4. 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
4.1. 定义策略
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
4.2. 运行策略
设置回测参数，运行策略
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：
====================================
| |
| 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=============
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
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：
====================================
| |
| 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=============
