def bar_with_brush() -> Bar:
c = (Bar().add_xaxis(Faker.choose()).add_yaxis(
"商家A",
Faker.values()).add_yaxis("商家B", Faker.values()).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-Brush示例", subtitle="我是副标题"),
brush_opts=opts.BrushOpts(),
))
return c
def bar_datazoom_slider(xaxis,yaxis_name,yaxis,title_name) -> Bar:
if isinstance(yaxis_name,str):
c=(
Bar()
.add_xaxis(xaxis)
.add_yaxis(yaxis_name,yaxis)
.set_global_opts(
title_opts=opts.TitleOpts(title=title_name),
datazoom_opts=[opts.DataZoomOpts()],
brush_opts=opts.BrushOpts(),
toolbox_opts=opts.ToolboxOpts(),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(name="我是 Y 轴"),
xaxis_opts=opts.AxisOpts(name="我是 X 轴"),
)
)
elif isinstance(yaxis,list):
c=Bar()
c.add_xaxis(xaxis)
for i in range(len(yaxis_name)):
c.add_yaxis(yaxis_name[i],yaxis[i], gap="0%",category_gap="10%",is_selected=True)
c.set_global_opts(
title_opts=opts.TitleOpts(title=title_name),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
#datazoom_opts=opts.DataZoomOpts(orient="vertical"),
brush_opts=opts.BrushOpts(),
toolbox_opts=opts.ToolboxOpts(),
legend_opts=opts.LegendOpts(is_show=False),
yaxis_opts=opts.AxisOpts(name="我是 Y 轴"),
xaxis_opts=opts.AxisOpts(name="我是 X 轴"),
)
c.set_series_opts(
label_opts=opts.LabelOpts(is_show=True),
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"),
]
)
)
return c
def test_bar_with_brush(fake_writer):
c = (Bar().add_xaxis(["A", "B", "C"]).add_yaxis(
"series0", [1, 2, 4]).add_yaxis("series1", [2, 3, 6]).set_global_opts(
title_opts=opts.TitleOpts(title="Bar-Brush示例", subtitle="我是副标题"),
brush_opts=opts.BrushOpts(),
))
c.render()
_, content = fake_writer.call_args[0]
assert_in("brush", content)
def draw_line(xlist, ylist):
c = (Bar(init_opts=opts.InitOpts(
width='1800px',
height='900px',
js_host="./",
)).add_xaxis(xlist).add_yaxis("视频弹幕数", ylist).set_global_opts(
title_opts=opts.TitleOpts("知识区TOP10弹幕视频"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
brush_opts=opts.BrushOpts(),
).render('./柱状图/知识区TOP10弹幕视频.html'))
def bar_with_brush():
obj_bar = Bar()
obj_bar.add_xaxis(Faker.choose())
obj_bar.add_yaxis("商家A", Faker.values())
obj_bar.add_yaxis("商家B", Faker.values())
obj_bar.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-Brush示例", subtitle="副标题"),
brush_opts=opts.BrushOpts()
)
return obj_bar
def bar_datazoom_slider(self):
bar = (
Bar(init_opts=opts.InitOpts(width="600px", height="300px"))
.add_xaxis(["运行", "失败", "平均排名"])
.add_yaxis("", self.bar_data)
.set_global_opts(
title_opts=opts.TitleOpts(title="局数统计"),
brush_opts=opts.BrushOpts(),
)
)
bar.render(f"{self.report_dir}/bar.html")
def draw_line(xlist, ylist, ylist2):
c = (Bar(init_opts=opts.InitOpts(
width='1800px',
height='900px',
js_host="./",
)).add_xaxis(xlist).add_yaxis("视频平均粉丝响应指数", ylist).add_yaxis(
"视频最高粉丝响应指数", ylist2).set_global_opts(
title_opts=opts.TitleOpts("各频道平均-最高粉丝响应指数"),
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(
rotate=-15)),
brush_opts=opts.BrushOpts(),
).render('./柱状图/各频道平均-最高粉丝响应指数.html'))
def timelineChart(df):
x = Faker.choose()
tl = Timeline()
for i in range(2015, 2020):
bar = (
Bar()
.add_xaxis(x)
.add_yaxis("商家A", Faker.values())
.add_yaxis("商家B", Faker.values())
.set_global_opts(
title_opts=opts.TitleOpts("某商店{}年营业额 - With Graphic 组件".format(i)),
toolbox_opts=opts.ToolboxOpts(), brush_opts=opts.BrushOpts(),
graphic_opts=[
opts.GraphicGroup(
graphic_item=opts.GraphicItem(
rotation=JsCode("Math.PI / 4"),
bounding="raw",
right=100,
bottom=110,
z=100,
),
children=[
opts.GraphicRect(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_shape_opts=opts.GraphicShapeOpts(
width=400, height=50
),
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="rgba(0,0,0,0.3)"
),
),
opts.GraphicText(
graphic_item=opts.GraphicItem(
left="center", top="center", z=100
),
graphic_textstyle_opts=opts.GraphicTextStyleOpts(
text="某商店{}年营业额".format(i),
font="bold 26px Microsoft YaHei",
graphic_basicstyle_opts=opts.GraphicBasicStyleOpts(
fill="#fff"
),
),
),
],
)
],
)
)
tl.add(bar, "{}年".format(i))
return tl
def _draw_line(result: ClassifierResult) -> Line:
# draw line chart
x_axis = result.get_timestamp_list()
y_axis = result.get_stage_list()
line = Line(init_opts=opts.InitOpts(
bg_color=constants.BACKGROUND_COLOR))
line.add_xaxis(x_axis)
line.add_yaxis("stage", y_axis, is_step=False, is_symbol_show=True)
line.set_global_opts(
title_opts=opts.TitleOpts(
title="Trend", subtitle="describe how these stages switching"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(is_show=True,
trigger="axis",
axis_pointer_type="cross"),
brush_opts=opts.BrushOpts(x_axis_index="all", tool_box=["lineX"]),
)
return line
def _draw_line(data_list: typing.List[ClassifierResult]) -> Line:
# draw line chart
x_axis = [str(i.timestamp) for i in data_list]
y_axis = [i.stage for i in data_list]
line = Line(init_opts=opts.InitOpts(bg_color=BACKGROUND_COLOR))
line.add_xaxis(x_axis)
line.add_yaxis("stage", y_axis, is_step=False, is_symbol_show=True)
line.set_global_opts(
title_opts=opts.TitleOpts(
title="Trend", subtitle="describe how these stages switching"
),
toolbox_opts=opts.ToolboxOpts(is_show=True),
tooltip_opts=opts.TooltipOpts(
is_show=True, trigger="axis", axis_pointer_type="cross"
),
brush_opts=opts.BrushOpts(x_axis_index="all", tool_box=["lineX"]),
)
return line
def bar_custom_bar_color():
# 小于50红色，50-100蓝色，其他绿色
color_function = """
function (params) {
if (params.value > 0 && params.value < 50) {
return 'red';
} else if (params.value > 50 && params.value < 100) {
return 'blue';
}
return 'green';
}
"""
obj_bar = Bar()
obj_bar.add_xaxis(Faker.choose())
# 这里设置color不起作用
obj_bar.add_yaxis("商家A", Faker.values(),color='#C1C1C1',itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
obj_bar.add_yaxis("商家B", Faker.values(),color='#909090',itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
obj_bar.add_yaxis("商家C", Faker.values(),color='#404040',itemstyle_opts=opts.ItemStyleOpts(color=JsCode(color_function)))
obj_bar.set_global_opts(
title_opts=opts.TitleOpts(title="Bar-自定义柱状颜色", subtitle="副标题"),
brush_opts=opts.BrushOpts()
)
return obj_bar
def get_chat_nums(message_path, contact_path):
"""
:param message_path: 预处理完成后的message表存储路径
:param contact_path: 预处理完成后的contact表存储路径
:return:
"""
# message_path = r'D:\wechet-anayze\pre-message-2.txt'
# contact_path = r'D:\wechet-anayze\pre-recontact.csv'
message = pd.read_csv(message_path, sep=',', encoding='utf-8', low_memory=False)
contact = pd.read_csv(contact_path, sep=',', encoding='utf-8', low_memory=False)
# 提取出联系人列表中用户名和备注名称
contact = contact[['username', 'conRemark']]
# 将用户名提取出来
username = contact['username'].tolist()
print(type(username))
