def test4_takeStep():
class DummyTollAgent:
def __init__(self):
self.USD = 3.0
def receiveUSD(self, USD):
self.USD += USD
class DummyKpis:
def mktsRNDToSalesRatio(self):
return 0.0
class DummySS:
def __init__(self):
self.time_step = S_PER_DAY
def annualMktsGrowthRate(self, dummy_ratio):
return 0.25
class DummySimState:
def __init__(self):
self.kpis, self.ss = DummyKpis(), DummySS()
self._toll_agent = DummyTollAgent()
def marketplacePercentTollToOcean(self) -> float:
return 0.05
def getAgent(self, name: str):
assert name == "toll_agent"
return self._toll_agent
state = DummySimState()
a = MarketplacesAgent(name="marketplaces",
USD=10.0,
OCEAN=20.0,
toll_agent_name="toll_agent",
n_marketplaces=100.0,
revenue_per_marketplace_per_s=2.0,
time_step=state.ss.time_step)
g = _annualToDailyGrowthRate(0.25)
a.takeStep(state)
assert a._n_marketplaces == (100.0 * (1.0 + g))
assert a._revenue_per_marketplace_per_s == (2.0 * (1.0 + g))
assert a._salesPerTick() == \
(a._n_marketplaces * a._revenue_per_marketplace_per_s * S_PER_DAY)
expected_toll = 0.05 * a._salesPerTick()
assert state._toll_agent.USD == (3.0 + expected_toll)
a.takeStep(state)
assert a._n_marketplaces == (100.0 * (1.0 + g) * (1.0 + g))
assert a._revenue_per_marketplace_per_s == (2.0 * (1.0 + g) * (1.0 + g))
for i in range(10):
a.takeStep(state)
assert pytest.approx(
a._n_marketplaces) == 100.0 * math.pow(1.0 + g, 1 + 1 + 10)
assert pytest.approx(a._revenue_per_marketplace_per_s) == \
2.0*math.pow(1.0+g,1+1+10)
def test3_growthRatePerTick_025():
a = MarketplacesAgent("mkts", 0.0, 0.0, "toll", 10.0, 0.1, S_PER_DAY)
assert a._growthRatePerTick(0.0) == 0.0
assert a._growthRatePerTick(0.25) == _annualToDailyGrowthRate(0.25)
def test2_growthRatePerTick_000():
a = MarketplacesAgent("mkts", 0.0, 0.0, "toll", 10.0, 0.1, S_PER_YEAR)
assert a._growthRatePerTick(0.0) == 0.0
assert a._growthRatePerTick(0.25) == 0.25
def test1_basic():
a = MarketplacesAgent("mkts", 0.0, 0.0, "toll", 10.0, 0.1, 1)
assert a.numMarketplaces() == 10.0
assert a.revenuePerMarketplacePerSecond() == 0.1
def __init__(self, ss: SimStrategy.SimStrategy):
log.debug("init:begin")
#main
self.ss = ss
self.tick = 0
#used to manage names
self._next_free_marketplace_number = 0
#used to add agents
self._marketplace_tick_previous_add = 0
#main storage of agents. Fill this below
self.agents = AgentDict() #agent_name : Agent instance
#<<Note many magic numbers below, for simplicity>>
#note: KPIs class also has some magic number
#as ecosystem improves, these parameters may change / improve
self._marketplace_percent_toll_to_ocean = 0.002 #magic number
self._percent_burn: float = 0.05 #to burning, vs to DAO #magic number
self._total_OCEAN_minted: float = 0.0
self._total_OCEAN_burned: float = 0.0
self._total_OCEAN_burned_USD: float = 0.0
self._speculation_valuation = 5e6 #in USD #magic number
self._percent_increase_speculation_valuation_per_s = 0.10 / S_PER_YEAR # ""
#Instantiate and connnect agent instances. "Wire up the circuit"
new_agents: Set[BaseAgent] = set()
#FIXME: replace MarketplacesAgent with DataecosystemAgent, when ready
new_agents.add(
MarketplacesAgent(
name="marketplaces1",
USD=0.0,
OCEAN=0.0,
toll_agent_name="opc_address",
n_marketplaces=float(ss.init_n_marketplaces),
revenue_per_marketplace_per_s=2e3 / S_PER_MONTH, #magic number
