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_mm_agents)
])
agent_count += num_mm_agents
agent_types.extend('MarketMakerAgent')
# 5) Momentum Agents
num_momentum_agents = 25
agents.extend([
MomentumAgent(
id=j,
name="MOMENTUM_AGENT_{}".format(j),
type="MomentumAgent",
symbol=symbol,
starting_cash=starting_cash,
min_size=1,
max_size=10,
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_momentum_agents)
])
agent_count += num_momentum_agents
agent_types.extend("MomentumAgent")
########################################################################################################################
########################################### KERNEL AND OTHER CONFIG ####################################################
kernel = Kernel("random_fund_diverse Kernel",
random_state=np.random.RandomState(
random_state=np.random.RandomState(
seed=np.random.randint(low=0, high=2**32)))
for idx, j in enumerate(range(agent_count, agent_count + num_mm_agents))
])
agent_count += num_mm_agents
agent_types.extend('AdaptiveMarketMakerAgent')
# 5) Momentum Agents
num_momentum_agents = 25
agents.extend([
MomentumAgent(id=j,
name="MOMENTUM_AGENT_{}".format(j),
type="MomentumAgent",
symbol=symbol,
starting_cash=starting_cash,
min_size=1,
max_size=10,
wake_up_freq='20s',
log_orders=True,
random_state=np.random.RandomState(
seed=np.random.randint(low=0, high=2**32)))
for j in range(agent_count, agent_count + num_momentum_agents)
])
agent_count += num_momentum_agents
agent_types.extend("MomentumAgent")
# 6) Execution Agent Config
trade = True if args.execution_agents else False
execution_agent_start_time = historical_date + pd.to_timedelta("10:00:00")
execution_agent_end_time = historical_date + pd.to_timedelta("11:00:00")
) for idx, j in enumerate(range(agent_count, agent_count + num_mm_agents))
])
agent_count += num_mm_agents
agent_types.extend("POVMarketMakerAgent")
# 5) Momentum Agents
num_momentum_agents = 5
agents.extend([
MomentumAgent(
id=j,
name="MOMENTUM_AGENT_{}".format(j),
type="MomentumAgent",
symbol=symbol,
starting_cash=starting_cash,
min_size=1,
max_size=10,
wake_up_freq="20s",
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_momentum_agents)
])
agent_count += num_momentum_agents
agent_types.extend("MomentumAgent")
# 6) Execution Agent
trade = True if args.execution_agents else False
#### Participation of Volume Agent parameters
agent_types.extend([
"HeuristicBeliefLearningAgent {}".format(strat_name)
for j in range(x[0])
])
agent_count += x[0]
# Trend followers agent
i = agent_count
lookback = 10
num_tf = 20
for j in range(num_tf):
agents.append(
MomentumAgent(i,
"Momentum Agent {}".format(i),
symbol=symbol1,
starting_cash=starting_cash,
lookback=lookback,
random_state=np.random.RandomState(
seed=np.random.randint(low=0, high=2**32)),
log_orders=log_orders))
agent_types.append("MomentumAgent {}".format(i))
i += 1
agent_count += num_tf
#for j in range(num_tf):
#agents.append(MomentumAgent(i, "Momentum Agent {}".format(i), symbol=symbol2, startingCash = starting_cash, lookback=lookback))
#agent_types.append("MomentumAgent {}".format(i))
#i+=1
#agent_count += num_tf
#for j in range(num_tf):
#agents.append(MomentumAgent(i, "Momentum Agent {}".format(i), symbol=symbol3, startingCash = starting_cash, lookback=lookback))
# Add an OBI agent to try to beat this market.
for i in range(num_obi):
random_state = get_rand_obj(agent_seeds[agent_id])
agents.extend([ OrderBookImbalanceAgent(agent_id, "OBI Agent {}".format(agent_id), "OrderBookImbalanceAgent", symbol = symbol, starting_cash = starting_cash, levels = levels, entry_threshold = entry_threshold, trail_dist = trail_dist, freq = obi_freq, random_state = random_state) ])
agent_id += 1
# Add market maker agents.
for i in range(num_mm):
random_state = get_rand_obj(agent_seeds[agent_id])
agents.extend([ MarketMakerAgent(agent_id, "Market Maker Agent {}".format(agent_id), "MarketMakerAgent", symbol=symbol, starting_cash=starting_cash, min_size=500, max_size=1000, subscribe=True, log_orders=False, random_state = random_state) ])
agent_id += 1
# Add momentum agents.
for i in range(num_mom):
random_state = get_rand_obj(agent_seeds[agent_id])
agents.extend([ MomentumAgent(agent_id, "Momentum Agent {}".format(agent_id), "MomentumAgent", symbol=symbol, starting_cash=starting_cash, min_size=1, max_size=10, subscribe=True, log_orders=False, random_state = random_state) ])
agent_id += 1
# Start the kernel running. This call will not return until the
# simulation is complete. (Eventually this should be made
# parallel for learning.)
agent_saved_states = kernel.runner(
agents = agents, startTime = kernelStartTime,
stopTime = kernelStopTime, agentLatency = latency,
latencyNoise = noise,
defaultComputationDelay = defaultComputationDelay,
oracle = oracle, log_dir = "{}_{}".format(log_dir,sim))
obi_perf.append(agent_saved_states['agent_state'][0])