def get_order_list(self, portfolio: Portfolio,
stock_market_data: StockMarketData):
stock_price_a = stock_market_data[Company.A].get_last()[-1]
stock_price_b = stock_market_data[Company.B].get_last()[-1]
order_list = []
if self.actions[self.last_action][0] > 0:
amount_to_buy_stock_a = int(portfolio.cash *
self.actions[self.last_action][0] //
stock_price_a)
order_list.append(
Order(OrderType.BUY, Company.A, amount_to_buy_stock_a))
elif self.actions[self.last_action][0] < 0:
# sell everything we have, look at the actions, we don't have -0.8 or sth just -1
# we don't need any calculation for "amount_to_sell_stock_a"
order_list.append(
Order(OrderType.SELL, Company.A, portfolio.get_stock(Company.A)))
if self.actions[self.last_action][1] > 0:
amount_to_buy_stock_b = int(portfolio.cash *
self.actions[self.last_action][1] //
stock_price_b)
order_list.append(
Order(OrderType.BUY, Company.B, amount_to_buy_stock_b))
elif self.actions[self.last_action][1] < 0:
# sell everything we have, look at the actions, we don't have -0.8 or sth just -1
# we don't need any calculation for "amount_to_sell_stock_b"
order_list.append(
Order(OrderType.SELL, Company.B, portfolio.get_stock(Company.B)))
return order_list
def make_order(self, company: Company, orderTyp: OrderType, percentage,
portfolio: Portfolio,
stock_market_data: StockMarketData) -> Order:
"""
creates an Order
Args:
company: the company for the order
orderTyp: the OrderTyp (as Vote instance)
percentage: an integer indicating how much percent should be bought or sold
Returns an order for one company of instance Order
"""
if orderTyp == OrderType.BUY:
stock_price = stock_market_data.get_most_recent_price(company)
port = portfolio.cash * percentage
amount_to_buy = int(port // stock_price)
logger.debug(
f"{self.get_name()}: Got best action to buy {company}: and bought {amount_to_buy}"
)
return Order(OrderType.BUY, company,
amount_to_buy) if amount_to_buy > 0 else None
elif orderTyp == OrderType.SELL:
amount_to_sell = portfolio.get_stock(company)
amount_to_sell *= percentage
logger.debug(
f"{self.get_name()}: Got best action to sell {company}: and sold {amount_to_sell}"
)
return Order(OrderType.SELL, company,
amount_to_sell) if amount_to_sell > 0 else None
else:
assert False
def test_update_action_order_does_not_matter(self):
stock_market_data = StockMarketData([Company.A, Company.B],
[Period.TESTING])
# Create two equal designed portfolios
portfolio1 = Portfolio(0, {Company.A: 100})
portfolio2 = Portfolio(0, {Company.A: 100})
# Create two order lists with the same entries, however in different order
order_list_1 = [
Order(OrderType.BUY, Company.A, 50),
Order(OrderType.SELL, Company.A, 100)
]
order_list_2 = [
Order(OrderType.SELL, Company.A, 100),
Order(OrderType.BUY, Company.A, 50)
]
# Execute the trade action lists on the two portfolios: Sell 100 stocks, skip buying because no cash available
portfolio1.update_with_order_list(stock_market_data, order_list_1)
portfolio2.update_with_order_list(stock_market_data, order_list_2)
# The portfolios should still be equal after applying the actions
self.assertEqual(portfolio1.cash, 10275.9895)
self.assertEqual(portfolio1.cash, portfolio2.cash)
self.assertEqual(portfolio1.stocks[Company.A], 0)
self.assertEqual(portfolio1.stocks, portfolio2.stocks)
def __follow_expert_vote(self, company: Company, stock_data: StockData,
vote: Vote, buy_weight: float,
portfolio: Portfolio, order_list: List[Order]):
assert company is not None
assert stock_data is not None
assert vote is not None
assert portfolio is not None
assert order_list is not None
if vote is Vote.BUY or vote is Vote.HOLD:
assert buy_weight is not None and 0 < buy_weight <= 1.0
stock_price = stock_data.get_last()[-1]
amount_to_buy = int(buy_weight * portfolio.cash // stock_price)
logger.debug(
f"{self.get_name()}: Got vote to buy {company}: {amount_to_buy} shares a {stock_price}"
)
if amount_to_buy > 0:
order_list.append(Order(OrderType.BUY, company, amount_to_buy))
elif vote == Vote.SELL:
# sell as many stocks as possible
amount_to_sell = portfolio.get_stock(company)
logger.debug(
f"{self.get_name()}: Got vote to sell {company}: {amount_to_sell} shares available"
)
if amount_to_sell > 0:
order_list.append(
Order(OrderType.SELL, company, amount_to_sell))
else:
# do nothing
assert vote == Vote.HOLD
logger.debug(f"{self.get_name()}: Got vote to hold {company}")
def test_update_do_not_drop_below_cash_0(self):