time_step=self.ss.time_step,
))
new_agents.add(
RouterAgent(name="opc_address",
USD=0.0,
OCEAN=0.0,
receiving_agents={
"ocean_dao": self.percentToOceanDao,
"opc_burner": self.percentToBurn
}))
new_agents.add(OCEANBurnerAgent(name="opc_burner", USD=0.0, OCEAN=0.0))
#func = MinterAgents.ExpFunc(H=4.0)
func = MinterAgents.RampedExpFunc(
H=4.0, #magic number
T0=0.5,
T1=1.0,
T2=1.4,
T3=3.0, #""
M1=0.10,
M2=0.25,
M3=0.50) #""
new_agents.add(
MinterAgents.OCEANFuncMinterAgent(
name="ocean_51",
receiving_agent_name="ocean_dao",
total_OCEAN_to_mint=UNMINTED_OCEAN_SUPPLY,
s_between_mints=S_PER_DAY,
func=func))
new_agents.add(
GrantGivingAgent(
name="opf_treasury_for_ocean_dao",
USD=0.0,
OCEAN=OPF_TREASURY_OCEAN_FOR_OCEAN_DAO, #magic number
receiving_agent_name="ocean_dao",
s_between_grants=S_PER_MONTH,
n_actions=12 * 3)) #""
new_agents.add(
GrantGivingAgent(
name="opf_treasury_for_opf_mgmt",
USD=OPF_TREASURY_USD,
OCEAN=OPF_TREASURY_OCEAN_FOR_OPF_MGMT, #magic number
receiving_agent_name="opf_mgmt",
s_between_grants=S_PER_MONTH,
n_actions=12 * 3)) #""
new_agents.add(
GrantGivingAgent(
name="bdb_treasury",
USD=BDB_TREASURY_USD,
OCEAN=BDB_TREASURY_OCEAN, #magic number
receiving_agent_name="bdb_mgmt",
s_between_grants=S_PER_MONTH,
n_actions=17)) #""
new_agents.add(
RouterAgent(name="ocean_dao",
receiving_agents={"opc_workers": funcOne},
USD=0.0,
OCEAN=0.0))
new_agents.add(
RouterAgent(name="opf_mgmt",
receiving_agents={"opc_workers": funcOne},
USD=0.0,
OCEAN=0.0))
new_agents.add(
RouterAgent(name="bdb_mgmt",
receiving_agents={"bdb_workers": funcOne},
USD=0.0,
OCEAN=0.0))
new_agents.add(GrantTakingAgent(name="opc_workers", USD=0.0,
OCEAN=0.0))
new_agents.add(GrantTakingAgent(name="bdb_workers", USD=0.0,
OCEAN=0.0))
for agent in new_agents:
self.agents[agent.name] = agent
#track certain metrics over time, so that we don't have to load
self.kpis = Kpis.KPIs(self.ss.time_step)
log.debug("init: end")
def test4_takeStep():
class DummyTollAgent:
def __init__(self):
self.USD = 3.0
def receiveUSD(self, USD):
self.USD += USD
class DummyKpis:
def mktsRNDToSalesRatio(self): # pylint: disable=no-self-use
return 0.0
class DummySS:
def __init__(self):
self.time_step = S_PER_DAY
self._percent_consume_sales_for_network = 0.05
def annualMktsGrowthRate(self, dummy_ratio): # pylint: disable=no-self-use, unused-argument
return 0.25
def networkRevenue(self, consume_sales: float) -> float:
return self._percent_consume_sales_for_network * consume_sales
class DummySimState:
def __init__(self):
self.kpis, self.ss = DummyKpis(), DummySS()
self._toll_agent = DummyTollAgent()
def getAgent(self, name: str):
assert name == "toll_agent"
return self._toll_agent
state = DummySimState()
a = MarketplacesAgent(
name="marketplaces",
USD=10.0,
OCEAN=20.0,
toll_agent_name="toll_agent",
n_marketplaces=100.0,
sales_per_marketplace_per_s=2.0,
time_step=state.ss.time_step,
)
g = _annualToDailyGrowthRate(0.25)
a.takeStep(state)
assert a._n_marketplaces == (100.0 * (1.0 + g))
assert a._consume_sales_per_marketplace_per_s == (2.0 * (1.0 + g))
assert a._consumeSalesPerTick() == (
a._n_marketplaces * a._consume_sales_per_marketplace_per_s * S_PER_DAY)
expected_toll = 0.05 * a._consumeSalesPerTick()
assert state._toll_agent.USD == (3.0 + expected_toll)
