def line_datazoom_CO2(dataframe) -> Line:
Line_CO2 = (
Line().add_xaxis(dataframe['time'].values.tolist(), ) # 添加x轴
.add_yaxis(
series_name="CO2浓度",
y_axis=dataframe['CO2'].values.tolist(),
is_smooth=True,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
# xaxis_opts=opts.AxisOpts(type_="time"),
title_opts=opts.TitleOpts(title='实时CO2浓度折线图'),
datazoom_opts=opts.DataZoomOpts(
is_show=True,
pos_bottom=-2,
xaxis_index=[0, 1, 2],
),
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
axis_pointer_type='cross',
),
))
return Line_CO2
def gen_line(df1, s1, price_min, price_max):
df1['datetime'] = df1['date']
dt_list1 = list(df1['datetime'])
dt_list1 = [s[5:10] for s in dt_list1]
# print( len(dt_list1) )
# dt_list1 = [s[5:10] for s in dt_list1]
close_list1 = df1.apply(lambda record: float(record['close']),
axis=1).tolist()
close_list1 = np.array(close_list1)
# print(close_list1)
line1 = Line(init_opts=opts.InitOpts(width='1500px', height='600px'))
line1.set_global_opts(
yaxis_opts=opts.AxisOpts(
min_=price_min,
max_=price_max,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=0,
range_end=100,
),
],
)
line1.add_xaxis(xaxis_data=dt_list1)
line1.add_yaxis(
s1,
y_axis=close_list1,
)
line1.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
return line1
def heliu() -> Bar:
a = [1, 10, 100, 150, 180, 200] #模拟确诊
b = [0, 2, 20, 50, 100, 102] #模拟治愈
c = [0, 1, 10, 15, 18, 18] #模拟死亡
v = list(map(lambda x: x[0] - x[1] - x[2], zip(a, b, c))) #等待治愈
x = [0, 1, 2, 3, 4, 5] #模拟日
c = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.VINTAGE)) #设置主题
.add_xaxis(x) #直接列表
#.add_yaxis('确诊',v,stack="stack1")
.add_yaxis('死亡', c, stack="stack3").add_yaxis(
'治愈', b,
stack="stack3").add_yaxis('确诊', v, stack="stack3").set_global_opts(
title_opts=opts.TitleOpts(title="2019-nCov 海外国家疫情分析",
subtitle=""),
toolbox_opts=opts.ToolboxOpts(is_show=True), #
datazoom_opts=opts.DataZoomOpts(),
).set_series_opts(
# 设置系列配置
areastyle_opts=opts.AreaStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
singleaxis_opts=opts.SingleAxisOpts(max_=80)))
return c
def _init_base_axis(self):
self.init_opt['page_title'] = self.title
self.global_opt['title_opts'] = opts.TitleOpts(title=self.title)
self.global_opt['toolbox_opts'] = opts.ToolboxOpts(is_show=True)
self.global_opt['datazoom_opts'] = opts.DataZoomOpts(is_show=True,
type_='slider',
range_start=0,
range_end=100)
self.global_opt['xaxis_opts'] = opts.AxisOpts(
name=self.x_axis_name,
type_=self._x_axis_type,
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True))
self.global_opt['yaxis_opts'] = opts.AxisOpts(
name=self.y_axis_name,
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True))
if self.width > 0:
self.init_opt['width'] = str(self.width) + 'px'
if self.height > 0:
self.init_opt['height'] = str(self.height) + 'px'
self.chart = self._main_chart_type(init_opts=opts.InitOpts(
**self.init_opt))
def bar_datazoom_inside() -> Timeline:
tl = Timeline()
for i in range(2014, 2020):
c = (Bar(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation_delay=1000, animation_easing="elasticOut"))).add_xaxis(
list(zip(list(data总.set_index('类别').index)))).add_yaxis(
"显示", list(data总["{}".format(i)])).set_global_opts(
title_opts=opts.TitleOpts(title="纵横小说月票榜"),
datazoom_opts=opts.DataZoomOpts(type_="inside"),
visualmap_opts=opts.VisualMapOpts(type_="color",
max_=250000,
min_=200,
pos_right='20',
pos_top='middle'),
toolbox_opts=opts.ToolboxOpts(),
).set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
))
tl.add(c, "{}年".format(i))
return tl
def day_word_count():
ddata = []
kait = dict()
wordCount = []
for i in json_data: #
if i[0] not in ddata:
ddata.append(i[0])
for i in json_data:
if ids[i[3]] not in kait:
kait[ids[i[3]]] = dict()
for k in ddata:
kait[ids[i[3]]][k] = 0
kait[ids[i[3]]][i[0]] += len(i[4])
ddata = sort(ddata)
for i in ddata:
wordCount.append(
[i, [kait[k][i] for k in [i for i, j in kait.items()]]])
c = (Line().add_xaxis([i[0] for i in wordCount]).add_yaxis(
"A", [i[1][0] for i in wordCount]).add_yaxis(
"B", [i[1][1] for i in wordCount]).add_yaxis(
"C", [i[1][2] for i in wordCount]).set_global_opts(
title_opts=opts.TitleOpts(title=""),
datazoom_opts=opts.DataZoomOpts(
type_="slider")).render("line_.html"))
def kline_pro(kline: List[dict],
fx: List[dict] = None,
xd=None,
bs: List[dict] = None,
title: str = "缠中说禅K线分析",
width: str = "1400px",
height: str = '580px') -> Grid:
"""绘制缠中说禅K线分析结果
:param kline: K线
:param fx: 分型识别结果
:param bi: 笔识别结果
:param xd: 线段识别结果
:param zs: 中枢
:param bs: 买卖点
:param title: 图表标题
:param width: 图表宽度
:param height: 图表高度
:return: 用Grid组合好的图表
"""
# 配置项设置
# ------------------------------------------------------------------------------------------------------------------
bg_color = "#1f212d" # 背景
up_color = "#F9293E"
down_color = "#00aa3b"
init_opts = opts.InitOpts(bg_color=bg_color,
width=width,
height=height,
animation_opts=opts.AnimationOpts(False))
title_opts = opts.TitleOpts(
title=title,
pos_top="1%",
title_textstyle_opts=opts.TextStyleOpts(color=up_color, font_size=20),
subtitle_textstyle_opts=opts.TextStyleOpts(color=down_color,
font_size=12))
label_not_show_opts = opts.LabelOpts(is_show=False)
legend_not_show_opts = opts.LegendOpts(is_show=False)
red_item_style = opts.ItemStyleOpts(color=up_color)
green_item_style = opts.ItemStyleOpts(color=down_color)
k_style_opts = opts.ItemStyleOpts(color=up_color,
color0=down_color,
border_color=up_color,
border_color0=down_color,
opacity=0.8)
legend_opts = opts.LegendOpts(is_show=True,
pos_top="1%",
pos_left="30%",
item_width=14,
item_height=8,
textstyle_opts=opts.TextStyleOpts(
font_size=12, color="#0e99e2"))
brush_opts = opts.BrushOpts(tool_box=["rect", "polygon", "keep", "clear"],
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX")
axis_pointer_opts = opts.AxisPointerOpts(is_show=True,
link=[{
"xAxisIndex": "all"
}])
range_start = int(100 - 216 / len(kline) * 100)
dz_inside = opts.DataZoomOpts(False,
"inside",
xaxis_index=[0, 1, 2],
range_start=range_start,
range_end=100)
dz_slider = opts.DataZoomOpts(True,
"slider",
xaxis_index=[0, 1, 2],
pos_top="96%",
pos_bottom="0%",
range_start=range_start,
range_end=100)
yaxis_opts = opts.AxisOpts(is_scale=True,
axislabel_opts=opts.LabelOpts(
color="#c7c7c7",
font_size=8,
position="inside"))
grid0_xaxis_opts = opts.AxisOpts(
type_="category",
grid_index=0,
axislabel_opts=label_not_show_opts,
split_number=20,
min_="dataMin",
max_="dataMax",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False))
tool_tip_opts = opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
position=JsCode("""
function (pos, params, el, elRect, size) {
var obj = {top: 10};
obj[['left', 'right'][+(pos[0] < size.viewSize[0] / 2)]] = 30;
return obj;
}
"""),
textstyle_opts=opts.TextStyleOpts(color="#000"),
)
# 数据预处理
# ------------------------------------------------------------------------------------------------------------------
# dts = [x.get('dt', x['date']) for x in kline]
try:
dts = [x['date'] for x in kline]
except:
dts = [x['dt'] for x in kline]
# k_data = [[x['open'], x['close'], x['low'], x['high']] for x in kline]
k_data = [
opts.CandleStickItem(
name=i, value=[x['open'], x['close'], x['low'], x['high']])
for i, x in enumerate(kline)
]
vol = []
for i, row in enumerate(kline):
item_style = red_item_style if row['close'] > row[
'open'] else green_item_style
bar = opts.BarItem(name=i,
value=row['volume'],
itemstyle_opts=item_style,
label_opts=label_not_show_opts)
vol.append(bar)
close = np.array([x['close'] for x in kline], dtype=np.double)
diff, dea, macd = MACD(close)
ma5 = SMA(close, timeperiod=5)
ma34 = SMA(close, timeperiod=34)
ma55 = SMA(close, timeperiod=55)
ma233 = SMA(close, timeperiod=233)
macd_bar = []
for i, v in enumerate(macd.tolist()):
item_style = red_item_style if v > 0 else green_item_style
bar = opts.BarItem(name=i,
value=round(v, 4),
itemstyle_opts=item_style,
label_opts=label_not_show_opts)
macd_bar.append(bar)
diff = diff.round(4)
dea = dea.round(4)
# K 线主图
# ------------------------------------------------------------------------------------------------------------------
chart_k = Kline()
chart_k.add_xaxis(xaxis_data=dts)
chart_k.add_yaxis(series_name="Kline",
y_axis=k_data,
itemstyle_opts=k_style_opts)
chart_k.set_global_opts(legend_opts=legend_opts,
datazoom_opts=[dz_inside, dz_slider],
yaxis_opts=yaxis_opts,
tooltip_opts=tool_tip_opts,
axispointer_opts=axis_pointer_opts,
brush_opts=brush_opts,
title_opts=title_opts,
xaxis_opts=grid0_xaxis_opts)
if xd:
index = 0
zs_colors = [
"yellow", "white", '#f034c1', "#7944b7", "#468b58", "#c17f2f",
"#9EA0A1"
]
data = []
temp_xd = xd
while temp_xd:
zs_color = zs_colors[index % len(zs_colors)]
data = data + [
opts.MarkAreaItem(
name='XD{}'.format(index),
x=(x['xd_list'][0]['date'], x['xd_list'][-1]['date']),
y=(x['ZG']['value'], x['ZD']['value']),
label_opts=opts.LabelOpts(color=zs_color),
itemstyle_opts=opts.ItemStyleOpts(
color=zs_color,
opacity=0.2,
)) for x in temp_xd.zs_list
]
temp_xd = temp_xd.next
index = index + 1
chart_k.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True, data=data))
# 均线图
# ------------------------------------------------------------------------------------------------------------------
chart_ma = Line()
