17906102104

Committed 22 Sep 2025 06:02AM UTC coverage: 65.213% (+0.03%) from 65.183%

Build # 17906102104

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push

github

Committed by

web-flow

Commit Message

Rqsdk 815 (#938)

* Remove debug print statement from the plot function in the system analyser module.

* Add debug logging for Matplotlib backend in plot function of system analyser module

* add FutureArbitrage

* update version

* update

* update

---------

Co-authored-by: 崔子琦 <oscarcui@ricequant.com>
Co-authored-by: Cuizi7 <Cuizi7@users.noreply.github.com>
Co-authored-by: pitaya <wangjiangfeng77qq@163.com>
Co-authored-by: Don <lin.dongzhao@ricequant.com>

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29.49

/rqalpha/mod/rqalpha_mod_sys_analyser/plot/plot.py

# -*- coding: utf-8 -*-
# 版权所有 2021 深圳米筐科技有限公司（下称“米筐科技”）
#
# 除非遵守当前许可，否则不得使用本软件。
#
#     * 非商业用途（非商业用途指个人出于非商业目的使用本软件，或者高校、研究所等非营利机构出于教育、科研等目的使用本软件）：
#         遵守 Apache License 2.0（下称“Apache 2.0 许可”），
#         您可以在以下位置获得 Apache 2.0 许可的副本：http://www.apache.org/licenses/LICENSE-2.0。
#         除非法律有要求或以书面形式达成协议，否则本软件分发时需保持当前许可“原样”不变，且不得附加任何条件。
#
#     * 商业用途（商业用途指个人出于任何商业目的使用本软件，或者法人或其他组织出于任何目的使用本软件）：
#         未经米筐科技授权，任何个人不得出于任何商业目的使用本软件（包括但不限于向第三方提供、销售、出租、出借、转让本软件、
#         本软件的衍生产品、引用或借鉴了本软件功能或源代码的产品或服务），任何法人或其他组织不得出于任何目的使用本软件，
#         否则米筐科技有权追究相应的知识产权侵权责任。
#         在此前提下，对本软件的使用同样需要遵守 Apache 2.0 许可，Apache 2.0 许可与本许可冲突之处，以本许可为准。
#         详细的授权流程，请联系 public@ricequant.com 获取。

import os
from typing import List, Mapping, Tuple, Sequence, Optional
from collections import ChainMap

import pandas as pd
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from matplotlib import gridspec, ticker, image as mpimg, pyplot

import rqalpha
from rqalpha.const import POSITION_EFFECT
from rqalpha.utils.logger import system_log
from .utils import IndicatorInfo, LineInfo, max_dd as _max_dd, SpotInfo, max_ddd as _max_ddd
from .utils import weekly_returns, trading_dates_index
from .consts import PlotTemplate, DefaultPlot
from .consts import IMG_WIDTH, INDICATOR_AREA_HEIGHT, PLOT_AREA_HEIGHT, USER_PLOT_AREA_HEIGHT, PLOT_TITLE_HEIGHT
from .consts import LABEL_FONT_SIZE, BLACK, SUPPORT_CHINESE, TITLE_FONT_SIZE
from .consts import MAX_DD, MAX_DDD, OPEN_POINT, CLOSE_POINT
from .consts import LINE_BENCHMARK, LINE_STRATEGY, LINE_WEEKLY_BENCHMARK, LINE_WEEKLY, LINE_EXCESS


class SubPlot:
    height: int
    right_pad: Optional[int] = None

    def plot(self, ax: Axes):
        raise NotImplementedError


class IndicatorArea(SubPlot):
    height: int = INDICATOR_AREA_HEIGHT
    right_pad = -1

    def __init__(
            self, indicators: List[List[IndicatorInfo]], indicator_values: Mapping[str, float],
            plot_template: PlotTemplate, strategy_name=None
    ):
        self._indicators = indicators
        self._values = indicator_values
        self._template = plot_template
        self._strategy_name = strategy_name