a.takeStep(state)
assert a._n_marketplaces == (100.0 * (1.0 + g) * (1.0 + g))
assert a._consume_sales_per_marketplace_per_s == (2.0 * (1.0 + g) *
(1.0 + g))
for _ in range(10):
a.takeStep(state)
assert pytest.approx(
a._n_marketplaces) == 100.0 * math.pow(1.0 + g, 1 + 1 + 10)
assert pytest.approx(
a._consume_sales_per_marketplace_per_s) == 2.0 * math.pow(
1.0 + g, 1 + 1 + 10)
def __init__(self, ss=None):
# initialize self.tick, ss, agents, kpis
super().__init__(ss)
# now, fill in actual values for ss, agents, kpis
if self.ss is None:
self.ss = SimStrategy()
ss = self.ss # for convenience as we go forward
# used to manage names
self._next_free_marketplace_number = 0
# used to add agents
self._marketplace_tick_previous_add = 0
# as ecosystem improves, these parameters may change
self._total_OCEAN_minted: float = 0.0
self._total_OCEAN_burned: float = 0.0
self._total_OCEAN_burned_USD: float = 0.0
self._speculation_valuation = ss._init_speculation_valuation
# Instantiate and connnect agent instances. "Wire up the circuit"
new_agents: Set[AgentBase.AgentBase] = set()
# Note: could replace MarketplacesAgent with DataecosystemAgent, for a
# higher-fidelity simulation using EVM agents
new_agents.add(
MarketplacesAgent(
name="marketplaces1",
USD=0.0,
OCEAN=0.0,
toll_agent_name="opc_address",
n_marketplaces=float(ss.init_n_marketplaces),
sales_per_marketplace_per_s=20e3 / S_PER_MONTH, # magic number
time_step=self.ss.time_step,
)
)
new_agents.add(
RouterAgent(
name="opc_address",
USD=0.0,
OCEAN=0.0,
receiving_agents={
"ocean_dao": self.ss.percentToOceanDao,
"opc_burner": self.ss.percentToBurn,
},
)
)
new_agents.add(OCEANBurnerAgent(name="opc_burner", USD=0.0, OCEAN=0.0))
# func = MinterAgents.ExpFunc(H=4.0)
func = MinterAgents.RampedExpFunc(
H=4.0, T0=0.5, T1=1.0, T2=1.4, T3=3.0, M1=0.10, M2=0.25, M3=0.50
)
new_agents.add(
MinterAgents.OCEANFuncMinterAgent(
name="ocean_51",
receiving_agent_name="ocean_dao",
total_OCEAN_to_mint=ss.UNMINTED_OCEAN_SUPPLY,
s_between_mints=S_PER_DAY,
func=func,
)
)
new_agents.add(
GrantGivingAgent(
name="opf_treasury_for_ocean_dao",
USD=0.0,
OCEAN=ss.OPF_TREASURY_OCEAN_FOR_OCEAN_DAO,
receiving_agent_name="ocean_dao",
s_between_grants=S_PER_MONTH,
n_actions=12 * 3,
)
)
new_agents.add(
GrantGivingAgent(
name="opf_treasury_for_opf_mgmt",
USD=ss.OPF_TREASURY_USD,
OCEAN=ss.OPF_TREASURY_OCEAN_FOR_OPF_MGMT,
receiving_agent_name="opf_mgmt",
s_between_grants=S_PER_MONTH,
n_actions=12 * 3,
)
)
new_agents.add(
GrantGivingAgent(
name="bdb_treasury",
USD=ss.BDB_TREASURY_USD,
OCEAN=ss.BDB_TREASURY_OCEAN,
receiving_agent_name="bdb_mgmt",
s_between_grants=S_PER_MONTH,
n_actions=17,
)
)
new_agents.add(
RouterAgent(
name="ocean_dao",
receiving_agents={"opc_workers": funcOne},
USD=0.0,
OCEAN=0.0,
)
)
new_agents.add(
RouterAgent(
name="opf_mgmt",
receiving_agents={"opc_workers": funcOne},
USD=0.0,
OCEAN=0.0,
)
)
new_agents.add(
RouterAgent(
name="bdb_mgmt",
receiving_agents={"bdb_workers": funcOne},
USD=0.0,
OCEAN=0.0,
)
)
new_agents.add(GrantTakingAgent(name="opc_workers", USD=0.0, OCEAN=0.0))
new_agents.add(GrantTakingAgent(name="bdb_workers", USD=0.0, OCEAN=0.0))
for agent in new_agents:
self.agents[agent.name] = agent
# track certain metrics over time, so that we don't have to load
self.kpis = KPIs(self.ss)