def computeVertPerc2Feature(input_dir, output_dir, feature, windows_str, ticker_file, info_dir):
windows = [int(w) for w in windows_str.split(',')]
tickers = util.readTickers(ticker_file)
feature_info = {w: [] for w in windows} # [[yyyy, feature] ...]
for window in windows:
target_dir = getTargetDir(output_dir, feature, window)
if not os.path.isdir(target_dir):
os.mkdir(target_dir)
for ticker in tickers:
feature_file = '%s/%s/%s' % (input_dir, feature, ticker)
if not os.path.isfile(feature_file):
continue
data = util.readKeyValueList(feature_file)
ofps = {w: open('%s/%s' % (getTargetDir(output_dir, feature, w), ticker), 'w')
for w in windows}
for index in range(len(data)):
date, percs = compute(data, index, windows)
for window, perc in percs.iteritems():
print >> ofps[window], '%s\t%f' % (date, perc)
year = util.ymdToY(date)
for window in windows:
feature_info[window].append((year, percs[window]))
for ofp in ofps.itervalues():
ofp.close()
for window in windows:
target = getTarget(feature, window)
util.writeFeatureInfo(
[input_dir, output_dir, feature, windows_str, ticker_file],
feature_info[window], '%s/%s' % (info_dir, target))
def computePerc(input_dir, output_dir):
tickers = sorted(os.listdir(input_dir))
dvalues = dict() # date => [[ticker, value] ...]
for ticker in tickers:
data = util.readKeyValueList('%s/%s' % (input_dir, ticker))
for date, value in data:
if date not in dvalues:
dvalues[date] = [[ticker, value]]
else:
dvalues[date].append([ticker, value])
# Convert raw value to perc.
for tvalues in dvalues.itervalues():
tvalues.sort(key=lambda item: item[1])
for i in range(len(tvalues)):
tvalues[i][1] = float(i) / len(tvalues)
tvalues = dict() # ticker => [[date, perc] ...]
for date, tpercs in dvalues.iteritems():
for ticker, perc in tpercs:
if ticker not in tvalues:
tvalues[ticker] = [[date, perc]]
else:
tvalues[ticker].append([date, perc])
# Write output.
for ticker, values in tvalues.iteritems():
values.sort(key=lambda value: value[0])
with open('%s/%s' % (output_dir, ticker), 'w') as fp:
for date, perc in values:
print >> fp, '%s\t%f' % (date, perc)
def computePerc(input_dir, output_dir):
tickers = sorted(os.listdir(input_dir))
dvalues = dict() # date => [[ticker, value] ...]
for ticker in tickers:
data = util.readKeyValueList('%s/%s' % (input_dir, ticker))
for date, value in data:
if date not in dvalues:
dvalues[date] = [[ticker, value]]
else:
dvalues[date].append([ticker, value])
# Convert raw value to perc.
for tvalues in dvalues.itervalues():
tvalues.sort(key=lambda item: item[1])
for i in range(len(tvalues)):
tvalues[i][1] = float(i)/len(tvalues)
tvalues = dict() # ticker => [[date, perc] ...]
for date, tpercs in dvalues.iteritems():
for ticker, perc in tpercs:
if ticker not in tvalues:
tvalues[ticker] = [[date, perc]]
else:
tvalues[ticker].append([date, perc])