    def plot(self, ax: Axes):
        ax.axis("off")
        for lineno, indicators in enumerate(self._indicators[::-1]):  # lineno: 自下而上的行号
            _extra_width = 0  # 用于保存加长的部分, 原因是部分label太长出现覆盖
            for index_in_line, i in enumerate(indicators):
                _extra_width += (i.label_width_multiplier - 1) * self._template.INDICATOR_WIDTH
                x = index_in_line * self._template.INDICATOR_WIDTH + _extra_width
                y_value = lineno * (self._template.INDICATOR_VALUE_HEIGHT + self._template.INDICATOR_LABEL_HEIGHT)
                y_label = y_value + self._template.INDICATOR_LABEL_HEIGHT
                try:
                    value = i.formatter.format(self._values[i.key])
                except KeyError:
                    value = "nan"
                ax.text(x, y_label, i.label, color=i.color, fontsize=LABEL_FONT_SIZE),
                ax.text(x, y_value, value, color=BLACK, fontsize=i.value_font_size)
        if self._strategy_name:
            p = TitlePlot(self._strategy_name, len(self._indicators), self._template)
            p.plot(ax)
        

class ReturnPlot(SubPlot):
    height: int = PLOT_AREA_HEIGHT

    def __init__(
            self,
            returns,
            lines: List[Tuple[pd.Series, LineInfo]],
            spots_on_returns: List[Tuple[Sequence[int], SpotInfo]]
    ):
        self._returns = returns
        self._lines = lines
        self._spots_on_returns = spots_on_returns

    @classmethod
    def _plot_line(cls, ax, returns, info: LineInfo):
        if returns is not None:
            ax.plot(returns, label=info.label, alpha=info.alpha, linewidth=info.linewidth, color=info.color)

    def _plot_spots_on_returns(self, ax, positions: Sequence[int], info: SpotInfo):
        ax.plot(
            self._returns.index[positions], self._returns[positions],
            info.marker, color=info.color, markersize=info.markersize, alpha=info.alpha, label=info.label
        )

    def plot(self, ax: Axes):
        ax.get_xaxis().set_minor_locator(ticker.AutoMinorLocator())
        ax.get_yaxis().set_minor_locator(ticker.AutoMinorLocator())
        ax.grid(visible=True, which='minor', linewidth=.2)
        ax.grid(visible=True, which='major', linewidth=1)
        ax.patch.set_alpha(0.6)

        # plot lines
        for returns, info in self._lines:
            self._plot_line(ax, returns, info)
        # plot MaxDD/MaxDDD
        for positions, info in self._spots_on_returns:
            self._plot_spots_on_returns(ax, positions, info)

        # place legend
        pyplot.legend(loc="best").get_frame().set_alpha(0.5)
        # manipulate axis
        ax.set_yticks(ax.get_yticks())  # make matplotlib happy
        ax.set_yticklabels(['{:3.2f}%'.format(x * 100) for x in ax.get_yticks()])


class UserPlot(SubPlot):
    height: int = USER_PLOT_AREA_HEIGHT

    def __init__(self, plots_df):
        self._df = plots_df

    def plot(self, ax: Axes):
        ax.patch.set_alpha(0.6)
        for column in self._df.columns:
            ax.plot(self._df[column], label=column)
        pyplot.legend(loc="best").get_frame().set_alpha(0.5)


class TitlePlot(SubPlot):
    height: int = PLOT_TITLE_HEIGHT

    def __init__(self, strategy_name, indicator_area_rows, plot_template: PlotTemplate):
        self._strategy_name = strategy_name
        self._indicator_area_rows = indicator_area_rows
        self._template = plot_template

    def plot(self, ax:Axes):
        x = 0.57  # title 为整图居中，而非子图居中
        y = (self._template.INDICATOR_LABEL_HEIGHT + self._template.INDICATOR_VALUE_HEIGHT) * self._indicator_area_rows + 0.1
        ax.text(x, y, self._strategy_name, ha='center', va='bottom', color=BLACK, fontsize=TITLE_FONT_SIZE)

