美股有个比较有趣的指标，52周新高和52周新低。为什么是52周，也不知道，大概不到1年的时间。一个股票创52周新低，就说明这个股票比较烂。但是，但很多股票都创52周新低的时候，感觉是股市的底部。符合巴菲特的”恐惧时间”。来看下过去十几年美股和A股的52周新低的股票数量展示。
废话不多说，直接看图，在quantopian 上运行代码，画出每天的52周新低的股票的数量：
可以看到基本上52周新低的股票数量到达600个，就算是底部了。2006年到现在符合的有4次，2008年的金融危机，2011欧债危机，2016年1月，2018年12月。
A股的是在joinquant上计算的，看起来疯狂些。2008年金融危机，一度70%的股票创52周新低，2010年年中，2012年，2016年年初，最近的2019年年初，A股创52周新低的股票还是很多啊。2018年确实是定投的好时机。
#导入各种包
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import timedelta, datetime
plt.style.use('ggplot')
#方法
#获取交易日期列表
#时间序列函数
class f():
date_now = '2019-2-21'
all_stock_info = get_all_securities(types='stock',date=date_now)
all_stock = list(all_stock_info.index)
#df = get_price(list(all_stock.index),start_date='2008-01-01',end_date=date,fields=['close','money','volume'])['close']
#交易日期列表
def get_tradeday_list(self,s='2017-1-1',e=date_now,count=None):
if count != None:
df = get_price('000001.XSHG',end_date=e,count=count)
return df.index
else:
df = get_price('000001.XSHG',start_date=s,end_date=e)
return df.index
#股票个数序列
def all_stock_num(s,e):
get_all_securities(t)
#返回52周新低股票个数占比
def f1(self,date_list):
all_low_percent_array = []
for i in range(len(date_list)):
start = date_list[i]
print(start)
end = start + timedelta(-252)
all_stock_info = get_all_securities(types='stock',date=date_list[i])
all_stock = list(all_stock_info.index)
df = get_price(all_stock,start_date=end,end_date=start,fields=['close'],fq='pre')
# df = history(252, unit='1d', field='close', security_list=all_stock, df=True, skip_paused=False, fq='pre')
low_count = 0
# print(df['close'])
df = df['close']
for stock in all_stock:
# print(df[stock][-1] <= min(df[stock]))
# print(min(df[stock]))
# print(stock + ' ' + str(df[stock][-1]) + ' ' + str(min(df[stock])))
if df[stock][-1] <= min(df[stock]):
low_count += 1
# print('low-- ' + stock + ' ' + str(df[stock][-1]) + ' ' + str(min(df[stock])))
# print(low_count)
all_low_percent_array.append(low_count / len(all_stock))
return all_low_percent_array
a = f()
date_list = a.get_tradeday_list('2006-01-04','2019-04-30')
df1 = a.f1(date_list)
# print(df1)
2006-01-04 00:00:00
/opt/conda/lib/python3.6/site-packages/jqresearch/api.py:87: FutureWarning: Panel is deprecated and will be removed in a future version. The recommended way to represent these types of 3-dimensional data are with a MultiIndex on a DataFrame, via the Panel.to_frame() method Alternatively, you can use the xarray package http://xarray.pydata.org/en/stable/. Pandas provides a `.to_xarray()` method to help automate this conversion. pre_factor_ref_date=_get_today())
