# 类网格交易策略 - 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 class GridTrading(qt.GeneralStg): def __init__(self, pars: tuple = (2.0, 3.0, 0.3, 0.5, 300)): super().__init__( pars=pars, par_count=5, par_types=['float', 'float', 'float', 'float', 'int'], # 仓位配置的阈值：参数1:低仓位阈值，参数2: 高仓位阈值，参数3：低仓位比例，参数4:高仓位比例，参数5:计算天数 par_range=[(0.5, 3.0), (2.0, 10.), (0.01, 0.5), (0.5, 0.99), (10, 300)], name='GridTrading', description='根据过去300份钟的股价均值和标准差，改变投资金额的仓位', strategy_run_timing='close', # 在周期结束（收盘）时运行 strategy_run_freq='1min', # 每份钟执行一次调整 strategy_data_types='close', # 使用份钟收盘价调整 data_freq='1min', # 数据频率（包括股票数据和参考数据） window_length=300, use_latest_data_cycle=False, # 高频数据不需要使用当前数据区间 reference_data_types='', # 不需要使用参考数据 ) def realize(self, h, r=None, t=None, pars=None): """策略输出PT信号，即仓位目标信号""" low_threshold, high_threshold, low_pos, hi_pos, days = self.pars # 读取最近N天的收盘价 close = h[:, - days:, 0] # 最新连续收盘价 current_close = h[:, -1, 0] # 当天的收盘价 # 计算N天的平均价和标准差，并计算仓位阈值 close_mean = np.nanmean(close, axis=1) close_std = np.nanstd(close, axis=1) hi_positive = close_mean + high_threshold * close_std low_positive = close_mean + low_threshold * close_std low_negative = close_mean - low_threshold * close_std hi_negative = close_mean - high_threshold * close_std # 根据当前的实际价格确定目标仓位，并将目标仓位作为信号输出 pos = np.zeros_like(close_mean) pos = np.where(current_close > hi_positive, hi_pos, pos) pos = np.where(hi_positive >= current_close > low_positive, low_pos, pos) pos = np.where(low_positive >= current_close > low_negative, 0, pos) pos = np.where(low_negative >= current_close > hi_negative, - low_pos, pos) pos = np.where(current_close >= hi_negative, - hi_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", 'close') op.run( mode=1, invest_start='20220401', invest_end='20220731', invest_cash_amounts=[1000000], asset_type='IDX', asset_pool=['000300.SH'], trade_batch_size=0, sell_batch_size=0, 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)