def printRecArray(recarr, precision=8):
"""
Print a record array in a mannerly fashion.
@param recarr: The record array.
@type recarr: recarray
@keyword precision: The precision of the floats shown when printed
(default: 8)
@type precision: int
"""
print mlab.rec2txt(recarr, precision=precision)
def printRecArray(recarr,precision=8):
"""
Print a record array in a mannerly fashion.
@param recarr: The record array.
@type recarr: recarray
@keyword precision: The precision of the floats shown when printed
(default: 8)
@type precision: int
"""
print mlab.rec2txt(recarr,precision=precision)
def __str__(self):
if self.names and self.size:
table_ = rec2txt(self, padding=3, precision=4)
#line = '.'*max(map(len, table_.partition('\n')))
#return '\n'.join([line, table_, line])
return table_
else:
return '[]'
def printResult(self):
string = "\n"
string+= "MeanVariance Optimization Result\n"
string+= "Optimization Mode : %s\n"%self.optimizationMode
weight_data = np.array(zip(self.nameList, self.optimizedWeights), dtype=[('Name', 'S20'), ('Weight', float)])
string+= "\n"
string+= "Optimization Result:\n"
string+= rec2txt(weight_data)
string+= "\n"
string+= "Optimization Metric:\n"
string+= " Mean: %f\n"%self.optimizedMean
string+= " (optimized)Risk: %f\n"%self.optimizedRisk
string+= " (targeted)Risk: %f\n"%self.targetRisk
string+= " Sharpe: %f\n"%(self.optimizedMean / self.optimizedRisk)
string+= "\n"
return string
def __str__(self):
str_ = list()
if self.globalmeta:
g = '\r\n'.join(
['globalmeta:'] +
['\t{0}:{1}'.format(k, v) for k, v in self.globalmeta.items()])
str_.append(g)
if any([bool(fmeta) for fmeta in self.fieldmeta]):
fieldmeta = zip(*(self.names, self.fieldmeta))
f = '\r\n'.join(['fieldmeta:'] +
['\t{0}:{1}'.format(n, f) for n, f in fieldmeta])
str_.append(f)
try:
d = rec2txt(self.view(recarray), padding=3, precision=4)
except:
d = super(LookUpTable, self).__str__()
str_.append(d)
return '\r\n'.join(str_)
from __future__ import print_function
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.cbook as cbook
datafile = cbook.get_sample_data('aapl.csv', asfileobj=False)
print('loading', datafile)
r = mlab.csv2rec(datafile)
r.sort()
r1 = r[-10:]
# Create a new array
r2 = np.empty(12, dtype=[('date', '|O4'), ('high', np.float),
('marker', np.float)])
r2 = r2.view(np.recarray)
r2.date = r.date[-17:-5]
r2.high = r.high[-17:-5]
r2.marker = np.arange(12)
print("r1:")
print(mlab.rec2txt(r1))
print("r2:")
print(mlab.rec2txt(r2))
defaults = {'marker': -1, 'close': np.NaN, 'low': -4444.}
for s in ('inner', 'outer', 'leftouter'):
rec = mlab.rec_join(['date', 'high'], r1, r2,
jointype=s, defaults=defaults)
print("\n%sjoin :\n%s" % (s, mlab.rec2txt(rec)))
rsum = mlab.rec_summarize(r, summaryfuncs)
# stats is a list of (dtype_name, function, output_dtype_name).
# rec_groupby will summarize the attribute identified by the
# dtype_name over the groups in the groupby list, and assign the
# result to the output_dtype_name
stats = (
('dreturn', len, 'rcnt'),
('dreturn', np.mean, 'rmean'),
('dreturn', np.median, 'rmedian'),
('dreturn', np.std, 'rsigma'),
)
# you can summarize over a single variable, like years or months
print 'summary by years'
ry = mlab.rec_groupby(rsum, ('years', ), stats)
print mlab.rec2txt(ry)
print 'summary by months'
rm = mlab.rec_groupby(rsum, ('months', ), stats)
print mlab.rec2txt(rm)
# or over multiple variables like years and months
print 'summary by year and month'
rym = mlab.rec_groupby(rsum, ('years', 'months'), stats)
print mlab.rec2txt(rym)
print 'summary by volume'
rv = mlab.rec_groupby(rsum, ('volcode', ), stats)
print mlab.rec2txt(rv)
rsum = mlab.rec_summarize(r, summaryfuncs)
