def test_eq__wrong_instance(self):
portfolio = Portfolio(10.0,
[SharesOfCompany(CompanyEnum.COMPANY_A, 200)])
assert portfolio != "a string"
def doTrade(self, portfolio: Portfolio, current_portfolio_value: float,
stock_market_data: StockMarketData) -> OrderList:
"""
Generate action to be taken on the "stock market"
Args:
portfolio : current Portfolio of this trader
current_portfolio_value : value of Portfolio at given moment
stock_market_data : StockMarketData for evaluation
Returns:
A OrderList instance, may be empty never None
"""
# TODO: Store experience and train the neural network only if doTrade was called before at least once
# TODO: Create actions for current state and decrease epsilon for fewer random actions
# TODO: Save created state, actions and portfolio value for the next call of doTrade
deltaA = self.stock_a_predictor.doPredict(
stock_market_data[CompanyEnum.COMPANY_A]) / stock_market_data.get_most_recent_price(CompanyEnum.COMPANY_A)
deltaB = self.stock_b_predictor.doPredict(
stock_market_data[CompanyEnum.COMPANY_B]) / stock_market_data.get_most_recent_price(CompanyEnum.COMPANY_B)
INPUT = numpy.asarray([[
(deltaA - 1.0) / 0.04,
(deltaB - 1.0) / 0.04,
]])
qualities = self.model.predict(INPUT)[0]
qmax = max(qualities[0], qualities[1], qualities[2])
currentValue = portfolio.total_value(stock_market_data.get_most_recent_trade_day(), stock_market_data)
if self.lastValue and self.train_while_trading:
lastReward = min(1, max(-1, (currentValue / self.lastValue - 1) / 0.04))
shouldBeQ = lastReward + GAMMA * qmax
self.lastOutput[self.lastAmax] = shouldBeQ
xtrain = [self.lastInput[0]]
ytrain = [self.lastOutput]
for m in self.memory:
xtrain.append(m[0][0])
qs = self.model.predict(m[0])[0]
qs[m[1]] = m[2] + GAMMA * qs[m[1]]
ytrain.append(qs)
self.model.fit(numpy.asarray(xtrain), numpy.asarray(ytrain))
self.memory.append([self.lastInput, self.lastAmax, lastReward])
if len(self.memory) > MEMOMRY_SIZE:
self.memory.pop(0)
self.lastValue = currentValue
self.lastInput = INPUT
self.lastOutput = qualities
result = OrderList()
actions = ["BUY_A__SELL_B", "BUY_A", "BUY_B__SELL_A", "BUY_B", "SELL_ALL"]
nextAction = None
if random.random() < self.epsilon and self.train_while_trading:
nextAction = actions[random.randint(0, self.action_size - 1)]
else:
i = 0 if qualities[0] > qualities[1] else 1
i = 2 if qualities[2] > qualities[i] else i
i = 3 if qualities[3] > qualities[i] else i
i = 4 if qualities[4] > qualities[i] else i
nextAction = actions[i]
self.epsilon = max(self.epsilon_decay * self.epsilon, self.epsilon_min)
if nextAction == "BUY_A__SELL_B":
result.sell(CompanyEnum.COMPANY_B, portfolio.get_amount(CompanyEnum.COMPANY_B))
count = math.floor(portfolio.cash / stock_market_data.get_most_recent_price(CompanyEnum.COMPANY_A))
result.buy(CompanyEnum.COMPANY_A, count)
self.lastAmax = 0
elif nextAction == "BUY_A":
count = math.floor(portfolio.cash / stock_market_data.get_most_recent_price(CompanyEnum.COMPANY_A))
result.buy(CompanyEnum.COMPANY_A, count)
self.lastAmax = 1
elif nextAction == "BUY_B__SELL_A":
result.sell(CompanyEnum.COMPANY_A, portfolio.get_amount(CompanyEnum.COMPANY_A))
count = math.floor(portfolio.cash / stock_market_data.get_most_recent_price(CompanyEnum.COMPANY_B))
result.buy(CompanyEnum.COMPANY_B, count)
self.lastAmax = 2
elif nextAction == "BUY_B":
count = math.floor(portfolio.cash / stock_market_data.get_most_recent_price(CompanyEnum.COMPANY_B))
