它已成为新闻。DJI正在创下历史新高，已经连续10个上涨日和9个历史高点。例如，请参阅：
当然，许多人已经注意到道琼斯指数处于这样的状态，这篇文章只是告诉我们它正在成为主流。但出现了一些问题：
-
这是正常的还是非凡的？
-
之后会发生什么？
让我们通过制作一个分析器来启动 backtrader 来回答问题：分析情况并回答问题（请参阅下面的代码）
我们的范例数据包含5923 交易时段。让我们看看当前的连胜适合在哪里。
这是正常的还是非凡的？
运行我们的代码表明，10 连续几天至少是非凡的，如果不是非凡的话：
$ ./updaystreaks.py --data 099I-DJI --upstreak hilo=True
count rank upstreak upleg upleg % drawdown rel drawdown
1987-01-02 1 1 13 219.069946 0.116193 0.017616 0.171407
2017-02-09 2 2 12 822.109375 0.041074 0.001875 0.047548
1970-11-19 3 2 12 66.900024 0.088986 0.010321 0.127055
1929-06-20 4 2 12 32.000000 0.101716 0.031134 0.340625
1991-12-18 5 3 11 315.100098 0.109167 0.011113 0.113614
1955-01-18 6 3 11 22.200012 0.057290 0.014334 0.265765
2017-07-25 7 4 10 622.289062 0.028949 NaN NaN
2013-03-01 8 4 10 488.959961 0.034801 0.008102 0.240919
1996-11-04 9 4 10 348.839844 0.058148 0.004792 0.087605
1973-07-16 10 4 10 53.600037 0.060695 0.095935 1.686565
1959-11-17 11 4 10 31.599976 0.049945 0.011216 0.237342
1959-06-24 12 4 10 36.200012 0.057680 0.020649 0.381215
1955-08-23 13 4 10 25.400024 0.056344 0.008772 0.165353
1933-03-03 14 4 10 12.600002 0.250497 0.142415 0.730158
1920-12-29 15 4 10 8.099998 0.119118 0.022339 0.209876
2016-07-08 16 5 9 778.378906 0.043688 0.016552 0.396003
1996-05-08 17 5 9 334.369629 0.061755 0.002442 0.041990
1989-07-03 18 5 9 141.890137 0.058804 0.007179 0.129677
1968-04-23 19 5 9 38.000000 0.043123 0.070535 1.736842
1967-04-13 20 5 9 49.700012 0.059061 0.006593 0.118713
1967-01-03 21 5 9 55.799988 0.071603 0.006321 0.094982
1965-01-22 22 5 9 18.500000 0.020838 0.031326 1.540541
1964-03-06 23 5 9 19.600037 0.024506 0.016127 0.678570
1955-06-15 24 5 9 12.399994 0.028343 0.005537 0.201613
1955-04-05 25 5 9 16.299988 0.039553 0.010465 0.276074
1954-09-01 26 5 9 18.599976 0.055822 0.009325 0.177419
1945-04-06 27 5 9 9.000000 0.058140 0.008526 0.155555
1929-02-18 28 5 9 21.800018 0.072812 0.086005 1.279815
1921-10-18 29 5 9 4.300003 0.061871 0.008130 0.139536
目前的连胜尚未结束，排名（并列）在第 4位。要注意：
-
当连胜为 10 天或更长时间时，以 % 为单位的上挽是最小的
-
连续9天上升的三（3）天在百分比上略小，可追溯到1955年，1964年和1965年
-
今年又一次连续12天排名第二
之后会发生什么？
即使表格已经显示了连胜结束后的回撤和相对回撤（从连胜开始，因此可以>100%），这个问题在视觉上得到更好的回答。
图表很快显示：
- 如此长的连胜似乎表明实力，没有大的回撤，这是真正的预期，因为反应
但是等等!!!
排名第一，也排名第二，我们有非凡的日期：
count rank upstreak upleg upleg % drawdown rel drawdown 1987-01-02 1 1 13 219.069946 0.116193 0.017616 0.171407 2017-02-09 2 2 12 822.109375 0.041074 0.001875 0.047548 1970-11-19 3 2 12 66.900024 0.088986 0.010321 0.127055 1929-06-20 4 2 12 32.000000 0.101716 0.031134 0.340625 ...