# Write output.
for ticker, values in tvalues.iteritems():
values.sort(key=lambda value: value[0])
with open('%s/%s' % (output_dir, ticker), 'w') as fp:
for date, perc in values:
print >> fp, '%s\t%f' % (date, perc)
def getGainDict(gain_file):
dgains = util.readKeyValueList(gain_file)
gain_dict = dict()
for date, gain in dgains:
ym = util.ymdToYm(date)
assert ym not in gain_dict
gain_dict[ym] = [date, gain]
return gain_dict
def computePreviousFeature(feature_dir, k, pfeature_dir):
tickers = sorted(os.listdir(feature_dir))
for ticker in tickers:
feature_file = '%s/%s' % (feature_dir, ticker)
dfeatures = util.readKeyValueList(feature_file)
with open('%s/%s' % (pfeature_dir, ticker), 'w') as fp:
for date, feature in dfeatures:
ym = util.ymdToYm(date)
pdate = util.getNextYm(ym, k)
print >> fp, '%s-01\t%f' % (pdate, feature)
def computePreviousFeature(feature_dir, k, pfeature_dir):
tickers = sorted(os.listdir(feature_dir))
for ticker in tickers:
feature_file = '%s/%s' % (feature_dir, ticker)
dfeatures = util.readKeyValueList(feature_file)
with open('%s/%s' % (pfeature_dir, ticker), 'w') as fp:
for date, feature in dfeatures:
ym = util.ymdToYm(date)
pdate = util.getNextYm(ym, k)
print >> fp, '%s-01\t%f' % (pdate, feature)
def computeVolatility(price_dir, k, volatility_dir):
assert k > 0
tickers = sorted(os.listdir(price_dir))
for ticker in tickers:
price_file = '%s/%s' % (price_dir, ticker)
dprices = util.readKeyValueList(price_file)
with open('%s/%s' % (volatility_dir, ticker), 'w') as fp:
for i in range(len(dprices)):
prices = [dprices[j][1] for j in range(max(0,i-k), i+1)]
gains = [(prices[j+1] - prices[j]) / (prices[j] + EPS) for j in range(len(prices) - 1)]
volatility = computeStd(gains)
print >> fp, '%s\t%f' % (dprices[i][0], volatility)
def computeWindowFeature(args):
assert args.do_raw or args.do_fd
tickers = sorted(os.listdir(args.value_dir))
windows = [int(window) for window in args.windows.split(',')]
assert min(windows) >= 0, 'cannot look at future values'
assert len(windows) > 0
assert len(windows) > 1 or not args.do_fd
max_window = max(windows)
for ticker in tickers:
raw_fps = None
fd_fps = None
if args.do_raw:
raw_fps = []
for window in windows:
raw_dir = '%s/%s%d' % (args.feature_dir, args.prefix, window)
if not os.path.isdir(raw_dir):
os.mkdir(raw_dir)
raw_fps.append(open('%s/%s' % (raw_dir, ticker), 'w'))
if args.do_fd:
fd_fps = []
for i in range(len(windows) - 1):
fd_dir = '%s/%sfd-%d' % (args.feature_dir, args.prefix,
windows[i])
if not os.path.isdir(fd_dir):
os.mkdir(fd_dir)
fd_fps.append(open('%s/%s' % (fd_dir, ticker), 'w'))
dvalues = util.readKeyValueList('%s/%s' % (args.value_dir, ticker))
for i in range(max_window, len(dvalues)):
values = [dvalues[i - window][1] for window in windows]
if raw_fps:
raws = util.normalize(values)
assert len(raws) == len(raw_fps)
for j in range(len(raws)):
print >> raw_fps[j], '%s\t%f' % (dvalues[i][0], raws[j])
if fd_fps:
derivatives = [
(values[j] - values[j + 1]) / (values[j + 1] + args.bonus)
for j in range(len(windows) - 1)
]
derivatives = util.normalize(derivatives)
assert len(derivatives) == len(fd_fps)
for j in range(len(derivatives)):
print >> fd_fps[j], '%s\t%f' % (dvalues[i][0],
derivatives[j])
if raw_fps:
for fp in raw_fps:
fp.close()
if fd_fps:
for fp in fd_fps:
fp.close()
def computeVolatility(price_dir, k, volatility_dir):
assert k > 0
tickers = sorted(os.listdir(price_dir))
for ticker in tickers:
price_file = '%s/%s' % (price_dir, ticker)
dprices = util.readKeyValueList(price_file)
with open('%s/%s' % (volatility_dir, ticker), 'w') as fp:
for i in range(len(dprices)):
prices = [dprices[j][1] for j in range(max(0, i - k), i + 1)]
gains = [(prices[j + 1] - prices[j]) / (prices[j] + EPS)
for j in range(len(prices) - 1)]
volatility = computeStd(gains)
print >> fp, '%s\t%f' % (dprices[i][0], volatility)
def computeRollingWindowFeature(args):
assert args.window > 0
tickers = sorted(os.listdir(args.input_dir))
for ticker in tickers:
dvalues = util.readKeyValueList('%s/%s' % (args.input_dir, ticker))
dates = [dvalue[0] for dvalue in dvalues]
values = [dvalue[1] for dvalue in dvalues]
for i in range(len(dates) - 1):
assert dates[i] < dates[i + 1]
with open('%s/%s' % (args.output_dir, ticker), 'w') as fp:
for i in range(args.window - 1, len(dates)):
wvalues = values[i - args.window + 1:i + 1]
if args.method == 'mean':
f = sum(wvalues) / args.window
print >> fp, '%s\t%f' % (dates[i], f)
def computeVertPercFeature(input_dir, output_dir, feature, windows_str,
ticker_file, info_dir):
windows = [int(w) for w in windows_str.split(',')]
tickers = util.readTickers(ticker_file)
feature_info = {w: [] for w in windows} # [[yyyy, feature] ...]