chart_ma.add_xaxis(xaxis_data=dts)
ma_keys = {"MA5": ma5, "MA34": ma34, "MA55": ma55, "MA233": ma233}
ma_colors = ["#39afe6", "#da6ee8", "#A02128", "#00940b"]
for i, (name, ma) in enumerate(ma_keys.items()):
chart_ma.add_yaxis(series_name=name,
y_axis=ma,
is_smooth=True,
is_selected=False,
symbol_size=0,
label_opts=label_not_show_opts,
linestyle_opts=opts.LineStyleOpts(
opacity=0.8, width=1.0, color=ma_colors[i]))
chart_ma.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_ma)
# 缠论结果
# ------------------------------------------------------------------------------------------------------------------
if fx:
try:
fx_dts = [x['date'] for x in fx]
except:
fx_dts = [x['dt'] for x in fx]
fx_val = [x['value'] for x in fx]
chart_fx = Scatter()
chart_fx.add_xaxis(fx_dts)
chart_fx.add_yaxis(series_name="FX",
y_axis=fx_val,
is_selected=False,
symbol="circle",
symbol_size=6,
label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(
color="rgba(152, 147, 193, 1.0)", ))
chart_fx.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_fx)
if xd:
index = 0
xd_colors = zs_colors
while xd:
xd_dts = [x['date'] for x in xd]
xd_val = [x['value'] for x in xd]
chart_xd = Line()
chart_xd.add_xaxis(xd_dts)
xd_color = xd_colors[index % len(xd_colors)]
chart_xd.add_yaxis(
series_name="XD{}".format(index),
y_axis=xd_val,
is_selected=True,
symbol="triangle",
symbol_size=10,
linestyle_opts=opts.LineStyleOpts(color=xd_color,
width=index + 1,
type_="solid"),
itemstyle_opts=opts.ItemStyleOpts(color=xd_color))
chart_xd.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_xd)
xd = xd.next
index = index + 1
if bs:
b_dts = [x['date'] for x in bs if x['bs'] == 'buy']
if len(b_dts) > 0:
b_val = [x['value'] for x in bs if x['bs'] == 'buy']
chart_b = Scatter()
chart_b.add_xaxis(b_dts)
chart_b.add_yaxis(series_name="BUY",
y_axis=b_val,
is_selected=False,
symbol="arrow",
symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(
color="#f31e1e", ))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_b)
s_dts = [x['date'] for x in bs if x['bs'] == 'sell']
if len(s_dts) > 0:
s_val = [x['value'] for x in bs if x['bs'] == 'sell']
chart_s = Scatter()
chart_s.add_xaxis(s_dts)
chart_s.add_yaxis(series_name="SELL",
y_axis=s_val,
is_selected=False,
symbol="pin",
symbol_size=12,
itemstyle_opts=opts.ItemStyleOpts(
color="#45b97d", ))
chart_s.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_s)
# 成交量图
# ------------------------------------------------------------------------------------------------------------------
chart_vol = Bar()
chart_vol.add_xaxis(dts)
chart_vol.add_yaxis(series_name="Volume", y_axis=vol, bar_width='60%')
chart_vol.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=True,
font_size=8,
color="#9b9da9"),
),
yaxis_opts=yaxis_opts,
legend_opts=legend_not_show_opts,
)
# MACD图
# ------------------------------------------------------------------------------------------------------------------
chart_macd = Bar()
chart_macd.add_xaxis(dts)
chart_macd.add_yaxis(series_name="MACD", y_axis=macd_bar, bar_width='60%')
chart_macd.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, color="#c7c7c7"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
line = Line()
line.add_xaxis(dts)
line.add_yaxis(series_name="DIFF",
y_axis=diff,
label_opts=label_not_show_opts,
is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8,
width=1.0,
color="#da6ee8"))
line.add_yaxis(series_name="DEA",
y_axis=dea,
label_opts=label_not_show_opts,
is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8,
width=1.0,
color="#39afe6"))
chart_macd = chart_macd.overlap(line)
grid0_opts = opts.GridOpts(pos_left="0%",
pos_right="1%",
pos_top="12%",
height="58%")
grid1_opts = opts.GridOpts(pos_left="0%",
pos_right="1%",
pos_top="74%",
height="8%")
grid2_opts = opts.GridOpts(pos_left="0%",
pos_right="1%",
pos_top="86%",
height="10%")
grid_chart = Grid(init_opts)
grid_chart.add(chart_k, grid_opts=grid0_opts)
grid_chart.add(chart_vol, grid_opts=grid1_opts)
grid_chart.add(chart_macd, grid_opts=grid2_opts)
return grid_chart
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
c = (Bar().add_xaxis(Faker.days_attrs).add_yaxis(
"商家A", Faker.days_values, color=Faker.rand_color()).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（slider+inside）"),
datazoom_opts=[opts.DataZoomOpts(),
opts.DataZoomOpts(type_="inside")],
))
c.width = "100%"
put_html(c.render_notebook())
import pandas as pd
from pyecharts import options as opts
from pyecharts.charts import Line, Kline, Scatter
kline1 = Kline()
kline2 = Kline()
kline1.set_global_opts(xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts()])
kline1.extend_axis(yaxis=opts.AxisOpts(type_='value', position='right'))
kline1.extend_axis(yaxis=opts.AxisOpts(type_='value', position='right'))
kline2.set_global_opts(xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(is_scale=True),
datazoom_opts=[opts.DataZoomOpts()])
kline2.extend_axis(yaxis=opts.AxisOpts(type_='value', position='right'))
kline2.extend_axis(yaxis=opts.AxisOpts(type_='value', position='right'))
df = pd.read_csv('F:/Stock/kline.csv')
str1 = str(df.iloc[0, 1])
str2 = str(df.iloc[1, 1])
#-------------------------------------------------------------------------------------------------------------------
df1 = pd.read_csv('F:/Stock/Data_Day/' + df.iloc[0, 1] + ".csv")
df1 = df1.sort_index(ascending=False).reset_index(drop=True)
date1 = df1.xs('Date Time', axis=1).tolist()
data1 = []
vol1 = []
def bar_base(self) -> Bar:
# start = datetime.datetime.now()
#
# dispose_unit = []
# done = []
# undo = []
# count_dispose_unit = 30
#
# event_counts = Event.objects.all().values('dispose_unit').annotate(count=Count('dispose_unit')).values('dispose_unit', 'count').order_by('-count')
# print(event_counts)
# event_counts = list(event_counts)
# sum_of_done = 0
# sum_of_undo = 0
# # print(len(event_counts))
# # print(event_counts)
# for i in range(0,count_dispose_unit):
# undo_event = Event.objects.filter(Q(achieve=3) & Q(dispose_unit=event_counts[i]['dispose_unit']))
# undo_event_num = len(undo_event)
# dispose_unit_name = DisposeUnit.objects.filter(id=event_counts[i]['dispose_unit']).values("name")[0]['name']
# dispose_unit.append(dispose_unit_name)
# done.append(event_counts[i]['count'])
# undo.append(undo_event_num)
# for i in range(count_dispose_unit,len(event_counts)):
# undo_event = Event.objects.filter(Q(achieve=3) & Q(dispose_unit=event_counts[i]['dispose_unit']))
# undo_event_num = len(undo_event)
# sum_of_done = sum_of_done + event_counts[i]['count']
# sum_of_undo = sum_of_undo + undo_event_num
# other_unit_num = len(event_counts) - count_dispose_unit
# dispose_unit.append("其他"+str(other_unit_num)+"个部门总和")
# done.append(sum_of_done)
# undo.append(sum_of_undo)
# c = (
# Bar()
# .add_xaxis(dispose_unit)
# .add_yaxis("按期完成", done, stack="stack1",category_gap="60%")
# .add_yaxis("逾期完成", undo, stack="stack1",category_gap="60%")
# .set_series_opts(label_opts=opts.LabelOpts(is_show=False))
# .set_global_opts(
# xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
# # title_opts=opts.TitleOpts(title="处理事件最多的部门"),
# datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
#
# )
# )
#
# grid = Grid()
#
# grid.add(c, grid_opts=opts.GridOpts(pos_bottom="20%"))
#
# c = grid.dump_options_with_quotes()
#
# end = datetime.datetime.now()
# print("Bar: " + str(end - start))
start = datetime.date(2016, 6, 6)
end = datetime.date(2019, 6, 6)
y_lists = []
x_list = []
b = Bar()
event_counts = DisposeUnit.objects.all().values(
'name', 'number').order_by('-number')
for achieve in achieves:
new_list = [achieve.name]
limit = 0
for dispose_unit_node in event_counts:
dispose_unit = DisposeUnit.objects.get(
name=dispose_unit_node['name'])
if limit < 30:
new_len = len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(achieve=achieve)))
new_list.append(new_len)
new_len = 0
else:
new_len += len(
Event.objects.filter(
Q(create_time__range=(start, end))
& Q(dispose_unit=dispose_unit)
& Q(achieve=achieve)))
limit += 1
new_list.append(new_len)
y_lists.append(new_list)
limit = 0
for dispose_unit_node in event_counts:
if limit >= 30:
break
x_list.append(dispose_unit_node['name'])
limit += 1
x_list.append("其他")
b.add_xaxis(x_list)
for y_list in y_lists:
b.add_yaxis(y_list[0],
y_list[1:],
stack="stack1",
category_gap="60%")
b.set_series_opts(label_opts=opts.LabelOpts(is_show=False)) \
.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
# title_opts=opts.TitleOpts(title="处理事件最多的部门"),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")], )
c = (b)
grid = Grid()
grid.add(c, grid_opts=opts.GridOpts(pos_bottom="20%"))
c = grid.dump_options_with_quotes()
return c
def draw(datelist, pricelist, title):
min_value = min(pricelist)
max_value = max(pricelist)
line = (
Line(init_opts=opts.InitOpts(
width='1800px',
height='800px',
js_host="./",
)).set_global_opts(
title_opts=opts.TitleOpts(title=title,
# subtitle='股票价格走势'
),
legend_opts=opts.LegendOpts(is_show=True,
pos_top=10,
pos_left="center",
item_width=30,
item_height=15,
textstyle_opts=opts.TextStyleOpts(
font_family='Microsoft Yahei',
font_size=14,
font_style='oblique')),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