的确，因为19871929后来真的有非常大的熊腿。但不是像统计数据所示的连胜之后立即：相对回撤没有超过100%，因此在这些连胜结束后出现了新的高点。
代码
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import argparse
import collections
import datetime
import itertools
import matplotlib.pyplot as plt
import pandas as pd
import backtrader as bt
class UpStreak(bt.Analyzer):
params = dict(
sep=',',
hilo=False,
)
def __init__(self):
self.upday = bt.ind.UpDayBool()
self.curdt = None # streak start date
self.incs = dict() # upleg in points
self.pincs = dict() # upleg in percentage
self.close0 = dict() # starting price for upleg
self.peaks = collections.deque() # endng price for upleg
self.ddown = dict() # absolute drawdowns
self.ddownrel = dict() # relative drawdown (% of upleg retraced)
self.rets = collections.defaultdict(int) # holds main results
def next(self):
curclose = self.data.close[0]
lastclose = self.data.close[-1]
self.peaks.append((None, None))
while True:
dt, peak = self.peaks.popleft()
if dt is None:
break # all elements seen
if peak > curclose: # peak not overdone, update drawdown
ddown = 1.0 - curclose / peak
self.ddown[dt] = max(self.ddown[dt], ddown)
self.peaks.append((dt, peak)) # not done yet
inc = self.incs[dt]
fall = peak - curclose
ddownrel = fall / inc
self.ddownrel[dt] = max(self.ddownrel[dt], ddownrel)
if self.upday:
if self.curdt is None: # streak begins
self.curdt = self.strategy.datetime.date()
if self.p.hilo:
lastclose = self.data.low[-1]
self.close0[self.curdt] = lastclose
self.incs[self.curdt] = inc = curclose - self.close0[self.curdt]
self.pincs[self.curdt] = inc / self.close0[self.curdt]
self.rets[self.curdt] += 1 # update current streak
else:
if self.curdt is not None: # streak ends
if self.p.hilo:
lastclose = self.data.high[-1]
inc = self.incs[self.curdt]
fall = lastclose - curclose
self.ddownrel[self.curdt] = fall / inc
self.ddown[self.curdt] = 1.0 - curclose / lastclose
self.peaks.append((self.curdt, lastclose))
self.curdt = None
def stop(self):
s = sorted(
self.rets.items(),
reverse=True,
key=lambda item: (item[1], item[0])
)
# keep it in dict format
self.rets = collections.OrderedDict(s)
self.s = collections.OrderedDict(s)
self.headers = [
'date',
'count', 'rank', 'upstreak',
'upleg', 'upleg %',
'drawdown', 'rel drawdown',
]
i = 0
count = itertools.count(1)
last = float('inf')
for dt, streak in self.s.items():
if streak < last:
i += 1
last = streak
ddown = self.ddown.get(dt, None)
ddownrel = self.ddownrel.get(dt, None)
inc = self.incs.get(dt, None)
pinc = self.pincs.get(dt, None)
self.s[dt] = [
next(count), i,
streak,
inc, pinc,
ddown, ddownrel
]
def get_dataframe(self):
return pd.DataFrame.from_items(
self.s.items(),
orient='index',
columns=self.headers[1:], # skip index
)
def print_ranking(self):
i = 0
last = float('inf')
print(self.p.sep.join(self.headers))
for dt, items in self.s.items():
print(
self.p.sep.join(
str(x) for x in itertools.chain([dt], items)
)
)
def runstrat(args=None):
args = parse_args(args)
cerebro = bt.Cerebro()
kwargs = dict() # Data feed kwargs
# Parse from/to-date
dtfmt, tmfmt = '%Y-%m-%d', 'T%H:%M:%S'
for a, d in ((getattr(args, x), x) for x in ['fromdate', 'todate']):
if a:
strpfmt = dtfmt + tmfmt * ('T' in a)
kwargs[d] = datetime.datetime.strptime(a, strpfmt)
fromdate = kwargs.get('fromdate', datetime.date.min)
store = bt.stores.VChartFile()
data = store.getdata(dataname=args.data, **kwargs)
cerebro.adddata(data)
cerebro.addanalyzer(UpStreak, **eval('dict(' + args.upstreak + ')'))
result = cerebro.run()
st0 = result[0]
a = st0.analyzers.upstreak
# Plot some things
# pd.set_option('display.max_columns', 500)
pd.set_option('display.expand_frame_repr', False)
df = a.get_dataframe()
up = df['upstreak']
up9 = df[up >= 9]
print(up9)
up7 = df[up >= 7]
x = up7['upstreak']
y = up7['rel drawdown'] * 100.0
plt.scatter(x, y)
plt.ylabel('% Relative Drawdown')
plt.xlabel('Updays streak')
plt.title('DJI Relative Drawdown after N consecutive UpDays')
plt.show()
# Plot some things
y = up7['drawdown'] * 100.0
plt.ylabel('% Absolute Drawdown')
plt.xlabel('Updays streak')
plt.title('DJI Drawdown after N consecutive UpDays')
plt.scatter(x, y)
plt.show()
def parse_args(pargs=None):
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
description=(
'UpDayStreaks'
)
)
parser.add_argument('--data', default='', required=True,
help='Data Ticker')
parser.add_argument('--fromdate', required=False, default='',
help='Date[time] in YYYY-MM-DD[THH:MM:SS] format')
parser.add_argument('--todate', required=False, default='',
help='Date[time] in YYYY-MM-DD[THH:MM:SS] format')
parser.add_argument('--cerebro', required=False, default='',
metavar='kwargs', help='kwargs in key=value format')
parser.add_argument('--upstreak', required=False, default='',
metavar='kwargs', help='kwargs in key=value format')
parser.add_argument('--strat', required=False, default='',
metavar='kwargs', help='kwargs in key=value format')
parser.add_argument('--plot', required=False, default='',
nargs='?', const='{}',
metavar='kwargs', help='kwargs in key=value format')
return parser.parse_args(pargs)
if __name__ == '__main__':
runstrat()