class WaterMark:
    def __init__(self, img_width, img_height, strategy_name):
        logo_file = os.path.join(
            os.path.dirname(os.path.realpath(rqalpha.__file__)),
            "resource", 'ricequant-logo.png')
        self.img_width = img_width
        self.img_height = img_height
        self.logo_img = mpimg.imread(logo_file)
        self.dpi = self.logo_img.shape[1] / img_width * 1.1

    def plot(self, fig: Figure):
        fig.figimage(
            self.logo_img, 
            xo = (self.img_width * self.dpi - self.logo_img.shape[1]) / 2,
            yo = (PLOT_AREA_HEIGHT * self.dpi - self.logo_img.shape[0]) / 2, 
            alpha=0.4
            )


def _plot(title: str, sub_plots: List[SubPlot], strategy_name):
    img_height = sum(s.height for s in sub_plots)
    water_mark = WaterMark(IMG_WIDTH, img_height, strategy_name)
    fig = pyplot.figure(title, figsize=(IMG_WIDTH, img_height), dpi=water_mark.dpi, clear=True)
    water_mark.plot(fig)

    gs = gridspec.GridSpec(img_height, 8, figure=fig)
    last_height = 0
    for p in sub_plots:
        p.plot(pyplot.subplot(gs[last_height:last_height + p.height, :p.right_pad]))
        last_height += p.height

    pyplot.tight_layout()
    return fig


def plot_result(
        result_dict, show=True, save=None, weekly_indicators: bool = False, open_close_points: bool = False,
        plot_template_cls=DefaultPlot, strategy_name=None
):
    summary = result_dict["summary"]
    portfolio = result_dict["portfolio"]

    return_lines: List[Tuple[pd.Series, LineInfo]] = [(portfolio.unit_net_value - 1, LINE_STRATEGY)]
    if "benchmark_portfolio" in result_dict:
        benchmark_portfolio = result_dict["benchmark_portfolio"]
        plot_template = plot_template_cls(portfolio.unit_net_value, benchmark_portfolio.unit_net_value)
        ex_returns = plot_template.geometric_excess_returns
        ex_max_dd_ddd = "MaxDD {}\nMaxDDD {}".format(
            _max_dd(ex_returns + 1, portfolio.index).repr, _max_ddd(ex_returns + 1, portfolio.index).repr
        )
        indicators = plot_template.INDICATORS + plot_template.EXCESS_INDICATORS

        # 在图例中输出基准信息
        _b_str = summary["benchmark_symbol"] if SUPPORT_CHINESE else summary["benchmark"]
        _INFO = LineInfo(
            LINE_BENCHMARK.label + "({})".format(_b_str), LINE_BENCHMARK.color,
            LINE_BENCHMARK.alpha, LINE_BENCHMARK.linewidth
        )

        return_lines.extend([
            (benchmark_portfolio.unit_net_value - 1, _INFO),
            (ex_returns, LINE_EXCESS),
        ])
        if weekly_indicators:
            return_lines.append((weekly_returns(benchmark_portfolio), LINE_WEEKLY_BENCHMARK))
    else:
        ex_max_dd_ddd = "nan"
        plot_template = plot_template_cls(portfolio.unit_net_value, None)
        indicators = plot_template.INDICATORS
    if weekly_indicators:
        return_lines.append((weekly_returns(portfolio), LINE_WEEKLY))
        indicators.extend(plot_template.WEEKLY_INDICATORS)
    max_dd = _max_dd(portfolio.unit_net_value.values, portfolio.index)
    max_ddd = summary["max_drawdown_duration"]
    spots_on_returns: List[Tuple[Sequence[int], SpotInfo]] = [
        ([max_dd.start, max_dd.end], MAX_DD),
        ([max_ddd.start, max_ddd.end], MAX_DDD)
    ]
    if open_close_points and not result_dict["trades"].empty:
        trades: pd.DataFrame = result_dict["trades"]
        spots_on_returns.append((trading_dates_index(trades, POSITION_EFFECT.CLOSE, portfolio.index), CLOSE_POINT))
        spots_on_returns.append((trading_dates_index(trades, POSITION_EFFECT.OPEN, portfolio.index), OPEN_POINT))

