def fetch_returns(self):
rshift, lshift = max(self.rshifts), max(self.lshifts)
dates = dateutil.to_datestr(self.alpha.index)
ei = DATES.index(dates[-1])
edate = DATES[min(len(DATES)-2, ei+rshift)]
ret = quote.fetch('returns', dates[0], edate, backdays=rshift+lshift)
return ret
def operate(self, alpha, date=None):
alpha = alpha[np.isfinite(alpha)]
if isinstance(alpha, pd.Series):
if self.group is None:
return rank(alpha)
if isinstance(self.group, pd.DataFrame):
group = self.group.ix[date]
else:
group = self.group
sids = group.dropna().index
nalpha = alpha.ix[sids]
nalpha = nalpha.groupby(group).transform(lambda x: rank(x))
return nalpha.reindex(index=alpha.index)
if self.group is None:
return rank(alpha)
if isinstance(self.group, pd.Series):
sids = self.group.dropna().index
nalpha = alpha.T.ix[sids]
nalpha = nalpha.groupby(self.group.dropna()).transform(lambda x: rank(x)).T
return nalpha.reindex(columns=alpha.columns)
dates = dateutil.to_datestr(alpha.index)
pool = multiprocessing.Pool(self.threads)
res = pool.imap_unordered(worker, [(dt1, row, self.group.ix[dt2]) for (dt1, row), dt2 in zip(alpha.iterrows(), dates)])
pool.close()
pool.join()
df = {}
for dt, row in res:
df[dt] = row
return pd.DataFrame(df).T.reindex(columns=alpha.columns)
def operate(self, alpha, date=None):
alpha = alpha[np.isfinite(alpha)]
if isinstance(alpha, pd.Series):
if self.group is None:
return rank(alpha)
if isinstance(self.group, pd.DataFrame):
group = self.group.ix[date]
else:
group = self.group
sids = group.dropna().index
nalpha = alpha.ix[sids]
nalpha = nalpha.groupby(group).transform(lambda x: rank(x))
return nalpha.reindex(index=alpha.index)
if self.group is None:
return rank(alpha)
if isinstance(self.group, pd.Series):
sids = self.group.dropna().index
nalpha = alpha.T.ix[sids]
nalpha = nalpha.groupby(
self.group.dropna()).transform(lambda x: rank(x)).T
return nalpha.reindex(columns=alpha.columns)
dates = dateutil.to_datestr(alpha.index)
pool = multiprocessing.Pool(self.threads)
res = pool.imap_unordered(
worker, [(dt1, row, self.group.ix[dt2])
for (dt1, row), dt2 in zip(alpha.iterrows(), dates)])
pool.close()
pool.join()
df = {}
for dt, row in res:
df[dt] = row
return pd.DataFrame(df).T.reindex(columns=alpha.columns)
def fetch_returns(self):
rshift, lshift = max(self.rshifts), max(self.lshifts)
dates = dateutil.to_datestr(self.alpha.index)
ei = DATES.index(dates[-1])
edate = DATES[min(len(DATES) - 2, ei + rshift)]
ret = quote.fetch('returns', dates[0], edate, backdays=rshift + lshift)
return ret
def to_frame(panel):
"""Transform a time-itemized, date-major_axised Panel into DataFrame with DatetimeIndex."""
if isinstance(panel.major_axis, DatetimeIndex):
panel.major_axis = dateutil.to_datestr(panel.major_axis)
df = panel.transpose(2, 1, 0).to_frame(filter_observations=False)
df.index = pd.to_datetime(pd.Series(df.index.get_level_values(0)) + ' ' + \
pd.Series(df.index.get_level_values(1)))
return df
def to_frame(panel):
"""Transform a time-itemized, date-major_axised Panel into DataFrame with DatetimeIndex."""
