class BuyOrderAgent(Agent):
state = None # save the state to be trained
def __init__(self, environment):
super().__init__(environment)
# technical indicator 4*8
self.model = OrderModel(7, [32], 50, str(self.__class__.__name__))
def process_action(self, action, date):
# buy order agent consider state on T-1, and place order on T day
market_data = self.environment.get_market_data_by_date(date)
if market_data is None:
# state is not available, restart from the top
return False
ma5 = market_data['ma5']
low = market_data['Low']
if ma5 is None or low is None:
# terminate
return False
bp = ma5 + action / 100 * ma5
d = bp - low
# print("processing buy order, buy price: " + str(bp))
if d >= 0:
reward = math.exp(-100 * d / low)
if not self.environment.get_evaluation_mode():
self.model.fit(self.state.value, reward, action)
else:
record = {'bp': bp, 'date': date}
self.environment.record(**record)
# last state date for sell signal becomes T day, start training on T+1
self.environment.set_buy_price(bp)
self.environment.invoke_sell_signal_agent()
else:
reward = 0
if not self.environment.get_evaluation_mode():
self.model.fit(self.state.value, reward, action)
self.environment.invoke_buy_signal_agent(True, self.state.date)
return True
def process_next_state(self, date):
# the date get here is already the next day, but we need the same day of BSA as the state
prev_date = self.environment.get_prev_day(date)
# print("Buy order - processing date: " + str(date))
self.state = self.environment.get_buy_order_states_by_date(prev_date)
action = self.get_action(self.state)
result = self.process_action(action, date)
if not result:
self.environment.process_epoch_end(None, True)
class SellOrderAgent(Agent):
state = None # save the state to be trained
action = None # save the action needed to pass to fit method
def __init__(self, environment):
super().__init__(environment)
# technical indicator 4*8
self.model = OrderModel(7, [32], 50, str(self.__class__.__name__))
def process_action(self, action, date):
# sell order agent consider state on T-1, and place order on T day
market_data = self.environment.get_market_data_by_date(date)
if market_data is None:
# terminated
return False
ma5 = market_data['ma5']
high = market_data['High']
close = market_data['Close']
if ma5 is None or high is None:
# terminate
return False
sp = ma5 + action / 100 * ma5
d = sp - high
if d <= 0:
reward = math.exp(100 * d / high)
else:
reward = 0
sp = close
if not self.environment.get_evaluation_mode():
self.model.fit(self.state.value, reward, action)
else:
profit = (1 - self.environment.transaction_cost
) * sp - self.environment.get_buy_price()
pf_return = ((1 - self.environment.transaction_cost) * sp -
self.environment.get_buy_price()
) / self.environment.get_buy_price()
record = {
'sp': sp,
'date': date,
'profit': profit,
'return': pf_return
}
self.environment.record(**record)
# print("processing sell order, sell price: " + str(sp))
self.environment.invoke_buy_signal_agent(
sp, date, self.environment.get_buy_price(), sp)
return True
def process_next_state(self, date):
# the date get here is already the next day, but we need the same day of SSA as the state
prev_date = self.environment.get_prev_day(date)
# print("Sell order - processing date: " + str(date))
self.state = self.environment.get_sell_order_states_by_date(prev_date)
action = self.get_action(self.state)
result = self.process_action(action, date)
if not result:
self.environment.process_epoch_end(None, True)