stock_market_data = StockMarketData([Company.A, Company.B], [Period.TESTING])
portfolio = Portfolio(110)
# Create a order list whose individual actions are within the limit but in sum are over the limit
# Most recent stock price of stock A is 102.759895
order_list = [Order(OrderType.BUY, Company.A, 1), Order(OrderType.BUY, Company.A, 1)]
portfolio.update_with_order_list(stock_market_data, order_list)
self.assertEqual(portfolio.cash, 7.240105)
self.assertEqual(portfolio.stocks[Company.A], 1)
def get_orders(order_index):
order_combination_dict = {0: ["BUY", "BUY"], 1: ["SELL", "SELL"], 2: ["BUY", "SELL"], 3: ["SELL", "BUY"]}
order_dict = {"BUY": 1, "SELL": 2}
action_combination = order_combination_dict[order_index]
order_a = order_dict[action_combination[0]]
order_b = order_dict[action_combination[1]]
order_a = Order(OrderType(order_a), Company.A, portfolio.get_stock(Company.A))
order_b = Order(OrderType(order_b), Company.B, portfolio.get_stock(Company.B))
order_list = [order_a, order_b]
return order_list
def trade(self, portfolio: Portfolio,
stock_market_data: StockMarketData) -> List[Order]:
"""
Generate actions to be taken on the "stock market"
Args:
portfolio : current Portfolio of this traders
stock_market_data : StockMarketData for evaluation
Returns:
A OrderList instance, may be empty never None
"""
assert portfolio is not None
assert stock_market_data is not None
if self.__bought_stocks:
return []
else:
self.__bought_stocks = True
# Calculate how many cash to spend per company
company_list = stock_market_data.get_companies()
# Invest (100 // `len(companies)`)% of cash into each stock
order_list = []
for company in company_list:
available_cash_per_stock = portfolio.cash / len(company_list)
most_recent_price = stock_market_data.get_most_recent_price(
company)
amount_to_buy = available_cash_per_stock // most_recent_price
order_list.append(Order(OrderType.BUY, company, amount_to_buy))
return order_list
def test_update_no_sufficient_cash_reserve(self):
stock_market_data = StockMarketData([Company.A, Company.B], [Period.TESTING])
portfolio = Portfolio(0, {Company.A: 200})
order_list = [Order(OrderType.BUY, Company.A, 100)]
# Trade volume is too high for current cash reserve. Nothing should happen
portfolio.update_with_order_list(stock_market_data, order_list)
self.assertEqual(portfolio.cash, 0)
self.assertEqual(portfolio.stocks[Company.A], 200)
def test_update_sufficient_cash_reserve(self):
stock_market_data = StockMarketData([Company.A, Company.B], [Period.TESTING])
portfolio = Portfolio(20000, {Company.A: 200})
order_list = [Order(OrderType.BUY, Company.A, 100)]
# Current cash reserve is sufficient for trade volume. Trade should happen
portfolio.update_with_order_list(stock_market_data, order_list)
self.assertEqual(portfolio.cash, 9724.0105)
self.assertEqual(portfolio.stocks[Company.A], 300)
def get_order(self, portfolio, company, voteOrder, voteOther, stock_data):
order = None
if voteOrder == Vote.BUY:
stock_price = stock_data.get_most_recent_price(company)
if (voteOrder == voteOther):
amount = (portfolio.cash / 2) / stock_price
else:
amount = portfolio.cash / stock_price
if amount > 0:
order = Order(OrderType.BUY, company, amount)
elif voteOrder == Vote.SELL:
amount = portfolio.get_stock(company)
if amount > 0:
order = Order(OrderType.SELL, company, amount)
else:
order = None
return order
def follow_orders(orders):
order_list = []
company_list = stock_market_data.get_companies()
for company, order in zip(company_list, orders):
stock_data = stock_market_data[company]
if order.type == OrderType.BUY:
# buy as many stocks as possible
stock_price = stock_data.get_last()[-1]
amount_to_buy = int(portfolio.cash // stock_price)
logger.debug(f"{self.get_name()}: Got order to buy {company}: {amount_to_buy} shares a {stock_price}")
if amount_to_buy > 0:
order_list.append(Order(OrderType.BUY, company, amount_to_buy))
elif order.type == OrderType.SELL:
# sell as many stocks as possible
amount_to_sell = portfolio.get_stock(company)
logger.debug(f"{self.get_name()}: Got order to sell {company}: {amount_to_sell} shares available")
if amount_to_sell > 0:
order_list.append(Order(OrderType.SELL, Company.A, amount_to_sell))
return order_list
def __follow_action(self, company: Company, stock_data: StockData,
vote: Vote, portfolio: Portfolio,
order_list: List[Order]):
"""
Protected helper method to calculate amount of stocks to be bought and sold.
