# 多因子选股策略 参考来源：`docs/_joinquant_migration_source/Example_04_多因子选股.ipynb` 第一个 Markdown cell。 本策略每隔1个月定时触发,根据Fama-French三因子模型对每只股票进行回归，得到其alpha值。 假设Fama-French三因子模型可以完全解释市场，则alpha为负表明市场低估该股，因此应该买入。 策略思路： 计算市场收益率、个股的账面市值比和市值,并对后两个进行了分类, 根据分类得到的组合分别计算其市值加权收益率、SMB和HML. 对各个股票进行回归(假设无风险收益率等于0)得到alpha值. 选取alpha值小于0并为最小的10只股票进入标的池，平掉不在标的池的股票并等权买入在标的池的股票 回测数据:SHSE.000300的成份股 回测时间为:2019-05-01 到 2022-05-01 ## 定义策略 ```python import qteasy as qt import numpy as np from qteasy import Parameter, StgData def market_value_weighted(stock_return, mv, mv_cat, bp_cat, mv_target, bp_target): """ 根据mv_target和bp_target计算市值加权收益率 """ sel = (mv_cat == mv_target) & (bp_cat == bp_target) mv_total = np.nansum(mv[sel]) mv_weight = mv / mv_total return_total = np.nansum(stock_return[sel] * mv_weight[sel]) return return_total class MultiFactors(qt.FactorSorter): def __init__(self, pars: tuple = (0.5, 0.3, 0.7)): super().__init__( name='MultiFactor', description='根据Fama-French三因子回归模型估算HS300成分股的alpha值选股', pars=[Parameter((0.01, 0.99), par_type='float', name='size_gate', value=0.5), # 参数1:大小市值分类界限 Parameter((0.01, 0.49), par_type='float', name='pb_s', value=0.3), # 参数2:小/中bp分界线 Parameter((0.50, 0.99), par_type='float', name='pb_l', value=0.7)], # 参数3，中/大bp分界线 data_types=[StgData('pb', freq='d', asset_type='E', window_length=20, use_latest_data_cycle=True), StgData('total_mv', freq='d', asset_type='E', window_length=2, use_latest_data_cycle=True), StgData('close', freq='d', asset_type='E', window_length=20, use_latest_data_cycle=True), StgData('close-000300.SH', freq='d', asset_type='IDX', window_length=20, use_latest_data_cycle=True)], # 执行选股需要用到的股票数据 max_sel_count=10, # 最多选出10支股票 sort_ascending=True, # 选择因子最小的股票 condition='less', # 仅选择因子小于某个值的股票 lbound=0, # 仅选择因子小于0的股票 ubound=0, # 仅选择因子小于0的股票 ) def realize(self): size_gate_percentile, bp_small_percentile, bp_large_percentile = self.get_pars('size_gate', 'pb_s', 'pb_l') # 读取投资组合的数据PB和total_MV的最新值 pb, mv, closes, market_closes = self.get_data('pb_E_d', 'total_mv_E_d', 'close_E_d', 'close-000300.SH_IDX_d') pb = pb[-1] # 当前所有股票的PB值 mv = mv[-1] # 当前所有股票的市值 pre_close = closes[-2] # 当前所有股票的前收盘价 close = closes[-1] # 当前所有股票的最新收盘价 # 读取参考数据(r) market_pre_close = market_closes[-2] # HS300的昨收价 market_close = market_closes[-1] # HS300的收盘价 # 计算账面市值比，为pb的倒数 bp = pb ** -1 # 计算市值的50%的分位点,用于后面的分类 size_gate = np.nanquantile(mv, size_gate_percentile) # 计算账面市值比的30%和70%分位点,用于后面的分类 bm_30_gate = np.nanquantile(bp, bp_small_percentile) bm_70_gate = np.nanquantile(bp, bp_large_percentile) # 计算每只股票的当日收益率 stock_return = pre_close / close - 1 # 根据每只股票的账面市值比和市值，给它们分配bp分类和mv分类 # 市值小于size_gate的cat为1，否则为2 mv_cat = np.ones_like(mv) mv_cat += (mv > size_gate).astype('float') # bp小于30%的cat为1，30%～70%之间为2，大于70%为3 bp_cat = np.ones_like(bp) bp_cat += (bp > bm_30_gate).astype('float') bp_cat += (bp > bm_70_gate).astype('float') # 获取小市值组合的市值加权组合收益率 smb_s = (market_value_weighted(stock_return, mv, mv_cat, bp_cat, 1, 1) + market_value_weighted(stock_return, mv, mv_cat, bp_cat, 1, 2) + market_value_weighted(stock_return, mv, mv_cat, bp_cat, 1, 3)) / 3 # 获取大市值组合的市值加权组合收益率 smb_b = (market_value_weighted(stock_return, mv, mv_cat, bp_cat, 2, 1) + market_value_weighted(stock_return, mv, mv_cat, bp_cat, 2, 2) + market_value_weighted(stock_return, mv, mv_cat, bp_cat, 2, 3)) / 3 smb = smb_s - smb_b # 获取大账面市值比组合的市值加权组合收益率 hml_b = (market_value_weighted(stock_return, mv, mv_cat, bp_cat, 1, 3) + market_value_weighted(stock_return, mv, mv_cat, bp_cat, 2, 3)) / 2 # 获取小账面市值比组合的市值加权组合收益率 hml_s = (market_value_weighted(stock_return, mv, mv_cat, bp_cat, 1, 1) + market_value_weighted(stock_return, mv, mv_cat, bp_cat, 2, 1)) / 2 hml = hml_b - hml_s # 计算市场收益率 market_return = market_pre_close / market_close - 1 coff_pool = [] # 对每只股票进行回归获取其alpha值 for rtn in stock_return: x = np.array([[market_return, smb, hml, 1.0]]) y = np.array([[rtn]]) # OLS估计系数 coff = np.linalg.lstsq(x, y)[0][3][0] coff_pool.append(coff) # 以alpha值为股票组合的选股因子执行选股 factors = np.array(coff_pool) return factors ``` ## 运行策略 设置回测参数，运行策略 ```python shares = qt.filter_stock_codes(index='000300.SH', date='20190501') alpha = MultiFactors() op = qt.Operator(alpha, signal_type='PT', run_freq='ME') qt.run(op=op, mode=1, invest_start='20160405', invest_end='20210201', asset_type='E', asset_pool=shares, trade_batch_size=100, sell_batch_size=1, trade_log=True, ) ``` 运行结果如下： ```bash ==================================== | | | BACK TESTING RESULT | | | ==================================== qteasy running mode: 1 - History back testing time consumption for operate signal creation: 0.0 ms time consumption for operation back looping: 6 sec 502.5 ms investment starts on 2019-05-06 00:00:00 ends on 2022-04-29 00:00:00 Total looped periods: 3.0 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 000063.SZ 1 1 2 2.7% 0.0% 97.3% 000100.SZ 2 2 4 5.9% 0.0% 94.1% 000157.SZ 3 3 6 8.6% 0.0% 91.4% 000333.SZ 1 1 2 2.7% 0.0% 97.3% 000338.SZ 2 2 4 5.5% 0.0% 94.5% 000413.SZ 1 1 2 2.9% 0.0% 97.1% 000423.SZ 1 1 2 2.7% 0.0% 97.3% 000425.SZ 1 1 2 2.7% 0.0% 97.3% 000625.SZ 2 2 4 5.6% 0.0% 94.4% 000651.SZ 1 1 2 2.7% 0.0% 97.3% ... ... ... ... ... ... ... 