for j in range(agent_count, agent_count + num_zi_agents)])
agent_types.extend("ZeroIntelligenceAgent")
agent_count += num_zi_agents
# 10) Heuristic Belief Learning Agents
num_hbl_agents = 500
agents.extend([HeuristicBeliefLearningAgent(id=j,
name="HBL_AGENT_{}".format(j),
type="HeuristicBeliefLearningAgent",
symbol=symbol,
starting_cash=starting_cash,
sigma_n=10000,
sigma_s=symbols[symbol]['fund_vol'],
kappa=symbols[symbol]['agent_kappa'],
r_bar=symbols[symbol]['r_bar'],
q_max=10,
sigma_pv=5e4,
R_min=0,
R_max=100,
eta=1,
lambda_a=1e-12,
L=2,
log_orders=False,
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_hbl_agents)])
agent_types.extend("HeuristicBeliefLearningAgent")
agent_count += num_hbl_agents
########################################################################################################################
########################################### KERNEL AND OTHER CONFIG ####################################################
# HBL strategy split.
for i, x in enumerate(hbl):
strat_name = "Type {} [{} <= R <= {}, eta={}, L={}]".format(
i + 1, x[1], x[2], x[3], x[4])
agents.extend([
HeuristicBeliefLearningAgent(
j,
"HBL Agent {} {}".format(j, strat_name),
"HeuristicBeliefLearningAgent {}".format(strat_name),
random_state=np.random.RandomState(
seed=np.random.randint(low=0, high=2**32)),
log_orders=log_orders,
symbol=symbol,
starting_cash=starting_cash,
sigma_n=sigma_n,
r_bar=s['r_bar'],
kappa=s['kappa'],
sigma_s=s['sigma_s'],
q_max=10,
sigma_pv=5000000,
R_min=x[1],
R_max=x[2],
eta=x[3],
lambda_a=0.005,
L=x[4]) for j in range(agent_count, agent_count + x[0])
])
agent_types.extend([
"HeuristicBeliefLearningAgent {}".format(strat_name)
for j in range(x[0])
])
agent_count += x[0]
agent_count += x[0]
# HBL strategy split.
for i, x in enumerate(hbl):
strat_name = "Type {} [{} <= R <= {}, eta={}, L={}]".format(
i + 1, x[1], x[2], x[3], x[4])
agents.extend([
HeuristicBeliefLearningAgent(
j,
"HBL Agent {} {}".format(j, strat_name),
"HeuristicBeliefLearningAgent {}".format(strat_name),
random_state=np.random.RandomState(
seed=np.random.randint(low=0, high=2**32)),
log_orders=log_orders,
symbol=symbol1,
starting_cash=starting_cash,
sigma_n=sigma_n,
r_bar=s1['r_bar'],
q_max=10,
sigma_pv=5000000,
R_min=x[1],
R_max=x[2],
eta=x[3],
lambda_a=0.005,
L=x[4]) for j in range(agent_count, agent_count + x[0])
])
agent_types.extend([
"HeuristicBeliefLearningAgent {}".format(strat_name)
for j in range(x[0])
])
agent_count += x[0]
lambda_a=0.005) for i in range(0, num_agents)
]
agent_types = ["ZeroIntelligenceAgent" for i in range(num_agents)]
agent_count = num_agents
# Here are the heuristic belief learning agents.
num_hbl_agents = 10
agents.extend([
HeuristicBeliefLearningAgent(i,
"HBL Agent {}".format(i),
symbol,
starting_cash,
sigma_n=sigma_n,
r_bar=s['r_bar'],
kappa=s['kappa'],
sigma_s=s['sigma_s'],
q_max=10,
sigma_pv=5000000,
R_min=250,
R_max=500,
eta=0.8,
lambda_a=0.005,
L=8)
for i in range(agent_count, agent_count + num_hbl_agents)
])
agent_types.extend(
["HeuristicBeliefLearningAgent" for i in range(num_hbl_agents)])
agent_count += num_hbl_agents
### Configure an exchange agent.
