5. Psuedo-Grid Trading Strategy

Reference source: docs/_joinquant_migration_source/Example_05_Class Grid Trading.ipynb First Markdown cell.

  • This strategy first calculates the mean and standard deviation of the past 300 price data points (the number of days is an adjustable parameter).

  • The grid interval boundaries are obtained by adding or subtracting 1 and 2 standard deviations from the mean (the multiplier for adding or subtracting standard deviations is an adjustable parameter).

  • They are assigned position weights of 0.3 and 0.5 respectively (position weights are adjustable parameters).

  • Then, configure the position size based on the price range (+/-40 is the upper and lower bound, which has no practical significance): (-40,-3],(-3,-2],(-2,2],(2,3],(3,40] (the specific price is equal to the mean plus a multiple of the standard deviation) [-0.5, -0.3, 0.0, 0.3, 0.5] (capital ratio; the negative sign here indicates opening a short position, and it is set to allow holding short positions during backtesting).

The backtesting data consists of 1-minute data points for the HS300 index. The backtesting period is from 09:30:00 on March 1, 2022 to 15:00:00 on July 31, 2022.

import qteasy as qt
print(qt.__version__)

5.1. Define trading strategy

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. Define the trader object, configure the trading settings, and perform trading backtesting.

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=============

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