:param company: Company
:param stock_data: StockData
:param vote: Vote
:param portfolio: Portfolio
:param order_list: List[Order]
:return: None (writes result to order_list)
"""
assert company is not None
assert stock_data is not None
assert vote is not None
assert portfolio is not None
assert order_list is not None
if vote == Vote.BUY:
# buy as many stocks as possible
stock_price = stock_data.get_last()[-1]
amount_to_buy = int(portfolio.cash // stock_price)
logger.debug(
f"{self.get_name()}: Got vote to buy {company}: {amount_to_buy} shares a {stock_price}"
)
if amount_to_buy > 0:
order_list.append(Order(OrderType.BUY, company, amount_to_buy))
elif vote == Vote.SELL:
# sell as many stocks as possible
amount_to_sell = portfolio.get_stock(company)
logger.debug(
f"{self.get_name()}: Got vote to sell {company}: {amount_to_sell} shares available"
)
if amount_to_sell > 0:
order_list.append(
Order(OrderType.SELL, company, amount_to_sell))
else:
# do nothing
assert vote == Vote.HOLD
logger.debug(f"{self.get_name()}: Got vote to hold {company}")
def __create_order_for_company(
self, company: Company, portfolio: Portfolio, vote: Vote,
stock_market_data: StockMarketData) -> Order:
order = None
if vote == Vote.SELL:
amount = portfolio.get_stock(company)
if amount > 0:
order = Order(OrderType.SELL, company, amount)
elif vote == Vote.BUY:
stock_price = stock_market_data.get_most_recent_price(company)
amount = 0
if (self.type_a == self.type_b):
# buy both - half portfolio value for each
amount = int((portfolio.cash // 2) // stock_price)
else:
amount = int(portfolio.cash // stock_price)
if amount > 0:
order = Order(OrderType.BUY, company, amount)
return order
def buy(self, stock_data, company, order_list, portfolio, percent):
stock_price = stock_data.get_last()[-1]
amount_to_buy = int(np.divide(portfolio.cash * percent, stock_price))
if amount_to_buy > 0:
order_list.append(Order(OrderType.BUY, company, amount_to_buy))
def trade(self, portfolio: Portfolio, stock_market_data: StockMarketData) -> List[Order]:
"""
Generate action to be taken on the "stock market"
Args:
portfolio : current Portfolio of this traders
stock_market_data : StockMarketData for evaluation
Returns:
A OrderList instance, may be empty never None
"""
assert portfolio is not None
assert stock_market_data is not None
assert stock_market_data.get_companies() == [Company.A, Company.B]
# INPUT layer: 1 (buy or sell A?)
# output layer: 2 ([buy_A, sell_A])
# TODO Compute the current state
stock_data_A = stock_market_data[Company.A]
expertA_voteA = self.expert_a.vote(stock_data_A)
expertB_voteA = self.expert_b.vote(stock_data_A)
stock_data_B = stock_market_data[Company.B]
expertA_voteB = self.expert_a.vote(stock_data_B)
expertB_voteB = self.expert_b.vote(stock_data_B)
state = np.array([[
self.vote_map[expertA_voteA] + self.vote_map[expertB_voteA],
self.vote_map[expertA_voteB] + self.vote_map[expertB_voteB],
]])
# do action 0 or 1?
predictions = self.model.predict(state)
'''
if random.random() < self.epsilon:
# use random actions for A and B
action_A = random.randrange(2)
action_B = random.randrange(2)
else:
# use prediction actions
action_A = np.argmax(predictions[0][0:2])
action_B = np.argmax(predictions[0][2:4])
'''
action_A = np.argmax(predictions[0][0:2])
action_B = np.argmax(predictions[0][2:4])
current_price_a = stock_market_data.get_most_recent_price(Company.A)
current_price_b = stock_market_data.get_most_recent_price(Company.B)
money_to_spend = portfolio.cash
order_list = []
# do stuff for A
if action_A == 0:
# buy all A
amount_to_buy = money_to_spend // current_price_a
if amount_to_buy > 0:
money_to_spend -= amount_to_buy * current_price_a
order_list.append(Order(OrderType.BUY, Company.A, amount_to_buy))
elif action_A == 1:
# sell all A
amount_to_sell = portfolio.get_stock(Company.A)
if amount_to_sell > 0:
order_list.append(Order(OrderType.SELL, Company.A, amount_to_sell))
else:
assert False
# do stuff for B
if action_B == 0:
# buy all B
amount_to_buy = money_to_spend // current_price_b
if amount_to_buy > 0:
order_list.append(Order(OrderType.BUY, Company.B, amount_to_buy))
elif action_B == 1:
# sell all B
amount_to_sell = portfolio.get_stock(Company.B)
if amount_to_sell > 0:
order_list.append(Order(OrderType.SELL, Company.B, amount_to_sell))
else:
assert False
if self.last_state is not None:
# train
diff_a = (current_price_a / self.last_price_a - 1)
diff_b = (current_price_b / self.last_price_b - 1)
fut_reward_a_buy = np.max(predictions[0][0])
fut_reward_a_buy = fut_reward_a_buy if fut_reward_a_buy > 0 else 0
fut_reward_a_sell = np.max(predictions[0][1])
fut_reward_a_sell = fut_reward_a_sell if fut_reward_a_sell > 0 else 0
fut_reward_b_buy = np.max(predictions[0][2])
fut_reward_b_buy = fut_reward_b_buy if fut_reward_b_buy > 0 else 0
fut_reward_b_sell = np.max(predictions[0][3])
fut_reward_b_sell = fut_reward_b_sell if fut_reward_b_sell > 0 else 0
reward_vec = np.array([[
diff_a + self.gamma * fut_reward_a_buy,
-diff_a + self.gamma * fut_reward_a_sell,
