def fun1():
i = 0
while i < float('inf'):
if i == 0 or i % 100 == 0:
ip_get.run()
i += 1
else:
query.run()
i += 1
def main():
if (len(sys.argv) not in [2, 3]) or (sys.argv[1]
not in ['load', 'query', 'clear']):
print 'Error: invalid usage\nUsage: python main.py load|query'
sys.exit(1)
client = connect()
if sys.argv[1] == 'load':
if len(sys.argv) == 3 and sys.argv[2] == "loopdata":
loader.load_loopdata(client)
else:
loader.load(client)
if sys.argv[1] == 'query':
query.run(client)
if sys.argv[1] == 'clear':
loader.clear(client)
# since we don't currently have any way of passing args into mappers
args = parser.parse_args()
PAST_CHANGE = args.past_change * 10**-4
if args.future_change:
FUTURE_CHANGE = args.future_change * 10 ** -4
else:
FUTURE_CHANGE = PAST_CHANGE
OFFSET_TICKS = args.time_offset * 10
START_HOUR = args.start_hour
END_HOUR = args.end_hour
assert START_HOUR < END_HOUR
print "past change %", PAST_CHANGE
print "future change %", FUTURE_CHANGE
print "time offset in ticks", OFFSET_TICKS
all_ccys = query.run(args.pattern,
map_hdf = mapper,
init = {}, combine = combine)
# Once all the counts have been collected, do the simple task of
# computing probs by dividing event counts by the totals
result = {}
for (ccy, (event_count, total)) in all_ccys.items():
if total == 0: prob = 0
else: prob = event_count / float(total)
result[ccy] = {'prob':prob, 'count':event_count, 'total':total}
print result
def combine(all_amts, (ccy, amts)):
all_amts.setdefault(ccy, []).extend(amts)
from argparse import ArgumentParser
parser = ArgumentParser(description='Process some integers.')
parser.add_argument('pattern',
metavar='P',
type=str,
help='s3://capk-bucket/some-hdf-pattern')
parser.add_argument(
'--min-duration',
dest='min_dur',
type=int,
default=None,
help=
'ignore crosses which last shorter than this min. duration in milliseconds'
)
if __name__ == '__main__':
args = parser.parse_args()
assert args.pattern
assert len(args.pattern) > 0
MIN_DUR = args.min_dur
df = query.run(args.pattern,
map_hdf=cross_amounts,
init={},
combine=combine,
post_process=query_helpers.summarize_continuous)
print df
import query r = query.run() r.full_query()
from optparse import OptionParser
import query
import cross_info
import query_helpers
def cross_durations(hdf):
ccy = hdf.attrs['ccy']
durs = [cross.dur for cross in cross_info.find_crossed_markets_in_hdf(hdf)]
return ccy, durs
def combine(all_durs, (ccy,durs)):
all_durs.setsdefault(ccy, []).extend(durs)
parser = OptionParser(usage = "usage: %prog s3://bucket-name/key-pattern")
if __name__ == '__main__':
(options, args) = parser.parse_args()
assert len(args)==1
df = query.run(args[0], map_hdf = cross_durations,
init = {}, combine = combine,
post_process = query_helpers.summarize_continuous)
print df
def combine(all_amts, (ccy, amts)):
all_amts.setdefault(ccy, []).extend(amts)
from argparse import ArgumentParser
parser = ArgumentParser(description="Process some integers.")
parser.add_argument("pattern", metavar="P", type=str, help="s3://capk-bucket/some-hdf-pattern")
parser.add_argument(
"--min-duration",
dest="min_dur",
type=int,
default=None,
help="ignore crosses which last shorter than this min. duration in milliseconds",
)
parser.add_argument("--future", dest="future", type=int, default=10000)
if __name__ == "__main__":
args = parser.parse_args()
print "Args", args
assert args.pattern
assert len(args.pattern) > 0
MIN_DUR = args.min_dur
FUTURE_HORIZON = args.future
df = query.run(
args.pattern, map_hdf=does_cross_return, init={}, combine=combine, post_process=query_helpers.summarize_bool
)
print df
import query
import numpy as np
def map(hdf):
"""Takes an HDF, returns a tuple of min/max midprices"""
# Note: I slice into the HDF's column to pull out a numpy array
midprice = (hdf['bid'][:]+hdf['offer'][:])/2.0
return np.min(midprice), np.max(midprice)
def combine(values, (curr_min, curr_max)):
values.append(curr_min)
values.append(curr_max)
from argparse import ArgumentParser
parser = ArgumentParser(description='min/max for ccy pair')
parser.add_argument('--ccy', dest='ccy', type=str, required=True, help="e.g. USDJPY")
parser.add_argument('--bucket', dest='bucket', type=str, default='capk-fxcm-hdf')
if __name__ == '__main__':
args = parser.parse_args()
print "args", args
pattern = '*' + args.ccy + '*.hdf'
values = query.run(args.bucket, pattern,
map_hdf = map, combine = combine, init = [])
print "Min: %s, Max: %s" % (np.min(values), np.max(values))
def count_crosses(hdf):
ccy = hdf.attrs['ccy']
date = (hdf.attrs['year'], hdf.attrs['month'], hdf.attrs['day'])
count = np.sum(hdf['bid'][:] > hdf['offer'][:])
print "Found", count, "crossed tick for", ccy, "on", date
return ccy, count
def combine(counts, (ccy, count)):
old = counts.get(ccy, 0)
counts[ccy] = old + count
def convert_to_dataframe(total_counts):
return pandas.DataFrame({"count": total_counts.values()},
index=total_counts.keys())
parser = OptionParser(usage="usage: %prog s3://bucket-name/key-pattern")
if __name__ == '__main__':
(options, args) = parser.parse_args()
assert len(args) == 1
df = query.run(args[0],
map_hdf=count_crosses,
init={},
combine=combine,
post_process=convert_to_dataframe)
print df
MIN_DUR = None
def cross_amounts(hdf):
ccy = hdf.attrs['ccy']
amts = [cross.amt * cross.min_vol \
for cross in cross_info.find_crossed_markets_in_hdf(hdf)
if MIN_DUR is None or cross.dur >= MIN_DUR]
return ccy, amts
def combine(all_amts, (ccy,amts)):
all_amts.setdefault(ccy, []).extend(amts)
from argparse import ArgumentParser
parser = ArgumentParser(description='Process some integers.')
