def test_update_and_draw(self):
"""
Tests: Evaluator#inspect_over_time
Inspects portfolios over time and checks the results
"""
trader = SimpleTrader(RandomPredictor(), RandomPredictor())
evaluator = PortfolioEvaluator([trader] * 3, draw_results=False)
full_stock_market_data = read_stock_market_data(
[CompanyEnum.COMPANY_A, CompanyEnum.COMPANY_B],
[PERIOD_1, PERIOD_2])
# Calculate and save the initial total portfolio value (i.e. the cash reserve)
portfolio1 = Portfolio(50000.0, [], 'portfolio 1')
portfolio2 = Portfolio(100000.0, [], 'portfolio 2')
portfolio3 = Portfolio(150000.0, [], 'portfolio 3')
portfolios_over_time = evaluator.inspect_over_time(
full_stock_market_data, [portfolio1, portfolio2, portfolio3],
evaluation_offset=100)
last_date = list(portfolios_over_time['portfolio 1'].keys())[-1]
assert last_date == datetime.strptime('2015-12-30', '%Y-%m-%d').date()
data_row_lengths = set(
[len(value_set) for value_set in portfolios_over_time.values()])
assert len(data_row_lengths) == 1
assert data_row_lengths.pop() == 100
def test_different_mappings(self):
"""
Tests: Evaluator#inspect_over_time and #inspect_over_time_with_mappings
Compares if both methods return the same values
"""
stock_market_data = read_stock_market_data(
[CompanyEnum.COMPANY_A], [PERIOD_1, PERIOD_2, PERIOD_3])
portfolio_1 = Portfolio(20000,
[SharesOfCompany(CompanyEnum.COMPANY_A, 200)])
portfolio_2 = Portfolio(20000,
[SharesOfCompany(CompanyEnum.COMPANY_A, 200)])
portfolios = [portfolio_1, portfolio_2]
perfect_predictor = PerfectPredictor(CompanyEnum.COMPANY_A)
trader_1 = SimpleTrader(perfect_predictor, perfect_predictor)
trader_2 = SimpleTrader(perfect_predictor, perfect_predictor)
trader_3 = SimpleTrader(RandomPredictor(), RandomPredictor())
traders = [trader_1, trader_2]
portfolio_trader_mappings = list(
zip(portfolios, traders, [None] * len(portfolios)))
evaluator_1 = PortfolioEvaluator([trader_1, trader_1])
evaluator_2 = PortfolioEvaluator([])
evaluator_3 = PortfolioEvaluator([trader_3, trader_3])
# 1 Use evaluator that is initialized *with* traders and call `#inspect_over_time` (i.e. without traders)
result_1 = evaluator_1.inspect_over_time(stock_market_data, portfolios,
100)
# 2 Use evaluator that is initialized *without* traders and call `#inspect_over_time_with_mapping`
# (i.e. with traders)
result_2 = evaluator_2.inspect_over_time_with_mapping(
stock_market_data, portfolio_trader_mappings, 100)
# 3 Use evaluator that is initialized *with* traders and call `#inspect_over_time_with_mapping`
# (i.e. with traders) - this should be no problem, because the traders given at initialization time are ignored
result_3 = evaluator_3.inspect_over_time_with_mapping(
stock_market_data, portfolio_trader_mappings, 100)
assert result_1 == result_2 == result_3
def test_inspect__date_offset(self):
"""
Tests: Evaluator#inspect_over_time
Flavour: Test with an date offset
"""
data = StockData([(date(2017, 1, 1), 150.0), (date(2017, 1, 2), 200.0),
(date(2017, 1, 3), 250.0)])
stock_market_data = StockMarketData({CompanyEnum.COMPANY_A: data})
portfolio = Portfolio(20000,
[SharesOfCompany(CompanyEnum.COMPANY_A, 200)])
evaluator = PortfolioEvaluator(
[SimpleTrader(RandomPredictor(), RandomPredictor())])
portfolio_over_time: dict = \
evaluator.inspect_over_time(stock_market_data, [portfolio], date_offset=date(2017, 1, 2))['nameless']
assert date(2016, 12, 31) not in portfolio_over_time.keys()
assert date(2017, 1, 1) in portfolio_over_time.keys()
assert date(2017, 1, 2) in portfolio_over_time.keys()
assert date(2017, 1, 3) not in portfolio_over_time.keys()
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 = DqlTrader(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 = TeamBlackDqlTrader(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]
final_values_training.append(
portfolio_over_time[final_training_day].total_value(
final_training_day, training_data))
trader.save_trained_model()
# Evaluation over training and visualization
# trader_test = TeamBlackDqlTrader(PerfectPredictor(CompanyEnum.COMPANY_A), PerfectPredictor(CompanyEnum.COMPANY_B), True, False, MODEL_FILENAME_DQLTRADER_PERFECT_PREDICTOR)
# trader_test = TeamBlackDqlTrader(StockANnPerfectBinaryPredictor(), StockBNnPerfectBinaryPredictor(), True, False, MODEL_FILENAME_DQLTRADER_PERFECT_NN_BINARY_PREDICTOR)
trader_test = TeamBlackDqlTrader(
('Simple Trader (Team Blue prediction)',
Traders.SimpleTrader_with_team_blue_prediction()),
('Simple Trader (Team Green prediction)',
Traders.SimpleTrader_with_team_green_prediction()),
('Simple Trader (Team Pink prediction)',
Traders.SimpleTrader_with_team_pink_prediction()),
('Simple Trader (Team Red prediction)',
Traders.SimpleTrader_with_team_red_prediction()),
# Code-Camp Task 2 traders
('Team Blue DQL Trader (perfect prediction)',
Traders.TeamBlueDqlTrader_with_perfect_prediction()),
('Team Green DQL Trader (perfect prediction)',
Traders.TeamGreenDqlTrader_with_perfect_prediction()),
('Team Pink DQL Trader (perfect prediction)',
Traders.TeamPinkDqlTrader_with_perfect_prediction()),
('Team Red DQL Trader (perfect prediction)',
Traders.TeamRedDqlTrader_with_perfect_prediction())
]
# Define portfolios for the traders and create a portfolio/trader mapping
portfolio_trader_mappings = initialize_portfolios(
10000.0, portfolio_name_trader_mappings)
# Evaluate their performance over the testing period
evaluator = PortfolioEvaluator([], True)
evaluator.inspect_over_time_with_mapping(stock_market_data,
portfolio_trader_mappings,
date_offset=datetime.date(
2012, 1, 3))