diff_b + self.gamma * fut_reward_b_buy,
-diff_b + self.gamma * fut_reward_b_sell
]])
#reward_vec = np.array([[portfolio.get_value(stock_market_data)]])
self.model.fit(self.last_state, reward_vec, verbose=0)
self.last_state = state
self.last_action_a = action_A
self.last_action_b = action_B
self.last_portfolio_value = portfolio.get_value(stock_market_data)
self.last_price_a = current_price_a
self.last_price_b = current_price_b
return order_list
def trade(self, portfolio: Portfolio, stock_market_data: StockMarketData) -> List[Order]:
"""
Generate actions to be taken on the "stock market"
Args:
portfolio : current Portfolio of this traders
stock_market_data : StockMarketData for evaluation
Returns:
A OrderList instance, may be empty never None
"""
assert portfolio is not None
assert stock_market_data is not None
assert stock_market_data.get_companies() == [Company.A, Company.B]
# Compute the current state
expert_votes = [
self.experts[i].vote(stock_market_data[company])
for i, company in enumerate(stock_market_data.get_companies())
]
shares_owned = [
portfolio.get_stock(company)
for company in stock_market_data.get_companies()
]
if State is StateExpertsOnly:
state = StateExpertsOnly(expert_votes, portfolio.get_value(stock_market_data))
elif State is StateExpertsCashShares:
state = StateExpertsCashShares(expert_votes, portfolio.cash, shares_owned, portfolio.get_value(stock_market_data))
else:
raise RuntimeError
if self.train_while_trading:
# store state as experience in memory
if len(self.memory) > 0:
self.memory[-1].state2 = state
experience = Experience(
state1=state
)
self.memory.append(experience)
# train
if len(self.memory) >= self.min_size_of_memory_before_training:
if self.days_passed % self.train_each_n_days == 0:
self.train()
# determine probability for random actions
if not self.is_evolved_model:
# first training episode
random_action_probability = (
(self.epsilon ** self.days_passed) * (1.0 - self.random_action_min_probability) +
self.random_action_min_probability
)
else:
# subsequent training episode
random_action_probability = self.random_action_min_probability
if self.training_occasions == 0 or random.random() < random_action_probability:
actions = [Action.get_random(), Action.get_random()]
else:
# choose actions by querying network
x = state.to_input()
y = self.model.predict(numpy.array([x]))
assert y.shape == (1, self.action_size)
actions = Action.from_model_prediction(y[0])
experience.actions = actions
else:
# not training -> always choose actions by querying network
actions = Action.from_model_prediction(self.model.predict(numpy.array([state.to_input()]))[0])
# translate actions into orders
orders: typing.List[Order] = []
companies_with_actions_and_magnitudes = list(zip(list(Company), actions, Action.get_action_magnitudes(actions)))
for comp, action, mag in companies_with_actions_and_magnitudes:
if action.is_buy():
cash_limit = portfolio.cash * mag
date, stock_price = stock_market_data[comp].get_last()
shares_amount = cash_limit / stock_price
if shares_amount > 0:
orders.append(Order(OrderType.BUY, comp, shares_amount))
elif action.is_sell():
shares_amount = portfolio.get_stock(comp) * mag
if shares_amount > 0:
orders.append(Order(OrderType.SELL, comp, shares_amount))
self.days_passed += 1
return orders
def trade(self, portfolio: Portfolio,
stock_market_data: StockMarketData) -> List[Order]:
"""
Generate action to be taken on the "stock market"
Args:
portfolio : current Portfolio of this traders
stock_market_data : StockMarketData for evaluation
Returns:
A OrderList instance, may be empty never None
"""
assert portfolio is not None
assert stock_market_data is not None
assert stock_market_data.get_companies() == [Company.A, Company.B]
# INPUT layer: 1 (buy or sell A?)
# output layer: 2 ([buy_A, sell_A])
# TODO Compute the current state
stock_data_A = stock_market_data[Company.A]
expertA_voteA = self.expert_a.vote(stock_data_A)
expertB_voteA = self.expert_b.vote(stock_data_A)
stock_data_B = stock_market_data[Company.B]
expertA_voteB = self.expert_a.vote(stock_data_B)
expertB_voteB = self.expert_b.vote(stock_data_B)
state = np.array([[
self.vote_map[expertA_voteA] + self.vote_map[expertB_voteA],
self.vote_map[expertA_voteB] + self.vote_map[expertB_voteB],
]])
# do action 0 or 1?
predictions = self.model.predict(state)
#print(f'predictions:{predictions}')
#input()
action_A = np.argmax(predictions[0][0:2])
action_B = np.argmax(predictions[0][2:4])
most_recent_price_A = stock_market_data.get_most_recent_price(
Company.A)
most_recent_price_B = stock_market_data.get_most_recent_price(
Company.B)
order_list = []
money_to_spend = portfolio.cash
# do stuff for A
if action_A == 0:
# buy all A
amount_to_buy = money_to_spend // most_recent_price_A
if amount_to_buy > 0:
money_to_spend -= amount_to_buy * most_recent_price_A
order_list.append(
Order(OrderType.BUY, Company.A, amount_to_buy))
elif action_A == 1:
# sell all A
amount_to_sell = portfolio.get_stock(Company.A)
if amount_to_sell > 0:
order_list.append(