603185.SH 1 1 2 5.8% 0.0% 94.2% 603290.SH 1 1 2 5.8% 0.0% 94.2% 688005.SH 3 3 6 7.9% 0.0% 92.1% 002756.SZ 1 1 2 2.7% 0.0% 97.3% 600039.SH 1 1 2 2.8% 0.0% 97.2% 600803.SH 1 1 2 2.9% 0.0% 97.1% 688187.SH 1 1 2 2.9% 0.0% 97.1% 000983.SZ 1 1 2 2.9% 0.0% 97.1% 600732.SH 3 3 6 8.2% 0.0% 91.8% 601699.SH 1 2 3 8.5% 0.0% 91.5% Total operation fee: ¥ 3,356.25 total investment amount: ¥ 100,000.00 final value: ¥ 252,942.40 Total return: 152.94% Avg Yearly return: 36.48% Skewness: -0.19 Kurtosis: 3.08 Benchmark return: 9.00% Benchmark Yearly return: 2.93% ------strategy loop_results indicators------ alpha: 0.413 Beta: 0.458 Sharp ratio: 1.511 Info ratio: 0.086 250 day volatility: 0.283 Max drawdown: 28.83% peak / valley: 2021-12-23 / 2022-04-26 recovered on: Not recovered! ===========END OF REPORT============= ``` ![png](img/output_4_3.png) 设置另外的回测区间从2016-04-05到2021-02-01，运行策略，可以看到在不同的区间下该策略都是有效的 ```python shares = qt.filter_stock_codes(index='000300.SH', date='20190501') alpha = MultiFactors() # 实例化策略 op = qt.Operator(alpha, signal_type='PT') # 创建Operator交易员对象，使用PT信号类型（仓位目标信号） op.op_type = 'stepwise' op.set_blender('1.0*s0') # 设置仓位调整公式，仓位目标为1.0*s0，即持仓百分比总和等于100% op.run(mode=1, invest_start='20160405', # 回测起始时间 invest_end='20210201', # 回测结束时间 asset_type='E', # 股票 asset_pool=shares, # 股票池 trade_batch_size=100, # 交易最小批量 sell_batch_size=1, # 卖出最小批量 trade_log=True, # 产生交易记录 ) print() ``` 运行结果如下： ```bash ==================================== | | | BACK TESTING RESULT | | | ==================================== qteasy running mode: 1 - History back testing time consumption for operate signal creation: 0.0 ms time consumption for operation back looping: 8 sec 335.0 ms 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 000063.SZ 2 2 4 3.4% 0.0% 96.6% 000100.SZ 3 3 6 5.2% 0.0% 94.8% 000157.SZ 1 1 2 1.8% 0.0% 98.2% 000333.SZ 2 2 4 3.4% 0.0% 96.6% 000338.SZ 1 1 2 1.7% 0.0% 98.3% 000413.SZ 2 2 4 3.6% 0.0% 96.4% 000596.SZ 1 1 2 1.8% 0.0% 98.2% 000625.SZ 3 3 6 5.3% 0.0% 94.7% 000629.SZ 1 1 2 1.7% 0.0% 98.3% 000651.SZ 1 1 2 1.7% 0.0% 98.3% ... ... ... ... ... ... ... 688005.SH 1 2 3 3.3% 0.0% 96.7% 000733.SZ 1 1 2 1.8% 0.0% 98.2% 002180.SZ 1 1 2 1.7% 0.0% 98.3% 600039.SH 1 1 2 1.7% 0.0% 98.3% 600803.SH 1 1 2 1.7% 0.0% 98.3% 601615.SH 1 1 2 1.8% 0.0% 98.2% 000983.SZ 2 2 4 3.3% 0.0% 96.7% 600732.SH 3 4 7 6.7% 0.0% 93.3% 600754.SH 1 1 2 1.8% 0.0% 98.2% 601699.SH 1 1 2 1.7% 0.0% 98.3% Total operation fee: ¥ 7,063.30 total investment amount: ¥ 100,000.00 final value: ¥ 584,928.02 Total return: 484.93% Avg Yearly return: 44.15% Skewness: -0.14 Kurtosis: 2.77 Benchmark return: 65.96% Benchmark Yearly return: 11.06% ------strategy loop_results indicators------ alpha: 0.428 Beta: 0.371 Sharp ratio: 1.376 Info ratio: 0.076 250 day volatility: 0.287 Max drawdown: 35.84% peak / valley: 2018-06-12 / 2019-01-02 recovered on: 2019-03-05 ===========END OF REPORT============= ``` ![png](img/output_4_4.png)