xaxis_opts=opts.AxisOpts(
# type_="time",
name='日期',
split_number=10,
name_gap=35,
axispointer_opts=opts.AxisPointerOpts(is_show=True),
name_textstyle_opts=opts.TextStyleOpts(
font_size=16, font_family='Microsoft Yahei')),
yaxis_opts=opts.AxisOpts(
type_="value",
# name='价格',
min_=min_value,
max_=max_value,
split_number=4,
axispointer_opts=opts.AxisPointerOpts(is_show=True),
name_textstyle_opts=opts.TextStyleOpts(
font_size=16, font_family='Microsoft Yahei'),
axistick_opts=opts.AxisTickOpts(is_show=True),
splitline_opts=opts.SplitLineOpts(is_show=True),
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1))),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
# xaxis_index=[0, 1],
range_start=30,
range_end=70,
),
opts.DataZoomOpts(
is_show=True,
# xaxis_index=[0, 1],
type_="slider",
pos_top="96%",
range_start=38,
range_end=70,
),
],
).add_xaxis(xaxis_data=datelist).add_yaxis(
series_name="走势情况",
is_selected=True,
y_axis=pricelist,
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
opts.MarkPointItem(type_="average", name="平均值")
])).render(title + '.html'))
pos_right="0%"), # 调整图例的位置
title_opts=opts.TitleOpts(title="行情显示图"),
))
line = (Line().add_xaxis(dates.tolist()).add_yaxis(
"Ma20", df_stockload["Ma20"].tolist()).add_yaxis(
"Ma30", df_stockload["Ma30"].tolist()).add_yaxis(
"Ma60", df_stockload["Ma60"].tolist()).set_series_opts(
label_opts=opts.LabelOpts(is_show=False)))
bar = (
Bar().add_xaxis(dates.tolist()).add_yaxis("rvolume",
volume_rise,
stack="stack1",
category_gap="50%") # 堆积柱形图
.add_yaxis("dvolume", volume_drop, stack="stack1",
category_gap="50%") # 堆积柱形图
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
#xaxis_index=[0, 1] 同时显示两幅图
.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(xaxis_index=[0, 1],
is_show=True)], # 图表数据缩放
legend_opts=opts.LegendOpts(orient="vertical", pos_right="0%")))
overlap_1 = kline.overlap(line)
grid = (
Grid().add(bar, grid_opts=opts.GridOpts(pos_top="60%"), grid_index=0).add(
overlap_1, grid_opts=opts.GridOpts(pos_bottom="40%"),
grid_index=1).render(r"grid_vertical.html") # raw string 非转义 string
)
def vtradevolume(cftable, freq="D", rendered=True):
"""
aid function on visualization of trade summary
:param cftable: cftable (pandas.DataFrame) with at least date and cash columns
:param freq: one character string, frequency label, now supporting D for date,
W for week and M for month, namely the trade volume is shown based on the time unit
:returns: the Bar object
"""
### WARN: datazoom and time conflict, sliding till 1970..., need further look into pyeacharts
startdate = cftable.iloc[0]["date"]
if freq == "D":
# datedata = [d.to_pydatetime() for d in cftable["date"]]
datedata = pd.date_range(startdate, yesterdayobj(), freq="D")
selldata = [[row["date"].to_pydatetime(), row["cash"]]
for _, row in cftable.iterrows() if row["cash"] > 0]
buydata = [[row["date"].to_pydatetime(), row["cash"]]
for _, row in cftable.iterrows() if row["cash"] < 0]
elif freq == "W":
cfmerge = cftable.groupby(
[cftable["date"].dt.year, cftable["date"].dt.week])["cash"].sum()
# datedata = [
# dt.datetime.strptime(str(a) + "4", "(%Y, %W)%w")
# for a, _ in cfmerge.iteritems()
# ]
datedata = pd.date_range(startdate,
yesterdayobj() + pd.Timedelta(days=7),
freq="W-THU")
selldata = [[dt.datetime.strptime(str(a) + "4", "(%G, %V)%w"), b]
for a, b in cfmerge.iteritems() if b > 0]
buydata = [[dt.datetime.strptime(str(a) + "4", "(%G, %V)%w"), b]
for a, b in cfmerge.iteritems() if b < 0]
# %V pandas gives iso weeknumber which is different from python original %W or %U,
# see https://stackoverflow.com/questions/5882405/get-date-from-iso-week-number-in-python for more details
# python3.6+ required for %G and %V
# but now seems no equal distance between sell and buy data, no idea why
elif freq == "M":
cfmerge = cftable.groupby(
[cftable["date"].dt.year, cftable["date"].dt.month])["cash"].sum()
# datedata = [
# dt.datetime.strptime(str(a) + "15", "(%Y, %m)%d")
# for a, _ in cfmerge.iteritems()
# ]
datedata = pd.date_range(startdate,
yesterdayobj() + pd.Timedelta(days=31),
freq="MS")
selldata = [[dt.datetime.strptime(str(a) + "1", "(%Y, %m)%d"), b]
for a, b in cfmerge.iteritems() if b > 0]
buydata = [[dt.datetime.strptime(str(a) + "1", "(%Y, %m)%d"), b]
for a, b in cfmerge.iteritems() if b < 0]
else:
raise ParserFailure("no such freq tag supporting")
buydata = [[d, round(x, 1)] for d, x in buydata]
selldata = [[d, round(x, 1)] for d, x in selldata]
bar = Bar()
datedata = list(datedata)
bar.add_xaxis(xaxis_data=datedata)
# buydata should before selldata, since emptylist in the first line would make the output fig empty: may be bug in pyecharts
bar.add_yaxis(series_name="买入", yaxis_data=buydata)
bar.add_yaxis(series_name="卖出", yaxis_data=selldata)
bar.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
datazoom_opts=[opts.DataZoomOpts(range_start=90, range_end=100)],
)
if rendered:
return bar.render_notebook()
else:
return bar
def plot_kline(ka, bs=None, file_html="kline.html", width="1400px", height="680px"):
"""
:param ka: KlineAnalyze
:param bs: pd.DataFrame
买卖点，包含三个字段 ["操作提示", "交易时间", "交易价格"]
:param file_html: str
:param width: str
:param height: str
:return: None
"""
df = ka.to_df(use_macd=True, ma_params=(5, 20,))
x = df.dt.to_list()
title = "%s | %s 至 %s" % (ka.symbol, ka.start_dt, ka.end_dt)
kline = (
Kline()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="",
y_axis=df[['open', 'close', 'low', 'high']].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
)
.set_series_opts(
markarea_opts=opts.MarkAreaOpts(is_silent=True)
)
.set_global_opts(
title_opts=opts.TitleOpts(title=title, pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True, splitline_opts=opts.SplitLineOpts(is_show=True),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside")
),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False, type_="inside", xaxis_index=[0, 0], range_end=100
),
opts.DataZoomOpts(
is_show=True, xaxis_index=[0, 1], pos_top="96%", range_end=100
),
opts.DataZoomOpts(is_show=False, xaxis_index=[0, 2], range_end=100),
],
# 三个图的 axis 连在一块
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
)
)
kline_line = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="笔",
y_axis=df.bi.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='diamond',
symbol_size=8,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='dotted', width=2),
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="线段",
y_axis=df.xd.tolist(),
is_smooth=False,
is_connect_nones=True,
symbol='triangle',
symbol_size=12,
linestyle_opts=opts.LineStyleOpts(opacity=1, type_='solid', width=2),
label_opts=opts.LabelOpts(is_show=True, position='right'),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
)
)
# Overlap Kline + Line
overlap_kline_line = kline.overlap(kline_line)
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
c = (
Scatter()
.add_xaxis(bs['交易时间'].to_list())
.add_yaxis(
"买卖点",
bs['交易价格'].to_list(),
label_opts=opts.LabelOpts(
is_show=True,
position="left",
formatter=JsCode(
"function(params){return bsName[params.dataIndex][0];}"
)
),
))
overlap_kline_line = overlap_kline_line.overlap(c)
# draw volume
bar_1 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="Volumn",
yaxis_data=df.vol.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
axislabel_opts=opts.LabelOpts(is_show=True, position='inside')
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Bar-2 (Overlap Bar + Line)
bar_2 = (
Bar()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="MACD",
yaxis_data=df.macd.tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (params.data >= 0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, position="inside"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
line_2 = (
Line()
.add_xaxis(xaxis_data=x)
.add_yaxis(
series_name="DIF",
y_axis=df['diff'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.add_yaxis(
series_name="DEA",
y_axis=df['dea'].tolist(),
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
)
# draw MACD
overlap_bar_line = bar_2.overlap(line_2)
# 最后的 Grid
grid_chart = Grid(init_opts=opts.InitOpts(width=width, height=height, page_title=title))
grid_chart.add_js_funcs("var barData = {}".format(df[['open', 'close', 'low', 'high']].values.tolist()))
if isinstance(bs, pd.DataFrame) and len(bs) > 0:
grid_chart.add_js_funcs("var bsName = {}".format(bs[["操作提示", "交易价格"]].values.tolist()))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="71%", height="10%"),
)
grid_chart.add(
overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", pos_top="82%", height="14%"),
)
grid_chart.render(path=file_html)
# V1 版本开始支持链式调用
place = np.array(province)
bar = (
Bar().add_xaxis(list(place[:, 0])).add_yaxis("热度", list(
place[:, 1])).set_global_opts(
# 设置全局参数
title_opts=opts.TitleOpts(title="国庆旅游热度"), # 设置title
xaxis_opts=opts.AxisOpts(splitline_opts=opts.SplitLineOpts(
is_show=True)), # 设置x轴
yaxis_opts=opts.AxisOpts(splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(
opacity=1))), # 设置y轴
toolbox_opts=opts.ToolboxOpts(is_show=True), # 设置工具箱
datazoom_opts=[
opts.DataZoomOpts(range_start=10,
range_end=80,
is_zoom_lock=False)
], # 设置slider
))
bar.render(path="国庆旅游热度1.html")