# stats is a list of (dtype_name, function, output_dtype_name).
# rec_groupby will summarize the attribute identified by the
# dtype_name over the groups in the groupby list, and assign the
# result to the output_dtype_name
stats = (
('dreturn', len, 'rcnt'),
('dreturn', np.mean, 'rmean'),
('dreturn', np.median, 'rmedian'),
('dreturn', np.std, 'rsigma'),
)
# you can summarize over a single variable, like years or months
print('summary by years')
ry = mlab.rec_groupby(rsum, ('years', ), stats)
print(mlab.rec2txt(ry))
print('summary by months')
rm = mlab.rec_groupby(rsum, ('months', ), stats)
print(mlab.rec2txt(rm))
# or over multiple variables like years and months
print('summary by year and month')
rym = mlab.rec_groupby(rsum, ('years', 'months'), stats)
print(mlab.rec2txt(rym))
print('summary by volume')
rv = mlab.rec_groupby(rsum, ('volcode', ), stats)
print(mlab.rec2txt(rv))
rall = np.load(os.path.join(outdir,
'stocks_combined.npy')).view(np.recarray)
if 1:
fig = plt.figure()
plt.plot(rall.price, rall.dreturn, 'o')
fig = plt.figure()
plt.hist(rall.price, 20)
data = []
ranges = [(0, 1), (1, 2), (2, 5), (5, 10), (10, 20), (20, 100), (100, np.inf)]
for price_min, price_max in ranges:
mask = (rall.price > price_min) & (rall.price <= price_max)
rmask = rall[mask]
prices = rmask.price
dreturn = rmask.dreturn * 100
median = np.median(dreturn)
mean = np.mean(dreturn)
count = len(dreturn)
std = np.std(dreturn)
rar = mean / std
data.append((price_min, price_max, count, mean, median, std, rar))
rsummary = np.rec.fromrecords(
data, names='price_min,price_max,count,mean,median,std,rar')
print mlab.rec2txt(rsummary)
plt.show()
"""
Demonstrate how get_sample_data works with git revisions in the data.
git clone [email protected]/matplotlib/sample_data.git
and edit testdata.csv to add a new row. After committing the changes,
when you rerun this script you will get the updated data (and the new
git version will be cached in ~/.matplotlib/sample_data)
"""
import matplotlib.mlab as mlab
import matplotlib.cbook as cbook
# get the file handle to the cached data and print the contents
datafile = 'testdir/subdir/testsub.csv'
fh = cbook.get_sample_data(datafile)
print fh.read()
# make sure we can read it using csv2rec
fh.seek(0)
r = mlab.csv2rec(fh)
print mlab.rec2txt(r)
fh.close()
def analyze_yahoo_data(ticker, date1=datetime.date(2004, 1, 1), date2=datetime.date.today()):
ticker, iso_week, data = load_yahoo_data(ticker, date1, date2)
result_all = estimate_var(data)
result_year = estimate_var(data, True)
print rec2txt(result_all)
print rec2txt(result_year)
def analyze_dzh_file(filename):
ticker, iso_week, data = load_file(filename)
result_all = estimate_var(data)
result_year = estimate_var(data, True)
print rec2txt(result_all)
print rec2txt(result_year)
"""
Demonstrate how get_sample_data works with git revisions in the data.