    sub_plots = [IndicatorArea(indicators, ChainMap(summary, {
        "max_dd_ddd": "MaxDD {}\nMaxDDD {}".format(max_dd.repr, max_ddd.repr),
        "excess_max_dd_ddd": ex_max_dd_ddd,
    }), plot_template, strategy_name), ReturnPlot(
        portfolio.unit_net_value - 1, return_lines, spots_on_returns
    )]
    if "plots" in result_dict:
        sub_plots.append(UserPlot(result_dict["plots"]))
    
    if strategy_name:
        for p in sub_plots:
            if (isinstance(p, IndicatorArea)): p.height += PLOT_TITLE_HEIGHT

    _plot(summary["strategy_file"], sub_plots, strategy_name)

    system_log.debug(f"Matplotlib backend: {pyplot.get_backend()}")
    
    if save:
        file_path = save
        if os.path.isdir(save):
            file_path = os.path.join(save, "{}.png".format(summary["strategy_name"]))
        pyplot.savefig(file_path, bbox_inches='tight')

    if show:
        pyplot.show()

1	# -- coding: utf-8 --
2	# 版权所有 2021 深圳米筐科技有限公司（下称“米筐科技”）
3	#
4	# 除非遵守当前许可，否则不得使用本软件。
5	#
6	# * 非商业用途（非商业用途指个人出于非商业目的使用本软件，或者高校、研究所等非营利机构出于教育、科研等目的使用本软件）：
7	# 遵守 Apache License 2.0（下称“Apache 2.0 许可”），
8	# 您可以在以下位置获得 Apache 2.0 许可的副本：http://www.apache.org/licenses/LICENSE-2.0。
9	# 除非法律有要求或以书面形式达成协议，否则本软件分发时需保持当前许可“原样”不变，且不得附加任何条件。
10	#
11	# * 商业用途（商业用途指个人出于任何商业目的使用本软件，或者法人或其他组织出于任何目的使用本软件）：
12	# 未经米筐科技授权，任何个人不得出于任何商业目的使用本软件（包括但不限于向第三方提供、销售、出租、出借、转让本软件、
13	# 本软件的衍生产品、引用或借鉴了本软件功能或源代码的产品或服务），任何法人或其他组织不得出于任何目的使用本软件，
14	# 否则米筐科技有权追究相应的知识产权侵权责任。
15	# 在此前提下，对本软件的使用同样需要遵守 Apache 2.0 许可，Apache 2.0 许可与本许可冲突之处，以本许可为准。
16	# 详细的授权流程，请联系 public@ricequant.com 获取。
17
18	import os	7✔
19	from typing import List, Mapping, Tuple, Sequence, Optional	7✔
20	from collections import ChainMap	7✔
21
22	import pandas as pd	7✔
23	from matplotlib.axes import Axes	7✔
24	from matplotlib.figure import Figure	7✔
25	from matplotlib import gridspec, ticker, image as mpimg, pyplot	7✔
26
27	import rqalpha	7✔
28	from rqalpha.const import POSITION_EFFECT	7✔
29	from rqalpha.utils.logger import system_log	7✔
30	from .utils import IndicatorInfo, LineInfo, max_dd as _max_dd, SpotInfo, max_ddd as _max_ddd	7✔
31	from .utils import weekly_returns, trading_dates_index	7✔
32	from .consts import PlotTemplate, DefaultPlot	7✔
33	from .consts import IMG_WIDTH, INDICATOR_AREA_HEIGHT, PLOT_AREA_HEIGHT, USER_PLOT_AREA_HEIGHT, PLOT_TITLE_HEIGHT	7✔
34	from .consts import LABEL_FONT_SIZE, BLACK, SUPPORT_CHINESE, TITLE_FONT_SIZE	7✔
35	from .consts import MAX_DD, MAX_DDD, OPEN_POINT, CLOSE_POINT	7✔
36	from .consts import LINE_BENCHMARK, LINE_STRATEGY, LINE_WEEKLY_BENCHMARK, LINE_WEEKLY, LINE_EXCESS	7✔
37
38
39	class SubPlot:	7✔
40	height: int	7✔
41	right_pad: Optional[int] = None	7✔
42
43	def plot(self, ax: Axes):	7✔
44	raise NotImplementedError	×
45
46
47	class IndicatorArea(SubPlot):	7✔
48	height: int = INDICATOR_AREA_HEIGHT	7✔