for window in windows:
target_dir = getTargetDir(output_dir, feature, window)
if not os.path.isdir(target_dir):
os.mkdir(target_dir)
for ticker in tickers:
feature_file = '%s/%s/%s' % (input_dir, feature, ticker)
if not os.path.isfile(feature_file):
continue
data = util.readKeyValueList(feature_file)
ofps = {
w: open('%s/%s' % (getTargetDir(output_dir, feature, w), ticker),
'w')
for w in windows
}
for index in range(len(data)):
date, ws, vs = collectData(data, index, windows)
ps = computePerc(vs)
for i in range(len(ws)):
print >> ofps[ws[i]], '%s\t%f' % (date, ps[i])
year = util.ymdToY(date)
for window in windows:
try:
i = ws.index(window)
feature_info[window].append((year, ps[i]))
except ValueError:
feature_info[window].append((year, None))
for ofp in ofps.itervalues():
ofp.close()
for window in windows:
target = getTarget(feature, window)
util.writeFeatureInfo(
[input_dir, output_dir, feature, windows_str, ticker_file],
feature_info[window], '%s/%s' % (info_dir, target))
def computeVertGainFeature(input_dir, output_dir, feature, windows_str,
ticker_file, info_dir):
windows = [int(w) for w in windows_str.split(',')]
tickers = util.readTickers(ticker_file)
feature_info = {w: [] for w in windows} # [[yyyy, feature] ...]
for window in windows:
target_dir = getTargetDir(output_dir, feature, window)
if not os.path.isdir(target_dir):
os.mkdir(target_dir)
for ticker in tickers:
feature_file = '%s/%s/%s' % (input_dir, feature, ticker)
if not os.path.isfile(feature_file):
continue
data = util.readKeyValueList(feature_file)
ofps = {
w: open('%s/%s' % (getTargetDir(output_dir, feature, w), ticker),
'w')
for w in windows
}
for index in range(len(data)):
date, gains = compute(data, index, windows)
year = util.ymdToY(date)
for window, gain in gains.iteritems():
print >> ofps[window], '%s\t%f' % (date, gain)
for window in windows:
if window in gains:
feature_info[window].append((year, gains[window]))
else:
feature_info[window].append((year, None))
for ofp in ofps.itervalues():
ofp.close()
for window in windows:
target = getTarget(feature, window)
util.writeFeatureInfo(
[input_dir, output_dir, feature, windows_str, ticker_file],
feature_info[window], '%s/%s' % (info_dir, target))
def filterMetadata(input_file, min_raw_price, raw_price_dir, max_volatility,
volatility_dir, min_marketcap, marketcap_dir, max_holes,
hole_dir, membership_file, remove_neg_labels, label_file,
output_file):
stats = {
'min_raw_price': 0,
'max_volatility': 0,
'min_marketcap': 0,
'max_holes': 0,
'membership': 0,
'neg_label': 0,
}
ifp = open(input_file, 'r')
if remove_neg_labels:
lfp = open(label_file, 'r')
ofp = open(output_file, 'w')
prev_ticker = None
price = None # for prev_ticker, date => price
volatility = None # for prev ticker, date => volatility
marketcap = None # for prev ticker, [dates, values]
hole = None # for prev ticker, [dates, holes]
if membership_file is None:
membership = None
else:
membership = readMembership(
membership_file) # ticker => [[start, end] ...]
