“““
=======================================
Visualizing?the?stock?market?structure
=======================================

This?example?employs?several?unsupervised?learning?techniques?to?extract
the?stock?market?structure?from?variations?in?historical?quotes.

The?quantity?that?we?use?is?the?daily?variation?in?quote?price:?quotes
that?are?linked?tend?to?cofluctuate?during?a?day.

..?_stock_market:

Learning?a?graph?structure
--------------------------

We?use?sparse?inverse?covariance?estimation?to?find?which?quotes?are
correlated?conditionally?on?the?others.?Specifically?sparse?inverse
covariance?gives?us?a?graph?that?is?a?list?of?connection.?For?each
symbol?the?symbols?that?it?is?connected?too?are?those?useful?to?explain
its?fluctuations.

Clustering
----------

We?use?clustering?to?group?together?quotes?that?behave?similarly.?Here
amongst?the?:ref:‘various?clustering?techniques?‘?available
in?the?scikit-learn?we?use?:ref:‘a(chǎn)ffinity_propagation‘?as?it?does
not?enforce?equal-size?clusters?and?it?can?choose?automatically?the
number?of?clusters?from?the?data.

Note?that?this?gives?us?a?different?indication?than?the?graph?as?the
graph?reflects?conditional?relations?between?variables?while?the
clustering?reflects?marginal?properties:?variables?clustered?together?can
be?considered?as?having?a?similar?impact?at?the?level?of?the?full?stock
market.

embedding?in?2D?space
---------------------

For?visualization?purposes?we?need?to?lay?out?the?different?symbols?on?a
2D?canvas.?For?this?we?use?:ref:‘manifold‘?techniques?to?retrieve?2D
embedding.


Visualization
-------------

The?output?of?the?3?models?are?combined?in?a?2D?graph?where?nodes
represents?the?stocks?and?edges?the:

-?cluster?labels?are?used?to?define?the?color?of?the?nodes
-?the?sparse?covariance?model?is?used?to?display?the?strength?of?the?edges
-?the?2D?embedding?is?used?to?position?the?nodes?in?the?plan

This?example?has?a?fair?amount?of?visualization-related?code?as
visualization?is?crucial?here?to?display?the?graph.?One?of?the?challenge
is?to?position?the?labels?minimizing?overlap.?For?this?we?use?an
heuristic?based?on?the?direction?of?the?nearest?neighbor?along?each
axis.
“““
from?__future__?import?print_function

#?Author:?Gael?Varoquaux?gael.varoquaux@normalesup.org
#?License:?BSD?3?clause

import?sys
from?datetime?import?datetime

import?numpy?as?np
import?matplotlib.pyplot?as?plt
from?matplotlib.collections?import?LineCollection

import?pandas?as?pd

from?sklearn?import?cluster?covariance?manifold

print（__doc__）


#?#############################################################################
#?Retrieve?the?data?from?Internet

#?The?data?is?from?2003?-?2008.?This?is?reasonably?calm:?（not?too?long?ago?so
#?that?we?get?high-tech?firms?and?before?the?2008?crash）.?This?kind?of
#?historical?data?can?be?obtained?for?from?APIs?like?the?quandl.com?and
#?alphavantage.co?ones.
start_date?=?datetime（2003?1?1）.date（）
end_date?=?datetime（2008?1?1）.date（）

symbol_di

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----

?????文件???????8508??2018-11-07?10:16??plot_stock_market.py

?????文件???????4427??2018-11-12?17:06??shop_item.py

-----------?---------??----------?-----??----

????????????????12935????????????????????2