seed=np.random.randint(low=0, high=2**32, dtype='uint64')))
kernelStartTime = historical_date
kernelStopTime = mkt_close + pd.to_timedelta('00:01:00')
defaultComputationDelay = 50 # 50 nanoseconds
# LATENCY
latency_rstate = np.random.RandomState(
seed=np.random.randint(low=0, high=2**32))
pairwise = (agent_count, agent_count)
# All agents sit on line from Seattle to NYC
nyc_to_seattle_meters = 3866660
pairwise_distances = util.generate_uniform_random_pairwise_dist_on_line(
0.0, nyc_to_seattle_meters, agent_count, random_state=latency_rstate)
pairwise_latencies = util.meters_to_light_ns(pairwise_distances)
model_args = {'connected': True, 'min_latency': pairwise_latencies}
latency_model = LatencyModel(latency_model='deterministic',
random_state=latency_rstate,
kwargs=model_args)
# KERNEL
kernel.runner(agents=agents,
startTime=kernelStartTime,
stopTime=kernelStopTime,
agentLatencyModel=latency_model,
defaultComputationDelay=defaultComputationDelay,
oracle=oracle,
kernelStartTime = historical_date
kernelStopTime = mkt_close + pd.to_timedelta('00:01:00')
defaultComputationDelay = 0 # 50 nanoseconds #there was 50, doesn't work for MarketReplay as this is history that should be
#executed at exact order book time.
# LATENCY
latency_rstate = np.random.RandomState(
seed=np.random.randint(low=0, high=2**32))
pairwise = (agent_count, agent_count)
# All agents sit on line from my PC to MICEX
me_to_micex_meters = 10000
pairwise_distances = util.generate_uniform_random_pairwise_dist_on_line(
0.0, me_to_micex_meters, agent_count, random_state=latency_rstate)
pairwise_latencies = util.meters_to_light_ns(pairwise_distances)
model_args = {'connected': True, 'min_latency': pairwise_latencies}
latency_model = LatencyModel(latency_model='deterministic',
random_state=latency_rstate,
kwargs=model_args)
# KERNEL
latency = np.zeros(
(agent_count,
agent_count)) #TODO: check this way to setup separate latency for agent
noise = [0.0]
kernel.runner(