while True:
line = ifp.readline()
if remove_neg_labels:
lline = lfp.readline()
if line == '':
if remove_neg_labels:
assert lline == '', 'inconsisten line count between meta and label files'
break
assert line.endswith('\n')
items = line[:-1].split('\t')
assert len(items) >= 2
ticker, date = items[0], items[1]
if ticker != prev_ticker:
prev_ticker = ticker
if raw_price_dir is not None:
price = util.readKeyValueDict('%s/%s' %
(raw_price_dir, ticker))
if volatility_dir is not None:
volatility = util.readKeyValueDict('%s/%s' %
(volatility_dir, ticker))
if marketcap_dir is not None:
tmp = util.readKeyValueList('%s/%s' % (marketcap_dir, ticker))
marketcap_dates = [t[0] for t in tmp]
marketcap_values = [t[1] for t in tmp]
marketcap = (marketcap_dates, marketcap_values)
if hole_dir is not None:
tmp = util.readKeyValueList('%s/%s' % (hole_dir, ticker))
hole_dates = [t[0] for t in tmp]
hole_values = [t[1] for t in tmp]
hole = (hole_dates, hole_values)
# Maybe check price.
if price is not None:
assert date in price, 'missing price for %s on %s' % (ticker, date)
if price[date] < min_raw_price:
stats['min_raw_price'] += 1
continue
# Maybe check volatility.
if volatility is not None:
assert date in volatility, 'missing volatility for %s on %s' % (
ticker, date)
if volatility[date] > max_volatility:
stats['max_volatility'] += 1
continue
# Maybe check marketcap.
if marketcap is not None:
marketcap_dates, marketcap_values = marketcap
index = bisect.bisect_right(marketcap_dates, date) - 1
if index < 0 or marketcap_values[index] < min_marketcap:
stats['min_marketcap'] += 1
continue
# Maybe check holes.
if hole is not None:
hole_dates, hole_values = hole
index = bisect.bisect_right(hole_dates, date) - 1
if index < 0 or hole_values[index] > max_holes:
stats['max_holes'] += 1
continue
# Maybe check membership.
if membership is not None:
if not isMember(membership, ticker, date):
stats['membership'] += 1
continue
# Maybe check label.
if remove_neg_labels:
assert lline.endswith('\n')
label = float(lline[:-1])
if label < 0:
stats['neg_label'] += 1
continue
print >> ofp, line[:-1]
logging.info('skip_stats: %s' % stats)
def collectData(gain_dir, date_file, max_neg, min_pos, feature_base_dir,
feature_list_file, feature_stats_file, min_date, max_date,
window, min_feature_perc, data_file, label_file, rlabel_file,
meta_file, weight_power, weight_file):
tickers = sorted(os.listdir(gain_dir))
feature_list = readFeatureList(feature_list_file)
min_feature_count = int(len(feature_list) * min_feature_perc)
feature_ranges = readFeatureRanges(feature_stats_file)
for feature in feature_list:
if feature not in feature_ranges:
assert (feature.find('gain') > 0 or feature.find('price') > 0
or feature.find('volume') > 0
or feature.find('volatility') > 0
or feature.find('_hp') > 0 or feature.startswith('sector')
or feature.startswith('industry')
or feature.startswith('window')), (
'no range info for feature %s' % feature)
feature_ranges[feature] = [float('-Inf'), float('Inf')]
lower, upper = feature_ranges[feature]
data_fp = open(data_file, 'w')
label_fp = open(label_file, 'w')
rlabel_fp = open(rlabel_file, 'w')
meta_fp = open(meta_file, 'w')
weight_fp = None
if weight_file:
weight_fp = open(weight_file, 'w')
skip_stats = {
'feature_file': 0,
'index': 0,
'min_date': 0,
'max_date': 0,
'filter_date': 0,
'neg_pos': 0,
'window': 0,
'min_perc': 0,
'1_perc': 0,
'99_perc': 0
}
dates = None
if date_file:
with open(date_file, 'r') as fp:
dates = set(fp.read().splitlines())
for ticker in tickers:
gain_file = '%s/%s' % (gain_dir, ticker)
gains = util.readKeyValueList(gain_file)
feature_items = [[] for i in range(len(feature_list))]
for i in range(len(feature_list)):
feature_file = '%s/%s/%s' % (feature_base_dir, feature_list[i],
ticker)
if not os.path.isfile(feature_file):
skip_stats['feature_file'] += 1
continue
keys, values = util.readKeyListValueList(feature_file)
for j in range(len(keys)):
if keys[j] == '*':
continue
ymd = keys[j].split('-')
if len(ymd) == 3:
continue
# Change yyyy-mm to yyyy-mm-01
assert len(ymd) == 2
keys[j] += '-01'
feature_items[i] = [keys, values]
for gain_date, gain in gains:
if gain_date < min_date:
skip_stats['min_date'] += 1
continue
if gain_date > max_date:
skip_stats['max_date'] += 1
continue
if dates is not None and gain_date not in dates:
skip_stats['filter_date'] += 1
continue
if max_neg < gain and gain < min_pos:
