def lc005(type_, country):
'''
report statistics matching (type_,country) by ad, location, and whole set,
then chart ads colored by location, styled by type_
Prints a line for all ads of this type and country styled by
location; preceded by a report.
'''
kxyl = sorted([(ad.id, k.snap, ad.diff_spotusd,
k.location.split('/')[1][:30]) for k, ads in pkl.items()
for ad in ads if k.type_ == type_
and ad.diff_spotusd != None and ad.country == country])
print("\n%s:%s stats per ad" % (type_, country))
keys = sorted(set([k for k, x, y, l in kxyl]))
for key in keys:
answer = describe([y for k, x, y, l in kxyl if k == key],
only=('mean', 'stddev'))
if answer: print('ad:%s ' % key + describe_str(answer))
print("\n%s:%s stats per location" % (type_, country))
labels = sorted(set([l for k, x, y, l in kxyl]))
for label in labels:
print('label:%s ' % label +
describe_str(describe([y for k, x, y, l in kxyl if l == label])))
print
print(describe_str(describe([y for k, x, y, l in kxyl])))
# create a lines chart
title = 'ads of type:%s in country:%s' % (type_, country)
xlabel, ylabel = '15m samples', 'difference from spot usd price'
lines_chart(kxyl, xlabel, ylabel, title)
def pdfapprox(samples):
"""Return a function that approximates the pdf of a set of samples
using a Gaussian expansion computed from the mean, variance, skewness
and Fisher's kurtosis.
"""
# Estimate mean, variance, skewness and kurtosis
mu,sig,sk,kur = stats.describe(samples)[2:]
# Get central moments
cnt = [None]*4
cnt[0] = mu
cnt[1] = sig*sig
cnt[2] = sk * sig**1.5
cnt[3] = (kur+3.0) * sig**2.0
return pdf_moments(cnt)
def pdfapprox(samples):
"""Return a function that approximates the pdf of a set of samples
using a Gaussian expansion computed from the mean, variance, skewness
and Fisher's kurtosis.
"""
# Estimate mean, variance, skewness and kurtosis
mu,sig,sk,kur = stats.describe(samples)[2:]
# Get central moments
cnt = [None]*4
cnt[0] = mu
cnt[1] = sig*sig
cnt[2] = sk * sig**1.5
cnt[3] = (kur+3.0) * sig**2.0
return pdf_moments(cnt)
def lc008(sortreversed):
'''
report and chart most severe stddev diff_spotusd by trader
if True then most else least
Each trader will have a line based on the standard deviation of their
historic above-spotusd prices (for both bids and asks). A greater
standard deviation means that their markup varied throughout their
history. A lesser standard deviation means they ran a consistent
markup over time... It says nothing about how much their markup was,
just weather it varied significantly or not. Depending on how this
recipe is called, it will answer for the the traders with the
greatest (most erratic) or least (most consistent) markup over time.
'''
if sortreversed: label = 'most'
else: label = 'least'
temp = sorted([((k.type_, ad.user), k.snap, ad.diff_spotusd)
for k, ads in pkl.items() for ad in ads
if ad.diff_spotusd != None])
keys = sorted(set(x[0] for x in temp))
kstats = {}
for key in keys:
kstats[key] = describe([y for k, x, y in temp if k == key],
only=('mean', 'stddev'))
print('40 traders with %s erratic diff_spotusd:' % label)
kvals = sorted([(k, v['mean'], v['stddev'])
for k, v in kstats.items() if v != None],
key=itemgetter(2),
reverse=sortreversed)[:40]
for kval in kvals:
print('%s %-30s %15.2f mean %15.2f stddev' %
(kval[0][0], kval[0][1][:30], kval[1], kval[2]))
kxyl = sorted([
(ad.id, k.snap, ad.diff_spotusd,
'%s %s %s' % (k.type_, ad.user, ad.country))
for k, ads in pkl.items() for ad in ads
if (k.type_, ad.user) in [x[0]
for x in kvals] and ad.diff_spotusd != None
])
title = 'ads from 40 %s erratic diff_spotusd traders' % label
xlabel, ylabel = '15m samples', 'difference from spot usd price'
lines_chart(kxyl, xlabel, ylabel, title)
def lc007(sortreversed):
'''
report and chart most severe mean diff_spotusd by trader
if True then highest else lowest
Each trader will have similarly-styled-lines for each of their ads
and the report will show the traders with the greatest or least mean
based on their historic above-spotusd prices (for both bids and asks).
A higher mean means that their markup was likely very pricey. A lower
mean means that their markup was closer to spot. A negative mean indicates
that they are advertising at prices which result in loss and may hint that
the trader is practicing bait-and-switch.
