5. 類網格交易策略
參考來源：docs/_joinquant_migration_source/Example_05_類網格交易.ipynb 第一個 Markdown cell。
本策略首先計算過去300個價格數據的均值和標準差 (天數是一個可調參數)
並根據均值加減1和2個標準差得到網格的區間分界線,（加減標準差的倍數是可調參數)
並分別配以0.3和0.5的倉位權重 (倉位權重是可調參數）
然後根據價格所在的區間來配置倉位(+/-40爲上下界,無實際意義): (-40,-3],(-3,-2],(-2,2],(2,3],(3,40](具體價格等於均值+數字倍標準差) [-0.5, -0.3, 0.0, 0.3, 0.5] (資金比例,此處負號表示開空倉，回測時設置爲允許持有空頭倉位)
回測數據爲:HS300指數的1min數據 回測時間爲:2022-03-01 09:30:00到2022-07-31 15:00:00
import qteasy as qt
print(qt.__version__)
5.1. 定義交易策略
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
5.2. 設定交易員對象，並且設置交易配置，實施交易回測
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=============
