vectorbt学习_41DMA之一基础策略

基于vectorbt的基础双均线策略

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)

svg

03,指标计算和可视化#

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# fig.show_svg()
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fast_window = 35
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slow_window = 60
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# Pre-calculate running windows on data with time buffer
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fast_ma = vbt.MA.run(ohlcv_wbuf['Close'], fast_window)
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slow_ma = vbt.MA.run(ohlcv_wbuf['Close'], slow_window)
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print(fast_ma.ma.shape)
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print(slow_ma.ma.shape)
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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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# there should be no nans after removing time buffer
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assert(~fast_ma.ma.isnull().any())
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assert(~slow_ma.ma.isnull().any())
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print(fast_ma.ma.shape)
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print(slow_ma.ma.shape)
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fig = ohlcv['Open'].vbt.plot(trace_kwargs=dict(name='Price'))
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fig = fast_ma.ma.vbt.plot(trace_kwargs=dict(name='Fast MA'), fig=fig)
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fig = slow_ma.ma.vbt.plot(trace_kwargs=dict(name='Slow MA'), fig=fig)
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fig.show_svg()

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(978,)
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(978,)
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(728,)
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(728,)

svg

04,信号计算,可视化#

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# 信号计算
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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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# 行情-指标-信号可视化
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fig = ohlcv['Close'].vbt.plot(trace_kwargs=dict(name='Price'))
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fig = fast_ma.ma.vbt.plot(trace_kwargs=dict(name='Fast MA'), fig=fig)
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fig = slow_ma.ma.vbt.plot(trace_kwargs=dict(name='Slow MA'), fig=fig)
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fig = dmac_entries.vbt.signals.plot_as_entry_markers(ohlcv['Close'], fig=fig)
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fig = dmac_exits.vbt.signals.plot_as_exit_markers(ohlcv['Close'], fig=fig)
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fig.show_svg()
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# (单独)信号可视化
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fig = dmac_entries.vbt.signals.plot(trace_kwargs=dict(name='Entries'))
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dmac_exits.vbt.signals.plot(trace_kwargs=dict(name='Exits'), fig=fig).show_svg()
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# 信号的统计信息
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dmac_entries.vbt.signals.stats(settings=dict(other=dmac_exits))

svg

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Start                       2020-01-02 00:00:00+00:00
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End                         2022-12-30 00:00:00+00:00
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Period                                            728
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Total                                               6
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Rate [%]                                     0.824176
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Total Overlapping                                   0
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Overlapping Rate [%]                              0.0
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First Index                 2020-01-08 00:00:00+00:00
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Last Index                  2022-12-16 00:00:00+00:00
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Norm Avg Index [-1, 1]                      -0.002751
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Distance -> Other: Min                            7.0
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Distance -> Other: Max                          200.0
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Distance -> Other: Mean                     76.333333
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Distance -> Other: Std                      66.503133
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Total Partitions                                    6
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Partition Rate [%]                              100.0
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Partition Length: Min                             1.0
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Partition Length: Max                             1.0
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Partition Length: Mean                            1.0
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Partition Length: Std                             0.0
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Partition Distance: Min                          90.0
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Partition Distance: Max                         252.0
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Partition Distance: Mean                        142.6
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Partition Distance: Std                     65.305436
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dtype: object

05,交易统计#

a,基准比对#

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dmac_pf = vbt.Portfolio.from_signals(ohlcv['Close'], dmac_entries, dmac_exits)
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# Print stats
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print(dmac_pf.stats())
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# Now build portfolio for a "Hold" strategy
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# Here we buy once at the beginning and sell at the end
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hold_entries = pd.Series.vbt.signals.empty_like(dmac_entries)
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hold_entries.iloc[0] = True
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hold_exits = pd.Series.vbt.signals.empty_like(hold_entries)
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hold_exits.iloc[-1] = True
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hold_pf = vbt.Portfolio.from_signals(ohlcv['Close'], hold_entries, hold_exits)
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# Equity
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fig = dmac_pf.value().vbt.plot(trace_kwargs=dict(name='Value (DMAC)'))
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hold_pf.value().vbt.plot(trace_kwargs=dict(name='Value (Hold)'), fig=fig).show_svg()

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Start                         2020-01-02 00:00:00+00:00
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End                           2022-12-30 00:00:00+00:00
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Period                                              728
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Start Value                                     10000.0
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End Value                                  56343.449364
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Total Return [%]                             463.434494
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Benchmark Return [%]                         433.464812
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Max Gross Exposure [%]                            100.0
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Total Fees Paid                             1154.406013
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Max Drawdown [%]                              37.462162
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Max Drawdown Duration                             319.0
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Total Trades                                          6
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Total Closed Trades                                   6
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Total Open Trades                                     0
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Open Trade PnL                                      0.0
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Win Rate [%]                                  66.666667
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Best Trade [%]                               192.432267
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Worst Trade [%]                              -14.196623
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Avg Winning Trade [%]                         72.994385
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Avg Losing Trade [%]                          -9.136247
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Avg Winning Trade Duration                        104.0
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Avg Losing Trade Duration                          21.0
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Profit Factor                                   5.95588
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Expectancy                                  7723.908227
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dtype: object

svg

b,交易详情和可视化#

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# Plot trades
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print(dmac_pf.trades.records.head(5))
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dmac_pf.trades.plot().show_svg()

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   id  col        size  entry_idx  entry_price  entry_fees  exit_idx  exit_price   exit_fees           pnl    return  direction  status  parent_id
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0   0    0  210.452345          4    47.398200   24.937656        66   57.775200   30.397316   2128.529014  0.213385          0       1          0
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1   1    0  210.612793         94    57.443250   30.245708       294  168.547575   88.745689  23281.000774  1.924323          0       1          1
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2   2    0  174.421504        346   202.505000   88.303067       430  282.621675  123.238244  13762.529659  0.389639          0       1          2
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3   3    0  157.697151        448   311.035650  122.623590       483  268.327500  105.786206  -6963.363374 -0.141966          0       1          3
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4   4    0  179.995892        566   233.913325  105.258594       636  327.110175  147.196219  16522.595293  0.392429          0       1          4

svg

黄金矿工

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#

03,指标计算和可视化#

04,信号计算,可视化#

05,交易统计#

a,基准比对#

b,交易详情和可视化#