# 将用户名及备注名提取为一个字典
contact_dict = dict(zip(contact['username'], contact['conRemark']))
# 联系人及其聊天次数集合
contact_sum_message = {}
# 全部联系人聊天次数集合
sum_message = 0
# 联系人列表
uname_list = []
# 联系人列表对应的聊天次数列表
chat_num_list = []
# 遍历联系人列表，并逐一统计聊天次数
for uname in username:
# 根据微信id获取真实姓名,key为真实姓名
key = contact_dict.get(uname)
# 根据微信id统计聊天次数，value:聊天次数
value = (message['talker'] == uname).sum()
# 过滤聊天次数为0的联系人，只保留聊天次数不为0的联系人
if value != 0:
contact_sum_message[key] = value
sum_message += value
uname_list.append(key)
# 这里需特别注意：value值也即聊天的次数格式是int64,但是pyecharts中如果传入的是int64时，最终渲染出的html文件中会数据会丢失，
# 所以需转为int值（血泪教训）
chat_num_list.append(int(value))
# print(contact_sum_message)
print("总聊天次数： ", sum_message)
# 使用pyecharts绘制柱状图
c = (
Bar(init_opts=opts.InitOpts(width="1600px", height="600px", page_title="聊天次数统计"))
.add_xaxis(uname_list)
.add_yaxis(series_name="聊天次数", y_axis=chat_num_list, color='#FF6666')
.set_global_opts(
# 标题配置
title_opts=opts.TitleOpts(title="聊天次数统计"),
# X轴区域缩放配置项,可使用list同时配置多个配置项
datazoom_opts=[opts.DataZoomOpts(range_start=20, range_end=40), opts.DataZoomOpts(type_="inside")],
# 区域选择组件
brush_opts=opts.BrushOpts(),
# X坐标轴旋转
xaxis_opts=opts.AxisOpts(axislabel_opts=opts.LabelOpts(rotate=-15)),
# 工具箱组件
toolbox_opts=opts.ToolboxOpts(),
# 图例配置
legend_opts=opts.LegendOpts(is_show=False),
)
.set_series_opts(
label_opts=opts.LabelOpts(is_show=False),
# 配置最大值最小值刻度线
markline_opts=opts.MarkLineOpts(
data=[
opts.MarkLineItem(type_="min", name="最小值"),
opts.MarkLineItem(type_="max", name="最大值"),
opts.MarkLineItem(type_="average", name="平均值"),
]
),
)
.render("chat_num_count.html")
)
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 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 draw_kine(stock_data):
'''
pyecharts V1 版本开始支持链式调用
文档地址 https://pyecharts.org/#/zh-cn/
'''
stock_data.index = pd.to_datetime(stock_data['TIME'], format="%Y/%m/%d")
stock_data = stock_data[["TIME", "OPEN", "CLOSE", "LOW", "HIGH", "VOLUME"]]
# stock_data = stock_data.sort_index(ascending=True) # 倒序，看时间顺序是否正常
# k线图
kline = (
Kline().add_xaxis(stock_data[["TIME"]].values.tolist()).add_yaxis(
"K线图",
stock_data.iloc[:, 1:5].values.tolist(),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
).set_global_opts(
xaxis_opts=opts.AxisOpts(is_scale=True, is_show=False),
# axis_opts=opts.AxisOpts(is_scale=True,min_=0), #y轴起始坐标可以设为0
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
), # y轴起始坐标可自动调整
title_opts=opts.TitleOpts(title="价格",
subtitle=ts_code,
pos_top="20%"),
axispointer_opts=opts.AxisPointerOpts(
is_show=True,
link=[{
"xAxisIndex": "all"
}],
label=opts.LabelOpts(background_color="#777"),
),
datazoom_opts=[ # 设置zoom参数后即可缩放
opts.DataZoomOpts(
is_show=True,
type_="inside",
xaxis_index=[0, 1], # 设置第0轴和第1轴同时缩放
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,
),
],
brush_opts=opts.BrushOpts(
x_axis_index="all",
brush_link="all",
out_of_brush={"colorAlpha": 0.1},
brush_type="lineX",
),
))
# 成交量柱形图
x = stock_data[["TIME"]].values.tolist()
y = stock_data[["VOLUME"]].values[:, 0].tolist()
bar = (Bar().add_xaxis(x).add_yaxis(
"成交量",
y,
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(
title_opts=opts.TitleOpts(title="成交量", pos_top="70%"),
legend_opts=opts.LegendOpts(is_show=False),
))
# 使用网格将多张图标组合到一起显示
grid_chart = Grid(init_opts=opts.InitOpts(
width="1200px",
height="600px",
animation_opts=opts.AnimationOpts(animation=False),
))
# 这个是为了把 data.datas 这个数据写入到 html 中,还没想到怎么跨 series 传值
# demo 中的代码也是用全局变量传的
grid_chart.add_js_funcs("var barData = {}".format(
stock_data.iloc[:, 1:5].values.tolist()))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="15%", pos_right="15%", height="55%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="15%",
pos_right="15%",
pos_top="70%",
height="20%"),
)
grid_chart.render('stock_{}.html'.format(ts_code)) #保存成用股票代码命名的文档
def ka_to_echarts(ka: KlineAnalyze, width="1500px", height='800px'):
# 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="{} - {}".format(ka.symbol, ka.name),
subtitle="from {} to {}".format(ka.start_dt, ka.end_dt),
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 ka.kline_raw]
k_data = [[x['open'], x['close'], x['low'], x['high']] for x in ka.kline_raw]
ma = ka.ma
vol = []
for row in ka.kline_raw:
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 = []
for row in ka.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.append(bar)
diff = [round(x['diff'], 4) for x in ka.macd]
dea = [round(x['dea'], 4) for x in ka.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
# ------------------------------------------------------------------------------------------------------------------
fx_dts = [x['dt'] for x in ka.fx_list]
fx_val = [x['fx'] for x in ka.fx_list]
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)
bi_dts = [x['dt'] for x in ka.bi_list]
bi_val = [x['bi'] for x in ka.bi_list]
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)
xd_dts = [x['dt'] for x in ka.xd_list]
xd_val = [x['xd'] for x in ka.xd_list]
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)
# 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_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
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 11 15:54:45 2020
@author: Administrator
"""
from pyecharts import options as opts
from pyecharts.charts import Bar
import pandas as pd
data = pd.read_excel('info_movies.xls')
score = data['score'].value_counts().sort_index()
x = score.index.tolist()
y = score.tolist()
bar = Bar()
bar.add_xaxis(x)
bar.add_yaxis('电影评分', y)
bar.set_global_opts(
title_opts={'text': "电影评分分布", 'subtext': "来源于豆瓣电影top250排名"},
brush_opts=opts.BrushOpts(),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
visualmap_opts=opts.VisualMapOpts(max_=50)
)
bar.render("templates/bar_score.html")
price_list = cursor.fetchall()
sum_price = 0
number = len(price_list)
for price in price_list:
sum_price += int(price[0].replace('元/月', ''))
average = sum_price / number # 5837.533109807208
average_list.append(format(average, '.2f'))
return average_list
price_list = select_average_price()
# 柱状图
bar_obj = (
Bar(init_opts=options.InitOpts(theme=ThemeType.MACARONS)) \
.add_xaxis((city_list)) \
.add_yaxis("", price_list) \
.set_global_opts(title_opts=options.TitleOpts("租房平均价格元/月", subtitle='仅供参考'),
toolbox_opts=options.ToolboxOpts(), # 工具栏选项
brush_opts=options.BrushOpts() # 工具刷对象
)
)
# 饼状图
b_list = [list(i) for i in zip(city_list, price_list)]
pie_ogj = (Pie().add("", b_list).set_series_opts(label_opts=options.LabelOpts(
formatter="{b}: {c}")))
# page对象用于整合地图
p = Page(layout=Page.SimplePageLayout)
p.add(pie_ogj, bar_obj)
p.render("一线城市租房信息整合.html")
def generate_kchart(self,MA=[5,10,21],renderfile=False) -> Grid:
if self.exists == False:
