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vectorbt学习_42DMA之二网格参数优选

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本文在上一篇文章(vectorbt学习_16DMA之一基础策略)基础上，采用网格分析法分析策略的最优参数。

01,基础配置信息

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#conda envs:vectorbt_env
import warnings
import vectorbt as vbt
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
import pytz
from dateutil.parser import parse
import ipywidgets as widgets
from copy import deepcopy
from tqdm import tqdm
import imageio
from IPython import display
import plotly.graph_objects as go
import itertools
import dateparser
import gc
import math
from tools import dbtools 

warnings.filterwarnings("ignore")

pd.set_option('display.max_rows',500)
pd.set_option('display.max_columns',500)
pd.set_option('display.width',1000)

02,行情获取和可视化

a,时间交易参数配置

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# Enter your parameters here
seed = 42
symbol = '002594.XSHE'
metric = 'total_return'

start_date = datetime(2020, 1, 1, tzinfo=pytz.utc)  # time period for analysis, must be timezone-aware
end_date = datetime(2023,1,1, tzinfo=pytz.utc)
time_buffer = timedelta(days=100)  # buffer before to pre-calculate SMA/EMA, best to set to max window
freq = '1D'

vbt.settings.portfolio['init_cash'] = 10000.  # 100$
vbt.settings.portfolio['fees'] = 0.0025  # 0.25%
vbt.settings.portfolio['slippage'] = 0.0025  # 0.25%

b,获取行情和行情mask

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# Download data with time buffer
cols = ['Open', 'High', 'Low', 'Close', 'Volume']
# ohlcv_wbuf = vbt.YFData.download(symbol, start=start_date-time_buffer, end=end_date).get(cols)

ohlcv_wbuf=dbtools.MySQLData.download(symbol).get() # 自带工具类查询
assert(~ohlcv_wbuf.empty)
ohlcv_wbuf = ohlcv_wbuf.astype(np.float64)

print("origin ohlcv_wbuf size:",ohlcv_wbuf.shape)
print(ohlcv_wbuf.columns)


# Create a copy of data without time buffer
wobuf_mask = (ohlcv_wbuf.index >= start_date) & (ohlcv_wbuf.index <= end_date) # mask without buffer

ohlcv = ohlcv_wbuf.loc[wobuf_mask, :]

print("wobuf_mask ohlcv size:",ohlcv.shape)

# Plot the OHLC data
ohlcv.vbt.ohlcv.plot().show_svg() # 绘制蜡烛图
# remove show_svg() to display interactive chart!
origin ohlcv_wbuf size: (978, 5)
Index(['Open', 'High', 'Low', 'Close', 'Volume'], dtype='object')
wobuf_mask ohlcv size: (728, 5)

svg

10,网格参数寻优

a,基础参数设置

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min_window = 10
max_window = 60

metric = 'total_return'

b,多维(联合索引)指标

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# Pre-calculate running windows on data with time buffer
fast_ma, slow_ma = vbt.MA.run_combs(
    ohlcv_wbuf['Close'], np.arange(min_window, max_window+1), 
    r=2, short_names=['fast_ma', 'slow_ma'])
print("##### 111 #####")
print(fast_ma.ma.shape)
print(slow_ma.ma.shape)
print(fast_ma.ma.columns)
print(slow_ma.ma.columns)

# Remove time buffer
fast_ma = fast_ma[wobuf_mask]
slow_ma = slow_ma[wobuf_mask]
print("##### 222 #####")
print(fast_ma.ma.shape)
print(slow_ma.ma.shape)
fast_ma.ma.columns
##### 111 #####
(978, 1275)
(978, 1275)
Int64Index([10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
            ...
            56, 56, 56, 56, 57, 57, 57, 58, 58, 59], dtype='int64', name='fast_ma_window', length=1275)
Int64Index([11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
            ...
            57, 58, 59, 60, 58, 59, 60, 59, 60, 60], dtype='int64', name='slow_ma_window', length=1275)
##### 222 #####
(728, 1275)
(728, 1275)





Int64Index([10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
            ...
            56, 56, 56, 56, 57, 57, 57, 58, 58, 59], dtype='int64', name='fast_ma_window', length=1275)

c,多维(联合索引)信号

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# We perform the same steps, but now we have 4851 columns instead of 1
# Each column corresponds to a pair of fast and slow windows
# Generate crossover signals
dmac_entries = fast_ma.ma_crossed_above(slow_ma)
dmac_exits = fast_ma.ma_crossed_below(slow_ma)
print(dmac_entries.columns) # the same for dmac_exits

MultiIndex([(10, 11),
            (10, 12),
            (10, 13),
            (10, 14),
            (10, 15),
            (10, 16),
            (10, 17),
            (10, 18),
            (10, 19),
            (10, 20),
            ...
            (56, 57),
            (56, 58),
            (56, 59),
            (56, 60),
            (57, 58),
            (57, 59),
            (57, 60),
            (58, 59),
            (58, 60),
            (59, 60)],
           names=['fast_ma_window', 'slow_ma_window'], length=1275)

d,多维(联合索引)信号回测,最佳参数组

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# Build portfolio
dmac_pf = vbt.Portfolio.from_signals(ohlcv['Close'], dmac_entries, dmac_exits)
# Calculate performance of each window combination
dmac_perf = dmac_pf.deep_getattr(metric)

print(dmac_perf.shape)
print(dmac_perf.index)

print("dmac_perf.idxmax()")
print(dmac_perf.idxmax()) # your optimal window combination
(1275,)
MultiIndex([(10, 11),
            (10, 12),
            (10, 13),
            (10, 14),
            (10, 15),
            (10, 16),
            (10, 17),
            (10, 18),
            (10, 19),
            (10, 20),
            ...
            (56, 57),
            (56, 58),
            (56, 59),
            (56, 60),
            (57, 58),
            (57, 59),
            (57, 60),
            (58, 59),
            (58, 60),
            (59, 60)],
           names=['fast_ma_window', 'slow_ma_window'], length=1275)
dmac_perf.idxmax()
(35, 60)

e,网格选优热力图

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# Convert this array into a matrix of shape (99, 99): 99 fast windows x 99 slow windows
dmac_perf_matrix = dmac_perf.vbt.unstack_to_df(symmetric=True,   index_levels='fast_ma_window', column_levels='slow_ma_window')
print("dmac_perf_matrix.shape")
print(dmac_perf_matrix.shape)

dmac_perf_matrix.vbt.heatmap(
    xaxis_title='Slow window', 
    yaxis_title='Fast window').show_svg()
# remove show_svg() for interactivity

dmac_perf_matrix.shape
(51, 51)
('fast_ma_window', 'slow_ma_window')   10   11   12   13   14   15   16   17   18   19   20   21   22   23   24   25   26   27   28   29   30   31   32   33   34   35   36   37   38   39   40   41   42   43   44   45   46   47   48   49   50   51   52   53   54   55   56   57   58   59   60
(fast_ma_window, slow_ma_window)

svg

11,最佳参数回测图

svg