# 不习惯链式调用的开发者依旧可以单独调用方法
bar = Bar()
bar.add_xaxis(list(place[:, 0]))
bar.add_yaxis("热度", list(place[:, 1]))
bar.set_global_opts(title_opts=opts.TitleOpts(title="国庆旅游热度"))
bar.set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts={"rotate": 45}),
toolbox_opts=opts.ToolboxOpts(is_show=True),
datazoom_opts=[
opts.DataZoomOpts(range_start=10,
range_end=80,
def show_xd_kline(kline_raw, show=True):
freq = '5min'
start_dt = kline_raw.iloc[0]["dt"]
end_dt = kline_raw.iloc[-1]["dt"]
x_data = kline_raw.dt.values.tolist()
oclh = kline_raw[['open', 'close', 'low', 'high']].values.tolist()
symbol = kline_raw.iloc[0]['symbol']
kline = (
Kline(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name=symbol,
y_axis=oclh,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
)
.set_global_opts(
title_opts=opts.TitleOpts(
title="缠论分析结果：%s-%s" % (symbol, freq),
subtitle="时间区间：%s 至 %s" % (start_dt, end_dt)
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)
),
),
legend_opts=opts.LegendOpts(
is_show=True, pos_top=10, pos_left="center"
),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=0,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{"value": 1, "color": "#ec0000"},
{"value": -1, "color": "#00da3c"},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{"xAxisIndex": "all"}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
)
)
chan_fx = (
Scatter(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis("分型标记", kline_raw.fx.values.tolist(),
label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
)
)
chan_bi = (
Scatter(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis("笔标记", kline_raw.bi.values.tolist(),
label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
)
)
chan_xd = (
Scatter(init_opts=opts.InitOpts(theme=ThemeType.WHITE))
.add_xaxis(xaxis_data=x_data)
.add_yaxis("线段标记", kline_raw.xd.values.tolist(),
label_opts=opts.LabelOpts(is_show=False))
.set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size", max_=150, min_=20),
)
)
bar = (
Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT))
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="Volume",
yaxis_data=kline_raw.vol.values.tolist(),
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
)
)
# Kline And Line
kline = kline.overlap(chan_fx)
kline = kline.overlap(chan_bi)
kline = kline.overlap(chan_xd)
# Grid Overlap + Bar
grid_chart = Grid(opts.InitOpts(width="1400px", height="800px", theme=ThemeType.WHITE))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(
pos_left="8%", pos_right="8%", height="60%"
),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(
pos_left="8%", pos_right="8%", pos_top="70%", height="16%"
),
)
graph_path = os.path.join(cache_path, "%s_kline_%s.html" % (symbol, "fx"))
grid_chart.render(path=graph_path)
# 调用浏览器打开可视化结果
if show:
webbrowser.open(graph_path)
data['yuanchuang'] = data['yuanchuanghefawen'].apply(get_yuanchuang)
data['fawen'] = data['yuanchuanghefawen'].apply(get_fawen)
data['toutiao_read'] = data['toutiao_read'].astype('int')
data['seeing'] = data['seeing'].astype('int')
# 次幂指数
index_data = data[['name', 'index']]
index_data_sort = index_data.sort_values(by='index', ascending=False)
bar = Bar()
bar.add_xaxis(index_data_sort['name'].values.tolist())
bar.add_yaxis("", index_data_sort['index'].values.tolist())
bar.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)),
title_opts=opts.TitleOpts(title="次幂指数"),
datazoom_opts=opts.DataZoomOpts(),
)
bar.render_notebook()
# 阅读量
read_data = data[['name', 'toutiao_read']]
read_data_sort = read_data.sort_values(by='toutiao_read', ascending=False)
bar = Bar()
bar.add_xaxis(read_data_sort['name'].values.tolist())
bar.add_yaxis("", read_data_sort['toutiao_read'].values.tolist())
bar.set_global_opts(
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-30)),
title_opts=opts.TitleOpts(title="头条平均阅读量"),
datazoom_opts=opts.DataZoomOpts(),
)
def vtradecost(self,
cftable,
unitcost=False,
start=None,
end=yesterdayobj(),
rendered=True):
"""
visualization giving the average cost line together with netvalue line as well as buy and sell points
:returns: pyecharts.line
"""
funddata = []
costdata = []
cashdata = [] #kahar add in order to observe the sum of the cash
funddata_test = []
pprice = self.price[self.price["date"] <= end]
pcftable = cftable
if start is not None:
pprice = pprice[pprice["date"] >= start]
pcftable = pcftable[pcftable["date"] >= start]
for _, row in pprice.iterrows():
date = row["date"]
funddata.append(row["netvalue"])
funddata_test.append(3.2)
if unitcost:
cost = 0
cash = 0
if (date - self.cftable.iloc[0].date).days >= 0:
cost = self.unitcost(date)
cash = self.get_datecashvalue(date)
costdata.append(cost)
cashdata.append(cash / 1000000.0)
coords = []
# pcftable = pcftable[abs(pcftable["cash"]) > threhold]
for i, r in pcftable.iterrows():
coords.append(
[r.date, pprice[pprice["date"] <= r.date].iloc[-1]["netvalue"]])
upper = pcftable.cash.abs().max()
lower = pcftable.cash.abs().min()
if upper == lower:
upper = 2 * lower + 1 # avoid zero in denominator
def marker_factory(x, y):
buy = pcftable[pcftable["date"] <= x].iloc[-1]["cash"]
if buy < 0:
color = "#ff7733"
else:
color = "#3366ff"
size = (abs(buy) - lower) / (upper - lower) * 5 + 5
return opts.MarkPointItem(
coord=[x.date(), y],
itemstyle_opts=opts.ItemStyleOpts(color=color),
# this nested itemstyle_opts within MarkPointItem is only supported for pyechart>1.7.1
symbol="circle",
symbol_size=size,
)
line = Line()
line.add_xaxis([d.date() for d in pprice.date])
print("costdata", costdata)
if unitcost:
line.add_yaxis(
series_name="持仓成本",
y_axis=costdata,
is_symbol_show=False,
)
line.add_yaxis(
series_name="持仓总额k6",
y_axis=cashdata,
is_symbol_show=False,
)
line.add_yaxis(
series_name="基金净值",
y_axis=funddata,
is_symbol_show=False,
markpoint_opts=opts.MarkPointOpts(
data=[marker_factory(*c) for c in coords], ),
)
line.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(is_show=True,
type_="slider",
range_start=50,
range_end=100),
opts.DataZoomOpts(
is_show=True,
type_="slider",
orient="vertical",
range_start=50,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
)
if rendered:
return line.render_notebook()
else:
return line
import pandas as pd
from pyecharts import options as opts
for i in range(1, 258):
df = pd.read_csv('D/' + str(i) + '.csv')
sizeNum = df.iloc[:, 0].size
#print(sizeNum)
t = 1
row = []
col = []
for j in range(0, sizeNum):
row.append(df['end_geo_id'][j])
#row.append(str(j))
col.append(df['ans'][j])
# if j%10==0 or j==sizeNum-1:
# bar = (
# Bar()
# #.set_global_opts(xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-20)))
# )
# bar.add_xaxis(row)
# bar.add_yaxis("people",col)
# # print(col)
# # print(row)
# bar.render("Ps/"+str(i)+"_Bar_From"+str(t)+"To"+str(j)+".html")
# t = j
# row = []
# col = []
bar = (Bar().add_xaxis(row).add_yaxis("people", col).set_global_opts(
title_opts=opts.TitleOpts(title="区域缩放柱状图"),
datazoom_opts=opts.DataZoomOpts(type_="slider"),
))
bar.render("Bar/" + str(i) + ".html")
def visual(self, name='model_visual', path=None, gif=False):
"""
Arguments:
name : str, train end save last image or gif file with html format name.
path : str, train end save last image or gif file with html format to path;
save_gif : bool, default False, if save_gif=True, train end save all image to gif;
Return:
a html file path.
"""
if path is not None:
assert tf.io.gfile.exists(path), "`path` not exist."
file = path + '/' + '{}.html'.format(name)
else:
file = '{}.html'.format(name)
page = Page(interval=1, layout=Page.SimplePageLayout)
plot_list = []
width_len = '750px'
height_len = '450px'
for metric_id, metric in enumerate(self.params.metrics):
if not gif:
line = Line(opts.InitOpts(width=width_len, height=height_len))
line = line.add_xaxis(list(range(1, self.params.polt_num + 1)))
line = line.add_yaxis(
'train',
Series(self.params.logs[metric]).round(4).tolist(),
is_smooth=True)
if self.params.valid_fmt.format(metric) in self.params.logs:
line = line.add_yaxis(
self.params.valid_fmt.split('_')[0],
Series(self.params.logs[self.params.valid_fmt.format(
metric)]).round(4).tolist(),
is_smooth=True)
line = line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='max_value'),
opts.MarkPointItem(type_='min', name='min_value')
]))
line = line.set_global_opts(
title_opts=opts.TitleOpts(title=metric),
xaxis_opts=opts.AxisOpts(
name=self.params.xlabel[self.params.mode],
name_location='center',
is_scale=True),
datazoom_opts=[
opts.DataZoomOpts(range_start=0, range_end=100)
],
toolbox_opts=opts.ToolboxOpts())
plot_list.append(line)
else:
timeline = Timeline(
opts.InitOpts(width=width_len,
height=height_len)).add_schema(
play_interval=100, is_auto_play=True)
for i in range(1, self.params.polt_num + 1):
line = Line(
opts.InitOpts(width=width_len, height=height_len))
line = line.add_xaxis(list(range(1, i + 1)))
line = line.add_yaxis(
'train',
Series(self.params.logs[metric])[:i].round(4).tolist(),
is_smooth=True)
if self.params.valid_fmt.format(
metric) in self.params.logs:
line = line.add_yaxis(
self.params.valid_fmt.split('_')[0],
Series(
self.params.logs[self.params.valid_fmt.format(
metric)])[:i].round(4).tolist(),
is_smooth=True)