return None
# reassign cursor tick to trigger populate attributes like self.histBar and etc.
self.cursorTick = self._cursorTick
df_toPlot = self.allBars.loc[self.allBars['unit_intdate'] <= self.cursorTick['unit_intdate']]
# 输入时间序列中带有未来日期，则取当下能取到的最新online数据模拟
# if len(self.allBars.loc[self.allBars['unit_intdate'] == self.cursorTick['unit_intdate']]) == 0:
# df_liveQuote = self.get_livebar(assign_timestamp=self.cursorTick['timeAxis'])
# if len(df_liveQuote) > 0:
# df_toPlot = df_toPlot.append(df_liveQuote, ignore_index = True, sort=False)
str_secname = self.sec_name
str_plotname = '{} - {}'.format(self.ts_code,str_secname)
'''-----------------画图部分---------------------'''
lst_ohlcv=df_toPlot.loc[:,['open','close','low','high']].values.tolist()
lst_vol=list(df_toPlot['vol'].values)
lst_amount=list(df_toPlot['amount'].values)
# 需从处理过的histbar中获得，由于长度比allbar短1，需将值list添加一个空值补齐
lst_peaks=list(abs(self.histBars['peaks'].values))
# lst_pivotdown=[*(float('nan') if i<0 else i for i in list(self.histBars['pivots'].values)),float('nan')]
lst_pivotdown=[*(float('nan') if i<0 else i for i in list(self.histBars['valid_pivots'].values)),float('nan')]
# lst_pivotup=[*(float('nan') if i>0 else abs(i) for i in list(self.histBars['pivots'].values)),float('nan')]
lst_pivotup=[*(float('nan') if i>0 else abs(i) for i in list(self.histBars['valid_pivots'].values)),float('nan')]
lst_xaxis=list(df_toPlot['unit_intdate'].astype(str)) # xaxis values has to be STRING type, int will not work
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
kline = (Kline()
.add_xaxis(lst_xaxis)
.add_yaxis(series_name=str_secname, y_axis=lst_ohlcv,
markpoint_opts=opts.MarkPointOpts(
data=[opts.MarkPointItem(type_="min",value_dim="close"),
opts.MarkPointItem(type_="max",value_dim="close")],
symbol_size = [20, 20], #表示标记宽为 20，高为 10
),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c",
border_color="#ec0000",
border_color0="#00da3c",
),)
.set_global_opts(
title_opts=opts.TitleOpts(
title=str_plotname,
subtitle='MA='+str(MA),
),
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=[0,1,2],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0,1,2],
type_="slider",
pos_top="90%",
range_start=75,
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",font_size=10),
),
# 阴量绿阳量红
visualmap_opts=opts.VisualMapOpts(
is_show=False,
dimension=2,
series_index=list(map(lambda x: x+len(MA),[4,5])), #动态算出vol和amt柱状图的series index
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",
),
)
)
trendline = (
Line()
.add_xaxis(lst_xaxis)
.add_yaxis('高低点', lst_peaks,
itemstyle_opts=opts.ItemStyleOpts(color="green"))
)
for i in MA:
if i is not None:
trendline.add_yaxis(
series_name='MA'+str(i),
y_axis=calculate_ma(day_count=i, d=lst_ohlcv),
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
trendline.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
trendline.set_global_opts(legend_opts=opts.LegendOpts(is_show=False))
keyPoints = (
EffectScatter()
.add_xaxis(lst_xaxis)
.add_yaxis("末跌高", lst_pivotdown,symbol=SymbolType.ARROW,symbol_rotate=180,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color="purple"))
.add_yaxis("末升低", lst_pivotup,symbol=SymbolType.ARROW,symbol_size=5,itemstyle_opts=opts.ItemStyleOpts(color="blue"))
.set_series_opts(label_opts=opts.LabelOpts(is_show=False))
)
vol_bar = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="交易量",
y_axis=[
[i, lst_vol[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]
for i in range(len(lst_vol))
],
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_="最低",
max_="最高",
),
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),
)
# .add_yaxis("交易量", lst_vol,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
amnt_bar = (
Bar()
.add_xaxis(lst_xaxis)
.add_yaxis(
series_name="交易额",
y_axis=[
[i, lst_amount[i], 1 if lst_ohlcv[i][0] < lst_ohlcv[i][1] else -1]
for i in range(len(lst_amount))
],
xaxis_index=2,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False),
)
.set_global_opts(
xaxis_opts=opts.AxisOpts(
type_="category",
is_scale=True,
grid_index=2,
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_="最低",
max_="最高",
),
yaxis_opts=opts.AxisOpts(
grid_index=2,
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),
)
# .add_yaxis("交易额", lst_amount,itemstyle_opts=opts.ItemStyleOpts(color="#456A76"))
)
priceChart = kline.overlap(trendline).overlap(keyPoints)
gridChart = Grid()
gridChart.add(
priceChart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_bottom='40%'),
)
gridChart.add(
vol_bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="60%", height='15%'),
)
gridChart.add(
amnt_bar,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="75%"),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'kline')
gridChart.render(fname) if renderfile else None
self.kchart = gridChart # 将结果输出到analytics对象属性中用于function以外的调用
return self
def gen_kline_plot(
stock_data: pd.DataFrame,
chart_data: typing.Dict[str, typing.List[typing.List[typing.Union[float,
int]]]],
) -> Kline:
xaxis_index, series_index = get_subgraph_indices(chart_data)
kline_data = [
list(kline_dict.values()) for kline_dict in stock_data[
["open", "close", "min", "max"]].to_dict("records")
]
kline = Kline(init_opts=opts.InitOpts(animation_opts=opts.AnimationOpts(
animation=False), ))
kline.add_xaxis(xaxis_data=chart_data["categoryData"])
kline.add_yaxis(
series_name="kline",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
)
kline.set_global_opts(
legend_opts=opts.LegendOpts(is_show=True, pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=xaxis_index,
range_start=85,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=xaxis_index,
type_="slider",
pos_top="85%",
range_start=85,
range_end=100,
),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
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=series_index,
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",
),
)
return kline
def kline_viewer(ts_code, freq, end_date, asset='E', show=True):
"""
:param show:
:param ts_code:
:param freq:
:param end_date:
:param asset:
:return:
example:
>>> from chan.web.vis_kline import kline_viewer
