vectorbt学习_42DMA之二网格参数优选

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
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import warnings
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import vectorbt as vbt
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import numpy as np
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import pandas as pd
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from datetime import datetime, timedelta
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import pytz
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from dateutil.parser import parse
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import ipywidgets as widgets
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from copy import deepcopy
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from tqdm import tqdm
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import imageio
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from IPython import display
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import plotly.graph_objects as go
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import itertools
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import dateparser
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import gc
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import math
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from tools import dbtools
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warnings.filterwarnings("ignore")
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pd.set_option('display.max_rows',500)
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pd.set_option('display.max_columns',500)
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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
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seed = 42
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symbol = '002594.XSHE'
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metric = 'total_return'
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start_date = datetime(2020, 1, 1, tzinfo=pytz.utc)  # time period for analysis, must be timezone-aware
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end_date = datetime(2023,1,1, tzinfo=pytz.utc)
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time_buffer = timedelta(days=100)  # buffer before to pre-calculate SMA/EMA, best to set to max window
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freq = '1D'
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vbt.settings.portfolio['init_cash'] = 10000.  # 100$
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vbt.settings.portfolio['fees'] = 0.0025  # 0.25%
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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
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cols = ['Open', 'High', 'Low', 'Close', 'Volume']
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# ohlcv_wbuf = vbt.YFData.download(symbol, start=start_date-time_buffer, end=end_date).get(cols)
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ohlcv_wbuf=dbtools.MySQLData.download(symbol).get() # 自带工具类查询
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assert(~ohlcv_wbuf.empty)
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ohlcv_wbuf = ohlcv_wbuf.astype(np.float64)
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print("origin ohlcv_wbuf size:",ohlcv_wbuf.shape)
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print(ohlcv_wbuf.columns)
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# Create a copy of data without time buffer
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wobuf_mask = (ohlcv_wbuf.index >= start_date) & (ohlcv_wbuf.index <= end_date) # mask without buffer
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ohlcv = ohlcv_wbuf.loc[wobuf_mask, :]
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print("wobuf_mask ohlcv size:",ohlcv.shape)
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# Plot the OHLC data
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ohlcv.vbt.ohlcv.plot().show_svg() # 绘制蜡烛图
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# remove show_svg() to display interactive chart!

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origin ohlcv_wbuf size: (978, 5)
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Index(['Open', 'High', 'Low', 'Close', 'Volume'], dtype='object')
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wobuf_mask ohlcv size: (728, 5)

10,网格参数寻优#

a,基础参数设置#

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min_window = 10
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max_window = 60
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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
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fast_ma, slow_ma = vbt.MA.run_combs(
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    ohlcv_wbuf['Close'], np.arange(min_window, max_window+1),
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    r=2, short_names=['fast_ma', 'slow_ma'])
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print("##### 111 #####")
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print(fast_ma.ma.shape)
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print(slow_ma.ma.shape)
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print(fast_ma.ma.columns)
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print(slow_ma.ma.columns)
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# Remove time buffer
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fast_ma = fast_ma[wobuf_mask]
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slow_ma = slow_ma[wobuf_mask]
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print("##### 222 #####")
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print(fast_ma.ma.shape)
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print(slow_ma.ma.shape)
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fast_ma.ma.columns

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##### 111 #####
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(978, 1275)
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(978, 1275)
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Int64Index([10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
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            ...
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            56, 56, 56, 56, 57, 57, 57, 58, 58, 59], dtype='int64', name='fast_ma_window', length=1275)
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Int64Index([11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
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            ...
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            57, 58, 59, 60, 58, 59, 60, 59, 60, 60], dtype='int64', name='slow_ma_window', length=1275)
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##### 222 #####
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(728, 1275)
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(728, 1275)
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Int64Index([10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
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            ...
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            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
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# Each column corresponds to a pair of fast and slow windows
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# Generate crossover signals
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dmac_entries = fast_ma.ma_crossed_above(slow_ma)
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dmac_exits = fast_ma.ma_crossed_below(slow_ma)
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print(dmac_entries.columns) # the same for dmac_exits

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MultiIndex([(10, 11),
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            (10, 12),
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            (10, 13),
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            (10, 14),
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            (10, 15),
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            (10, 16),
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            (10, 17),
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            (10, 18),
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            (10, 19),
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            (10, 20),
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            ...
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            (56, 57),
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            (56, 58),
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            (56, 59),
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            (56, 60),
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            (57, 58),
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            (57, 59),
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            (57, 60),
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            (58, 59),
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            (58, 60),
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            (59, 60)],
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           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
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dmac_pf = vbt.Portfolio.from_signals(ohlcv['Close'], dmac_entries, dmac_exits)
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# Calculate performance of each window combination
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dmac_perf = dmac_pf.deep_getattr(metric)
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print(dmac_perf.shape)
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print(dmac_perf.index)
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print("dmac_perf.idxmax()")
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print(dmac_perf.idxmax()) # your optimal window combination

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(1275,)
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MultiIndex([(10, 11),
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            (10, 12),
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            (10, 13),
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            (10, 14),
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            (10, 15),
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            (10, 16),
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            (10, 17),
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            (10, 18),
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            (10, 19),
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            (10, 20),
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            ...
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            (56, 57),
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            (56, 58),
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            (56, 59),
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            (56, 60),
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            (57, 58),
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            (57, 59),
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            (57, 60),
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            (58, 59),
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            (58, 60),
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            (59, 60)],
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           names=['fast_ma_window', 'slow_ma_window'], length=1275)
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dmac_perf.idxmax()
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(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
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dmac_perf_matrix = dmac_perf.vbt.unstack_to_df(symmetric=True,   index_levels='fast_ma_window', column_levels='slow_ma_window')
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print("dmac_perf_matrix.shape")
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print(dmac_perf_matrix.shape)
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dmac_perf_matrix.vbt.heatmap(
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    xaxis_title='Slow window',
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    yaxis_title='Fast window').show_svg()
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# remove show_svg() for interactivity

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dmac_perf_matrix.shape
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(51, 51)
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('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
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(fast_ma_window, slow_ma_window)

11,最佳参数回测图#

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01,基础配置信息#

02,行情获取和可视化#

a,时间交易参数配置#

b,获取行情和行情mask#

10,网格参数寻优#

a,基础参数设置#

b,多维(联合索引)指标#

c,多维(联合索引)信号#

d,多维(联合索引)信号回测,最佳参数组#

e,网格选优热力图#

11,最佳参数回测图#

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