line = line.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_='max', name='max_value'),
opts.MarkPointItem(type_='min', name='min_value')
]))
line = line.set_global_opts(
title_opts=opts.TitleOpts(title=metric),
xaxis_opts=opts.AxisOpts(
name=self.params.xlabel[self.params.mode],
name_location='center',
is_scale=True))
timeline.add(line, str(i))
plot_list.append(timeline)
page.add(*plot_list).render(file)
return file
'Q1: ' + param.data[1],
'median: ' + param.data[2],
'Q3: ' + param.data[3],
'lower: ' + param.data[4]
].join('<br/>') }"""))).set_global_opts(
title_opts=opts.TitleOpts(title="Multiple Categories",
pos_left="center"),
legend_opts=opts.LegendOpts(pos_top="3%"),
tooltip_opts=opts.TooltipOpts(trigger="item",
axis_pointer_type="shadow"),
xaxis_opts=opts.AxisOpts(
name_gap=30,
boundary_gap=True,
splitarea_opts=opts.SplitAreaOpts(
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
axislabel_opts=opts.LabelOpts(formatter="expr {value}"),
splitline_opts=opts.SplitLineOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(
type_="value",
min_=-400,
max_=600,
splitarea_opts=opts.SplitAreaOpts(is_show=False)),
datazoom_opts=[
opts.DataZoomOpts(type_="inside",
range_start=0,
range_end=20),
opts.DataZoomOpts(type_="slider",
xaxis_index=0,
is_show=True)
]).render("multiple_categories.html"))
def quanguo_wuhan_compare(city_list):
time = set()
for item in city_list:
time.add(item[0])
TIME = {}
for x in time:
TIME.update({x: (0, 0)})
for item in city_list:
if item[1] in province:
if item[1] != "湖北":
TIME.update(
{item[0]: (TIME[item[0]][0] + item[3], TIME[item[0]][1])})
else:
TIME.update(
{item[0]: (TIME[item[0]][0], TIME[item[0]][1] + item[3])})
time_others_wuhan = []
for k, v in TIME.items():
time_others_wuhan.append([k, v[0], v[1]])
time_others_wuhan.sort()
L1 = (Line().add_xaxis(
xaxis_data=[x[0] for x in time_others_wuhan]).add_yaxis(
series_name="全国其他地区",
y_axis=[x[1] for x in time_others_wuhan],
symbol_size=8,
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=1.5),
is_smooth=True,
).set_global_opts(
title_opts=opts.TitleOpts(title="湖北地区和全国其他地区确诊人数对比图",
subtitle="截止至2020年5月21日",
pos_left="center"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
axispointer_opts=opts.AxisPointerOpts(is_show=True,
link=[{
"xAxisIndex": "all"
}]),
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
is_realtime=True,
start_value=30,
end_value=70,
xaxis_index=[0, 1],
)
],
xaxis_opts=opts.AxisOpts(
type_="category",
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=True),
),
yaxis_opts=opts.AxisOpts(is_inverse=False, name="人数"),
legend_opts=opts.LegendOpts(pos_left="left"),
toolbox_opts=opts.ToolboxOpts(
is_show=True,
feature={
"dataZoom": {
"yAxisIndex": "none"
},
"restore": {},
"saveAsImage": {},
},
),
))
L2 = (Line().add_xaxis(
xaxis_data=[x[0] for x in time_others_wuhan]).add_yaxis(
series_name="湖北地区",
y_axis=[x[2] for x in time_others_wuhan],
xaxis_index=1,
yaxis_index=1,
symbol_size=8,
is_hover_animation=False,
label_opts=opts.LabelOpts(is_show=False),
linestyle_opts=opts.LineStyleOpts(width=1.5),
is_smooth=True,
).set_global_opts(
axispointer_opts=opts.AxisPointerOpts(is_show=True,
link=[{
"xAxisIndex": "all"
}]),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
xaxis_opts=opts.AxisOpts(
grid_index=1,
type_="category",
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=True),
position="top",
),
datazoom_opts=[
opts.DataZoomOpts(
is_realtime=True,
type_="inside",
start_value=30,
end_value=70,
xaxis_index=[0, 1],
)
],
yaxis_opts=opts.AxisOpts(is_inverse=True, name="确诊人数"),
legend_opts=opts.LegendOpts(pos_left="15%"),
))
(Grid(init_opts=opts.InitOpts(width="1024px", height="768px")).add(
chart=L1,
grid_opts=opts.GridOpts(pos_left=50, pos_right=50, height="35%")).add(
chart=L2,
grid_opts=opts.GridOpts(pos_left=50,
pos_right=50,
pos_top="55%",
height="35%"),
).render(path="compare.html"))
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(
color="#90979c"
)
),
axislabel_opts=opts.LabelOpts(
interval=0
),
splitarea_opts=opts.SplitAreaOpts(
is_show=False
)
),
datazoom_opts=opts.DataZoomOpts(
is_show=True,
xaxis_index=[0],
pos_bottom=30,
start_value=10,
end_value=80
)
)
.add_xaxis(xaxis_data=x_data)
.add_yaxis(
series_name="女",
y_axis=y_data,
stack="总量",
label_opts=opts.LabelOpts(
is_show=True,
color="#fff",
position="inside"
),
itemstyle_opts=opts.ItemStyleOpts(
def kline_pro(kline: List[dict],
ma: List[dict],
macd: List[dict],
fx: List[dict] = None,
bi: List[dict] = None,
xd: List[dict] = None,
bs: List[dict] = None,
title: str = "缠中说禅K线分析",
width: str = "1200px",
height: str = '680px') -> Grid:
"""绘制缠中说禅K线分析结果
:param kline: K线
:param ma: 均线
:param macd: MACD
:param fx: 分型识别结果
:param bi: 笔识别结果
:param xd: 线段识别结果
:param bs: 买卖点
:param title: 图表标题
:param width: 图表宽度
:param height: 图表高度
:return: 用Grid组合好的图表
"""
# 配置项设置
# ------------------------------------------------------------------------------------------------------------------
bg_color = "#1f212d" # 背景
up_color = "#F9293E"
down_color = "#00aa3b"
init_opts = opts.InitOpts(bg_color=bg_color,
width=width,
height=height,
animation_opts=opts.AnimationOpts(False))
title_opts = opts.TitleOpts(
title=title,
pos_top="1%",
title_textstyle_opts=opts.TextStyleOpts(color=up_color, font_size=20),
subtitle_textstyle_opts=opts.TextStyleOpts(color=down_color,
font_size=12))
label_not_show_opts = opts.LabelOpts(is_show=False)
legend_not_show_opts = opts.LegendOpts(is_show=False)
red_item_style = opts.ItemStyleOpts(color=up_color)
green_item_style = opts.ItemStyleOpts(color=down_color)
k_style_opts = opts.ItemStyleOpts(color=up_color,
color0=down_color,
border_color=up_color,
border_color0=down_color,
opacity=0.8)
legend_opts = opts.LegendOpts(is_show=True,
pos_top="1%",
pos_left="30%",
item_width=14,
item_height=8,
textstyle_opts=opts.TextStyleOpts(
font_size=12, color="#0e99e2"))
brush_opts = opts.BrushOpts(tool_box=["rect", "polygon", "keep", "clear"],
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX")
axis_pointer_opts = opts.AxisPointerOpts(is_show=True,
link=[{
"xAxisIndex": "all"
}])
dz_inside = opts.DataZoomOpts(False, "inside", xaxis_index=[0, 1, 2])
dz_slider = opts.DataZoomOpts(True,
"slider",
xaxis_index=[0, 1, 2],
pos_top="96%",
pos_bottom="0%")
yaxis_opts = opts.AxisOpts(is_scale=True,
axislabel_opts=opts.LabelOpts(
color="#c7c7c7",
font_size=8,
position="inside"))
grid0_xaxis_opts = opts.AxisOpts(
type_="category",
grid_index=0,
axislabel_opts=label_not_show_opts,
split_number=20,
min_="dataMin",
max_="dataMax",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False))
tool_tip_opts = opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
position=JsCode("""
function (pos, params, el, elRect, size) {
var obj = {top: 10};
obj[['left', 'right'][+(pos[0] < size.viewSize[0] / 2)]] = 30;
return obj;
}
"""),
textstyle_opts=opts.TextStyleOpts(color="#000"),
)
# 数据预处理
# ------------------------------------------------------------------------------------------------------------------
dts = [x['dt'] for x in kline]
k_data = [[x['open'], x['close'], x['low'], x['high']] for x in kline]
vol = []
for row in kline:
item_style = red_item_style if row['close'] > row[
'open'] else green_item_style
bar = opts.BarItem(value=row['vol'],
itemstyle_opts=item_style,
label_opts=label_not_show_opts)
vol.append(bar)
macd_bar = []
for row in macd:
item_style = red_item_style if row['macd'] > 0 else green_item_style
bar = opts.BarItem(value=round(row['macd'], 4),
itemstyle_opts=item_style,
label_opts=label_not_show_opts)
macd_bar.append(bar)
diff = [round(x['diff'], 4) for x in macd]
dea = [round(x['dea'], 4) for x in macd]
# K 线主图
# ------------------------------------------------------------------------------------------------------------------
chart_k = Kline()
chart_k.add_xaxis(xaxis_data=dts)
chart_k.add_yaxis(series_name="Kline",
y_axis=k_data,
itemstyle_opts=k_style_opts)
chart_k.set_global_opts(legend_opts=legend_opts,
datazoom_opts=[dz_inside, dz_slider],
yaxis_opts=yaxis_opts,
tooltip_opts=tool_tip_opts,
axispointer_opts=axis_pointer_opts,
brush_opts=brush_opts,
title_opts=title_opts,
xaxis_opts=grid0_xaxis_opts)
# 均线图
# ------------------------------------------------------------------------------------------------------------------
chart_ma = Line()
chart_ma.add_xaxis(xaxis_data=dts)
ma_keys = [x for x in ma[0].keys() if "ma" in x][:3]
ma_colors = ["#39afe6", "#da6ee8", "#00940b"]
for i, k in enumerate(ma_keys):
y_data = [x[k] for x in ma]
chart_ma.add_yaxis(series_name=k.upper(),
y_axis=y_data,
is_smooth=True,
is_selected=False,
symbol_size=0,
label_opts=label_not_show_opts,
linestyle_opts=opts.LineStyleOpts(
opacity=0.8, width=1.0, color=ma_colors[i]))
chart_ma.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_ma)
# 缠论结果
# ------------------------------------------------------------------------------------------------------------------
if fx:
fx_dts = [x['dt'] for x in fx]
fx_val = [x['fx'] for x in fx]
chart_fx = Scatter()
chart_fx.add_xaxis(fx_dts)
chart_fx.add_yaxis(series_name="FX",
y_axis=fx_val,
is_selected=False,
symbol="circle",
symbol_size=6,
label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(
color="rgba(152, 147, 193, 1.0)", ))