>>> kline_viewer(ts_code='002739.SZ', freq='1min', end_date="20190809", asset='E')
"""
kline_raw = get_kline(ts_code,
freq=freq,
end_date=end_date,
asset=asset,
indicators=('ma', 'macd'))
for col in ['open', 'close', 'high', 'low']:
kline_raw[col] = kline_raw[col].apply(round, args=(2, ))
kline_chan = find_xd(find_bi(find_fx(preprocess(kline_raw))))
kline_chan = kline_chan[[
'dt', 'fx', 'fx_mark', 'bi', 'bi_mark', 'xd', 'xd_mark'
]]
kline_raw = kline_raw.merge(kline_chan, how='left', on='dt')
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=5,
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="60%",
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(init_opts=opts.InitOpts(theme=ThemeType.WHITE)).add_xaxis(
xaxis_data=x_data).add_yaxis(
series_name="MA5",
y_axis=kline_raw.ma5.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA10",
y_axis=kline_raw.ma10.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA20",
y_axis=kline_raw.ma20.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA60",
y_axis=kline_raw.ma60.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA120",
y_axis=kline_raw.ma120.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="MA250",
y_axis=kline_raw.ma250.values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
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()).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()).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()).set_global_opts(
visualmap_opts=opts.VisualMapOpts(type_="size",
max_=150,
min_=20), ))
# Kline And Line
kline = kline.overlap(line)
kline = kline.overlap(chan_fx)
kline = kline.overlap(chan_bi)
kline = kline.overlap(chan_xd)
# ==========================================================
macd_line = (Line(init_opts=opts.InitOpts(
theme=ThemeType.WHITE)).add_xaxis(xaxis_data=x_data).add_yaxis(
series_name="DEA",
y_axis=kline_raw['dea'].values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).add_yaxis(
series_name="DIFF",
y_axis=kline_raw['diff'].values.tolist(),
is_smooth=True,
is_symbol_show=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
macd_bar = (Bar(init_opts=opts.InitOpts(theme=ThemeType.LIGHT)).add_xaxis(
xaxis_data=x_data).add_yaxis(
series_name="Volume",
yaxis_data=kline_raw.macd.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),
))
macd = macd_line.overlap(macd_bar)
# ==========================================================
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),
))
# Grid Overlap + Bar
grid_chart = Grid(
opts.InitOpts(width="1500px", height="800px", theme=ThemeType.WHITE))
grid_chart.add(
kline,
grid_opts=opts.GridOpts(pos_left="8%", pos_right="8%", height="50%"),
)
grid_chart.add(
macd,
grid_opts=opts.GridOpts(pos_left="8%",
pos_right="8%",
pos_top="60%",
height="16%"),
)
grid_chart.add(
bar,
grid_opts=opts.GridOpts(pos_left="8%",
pos_right="8%",
pos_top="70%",
height="16%"),
)
# 调用浏览器打开可视化结果
if show:
graph_path = os.path.join(cache_path,
"%s_kline_%s.html" % (symbol, freq))
grid_chart.render(path=graph_path)
webbrowser.open(graph_path)
return grid_chart
def draw_charts():
df_merge = get_data(3650)
kline_data = pd.DataFrame(df_merge, columns=[2, 5, 4, 3]).values.tolist()
xaxis = pd.to_datetime(df_merge[0],
unit='ms').dt.strftime('%Y-%m-%d').to_list()
kline = (Kline().add_xaxis(xaxis_data=xaxis).add_yaxis(
series_name="证券板块指数",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000", color0="#00da3c"),
).set_global_opts(
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="85%",
range_start=0,
range_end=100,
),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
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": "#00da3c"
},
{
"value": -1,
"color": "#ec0000"
},
],
),
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",
),
))
array_volume = df_merge['两市成交额'].values.tolist()
bar_yaxis = [round(i / 100000000, 1) for i in array_volume]
bar = (Bar().add_xaxis(xaxis_data=xaxis).add_yaxis(
series_name="两市成交量（亿元）",
y_axis=bar_yaxis,
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=3,
axislabel_opts=opts.LabelOpts(is_show=True),
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),
title_opts=opts.TitleOpts(title="证券板块与两市成交量（亿元）对比")).set_series_opts(
markline_opts=opts.MarkLineOpts(data=[opts.MarkLineItem(
y=10000)])))
# Grid Overlap + Bar
grid_chart = Grid(init_opts=opts.InitOpts(
width="1000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(
kline,
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="63%",
height="16%"),
)
grid_chart.render("证券-两市成交额对比.html")
print("done")
def draw_charts(kline_data ,canshu ={} ,canshu2={},vol_bar=False,markline_show1 =False ,markline_show2 =False, path = '0501'):
'''
df['candle_begin_time','open','high','low','close','volume']
[['candle_begin_time','open','high','low','close','volume']]
kdata = df[['open', 'high', 'low', 'close']].values.tolist()
df['candle_begin_time'].values.tolist()
:return:
'''
df = kline_data.copy()
df['candle_begin_time'] = pd.to_datetime(df['candle_begin_time'])
time_list = df['candle_begin_time'].apply(lambda x: x.strftime('%Y-%m-%d %H:%M:%S')).values.tolist()
vol = df['volume'].values.tolist()
kline = Kline()
kline.add_xaxis(xaxis_data=time_list)
signal_pos_list = []
if 'signal' in df.columns:
print('signal,存在！')
df['pos'] = df['signal'].shift(1)
for i in df[df['pos'] > 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="做多",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.low -2],
value='买',
symbol ='circle',
symbol_size =[20,40],
itemstyle_opts = {'color': 'red'})
signal_pos_list.append(sig_pos0)
for i in df[df['pos'] < 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="做空",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.high +3],
value='卖',
symbol='circle',
symbol_size=[20, 40],
itemstyle_opts={'color': 'blue'})
signal_pos_list.append(sig_pos0)
for i in df[df['pos'] == 0].index:
bar0 = df.iloc[i]
sig_pos0 = opts.MarkPointItem(name="平仓",
coord=[bar0['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),bar0.low - 2],
value='平',
symbol ='triangle',
symbol_size =[20,40],
itemstyle_opts = {'color': 'green'})
signal_pos_list.append(sig_pos0)
else :
df['pos'] =None
markline =[]
