def jobqueueRefresh(self, inception=False):
mtime = os.path.getmtime(os.path.abspath("menuConfig.json"))
if self.menus_mtime > mtime:
self.menus = json.load(open("menuConfig.json"))
self.menus_mtime = mtime
baseDt = dateoffset(self.sysdate, -1,"%d/%m/%Y")
for loc, v in self.menus.items():
farm = v["mad"]
for scene, vv in v["scenes"].items():
for batch, jobs in vv["monitor"].items():
self.mongo.refreshQueue(farm, baseDt, batch, jobs, scene, inception)
self.mongo.rebuildIndex(self.reglib)
def spotcorrection():
tday = dateoffset(datetime.today(), 0)
rampset = "OSPG TEST 6"
rampdate = xldate(tday)
mkt = load_market("mkt", rampset, rampdate, rampdate)
ccy1ccy2 = [['USD','BRL'], ['USD','JPY'], ['USD','TRY'], ['TRY','JPY'], ['BRL','JPY']]
df = DataFrame(ccy1ccy2, columns=['CCY1','CCY2'])
df['Mid'] = df[['CCY1','CCY2']].apply(lambda x: west.FXGetSpot("mkt", x[0], x[1]), axis=1)
df['SpotDate'] = df[['CCY1','CCY2']].apply(lambda x: west.GetFXSpotDate2(rampdate, x[0], x[1], 'X', False), axis=1)
df["SpotDate"][3] = west.Add(rampdate,1,"bd",'',"TYO#IST#NYK")
df['DF1'] = df[['CCY1', 'SpotDate']].apply(lambda x: west.Df(x[1],"mkt",rampdate,'-5','',x[0]), axis=1)
df['DF2'] = df[['CCY2', 'SpotDate']].apply(lambda x: west.Df(x[1],"mkt",rampdate,'-5','',x[0]), axis=1)
df['CashRate'] = df['Mid']*df['DF2']/df['DF1']
df['SpotRate'] = df['CashRate']*df['DF1']/df['DF2']
#df = df.set_index(['CCY1', 'CCY2'])
fxspot=[]
for t in ramp.RampReadCurve("SPOT FX RATES", rampset, rampdate, 2).to_tuple()[0]:
if not t[0]+t[1] in ["TRYJPY", "BRLJPY"]:
fxspot.append(t)
fxspot.extend([('TRY', 'JPY',df["SpotRate"][3],df["SpotRate"][3]), ('BRL', 'JPY',df["SpotRate"][4],df["SpotRate"][4])])
sts = ramp.RampWriteCurve(tuple3d(fxspot), "SPOT FX RATES", rampset, rampdate, 2)
if sts[:2].upper() != "OK":
logger.error("SPOT FX contribution failed")
#correct eurcny vol
df_eurusd = read_ramp('HYB EURUSD VOL MKT VOLS', rampset, rampdate)
df_eurusd = df_eurusd.ix[:14]
df_usdcny = read_ramp('HYB USDCNY VOL MKT VOLS', rampset, rampdate)
df_eurcny = read_ramp('HYB EURCNY VOL MKT VOLS', rampset, rampdate)
df_cross = np.sqrt(np.power(df_eurusd["ATM"],2)+np.power(df_usdcny["ATM"],2))
df_check = np.abs(df_eurcny["ATM"]-df_eurusd["ATM"])>0.5*df_usdcny["ATM"]
df_eurcny["ATM"] = concat([df_eurcny["ATM"], df_cross, df_check], axis=1).apply(lambda x: x.values[1] if x.values[2] else x.values[0], axis=1)
df_eurcny = df_eurcny.reset_index()
curvedata = [["Vols"]+df_eurcny.columns.tolist()]
curvedata.extend([['']+t for t in df_eurcny.values.tolist()])
sts = write_ramp(curvedata, 'HYB EURCNY VOL MKT VOLS', rampset, rampdate)
if sts[:2].upper() != "OK":
console.error("EURCNY vol contribution failed")
farm = v["mad"]
for scene, vv in v["scenes"].items():
for batch, jobs in vv["monitor"].items():
self.mongo.refreshQueue(farm, baseDt, batch, jobs, scene, inception)
self.mongo.rebuildIndex(self.reglib)
def livefeed(self):
logger.info("PriceChangeLive")
df = PriceChangeLive()
self.mongo.updateLiveFeed(json.loads(df.to_json(orient="records")))
