from jqdata import *
import datetime
import pandas as pd
# 画图相关的包
from pyecharts.charts import Bar
from pyecharts.charts import Line
from pyecharts import options as opts
from pyecharts.render import make_snapshot
# 使用 snapshot-selenium 渲染图片
from snapshot_selenium import snapshot
def crowd_line(daycount, date_now=datetime.datetime.now().strftime('%Y-%m-%d'), piece_count=100):
all_df_crowd = get_all_crowd_rate_df(daycount, date_now, piece_count)
time_list = list(all_df_crowd.index)
hs300_df = get_price('000001.XSHG', start_date=time_list[0] ,end_date=time_list[-1], frequency='1d', fields='close',panel=False)
hs300_list = list(hs300_df['close'])
hs300_list = [round(x/100,2) for x in hs300_list]
crowd_rate_list = all_df_crowd['crowd_rate']
line = produce_line(time_list, hs300_list, crowd_rate_list)
return line
def produce_line(time_list, hs300_list, crowd_rate_list):
line = (
Line()
.add_xaxis(xaxis_data = time_list)
.add_yaxis(series_name = "拥挤率",
y_axis = crowd_rate_list,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]
),
)
.add_yaxis(series_name = "上证指数收盘价(已处理过，除以100)",
y_axis = hs300_list,
markpoint_opts=opts.MarkPointOpts(
data=[
opts.MarkPointItem(type_="max", name="最大值"),
opts.MarkPointItem(type_="min", name="最小值"),
]
),
markline_opts=opts.MarkLineOpts(
data=[opts.MarkLineItem(type_="average", name="平均值")]
),
)
.set_global_opts(
title_opts=opts.TitleOpts(title="成交前5%拥挤率"),
tooltip_opts=opts.TooltipOpts(trigger="axis"),
toolbox_opts=opts.ToolboxOpts(is_show=True),
datazoom_opts=[opts.DataZoomOpts(orient="horizontal"),opts.DataZoomOpts(type_="inside")],
yaxis_opts=opts.AxisOpts(name = '拥挤率', is_scale= True),
xaxis_opts=opts.AxisOpts(type_="category", name = '交易日'),
)
)
return line
# 以下方法可以设置双y轴
# x = Faker.choose()
# scatter1 = (
# Line()
# .add_xaxis(x)
# .add_yaxis("商家A", Faker.values(), yaxis_index=0,)
# .extend_axis(yaxis=opts.AxisOpts())
# # .set_global_opts(yaxis_opts=opts.AxisOpts(type_="value", name="商家A", position="right"))
# )
# scatter2 = (
# Line()
# .add_xaxis(x)
# .add_yaxis("商家B", [v/1000 for v in Faker.values()], yaxis_index=1)
# # .extend_axis(yaxis=opts.AxisOpts(type_="value", name="商家B", position="left"))
# # .set_global_opts(yaxis_opts=opts.AxisOpts(type_="value", name="商家B", position="left"))
# )
# scatter1.overlap(scatter2)
# scatter1.render_notebook()
def get_all_crowd_rate_df(daycount, date_now, piece_count):
# 数据量比较大，需要分开取数据, 每次取100条
data_count = int((daycount-1) / piece_count) + 1
end_date = date_now
all_stocks = list(get_all_securities(date=date_now).index)
all_df_crowd = pd.DataFrame(columns=['date', 'crowd_rate']).set_index('date')
trade_dates = get_trade_days(start_date=None, end_date=end_date, count=daycount)
frames = []
for i in range(data_count):
last_date = None
last_count = piece_count*(i+1)
if piece_count*(i+1) > daycount:
last_count = daycount
piece_trade_dates = trade_dates[piece_count*i : last_count]
piece_trade_dates_str = [trade.strftime('%Y-%m-%d') for trade in piece_trade_dates]
df = get_all_df(piece_trade_dates[0], piece_trade_dates[-1], all_stocks)
df_crowd = get_crowd_rate_df(df)
frames.append(df_crowd)
d = pd.concat(frames)
return d
def get_all_df(start_date, end_date, all_stocks):
dict_crowd = {}
# trade_days = get_trade_days(end_date=date_now, count=day_count)
all_df = get_price(all_stocks, start_date=start_date, end_date=end_date, frequency='1d', fields='money',panel=False)
all_df = all_df.sort_values(['time', 'money'], ascending = [True, False])
all_df = all_df.set_index('time',drop=True)
return all_df
def get_crowd_rate_df(all_df):
date_set = set(list(all_df.index))
df_crowd = pd.DataFrame(columns=['date', 'crowd_rate']).set_index('date')
for date_str in date_set:
date_str = date_str.strftime('%Y-%m-%d')
df = all_df[all_df.index == date_str]
df = df.sort_values('money', ascending = False)
all_money = df['money'].sum()
df = df[df['money'] >= df.iloc[int(len(df) * 0.05)]['money']]
crowd_rate = df['money'].sum() / all_money
df_crowd.loc[date_str, 'crowd_rate'] = round(crowd_rate*100,1)
df_crowd = df_crowd.sort_values('date')
return df_crowd
print('over')