if markline_show1 and ('signal' in df.columns) :
area_index_list =[i for i in df[(df['pos'] ==0)|(df['pos'] >0)|(df['pos'] <0)].index]
for ix,i in enumerate(area_index_list):
if ix+1 > len(area_index_list)-1:
break
i_now = df.iloc[area_index_list[ix]]
i_next = df.iloc[area_index_list[ix+1]]
if (i_now['pos'] >0) or (i_now['pos'] <0) :
log_info = f"价差：={i_next['open']-i_now['open']}--({i_next['open']}-{i_now['open']})"
else :
log_info =f"平仓：{i_next['open']}---开仓：{i_now['open']}"
sig_area = [{"xAxis": i_now['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'],
"value":log_info}]
sig_area_v = [{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": i_next['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_next['open'],
"value":None}]
markline.append(sig_area)
markline.append(sig_area_v)
elif markline_show2 and ('signal' in df.columns):
area_index_list =[i for i in df[(df['pos'] ==0)|(df['pos'] >0)|(df['pos'] <0)].index]
for ix,i in enumerate(area_index_list):
i_now = df.iloc[area_index_list[ix]]
i_1_now = df.iloc[area_index_list[ix-1]] if ix != 0 else 0
if i_now['pos'] == 0:
log_info = f"交易价: {round(i_now['open'], 1)} +- 2__盈亏:{round(i_now['open'], 1) - round(i_1_now['open'], 1)}"
else:
log_info = f"交易价: {round(i_now['open'],1)} +- 2"
sig_area = [{"xAxis": i_now['candle_begin_time'].strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'] ,
"value": None},
{"xAxis": (i_now['candle_begin_time']+timedelta(minutes=15)).strftime('%Y-%m-%d %H:%M:%S'),
"yAxis": i_now['open'],
"value":log_info}]
markline.append(sig_area)
colors = {'red': 'rgb((220,20,60)','red2': 'rgb((250,20,40)',
'yangzi': 'rgb(255,0,255)', 'zi': 'rgb(128,0,128)',
'sehnzi': 'rgb(148,0,211)', 'blue': 'rgb(0,0,255)',
'qing': 'rgb(0,255,255)', 'black': 'rgb(0,0,0)',
'shengreen': 'rgb(157,255,212)', 'green': 'rgb(0,255,0)',
'yellow': 'rgb(255,255,0)', 'glod': 'rgb(218,165,32)',
'orange': 'rgb(255,165,0)', 'brown': 'rgb(165,42,42)'}
kline.add_xaxis(xaxis_data=time_list)
kline.add_yaxis(series_name="oclh",
xaxis_index=1,
yaxis_index=1,
y_axis =df.loc[:, ['open', 'close', 'low', 'high']].values.tolist(),
markline_opts=opts.MarkLineOpts(data=markline,
linestyle_opts=opts.LineStyleOpts(type_='dotted',width=3,color='red',opacity=0.5),
label_opts=opts.LabelOpts(position="right", color="blue", font_size=13),),
#官网给出的样本
markpoint_opts=opts.MarkPointOpts(data=signal_pos_list),
itemstyle_opts=opts.ItemStyleOpts(color="#ec0090", color0="#00aa3c"), )
kline.set_global_opts(
legend_opts=opts.LegendOpts(is_show=True,pos_top=30, pos_left="left",orient='vertical'),
datazoom_opts=[ opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0, 1],
range_start=90,
range_end=100,
orient='vertical'),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="20%",
range_start=90,
range_end=100,orient='vertical'),],
yaxis_opts =opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),),
title_opts=opts.TitleOpts(
title = 'K_line',
pos_top='middle',
title_textstyle_opts=opts.TextStyleOpts(
color='black',font_weight='bold' ,font_size=20)),
tooltip_opts=opts.TooltipOpts(
trigger="axis",
trigger_on='"mousemove"',#click|mousemove
axis_pointer_type="cross",
is_show_content=True,
is_always_show_content=True,
background_color="rgba(20, 105, 245, 0.1)",
border_width=1,
border_color= "#ccc",
position = ['70%','30%'],
textstyle_opts=opts.TextStyleOpts(font_size=10,color="#000"),),
visualmap_opts=opts.VisualMapOpts(
is_show=True,
dimension=2,
series_index=5,
is_piecewise=True,
pieces=[{"value": 1, "color": "#00da3c"},{"value": -1, "color": "#ec0000"},],),
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",),
)
if len(canshu.keys())>0:
cos = list(colors.values())
line = Line()
for k,v in canshu.items():
line.add_xaxis(xaxis_data=time_list)
line.set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
co = cos.pop()
line.add_yaxis(
series_name=k,
y_axis = [y for y in v.values.tolist() if y != np.nan],
xaxis_index=1,
yaxis_index=1,
is_smooth=False,
is_connect_nones=False,# 是否连接空数据
is_symbol_show=False,#是否显示值的位置，默认显示。
color = co,
is_hover_animation = False, # 是否开启 hover 在拐点标志上的提示动画效果。
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.9,color=co),
label_opts=opts.LabelOpts(is_show =True,position='middle',distance=2,rotate=5,color = 'rgb(165,42,42)'),
itemstyle_opts=opts.ItemStyleOpts(color=co),)
kline = kline.overlap(line)
grid_chart = Grid(init_opts=opts.InitOpts(width = "1500px",height= "700px",theme=ThemeType.DARK))
grid_chart.add(kline,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%",pos_top='4%' ,height="70%"))
if( vol_bar == True) or (len(canshu2.keys())==0):
bar = Bar()
bar.add_xaxis(xaxis_data=time_list)
bar.add_yaxis(
series_name="volume",
y_axis=vol,
xaxis_index=1,
yaxis_index=2,
label_opts=opts.LabelOpts(is_show=False), )
bar.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=True), )
grid_chart.add(bar,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="80%", height="15%"),)
if len(canshu2.keys())>0 :
line2 = Line()
line2.add_xaxis(xaxis_data=time_list)
for i, k in enumerate(canshu2.keys()):
co = random.choice(list(colors.values()))
line2.add_yaxis(
series_name=k,
y_axis=[y for y in canshu2[k].values.tolist()],
xaxis_index=1,
yaxis_index=i + 1,
is_smooth=True,
is_symbol_show=False,
is_hover_animation=True,
color=co,
z_level=0,
linestyle_opts=opts.LineStyleOpts(width=2, opacity=0.5, color=co),
label_opts=opts.LabelOpts(is_show=False), )
if k == list(canshu2.keys())[-1]: continue
line2.extend_axis(yaxis=opts.AxisOpts(name=k, type_="value", position="right", min_=None, max_=None,
axisline_opts=opts.AxisLineOpts(linestyle_opts=opts.LineStyleOpts(color=co)),
axislabel_opts=opts.LabelOpts(formatter="{value}"), ))
line2.set_global_opts(xaxis_opts=opts.AxisOpts(
type_="category", is_scale=True, grid_index=1, split_number=20, min_="dataMin", max_="dataMax",
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), ),
yaxis_opts=opts.AxisOpts(name=k, grid_index=i + 1, position='right',
splitarea_opts=opts.SplitAreaOpts(is_show=True, areastyle_opts=opts.AreaStyleOpts(opacity=1)),
min_=None, max_=None, is_scale=True, offset=50, ), )
