threading_lob_test.py

# -*- coding: utf-8 -*-

import threading
import time
#import queue
#import logging
from unittest import TestCase

from HFTOrderbook.lob import LimitOrderBook, Order


#I define a dict here that I will use globally to store the results
results_dict ={}

#I regularly tend to use verb phrases for functions and nouns for objects and classes
#nicety, but helps to orient yourself
def simulate_orderbook(n, name):
    #I use the trick to access the variable in a global namespace, 
    #that is outside of this function, and expose this inside the function also
    #this is not a threading based solution, just plain python function call stuff
    # See Python namespacing 
    global results_dict
    
    lob = LimitOrderBook()
    for i in range(n):
        bid_order = Order(uid=i, is_bid=True, size=5, price=100)
        ask_order = Order(uid=i, is_bid=False, size=5, price=200)
        lob.process(bid_order)
        lob.process(ask_order)    

    #TO ensure that I do not overwrite the contents generated by the other thread, 
    #I use the thread's given name parameter as key in a global dict
    # Ugly but works 
    results_dict[name]=lob._orders

       


t1=threading.Thread(target = simulate_orderbook, name='thread1', 
                   args=(100000, 'thread1'))
t2=threading.Thread(target = simulate_orderbook, name='thread2', 
                   args=(100000, 'thread2'))


t1.start()
t2.start()
#Let's halt the main process till thread2 finishes 
#- maybe I am not using this fully right, but seems to work
t2.join()

#waiting for thread1 to surely finish.
#thread2 is surely done, that's what is ensured by the join()
time.sleep(15) 


#I access the end results with the dict keys
print(results_dict.keys())
print(len(results_dict['thread1']))
print(len(results_dict['thread2']))