5. 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
import qteasy as qt
print(qt.__version__)
5.1. 定义交易策略
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
5.2. 设定交易员对象，并且设置交易配置，实施交易回测
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=============