parser.add_argument('pattern', metavar='P', type=str,
help='s3://capk-bucket/some-hdf-pattern')
parser.add_argument('--min-duration', dest='min_dur', type=int, default=None,
help = 'ignore crosses which last shorter than this min. duration in milliseconds')
if __name__ == '__main__':
args = parser.parse_args()
assert args.pattern
assert len(args.pattern) > 0
MIN_DUR = args.min_dur
df = query.run(args.pattern,
map_hdf = cross_amounts,
init = {}, combine = combine,
post_process = query_helpers.summarize_continuous)
print df
from optparse import OptionParser
import numpy as np
import query
import pandas
def count_crosses(hdf):
ccy = hdf.attrs["ccy"]
date = (hdf.attrs["year"], hdf.attrs["month"], hdf.attrs["day"])
count = np.sum(hdf["bid"][:] > hdf["offer"][:])
print "Found", count, "crossed tick for", ccy, "on", date
return ccy, count
def combine(counts, (ccy, count)):
old = counts.get(ccy, 0)
counts[ccy] = old + count
def convert_to_dataframe(total_counts):
return pandas.DataFrame({"count": total_counts.values()}, index=total_counts.keys())
parser = OptionParser(usage="usage: %prog s3://bucket-name/key-pattern")
if __name__ == "__main__":
(options, args) = parser.parse_args()
assert len(args) == 1
df = query.run(args[0], map_hdf=count_crosses, init={}, combine=combine, post_process=convert_to_dataframe)
print df
# either the cross was shorter than 50ms or both prices changed
# at the end
returns = np.sum(future_bid_ok & future_offer_ok & future_vol_ok)
print cross, "returns", returns, "times in", FUTURE_HORIZON / 1000.0, "seconds"
result.append(returns > 0)
return ccy, result
def combine(all_amts, (ccy,amts)):
all_amts.setdefault(ccy, []).extend(amts)
from argparse import ArgumentParser
parser = ArgumentParser(description='Process some integers.')
parser.add_argument('pattern', metavar='P', type=str,
help='s3://capk-bucket/some-hdf-pattern')
parser.add_argument('--min-duration', dest='min_dur', type=int, default=None,
help = 'ignore crosses which last shorter than this min. duration in milliseconds')
parser.add_argument('--future', dest = 'future', type=int, default=10000)
if __name__ == '__main__':
args = parser.parse_args()
print "Args", args
assert args.pattern
assert len(args.pattern) > 0
MIN_DUR = args.min_dur
FUTURE_HORIZON = args.future
df = query.run(args.pattern,
map_hdf = does_cross_return,
init = {}, combine = combine,
post_process = query_helpers.summarize_bool)
print df
midprice = (hdf['bid'][:] + hdf['offer'][:]) / 2.0
return np.min(midprice), np.max(midprice)
def combine(values, (curr_min, curr_max)):
values.append(curr_min)
values.append(curr_max)
from argparse import ArgumentParser
parser = ArgumentParser(description='min/max for ccy pair')
parser.add_argument('--ccy',
dest='ccy',
type=str,
required=True,
help="e.g. USDJPY")
parser.add_argument('--bucket',
dest='bucket',
type=str,
default='capk-fxcm-hdf')
if __name__ == '__main__':
args = parser.parse_args()
print "args", args
pattern = '*' + args.ccy + '*.hdf'
values = query.run(args.bucket,
pattern,
map_hdf=map,
combine=combine,
init=[])
print "Min: %s, Max: %s" % (np.min(values), np.max(values))
args = parser.parse_args()
PAST_CHANGE = args.past_change * 10**-4
if args.future_change:
FUTURE_CHANGE = args.future_change * 10**-4
else:
FUTURE_CHANGE = PAST_CHANGE
OFFSET_TICKS = args.time_offset * 10
START_HOUR = args.start_hour
END_HOUR = args.end_hour
assert START_HOUR < END_HOUR
print "past change %", PAST_CHANGE
print "future change %", FUTURE_CHANGE
print "time offset in ticks", OFFSET_TICKS
all_ccys = query.run(args.pattern,
map_hdf=mapper,
init={},
combine=combine)
# Once all the counts have been collected, do the simple task of
# computing probs by dividing event counts by the totals
result = {}
for (ccy, (event_count, total)) in all_ccys.items():
if total == 0: prob = 0
else: prob = event_count / float(total)
result[ccy] = {'prob': prob, 'count': event_count, 'total': total}
print result
