<commit_before># -*- coding: utf-8 -*-
#
# Copyright 2017 Ricequant, Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


def plot_result(result_dict, show_windows=True, savefile=None):
    import os
    from matplotlib import rcParams
    rcParams['font.family'] = 'sans-serif'
    rcParams['font.sans-serif'] = [
        u'Microsoft Yahei',
        u'Heiti SC',
        u'Heiti TC',
        u'STHeiti',
        u'WenQuanYi Zen Hei',
        u'WenQuanYi Micro Hei',
        u"文泉驿微米黑",
        u'SimHei',
    ] + rcParams['font.sans-serif']
    rcParams['axes.unicode_minus'] = False
    import numpy as np
    import matplotlib
    from matplotlib import gridspec
    import matplotlib.image as mpimg
    import matplotlib.pyplot as plt

    summary = result_dict["summary"]
    title = summary['strategy_file']

    total_portfolios = result_dict["total_portfolios"]
    benchmark_portfolios = result_dict.get("benchmark_portfolios")

    index = total_portfolios.index

    # maxdrawdown
    xs = total_portfolios.portfolio_value.values
    rt = total_portfolios.total_returns.values
    max_dd_end = np.argmax(np.maximum.accumulate(xs) / xs)
    if max_dd_end == 0:
        max_dd_end = len(xs) - 1
    max_dd_start = np.argmax(xs[:max_dd_end])

    # maxdrawdown duration
    al_cum = np.maximum.accumulate(xs)
    a = np.unique(al_cum, return_counts=True)
    start_idx = np.argmax(a[1])
    m = a[0][start_idx]
    al_cum_array = np.where(al_cum == m)
    max_ddd_start_day = al_cum_array[0][0]
    max_ddd_end_day = al_cum_array[0][-1]

    max_dd_info = "MaxDD  {}~{}, {} days".format(index[max_dd_start], index[max_dd_end],
                                                 (index[max_dd_end] - index[max_dd_start]).days)
    max_dd_info += "\nMaxDDD {}~{}, {} days".format(index[max_ddd_start_day], index[max_ddd_end_day],
                                                    (index[max_ddd_end_day] - index[max_ddd_start_day]).days)

    plt.style.use('ggplot')

    red = "#aa4643"
    blue = "#4572a7"
    black = "#000000"

    plots_area_size = 0
    if "plots" in result_dict:
        plots_area_size = 5

    figsize = (18, 6 + int(plots_area_size * 0.9))
    plt.figure(title, figsize=figsize)
    max_height = 10 + plots_area_size
    gs = gridspec.GridSpec(max_height, 8)

    # draw logo
    ax = plt.subplot(gs[:3, -1:])
    ax.axis("off")
    filename = os.path.join(os.path.dirname(os.path.realpath(__file__)), "resource")
    filename = os.path.join(filename, "ricequant-logo.png")
    img = mpimg.imread(filename)
    ax.imshow(img, interpolation="nearest")
    ax.autoscale_view()

    # draw risk and portfolio

    font_size = 12
    value_font_size = 11
    label_height, value_height = 0.8, 0.6
    label_height2, value_height2 = 0.35, 0.15

    fig_data = [
        (0.00, label_height, value_height, u"回测收益", "{0:.3%}".format(summary["total_returns"]), red, black),
        (0.15, label_height, value_height, u"回测年化收益", "{0:.3%}".format(summary["annualized_returns"]), red, black),
        (0.00, label_height2, value_height2, u"基准收益", "{0:.3%}".format(summary.get("benchmark_total_returns", 0)), blue,
         black),
        (0.15, label_height2, value_height2, u"基准年化收益", "{0:.3%}".format(summary.get("benchmark_annualized_returns", 0)),
         blue, black),

        (0.30, label_height, value_height, u"Alpha", "{0:.4}".format(summary["alpha"]), black, black),
        (0.40, label_height, value_height, u"Beta", "{0:.4}".format(summary["beta"]), black, black),
        (0.55, label_height, value_height, u"Sharpe", "{0:.4}".format(summary["sharpe"]), black, black),
        (0.70, label_height, value_height, u"Sortino", "{0:.4}".format(summary["sortino"]), black, black),
        (0.85, label_height, value_height, u"Information Ratio", "{0:.4}".format(summary["information_ratio"]), black, black),