result.buy(CompanyEnum.COMPANY_B, count)
self.lastAmax = 3
elif nextAction == "SELL_ALL":
result.sell(CompanyEnum.COMPANY_A, portfolio.get_amount(CompanyEnum.COMPANY_A))
result.sell(CompanyEnum.COMPANY_B, portfolio.get_amount(CompanyEnum.COMPANY_B))
self.lastAmax = 4
return result
"""
save_keras_sequential(self.model, self.RELATIVE_DATA_DIRECTORY, self.network_filename)
# This method retrains the trader from scratch using training data from PERIOD_1 and test data from PERIOD_2
EPISODES = 7
if __name__ == "__main__":
# Read the training data
training_data = read_stock_market_data([CompanyEnum.COMPANY_A, CompanyEnum.COMPANY_B], [PERIOD_1])
test_data = read_stock_market_data([CompanyEnum.COMPANY_A, CompanyEnum.COMPANY_B], [PERIOD_1, PERIOD_2])
start_training_day, final_training_day = dt.date(2009, 1, 2), dt.date(2011, 12, 29)
start_test_day, final_test_day = dt.date(2012, 1, 3), dt.date(2015, 12, 30)
# Define initial portfolio
name = 'DQL trader portfolio'
portfolio = Portfolio(10000.0, [], name)
# Initialize trader: use perfect predictors, don't use an already trained model, but learn while trading
trader = TeamGreenDqlTrader(PerfectPredictor(CompanyEnum.COMPANY_A), PerfectPredictor(CompanyEnum.COMPANY_B), False,
True, MODEL_FILENAME_DQLTRADER_PERFECT_PREDICTOR)
# trader = DqlTrader(StockANnPerfectBinaryPredictor(), StockBNnPerfectBinaryPredictor(), False, True, MODEL_FILENAME_DQLTRADER_PERFECT_NN_BINARY_PREDICTOR)
# trader = TeamGreenDqlTrader(StockANnBinaryPredictor(), StockBNnBinaryPredictor(), False, True, MODEL_FILENAME_DQLTRADER_NN_BINARY_PREDICTOR)
# Start evaluation and train correspondingly; don't display the results in a plot but display final portfolio value
evaluator = PortfolioEvaluator([trader], False)
final_values_training, final_values_test = [], []
for i in range(EPISODES):
logger.info(f"DQL Trader: Starting training episode {i}")
all_portfolios_over_time = evaluator.inspect_over_time(training_data, [portfolio],
date_offset=start_training_day)
portfolio_over_time = all_portfolios_over_time[name]
def doTrade(self, portfolio: Portfolio, current_portfolio_value: float,
stock_market_data: StockMarketData) -> OrderList:
"""
Generate action to be taken on the "stock market"
Args:
portfolio : current Portfolio of this trader
current_portfolio_value : value of Portfolio at given moment
stock_market_data : StockMarketData for evaluation
Returns:
A OrderList instance, may be empty never None
"""
# TODO: Build and store current state object
## cash %, portfolio a value %, portfolio b %, pred a, pred b
account_value = portfolio.cash + current_portfolio_value
a_value = portfolio.get_amount(CompanyEnum.COMPANY_A) * stock_market_data.get_most_recent_price(CompanyEnum.COMPANY_A)
b_value = portfolio.get_amount(CompanyEnum.COMPANY_B) * stock_market_data.get_most_recent_price(CompanyEnum.COMPANY_B)
a_value_percent = a_value / account_value
b_value_percent = b_value / account_value
cash_percent = portfolio.cash / account_value
pred_a_value = self.stock_a_predictor.doPredict(stock_market_data[CompanyEnum.COMPANY_A])
pred_b_value = self.stock_b_predictor.doPredict(stock_market_data[CompanyEnum.COMPANY_B])
stock_a_value = stock_market_data.get_most_recent_price(CompanyEnum.COMPANY_A)
stock_b_value = stock_market_data.get_most_recent_price(CompanyEnum.COMPANY_B)