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2017-05-03 00:00:00 2017-05-04 00:00:00 2017-05-05 00:00:00 2017-05-08 00:00:00 2017-05-09 00:00:00 2017-05-10 00:00:00 2017-05-11 00:00:00 2017-05-12 00:00:00 2017-05-15 00:00:00 2017-05-16 00:00:00 2017-05-17 00:00:00 2017-05-18 00:00:00 2017-05-19 00:00:00 2017-05-22 00:00:00 2017-05-23 00:00:00 2017-05-24 00:00:00 2017-05-25 00:00:00 2017-05-26 00:00:00 2017-05-31 00:00:00 2017-06-01 00:00:00 2017-06-02 00:00:00 2017-06-05 00:00:00 2017-06-06 00:00:00 2017-06-07 00:00:00 2017-06-08 00:00:00 2017-06-09 00:00:00 2017-06-12 00:00:00 2017-06-13 00:00:00 2017-06-14 00:00:00 2017-06-15 00:00:00 2017-06-16 00:00:00 2017-06-19 00:00:00 2017-06-20 00:00:00 2017-06-21 00:00:00 2017-06-22 00:00:00 2017-06-23 00:00:00 2017-06-26 00:00:00 2017-06-27 00:00:00 2017-06-28 00:00:00 2017-06-29 00:00:00 2017-06-30 00:00:00 2017-07-03 00:00:00 2017-07-04 00:00:00 2017-07-05 00:00:00 2017-07-06 00:00:00 2017-07-07 00:00:00 2017-07-10 00:00:00 2017-07-11 00:00:00 2017-07-12 00:00:00 2017-07-13 00:00:00 2017-07-14 00:00:00 2017-07-17 00:00:00 2017-07-18 00:00:00 2017-07-19 00:00:00 2017-07-20 00:00:00 2017-07-21 00:00:00 2017-07-24 00:00:00 2017-07-25 00:00:00 2017-07-26 00:00:00 2017-07-27 00:00:00 2017-07-28 00:00:00 2017-07-31 00:00:00 2017-08-01 00:00:00 2017-08-02 00:00:00 2017-08-03 00:00:00 2017-08-04 00:00:00 2017-08-07 00:00:00 2017-08-08 00:00:00 2017-08-09 00:00:00 2017-08-10 00:00:00 2017-08-11 00:00:00 2017-08-14 00:00:00 2017-08-15 00:00:00 2017-08-16 00:00:00 2017-08-17 00:00:00 2017-08-18 00:00:00 2017-08-21 00:00:00 2017-08-22 00:00:00 2017-08-23 00:00:00 2017-08-24 00:00:00 2017-08-25 00:00:00 2017-08-28 00:00:00 2017-08-29 00:00:00 2017-08-30 00:00:00 2017-08-31 00:00:00 2017-09-01 00:00:00 2017-09-04 00:00:00 2017-09-05 00:00:00 2017-09-06 00:00:00 2017-09-07 00:00:00 2017-09-08 00:00:00 2017-09-11 00:00:00 2017-09-12 00:00:00 2017-09-13 00:00:00 2017-09-14 00:00:00 2017-09-15 00:00:00 2017-09-18 00:00:00 2017-09-19 00:00:00 2017-09-20 00:00:00 2017-09-21 00:00:00 2017-09-22 00:00:00 2017-09-25 00:00:00 2017-09-26 00:00:00 2017-09-27 00:00:00 2017-09-28 00:00:00 2017-09-29 00:00:00 2017-10-09 00:00:00 2017-10-10 00:00:00 2017-10-11 00:00:00 2017-10-12 00:00:00 2017-10-13 00:00:00 2017-10-16 00:00:00 2017-10-17 00:00:00 2017-10-18 00:00:00 2017-10-19 00:00:00 2017-10-20 00:00:00 2017-10-23 00:00:00 2017-10-24 00:00:00 2017-10-25 00:00:00 2017-10-26 00:00:00 2017-10-27 00:00:00 2017-10-30 00:00:00 2017-10-31 00:00:00 2017-11-01 00:00:00 2017-11-02 00:00:00 2017-11-03 00:00:00 