def test_transformers():
"""Verify transformers work."""
pos_from_price = PositionsFromPricePrediction()
df = simulated_market_data_4_years_gen()
df["forecast_price_change"] = df["close"] * 0.000_1
df_with_positions = pos_from_price.fit_transform(df)
predictions_from_positions = PricePredictionFromPositions()
df0 = predictions_from_positions.fit_transform(df_with_positions)
df0 = df0.round()
df = df_with_positions.round()
assert list(df["forecast_price_change"]) == list(df0["forecast_price_change"])
def test_readme_example_three():
"""Get price prediction and positions from a signal transformer"""
pipeline = make_pipeline(scikit_signal_factory(normalised_close),
PricePredictionFromSignalRegression(),
PositionsFromPricePrediction())
pipeline.fit_transform(simulated_market_data_4_years_gen())
def test_readme_example_four():
"""Get price prediction and positions from an external signal transformer"""
adapted_aroon = ta_adaptor(AroonIndicator, "aroon_down", window=1)
pipeline = make_pipeline(
scikit_signal_factory(adapted_aroon), PricePredictionFromSignalRegression(), PositionsFromPricePrediction()
)
pipeline.fit_transform(simulated_market_data_4_years_gen())
def test_pipeline_signal_to_position():
"""Checks we can use a signal in conjunction with a rule to calculate a position."""
signal_to_positions = make_pipeline(
scikit_signal_factory(normalised_close, ),
PricePredictionFromSignalRegression(), PositionsFromPricePrediction())
df = signal_to_positions.fit_transform(simulated_market_data_4_years_gen())
assert isinstance(df, pd.DataFrame)