        (0.30, label_height2, value_height2, u"Volatility", "{0:.4}".format(summary["volatility"]), black, black),
        (0.40, label_height2, value_height2, u"最大回撤", "{0:.3%}".format(summary["max_drawdown"]), black, black),
        (0.55, label_height2, value_height2, u"Tracking Error", "{0:.4}".format(summary["tracking_error"]), black, black),
        (0.70, label_height2, value_height2, u"Downside Risk", "{0:.4}".format(summary["downside_risk"]), black, black),
    ]

    ax = plt.subplot(gs[:3, :-1])
    ax.axis("off")
    for x, y1, y2, label, value, label_color, value_color in fig_data:
        ax.text(x, y1, label, color=label_color, fontsize=font_size)
        ax.text(x, y2, value, color=value_color, fontsize=value_font_size)
    for x, y1, y2, label, value, label_color, value_color in [
        (0.85, label_height2, value_height2, u"最大回撤/最大回撤持续期", max_dd_info, black, black)]:
        ax.text(x, y1, label, color=label_color, fontsize=font_size)
        ax.text(x, y2, value, color=value_color, fontsize=8)

    # strategy vs benchmark
    ax = plt.subplot(gs[4:10, :])

    ax.get_xaxis().set_minor_locator(matplotlib.ticker.AutoMinorLocator())
    ax.get_yaxis().set_minor_locator(matplotlib.ticker.AutoMinorLocator())
    ax.grid(b=True, which='minor', linewidth=.2)
    ax.grid(b=True, which='major', linewidth=1)

    # plot two lines
    ax.plot(total_portfolios["total_returns"], label=u"策略", alpha=1, linewidth=2, color=red)
    if benchmark_portfolios is not None:
        ax.plot(benchmark_portfolios["total_returns"], label=u"基准", alpha=1, linewidth=2, color=blue)

    # plot MaxDD/MaxDDD
    ax.plot([index[max_dd_end], index[max_dd_start]], [rt[max_dd_end], rt[max_dd_start]],
            'v', color='Green', markersize=8, alpha=.7, label=u"最大回撤")
    ax.plot([index[max_ddd_start_day], index[max_ddd_end_day]],
            [rt[max_ddd_start_day], rt[max_ddd_end_day]], 'D', color='Blue', markersize=8, alpha=.7, label=u"最大回撤持续期")

    # place legend
    leg = plt.legend(loc="best")
    leg.get_frame().set_alpha(0.5)

    # manipulate axis
    vals = ax.get_yticks()
    ax.set_yticklabels(['{:3.2f}%'.format(x * 100) for x in vals])

    # plot user plots
    if "plots" in result_dict:
        plots_df = result_dict["plots"]

        ax2 = plt.subplot(gs[11:, :])
        for column in plots_df.columns:
            ax2.plot(plots_df[column], label=column)

        leg = plt.legend(loc="best")
        leg.get_frame().set_alpha(0.5)

    if show_windows:
        plt.show()

    if savefile:<commit_msg>use English to plot when missing Chinese fonts<commit_after># -*- coding: utf-8 -*-
#
# Copyright 2017 Ricequant, Inc
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from rqalpha.utils.logger import system_log


def plot_result(result_dict, show_windows=True, savefile=None):
    import os
    from matplotlib import rcParams
    from matplotlib.font_manager import findfont, FontProperties

    rcParams['font.family'] = 'sans-serif'
    rcParams['font.sans-serif'] = [
        u'Microsoft Yahei',
        u'Heiti SC',
        u'Heiti TC',
        u'STHeiti',
        u'WenQuanYi Zen Hei',
        u'WenQuanYi Micro Hei',
        u"文泉驿微米黑",
        u'SimHei',
    ] + rcParams['font.sans-serif']
    rcParams['axes.unicode_minus'] = False

    use_chinese_fonts = True
    font = findfont(FontProperties(family=['sans-serif']))
    if "/matplotlib/" in font:
        use_chinese_fonts = False
        system_log.warn("Missing Chinese fonts. Fallback to English.")

