def find_data_based_trading_opportunities(candidates=[ ]):
if len(candidates)==0:
for q_id in summarizequestions.by_id.keys():
if opinions.has_key(q_id):
continue
if q_id in DO_NOT_TRUST_CONSENSUS:
continue
q = summarizequestions.by_id[q_id]
if q['question']['settlement_at'] == None:
settled_at = datetime(2050,1,1,0,0,0)
else:
settled_at = datetime.strptime(q['question']['settlement_at'][0:19], "%Y-%m-%dT%H:%M:%S")
if settled_at < datetime.now() + timedelta(2):
continue
if q['trade_count']>60:
candidates.append(q_id)
candidates = list(set(candidates))
candidates.sort(key=lambda q_id: summarizequestions.by_id[q_id]['trade_count'], reverse=True)
candidates = candidates#[:60]
# (my_final_score, credit, final_standing, estdeb, s)
output = [ ]
for candidate in candidates:
res = determine_trade_from_data(candidate)
if res:
output.append(res)
output.sort(key=lambda x: x[3], reverse=True)
for score, credit, final_standing, estdeb, v, steps in output:
if estdeb > 0.666 or VERBOSE:
print v
comment_range[1] = max(comment_range[1], q['comment_count'])
by_id[ q['question']['id'] ] = q
if options.show_stats:
print "questions:", len(data)
print "trade range:", trade_range
print "comment range:", comment_range
print "low trade count questions:", len(low_trade_qs)
new = set([])
if options.show_new:
print "************ NEW QUESTIONS"
for q in sorted(low_trade_qs, key=lambda q: (q['trade_count'], q['question']['id'])):
qid = q['question']['id']
if opinions.has_key(qid) or (qid in boring):
# i'm already aware of this one
continue
new.add(qid)
print "(%i) id %i \"%s\"" % (q['trade_count'], q['question']['id'], q['question']['name'])
def outcomes(start, stop):
if start>=0.999:
start = 0.999
elif start<=0.001:
start = 0.001
if stop>=0.999:
stop = 0.999
elif stop<=0.001:
stop = 0.001
true = 100.0 * math.log(start / stop) / math.log(2.0)
by_id[q['question']['id']] = q
if options.show_stats:
print "questions:", len(data)
print "trade range:", trade_range
print "comment range:", comment_range
print "low trade count questions:", len(low_trade_qs)
new = set([])
if options.show_new:
print "************ NEW QUESTIONS"
for q in sorted(low_trade_qs,
key=lambda q: (q['trade_count'], q['question']['id'])):
qid = q['question']['id']
if opinions.has_key(qid) or (qid in boring):
# i'm already aware of this one
continue
new.add(qid)
print "(%i) id %i \"%s\"" % (q['trade_count'], q['question']['id'],
q['question']['name'])
def outcomes(start, stop):
if start >= 0.999:
start = 0.999
elif start <= 0.001:
start = 0.001
if stop >= 0.999:
stop = 0.999
elif stop <= 0.001:
if __name__ == '__main__':
from optparse import OptionParser
parser = OptionParser()
parser.add_option("-b", action="store_true", default=False,
dest="back_out", help="Back out of existing position?")
parser.add_option("-o", action="store_true", default=False,
dest="optimal", help="Find optimal, opinion-based, trades")
parser.add_option("-d", action="store_true", default=False,
dest="data_based", help="Find optimal, data-based, trades")
(options, args) = parser.parse_args()
args = map(int, args)
have_opinions = filter(lambda id: opinions.has_key(id), args)
no_opinion = filter(lambda id: not opinions.has_key(id), args)
if len(args)>0:
if len(have_opinions) and (options.optimal==True or options.data_based==False):
find_optimal_trading_opportunities(have_opinions, back_out=options.back_out)
if len(no_opinion) and (options.optimal==False or options.data_based==True):
find_data_based_trading_opportunities(no_opinion)
else:
if options.optimal or options.data_based==False:
find_optimal_trading_opportunities(back_out=options.back_out)
if options.data_based:
find_data_based_trading_opportunities()