chart_fx.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_fx)
if bi:
bi_dts = [x['dt'] for x in bi]
bi_val = [x['bi'] for x in bi]
chart_bi = Scatter()
chart_bi.add_xaxis(bi_dts)
chart_bi.add_yaxis(series_name="BI",
y_axis=bi_val,
is_selected=True,
symbol="diamond",
symbol_size=10,
label_opts=label_not_show_opts,
itemstyle_opts=opts.ItemStyleOpts(
color="rgba(184, 117, 225, 1.0)", ))
chart_bi.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_bi)
if xd:
xd_dts = [x['dt'] for x in xd]
xd_val = [x['xd'] for x in xd]
chart_xd = Scatter()
chart_xd.add_xaxis(xd_dts)
chart_xd.add_yaxis(series_name="XD",
y_axis=xd_val,
is_selected=True,
symbol="triangle",
symbol_size=10,
itemstyle_opts=opts.ItemStyleOpts(
color="rgba(37, 141, 54, 1.0)", ))
chart_xd.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_xd)
if bs:
b_dts = [x['dt'] for x in bs if x['mark'] == 'buy']
if len(b_dts) > 0:
b_val = [x['buy'] for x in bs if x['mark'] == 'buy']
chart_b = Scatter()
chart_b.add_xaxis(b_dts)
chart_b.add_yaxis(series_name="BUY",
y_axis=b_val,
is_selected=False,
symbol="arrow",
symbol_size=8,
itemstyle_opts=opts.ItemStyleOpts(
color="#f31e1e", ))
chart_b.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_b)
s_dts = [x['dt'] for x in bs if x['mark'] == 'sell']
if len(s_dts) > 0:
s_val = [x['sell'] for x in bs if x['mark'] == 'sell']
chart_s = Scatter()
chart_s.add_xaxis(s_dts)
chart_s.add_yaxis(series_name="SELL",
y_axis=s_val,
is_selected=False,
symbol="pin",
symbol_size=12,
itemstyle_opts=opts.ItemStyleOpts(
color="#45b97d", ))
chart_s.set_global_opts(xaxis_opts=grid0_xaxis_opts,
legend_opts=legend_not_show_opts)
chart_k = chart_k.overlap(chart_s)
# 成交量图
# ------------------------------------------------------------------------------------------------------------------
chart_vol = Bar()
chart_vol.add_xaxis(dts)
chart_vol.add_yaxis(series_name="Volume", y_axis=vol, bar_width='60%')
chart_vol.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=1,
axislabel_opts=opts.LabelOpts(is_show=True,
font_size=8,
color="#9b9da9"),
),
yaxis_opts=yaxis_opts,
legend_opts=legend_not_show_opts,
)
# MACD图
# ------------------------------------------------------------------------------------------------------------------
chart_macd = Bar()
chart_macd.add_xaxis(dts)
chart_macd.add_yaxis(series_name="MACD", y_axis=macd_bar, bar_width='60%')
chart_macd.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
grid_index=2,
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True, color="#c7c7c7"),
),
legend_opts=opts.LegendOpts(is_show=False),
)
line = Line()
line.add_xaxis(dts)
line.add_yaxis(series_name="DIFF",
y_axis=diff,
label_opts=label_not_show_opts,
is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8,
width=1.0,
color="#da6ee8"))
line.add_yaxis(series_name="DEA",
y_axis=dea,
label_opts=label_not_show_opts,
is_symbol_show=False,
linestyle_opts=opts.LineStyleOpts(opacity=0.8,
width=1.0,
color="#39afe6"))
chart_macd = chart_macd.overlap(line)
grid0_opts = opts.GridOpts(pos_left="0%",
pos_right="1%",
pos_top="12%",
height="58%")
grid1_opts = opts.GridOpts(pos_left="0%",
pos_right="1%",
pos_top="74%",
height="8%")
grid2_opts = opts.GridOpts(pos_left="0%",
pos_right="1%",
pos_top="86%",
height="10%")
grid_chart = Grid(init_opts)
grid_chart.add(chart_k, grid_opts=grid0_opts)
grid_chart.add(chart_vol, grid_opts=grid1_opts)
grid_chart.add(chart_macd, grid_opts=grid2_opts)
return grid_chart
def plot_kline(df, rendered=True, col=""):
"""
针对 dataframe 直接画出标准看盘软件的上k线图下成交量图的形式
:param df:
:param rendered:
:param col:
:return:
"""
kline = (
Kline()
.add_xaxis(xaxis_data=list(df["date"]))
.add_yaxis(
series_name="",
itemstyle_opts=opts.ItemStyleOpts(
color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143",
),
y_axis=list(zip(df["open"], df["close"], df["high"], df["low"])),
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
],
symbol="pin",
symbol_size=[56, 40],
# label_opts=opts.LabelOpts(color="#CCFFFF",position=["top", "bottom"])
),
)
.set_global_opts(
datazoom_opts=[
opts.DataZoomOpts(
is_show=True,
type_="slider",
range_start=50,
range_end=100,
xaxis_index=[0, 1],
),
opts.DataZoomOpts(
is_show=False,
type_="inside",
range_start=50,
range_end=100,
xaxis_index=1,
),
],
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
)
)
if col:
line = (
Line()
.add_xaxis(xaxis_data=list(df["date"]))
.add_yaxis(
series_name="",
y_axis=list(df[col]),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
)
kline = kline.overlap(line)
if "volume" in df.columns:
vl = list(df["volume"])
elif "amount" in df.columns:
vl = list(df["amount"])
else:
vl = [0 for _ in range(len(df))]
bar = (
Bar()
.add_js_funcs("var barData = {}".format(list(df["close"] - df["open"])))
.add_xaxis(xaxis_data=list(df["date"]))
.add_yaxis(
series_name="",
yaxis_data=vl,
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color=JsCode(
"""
function(params) {
var colorList;
if (barData[params.dataIndex]>0) {
colorList = '#ef232a';
} else {
colorList = '#14b143';
}
return colorList;
}
"""
)
),
)
.set_global_opts(
tooltip_opts=opts.TooltipOpts(
is_show=True,
trigger="axis",
trigger_on="mousemove",
axis_pointer_type="cross",
),
)
)
grid_chart = Grid()
grid_chart.add_js_funcs("var barData = {}".format(list(df["close"] - df["open"])))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(
pos_left="10%", pos_right="1%", pos_top="2%", height="65%"
),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(
pos_left="10%", pos_right="1%", pos_top="71%", height="22%"
),
)
if rendered:
return grid_chart.render_notebook()
else:
return grid_chart
def kline_profession_example() -> Grid:
data = [
[2320.26, 2320.26, 2287.3, 2362.94],
[2300, 2291.3, 2288.26, 2308.38],
[2295.35, 2346.5, 2295.35, 2345.92],
[2347.22, 2358.98, 2337.35, 2363.8],
[2360.75, 2382.48, 2347.89, 2383.76],
[2383.43, 2385.42, 2371.23, 2391.82],
[2377.41, 2419.02, 2369.57, 2421.15],
[2425.92, 2428.15, 2417.58, 2440.38],
[2411, 2433.13, 2403.3, 2437.42],
[2432.68, 2334.48, 2427.7, 2441.73],
[2430.69, 2418.53, 2394.22, 2433.89],
[2416.62, 2432.4, 2414.4, 2443.03],
[2441.91, 2421.56, 2418.43, 2444.8],
[2420.26, 2382.91, 2373.53, 2427.07],
[2383.49, 2397.18, 2370.61, 2397.94],
[2378.82, 2325.95, 2309.17, 2378.82],
[2322.94, 2314.16, 2308.76, 2330.88],
[2320.62, 2325.82, 2315.01, 2338.78],
[2313.74, 2293.34, 2289.89, 2340.71],
[2297.77, 2313.22, 2292.03, 2324.63],
[2322.32, 2365.59, 2308.92, 2366.16],
[2364.54, 2359.51, 2330.86, 2369.65],
[2332.08, 2273.4, 2259.25, 2333.54],
[2274.81, 2326.31, 2270.1, 2328.14],
[2333.61, 2347.18, 2321.6, 2351.44],
[2340.44, 2324.29, 2304.27, 2352.02],
[2326.42, 2318.61, 2314.59, 2333.67],
[2314.68, 2310.59, 2296.58, 2320.96],
[2309.16, 2286.6, 2264.83, 2333.29],
[2282.17, 2263.97, 2253.25, 2286.33],
[2255.77, 2270.28, 2253.31, 2276.22],
]
def calculate_ma(day_count: int, d):
result: List[Union[float, str]] = []
for i in range(len(d)):
if i < day_count:
result.append("-")
continue
sum_total = 0.0
for j in range(day_count):
sum_total += float(d[i - j][1])
result.append(abs(float("%.3f" % (sum_total / day_count))))
return result
x_data = ["2017-7-{}".format(i + 1) for i in range(31)]
kline = (Kline().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="Dow-Jones index",
y_axis=data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
).set_global_opts(
title_opts=opts.TitleOpts(
title="复杂版 Kline 示例 (Kline + Line + Bar)",
subtitle="MA 就以(2, 4, 6, 8为例)",
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
legend_opts=opts.LegendOpts(is_show=False,
pos_bottom=10,
pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=0,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=0,
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross",
background_color="rgba(245, 245, 245, 0.8)",
border_width=1,
border_color="#ccc",
textstyle_opts=opts.TextStyleOpts(color="#000"),
),
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[
{
"value": 1,
"color": "#ec0000"
},
{
"value": -1,
"color": "#00da3c"
},
],
),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
line = (Line().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="MA2",
y_axis=calculate_ma(day_count=2, d=data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA4",
y_axis=calculate_ma(day_count=4, d=data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA6",
y_axis=calculate_ma(day_count=6, d=data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA8",
y_axis=calculate_ma(day_count=8, d=data),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
bar = (Bar().add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="Volume",
yaxis_data=[[i, data[i][3], 1 if data[i][0] > data[i][1] else -1]
for i in range(len(data))],
xaxis_index=1,
yaxis_index=1,
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=1,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
grid_index=1,
is_scale=True,
split_number=2,
axislabel_opts=opts.LabelOpts(is_show=False),
axisline_opts=opts.AxisLineOpts(is_show=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
# Kline And Line
overlap_kline_line = kline.overlap(line)
# Grid Overlap + Bar
grid_chart = Grid()
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", height="50%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="10%",
