def test_generator_is_stable(snapshot):
# TODO Decrease sizes
for i, scenario in enumerate(get_standard_scenarios(42)):
snapshot.assert_match(scenario.train_signal.tolist(),
f"train_signal {i}")
snapshot.assert_match(
np.round(scenario.test_signal, 8).tolist(), f"test_signal {i}")
def test_calculate_score(snapshot):
scenarios = generation.get_standard_scenarios(2)
results = [
my_solver.solve(0, s.train_signal, s.test_size, max_wave_count=1)[0]
for s in scenarios
]
snapshot.assert_match(
evaluate.calculate_coordinator_score(scenarios, results))
def test_my_solver():
for seed in range(1, 3):
for scenario_index, scenario in enumerate(
generation.get_standard_scenarios(seed)):
trades, _, _ = my_solver.solve(0, scenario.train_signal,
scenario.test_size,
scenario.sine_count)
result = trade_simulator.simulate(scenario.test_signal,
scenario.train_size, trades)
optimal_trades = list(get_optimal_trades(scenario.test_signal))
max_result = trade_simulator.simulate(scenario.test_signal, 0,
optimal_trades)
quality = result / max_result
# pylint: disable=line-too-long
assert (
0.93 <= quality <= 1
), f"Seed {seed}, scenario {scenario_index}, result {result:.2f}, max {max_result:.2f}, model_parameters {scenario.model_parameters}"
def test_generator_default_sizes():
for scenario in get_standard_scenarios(None):
assert scenario.train_size == 100
assert scenario.test_size == 1000
assert scenario.train_signal.shape == (scenario.train_size, )
assert scenario.test_signal.shape == (scenario.test_size, )
def test_create_challenge_input(snapshot):
scenarios = generation.get_standard_scenarios(15)
challenge = evaluate.create_challenge_input(scenarios)
snapshot.assert_match(challenge)
# print(f"Trades: {trades}")
# print(f"Optimal trades: {optimal_trades}")
print()
def display_scenarios(scenarios):
challenge_input = evaluate.create_challenge_input(scenarios)
pprint.PrettyPrinter(width=120, compact=True).pprint(challenge_input)
for i, scenario in enumerate(scenarios):
name = str(i + 1)
print(name)
simulate(name, scenario)
display_scenarios(generation.get_standard_scenarios(1))
#%%
import importlib
from scipy.optimize import curve_fit
import numpy as np
import matplotlib.pyplot as plt
import pprint
from scipy.optimize import differential_evolution
from app import generation, trade_simulator, trade_optimizer
importlib.reload(generation)
def test_curve_fit():
scenario = (generation.ScenarioBuilder(