'''
if sortreversed: label = 'highest'
else: label = 'lowest'
temp = sorted([((k.type_, ad.user), k.snap, ad.diff_spotusd)
for k, ads in pkl.items() for ad in ads
if ad.diff_spotusd != None])
keys = sorted(set(x[0] for x in temp))
kstats = {}
for key in keys:
kstats[key] = describe([y for k, x, y in temp if k == key],
only=('mean', 'stddev'))
print('40 traders with %s diff_spotusd:' % label)
kvals = sorted([(k, v['mean'], v['stddev'])
for k, v in kstats.items() if v != None],
key=itemgetter(1),
reverse=sortreversed)[:40]
for kval in kvals:
print('%s %-30s %15.2f mean %15.2f stddev' %
(kval[0][0], kval[0][1][:30], kval[1], kval[2]))
kxyl = sorted([
(ad.id, k.snap, ad.diff_spotusd,
'%s %s %s' % (k.type_, ad.user, ad.country))
for k, ads in pkl.items() for ad in ads
if (k.type_, ad.user) in [x[0]
for x in kvals] and ad.diff_spotusd != None
])
title = 'ads from 40 %s diff_spotusd traders' % label
xlabel, ylabel = '15m samples', 'difference from spot usd price'
lines_chart(kxyl, xlabel, ylabel, title)
def lc009(type_, stat):
'''
for ads of (type_), chart (stat) of diff_spotusd by location
type_ in ('a','b'), stat in ('min','max','mean','median','stddev')
Each location will have a single line based on the average
above-spotusd-markup for all ads of this type at that location.
'''
keys = sorted(set(k for k in pkl.keys() if k.type_ == type_))
kstats = {}
for key in keys:
astat = describe(
[ad.diff_spotusd for ad in pkl[key] if ad.diff_spotusd != None],
only=[stat])
if astat: kstats[key] = astat[stat]
kxyl = sorted([('%s %s' % (k.type_, k.loc), k.snap, kstats[k],
'%s %s' % (k.type_, k.location.split('/')[1][:30]))
for k, y in kstats.items()])
title = '%s diff_spotusd of type:%s by location' % (stat, type_)
xlabel, ylabel = '15m samples', 'difference from spot usd price'
lines_chart(kxyl, xlabel, ylabel, title)
def lc006(type_):
'''
chart mean and stddev for ads of (type_) by location
Prints two lines per location (mean and stddev) for ads of this type,
'''
## below kept as an example of how NOT to access pkl
#temp = sorted([(k.location.split('/')[1][:30], k.snap, ad.diff_spotusd)
# for k, ads in pkl.items() for ad in ads
# if k.type_==type_ and ad.diff_spotusd != None])
#keys = sorted(set([x[0] for x in temp]))
#xs = sorted(set([x[1] for x in temp]))
#kxyl = []
#for snap in xs:
# print(snap)
# for key in keys:
# ystats = describe([y for k,x,y in temp if x==snap and k==key],
# only=['mean','stddev'])
# if not ystats: continue
# kxyl.append(('%s mean' % key, snap, ystats['mean'], '%s mean' % key))
# kxyl.append(('%s stddev' % key, snap, ystats['stddev'], '%s stddev' % key))
# print(' %-37s, %15.2f, %15.2f' % (key, ystats['mean'], ystats['stddev']))
temp = [(k,
describe([ad.diff_spotusd
for ad in ads], only=('mean', 'stddev')))
for k, ads in pkl.items() if k.type_ == type_]
kxyl = sorted([('m%d' % k.loc, k.snap, v['mean'],
'%s mean' % k.location.split('/')[1][:30]) for k, v in temp
if v != None])
kxyl.extend(
sorted([('s%d' % k.loc, k.snap, v['stddev'],
'%s stddev' % k.location.split('/')[1][:30]) for k, v in temp
if v != None]))
title = 'stddev, mean of diff_spotusd for all ads of type:%s by location' % type_
xlabel, ylabel = '15m samples', 'stddev, mean of diff_spotusd'
lines_chart(sorted(kxyl),
xlabel,
ylabel,
title,
stylecnt=2,
verbose=verbose)
print('harmonicmean:',stats.harmonicmean(l), stats.harmonicmean(lf), stats.harmonicmean(a), stats.harmonicmean(af))
print('mean:',stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af))
print('median:',stats.median(l),stats.median(lf),stats.median(a),stats.median(af))
print('medianscore:',stats.medianscore(l),stats.medianscore(lf),stats.medianscore(a),stats.medianscore(af))
print('mode:',stats.mode(l),stats.mode(a))
print('\nMOMENTS')
print('moment:',stats.moment(l),stats.moment(lf),stats.moment(a),stats.moment(af))