49	right_pad = -1	7✔
50
51	def __init__(	7✔
52	self, indicators: List[List[IndicatorInfo]], indicator_values: Mapping[str, float],
53	plot_template: PlotTemplate, strategy_name=None
54	):
55	self._indicators = indicators	×
56	self._values = indicator_values	×
57	self._template = plot_template	×
58	self._strategy_name = strategy_name	×
59
60	def plot(self, ax: Axes):	7✔
61	ax.axis("off")	×
62	for lineno, indicators in enumerate(self._indicators[::-1]): # lineno: 自下而上的行号	×
63	_extra_width = 0 # 用于保存加长的部分, 原因是部分label太长出现覆盖	×
64	for index_in_line, i in enumerate(indicators):	×
65	_extra_width += (i.label_width_multiplier - 1) * self._template.INDICATOR_WIDTH	×
66	x = index_in_line * self._template.INDICATOR_WIDTH + _extra_width	×
67	y_value = lineno * (self._template.INDICATOR_VALUE_HEIGHT + self._template.INDICATOR_LABEL_HEIGHT)	×
68	y_label = y_value + self._template.INDICATOR_LABEL_HEIGHT	×
69	try:	×
70	value = i.formatter.format(self._values[i.key])	×
71	except KeyError:	×
72	value = "nan"	×
73	ax.text(x, y_label, i.label, color=i.color, fontsize=LABEL_FONT_SIZE),	×
74	ax.text(x, y_value, value, color=BLACK, fontsize=i.value_font_size)	×
75	if self._strategy_name:	×
76	p = TitlePlot(self._strategy_name, len(self._indicators), self._template)	×
77	p.plot(ax)	×
78
79
80	class ReturnPlot(SubPlot):	7✔
81	height: int = PLOT_AREA_HEIGHT	7✔
82
83	def __init__(	7✔
84	self,
85	returns,
86	lines: List[Tuple[pd.Series, LineInfo]],
87	spots_on_returns: List[Tuple[Sequence[int], SpotInfo]]
88	):
89	self._returns = returns	×
90	self._lines = lines	×
91	self._spots_on_returns = spots_on_returns	×
92
93	@classmethod	7✔
94	def _plot_line(cls, ax, returns, info: LineInfo):	7✔
95	if returns is not None:	×
96	ax.plot(returns, label=info.label, alpha=info.alpha, linewidth=info.linewidth, color=info.color)	×
97
98	def _plot_spots_on_returns(self, ax, positions: Sequence[int], info: SpotInfo):	7✔
99	ax.plot(	×
100	self._returns.index[positions], self._returns[positions],
101	info.marker, color=info.color, markersize=info.markersize, alpha=info.alpha, label=info.label
102	)
103
104	def plot(self, ax: Axes):	7✔
105	ax.get_xaxis().set_minor_locator(ticker.AutoMinorLocator())	×
106	ax.get_yaxis().set_minor_locator(ticker.AutoMinorLocator())	×
107	ax.grid(visible=True, which='minor', linewidth=.2)	×
108	ax.grid(visible=True, which='major', linewidth=1)	×
109	ax.patch.set_alpha(0.6)	×
110
111	# plot lines
112	for returns, info in self._lines:	×
113	self._plot_line(ax, returns, info)	×
114	# plot MaxDD/MaxDDD
115	for positions, info in self._spots_on_returns:	×
116	self._plot_spots_on_returns(ax, positions, info)	×
117
118	# place legend
119	pyplot.legend(loc="best").get_frame().set_alpha(0.5)	×
120	# manipulate axis
121	ax.set_yticks(ax.get_yticks()) # make matplotlib happy	×
