import os

from matplotlib import rcParams

rcParams['font.family'] = 'sans-serif'

rcParams['font.sans-serif'] = ["Microsoft Yahei", "Heiti SC", "Heiti TC", "STHeiti", "WenQuanYi Zen Hei",

'WenQuanYi Micro Hei', "文泉驿微米黑", '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, "回测收益", "{0:.3%}".format(summary["total_returns"]), red, black),

(0.15, label_height, value_height, "回测年化收益", "{0:.3%}".format(summary["annualized_returns"]), red, black),

(0.00, label_height2, value_height2, "基准收益", "{0:.3%}".format(summary.get("benchmark_total_returns", 0)), blue,

black),

(0.15, label_height2, value_height2, "基准年化收益", "{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, "最大回撤", "{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, "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="策略", alpha=1, linewidth=2, color=red)

if benchmark_portfolios is not None:

ax.plot(benchmark_portfolios["total_returns"], label="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="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="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:

fnmame = savefile

if os.path.isdir(savefile):

fnmame = os.path.join(savefile, "{}.png".format(summary["strategy_name"]))