2017-11-06 00:00:00 2017-11-07 00:00:00 2017-11-08 00:00:00 2017-11-09 00:00:00 2017-11-10 00:00:00 2017-11-13 00:00:00 2017-11-14 00:00:00 2017-11-15 00:00:00 2017-11-16 00:00:00 2017-11-17 00:00:00 2017-11-20 00:00:00 2017-11-21 00:00:00 2017-11-22 00:00:00 2017-11-23 00:00:00 2017-11-24 00:00:00 2017-11-27 00:00:00 2017-11-28 00:00:00 2017-11-29 00:00:00 2017-11-30 00:00:00 2017-12-01 00:00:00 2017-12-04 00:00:00 2017-12-05 00:00:00 2017-12-06 00:00:00 2017-12-07 00:00:00 2017-12-08 00:00:00 2017-12-11 00:00:00 2017-12-12 00:00:00 2017-12-13 00:00:00 2017-12-14 00:00:00 2017-12-15 00:00:00 2017-12-18 00:00:00 2017-12-19 00:00:00 2017-12-20 00:00:00 2017-12-21 00:00:00 2017-12-22 00:00:00 2017-12-25 00:00:00 2017-12-26 00:00:00 2017-12-27 00:00:00 2017-12-28 00:00:00 2017-12-29 00:00:00 2018-01-02 00:00:00 2018-01-03 00:00:00 2018-01-04 00:00:00 2018-01-05 00:00:00 2018-01-08 00:00:00 2018-01-09 00:00:00 2018-01-10 00:00:00 2018-01-11 00:00:00 2018-01-12 00:00:00 2018-01-15 00:00:00 2018-01-16 00:00:00 2018-01-17 00:00:00 2018-01-18 00:00:00 2018-01-19 00:00:00 2018-01-22 00:00:00 2018-01-23 00:00:00 2018-01-24 00:00:00 2018-01-25 00:00:00 2018-01-26 00:00:00 2018-01-29 00:00:00 2018-01-30 00:00:00 2018-01-31 00:00:00 2018-02-01 00:00:00 2018-02-02 00:00:00 2018-02-05 00:00:00 2018-02-06 00:00:00 2018-02-07 00:00:00 2018-02-08 00:00:00 2018-02-09 00:00:00 2018-02-12 00:00:00 2018-02-13 00:00:00 2018-02-14 00:00:00 2018-02-22 00:00:00 2018-02-23 00:00:00 2018-02-26 00:00:00 2018-02-27 00:00:00 2018-02-28 00:00:00 2018-03-01 00:00:00 2018-03-02 00:00:00 2018-03-05 00:00:00 2018-03-06 00:00:00 2018-03-07 00:00:00 2018-03-08 00:00:00 2018-03-09 00:00:00 2018-03-12 00:00:00 2018-03-13 00:00:00 2018-03-14 00:00:00 2018-03-15 00:00:00 2018-03-16 00:00:00 2018-03-19 00:00:00 2018-03-20 00:00:00 2018-03-21 00:00:00 2018-03-22 00:00:00 2018-03-23 00:00:00 2018-03-26 00:00:00 2018-03-27 00:00:00 2018-03-28 00:00:00 2018-03-29 00:00:00 2018-03-30 00:00:00 2018-04-02 00:00:00 2018-04-03 00:00:00 2018-04-04 00:00:00 2018-04-09 00:00:00 2018-04-10 00:00:00 2018-04-11 00:00:00 2018-04-12 00:00:00 2018-04-13 00:00:00 2018-04-16 00:00:00 2018-04-17 00:00:00 2018-04-18 00:00:00 2018-04-19 00:00:00 2018-04-20 00:00:00 2018-04-23 00:00:00 2018-04-24 00:00:00 2018-04-25 00:00:00 2018-04-26 00:00:00 2018-04-27 00:00:00 2018-05-02 00:00:00 2018-05-03 00:00:00 2018-05-04 00:00:00 2018-05-07 00:00:00 2018-05-08 00:00:00 2018-05-09 00:00:00 2018-05-10 00:00:00 2018-05-11 00:00:00 2018-05-14 00:00:00 2018-05-15 00:00:00 2018-05-16 00:00:00 