skip_stats['neg_pos'] += 1
# Do not skip these they need to be part of testing data.
# Instead output negative label and postpone filtering to filter_metadata
# (remove negative labels for training and not for testing).
#continue
if DEBUG:
print 'gain: %f (%s)' % (gain, gain_date)
if gain <= max_neg:
weight = max_neg - gain
label = 0.0
elif gain >= min_pos:
weight = gain - min_pos
label = 1.0
else:
weight = 0.0
label = -1.0
weight = weight**weight_power
features = [MISSING_VALUE for i in range(len(feature_list))]
feature_count = 0
for i in range(len(feature_list)):
keys, values = feature_items[i]
if len(keys) == 1 and keys[0] == '*':
# undated feature, eg sector
index = 0
else:
# dated feature, eg pgain
index = bisect.bisect_right(keys, gain_date) - 1
if index < 0:
skip_stats['index'] += 1
continue
gain_date_obj = datetime.datetime.strptime(
gain_date, '%Y-%m-%d')
feature_date_obj = datetime.datetime.strptime(
keys[index], '%Y-%m-%d')
delta = (gain_date_obj - feature_date_obj).days
if delta > window:
skip_stats['window'] += 1
continue
feature = values[index]
lower, upper = feature_ranges[feature_list[i]]
if feature < lower:
skip_stats['1_perc'] += 1
continue
if feature > upper:
skip_stats['99_perc'] += 1
continue
if DEBUG:
print 'feature %s: (%s, %f)' % (feature_list[i],
keys[index], feature)
features[i] = feature
feature_count += 1
if feature_count < min_feature_count:
skip_stats['min_perc'] += 1
continue
print >> data_fp, ' '.join(
['%f' % feature for feature in features])
print >> label_fp, '%f' % label
print >> rlabel_fp, '%f' % gain
print >> meta_fp, '%s\t%s\t%d\t%f' % (ticker, gain_date,
feature_count, gain)
if weight_fp:
print >> weight_fp, '%f' % weight
if DEBUG: break
data_fp.close()
label_fp.close()
rlabel_fp.close()
meta_fp.close()
if weight_fp:
weight_fp.close()
logging.info('skip_stats: %s' % skip_stats)
def computePercFeature(input_dir, tickers, rank, output_dir):
# ticker => [[date, value] ...]
# where date is the first yyyy-mm after data is published.
# Dates are deduped (any yyyy-mm with more than one values available,
# the latest one wins).
data = dict()
for ticker in tickers:
dvalues = util.readKeyValueList('%s/%s' % (input_dir, ticker))
udvalues = []
for i in range(len(dvalues)):
date = util.getNextYm(util.ymdToYm(dvalues[i][0]))
if len(udvalues) > 0 and udvalues[-1][0] == date:
udvalues[-1][1] = dvalues[i][1]
else:
if len(udvalues) > 0:
assert udvalues[-1][0] < date
udvalues.append([date, dvalues[i][1]])
data[ticker] = udvalues
min_date = '9999-99'
max_date = '0000-00'
for dvalues in data.itervalues():
if len(dvalues) == 0:
continue
min_date = min(min_date, dvalues[0][0])
max_date = max(max_date, dvalues[-1][0])
percs = dict() # date => [[ticker, value] ...]
