_buy_threshold=0.0, PT_sell_threshold=0.0, trade_batch_size=100, sell_batch_size=1) ``` 回测结果如下： ==================================== | | | BACK TESTING RESULT | | | ==================================== qteasy running mode: 1 - History back testing time consumption for operate signal creation: 9.4ms time consumption for operation back looping: 5s 831.0ms 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 000301.SZ 1 2 3 10.3% 0.0% 89.7% 000786.SZ 2 3 5 27.5% 0.0% 72.5% 000895.SZ 1 0 1 62.6% 0.0% 37.4% 002001.SZ 2 2 4 55.8% 0.0% 44.2% 002007.SZ 3 1 4 68.3% 0.0% 31.7% 002027.SZ 2 9 11 41.3% 0.0% 58.7% 002032.SZ 2 0 2 5.9% 0.0% 94.1% 002044.SZ 1 1 2 1.8% 0.0% 98.2% 002049.SZ 1 1 2 5.1% 0.0% 94.9% 002050.SZ 4 5 9 13.8% 0.0% 86.2% ... ... ... ... ... ... ... 603517.SH 1 1 2 1.8% 0.0% 98.2% 603806.SH 6 3 9 39.8% 0.0% 60.2% 603899.SH 1 1 2 31.0% 0.0% 69.0% 000408.SZ 3 6 9 35.5% 0.0% 64.5% 002648.SZ 1 1 2 5.2% 0.0% 94.8% 002920.SZ 1 1 2 1.7% 0.0% 98.3% 300223.SZ 1 1 2 5.2% 0.0% 94.8% 600219.SH 1 1 2 6.1% 0.0% 93.9% 603185.SH 1 1 2 5.2% 0.0% 94.8% 688005.SH 1 1 2 5.2% 0.0% 94.8% Total operation fee: ¥ 928.22 total investment amount: ¥ 100,000.00 final value: ¥ 159,072.14 Total return: 59.07% Avg Yearly return: 10.09% Skewness: -0.28 Kurtosis: 3.29 Benchmark return: 65.96% Benchmark Yearly return: 11.06% ------strategy loop_results indicators------ alpha: -0.012 Beta: 1.310 Sharp ratio: 1.191 Info ratio: -0.010 250 day volatility: 0.105 Max drawdown: 20.49% peak / valley: 2018-05-22 / 2019-01-03 recovered on: 2019-12-26 ===========END OF REPORT============= ![png](img/output_9_1.png)