2018-05-17 00:00:00 2018-05-18 00:00:00 2018-05-21 00:00:00 2018-05-22 00:00:00 2018-05-23 00:00:00 2018-05-24 00:00:00 2018-05-25 00:00:00 2018-05-28 00:00:00 2018-05-29 00:00:00 2018-05-30 00:00:00 2018-05-31 00:00:00 2018-06-01 00:00:00 2018-06-04 00:00:00 2018-06-05 00:00:00 2018-06-06 00:00:00 2018-06-07 00:00:00 2018-06-08 00:00:00 2018-06-11 00:00:00 2018-06-12 00:00:00 2018-06-13 00:00:00 2018-06-14 00:00:00 2018-06-15 00:00:00 2018-06-19 00:00:00 2018-06-20 00:00:00 2018-06-21 00:00:00 2018-06-22 00:00:00 2018-06-25 00:00:00 2018-06-26 00:00:00 2018-06-27 00:00:00 2018-06-28 00:00:00 2018-06-29 00:00:00 2018-07-02 00:00:00 2018-07-03 00:00:00 2018-07-04 00:00:00 2018-07-05 00:00:00 2018-07-06 00:00:00 2018-07-09 00:00:00 2018-07-10 00:00:00 2018-07-11 00:00:00 2018-07-12 00:00:00 2018-07-13 00:00:00 2018-07-16 00:00:00 2018-07-17 00:00:00 2018-07-18 00:00:00 2018-07-19 00:00:00 2018-07-20 00:00:00 2018-07-23 00:00:00 2018-07-24 00:00:00 2018-07-25 00:00:00 2018-07-26 00:00:00 2018-07-27 00:00:00 2018-07-30 00:00:00 2018-07-31 00:00:00 2018-08-01 00:00:00 2018-08-02 00:00:00 2018-08-03 00:00:00 2018-08-06 00:00:00 2018-08-07 00:00:00 2018-08-08 00:00:00 2018-08-09 00:00:00 2018-08-10 00:00:00 2018-08-13 00:00:00 2018-08-14 00:00:00 2018-08-15 00:00:00 2018-08-16 00:00:00 2018-08-17 00:00:00 2018-08-20 00:00:00 2018-08-21 00:00:00 2018-08-22 00:00:00 2018-08-23 00:00:00 2018-08-24 00:00:00 2018-08-27 00:00:00 2018-08-28 00:00:00 2018-08-29 00:00:00 2018-08-30 00:00:00 2018-08-31 00:00:00 2018-09-03 00:00:00 2018-09-04 00:00:00 2018-09-05 00:00:00 2018-09-06 00:00:00 2018-09-07 00:00:00 2018-09-10 00:00:00 2018-09-11 00:00:00 2018-09-12 00:00:00 2018-09-13 00:00:00 2018-09-14 00:00:00 2018-09-17 00:00:00 2018-09-18 00:00:00 2018-09-19 00:00:00 2018-09-20 00:00:00 2018-09-21 00:00:00 2018-09-25 00:00:00 2018-09-26 00:00:00 2018-09-27 00:00:00 2018-09-28 00:00:00 2018-10-08 00:00:00 2018-10-09 00:00:00 2018-10-10 00:00:00 2018-10-11 00:00:00 2018-10-12 00:00:00 2018-10-15 00:00:00 2018-10-16 00:00:00 2018-10-17 00:00:00 2018-10-18 00:00:00 2018-10-19 00:00:00 2018-10-22 00:00:00 2018-10-23 00:00:00 2018-10-24 00:00:00 2018-10-25 00:00:00 2018-10-26 00:00:00 2018-10-29 00:00:00 2018-10-30 00:00:00 2018-10-31 00:00:00 2018-11-01 00:00:00 2018-11-02 00:00:00 2018-11-05 00:00:00 2018-11-06 00:00:00 2018-11-07 00:00:00 2018-11-08 00:00:00 2018-11-09 00:00:00 2018-11-12 00:00:00 2018-11-13 00:00:00 2018-11-14 00:00:00 2018-11-15 00:00:00 2018-11-16 00:00:00 2018-11-19 00:00:00 2018-11-20 00:00:00 2018-11-21 00:00:00 2018-11-22 