Order(OrderType.SELL, Company.A, amount_to_sell))
else:
assert False
# do stuff for B
if action_B == 0:
# buy all B
amount_to_buy = money_to_spend // most_recent_price_B
if amount_to_buy > 0:
order_list.append(
Order(OrderType.BUY, Company.B, amount_to_buy))
elif action_B == 1:
# sell all B
amount_to_sell = portfolio.get_stock(Company.B)
if amount_to_sell > 0:
order_list.append(
Order(OrderType.SELL, Company.B, amount_to_sell))
else:
assert False
if self.last_state is not None:
# train
diff = (portfolio.get_value(stock_market_data) /
self.last_portfolio_value - 1)
rec_vec = np.array([[-diff, -diff, -diff, -diff]])
rec_vec[0][self.last_action_a] = diff
rec_vec[0][2 + self.last_action_b] = diff
#reward_vec = np.array([[portfolio.get_value(stock_market_data)]])
self.model.fit(self.last_state, rec_vec)
self.last_state = state
self.last_action_a = action_A
self.last_action_b = action_B
self.last_portfolio_value = portfolio.get_value(stock_market_data)
return order_list
def trade(self, portfolio: Portfolio, stock_market_data: StockMarketData) -> List[Order]:
"""
Generate action to be taken on the "stock market"
Args:
portfolio : current Portfolio of this traders
stock_market_data : StockMarketData for evaluation
Returns:
A OrderList instance, may be empty never None
"""
assert portfolio is not None
assert stock_market_data is not None
assert stock_market_data.get_companies() == [Company.A, Company.B]
self.day += 1
# TODO Compute the current state
order_list = []
stock_data_a = stock_market_data[Company.A]
stock_data_b = stock_market_data[Company.B]
# Expert A
expert_a = self.expert_a.vote(stock_data_a)
# Expert B
expert_b = self.expert_b.vote(stock_data_b)
state = np.array([[
self.vote_num[expert_a], self.vote_num[expert_b], ]])
action = self.decide_action(state)
# TODO Store state as experience (memory) and train the neural network only if trade() was called before at least once
if self.last_state is not None:
reward = (portfolio.get_value(stock_market_data) - self.last_portfolio_value) / self.last_portfolio_value
self.memory.append((self.last_state, self.last_action, reward, state))
self.train_network(self.batch_size)
# TODO Create actions for current state and decrease epsilon for fewer random actions
act0 = 0
act1 = 0
act2 = 0
act3 = 0
act4 = 0
act5 = 0
act6 = 0
act7 = 0
act8 = 0
# What amount of the stocks should be bought or sold
percent_buy = 1
percent_sell = 1
if action == 0:
# Buy A
stock_price_a = stock_market_data.get_most_recent_price(Company.A)
amount_to_buy_a = int((portfolio.cash*percent_buy/2) // stock_price_a)
if amount_to_buy_a > 0:
order_list.append(Order(OrderType.BUY, Company.A, amount_to_buy_a))
# Buy B
stock_price_b = stock_market_data.get_most_recent_price(Company.B)
amount_to_buy_b = int((portfolio.cash*percent_buy/2) // stock_price_b)
if amount_to_buy_b > 0:
order_list.append(Order(OrderType.BUY, Company.B, amount_to_buy_b))
act0 += 1
elif action == 1:
# Buy A
stock_price_a = stock_market_data.get_most_recent_price(Company.A)
amount_to_buy_a = int(portfolio.cash *percent_buy// stock_price_a)
if amount_to_buy_a > 0:
order_list.append(Order(OrderType.BUY, Company.A, amount_to_buy_a))
# Sell B
amount_to_sell_b = int(portfolio.get_stock(Company.B)*percent_sell)
if amount_to_sell_b > 0:
order_list.append(Order(OrderType.SELL, Company.B, amount_to_sell_b))
act1 += 1
elif action == 2:
# Sell A
amount_to_sell_a = int(portfolio.get_stock(Company.A)*percent_sell)
if amount_to_sell_a > 0:
order_list.append(Order(OrderType.SELL, Company.A, amount_to_sell_a))
# Buy B
stock_price_b = stock_market_data.get_most_recent_price(Company.B)
amount_to_buy_b = int(portfolio.cash*percent_buy // stock_price_b)
if amount_to_buy_b > 0:
order_list.append(Order(OrderType.BUY, Company.B, amount_to_buy_b))
act2 += 1
elif action == 3:
# Sell A
amount_to_sell_a = int(portfolio.get_stock(Company.A)*percent_sell)
if amount_to_sell_a > 0:
order_list.append(Order(OrderType.SELL, Company.A, amount_to_sell_a))
# Sell B
amount_to_sell_b = int(portfolio.get_stock(Company.B)*percent_sell)
if amount_to_sell_b > 0:
order_list.append(Order(OrderType.SELL, Company.B, amount_to_sell_b))
act3 += 1
elif action == 4:
# Sell A
amount_to_sell_a = int(portfolio.get_stock(Company.A)*percent_sell)
if amount_to_sell_a > 0:
order_list.append(Order(OrderType.SELL, Company.A, amount_to_sell_a))
# Hold B
act4 += 1
elif action == 5:
# Hold A
# Sell B
amount_to_sell_b = int(portfolio.get_stock(Company.B)*percent_sell)
if amount_to_sell_b > 0:
order_list.append(Order(OrderType.SELL, Company.B, amount_to_sell_b))
act5 += 1
elif action == 6:
# Buy A
stock_price_a = stock_market_data.get_most_recent_price(Company.A)
amount_to_buy_a = int((portfolio.cash*percent_buy) // stock_price_a)
if amount_to_buy_a > 0:
order_list.append(Order(OrderType.BUY, Company.A, amount_to_buy_a))
# Hold B
act6 += 1
elif action == 7:
# Hold A
# Buy B
stock_price_b = stock_market_data.get_most_recent_price(Company.B)
amount_to_buy_b = int((portfolio.cash*percent_buy) // stock_price_b)
