def run(traders, timesteps=100, lmsr_b=150):
market_fact = prices.LMSRFactory(lmsr_b)
sim_obj = simulation.Simulation(timesteps, market_fact, traders)
sim_obj.simulate()
pyplot.figure()
x_overall = plot_beliefs(sim_obj.log.beliefs)
pyplot.plot(range(len(sim_obj.p_vec)), [a * 100.0 for a in sim_obj.p_vec],
ls='--',
color='r')
pyplot.ylim((0, 100))
print sim_obj.profits_by_user()
pyplot.show()
def run(trader_list,
timesteps=100,
num_processes=2,
simulations=2000,
lmsr_b=150):
marketmakers = [prices.LMSRFactory(lmsr_b)]
pool = multiprocessing.Pool(num_processes)
sim_objects = []
for marketmaker_fact in marketmakers:
for i in range(simulations):
sim_objects.append(
simulation.Simulation(timesteps, marketmaker_fact,
trader_list))
results = pool.map(worker_process, sim_objects)
results_by_market = {}
max_profit_by_market = {}
min_profit_by_market = {}
for market_name, profits_by_user in results:
if market_name is None:
continue
for user_type, profit_list in profits_by_user.iteritems():
results_by_market.setdefault(market_name,
{}).setdefault(user_type,
[]).append(profit_list)
if user_type == market_name:
if profit_list > max_profit_by_market.get(
market_name, float('-inf')):
max_profit_by_market[market_name] = profit_list
if profit_list < min_profit_by_market.get(
market_name, float('inf')):
min_profit_by_market[market_name] = profit_list
for market_name, profit_dict in results_by_market.iteritems():
print('%s profit: %1.2f (min %1.2f, '
'max %1.2f, %d samples)') % (
market_name, numpy.mean(profit_dict[market_name]),
min_profit_by_market[market_name],
max_profit_by_market[market_name],
len(profit_dict[market_name]))
for user_type, profit_list in profit_dict.iteritems():
if user_type == market_name:
continue
print(
' %s profit: %1.2f (%1.2f min, %1.2f max, '
'%1.2f std, %d samples)') % (
user_type, numpy.mean(profit_list), min(profit_list),
max(profit_list), numpy.std(profit_list), len(profit_list))