grid_chart.add(line2,grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top="75%", height="23%"),is_control_axis_index=True)
tab = Tab()
zijin_data = (kline_data['per_lr'].cumsum())
zijin_data.fillna(method='ffill', inplace=True)
zijin_line = Line(init_opts=opts.InitOpts(width = "1500px",height= "700px",theme=ThemeType.DARK)).add_xaxis(time_list)
zijin_line.add_yaxis(series_name="zijin_line:", y_axis=zijin_data.values, color="#FF0000")
zijin_line.set_global_opts(
datazoom_opts=[opts.DataZoomOpts(), opts.DataZoomOpts(type_="inside")],
title_opts=opts.TitleOpts(title="资金曲线变化"),
tooltip_opts=opts.TooltipOpts(trigger="axis", axis_pointer_type="cross"),
yaxis_opts = opts.AxisOpts(
is_scale=True,
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=True)
)
tab.add(grid_chart,'kline' )
tab.add(zijin_line,'资金曲线变化' )
tab.render(f"{path}.html")
html0 = tab.render(f"{path}.html")
if os.path.exists(html0):
print("ok!保存在：")
print(html0)
else:
print('保存失败！')
def draw_chan(stock, stock_df, kc, end_time, period): #
stock_df_original = stock_df[[
'date', 'open', 'close', 'low', 'high', 'money'
]]
stock_df_bi = kc.getFenBI_df()[['date', 'chan_price']]
stock_df_xd = kc.getFenDuan_df()[['date', 'chan_price']]
kline = (
# Kline({"theme": ThemeType.DARK})
Kline().add_xaxis(
xaxis_data=stock_df_original['date'].tolist(),
).add_yaxis(
series_name=stock,
y_axis=stock_df_original[['open', 'close', 'low',
'high']].tolist(),
itemstyle_opts=opts.ItemStyleOpts(
color="#ec0000",
color0="#00da3c"), #添加用 Hex 字符串表示的红和绿两种颜色，对应着 K线涨和跌的颜色。
).set_global_opts(
title_opts=opts.TitleOpts(title="缠论", ),
xaxis_opts=opts.AxisOpts(type_="category"),
# 分割线
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),
toolbox_opts=opts.ToolboxOpts(),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
#xaxis_index=[0, 1],
range_start=
0, #添加两个「数据区域缩放」功能，一个看的到（用鼠标拉缩图最下面的 slider），一个看不到（用鼠标直接在图中拉缩），并且设置 xaxis_index =[0,1]，表示用 K 线图（index 0）来控制柱状图（index 1）。
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
#xaxis_index=[0, 1],
type_="slider",
pos_top="90%",
range_start=
0, #index为1和2的两幅图的数据局部伸缩跟着index0那幅图，这样就实现了用一根x轴的slider可以任意缩放三幅图的数据
range_end=100,
),
],
tooltip_opts=opts.TooltipOpts(
trigger="axis",
axis_pointer_type="cross", #将两幅图的提示框合并在一起。
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=3,
is_piecewise=True,
pieces=[
{
"value": -1,
"color": "#ec0000"
},
{
"value": 1,
"color": "#00da3c"
},
],
), #坐标轴指示器配置和区域选择组件配置使得数据和轴可以一起联动。
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",
),
))
Biline = (
Line().add_xaxis(xaxis_data=stock_df_bi['date'].tolist()).add_yaxis(
series_name='笔',
y_axis=stock_df_bi['chan_price'].tolist(),
is_smooth=False,
is_connect_nones=True,
is_hover_animation=False,
#linestyle_opts=opts.LineStyleOpts(color="red",width=2, type_="dashed"),
linestyle_opts=opts.LineStyleOpts(color="red",
width=2,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=True),
)
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
overlap = kline.overlap(Biline)
crp = CentralRegionProcess(kc.getFenDuan_df(), kc, isdebug=False)
crp.define_central_region()
stock_zs_x, stock_zs_y = crp.convert_to_graph_data()
if (stock_df_xd is not None) and (len(stock_zs_x) != 0):
# print('if')
for i in range(0, len(stock_zs_x), 2):
XD_line = (
Line().add_xaxis(xaxis_data=stock_df_xd['date'].tolist()).
add_yaxis(
series_name='段',
y_axis=stock_df_xd['chan_price'].tolist(),
is_smooth=False,
is_connect_nones=True,
is_hover_animation=False,
#linestyle_opts=opts.LineStyleOpts(color="red",width=2, type_="dashed"),
linestyle_opts=opts.LineStyleOpts(color="blue",
width=2,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_series_opts(markarea_opts=opts.MarkAreaOpts(
is_silent=True,
data=[[{
'xAxis': stock_zs_x[i],
'yAxis': stock_zs_y[i]
}, {
'xAxis': stock_zs_x[i + 1],
'yAxis': stock_zs_y[i + 1]
}]]))
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
)
elif stock_df_xd is not None:
# print('elif')
XD_line = (
Line().add_xaxis(xaxis_data=stock_df_xd['date'].tolist()).
add_yaxis(
series_name='段',
y_axis=stock_df_xd['chan_price'].tolist(),
is_smooth=False,
is_connect_nones=True,
is_hover_animation=False,
#linestyle_opts=opts.LineStyleOpts(color="red",width=2, type_="dashed"),
linestyle_opts=opts.LineStyleOpts(color="blue",
width=2,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
))
overlap = overlap.overlap(XD_line)
# MA_line = (
# Line()
# .add_xaxis(xaxis_data=stock_df_original['date'].tolist(),)
# .add_yaxis(
# series_name="MA5",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 5).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add_yaxis(
# series_name="MA10",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 10).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add_yaxis(
# series_name="MA13",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 13).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .add_yaxis(
# series_name="MA34",
# y_axis= talib.SMA(stock_df['close'],timeperiod = 34).tolist(),
# is_smooth=True,
# is_hover_animation=False,
# linestyle_opts=opts.LineStyleOpts(width=1, opacity=0.5),
# label_opts=opts.LabelOpts(is_show=False),
# )
# .set_global_opts(xaxis_opts=opts.AxisOpts(type_="category"))
# )
# Volume_Bar = (
# Bar()
# .add_xaxis(xaxis_data=stock_df_original['date'].tolist())
# .add_yaxis(
# series_name="money",
# yaxis_data=stock_df_original['money'].tolist(),
# xaxis_index=2,
# yaxis_index=2,
# label_opts=opts.LabelOpts(is_show=False),
# markline_opts=opts.MarkLineOpts(
# data=[opts.MarkLineItem(type_="average", name="平均值")]
# ),
# itemstyle_opts=opts.ItemStyleOpts(
# color=JsCode(
# """
# function(params) {
# var colorList;
# if (params.data >= 0) {
# colorList = '#ef232a';
# } else {
# colorList = '#14b143';
# }
# return colorList;
# }
# """
# # """
# # function(params) {
# # var colorList;
# # if (barData[params.dataIndex][1] > barData[params.dataIndex][0]) {
# # colorList = '#ef232a';
# # } else {
# # colorList = '#14b143';
# # }
# # return colorList;
# # }
# # """
# )
# ),
# )
# .set_global_opts(
# title_opts=opts.TitleOpts(
# title='',
# ),
# xaxis_opts=opts.AxisOpts(
# type_="category",
# grid_index=2,
# is_scale=True,
# # axislabel_opts=opts.LabelOpts(is_show=False),
# 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=2,
# 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),
# splitarea_opts=opts.SplitAreaOpts(
# is_show=True,
# areastyle_opts=opts.AreaStyleOpts(opacity=1)
# ),
# ),
# # xaxis_opts=opts.AxisOpts(is_scale=True),
# legend_opts=opts.LegendOpts(is_show=False),
# datazoom_opts=opts.DataZoomOpts(type_="inside"),
# )
# )
#
# overlap = overlap.overlap(Volume_Bar)
print("it's here")