if amount_to_buy_b > 0:
order_list.append(Order(OrderType.BUY, Company.B, amount_to_buy_b))
act7 += 1
elif action == 8:
# Hold A
# Hold B
order_list.append(Order(OrderType.BUY, Company.B, 0))
act8 += 1
else:
print("undefined action called"+str(action))
# Decrease the epsilon for fewer random actions
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
# TODO Save created state, actions and portfolio value for the next call of trade()
self.last_state = state
self.last_action = action
self.last_portfolio_value = portfolio.get_value(stock_market_data)
return order_list
def sell(self, company, order_list, portfolio):
amount_to_sell = portfolio.get_stock(company)
if amount_to_sell > 0:
order_list.append(Order(OrderType.SELL, company, amount_to_sell))
def choose_actions(self,
stock_data_a: StockData,
stock_data_b: StockData,
portfolio: Portfolio,
order_list: List[Order],
epsilon=None,
model_choice=None):
assert epsilon is not None
assert model_choice is not None
action_a = None
action_b = None
if random.random() < self.epsilon:
action_comb = random.randrange(10)
else:
action_comb = model_choice
potential_buy_a = int(portfolio.cash // stock_data_a.get_last()[-1])
potential_buy_b = int(portfolio.cash // stock_data_b.get_last()[-1])
potential_sell_a = portfolio.get_stock(Company.A)
potential_sell_b = portfolio.get_stock(Company.B)
"""10 action combinations:
- buy 100% A, buy 0% B # buy only A completely
- buy 100% A, sell 100% B # sell all B, buy all A
- buy 50% A, buy 50% B # buy both
- buy 0% A, buy 100% B # buy only B completely
- sell 100% A, sell 0% B # sell only A completely
- sell 100% A, sell 100% B # sell both completely
- sell 100% A, buy 100% B # sell all A, buy all B
- sell 50% A, sell 50% B # sell both half
- sell 0% A, sell 100% B # sell only B completely
- hold # do nothing
"""
logger.debug(f"{self.get_name()}: chooses action comb {action_comb}")
if action_comb == 0:
# buy 100% A, buy 0% B # buy only A completely
action_a = OrderType.BUY.value
action_b = 0
order_list.append(Order(OrderType.BUY, Company.A, potential_buy_a))
elif action_comb == 1:
# buy 100% A, sell 100% B # sell all B, buy all A
action_a = OrderType.BUY.value
action_b = OrderType.SELL.value
order_list.append(Order(OrderType.BUY, Company.A, potential_buy_a))
order_list.append(
Order(OrderType.SELL, Company.B, potential_sell_b))
elif action_comb == 2:
# buy 50% A, buy 50% B # buy both
action_a = OrderType.BUY.value
action_b = OrderType.SELL.value
order_list.append(
Order(OrderType.BUY, Company.A, potential_buy_a // 2))
remaining_cash = portfolio.cash - (potential_buy_a //
2) * stock_data_a.get_last()[-1]
potential_buy_b = int(remaining_cash //
stock_data_b.get_last()[-1])
order_list.append(Order(OrderType.SELL, Company.B,
potential_buy_b))
elif action_comb == 3:
# buy 0% A, buy 100% B # buy only B completely
action_a = 0
action_b = OrderType.BUY.value
order_list.append(Order(OrderType.BUY, Company.B, potential_buy_b))
elif action_comb == 4:
# sell 100% A, sell 0% B # sell only A completely
action_a = OrderType.SELL.value
action_b = 0
order_list.append(
Order(OrderType.SELL, Company.A, potential_sell_a))
elif action_comb == 5:
# sell 100% A, sell 100% B # sell both completely
action_a = OrderType.SELL.value
action_b = OrderType.SELL.value
order_list.append(
Order(OrderType.SELL, Company.A, potential_sell_a))
order_list.append(
Order(OrderType.SELL, Company.B, potential_sell_b))
elif action_comb == 6:
# sell 100% A, buy 100% B # sell all A, buy all B
action_a = OrderType.SELL.value
action_b = OrderType.BUY.value
order_list.append(
Order(OrderType.SELL, Company.A, potential_sell_a))
order_list.append(Order(OrderType.BUY, Company.B, potential_buy_b))
elif action_comb == 7:
# sell 50% A, sell 50% B # sell both half
action_a = OrderType.SELL.value
action_b = OrderType.SELL.value
order_list.append(
Order(OrderType.SELL, Company.A, potential_sell_a // 2))
order_list.append(
Order(OrderType.SELL, Company.B, potential_sell_b // 2))
elif action_comb == 8:
# sell 0% A, sell 100% B # sell only B completely
action_a = 0
action_b = OrderType.SELL.value
order_list.append(
Order(OrderType.SELL, Company.B, potential_sell_b))
elif action_comb == 9:
# hold # do nothing
action_a = 0
action_b = 0
return action_a, action_b, order_list
def get_order_item(self, action, order, sell_off, company):
if action > 0:
return Order(OrderType.BUY, company, order)
else:
return Order(OrderType.SELL, company, sell_off)
def trade(self, portfolio: Portfolio,
stock_market_data: StockMarketData) -> List[Order]:
"""
Generate action to be taken on the "stock marketf"
Args:
portfolio : current Portfolio of this traders
stock_market_data : StockMarketData for evaluation
Returns:
A OrderList instance, may be empty never None
"""
assert portfolio is not None
assert stock_market_data is not None
assert stock_market_data.get_companies() == [Company.A, Company.B]
# TODO Compute the current state
stock_data_a = None
stock_data_b = None
last_stock_data_a = None
last_stock_data_b = None
