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(