print('variation:',stats.variation(l),stats.variation(a),stats.variation(lf),stats.variation(af))
print('skew:',stats.skew(l),stats.skew(lf),stats.skew(a),stats.skew(af))
print('kurtosis:',stats.kurtosis(l),stats.kurtosis(lf),stats.kurtosis(a),stats.kurtosis(af))
print('mean:',stats.mean(a),stats.mean(af))
print('var:',stats.var(a),stats.var(af))
print('stdev:',stats.stdev(a),stats.stdev(af))
print('sem:',stats.sem(a),stats.sem(af))
print('describe:')
print(stats.describe(l))
print(stats.describe(lf))
print(stats.describe(a))
print(stats.describe(af))
print('\nFREQUENCY')
print('freqtable:')
print('itemfreq:')
print(stats.itemfreq(l))
print(stats.itemfreq(a))
print('scoreatpercentile:',stats.scoreatpercentile(l,40),stats.scoreatpercentile(lf,40),stats.scoreatpercentile(a,40),stats.scoreatpercentile(af,40))
print('percentileofscore:',stats.percentileofscore(l,12),stats.percentileofscore(lf,12),stats.percentileofscore(a,12),stats.percentileofscore(af,12))
print('histogram:',stats.histogram(l),stats.histogram(a))
print('cumfreq:')
print(stats.cumfreq(l))
print(stats.cumfreq(lf))
print(stats.cumfreq(a))
print 'mean:',stats.mean(l), stats.mean(lf), stats.mean(a), stats.mean(af) print 'median:',stats.median(l),stats.median(lf),stats.median(a),stats.median(af) print 'medianscore:',stats.medianscore(l),stats.medianscore(lf),stats.medianscore(a),stats.medianscore(af) print 'mode:',stats.mode(l),stats.mode(a) print '\nMOMENTS' print 'moment:',stats.moment(l),stats.moment(lf),stats.moment(a),stats.moment(af) print 'variation:',stats.variation(l),stats.variation(a),stats.variation(lf),stats.variation(af) print 'skew:',stats.skew(l),stats.skew(lf),stats.skew(a),stats.skew(af) print 'kurtosis:',stats.kurtosis(l),stats.kurtosis(lf),stats.kurtosis(a),stats.kurtosis(af) print 'tmean:',stats.tmean(a,(5,17)),stats.tmean(af,(5,17)) print 'tvar:',stats.tvar(a,(5,17)),stats.tvar(af,(5,17)) print 'tstdev:',stats.tstdev(a,(5,17)),stats.tstdev(af,(5,17)) print 'tsem:',stats.tsem(a,(5,17)),stats.tsem(af,(5,17)) print 'describe:' print stats.describe(l) print stats.describe(lf) print stats.describe(a) print stats.describe(af) print '\nFREQUENCY' print 'freqtable:' print 'itemfreq:' print stats.itemfreq(l) print stats.itemfreq(a) print 'scoreatpercentile:',stats.scoreatpercentile(l,40),stats.scoreatpercentile(lf,40),stats.scoreatpercentile(a,40),stats.scoreatpercentile(af,40) print 'percentileofscore:',stats.percentileofscore(l,12),stats.percentileofscore(lf,12),stats.percentileofscore(a,12),stats.percentileofscore(af,12) print 'histogram:',stats.histogram(l),stats.histogram(a) print 'cumfreq:' print stats.cumfreq(l) print stats.cumfreq(lf)
print 'mode:', stats.mode(l), stats.mode(a)
print '\nMOMENTS'
print 'moment:', stats.moment(l), stats.moment(lf), stats.moment(
a), stats.moment(af)
print 'variation:', stats.variation(l), stats.variation(a), stats.variation(
lf), stats.variation(af)
print 'skew:', stats.skew(l), stats.skew(lf), stats.skew(a), stats.skew(af)
print 'kurtosis:', stats.kurtosis(l), stats.kurtosis(lf), stats.kurtosis(
a), stats.kurtosis(af)
print 'mean:', stats.mean(a), stats.mean(af)
print 'var:', stats.var(a), stats.var(af)
print 'stdev:', stats.stdev(a), stats.stdev(af)
print 'sem:', stats.sem(a), stats.sem(af)
print 'describe:'
print stats.describe(l)
print stats.describe(lf)
print stats.describe(a)
print stats.describe(af)
print '\nFREQUENCY'
print 'freqtable:'
print 'itemfreq:'
print stats.itemfreq(l)
print stats.itemfreq(a)
print 'scoreatpercentile:', stats.scoreatpercentile(
l, 40), stats.scoreatpercentile(lf, 40), stats.scoreatpercentile(
a, 40), stats.scoreatpercentile(af, 40)
print 'percentileofscore:', stats.percentileofscore(
l, 12), stats.percentileofscore(lf, 12), stats.percentileofscore(
a, 12), stats.percentileofscore(af, 12)
