# 类网格交易策略 参考来源：`docs/_joinquant_migration_source/Example_05_类网格交易.ipynb` 第一个 Markdown cell。 - 本策略首先计算过去300个价格数据的均值和标准差 (天数是一个可调参数) - 并根据均值加减1和2个标准差得到网格的区间分界线,（加减标准差的倍数是可调参数) - 并分别配以0.3和0.5的仓位权重 (仓位权重是可调参数） - 然后根据价格所在的区间来配置仓位(+/-40为上下界,无实际意义): (-40,-3],(-3,-2],(-2,2],(2,3],(3,40](具体价格等于均值+数字倍标准差) [-0.5, -0.3, 0.0, 0.3, 0.5] (资金比例,此处负号表示开空仓，回测时设置为允许持有空头仓位) 回测数据为:HS300指数的1min数据 回测时间为:2022-03-01 09:30:00到2022-07-31 15:00:00 ```python import qteasy as qt print(qt.__version__) ``` ## 定义交易策略 ```python import numpy as np from qteasy import Parameter, StgData class GridTrading(qt.GeneralStg): def __init__(self): super().__init__( name='GridTrading', description='根据过去窗口的均值和标准差分档生成目标仓位', pars=[ Parameter((0.5, 3.0), name='low_th', par_type='float', value=2.0), Parameter((2.0, 8.0), name='high_th', par_type='float', value=3.0), Parameter((0.01, 0.6), name='low_pos', par_type='float', value=0.3), Parameter((0.1, 1.0), name='high_pos', par_type='float', value=0.5), Parameter((60, 500), name='lookback', par_type='int', value=300), ], data_types=StgData('close', freq='1min', asset_type='ANY', window_length=500), use_latest_data_cycle=False, ) def realize(self): low_th, high_th, low_pos, high_pos, lookback = self.get_pars( 'low_th', 'high_th', 'low_pos', 'high_pos', 'lookback' ) close = self.get_data('close_ANY_1min') close = close[-lookback:] close_mean = np.nanmean(close, axis=0) close_std = np.nanstd(close, axis=0) current_close = close[-1] hi_positive = close_mean + high_th * close_std low_positive = close_mean + low_th * close_std low_negative = close_mean - low_th * close_std hi_negative = close_mean - high_th * close_std pos = np.zeros_like(close_mean, dtype=float) pos = np.where(current_close > hi_positive, high_pos, pos) pos = np.where((current_close <= hi_positive) & (current_close > low_positive), low_pos, pos) pos = np.where((current_close <= low_positive) & (current_close > low_negative), 0.0, pos) pos = np.where((current_close <= low_negative) & (current_close > hi_negative), -low_pos, pos) pos = np.where(current_close <= hi_negative, -high_pos, pos) return pos ``` ## 设定交易员对象，并且设置交易配置，实施交易回测 ```python alpha = GridTrading() op = qt.Operator(alpha, signal_type='PT') op.op_type = 'batch' op.set_blender("1.0*s0") res = qt.run(op, mode=1, invest_start='20220401', invest_end='20220731', invest_cash_amounts=[1000000], asset_type='IDX', asset_pool=['000300.SH'], trade_batch_size=0.01, sell_batch_size=0.01, trade_log=True, allow_sell_short=True, ) ``` ==================================== | | | BACK TESTING RESULT | | | ==================================== qteasy running mode: 1 - History back testing time consumption for operate signal creation: 3 sec 180.8 ms time consumption for operation back looping: 14 sec 844.9 ms investment starts on 2022-04-01 09:30:00 ends on 2022-07-30 15:00:00 Total looped periods: 0.3 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 000300.SH 79 79 158 0.0% 98.1% 1.9% Total operation fee: ¥ -3,595.52 total investment amount: ¥1,000,000.00 final value: ¥1,012,663.10 Total return: inf% Avg Yearly return: inf% Skewness: 3.34 Kurtosis: 116.90 Benchmark return: -0.68% Benchmark Yearly return: -2.07% ------strategy loop_results indicators------ alpha: inf Beta: inf Sharp ratio: -inf Info ratio: 0.001 250 day volatility: 0.004 Max drawdown: 7.95% peak / valley: 2022-05-10 / 2022-06-30 recovered on: Not recovered! ===========END OF REPORT============= ![png](img/output_3_1.png)