6. 指数增强选股策略
参考来源：docs/_joinquant_migration_source/Example_06_指数增强选股.ipynb 第一个 Markdown cell。
本策略以0.8为初始权重跟踪指数标的沪深300中权重大于0.35%的成份股. 个股所占的百分比为(0.8*成份股权重)*100%.然后根据个股是否: 1.连续上涨5天 2.连续下跌5天 来判定个股是否为强势股/弱势股,并对其把权重由0.8调至1.0或0.6
策略运行频率：每日运行 策略运行时间：每日收盘前
回测时间为:2021-01-01到2022-12-31
6.1. 1. 策略代码
创建自定义交易策略：
import qteasy as qt
import numpy as np
from qteasy import Parameter, StgData
class IndexEnhancement(qt.GeneralStg):
def __init__(self):
super().__init__(
pars=[
Parameter((0.01, 0.99), name='weight_threshold', par_type='float', value=0.35),
Parameter((0.51, 0.99), name='init_weight', par_type='float', value=0.8),
Parameter((2, 20), name='price_days', par_type='int', value=5),
],
name='IndexEnhancement',
description='跟踪HS300指数选股，并根据连续上涨/下跌趋势判断强弱势以增强权重',
data_types=[
StgData('wt_idx|000300.SH', freq='m', asset_type='E', window_length=2),
StgData('close', freq='d', asset_type='E', window_length=40),
],
)
def realize(self):
weight_threshold, init_weight, price_days = self.get_pars('weight_threshold', 'init_weight', 'price_days')
# 读取投资组合的权重wt和最近price_days天的收盘价
wt = self.get_data('wt_idx|000300.SH_E_m')[-1]
close_windows = self.get_data('close_E_d')
pre_close = close_windows[-price_days - 1:-1]
close = close_windows[-price_days:] # 当前所有股票的最新连续收盘价
# 计算连续price_days天的收益
stock_returns = close - pre_close
# 设置初始选股权重为0.8
weights = init_weight * np.ones_like(wt)
# 剔除掉权重小于weight_threshold的股票
weights[wt < weight_threshold] = 0
# 找出强势股，将其权重设为1, 找出弱势股，将其权重设置为 init_weight - (1 - init_weight)
up_trends = np.all(stock_returns > 0, axis=1)
weights[up_trends] = 1.0
down_trend_weight = init_weight - (1 - init_weight)
down_trends = np.all(stock_returns < 0, axis=1)
weights[down_trends] = down_trend_weight
# 实际选股权重为weights * HS300权重
weights *= wt
return weights
6.2. 2. 策略回测
回测参数：
回测时间：2021-01-01到2022-12-31
资产类型：股票
资产池：沪深300成份股
初始资金：100万
买入批量：100股
卖出批量：1股
shares = qt.filter_stock_codes(index='000300.SH', date='20210101')
print(len(shares), shares[:10])
alpha = IndexEnhancement()
op = qt.Operator(alpha, signal_type='PT')
op.op_type = 'stepwise'
op.set_blender('1.0*s0')
res = qt.run(op, mode=1,
invest_start='20210101',
invest_end='20221231',
invest_cash_amounts=[1000000],
asset_type='E',
asset_pool=shares,
trade_batch_size=100,
sell_batch_size=1,
trade_log=True,
)
print()
6.3. 回测结果
419 ['000001.SZ', '000002.SZ', '000063.SZ', '000066.SZ', '000069.SZ', '000100.SZ', '000157.SZ', '000166.SZ', '000333.SZ', '000338.SZ']
No match found! To get better result, you can
- pass "match_full_name=True" to match full names of stocks and funds
====================================
| |
| 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: 13 sec 461.8 ms
investment starts on 2021-01-04 00:00:00
ends on 2022-12-30 00:00:00
Total looped periods: 2.0 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
000001.SZ 0 3 3 100.0% 0.0% 0.0%
000002.SZ 0 2 2 100.0% 0.0% 0.0%
000063.SZ 0 0 0 100.0% 0.0% 0.0%
000100.SZ 1 5 6 66.9% 0.0% 33.1%
000333.SZ 0 1 1 100.0% 0.0% 0.0%
000338.SZ 1 1 2 62.3% 0.0% 37.7%
000651.SZ 0 1 1 100.0% 0.0% 0.0%
000725.SZ 0 95 95 100.0% 0.0% 0.0%
000858.SZ 0 0 0 100.0% 0.0% 0.0%
002027.SZ 1 3 4 62.3% 0.0% 37.7%
... ... ... ... ... ... ...
601229.SH 1 3 4 50.2% 0.0% 49.8%
601288.SH 0 76 76 100.0% 0.0% 0.0%
601318.SH 0 3 3 100.0% 0.0% 0.0%
601328.SH 0 30 30 100.0% 0.0% 0.0%
601398.SH 0 106 106 100.0% 0.0% 0.0%
601601.SH 1 0 1 78.8% 0.0% 21.2%
601668.SH 0 15 15 100.0% 0.0% 0.0%
601688.SH 0 1 1 100.0% 0.0% 0.0%
601899.SH 0 4 4 100.0% 0.0% 0.0%
603259.SH 0 0 0 100.0% 0.0% 0.0%
Total operation fee: ¥ 2,388.29
total investment amount: ¥1,000,000.00
final value: ¥ 703,480.41
Total return: -29.65%
Avg Yearly return: -16.23%
Skewness: -0.02
Kurtosis: 1.63
Benchmark return: -26.50%
Benchmark Yearly return: -14.36%
------strategy loop_results indicators------
alpha: -0.026
Beta: 0.941
Sharp ratio: -1.237
Info ratio: -0.139
250 day volatility: 0.168
Max drawdown: 43.41%
peak / valley: 2021-02-19 / 2022-10-31
recovered on: Not recovered!
===========END OF REPORT=============