00:00:00 2018-11-23 00:00:00 2018-11-26 00:00:00 2018-11-27 00:00:00 2018-11-28 00:00:00 2018-11-29 00:00:00 2018-11-30 00:00:00 2018-12-03 00:00:00 2018-12-04 00:00:00 2018-12-05 00:00:00 2018-12-06 00:00:00 2018-12-07 00:00:00 2018-12-10 00:00:00 2018-12-11 00:00:00 2018-12-12 00:00:00 2018-12-13 00:00:00 2018-12-14 00:00:00 2018-12-17 00:00:00 2018-12-18 00:00:00 2018-12-19 00:00:00 2018-12-20 00:00:00 2018-12-21 00:00:00 2018-12-24 00:00:00 2018-12-25 00:00:00 2018-12-26 00:00:00 2018-12-27 00:00:00 2018-12-28 00:00:00 2019-01-02 00:00:00 2019-01-03 00:00:00 2019-01-04 00:00:00 2019-01-07 00:00:00 2019-01-08 00:00:00 2019-01-09 00:00:00 2019-01-10 00:00:00 2019-01-11 00:00:00 2019-01-14 00:00:00 2019-01-15 00:00:00 2019-01-16 00:00:00 2019-01-17 00:00:00 2019-01-18 00:00:00 2019-01-21 00:00:00 2019-01-22 00:00:00 2019-01-23 00:00:00 2019-01-24 00:00:00 2019-01-25 00:00:00 2019-01-28 00:00:00 2019-01-29 00:00:00 2019-01-30 00:00:00 2019-01-31 00:00:00 2019-02-01 00:00:00 2019-02-11 00:00:00 2019-02-12 00:00:00 2019-02-13 00:00:00 2019-02-14 00:00:00 2019-02-15 00:00:00 2019-02-18 00:00:00 2019-02-19 00:00:00 2019-02-20 00:00:00 2019-02-21 00:00:00 2019-02-22 00:00:00 2019-02-25 00:00:00 2019-02-26 00:00:00 2019-02-27 00:00:00 2019-02-28 00:00:00 2019-03-01 00:00:00 2019-03-04 00:00:00 2019-03-05 00:00:00 2019-03-06 00:00:00 2019-03-07 00:00:00 2019-03-08 00:00:00 2019-03-11 00:00:00 2019-03-12 00:00:00 2019-03-13 00:00:00 2019-03-14 00:00:00 2019-03-15 00:00:00 2019-03-18 00:00:00 2019-03-19 00:00:00 2019-03-20 00:00:00 2019-03-21 00:00:00 2019-03-22 00:00:00 2019-03-25 00:00:00 2019-03-26 00:00:00 2019-03-27 00:00:00 2019-03-28 00:00:00 2019-03-29 00:00:00 2019-04-01 00:00:00 2019-04-02 00:00:00 2019-04-03 00:00:00 2019-04-04 00:00:00 2019-04-08 00:00:00 2019-04-09 00:00:00 2019-04-10 00:00:00 2019-04-11 00:00:00 2019-04-12 00:00:00 2019-04-15 00:00:00 2019-04-16 00:00:00 2019-04-17 00:00:00 2019-04-18 00:00:00 2019-04-19 00:00:00 2019-04-22 00:00:00 2019-04-23 00:00:00 2019-04-24 00:00:00 2019-04-25 00:00:00 2019-04-26 00:00:00 2019-04-29 00:00:00 2019-04-30 00:00:00
import matplotlib.pyplot as pltpe_array
import matplotlib.pyplot as plt
x = date_list
y = df1
plt.figure(figsize=(18,14)) #创建绘图对象
# plt.plot(x,y,linewidth=1, label="High Count") #在当前绘图对象绘图（X轴，Y轴，蓝色，线宽度）
plt.plot(x,y,linewidth=1, label="Low Count Percent")
# handles, labels = plt.get_legend_handles_labels()
plt.legend()
plt.xlabel("Date)") #X轴标签
plt.ylabel("Low Count") #Y轴标签
Text(0, 0.5, 'Low Count')
本社区仅针对特定人员开放
查看需注册登录并通过风险意识测评
5秒后跳转登录页面...
移动端课程