company_list = stock_market_data.get_companies()
for company in company_list:
if company == Company.A:
stock_data_a = stock_market_data[Company.A]
last_stock_data_a = stock_data_a.get_from_offset(-2)
elif company == Company.B:
stock_data_b = stock_market_data[Company.B]
last_stock_data_b = stock_data_b.get_from_offset(-2)
else:
assert False
vote_a = self.expert_a.vote(stock_data_a)
vote_b = self.expert_b.vote(stock_data_b)
state = State(last_stock_data_a, last_stock_data_b, vote_a, vote_b)
# TODO Q-Learning
nn_input = np.array(
[np.array([state.aDiff, state.vote_a, state.bDiff, state.vote_b])])
action_vals = self.model.predict(nn_input)
# TODO Store state as experience (memory) and train the neural network only if trade() was called before at least once
# TODO Create actions for current state and decrease epsilon for fewer random actions
actions = [[
Order(OrderType.BUY, Company.A,
int((portfolio.cash / 2) // stock_data_a.get_last()[-1])),
Order(OrderType.BUY, Company.B,
int((portfolio.cash / 2) // stock_data_b.get_last()[-1]))
],
[
Order(
OrderType.BUY, Company.A,
int((portfolio.cash) //
stock_data_a.get_last()[-1])),
Order(OrderType.SELL, Company.B,
portfolio.get_stock(Company.B))
],
[
Order(OrderType.SELL, Company.A,
portfolio.get_stock(Company.A)),
Order(
OrderType.BUY, Company.B,
int(portfolio.cash // stock_data_b.get_last()[-1]))
],
[
Order(OrderType.SELL, Company.A,
portfolio.get_stock(Company.A)),
Order(OrderType.SELL, Company.B,
portfolio.get_stock(Company.B))
],
[
Order(OrderType.SELL, Company.A, 0),
Order(OrderType.SELL, Company.B, 0)
]]
if not self.train_while_trading:
self.epsilon = 0.0
else:
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
else:
self.epsilon = self.epsilon_min
# randomize action
if random.random() < self.epsilon:
next_action = random.choice(list(range(self.action_size)))
else:
next_action = np.argmax(action_vals[0])
order_list = actions[next_action]
portfolio_value = portfolio.get_value(
stock_market_data, stock_market_data.get_most_recent_trade_day())
if (self.last_state != None and self.train_while_trading):
def reward(oldVal, newVal):
neg = -100.0
pos = 100.0
q = newVal / oldVal
if q < 1:
return neg
elif q == 1:
return -10
else:
print("Q: ", q)
return pos / 2 * oldVal / newVal
r = reward(self.last_portfolio_value, portfolio_value)
action_vals[0][self.last_order] = r
self.memory.append([self.last_input, action_vals])
if (len(self.memory) > self.min_size_of_memory_before_training):
sample = random.sample(self.memory, self.batch_size)
trainSample = list()
testSample = list()
for [sampleIn, sampleOut] in sample:
trainSample.append(sampleIn[0])
testSample.append(sampleOut[0])
self.model.fit(np.array(trainSample), np.array(testSample),
self.batch_size)
# Save created state, actions and portfolio value for the next call of trade()
self.last_input = nn_input
self.last_state = state
self.last_order = next_action
self.last_portfolio_value = portfolio_value
print(next_action, action_vals, portfolio.cash,
portfolio.get_stock(Company.A), portfolio.get_stock(Company.B))
return order_list
def mapping_action_order(self, action, portfolio,
stock_market_data: StockMarketData):
all_orders = []
sell_price_A = portfolio.get_stock(Company.A)
sell_price_B = portfolio.get_stock(Company.B)
stock_price_A = stock_market_data[Company.A].get_last()[-1]
stock_price_B = stock_market_data[Company.B].get_last()[-1]
buy_price_A = int(portfolio.cash // stock_price_A)
buy_price_B = int(portfolio.cash // stock_price_B)
if action == 0:
invest = int(portfolio.cash // 2)
buy_price_A = int(invest // stock_price_A)
all_orders.append(Order(OrderType.BUY, Company.A, buy_price_A))
buy_price_B = int(invest // stock_price_B)
all_orders.append(Order(OrderType.BUY, Company.B, buy_price_B))
elif action == 1:
all_orders.append(Order(OrderType.BUY, Company.A, buy_price_A))
all_orders.append(Order(OrderType.SELL, Company.B, sell_price_B))
elif action == 2:
all_orders.append(Order(OrderType.BUY, Company.A, buy_price_A))
elif action == 3:
all_orders.append(Order(OrderType.SELL, Company.A, sell_price_A))
all_orders.append(Order(OrderType.BUY, Company.B, buy_price_B))
elif action == 4:
all_orders.append(Order(OrderType.SELL, Company.A, sell_price_A))
all_orders.append(Order(OrderType.SELL, Company.B, sell_price_B))
elif action == 5:
all_orders.append(Order(OrderType.SELL, Company.A, sell_price_A))
elif action == 6:
all_orders.append(Order(OrderType.BUY, Company.B, buy_price_B))
elif action == 7:
all_orders.append(Order(OrderType.SELL, Company.B, sell_price_B))
elif action == 8:
all_orders = all_orders
return all_orders
def trade(self, portfolio: Portfolio,
stock_market_data: StockMarketData) -> List[Order]:
"""
Generate action to be taken on the "stock market"
Args:
portfolio : current Portfolio of this traders
stock_market_data : StockMarketData for evaluation
Returns:
A OrderList instance, may be empty never None
"""
assert portfolio is not None
assert stock_market_data is not None
assert stock_market_data.get_companies() == [Company.A, Company.B]