git clone [email protected]/matplotlib/sample_data.git
and edit testdata.csv to add a new row. After committing the changes,
when you rerun this script you will get the updated data (and the new
git version will be cached in ~/.matplotlib/sample_data)
"""
from __future__ import print_function
import matplotlib.mlab as mlab
import matplotlib.cbook as cbook
# get the file handle to the cached data and print the contents
datafile = 'testdir/subdir/testsub.csv'
fh = cbook.get_sample_data(datafile)
print(fh.read())
# make sure we can read it using csv2rec
fh.seek(0)
r = mlab.csv2rec(fh)
print(mlab.rec2txt(r))
fh.close()
if 1:
rall = np.load(os.path.join(outdir, 'stocks_combined.npy')).view(np.recarray)
if 1:
fig = plt.figure()
plt.plot(rall.price, rall.dreturn, 'o')
fig = plt.figure()
plt.hist(rall.price, 20)
data = []
ranges = [(0, 1), (1, 2), (2, 5), (5, 10), (10, 20), (20, 100), (100, np.inf)]
for price_min, price_max in ranges:
mask = (rall.price > price_min) & (rall.price <= price_max)
rmask = rall[mask]
prices = rmask.price
dreturn = rmask.dreturn * 100
median = np.median(dreturn)
mean = np.mean(dreturn)
count = len(dreturn)
std = np.std(dreturn)
rar = mean / std
data.append((price_min, price_max, count, mean, median, std, rar))
rsummary = np.rec.fromrecords(data, names='price_min,price_max,count,mean,median,std,rar')
print mlab.rec2txt(rsummary)
plt.show()
rsum = mlab.rec_summarize(r, summaryfuncs)
# stats is a list of (dtype_name, function, output_dtype_name).
# rec_groupby will summarize the attribute identified by the
# dtype_name over the groups in the groupby list, and assign the
# result to the output_dtype_name
stats = (
('dreturn', len, 'rcnt'),
('dreturn', np.mean, 'rmean'),
('dreturn', np.median, 'rmedian'),
('dreturn', np.std, 'rsigma'),
)
# you can summarize over a single variable, like years or months
print('summary by years')
ry = mlab.rec_groupby(rsum, ('years',), stats)
print(mlab. rec2txt(ry))
print('summary by months')
rm = mlab.rec_groupby(rsum, ('months',), stats)
print(mlab.rec2txt(rm))
# or over multiple variables like years and months
print('summary by year and month')
rym = mlab.rec_groupby(rsum, ('years', 'months'), stats)
print(mlab.rec2txt(rym))
print('summary by volume')
rv = mlab.rec_groupby(rsum, ('volcode',), stats)
print(mlab.rec2txt(rv))
def volume_code(volume):
'code the continuous volume data categorically'
ind = np.searchsorted([1e5,1e6, 5e6,10e6, 1e7], volume)
return ind
summaryfuncs = (
('date', lambda x: [thisdate.year for thisdate in x], 'years'),
('date', lambda x: [thisdate.month for thisdate in x], 'months'),
('date', lambda x: [thisdate.weekday() for thisdate in x], 'weekday'),
('adj_close', daily_return, 'dreturn'),
('volume', volume_code, 'volcode'),
)
rsum = mlab.rec_summarize(r, summaryfuncs)
stats = (
('dreturn', len, 'rcnt'),
('dreturn', np.mean, 'rmean'),
('dreturn', np.median, 'rmedian'),
('dreturn', np.std, 'rsigma'),
)
print 'summary by years'
ry = mlab.rec_groupby(rsum, ('years',), stats)
print mlab. rec2txt(ry)
print 'summary by months'
rm = mlab.rec_groupby(rsum, ('months',), stats)
print mlab.rec2txt(rm)
print 'summary by year and month'
rym = mlab.rec_groupby(rsum, ('years','months'), stats)
print mlab.rec2txt(rym)
print 'summary by volume'
rv = mlab.rec_groupby(rsum, ('volcode',), stats)
print mlab.rec2txt(rv)
import numpy as np
import matplotlib.mlab as mlab
r = mlab.csv2rec('../data/aapl.csv')
r.sort()
r1 = r[-10:]
# Create a new array
r2 = np.empty(12,
dtype=[('date', '|O4'), ('high', np.float),
('marker', np.float)])
r2 = r2.view(np.recarray)
r2.date = r.date[-17:-5]
r2.high = r.high[-17:-5]
r2.marker = np.arange(12)
print "r1:"
print mlab.rec2txt(r1)
print "r2:"
print mlab.rec2txt(r2)
defaults = {'marker': -1, 'close': np.NaN, 'low': -4444.}
for s in ('inner', 'outer', 'leftouter'):
rec = mlab.rec_join(['date', 'high'],
r1,
r2,
jointype=s,
defaults=defaults)
print "\n%sjoin :\n%s" % (s, mlab.rec2txt(rec))
import numpy as np
import matplotlib.mlab as mlab
r = mlab.csv2rec('../data/aapl.csv')
r.sort()
r1 = r[-10:]
# Create a new array
r2 = np.empty(12, dtype=[('date', '|O4'), ('high', np.float),
('marker', np.float)])
r2 = r2.view(np.recarray)
r2.date = r.date[-17:-5]
r2.high = r.high[-17:-5]
r2.marker = np.arange(12)
print "r1:"
print mlab.rec2txt(r1)
print "r2:"
print mlab.rec2txt(r2)
defaults = {'marker':-1, 'close':np.NaN, 'low':-4444.}
for s in ('inner', 'outer', 'leftouter'):
rec = mlab.rec_join(['date', 'high'], r1, r2,
jointype=s, defaults=defaults)
print "\n%sjoin :\n%s" % (s, mlab.rec2txt(rec))
def robustSVD(D,nMode=nMode,sigOut=sigOut,maxIt=maxIt,verbose=True):
"""
Robust SVD
D = U S V.T
This decomposition is computed using SVD. SVD is sensitive to
outliers, so we perform iterative sigma clipping in the `\chi^2`
sense, but also in the distribution of best fit parameters.
