finopt/cep/md_std.py

import sys

from pyspark import SparkContext
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
from numpy import *
import pylab
from scipy import stats
import time, datetime
import threading
import time
import os
from finopt import ystockquote
##
##
##
## This example demonstrates the use of accumulators and broadcast 
## and how to terminate spark running jobs
## 
## it also demonstrates how to send alerts via xmpp
## (requires prosody server running and redisQueue)
##
##

##
##
## insert the path so spark-submit knows where
## to look for a file located in a given directory
##
## the other method is to export PYTHONPATH before 
## calling spark-submit
##
# import sys
# sys.path.insert(0, '/home/larry-13.04/workspace/finopt/cep')
print sys.path


#import optcal
import json
import numpy
#from finopt.cep.redisQueue import RedisQueue
from comms.redisQueue import RedisQueue
from comms.alert_bot import AlertHelper


def f1(time, rdd):
    lt =  rdd.collect()
    print '**** f1'
    print lt
    print '**** end f1'
    f = open('/home/larry/l1304/workspace/finopt/data/mds_files/std/std.txt', 'a')
    f.write(''.join('%s,%s,%s\n'%(s[0].strftime('%Y-%m-%d %H:%M:%S.%f'),s[1],s[2]) for s in lt))
    d = Q.value
    if float(lt[0][1]) > 8.0:
        msg = 'Stock SD alert triggered: '.join('%s,%s,%s\n'%(s[0].strftime('%Y-%m-%d %H:%M:%S.%f'),s[1],s[2]) for s in lt)
        print msg
        q = RedisQueue(d['alert_bot_q'][1], d['alert_bot_q'][0], d['host'], d['port'], d['db'])
        q.put(msg)

def f2(time, rdd):
    lt =  rdd.collect()
    if lt:
        change = lt[0][0]
        d = Q.value
        print '********** f2'
        print lt[0][0], Threshold.value, lt[0][1]
        print '********** end f2'

        
        if change > Threshold.value:
            msg = 'Stock alert triggered: %0.6f, mean: %0.2f' % (change, lt[0][1])
            print msg
#             q = RedisQueue(d['alert_bot_q'][1], d['alert_bot_q'][0], d['host'], d['port'], d['db'])
#             q.put(msg)
    


 


       
        
       



    

# to run from command prompt
# 0. start kafka broker
# 1. edit subscription.txt and prepare 2 stocks
# 2. run ib_mds.py 
# 3. spark-submit  --jars spark-streaming-kafka-assembly_2.10-1.4.1.jar ./alerts/pairs_corr.py vsu-01:2181 

# http://stackoverflow.com/questions/3425439/why-does-corrcoef-return-a-matrix
# 

if __name__ == "__main__":
    if len(sys.argv) != 5:
        print("Usage: %s <broker_list ex: vsu-01:2181>  <rdd_name> <tick id> <fn name>" % sys.argv[0])
        print("Usage: to gracefully shutdown type echo 1 > /tmp/flag at the terminal")
        exit(-1)

    app_name = "std_deviation_analysis"
    sc = SparkContext(appName= app_name) #, pyFiles = ['./cep/redisQueue.py'])
    ssc = StreamingContext(sc, 2)
    ssc.checkpoint('../checkpoint')

    

    brokers, qname, id, fn  = sys.argv[1:]
    id = int(id)
    
    #
    # demonstrate how to use broadcast variable
    #
    NumProcessed = sc.accumulator(0)
    
    cls = float(ystockquote.get_historical_prices('^HSI', '20150930', '20150930')[1][4])
    
    print 'closing price of HSI %f' % cls
    
    Q = sc.broadcast({'cls': cls, \
                      'rname': 'rname', 'qname': qname, 'namespace': 'mdq', 'host': 'localhost', 'port':6379, 'db': 3, 'alert_bot_q': ('alert_bot', 'chatq')})
    Threshold = sc.broadcast(0.020)
    #kvs = KafkaUtils.createDirectStream(ssc, ['ib_tick_price', 'ib_tick_size'], {"metadata.broker.list": brokers})
    kvs = KafkaUtils.createStream(ssc, brokers, app_name, {'ib_tick_price':1, 'ib_tick_size':1})

    lns = kvs.map(lambda x: x[1])

    mdl = lns.map(lambda x: json.loads(x))\
            .filter(lambda x: (x['typeName'] == 'tickPrice' and x['contract'] == "HSI-20151029-0--FUT-HKD-102"))\
            .map(lambda x: (x['contract'], (x['ts'], x['price']) ))\
            .groupByKeyAndWindow(12, 10, 1)
            
    s1 = mdl.map(lambda x: (datetime.datetime.fromtimestamp( [a[0] for a in x[1]][0]  ), numpy.std([a[1] for a in x[1]]),\
                 numpy.mean([a[1] for a in x[1]])\
                 )) 

    s2 = s1.map(lambda x: (abs(x[2] - Q.value['cls']) / Q.value['cls'], x[2]))   

#     s1 = lines.map(lambda line: json.loads(line)).filter(lambda x: (x['tickerId'] in [1,2] and x['typeName']== 'tickPrice'))\
#                 .filter(lambda x: (x['field'] == 4))\
#                 .map(lambda x: (x['tickerId'], x['price'])).reduceByKey(lambda x,y: (x+y)/2).groupByKeyAndWindow(30, 20, 1)    
    
    
    s1.foreachRDD(f1)
    s2.foreachRDD(f2)

    #trades.pprint()
    #trades.foreachRDD(eval(fn))
    
        
    def do_work():

        while 1:
            # program will stop after processing 40 rdds
#             if NumProcessed.value == 70:
#                 break            
            # program will stop on detecting a 1 in the flag file
            try:
                f = open('/tmp/flag')
                l = f.readlines()
                print 'reading %s' % l[0]
                if '1' in l[0]:
                    os.remove('/tmp/flag') 
                    print 'terminating..........'        
                    ssc.stop(True, False) 
                    sys.exit()          
                f.close()
                time.sleep(2)
            except IOError:
                continue
            
            
        
    t = threading.Thread(target = do_work, args=())
    t.start()
    ssc.start()
    ssc.awaitTermination()