# 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 ## 1. 策略代码 创建自定义交易策略： ```python 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 ``` ## 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 ```python 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() ``` ## 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============= ![png](img/output_4_1_3.png)