pos_right="8%",
pos_top="70%",
height="16%"),
)
return grid_chart
def heat_map(data: List[dict],
x_label: List[str] = None,
y_label: List[str] = None,
title: str = "热力图",
width: str = "900px",
height: str = "680px") -> HeatMap:
"""绘制热力图
:param data: 用于绘制热力图的数据，示例如下
[{'x': '0hour', 'y': '0day', 'heat': 11},
{'x': '0hour', 'y': '1day', 'heat': 40},
{'x': '0hour', 'y': '2day', 'heat': 38},
{'x': '0hour', 'y': '3day', 'heat': 36},
{'x': '0hour', 'y': '4day', 'heat': 11}]
:param x_label: x轴标签
:param y_label: y轴标签
:param title: 图表标题
:param width: 图表宽度
:param height: 图表高度
:return: 图表
"""
value = [[s['x'], s['y'], s['heat']] for s in data]
heat = [s['heat'] for s in data]
if not x_label:
x_label = sorted(list(set([s['x'] for s in data])))
if not y_label:
y_label = sorted(list(set([s['y'] for s in data])))
vis_map_opts = opts.VisualMapOpts(pos_left="90%",
pos_top="20%",
min_=min(heat),
max_=max(heat))
title_opts = opts.TitleOpts(title=title)
init_opts = opts.InitOpts(page_title=title, width=width, height=height)
dz_inside = opts.DataZoomOpts(False,
"inside",
xaxis_index=[0],
range_start=80,
range_end=100)
dz_slider = opts.DataZoomOpts(True,
"slider",
xaxis_index=[0],
pos_top="96%",
pos_bottom="0%",
range_start=80,
range_end=100)
legend_opts = opts.LegendOpts(is_show=False)
hm = HeatMap(init_opts=init_opts)
hm.add_xaxis(x_label)
hm.add_yaxis("heat",
y_label,
value,
label_opts=opts.LabelOpts(is_show=True, position="inside"))
hm.set_global_opts(title_opts=title_opts,
visualmap_opts=vis_map_opts,
legend_opts=legend_opts,
xaxis_opts=opts.AxisOpts(grid_index=0),
datazoom_opts=[dz_inside, dz_slider])
return hm
def draw_chart():
kline = (Kline().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="",
y_axis=data["datas"],
itemstyle_opts=opts.ItemStyleOpts(color="#ef232a",
color0="#14b143",
border_color="#ef232a",
border_color0="#14b143"),
markpoint_opts=opts.MarkPointOpts(data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]),
markline_opts=opts.MarkLineOpts(
label_opts=opts.LabelOpts(position="middle",
color="blue",
font_size=15),
data=split_data_part(),
symbol=["circle", "none"],
),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True, data=split_data_part())).set_global_opts(
title_opts=opts.TitleOpts(title="K线周期图表", pos_left="0"),
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
boundary_gap=False,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
split_number=20,
min_="dataMin",
max_="dataMax",
),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitline_opts=opts.SplitLineOpts(is_show=True)),
tooltip_opts=opts.TooltipOpts(trigger="axis",
axis_pointer_type="line"),
datazoom_opts=[
opts.DataZoomOpts(is_show=True,
type_="inside",
xaxis_index=[0, 0],
range_end=100),
opts.DataZoomOpts(is_show=True,
xaxis_index=[0, 1],
pos_top="97%",
range_end=100),
opts.DataZoomOpts(is_show=False,
xaxis_index=[0, 2],
range_end=100),
],
))
kline_line = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5),
is_smooth=True,
linestyle_opts=opts.LineStyleOpts(opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
split_number=3,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
))
overlap_kline_line = kline.overlap(kline_line)
bar_1 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="Volumn",
y_axis=data["vols"],
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params){
var colorList;
if(barData[params.dataIndex][1] > barData[params.dataIndex][0]){
colorList = '#ef232a';
}else{
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
),
legend_opts=opts.LegendOpts(is_show=False),
))
bar_2 = (Bar().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="MACD",
y_axis=data["macds"],
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(color=JsCode("""
function(params){
var colorList;
if(params.data >= 0){
colorList = '#ef232a';
}else{
colorList = '#14b143';
}
return colorList;
}
""")),
).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
axislabel_opts=opts.LabelOpts(is_show=False),
),
yaxis_opts=opts.AxisOpts(
split_number=4,
axisline_opts=opts.AxisLineOpts(is_on_zero=False),
axistick_opts=opts.AxisTickOpts(is_show=False),
splitline_opts=opts.SplitLineOpts(is_show=False),
axislabel_opts=opts.LabelOpts(is_show=True),
),
legend_opts=opts.LegendOpts(is_show=False),
))
line_2 = (Line().add_xaxis(xaxis_data=data["times"]).add_yaxis(
series_name="DIF",
y_axis=data["difs"],
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(legend_opts=opts.LegendOpts(is_show=False)))
overlap_bar_line = bar_2.overlap(line_2)
grid_chart = Grid(init_opts=opts.InitOpts(
width="1400px", height="800px", theme=ThemeType.ESSOS))
grid_chart.add_js_funcs("var barData={}".format(data["datas"]))
grid_chart.add(
overlap_kline_line,
grid_opts=opts.GridOpts(pos_left="3%", pos_right="1%", height="60%"),
)
grid_chart.add(
bar_1,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="71%",
height="10%"),
)
grid_chart.add(overlap_bar_line,
grid_opts=opts.GridOpts(pos_left="3%",
pos_right="1%",
pos_top="82%",
height="14%"))
grid_chart.render("professional_kline_chart.html")
from pywebio.output import put_html
from pyecharts import options as opts
from pyecharts.charts import Bar
from pyecharts.faker import Faker
c = (
Bar()
.add_xaxis(Faker.days_attrs)
.add_yaxis("商家A", Faker.days_values, color=Faker.rand_color())
.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-DataZoom（slider-垂直）"),
datazoom_opts=opts.DataZoomOpts(orient="vertical"),
)
)
c.width = "100%"
put_html(c.render_notebook())
def main():
global dps
new_input = {}
st.sidebar.markdown('## Set A DRFI Strategy:')
for k in show_name:
try:
dvalue = dps[user_input][0][k] * 100 if k in input_pct else dps[
user_input][0][k]
nv = st.sidebar.text_input(show_name[k], dvalue, key=k)
nv = float(nv) / 100.0 if k in input_pct else float(nv)
new_input[k] = nv
except:
pass
bg_params = st.sidebar.empty()
bg_expander = bg_params.beta_expander('Other model settings')
new_bg_input = {}
for k in dps[bg_input][0]:
new_bg_input[k] = float(
bg_expander.text_input(k, value=dps[bg_input][0][k], key=k))
st.sidebar.text('')
st.sidebar.text('')
if st.sidebar.button('Save this strategy'):
dps[bg_input].append(new_bg_input)
dps[user_input].append(new_input)
if st.sidebar.button('reset the strategies'):
reset()
pass
pses = [] # 计算过程的多组参数
cb_AMTs = []
for i in range(len(dps[user_input])):
dps_ = copy.deepcopy(dps)
uinp = copy.deepcopy(dps_[user_input][i])
bginp = copy.deepcopy(dps_[bg_input][i])
dps_.update(uinp)
dps_.update(bginp)
del dps_[user_input]
bginp = copy.deepcopy(dps_[bg_input][i])
dps_.update(bginp)
del dps_[bg_input]
dps_[cb_AMT] = dps_[cb_limit] - dps_[cb_att]
cb_AMTs.append(dps_[cb_AMT])
dps_[tier1_RL] = dps_[tier1_AMT] * dps_[tier1_BenRate]
dps_[tier1_Int] = dps_[tier1_AMT] * dps_[tier1_IntRate]
dps_[tier2_RL] = dps_[tier1_RL] * dps_[tier2_BenRate]
dps_[tier2_Int] = dps_[tier2_RL] * dps_[tier2_IntRate]
dps_[dataSR] = min(dps_[data_Invest] * dps_[dataSR_ImpRatio],
dps_[dataSR_Cap])
dps_[dataCBLoad] = dps_[dataCBLoad_Base] * max(
0, 1 - dps_[data_Invest] * dps_[dataCBLoad_ImpRatio])
dps_[ipSR] = min(dps_[ip_Invest] * dps_[ipSR_ImpRate], dps_[ipSR_Cap])
dps_[ipCBLoad] = dps_[ipCBLoad_Base] * max(
0, 1 - dps_[ip_Invest] * dps_[ipCBLoad_ImpRatio])
dps_[ipRLLoad] = dps_[ipRLLoad_Base] * max(
0, 1 - min(dps_[ip_Invest] * dps_[ipRLLoad_ImpRatio],
dps_[ipRLLoad_ImpRatio_Cap]))
dps_[inSize_Base] = dps_[GDP] * dps_[ip_Base] * dps_[cap_Converter]
# dps_[inSize] = dps_[inSize_Base] * (1 + min(dps_[ip_Invest] * dps_[inSize_ImpRatio], dps_[inSize_ImpRatio_Cap]))
dps_[inSize] = dps_[GDP] * dps_[ip_Revised] * dps_[cap_Converter]
dps_[unit] = round(3 * dps_[L200Y] / dps_[no_cells], 0)
dps_[SharpeRatio] = dps_[SharpeRatioBaseline] * (1 - dps_[dataSR]) * (
1 - dps_[ipSR])
pses.append(dps_)
pass
AnInt = np.array(range(0, int(dps[no_cells]) + 1, 1))
Unit = round(3 * dps[L200Y] / dps[no_cells], 0)
LossAmountMillion: np.ndarray = AnInt * Unit
pencentile = [0.75, 0.9, 0.95, 0.975, 0.99, 0.995]
pctile_year = [4, 10, 20, 40, 100, 200]
# x axis
EP_X = np.zeros(LossAmountMillion.size)
S2 = 1 / 2 * (0.01 + 0.05) * (dps[L100Y] - dps[L20Y])
S3 = 1 / 2 * (0.01 + 0.005) * (dps[L200Y] - dps[L100Y])
S3 = 1 / 2 * (0.01 + 0.005) * (dps[L200Y] - dps[L100Y])
S4 = dps[PctAELTail] * dps[AEL]
S1 = dps[AEL] - S2 - S3 - S4
f1 = S1 - dps[L20Y] * 0.05
f2 = f1 / (dps[L0Y] - 0.05)
b = 1 / f2
r = S4 / 0.005
t1 = LossAmountMillion < dps[L20Y]
t2 = LossAmountMillion >= dps[L20Y]
t3 = LossAmountMillion < dps[L100Y]
t4 = LossAmountMillion >= dps[L100Y]
t5 = LossAmountMillion < dps[L200Y]
t6 = LossAmountMillion >= dps[L200Y]
EP_X[t1] = func1(LossAmountMillion[t1], b, dps[L0Y])
EP_X[t2 & t3] = func2(LossAmountMillion[t2 & t3], dps[L20Y], dps[L100Y])
EP_X[t4 & t5] = func3(LossAmountMillion[t4 & t5], dps[L100Y], dps[L200Y])
EP_X[t6] = func4(LossAmountMillion[t6], r, dps[L200Y])
CDF = 1 - EP_X
st.markdown('*****')
st.markdown('### I. Exceedance Probablility Curve')
linedata = [e for e in zip(LossAmountMillion, EP_X)]
xdata = [e[0] for e in linedata]
ydata = [e[1] for e in linedata]
c2 = (Line(init_opts=opts.InitOpts()).add_xaxis(xdata).add_yaxis(
'Exceedance Probablility',
ydata,
is_smooth=True,
is_symbol_show=False,