if len(stock_zs_x) != 0:
for i in range(0, len(stock_zs_x), 2):
zs1_line = (Line().add_xaxis(
xaxis_data=stock_zs_x[i:i + 2], ).add_yaxis(
series_name="中枢",
y_axis=np.append(stock_zs_y[i], stock_zs_y[i]),
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs2_line = (Line().add_xaxis(
xaxis_data=stock_zs_x[i:i + 2], ).add_yaxis(
series_name="中枢",
y_axis=np.append(stock_zs_y[i + 1:i + 2],
stock_zs_y[i + 1:i + 2]),
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs3_line = (Line().add_xaxis(xaxis_data=np.append(
stock_zs_x[i:i + 1], stock_zs_x[i:i + 1])).add_yaxis(
series_name="中枢",
y_axis=stock_zs_y[i:i + 2],
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs4_line = (Line().add_xaxis(xaxis_data=np.append(
stock_zs_x[i + 1:i + 2], stock_zs_x[i + 1:i + 2])).add_yaxis(
series_name="中枢",
y_axis=stock_zs_y[i:i + 2],
is_smooth=False,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(color="red",
width=1.5,
opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
).set_global_opts(xaxis_opts=opts.AxisOpts(type_="category")))
zs1 = zs1_line.overlap(zs2_line)
zs2 = zs3_line.overlap(zs4_line)
zs = zs1.overlap(zs2)
overlap.overlap(zs)
overlap.render("diagram/{0}@{1}#{2}.html".format(stock[0:6],
end_time[0:10], period))
print("it's done")
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)
def generate_mchart(self,lst_tscode=[],lst_macro=['shibor.1w','pimon.nt_yoy','pimon.ppi_yoy','pimon.m1_yoy','pimon.m2_yoy','pimon.allstock_circ_mv_sum'],
lst_findmedian=['pe',()],renderfile=False) -> Grid:
if self.exists == False:
return None
''' testing findmedian with a given index '''
# df=self._dataagent.query('index_weight',index_code='000300.SH',start_date='20200630', end_date='20200630')
# lst_findmedian=['pb',tuple(df['con_code'].values)]
''' ----------test end--------------------'''
lst_tscode=list(set([self.ts_code,*lst_tscode]))
# lst_findmedian[1] = tuple(self._dataagent.registry.loc[self._dataagent.registry['sec_type']=='stk']['ts_code']) if lst_findmedian[1] is None or len(lst_findmedian[1])==0 else lst_findmedian[1]
dat_cursor = self.cursorTick['timeAxis']
int_latestViewDate = self._dataagent.int_findClosestTradeDate(datetime.strftime(dat_cursor,'%Y%m%d') )
df_baseCal = self._dataagent.df_calTbl.loc[self._dataagent.df_calTbl['cal_date']<=int_latestViewDate]['cal_date'].copy()
lst_xaxis = ['{}/{}/{}'.format(i[:4],i[4:6],i[6:]) for i in df_baseCal.values.astype('str')] # xaxis values has to be STRING type, int will not work
linechart = (
Line()
.add_xaxis(lst_xaxis)
)
# 生成主线，即目标证券/指数，及比较用的指数
for str_tscode in lst_tscode:
# str_tscode = DataAgent.formatCode(code)
df_target = self._dataagent.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)
if len(df_target)>0:
df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')
else:
continue
'''=====主图画图部分包括主标的和副标的价格曲线，在主图中续把主图及后面叠加图的option，y轴等设置好==='''
linename = '{} - {}'.format(str_tscode,self.sec_name)
lst_values = list(df_toPlot['close'])
lst_values = list(libs.normByRange(lst_values))
linechart.add_yaxis(linename, lst_values,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.3),
label_opts=opts.LabelOpts(is_show=False),
itemstyle_opts=opts.ItemStyleOpts(
color="#00da3c",
),
)
linechart.extend_axis( # 所有保持原比例的折线都放在linechart2里，使用右侧y轴
yaxis=opts.AxisOpts(
name="倍数",
type_="value",
position="right",
axisline_opts=opts.AxisLineOpts(
linestyle_opts=opts.LineStyleOpts(color="black")
),
axislabel_opts=opts.LabelOpts(formatter="{value}倍"),
)
)
linechart.set_global_opts(
title_opts=opts.TitleOpts(
title='宏观比较',
),
xaxis_opts=opts.AxisOpts(type_="category"),
yaxis_opts=opts.AxisOpts(name="等比例",
is_scale=True,
),
legend_opts=opts.LegendOpts(is_show=True, pos_top='5%', pos_left="center"),
datazoom_opts=[
opts.DataZoomOpts(
is_show=False,
type_="inside",
xaxis_index=[0],
range_start=75,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0],
type_="slider",
pos_top="90%",
range_start=75,
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",font_size=10),
),
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",
),
)
'''=============================主图画图部分结束================='''
'''=========叠加图1 尝试读取pe等股票才有的属性，如失败则说明不是股票，循环继续============='''
try:
lst_pe = df_toPlot['pe'].values.tolist()
lst_pb = df_toPlot['pb'].values.tolist()
lst_pepb = [round(lst_pe[index]/value,2) for index, value in enumerate(lst_pb) if value!=0]
linechart.add_yaxis(str_tscode+"_PE", lst_pe, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
linechart.add_yaxis(str_tscode+"_PE/PB", lst_pepb, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
except:
continue
'''==========================叠加图1结束================================'''
df_shibor = self._dataagent.query('load_shibor')
# print('Supported shibor columns: {}'.format(df_shibor.columns))
df_aggDay = self._dataagent.query('load_aggDayKPI')
# print('Supported macro eco columns: {}'.format(df_aggDay.columns))
df_pimon = self._dataagent.query('load_piMon')
df_pimon = pd.merge(df_pimon, df_aggDay, how='left', left_on='date', right_on='trade_date') #与aggday按每月末日期合并以获得如“流通市值”等kpi
# print('Supported macro eco columns: {}'.format(df_pimon.columns))
'''=================叠加图2 所有股票的倍数类KPI，使用倍数类y轴============='''
lst_aggDayKPIloop = ['allstock_pe_median','allstock_pe_ttm_median','allstock_pb_median','allstock_pepb_median']
for kpi in lst_aggDayKPIloop:
lst_values = df_aggDay[kpi].values.tolist()
linechart.add_yaxis(kpi, lst_values, yaxis_index=1, #倍数使用右y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
'''==========================叠加图2结束================================'''
'''===========叠加图3 宏观数据如shibor，cpi，m1等，按“。”分割类别进行图表生成，使用等比类y轴 ========'''
for item in lst_macro:
try:
topic,colname = item.split('.')