date = min_date
while date <= max_date:
percs[date] = []
date = util.getNextYm(date)
for ticker, dvalues in data.iteritems():
for i in range(len(dvalues)):
date, value = dvalues[i]
if i < len(dvalues) - 1:
# Populate value up to next date (not inclusive).
next = dvalues[i+1][0]
else:
# Populate value up to max date (inclusive).
next = util.getNextYm(max_date)
while date < next:
percs[date].append([ticker, value])
date = util.getNextYm(date)
# Calculate percentiles.
for date, tvalues in percs.iteritems():
assert len(tvalues) > 0
# Use 0.5 if there is a single element.
if len(tvalues) == 1:
tvalues[0][1] = 0.5
continue
if rank:
tvalues.sort(key=lambda item: item[1])
for i in range(len(tvalues)):
tvalues[i][1] = float(i)/len(tvalues)
else:
values = [item[1] for item in tvalues]
maxv = max(values)
minv = min(values)
span = maxv - minv
for i in range(len(tvalues)):
if span < EPS:
tvalues[i][1] = 0.5
else:
tvalues[i][1] = (tvalues[i][1] - minv) / span
# Write output.
data = dict() # ticker => [[date, perc] ...]
for date, tpercs in percs.iteritems():
for ticker, perc in tpercs:
if ticker not in data:
data[ticker] = [[date, perc]]
else:
data[ticker].append([date, perc])
for ticker, dpercs in data.iteritems():
dpercs.sort(key=lambda item: item[0])
with open('%s/%s' % (output_dir, ticker), 'w') as fp:
for date, perc in dpercs:
print >> fp, '%s\t%f' % (date, perc)
def computePercFeature(input_dir, tickers, rank, output_dir):
# ticker => [[date, value] ...]
# where date is the first yyyy-mm after data is published.
# Dates are deduped (any yyyy-mm with more than one values available,
# the latest one wins).
data = dict()
for ticker in tickers:
dvalues = util.readKeyValueList('%s/%s' % (input_dir, ticker))
udvalues = []
for i in range(len(dvalues)):
date = util.getNextYm(util.ymdToYm(dvalues[i][0]))
if len(udvalues) > 0 and udvalues[-1][0] == date:
udvalues[-1][1] = dvalues[i][1]
else:
if len(udvalues) > 0:
assert udvalues[-1][0] < date
udvalues.append([date, dvalues[i][1]])
data[ticker] = udvalues
min_date = '9999-99'
max_date = '0000-00'
for dvalues in data.itervalues():
if len(dvalues) == 0:
continue
min_date = min(min_date, dvalues[0][0])
max_date = max(max_date, dvalues[-1][0])
percs = dict() # date => [[ticker, value] ...]
date = min_date
while date <= max_date:
percs[date] = []
date = util.getNextYm(date)
for ticker, dvalues in data.iteritems():
for i in range(len(dvalues)):
date, value = dvalues[i]
if i < len(dvalues) - 1:
# Populate value up to next date (not inclusive).
next = dvalues[i + 1][0]
else:
# Populate value up to max date (inclusive).
next = util.getNextYm(max_date)
while date < next:
percs[date].append([ticker, value])
date = util.getNextYm(date)
# Calculate percentiles.
for date, tvalues in percs.iteritems():
assert len(tvalues) > 0
# Use 0.5 if there is a single element.
if len(tvalues) == 1:
tvalues[0][1] = 0.5
continue
if rank:
tvalues.sort(key=lambda item: item[1])
for i in range(len(tvalues)):
tvalues[i][1] = float(i) / len(tvalues)
else:
values = [item[1] for item in tvalues]
maxv = max(values)
minv = min(values)
span = maxv - minv
for i in range(len(tvalues)):
if span < EPS:
tvalues[i][1] = 0.5
else:
tvalues[i][1] = (tvalues[i][1] - minv) / span
# Write output.
data = dict() # ticker => [[date, perc] ...]