increase_a = (pred_a_value - stock_a_value) / stock_a_value
increase_b = (pred_b_value - stock_b_value) / stock_b_value
current_status = [[cash_percent, a_value_percent, b_value_percent, increase_a, increase_b]]
np_current_status = np.array(current_status)
# TODO: Store experience and train the neural network only if doTrade was called before at least once
## calc rewards = was cash + portfolio - (old cash + old portfolio), map auf 1 0 -1, now simple:
if self.stored_action >= 0:
if current_portfolio_value > self.stored_portfolio_value:
reward = 1.0
elif current_portfolio_value < self.stored_portfolio_value:
reward = -1.0
else:
reward = 0
reward_array = [0, 0, 0]
reward_array[self.stored_action] = reward
np_reward_array = np.array([reward_array])
## train again
# print(np_reward_array)
if self.train_while_trading:
self.model.fit(self.np_previous_status, np_reward_array, epochs=1, batch_size=1, verbose=0)
# TODO: Create actions for current state and decrease epsilon for fewer random actions
action = -1
random_value = uniform(0.0, 1.0)
if random_value > self.epsilon:
action = randint(0, 2)
else:
pred = self.model.predict(np_current_status)
prediction = pred[0]
if len(prediction) != 3:
print("komische prediction")
action = 2
max = prediction[action]
for i in range(3):
if prediction[i] > max:
action = i
self.count[action] = self.count[action] + 1
# print("actions")
# print(self.count)
self.epsilon = self.epsilon * self.epsilon_decay
if self.epsilon < self.epsilon_min:
self.epsilon = self.epsilon_min
order_list = OrderList()
if action < 0 or action > 2:
print("komische action")
print(action)
else:
stock_a = portfolio.get_amount(CompanyEnum.COMPANY_A)
stock_b = portfolio.get_amount(CompanyEnum.COMPANY_B)
if action == 0:
# sell a
if stock_a > 0:
order_list.sell(CompanyEnum.COMPANY_A, stock_a)
# buy b
count_b = portfolio.cash / stock_b_value
if count_b > 0:
order_list.buy(CompanyEnum.COMPANY_B, int(count_b))
if action == 1:
# sell b
if stock_b > 0:
order_list.sell(CompanyEnum.COMPANY_B, stock_b)
# boy a
count_a = portfolio.cash / stock_a_value
if count_a > 0:
order_list.buy(CompanyEnum.COMPANY_A, int(count_a))
# TODO: Save created state, actions and portfolio value for the next call of
self.stored_action = action
self.stored_portfolio_value = current_portfolio_value
self.np_previous_status = np_current_status
## save current state as laststate, current portfolio and cash
return order_list
def testCreateActionList(self):
trader = DqlTrader(PerfectPredictor(CompanyEnum.COMPANY_A), PerfectPredictor(CompanyEnum.COMPANY_B), False)
self.assertIsNotNone(trader)
portfolio = Portfolio(10000, [])
stock_market_data = read_stock_market_data([CompanyEnum.COMPANY_A, CompanyEnum.COMPANY_B], [PERIOD_3])
self.assertIsNotNone(stock_market_data)
# Check doing nothing
# commented because that STOCKACTION is not used anymore
# action_a, action_b = 0.0, 0.0
# order_list = trader.create_order_list(action_a, action_b, portfolio, stock_market_data)
# self.assertIsNotNone(order_list)
# self.assertTrue(order_list.is_empty())
# Check buying halve stock
# commented because that STOCKACTION is not used anymore
# action_a, action_b = 0.5, 0.5
# order_list = trader.create_order_list(action_a, action_b, portfolio, stock_market_data)
# self.assertIsNotNone(order_list)
# self.assertEqual(order_list.get(0).action, OrderType.BUY)
# self.assertEqual(order_list.get(0).shares.company_enum, CompanyEnum.COMPANY_A)
# self.assertEqual(order_list.get(0).shares.amount, 49)
# self.assertEqual(order_list.get(1).action, OrderType.BUY)
# self.assertEqual(order_list.get(1).shares.company_enum, CompanyEnum.COMPANY_B)
# self.assertEqual(order_list.get(1).shares.amount, 33)
# Check buying full stock
action_a, action_b = 1.0, 1.0