except:
print('WARNING! input code {} not recognized...'.format(item))
if topic=='shibor':
df_target = df_shibor
maxarr = df_target.max(axis=0, skipna=True).values[1:]
minarr = df_target.min(axis=0, skipna=True).values[1:]
deltaRng = max(maxarr)-min(minarr) # overwrite the normalization function delta to put all shibor rates comparable
elif topic=='pimon':
df_target = df_pimon
deltaRng = None
else:
print('WARNING! subject code {} is not recognized...'.format(topic))
if len(df_target)>0:
df_toPlot = pd.merge(df_baseCal, df_target, how='left', left_on='cal_date', right_on='date')
df_toPlot[colname] = df_toPlot[colname].bfill()
else:
continue
linename = item
lst_values = list(df_toPlot[colname])
lst_values = list(libs.normByRange(lst_values,delta=deltaRng))
linechart.add_yaxis(linename, lst_values, # 所有等比例缩放的折线都放在linechart里，使用左侧y轴
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1,opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
)
'''==========================叠加图3结束================================'''
'''========叠加图4 如参数给出需要寻找中位数的股票列表则读取相应所有本地文件后计算并生成叠加图标========'''
if lst_findmedian[1] is not None and len(lst_findmedian[1])>0 :
str_cat = lst_findmedian[0]
df_result = pd.DataFrame()
for str_tscode in lst_findmedian[1]:
try:
df = self._dataagent.query('load_alldaily',tpl_dateindex=tuple(df_baseCal),ts_code=str_tscode)[['ts_code','trade_date',str_cat]]
print('{} csv file loaded for {} trend analysis...'.format(str_tscode,str_cat),end='\r')
except Exception as e:
print('WARNING! {} csv file not found...'.format(str_tscode))
continue
df_result = pd.concat([df_result,df])
df_result.dropna(subset=[str_cat],inplace=True)
df_result = df_result.loc[df_result['trade_date']>19940301]
# libs.df_csv(cfg.PATH_BUGFILE,(df_result.groupby('trade_date')['ts_code'].count(),))
# print(df_result.groupby('trade_date')['ts_code'].count())
df_target = df_result.groupby('trade_date')[str_cat].median()
if len(df_target)>0:
df_toPlot = pd.merge(left=df_baseCal, right=df_target, how='left', left_on='cal_date', right_on='trade_date')
linename = '{}-median'.format(str_cat)
lst_values = list(df_toPlot[str_cat])
lst_values = list(lst_values)
linechart.add_yaxis(linename, lst_values, yaxis_index=1,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=1),
label_opts=opts.LabelOpts(is_show=False),
)
else:
pass
'''==========================叠加图4结束================================'''
# put everything in a grid for easy resizing and positioning
gridChart = Grid()
gridChart.add(
linechart,
grid_opts=opts.GridOpts(pos_left="10%", pos_right="8%", pos_top='25%'),
)
fname = '{}{}.html'.format(cfg.PATH_ANAFILE,'macro')
gridChart.render(fname) if renderfile else None
self.mchart = gridChart #输出至可访问对象属性中
return self
def draw_charts(x_axis_list, oclh_data_list, volume_list, tech_line_dict):
upColor = '#ec0000'
downColor = '#00da3c'
upBorderColor = '#8A0000'
downBorderColor = '#008F2'
kline = (Kline().add_xaxis(x_axis_list).add_yaxis(
series_name="kline",
y_axis=oclh_data_list,
markpoint_opts=opts.MarkPointOpts(
data=gen_mark_points_data_list())).set_series_opts(
itemstyle_opts=opts.ItemStyleOpts(
color=upColor,
color0=downColor,
border_color=upBorderColor,
border_color0=downBorderColor), ).set_global_opts(
legend_opts=opts.LegendOpts(pos_bottom=10,
pos_left="center"),
xaxis_opts=opts.AxisOpts(is_scale=True),
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
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": downColor
},
{
"value": -1,
"color": upColor
},
],
),
datazoom_opts=[
opts.DataZoomOpts(
type_="inside",
xaxis_index=[0, 1],
range_start=98,
range_end=100,
),
opts.DataZoomOpts(
type_="slider",
xaxis_index=[0, 1],
range_start=98,
range_end=100,
pos_top='85%',
)
],
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",
),
title_opts=opts.TitleOpts(title="Kline")))
if tech_line_dict:
line = (Line().add_xaxis(xaxis_data=x_axis_list).set_global_opts(
xaxis_opts=opts.AxisOpts(type_="category")))
[
line.add_yaxis(
series_name=name,
y_axis=value_list,
is_smooth=True,
is_hover_animation=False,
linestyle_opts=opts.LineStyleOpts(width=3, opacity=0.5),
label_opts=opts.LabelOpts(is_show=False),
) for name, value_list in tech_line_dict.items()
]
kline = kline.overlap(line)
bar = (Bar().add_xaxis(x_axis_list).add_yaxis(
series_name="成交量",
yaxis_data=volume_list,
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),
))
grid_chart = Grid(init_opts=opts.InitOpts(
width="2000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
))
grid_chart.add(kline,
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="63%",
height="16%"))
folder_path = get_folder_path("backtesting_result_pyecharts")
file_path = f"{folder_path}/render.html"
grid_chart.render(path=file_path)
return file_path
import requests
from lxml import etree
from pyecharts.charts import Bar
from pyecharts import options
def get_city_money():
url = 'https://www.maigoo.com/news/480610.html'
res = requests.get(url).text
ele = etree.HTML(res)
city = ele.xpath('//table[@class="mod_table"]/tr/td[2]/text()')
money = ele.xpath('//table[@class="mod_table"]/tr/td[4]/text()')
return city[1:-1], money[1:-1]
(Bar().add_xaxis(get_city_money()[0]).add_yaxis(
'价格',
get_city_money()[1]).set_global_opts(
title_opts=options.TitleOpts(title="各省房价"),
visualmap_opts=options.VisualMapOpts(max_=15000, min_=5000), # 设置阈值
datazoom_opts=[options.DataZoomOpts()], # 拉的进度条
brush_opts=options.BrushOpts(), # 工具栏
yaxis_opts=options.AxisOpts(name="均价"),
xaxis_opts=options.AxisOpts(name="城市"),
).render('房价.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
def draw_kline(chart_data):
# [open, close, lowest, highest]
kline_data = [data[1:-1] for data in chart_data["values"]]
markpoint_item = [
opts.MarkPointItem(name="AI", coord=coord, value=value, symbol='pin', symbol_size=[35, 20],
itemstyle_opts=opts.ItemStyleOpts(color='rgb(255,0,0)'))
for coord, value in zip(chart_data["ai_markpoint_coord"], chart_data["ai_markpoint_value"])
# ]
] + \
[
opts.MarkPointItem(name="每日周線談整突破", coord=coord, value=value, symbol='pin',
symbol_size=[35, 20],
itemstyle_opts=opts.ItemStyleOpts(color='rgb(0,255,0)'))
for coord, value in zip(chart_data["other1_markpoint_coord"], chart_data["other1_markpoint_value"])
] + \
[
opts.MarkPointItem(name="每日日線", coord=coord, value=value, symbol='pin',
symbol_size=[35, 20],
itemstyle_opts=opts.ItemStyleOpts(color='rgb(0,0,255)'))
for coord, value in zip(chart_data["other2_markpoint_coord"], chart_data["other2_markpoint_value"])
] + \
[
opts.MarkPointItem(name="每日日線盤整突破", coord=coord, value=value, symbol='pin',
symbol_size=[35, 20],
itemstyle_opts=opts.ItemStyleOpts(color='rgb(255,255,0)'))
for coord, value in zip(chart_data["other3_markpoint_coord"], chart_data["other3_markpoint_value"])
] + \
[
opts.MarkPointItem(name="每日日線布林條件", coord=coord, value=value, symbol='pin',
symbol_size=[35, 20],
itemstyle_opts=opts.ItemStyleOpts(color='rgb(0,255,255)'))
for coord, value in zip(chart_data["other4_markpoint_coord"], chart_data["other4_markpoint_value"])
]
kline = (Kline().add_xaxis(
xaxis_data=chart_data["categoryData"]).add_yaxis(
series_name="Dow-Jones index",
y_axis=kline_data,
itemstyle_opts=opts.ItemStyleOpts(color="#ec0000",
color0="#00da3c"),
markpoint_opts=opts.MarkPointOpts(data=markpoint_item),
).set_global_opts(
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=98,
range_end=100,
),
opts.DataZoomOpts(
is_show=True,
xaxis_index=[0, 1],
type_="slider",
pos_top="85%",
range_start=98,
range_end=100,
),
],
yaxis_opts=opts.AxisOpts(
is_scale=True,
splitarea_opts=opts.SplitAreaOpts(
is_show=True,
areastyle_opts=opts.AreaStyleOpts(opacity=1)),
),
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": "#00da3c"
},
{
"value": -1,
"color": "#ec0000"
},
],
),
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=chart_data["categoryData"]).add_yaxis(
series_name="MA5",
y_axis=calculate_ma(day_count=5, data=chart_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="MA10",
y_axis=calculate_ma(day_count=10, data=chart_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="MA20",
y_axis=calculate_ma(day_count=20, data=chart_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="MA30",
y_axis=calculate_ma(day_count=30, data=chart_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=chart_data["categoryData"]).add_yaxis(
series_name="Volume",
y_axis=chart_data["volumes"],
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(init_opts=opts.InitOpts(
width="1000px",
height="800px",
animation_opts=opts.AnimationOpts(animation=False),
))
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="63%",
height="16%"),
)
return grid_chart.render_embed()