    import numpy as np
    import matplotlib
    from matplotlib import gridspec
    import matplotlib.image as mpimg
    import matplotlib.pyplot as plt

    summary = result_dict["summary"]
    title = summary['strategy_file']

    total_portfolios = result_dict["total_portfolios"]
    benchmark_portfolios = result_dict.get("benchmark_portfolios")

    index = total_portfolios.index

    # maxdrawdown
    xs = total_portfolios.portfolio_value.values
    rt = total_portfolios.total_returns.values
    max_dd_end = np.argmax(np.maximum.accumulate(xs) / xs)
    if max_dd_end == 0:
        max_dd_end = len(xs) - 1
    max_dd_start = np.argmax(xs[:max_dd_end])

    # maxdrawdown duration
    al_cum = np.maximum.accumulate(xs)
    a = np.unique(al_cum, return_counts=True)
    start_idx = np.argmax(a[1])
    m = a[0][start_idx]
    al_cum_array = np.where(al_cum == m)
    max_ddd_start_day = al_cum_array[0][0]
    max_ddd_end_day = al_cum_array[0][-1]

    max_dd_info = "MaxDD  {}~{}, {} days".format(index[max_dd_start], index[max_dd_end],
                                                 (index[max_dd_end] - index[max_dd_start]).days)
    max_dd_info += "\nMaxDDD {}~{}, {} days".format(index[max_ddd_start_day], index[max_ddd_end_day],
                                                    (index[max_ddd_end_day] - index[max_ddd_start_day]).days)

    plt.style.use('ggplot')

    red = "#aa4643"
    blue = "#4572a7"
    black = "#000000"

    plots_area_size = 0
    if "plots" in result_dict:
        plots_area_size = 5

    figsize = (18, 6 + int(plots_area_size * 0.9))
    plt.figure(title, figsize=figsize)
    max_height = 10 + plots_area_size
    gs = gridspec.GridSpec(max_height, 8)

    # draw logo
    ax = plt.subplot(gs[:3, -1:])
    ax.axis("off")
    filename = os.path.join(os.path.dirname(os.path.realpath(__file__)), "resource")
    filename = os.path.join(filename, "ricequant-logo.png")
    img = mpimg.imread(filename)
    ax.imshow(img, interpolation="nearest")
    ax.autoscale_view()

    # draw risk and portfolio

    font_size = 12
    value_font_size = 11
    label_height, value_height = 0.8, 0.6
    label_height2, value_height2 = 0.35, 0.15

    transaltion = {
        "strategy": u"策略",
        "benchmark": u"基准",
        "Total Returns": u"回测收益",
        "Annual Returns": u"回测年化收益",
        "Benchmark Returns": u"基准收益",
        "Benchmark Annual": u"基准年化收益",
        "MaxDrawdown": u"最大回撤",
        "MaxDD/MaxDDD": u"最大回撤/最大回撤持续期",
        "MaxDDD": u"最大回撤持续期",
    }

    def _(txt):
        try:
            return transaltion[txt] if use_chinese_fonts else txt
        except:
            return txt

    fig_data = [
        (0.00, label_height, value_height, _("Total Returns"), "{0:.3%}".format(summary["total_returns"]), red, black),
        (0.15, label_height, value_height, _("Annual Returns"), "{0:.3%}".format(summary["annualized_returns"]), red, black),
        (0.00, label_height2, value_height2, _("Benchmark Returns"), "{0:.3%}".format(summary.get("benchmark_total_returns", 0)), blue,
         black),
        (0.15, label_height2, value_height2, _("Benchmark Annual"), "{0:.3%}".format(summary.get("benchmark_annualized_returns", 0)),
         blue, black),