D_fit[i] = a_1 V_1 + a_2 V_2 + ... a_nMode V_nMode
nMode is the (small) number of principle components we wish to fit
our data with.
If any of the a_j, or \chi^2_i = sum((D_fit[i] - D[i])**2)/D.size
is an outlier, remove that row from D.
Parameters
----------
D : Data matrix. 1-D vectors stacked vertically (row-wise). May
not contain nans, or masked values.
nMode : Number of modes
sigOut : Clip outliers that are more than sigOut away from the
median. Defaults to the config value.
maxIt : Maximum number of iterations to perform before exiting.
Defaults to the config value.
"""
Dnrow,Dncol = D.shape
D = D.copy()
gRow = np.ones(Dnrow,dtype=bool) # Good rows (not outliers)
goodid = np.arange(D.shape[0])
# Iterate SVD fits.
count = 0
finished = False
while finished is False:
print(count)
if count == maxIt:
finished=True
D = D[gRow]
Dnrow,Dncol = D.shape
U, s, V = np.linalg.svd(D,full_matrices=False)
S = np.zeros(V.shape)
S[:Dnrow,:Dnrow] = np.diag(s)
A = np.dot(U,S) # A is matrix of best fit coeff
A = A[:,:nMode]
Dfit = np.dot(A,V[:nMode]) # Dfit is D represented by a
# trucated series of modes
# Evaluate Chi2
X2 = np.sum( (Dfit - D)**2,axis=1) / Dncol
rL = moments(A)
if verbose:
print("Moments of principle component weight")
print((mlab.rec2txt(rL,precision=1)))
# Determine which rows of D are outliers
dev = (A - rL['med'])/rL['mad']
# nMode x Dncol matrix of outlier coeffients
Aout = abs(dev) > sigOut
# Dncol matrix of red-Chi2 outliers
Xout = (X2 > 3) | (X2 < 0.5)
Xout = Xout.reshape(Xout.size,1)
# Dncol matrix with the number of coeff that failed.
out = np.hstack([Xout,Aout])
# All coefficients must be inliers
gRow = out.astype(int).sum(axis=1) == 0
# If there are no outliers or we've reached the max number of
# iterations return the input U,S,V,goodid,X2. If not, clip off
# the outliers and repeat.
if gRow.all() or (count == maxIt):
finished = True
else:
names = ['ID'] + ['Chi2'] + ['a%i' % (i+1) for i in range(nMode)]
dtype = list(zip(names,[float]*len(names)))
routData = np.hstack([np.vstack(goodid),np.vstack(X2),dev])
routData = [tuple(r) for r in routData]
rout = np.array(routData,dtype=dtype)
if verbose:
print("First 10 a/MAD(a)")
print((mlab.rec2txt(rout[~gRow][:10],precision=1)))
print(("%i there are %i outliers " % (count,goodid[~gRow].size)))
goodid = goodid[gRow]
count+=1
return U,S,V,goodid,X2