def test_create_tt_trader(self):
expert_a = PerfectExpert(Company.A)
expert_b = PerfectExpert(Company.B)
trader = TrustingTrader(expert_a, expert_b, 'test_color', 'test_name')
self.assertIsNotNone(trader)
self.assertEqual(trader.get_color(), 'test_color')
self.assertEqual(trader.get_name(), 'test_name')
def main_orig():
"""
Code from the original main routine.
Visualizes portfolio value over the testing period for different traders.
"""
# Load stock market data for testing period
stock_market_data = StockMarketData([Company.A, Company.B],
[Period.TESTING])
# create new stock exchange with initial portfolio cash for each traders
stock_exchange = StockExchange(2000.0)
# create the traders
bah_trader = BuyAndHoldTrader('black', 'Buy and hold')
tt_trader_obscure = TrustingTrader(ObscureExpert(Company.A),
ObscureExpert(Company.B), 'green',
'Trust experts')
tt_trader2 = TrustingTrader2(ObscureExpert(Company.A),
ObscureExpert(Company.B), 'limegreen',
'Trust experts for sell only')
dql_trader = deep_q_learning_trader.DeepQLearningTrader(
ObscureExpert(Company.A),
ObscureExpert(Company.B),
True,
False,
'red',
plot_name='Deep Q-learning trader')
# run the stock exchange over the testing period, with 100 skipped trading days
stock_exchange.run(stock_market_data,
[bah_trader, tt_trader_obscure, tt_trader2, dql_trader])
# visualize the results
stock_exchange.visualize_last_run()
def test_trade_vote_down_stock_a(self):
expert_a = PerfectExpert(Company.A)
expert_b = PerfectExpert(Company.B)
trader = TrustingTrader(expert_a, expert_b, 'test_color', 'test_name')
portfolio = Portfolio(1000.0, {Company.A: 10, Company.B: 10})
stock_market_data = StockMarketData([Company.A, Company.B], [Period.TESTING]).deepcopy_first_n_items(4)
order_list = trader.trade(portfolio, stock_market_data)
self.assertIsNotNone(order_list)
self.assertEqual(len(order_list), 2)
self.assertEqual(order_list[0].type, OrderType.SELL)
self.assertEqual(order_list[0].company, Company.A)
self.assertEqual(order_list[0].amount, 10.0)
self.assertEqual(order_list[1].type, OrderType.SELL)
self.assertEqual(order_list[1].company, Company.B)
self.assertEqual(order_list[1].amount, 10.0)
def main_print_model_performance():
"""
Just prints the final portfolio value and v score (return % per trading day)
for training and testing data set for different traders.
"""
stock_market_data_train = StockMarketData([Company.A, Company.B],
[Period.TRAINING])
# stock_market_data_train = stock_market_data_train.deepcopy_first_n_items(
# int(stock_market_data_train.get_row_count() / 5))
stock_market_data_test = StockMarketData([Company.A, Company.B],
[Period.TESTING])
bah_trader = BuyAndHoldTrader(name='buy and hold')
tt_trader1 = TrustingTrader(ObscureExpert(Company.A),
ObscureExpert(Company.B), 'green',
'trust experts, prefer A')
tt_trader2 = TrustingTrader2(ObscureExpert(Company.A),
ObscureExpert(Company.B), 'limegreen',
'trust experts for sell only')
dql_trader = deep_q_learning_trader.DeepQLearningTrader(
ObscureExpert(Company.A), ObscureExpert(Company.B), True, False, 'red')
all_traders = [bah_trader, tt_trader1, tt_trader2, dql_trader]
trader_names = []
for trader in all_traders:
if isinstance(trader, deep_q_learning_trader.DeepQLearningTrader
) and trader.plot_name is not None:
trader_name = trader.plot_name
else:
trader_name = trader.get_name()
trader_names.append(trader_name)
max_trader_name_length = max((len(name) for name in trader_names))
for trader, trader_name in zip(all_traders, trader_names):
is_first_line = True
for dataset, dataset_name, starting_cash in [
(stock_market_data_train, 'train', 10000.0),
(stock_market_data_test, 'test', 2000.0)
]:
stock_exchange = StockExchange(starting_cash)
stock_exchange.run(dataset, [trader])
p = stock_exchange.get_final_portfolio_value(trader)
samples = dataset.get_row_count()
v = 100.0 * (math.pow(p / starting_cash, 1 / samples) - 1.0)
if is_first_line:
header = ('{: <' + str(max_trader_name_length) +
'}').format(trader_name)
else:
header = ' ' * max_trader_name_length
header += f' ({dataset_name: <5}): '
print(f'{header}{v:.5f}% return per trading day'
f' (final portfolio value of {p:.1e})')
is_first_line = False
if isinstance(trader, BuyAndHoldTrader):
trader.reset()
pyplot.legend(trader_names)
pyplot.show()
# This main method evaluates all traders over the testing period and visualize the results.
if __name__ == "__main__":
mpl.rcParams["savefig.directory"] = os.chdir(
os.path.dirname(__file__) + '\\eval')
# Load stock market data for testing period
stock_market_data = StockMarketData([Company.A, Company.B],
[Period.TESTING])
# create new stock exchange with initial portfolio cash for each traders
stock_exchange = StockExchange(2000.0)
# create the traders
bah_trader = BuyAndHoldTrader()
tt_trader_obscure = TrustingTrader(ObscureExpert(Company.A),
ObscureExpert(Company.B), 'green',
'tt obscure')
dql_trader = deep_q_learning_trader.DeepQLearningTrader(
ObscureExpert(Company.A), ObscureExpert(Company.B), True, False, 'red')
# run the stock exchange over the testing period, with 100 skipped trading days
stock_exchange.run(stock_market_data,
[bah_trader, tt_trader_obscure, dql_trader])
# visualize the results
stock_exchange.visualize_last_run()