order_list = trader.create_order_list(action_a, action_b, portfolio, stock_market_data)
self.assertIsNotNone(order_list)
order_1 = order_list[0]
order_2 = order_list[1]
self.assertEqual(order_1.action, OrderType.BUY)
self.assertEqual(order_1.shares.company_enum, CompanyEnum.COMPANY_A)
self.assertEqual(order_1.shares.amount, 98)
self.assertEqual(order_2.action, OrderType.BUY)
self.assertEqual(order_2.shares.company_enum, CompanyEnum.COMPANY_B)
self.assertEqual(order_2.shares.amount, 66)
# Check selling stock without enough owned shares
action_a, action_b = -1.0, -1.0
order_list = trader.create_order_list(action_a, action_b, portfolio, stock_market_data)
self.assertIsNotNone(order_list)
self.assertTrue(order_list.is_empty())
# Check selling halve stock with enough owned shares
# commented because that STOCKACTION is not used anymore
# portfolio = Portfolio(10000, [SharesOfCompany(CompanyEnum.COMPANY_A, 2), SharesOfCompany(CompanyEnum.COMPANY_B, 2)])
# action_a, action_b = -0.5, -0.5
# order_list = trader.create_order_list(action_a, action_b, portfolio, stock_market_data)
# self.assertIsNotNone(order_list)
# self.assertEqual(order_list.get(0).action, OrderType.SELL)
# self.assertEqual(order_list.get(0).shares.company_enum, CompanyEnum.COMPANY_A)
# self.assertEqual(order_list.get(0).shares.amount, 2)
# self.assertEqual(order_list.get(1).action, OrderType.SELL)
# self.assertEqual(order_list.get(1).shares.company_enum, CompanyEnum.COMPANY_B)
# self.assertEqual(order_list.get(1).shares.amount, 1)
# Check selling full stock with enough owned shares
portfolio = Portfolio(10000,
[SharesOfCompany(CompanyEnum.COMPANY_A, 2), SharesOfCompany(CompanyEnum.COMPANY_B, 2)])
action_a, action_b = -1.0, -1.0
order_list = trader.create_order_list(action_a, action_b, portfolio, stock_market_data)
self.assertIsNotNone(order_list)
order_1 = order_list[0]
order_2 = order_list[1]
self.assertEqual(order_1.action, OrderType.SELL)
self.assertEqual(order_1.shares.company_enum, CompanyEnum.COMPANY_A)
self.assertEqual(order_1.shares.amount, 2)
self.assertEqual(order_2.action, OrderType.SELL)
self.assertEqual(order_2.shares.company_enum, CompanyEnum.COMPANY_B)
self.assertEqual(order_2.shares.amount, 2)
if __name__ == '__main__':
portfolio = 'Norway'
portfolio_obj = Portfolio('Norway')
powerplant_df = get_powerplants_by_portfolio(portfolio)
# powerplant_df.to_csv("ppd.csv")
""" 1 Convert PCUC file and save it to KEAN """
# plant_tech_master_file = r"C:\Users\cliu\Kindle Energy Dropbox\Chang Liu\LBO\data\Norway\pcuc\Norway plant char assumption_input v11.xlsx"
# pc_date_start = date(2020, 1, 1)
# pc_date_end = date(2027,12,31)
# pc_scenario = 'Norway Converted'
# pc_version = 'v1'
# # run_convert_uc(plant_tech_master_file, pc_date_start, pc_date_end, pc_scenario, pc_version)
# run_convert_uc('Norway', pc_date_start, pc_date_end, pc_scenario, pc_version, plant_tech_master_file=plant_tech_master_file, push_to_powerplant=False, push_to_technology=True, push_to_plant_characteristics=False)
#
