def gen_features(condition):
k = utils.kline(condition["currency"], condition["t"], "",
condition["size"])
points = pick(k, condition)
box = []
for i in points:
p = {}
#p["currency"], p["t"], p["since"], p["size"], p["feature_size"] = condition["currency"], condition["t"], i - 1000 * utils.str2sec(condition["t"]) * condition["size"], condition["size"], 30
p["k"], p["point"], p["feature_size"] = k, i, condition["feature_size"]
box.append(p)
pool = ThreadPool(16)
feas = pool.map(mfeature, box)
pool.close()
pool.join()
conn = sqlite3.connect("%s/%s" % (config.db_dir, config.db_file))
c = conn.cursor()
feature_items = []
for f in range(len(feas)):
content = json.dumps(feas[f])
digest = "".join(hashlib.md5(content).hexdigest())
point = points[f]
c.execute("select content from features where digest = ? or point = ?",
(digest, point))
r = c.fetchone()
if r != None:
#print "digest(%s) or point(%s) exists." % (digest, point)
#print json.loads(r[0])["v"]
#print datetime.datetime.fromtimestamp(point/1000).strftime('%Y-%m-%d %H:%M:%S')
continue
currency = condition["currency"]
size = condition["size"]
n = condition["n"]
increase = condition["increase"]
feature_size = 30
feature_items.append(
(digest, content, condition["currency"], condition["t"],
condition["size"], condition["n"], condition["increase"],
feature_size, point))
c.executemany("insert into features values (?, ?, ?, ?, ?, ?, ?, ?, ?)",
feature_items)
conn.commit()
conn.close()
return feature_items
def real_buy():
conn = sqlite3.connect("%s/%s" % (config.db_dir, config.db_file))
c = conn.cursor()
k = {}
digests = [i[0] for i in rank() if i[1] > 0]
c.execute(
"select digest, content, currency, t, size, n, increase, feature_size, point from features where digest in (%s)order by point"
% (",".join('?' * len(digests)), ), digests)
for f in c.fetchall():
digest, content, currency, t, size, n, increase, feature_size, point = f
if "%s_%s_%s" % (currency, t, size) not in k:
k["%s_%s_%s" % (currency, t, size)] = utils.kline(
currency, t, "", size)
print "Real comparing to %s @ %s %s %s" % (
digest, datetime.datetime.fromtimestamp(
point / 1000).strftime('%Y-%m-%d %H:%M:%S'), increase, t)
s = kline_similarity(k["%s_%s_%s" % (currency, t, size)],
json.loads(content), feature_size)
v = float(json.loads(content)["v"])
if not math.isnan(s) and s <= v:
#if True:
created_at = int(time.time() * 1000)
c.execute(
"select * from trade where digest = ? and created_at > ? and updated_at = 0",
(digest, created_at - 1000 * n * utils.str2sec(t)))
if len(c.fetchall()) > 0:
continue
print "Real match"
r = trade(currency, "b", 1.0)
print r
if r != False:
#ticker = utils.tick(currency)
#buy, sell = float(ticker["ticker"]["buy"]), float(ticker["ticker"]["sell"])
print "real buy %s @ %s, amount %s" % (currency, r["price"],
r['total_amount'])
c.execute(
"Insert into trade (created_at, updated_at, digest, buy, sell) values (?, 0, ?, ?, 0.0)",
(r["trade_date"], digest, r["price"]))
conn.commit()
else:
#print "Not Match"
pass
conn.close()
def train_buy():
conn = sqlite3.connect("%s/%s" % (config.db_dir, config.db_file))
c = conn.cursor()
k = {}
c.execute(
"select digest, content, currency, t, size, n, increase, feature_size, point from features order by point"
)
for f in c.fetchall():
digest, content, currency, t, size, n, increase, feature_size, point = f
if "%s_%s_%s" % (currency, t, size) not in k:
k["%s_%s_%s" % (currency, t, size)] = utils.kline(
currency, t, "", size)
print "Comparing to %s @ %s %s %s" % (
digest, datetime.datetime.fromtimestamp(
point / 1000).strftime('%Y-%m-%d %H:%M:%S'), increase, t)
s = kline_similarity(k["%s_%s_%s" % (currency, t, size)],
json.loads(content), feature_size)
v = float(json.loads(content)["v"])
if not math.isnan(s) and s <= v:
created_at = int(time.time() * 1000)
c.execute(
"select * from training where digest = ? and created_at > ? and updated_at = 0",
(digest, created_at - 1000 * n * utils.str2sec(t)))
if len(c.fetchall()) > 0:
continue
print "Match"
ticker = utils.tick(currency)
buy, sell = float(ticker["ticker"]["buy"]), float(
ticker["ticker"]["sell"])
print "buy %s @ %s" % (currency, sell)
c.execute(
"Insert into training (created_at, updated_at, digest, buy, sell) values (?, 0, ?, ?, 0.0)",
(created_at, digest, sell))
conn.commit()
else:
#print "Not Match"
pass
conn.close()