book_freq=book_freq,
random_state=np.random.RandomState(
seed=np.random.randint(low=0, high=2**32, dtype='uint64')))
])
agent_types.extend("ExchangeAgent")
agent_count += 1
# 2) Noise Agents
num_noise = 5000
agents.extend([
NoiseAgent(id=j,
name="NoiseAgent {}".format(j),
type="NoiseAgent",
symbol=symbol,
starting_cash=starting_cash,
wakeup_time=util.get_wake_time(mkt_open, mkt_close),
log_orders=log_orders,
random_state=np.random.RandomState(
seed=np.random.randint(low=0, high=2**32, dtype='uint64')))
for j in range(agent_count, agent_count + num_noise)
])
agent_count += num_noise
agent_types.extend(['NoiseAgent'])
# 3) Value Agents
num_value = 100
agents.extend([
ValueAgent(id=j,
name="Value Agent {}".format(j),
type="ValueAgent",
symbol=symbol,
wide_book=True,
random_state=np.random.RandomState(seed=np.random.randint(low=0, high=2 ** 32, dtype='uint64')))])
agent_types.extend("ExchangeAgent")
agent_count += 1
# 2) Noise Agents
num_noise = 5000
noise_mkt_open = historical_date + pd.to_timedelta("09:00:00") # These times needed for distribution of arrival times
# of Noise Agents
noise_mkt_close = historical_date + pd.to_timedelta("16:00:00")
agents.extend([NoiseAgent(id=j,
name="NoiseAgent {}".format(j),
type="NoiseAgent",
symbol=symbol,
starting_cash=starting_cash,
wakeup_time=util.get_wake_time(noise_mkt_open, noise_mkt_close),
log_orders=log_orders,
random_state=np.random.RandomState(seed=np.random.randint(low=0, high=2 ** 32, dtype='uint64')))
for j in range(agent_count, agent_count + num_noise)])
agent_count += num_noise
agent_types.extend(['NoiseAgent'])
# 3) Value Agents
num_value = 100
agents.extend([ValueAgent(id=j,
name="Value Agent {}".format(j),
type="ValueAgent",
symbol=symbol,
starting_cash=starting_cash,
sigma_n=sigma_n,
r_bar=r_bar,