if isinstance(panel.major_axis, DatetimeIndex):
panel.major_axis = dateutil.to_datestr(panel.major_axis)
df = panel.transpose(2, 1, 0).to_frame(filter_observations=False)
df.index = pd.to_datetime(pd.Series(df.index.get_level_values(0)) + ' ' + \
pd.Series(df.index.get_level_values(1)))
return df
def _get_analyser(perf, mode):
if mode == 'longshort':
return perf.get_longshort()
elif mode == 'BTOP70Q':
univ = univ_fetcher.fetch_window('BTOP70Q', to_datestr(perf.alpha.index))
return perf.get_universe(univ).get_longshort()
elif mode == 'quantile30':
return perf.get_qtail(0.3)
elif mode == 'top30':
return perf.get_qtop(0.3)
def _get_analyser(perf, mode):
if mode == 'longshort':
return perf.get_longshort()
elif mode == 'BTOP70Q':
univ = univ_fetcher.fetch_window('BTOP70Q',
to_datestr(perf.alpha.index))
return perf.get_universe(univ).get_longshort()
elif mode == 'quantile30':
return perf.get_qtail(0.3)
elif mode == 'top30':
return perf.get_qtop(0.3)
def operate(self, alpha, group='sector', simple=False, date=None):
alpha = alpha[np.isfinite(alpha)]
if isinstance(alpha, pd.Series):
group = self.industry.fetch_daily(group, date).dropna()
nalpha = alpha.ix[group.index]
nalpha = nalpha.groupby(group).transform(lambda x: rank(x))
return nalpha.reindex(index=alpha.index)
window = np.unique(dateutil.to_datestr(alpha.index))
group = self.industry.fetch_window(group, window)
self.group = group.iloc[-1] if simple else group
return super(IndustryRankOperation, self).operate(alpha)
def operate(self, alpha, group='sector', simple=False, date=None):
alpha = alpha[np.isfinite(alpha)]
if isinstance(alpha, pd.Series):
group = self.industry.fetch_daily(group, date).dropna()
nalpha = alpha.ix[group.index]
nalpha = nalpha.groupby(group).transform(lambda x: rank(x))
return nalpha.reindex(index=alpha.index)
window = np.unique(dateutil.to_datestr(alpha.index))
group = self.industry.fetch_window(group, window)
self.group = group.iloc[-1] if simple else group
return super(IndustryRankOperation, self).operate(alpha)
def rebase_index(alpha):
res = {}
for date in np.unique(dateutil.to_datestr(alpha.index)):
sdf = alpha.ix[date]
date = dateutil.find_le(DATES, date)[1]
if isinstance(sdf, pd.DataFrame):
res[date] = sdf.astype(int).max().astype(bool)
else:
res[date] = sdf
res = pd.DataFrame(res).T.sort_index()
res.index = pd.to_datetime(res.index)
return res
def rebase_index(alpha):
res = {}
for date in np.unique(dateutil.to_datestr(alpha.index)):
sdf = alpha.ix[date]
date = dateutil.find_le(DATES, date)[1]
if isinstance(sdf, pd.DataFrame):
res[date] = sdf.astype(int).max().astype(bool)
else:
res[date] = sdf
res = pd.DataFrame(res).T.sort_index()
res.index = pd.to_datetime(res.index)
return res
def comply(df, parent=None, value=None):
if parent is None:
return
if type(df.index) != type(parent.index):
parent = parent.copy()
if isinstance(df.index, DatetimeIndex):
parent.index = dateutil.to_datetime(parent.index)
else:
parent.index = dateutil.to_datestr(parent.index)
parent = parent.ix[df.index].fillna(method='bfill').fillna(False)
df[~parent] = value
return parent
def __init__(self, alpha, n, rank=None):
self.alpha = api.format(alpha)
self.rank_alpha = self.alpha.rank(axis=1, ascending=False)
self.rank_alpha = self.rank_alpha[self.rank_alpha <= n]
if rank is None:
self.alpha = (self.rank_alpha <= n).astype(float)
else:
if rank < 0:
self.alpha = self.alpha[self.rank_alpha <= n]
else:
self.alpha = rank - np.floor(self.rank_alpha / (n + 1) * rank)
self.alpha = api.scale(self.alpha)
self.dates = dateutil.to_datestr(self.alpha.index)
def comply(df, parent=None, value=None):
if parent is None:
return
if type(df.index) != type(parent.index):
parent = parent.copy()
if isinstance(df.index, DatetimeIndex):
parent.index = dateutil.to_datetime(parent.index)
else:
parent.index = dateutil.to_datestr(parent.index)
parent = parent.ix[df.index].fillna(method='bfill').fillna(False)
df[~parent] = value
return parent
def fetch_dates(self, dname, dates, rshift=0, lshift=0, **kwargs):
"""Use :py:meth:`fetch_window` behind the scene."""