symbol_size=0).set_global_opts(
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger='axis',
axis_pointer_type='line'),
xaxis_opts=opts.AxisOpts(type_='value', name='Loss Amount'),
yaxis_opts=opts.AxisOpts(type_='value',
name='Exceedance Probablility',
is_scale=True),
datazoom_opts=opts.DataZoomOpts(is_show=True,
type_='slider',
range_start=0,
range_end=100),
title_opts=opts.TitleOpts('')))
st_pyecharts(c2, height='400%', width='100%')
st.markdown('*****')
st.markdown('### II. Loss by return year period ($M)')
LossByReturnYearPeriod_empty = st.empty()
st.markdown('*****')
st.markdown('### III. Cost Comparison')
CostComparison_empty = st.empty()
st.markdown('****')
st.markdown('### IV. Disaster Risk Layering')
RetainedLossWithoutPolicy = LossAmountMillion / dps[natBdt]
series_num = 1
c = (Bar(init_opts=opts.InitOpts()).add_xaxis([
str(y) + '-Year' for y in pctile_year
]).add_yaxis(
'Without DRFI Strategy', [
round(
RetainedLossWithoutPolicy[np.argmin(np.abs(CDF - pctile))] *
100, 2) for pctile in pencentile
],
label_opts=opts.LabelOpts(
is_show=True, formatter='{c}%')).set_global_opts(
xaxis_opts=opts.AxisOpts(type_='category',
name='\n\nReturn\nYear\nPeriod'),
yaxis_opts=opts.AxisOpts(
type_='value',
name=' % of\nNational Budget',
is_scale=True,
axislabel_opts=opts.LabelOpts(
formatter=JsCode("function (x){return x + '%'}"))),
legend_opts=opts.LegendOpts(),
datazoom_opts=opts.DataZoomOpts(xaxis_index=[0, 1],
type_='inside',
range_start=0,
range_end=100,
pos_bottom='0%'),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger='axis',
axis_pointer_type='shadow',
is_show_content=True)))
c1 = (Bar(init_opts=opts.InitOpts()).add_xaxis(
[str(y) + '-Year' for y in pctile_year]).add_yaxis(
'', [0] * len(pctile_year),
label_opts=opts.LabelOpts(is_show=False)).set_global_opts(
xaxis_opts=opts.AxisOpts(
type_='category',
name='',
axislabel_opts=opts.LabelOpts(is_show=False)),
yaxis_opts=opts.AxisOpts(
type_='value',
name='Financing\nUtilization',
is_scale=True,
is_inverse=True,
axislabel_opts=opts.LabelOpts(
formatter=JsCode("function (x){return x + ' m'}"))),
legend_opts=opts.LegendOpts(pos_top='3%'),
tooltip_opts=opts.TooltipOpts(is_show=True,
is_show_content=True),
))
c1_label = opts.LabelOpts(
is_show=True,
position='inside',
color='white',
formatter=JsCode(
"function(x) {d=x.data; if(d!==0.0){return d.toFixed() + ' m'}else{return ''};}"
))
for i in range(len(pses)):
ps = pses[i]
if i == len(pses) - 1:
funnel_data = [
[
f'\n\n\nLayer 1:\n\nContingent budget or reserve\n\n0~{int(ps[tier1_UL])} m\n\n\nHigh{" " * 150}Low\nFrequency{" " * 140}Severity',
30
],
[
f'Layer 2:\n\nContingent credit\n\n{int(ps[tier1_UL])}~{int(ps[tier2_UL])} m',
20
],
[
f'Low Frequency{" " * 20}High Severity\n\n\n\nLayer 3:\n\nCapacity\nbuilding\nvehicles\n\n{int(ps[tier2_UL])}~{int(ps[tier3_UL])} m\n',
10
]
]
layerc = (Funnel(init_opts=opts.InitOpts(theme='dark')).add(
'layers',
funnel_data,
sort_='ascending',
label_opts=opts.LabelOpts(
position='inside', color='black',
font_weight=['bold'])).set_global_opts(
legend_opts=opts.LegendOpts(is_show=False),
tooltip_opts=opts.TooltipOpts(is_show=False)))
st_pyecharts(layerc, width='61.8%', height='600%')
Limit = ps[inSize]
RevisedLossRetainedRatio = ps[ipRLLoad]
PctLossRetained = RevisedLossRetainedRatio
LossTransfer = 1 - PctLossRetained
InsurancePayout = (LossAmountMillion - ps[attachment]).clip(
0, Limit) * LossTransfer
CatbondSize = ps[cb_limit] - ps[cb_att]
RetainedLossAfterInsurance = LossAmountMillion - InsurancePayout
tier1_utilization = np.minimum(RetainedLossAfterInsurance,
ps[tier1_AMT])
Layer1FinancingBenefit = tier1_utilization * ps[tier1_BenRate]
RetainedLossAfterLayer1Financing = np.maximum(
0, RetainedLossAfterInsurance - tier1_utilization -
Layer1FinancingBenefit)
tier2_utilization = np.minimum(RetainedLossAfterLayer1Financing,
ps[tier2_AMT])
Layer2FinancingBenefit = tier2_utilization * ps[tier2_BenRate]
RetainedLossAfterLayer2Financing = np.maximum(
0, RetainedLossAfterLayer1Financing - tier2_utilization -
Layer2FinancingBenefit)
CatbondRecovery = (RetainedLossAfterLayer2Financing - ps[cb_att]).clip(
0, CatbondSize)
rl_pct_NB = (RetainedLossAfterLayer2Financing -
CatbondRecovery) / ps[natBdt]
layer1_insurance_payout = np.minimum(LossAmountMillion, ps[tier1_UL])
layer2_insurance_payout = np.maximum(
np.minimum(LossAmountMillion, ps[tier2_UL]) - ps[tier1_UL], 0)
layer3_insurance_payout = np.maximum(
np.minimum(LossAmountMillion, ps[tier3_UL]) - ps[tier2_UL], 0)
WT_CDF = np.zeros(CDF.size)
WT_CDF[CDF != 0] = norm.cdf(norm.ppf(CDF[CDF != 0]) - ps[SharpeRatio])
TransformedProbability = np.diff(WT_CDF)
TransformedProbability = np.insert(TransformedProbability, 0,
WT_CDF[0])
InsurancePremium = (TransformedProbability * InsurancePayout).sum()
InsurancePremiumAsPctNationalBudget = InsurancePremium / ps[natBdt]
tier1_wtCost = (TransformedProbability * layer1_insurance_payout).sum()
tier1_wtCost /= float(ps[tier1_UL])
tier2_wtCost = (TransformedProbability * layer2_insurance_payout).sum()
tier2_wtCost /= (ps[tier2_UL] - ps[tier1_UL])
tier3_wtCost = (TransformedProbability * layer3_insurance_payout).sum()
tier3_wtCost /= (ps[tier3_UL] - ps[tier2_UL])
cc_df1 = pd.DataFrame([
str(round(ps[tier1_IntRate] * 100, 0)) + '%',
str(round(ps[tier2_IntRate] * 100, 0)) + '%'
],
index=['Debt', 'Credit Line'],
columns=[''])
cc_df2 = pd.DataFrame([
str(round(tier1_wtCost * 100, 1)) + '%',
str(round(tier2_wtCost * 100, 1)) + '%',
str(round(tier3_wtCost * 100, 1)) + '%'
],
index=[f'Layer {i}' for i in range(1, 4)],
columns=[''])
sb, ertcc = CostComparison_empty.beta_columns(2)
sb.markdown(
'               **Sovereign Borrowing**'
)
ertcc.markdown(
'          **Estimated Risk Transfer Capacity Cost**'
)
sb.table(cc_df1.T)
ertcc.table(cc_df2.T)
PDF = np.diff(CDF)
PDF = np.insert(PDF, 0, CDF[0])
ExpectedLoss = (CatbondRecovery * PDF).sum()
CatbondAnnualCost = ExpectedLoss * ps[hisMul] * (1 + ps[ipCBLoad]) * (
1 + ps[dataCBLoad])
CatbondCoupon = CatbondAnnualCost
CatbondCostAsPctNationalBudget = CatbondCoupon / ps[natBdt]
InsurancePenetrationCostAsPctNationalBudget = ps[ip_Invest]
DataInfrastractureCostAsPctNationalBudget = ps[data_Invest]
Layer1BorrowingCost = ps[tier1_Int]
Layer1CostAsPctNationalBudget = Layer1BorrowingCost / ps[natBdt]
Layer2BorrowingCost = ps[tier2_Int]
Layer2CostAsPctNationalBudget = Layer2BorrowingCost / ps[natBdt]
TotalDRFIStrategyCostAsPctNatlBudget = InsurancePremiumAsPctNationalBudget + CatbondCostAsPctNationalBudget \
+ DataInfrastractureCostAsPctNationalBudget + InsurancePenetrationCostAsPctNationalBudget \
+ Layer1CostAsPctNationalBudget + Layer2CostAsPctNationalBudget
VaR_pct_NationalBudget = rl_pct_NB + TotalDRFIStrategyCostAsPctNatlBudget
serie_name = f'DRFI Strategy {n2c[series_num]}'
serie_data = []
LossByReturnYearPeriod = []
l1FUs, l2Fus, cbrs = [], [], []
for pctile in pencentile:
position = np.argmin(np.abs(CDF - pctile))
var = VaR_pct_NationalBudget[position]
var = round(var * 100, 2)
serie_data.append(var)
loss = LossAmountMillion[position]
LossByReturnYearPeriod.append(loss)
l1FU = tier1_utilization[position]
l1FU = round(l1FU, 0)
l1FUs.append(l1FU)
l2FU = tier2_utilization[position]
l2FU = round(l2FU, 0)
l2Fus.append(l2FU)
cbr = CatbondRecovery[position]
cbr = round(cbr, 0)
cbrs.append(cbr)
c.add_yaxis(serie_name,
serie_data,
label_opts=opts.LabelOpts(is_show=True, formatter='{c}%'))
c1.add_yaxis('Layer 1 Financing Utilization', l1FUs, stack=serie_name, label_opts=c1_label) \
.add_yaxis('Layer 2 Financing Utilization', l2Fus, stack=serie_name, label_opts=c1_label) \
.add_yaxis('Capacity Building Vehicles Recovery', cbrs, stack=serie_name, label_opts=c1_label)
LossByReturnYearPeriod_df = pd.DataFrame(
{
'Loss ($m)': LossByReturnYearPeriod
},
index=[str(y) + '-year' for y in pctile_year]).T
LossByReturnYearPeriod_empty.table(LossByReturnYearPeriod_df)
series_num += 1
grid = (Grid().add(c,
grid_opts=opts.GridOpts(pos_top='15%',
pos_left='8%',
height='43%',
width='79%'),
grid_index=0).add(c1,
grid_opts=opts.GridOpts(
pos_bottom='8%',
pos_left='8%',
height='30%',
width='79%'),
grid_index=1))
df = pd.DataFrame(dps[user_input])
df[cb_AMT] = cb_AMTs
params_df = df.copy(deep=True)
rn = copy.deepcopy(show_name)
for pname in change_to_amt:
params_df[pname] = params_df[pname].map(
lambda x: str(round(x * dps[natBdt], 1))[:3])
for pname in pct_params:
params_df[pname] = params_df[pname].map(
lambda x: str(round(x * 100, 2)) + '%')
rn.update(bg_show_name)
params_df.rename(columns=rn, inplace=True)
params_df = params_df.loc[:, [show_name[k] for k in show_name]]
name_change = {}
for pname in change_to_amt:
if pname in show_name:
name_change[show_name[pname]] = show_name[pname].replace(
' as % National Budget', '')
params_df.rename(columns=name_change, inplace=True)
params_df = pd.DataFrame(
params_df.values.T,
index=params_df.columns,
columns=[f'DRFI Strategy {n2c[i + 1]}' for i in params_df.index])
st.markdown('*****')
st.markdown('### V. The DRFI Strategies')
st.dataframe(params_df)
st.markdown('*****')
st.markdown(
'### VI. Loss Impact as % of National Budget under Various Scenarios')
st_pyecharts(grid, width='100%', height='618%', renderer='canvas')