122	ax.set_yticklabels(['{:3.2f}%'.format(x * 100) for x in ax.get_yticks()])	×
123
124
125	class UserPlot(SubPlot):	7✔
126	height: int = USER_PLOT_AREA_HEIGHT	7✔
127
128	def __init__(self, plots_df):	7✔
129	self._df = plots_df	×
130
131	def plot(self, ax: Axes):	7✔
132	ax.patch.set_alpha(0.6)	×
133	for column in self._df.columns:	×
134	ax.plot(self._df[column], label=column)	×
135	pyplot.legend(loc="best").get_frame().set_alpha(0.5)	×
136
137
138	class TitlePlot(SubPlot):	7✔
139	height: int = PLOT_TITLE_HEIGHT	7✔
140
141	def __init__(self, strategy_name, indicator_area_rows, plot_template: PlotTemplate):	7✔
142	self._strategy_name = strategy_name	×
143	self._indicator_area_rows = indicator_area_rows	×
144	self._template = plot_template	×
145
146	def plot(self, ax:Axes):	7✔
147	x = 0.57 # title 为整图居中，而非子图居中	×
148	y = (self._template.INDICATOR_LABEL_HEIGHT + self._template.INDICATOR_VALUE_HEIGHT) * self._indicator_area_rows + 0.1	×
149	ax.text(x, y, self._strategy_name, ha='center', va='bottom', color=BLACK, fontsize=TITLE_FONT_SIZE)	×
150
151	class WaterMark:	7✔
152	def __init__(self, img_width, img_height, strategy_name):	7✔
153	logo_file = os.path.join(	×
154	os.path.dirname(os.path.realpath(rqalpha.__file__)),
155	"resource", 'ricequant-logo.png')
156	self.img_width = img_width	×
157	self.img_height = img_height	×
158	self.logo_img = mpimg.imread(logo_file)	×
159	self.dpi = self.logo_img.shape[1] / img_width * 1.1	×
160
161	def plot(self, fig: Figure):	7✔
162	fig.figimage(	×
163	self.logo_img,
164	xo = (self.img_width * self.dpi - self.logo_img.shape[1]) / 2,
165	yo = (PLOT_AREA_HEIGHT * self.dpi - self.logo_img.shape[0]) / 2,
166	alpha=0.4
167	)
168
169
170	def _plot(title: str, sub_plots: List[SubPlot], strategy_name):	7✔
171	img_height = sum(s.height for s in sub_plots)	×
172	water_mark = WaterMark(IMG_WIDTH, img_height, strategy_name)	×
173	fig = pyplot.figure(title, figsize=(IMG_WIDTH, img_height), dpi=water_mark.dpi, clear=True)	×
174	water_mark.plot(fig)	×
175
176	gs = gridspec.GridSpec(img_height, 8, figure=fig)	×
177	last_height = 0	×
178	for p in sub_plots:	×
179	p.plot(pyplot.subplot(gs[last_height:last_height + p.height, :p.right_pad]))	×
180	last_height += p.height	×
181
182	pyplot.tight_layout()	×
183	return fig	×
184
185
186	def plot_result(	7✔
187	result_dict, show=True, save=None, weekly_indicators: bool = False, open_close_points: bool = False,
188	plot_template_cls=DefaultPlot, strategy_name=None
189	):
190	summary = result_dict["summary"]	×
191	portfolio = result_dict["portfolio"]	×
192
193	return_lines: List[Tuple[pd.Series, LineInfo]] = [(portfolio.unit_net_value - 1, LINE_STRATEGY)]	×
194	if "benchmark_portfolio" in result_dict:	×
195	benchmark_portfolio = result_dict["benchmark_portfolio"]	×