# INPUT layer: 1 (buy or sell A?)
# output layer: 2 ([buy_A, sell_A])
# TODO Compute the current state
stock_data_A = stock_market_data[Company.A]
expertA_voteA = self.expert_a.vote(stock_data_A)
stock_data_B = stock_market_data[Company.B]
expertB_voteB = self.expert_b.vote(stock_data_B)
state = np.array([[
self.vote_map[expertA_voteA],
self.vote_map[expertB_voteB],
]])
# do action 0 or 1?
predictions = self.model.predict(state)
# TODO Create actions for current state and decrease epsilon for fewer random actions
if random.random() < self.epsilon:
# use random actions for A and B
action_A = random.randrange(2)
action_B = random.randrange(2)
else:
# use prediction actions
action_A = np.argmax(predictions[0][0:2])
action_B = np.argmax(predictions[0][2:4])
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
current_price_a = stock_market_data.get_most_recent_price(Company.A)
current_price_b = stock_market_data.get_most_recent_price(Company.B)
money_to_spend = portfolio.cash
order_list = []
# do stuff for A
if action_A == 0:
# buy all A
amount_to_buy = money_to_spend // current_price_a
if amount_to_buy > 0:
money_to_spend -= amount_to_buy * current_price_a
order_list.append(
Order(OrderType.BUY, Company.A, amount_to_buy))
elif action_A == 1:
# sell all A
amount_to_sell = portfolio.get_stock(Company.A)
if amount_to_sell > 0:
order_list.append(
Order(OrderType.SELL, Company.A, amount_to_sell))
else:
assert False
# do stuff for B
if action_B == 0:
# buy all B
amount_to_buy = money_to_spend // current_price_b
if amount_to_buy > 0:
order_list.append(
Order(OrderType.BUY, Company.B, amount_to_buy))
elif action_B == 1:
# sell all B
amount_to_sell = portfolio.get_stock(Company.B)
if amount_to_sell > 0:
order_list.append(
Order(OrderType.SELL, Company.B, amount_to_sell))
else:
assert False
# TODO train the neural network only if trade() was called before at least once
if self.last_state is not None:
# train
diff_a = (current_price_a / self.last_price_a - 1)
diff_b = (current_price_b / self.last_price_b - 1)
fut_reward_a = np.max(predictions[0][0:2])
fut_reward_b = np.max(predictions[0][2:4])
reward_vec = np.array([[
diff_a + self.gamma * fut_reward_a,
-diff_a + self.gamma * fut_reward_a,
diff_b + self.gamma * fut_reward_b,
-diff_b + self.gamma * fut_reward_b
]])
# TODO Store state as experience (memory) and replay
# slides: <s, a, r, s'>
# mine: <s, r>
if self.min_size_of_memory_before_training <= len(self.memory):
# take self.batch_size - 1 from memory
batch = random.sample(self.memory, self.batch_size - 1)
# append current state, reward
batch.append((self.last_state, reward_vec))
for x, y in batch:
self.model.fit(x, y, batch_size=self.batch_size, verbose=0)
else:
# only train with current (state, reward)
self.model.fit(self.last_state,
reward_vec,
batch_size=1,
verbose=0)
self.memory.append((self.last_state, reward_vec))
# TODO Save created state, actions and portfolio value for the next call of trade()
self.last_state = state
self.last_action_a = action_A
self.last_action_b = action_B
self.last_portfolio_value = portfolio.get_value(stock_market_data)
self.last_price_a = current_price_a
self.last_price_b = current_price_b
return order_list
def trade(self, portfolio: Portfolio,
stock_market_data: StockMarketData) -> List[Order]:
"""
Generate action to be taken on the "stock market"
Args:
portfolio : current Portfolio of this traders
stock_market_data : StockMarketData for evaluation
Returns:
A OrderList instance, may be empty never None
"""
assert portfolio is not None
assert stock_market_data is not None
assert stock_market_data.get_companies() == [Company.A, Company.B]
# INPUT layer: 1 (buy or sell A?)
# output layer: 2 ([buy_A, sell_A])
# TODO Compute the current state
stock_data_a = stock_market_data[Company.A]
vote_a_for_a = self.expert_a.vote(stock_data_a)
vote_b_for_a = self.expert_b.vote(stock_data_a)
#stock_data_b = stock_market_data[Company.B]
#vote_b = self.expert_a.vote(stock_data_b)
state = np.array([[
self.vote_map[vote_a_for_a] + self.vote_map[vote_b_for_a]
]]) #, self.vote_map[vote_b]])
# do action 0 or 1?
predictions = self.model.predict(state)
action = np.argmax(predictions)
current_price_a = stock_market_data.get_most_recent_price(Company.A)
order_list = []
if action == 0:
# buy all A
amount_to_buy = portfolio.cash // current_price_a
if amount_to_buy > 0:
order_list.append(
Order(OrderType.BUY, Company.A, amount_to_buy))
elif action == 1:
# sell all A
amount_to_sell = portfolio.get_stock(Company.A)
if amount_to_sell > 0:
order_list.append(
Order(OrderType.SELL, Company.A, amount_to_sell))
else:
assert False
if self.last_state is not None:
# train
diff_a = (current_price_a / self.last_price_a - 1)
if self.last_action_a == 0:
rec_vec = np.array([[diff_a, -diff_a]])
elif self.last_action_a == 1:
rec_vec = np.array([[diff_a, -diff_a]])
else:
assert False # wtf
#reward_vec = np.array([[portfolio.get_value(stock_market_data)]])
self.model.fit(self.last_state, rec_vec)
self.last_state = state
self.last_action_a = action
self.last_portfolio_value = portfolio.get_value(stock_market_data)
self.last_price_a = current_price_a
return order_list