dates_str = dateutil.to_datestr(dates)
res, is_df = {}, False
for dt, date in zip(dates, dates_str):
di, date = dateutil.parse_date(DATES, date, -1)
if di-lshift < 0 or di+rshift+1 > len(DATES):
continue
if rshift+lshift == 0:
res[dt] = self.fetch_daily(dname, DATES[di-lshift], **kwargs)
if isinstance(res[dt], pd.DataFrame):
is_df = True
else:
res[dt] = self.fetch_window(dname, DATES[di-lshift: di+rshift+1], **kwargs)
if rshift+lshift == 0:
res = pd.Panel(res).transpose(1, 2, 0) if is_df else pd.DataFrame(res).T
return res
def fetch_dates(self, dates, rshift=0, lshift=0, **kwargs):
"""Use :py:meth:`fetch_window` behind the scene."""
dates_str = dateutil.to_datestr(dates)
res, is_df = {}, False
for dt, date in zip(dates, dates_str):
di, date = dateutil.parse_date(DATES, date, -1)
if di - lshift < 0 or di + rshift + 1 > len(DATES):
continue
if rshift + lshift == 0:
res[dt] = self.fetch_daily(DATES[di - lshift], **kwargs)
if isinstance(res[dt], pd.DataFrame):
is_df = True
else:
res[dt] = self.fetch_window(DATES[di - lshift:di + rshift + 1],
**kwargs)
if rshift + lshift == 0:
res = pd.Panel(res).transpose(1, 2,
0) if is_df else pd.DataFrame(res).T
return res
if args.file:
with open(args.file) as file:
for line in file:
name, fpath = line.strip().split()
ext_alphas[name] = read_frame(fpath, args.ftype)
if args.dir:
assert os.path.exists(args.dir)
for name in os.listdir(args.dir):
ext_alphas[name] = read_frame(os.path.join(args.dir, name),
args.ftype)
extalpha_metric = {}
if args.db:
assert args.alpha
db_metrics = perf_fetcher.fetch_window(args.metric,
to_datestr(dates),
mode=args.mode)
for name, metric in db_metrics.iteritems():
extalpha_metric[name] = metric
for name, alpha in ext_alphas.iteritems():
perf = Performance(alpha)
extalpha_metric[name] = get_metric(perf, args.mode, args.metric)
extmetric_df = pd.DataFrame(extalpha_metric)
if not args.alpha:
if len(extmetric_df) > args.days:
extmetric_df = extmetric_df.iloc[-args.days:]
print extmetric_df.corr()
else:
if len(extmetric_df) > 0:
ext_alphas = {}
if args.file:
with open(args.file) as file:
for line in file:
name, fpath = line.strip().split()
ext_alphas[name] = read_frame(fpath, args.ftype)
if args.dir:
assert os.path.exists(args.dir)
for name in os.listdir(args.dir):
ext_alphas[name] = read_frame(os.path.join(args.dir, name), args.ftype)
extalpha_metric = {}
if args.db:
assert args.alpha
db_metrics = perf_fetcher.fetch_window(args.metric, to_datestr(dates), mode=args.mode)
for name, metric in db_metrics.iteritems():
extalpha_metric[name] = metric
for name, alpha in ext_alphas.iteritems():
perf = Performance(alpha)
extalpha_metric[name] = get_metric(perf, args.mode, args.metric)
extmetric_df = pd.DataFrame(extalpha_metric)
if not args.alpha:
if len(extmetric_df) > args.days:
extmetric_df = extmetric_df.iloc[-args.days:]
print extmetric_df.corr()
else:
if len(extmetric_df) > 0:
extmetric_df = extmetric_df.ix[dates]
def test_to_datestr_pddt_input(self):
res = dateutil.to_datestr(DateutilTestCase.dates_pddt)
self.assertListEqual(DateutilTestCase.dates_str, res)