for date, tpercs in percs.iteritems():
for ticker, perc in tpercs:
if ticker not in data:
data[ticker] = [[date, perc]]
else:
data[ticker].append([date, perc])
for ticker, dpercs in data.iteritems():
dpercs.sort(key=lambda item: item[0])
with open('%s/%s' % (output_dir, ticker), 'w') as fp:
for date, perc in dpercs:
print >> fp, '%s\t%f' % (date, perc)
def collectData(gain_dir, feature_base_dir, feature_list_file,
feature_stats_file, min_date, max_date,
window, min_feature_perc, data_file, meta_file):
tickers = sorted(os.listdir(gain_dir))
feature_list = readFeatureList(feature_list_file)
min_feature_count = int(len(feature_list) * min_feature_perc)
feature_ranges = readFeatureRanges(feature_stats_file)
for feature in feature_list:
if feature not in feature_ranges:
assert (feature.find('gain') > 0 or
feature.find('price') > 0 or
feature.find('volume') > 0 or
feature.find('volatility') > 0 or
feature.find('_hp') > 0 or
feature.startswith('sector') or
feature.startswith('industry')), (
'no range info for feature %s' % feature)
feature_ranges[feature] = [float('-Inf'), float('Inf')]
lower, upper = feature_ranges[feature]
data_fp = open(data_file, 'w')
meta_fp = open(meta_file, 'w')
skip_stats = {'feature_file': 0,
'index': 0,
'min_date': 0,
'max_date': 0,
'window': 0,
'min_perc': 0,
'1_perc': 0,
'99_perc': 0}
for ticker in tickers:
gain_file = '%s/%s' % (gain_dir, ticker)
gains = util.readKeyValueList(gain_file)
feature_items = [[] for i in range(len(feature_list))]
for i in range(len(feature_list)):
feature_file = '%s/%s/%s' % (feature_base_dir, feature_list[i], ticker)
if not os.path.isfile(feature_file):
skip_stats['feature_file'] += 1
continue
items = util.readKeyValueList(feature_file)
for j in range(len(items)):
if items[j][0] == '*':
continue
ymd = items[j][0].split('-')
if len(ymd) == 3:
continue
# Change yyyy-mm to yyyy-mm-01
assert len(ymd) == 2
items[j][0] += '-01'
feature_items[i] = items
for gain_date, gain in gains:
if gain_date < min_date:
skip_stats['min_date'] += 1
continue
if gain_date > max_date:
skip_stats['max_date'] += 1
continue
if DEBUG:
print 'gain: %f (%s)' % (gain, gain_date)
features = [MISSING_VALUE for i in range(len(feature_list))]
feature_count = 0
for i in range(len(feature_list)):
if len(feature_items[i]) == 1 and feature_items[i][0][0] == '*':
# undated feature, eg sector
index = 0
else:
# dated feature, eg pgain
feature_dates = [item[0] for item in feature_items[i]]
index = bisect.bisect_right(feature_dates, gain_date) - 1
if index < 0:
skip_stats['index'] += 1
continue
gain_date_obj = datetime.datetime.strptime(gain_date, '%Y-%m-%d')
feature_date_obj = datetime.datetime.strptime(feature_dates[index],
'%Y-%m-%d')
delta = (gain_date_obj - feature_date_obj).days
if delta > window:
skip_stats['window'] += 1
continue
feature = feature_items[i][index][1]
lower, upper = feature_ranges[feature_list[i]]
if feature < lower:
skip_stats['1_perc'] += 1
continue
if feature > upper:
skip_stats['99_perc'] += 1
continue
if DEBUG:
print 'feature %s: (%s, %f)' % (
feature_list[i], feature_items[i][index][0], feature)
features[i] = feature
feature_count += 1
if feature_count < min_feature_count:
skip_stats['min_perc'] += 1
continue
print >> data_fp, ' '.join(['%f' % feature for feature in features])
print >> meta_fp, '%s\t%s\t%d\t%f' % (
ticker, gain_date, feature_count, gain)
if DEBUG: break
data_fp.close()
meta_fp.close()
logging.info('skip_stats: %s' % skip_stats)