196	plot_template = plot_template_cls(portfolio.unit_net_value, benchmark_portfolio.unit_net_value)	×
197	ex_returns = plot_template.geometric_excess_returns	×
198	ex_max_dd_ddd = "MaxDD {}\nMaxDDD {}".format(	×
199	_max_dd(ex_returns + 1, portfolio.index).repr, _max_ddd(ex_returns + 1, portfolio.index).repr
200	)
201	indicators = plot_template.INDICATORS + plot_template.EXCESS_INDICATORS	×
202
203	# 在图例中输出基准信息
204	_b_str = summary["benchmark_symbol"] if SUPPORT_CHINESE else summary["benchmark"]	×
205	_INFO = LineInfo(	×
206	LINE_BENCHMARK.label + "({})".format(_b_str), LINE_BENCHMARK.color,
207	LINE_BENCHMARK.alpha, LINE_BENCHMARK.linewidth
208	)
209
210	return_lines.extend([	×
211	(benchmark_portfolio.unit_net_value - 1, _INFO),
212	(ex_returns, LINE_EXCESS),
213	])
214	if weekly_indicators:	×
215	return_lines.append((weekly_returns(benchmark_portfolio), LINE_WEEKLY_BENCHMARK))	×
216	else:
217	ex_max_dd_ddd = "nan"	×
218	plot_template = plot_template_cls(portfolio.unit_net_value, None)	×
219	indicators = plot_template.INDICATORS	×
220	if weekly_indicators:	×
221	return_lines.append((weekly_returns(portfolio), LINE_WEEKLY))	×
222	indicators.extend(plot_template.WEEKLY_INDICATORS)	×
223	max_dd = _max_dd(portfolio.unit_net_value.values, portfolio.index)	×
224	max_ddd = summary["max_drawdown_duration"]	×
225	spots_on_returns: List[Tuple[Sequence[int], SpotInfo]] = [	×
226	([max_dd.start, max_dd.end], MAX_DD),
227	([max_ddd.start, max_ddd.end], MAX_DDD)
228	]
229	if open_close_points and not result_dict["trades"].empty:	×
230	trades: pd.DataFrame = result_dict["trades"]	×
231	spots_on_returns.append((trading_dates_index(trades, POSITION_EFFECT.CLOSE, portfolio.index), CLOSE_POINT))	×
232	spots_on_returns.append((trading_dates_index(trades, POSITION_EFFECT.OPEN, portfolio.index), OPEN_POINT))	×
233
234	sub_plots = [IndicatorArea(indicators, ChainMap(summary, {	×
235	"max_dd_ddd": "MaxDD {}\nMaxDDD {}".format(max_dd.repr, max_ddd.repr),
236	"excess_max_dd_ddd": ex_max_dd_ddd,
237	}), plot_template, strategy_name), ReturnPlot(
238	portfolio.unit_net_value - 1, return_lines, spots_on_returns
239	)]
240	if "plots" in result_dict:	×
241	sub_plots.append(UserPlot(result_dict["plots"]))	×
242
243	if strategy_name:	×
244	for p in sub_plots:	×
245	if (isinstance(p, IndicatorArea)): p.height += PLOT_TITLE_HEIGHT	×
246
247	_plot(summary["strategy_file"], sub_plots, strategy_name)	×
248
NEW 249	system_log.debug(f"Matplotlib backend: {pyplot.get_backend()}")	×
250
251	if save:	×
252	file_path = save	×
253	if os.path.isdir(save):	×
254	file_path = os.path.join(save, "{}.png".format(summary["strategy_name"]))	×
255	pyplot.savefig(file_path, bbox_inches='tight')	×
256
257	if show:	×
258	pyplot.show()	×

ricequant / rqalpha / 17906102104

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