stream_history=10,
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",
# Some configs for ZI agents only (among seven parameter settings).
num = 100
# number of noise agents
num_noise = int(num * proportion)
# ZI strategy split. Note that agent arrival rates are quite small, because our minimum
# time step is a nanosecond, and we want the agents to arrive more on the order of
# minutes.
agents.extend([
NoiseAgent(j,
"NoiseAgent {}".format(j),
"NoiseAgent",
random_state=np.random.RandomState(
seed=np.random.randint(low=0, high=2**32, dtype='uint64')),
log_orders=log_orders,
symbol=symbol,
starting_cash=starting_cash,
wakeup_time=mkt_open + np.random.rand() *
(mkt_close - mkt_open))
for j in range(agent_count, agent_count + num_noise)
])
agent_count += num_noise
agent_types.extend(
['NoiseAgent' for j in range(agent_count, agent_count + num_noise)])
# 100 agents
num_value = num - num_noise
# ZI strategy split. Note that agent arrival rates are quite small, because our minimum
# time step is a nanosecond, and we want the agents to arrive more on the order of
random_state=np.random.RandomState(
seed=np.random.randint(low=0, high=2**32)))
])
agent_types.extend("ExchangeAgent")
agent_count += 1
# 2) Noise Agents
num_noise = 5000
noise_mkt_open = historical_date + pd.to_timedelta("09:00:00")
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=False,
log_to_file=False,
random_state=np.random.RandomState(
seed=np.random.randint(low=0, high=2**32)))
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="ValueAgent_{}".format(j),
type="ValueAgent",
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),