if __name__ == "__main__":
app = Application()
http_server = tornado.httpserver.HTTPServer(app)
http_server.listen(8080)
#app.listen(443)
delta = trunc(dateoffset(app.sysdate, 1))-app.sysdate
tornado.ioloop.IOLoop.instance().add_timeout(timedelta(seconds=delta.seconds+7*60*60), app.rollsysdate)
app.jobqueueRefresh(True)
tornado.ioloop.PeriodicCallback(app.jobqueueRefresh, 5*1000*60).start()
app.livefeed()
tornado.ioloop.PeriodicCallback(app.livefeed, 1*1000*60).start()
tornado.ioloop.PeriodicCallback(app.rrqueueRefresh, 1000*1).start()
logger.info("interstellar started")
tornado.ioloop.IOLoop.instance().start()
def calcOfficial(df, tday):
calcdate = xldate(dateoffset(tday,-1))
mkt = load_market('mesaofficial', 'OFFICIAL', calcdate, calcdate)
return DataFrame({"OFFICIAL":df.apply(getRate, axis=1, args=(mkt, calcdate))})
def buildcmd(self, groupindex):
cmds = []
for g, batches in self.riskconfig.items():
if g in groupindex.keys():
for batch, v in batches.items():
jobs = filter(lambda x: x[0]!="*" and (x[0] in groupindex[g] or groupindex[g][0]=="All"), [(t["JOBNAME"],t["RISKFILE"]) for t in v["JOBS"]])
jobs = list(jobs)
self.mapping.update(dict(jobs))
cmd ='"%s" %s %s' % (batch, self.db, " ".join(map(lambda x: '--job "%s"' % x[0], jobs)))
timingcat = self.request["timing-cat"] if "timing-cat" in self.request else v["TIMINGCALC"]
c = ' --base-date %s --sub-book "%s" "%s" --force-farm "%s" --force-priority %s --timing-cat %s ' % (dateoffset(self.basedate, int(v["DAYOFFSET"]), "%d/%m/%Y"), v["BOOK"], self.request["sub-book"], v["GARDEN"], v["PRIORITY"], timingcat)
if "force-ramp" in self.request:
c = c + ' --force-ramp "%s"' % self.request["force-ramp"]
cmd = cmd + c + " ".join(self.overrides)
cmd = cmd + " --sub-batch %s" % self.request["sub-batch"]
cmd = cmd + " --no-fltr --no-tfc --ignore-day --add-co FORCE_VALID_MODEL FALSE --add-co TARGET_PKT_TM 800"
if "eod-fixing" in self.request:
cmd = cmd + " --eod-fixing %s" % self.request["eod-fixing"]
cmds.append(cmd)
return cmds
from rampdiff import read_ramp, write_ramp
from pandas import DataFrame, concat
import numpy as np
import pdb
logger = logging.getLogger('')
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
console = logging.StreamHandler()
console.setFormatter(formatter)
logger.addHandler(console)
dstr = datetime.today().strftime("%d%m")
yestdstr = dateoffset(datetime.today(), -1, "%d%m")
def waitforcash():
cashfile = r"\\sins00120165\Reporting\Risk\rvg\EurOpen\BkLonxHybrids\ActRpt."+dstr
logger.info("waiting cash....")
while not os.path.exists(cashfile):
time.sleep(60*5)
logger.info("cash arrived")
def intradaysnapshot():
logger.info("intraday snapshot")
cmdline = "LON_MM_EVENING_INTRADAY GFTBSGL --force-priority 100 --job MKT_SNAPSHOT_LIVE_FASTINTRA --user-name SG435551 --now --no-tfc --sub-batch _FAST --ignore-day"
ntmad_ret = ntmadsubmit(cmdline)
jobid = ntmad_ret["job_id"]
mad = maddb("MADBUKL", {"select_progress":"""select job_id, proportion_done, sts_cod from exo_evt_log where job_id in (%s)"""})