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)
def finalizeOrder(orderId):
order = OrderModel.get(orderId)
order.setOrderTransctionDone()
order.save()
print(order.to_dict())
return order.to_dict()
def getOrderStatus(orderId):
order = OrderModel.get(orderId)
return order.to_dict()
def utc_8():
utc_now = datetime.utcnow().replace(tzinfo=timezone.utc)
obj = utc_now.astimezone(timezone(timedelta(hours=8)))
obj = datetime(obj.year, obj.month, obj.day, obj.hour, obj.minute,
obj.second, obj.microsecond)
zone = get_localzone()
return obj.strftime("%Y-%m-%d %H:%M:%S")
# data model
user = UserModel()
spot = SpotModel()
order = OrderModel()
response = ResponseModel()
# firebase
cred = credentials.Certificate("firebase-key.json")
initialize_app(cred)
db = firestore.client()
# firebase data collection
users_ref = db.collection("users")
spots_ref = db.collection("spots")
orders_ref = db.collection("orders")
def create(request):
# 获取参数
def __init__(self, environment):
super().__init__(environment)
# technical indicator 4*8
self.model = OrderModel(7, [32], 50, str(self.__class__.__name__))
class SellOrderAgent(Agent):
# technical indicator 4*8
model = OrderModel(7, 32, 50)
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)
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)
def createOrderReservation(input):
order = OrderModel()
order.init()
paymentIntent = stripe.PaymentIntent.create(
amount=1099,
currency='jpy',
metadata={
'orderId': str(order.id),
},
)
order.paymentId = paymentIntent.id
order.itemId = input['itemId']
order.title = input['title']
order.subtitle = input['subtitle']
order.price = input['price']
order.save()
result = {}
result['paymentIntent'] = paymentIntent
result['order'] = order.to_dict()
return result
def confirmOrderReservation(orderId):
order = OrderModel.get(orderId)
order.setOrderStatusSucceeded()
order.save()
return order.to_dict()
def cancelOrderReservation(orderId):
order = OrderModel.get(orderId)
order.setOrderStatusFailed()
order.save()
return order.to_dict()