        (0.30, label_height, value_height, _("Alpha"), "{0:.4}".format(summary["alpha"]), black, black),
        (0.40, label_height, value_height, _("Beta"), "{0:.4}".format(summary["beta"]), black, black),
        (0.55, label_height, value_height, _("Sharpe"), "{0:.4}".format(summary["sharpe"]), black, black),
        (0.70, label_height, value_height, _("Sortino"), "{0:.4}".format(summary["sortino"]), black, black),
        (0.85, label_height, value_height, _("Information Ratio"), "{0:.4}".format(summary["information_ratio"]), black, black),

        (0.30, label_height2, value_height2, _("Volatility"), "{0:.4}".format(summary["volatility"]), black, black),
        (0.40, label_height2, value_height2, _("MaxDrawdown"), "{0:.3%}".format(summary["max_drawdown"]), black, black),
        (0.55, label_height2, value_height2, _("Tracking Error"), "{0:.4}".format(summary["tracking_error"]), black, black),
        (0.70, label_height2, value_height2, _("Downside Risk"), "{0:.4}".format(summary["downside_risk"]), black, black),
    ]

    ax = plt.subplot(gs[:3, :-1])
    ax.axis("off")
    for x, y1, y2, label, value, label_color, value_color in fig_data:
        ax.text(x, y1, label, color=label_color, fontsize=font_size)
        ax.text(x, y2, value, color=value_color, fontsize=value_font_size)
    for x, y1, y2, label, value, label_color, value_color in [
        (0.85, label_height2, value_height2, _("MaxDD/MaxDDD"), max_dd_info, black, black)]:
        ax.text(x, y1, label, color=label_color, fontsize=font_size)
        ax.text(x, y2, value, color=value_color, fontsize=8)

    # strategy vs benchmark
    ax = plt.subplot(gs[4:10, :])

    ax.get_xaxis().set_minor_locator(matplotlib.ticker.AutoMinorLocator())
    ax.get_yaxis().set_minor_locator(matplotlib.ticker.AutoMinorLocator())
    ax.grid(b=True, which='minor', linewidth=.2)
    ax.grid(b=True, which='major', linewidth=1)

    # plot two lines
    ax.plot(total_portfolios["total_returns"], label=_("strategy"), alpha=1, linewidth=2, color=red)
    if benchmark_portfolios is not None:
        ax.plot(benchmark_portfolios["total_returns"], label=_("benchmark"), alpha=1, linewidth=2, color=blue)

    # plot MaxDD/MaxDDD
    ax.plot([index[max_dd_end], index[max_dd_start]], [rt[max_dd_end], rt[max_dd_start]],
            'v', color='Green', markersize=8, alpha=.7, label=_("MaxDrawdown"))
    ax.plot([index[max_ddd_start_day], index[max_ddd_end_day]],
            [rt[max_ddd_start_day], rt[max_ddd_end_day]], 'D', color='Blue', markersize=8, alpha=.7, label=_("MaxDDD"))

    # place legend
    leg = plt.legend(loc="best")
    leg.get_frame().set_alpha(0.5)

    # manipulate axis
    vals = ax.get_yticks()
    ax.set_yticklabels(['{:3.2f}%'.format(x * 100) for x in vals])

    # plot user plots
    if "plots" in result_dict:
        plots_df = result_dict["plots"]

        ax2 = plt.subplot(gs[11:, :])
        for column in plots_df.columns:
            ax2.plot(plots_df[column], label=column)

        leg = plt.legend(loc="best")
        leg.get_frame().set_alpha(0.5)

    if show_windows:
        plt.show()

    if savefile:
{'fraction_non_alphanumeric': 0.14944950387386163, 'fraction_numerical': 0.02460241946445562, 'mean_word_length': 3.4043699491170307, 'pattern_counts': {'":': 9, '<': 3, '<?xml version=': 0, '>': 3, 'https://': 0, 'lorem ipsum': 0, 'www.': 2, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 8, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '3658997', 'n_tokens_mistral': 5539, 'n_tokens_neox': 5272, 'n_words': 1048}