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)
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_regression():
"""Check regression"""
simulated_market_data = simulated_market_data_4_years_gen()
simulated_market_data["signal"] = simulated_market_data["close"].shift(-1)
price_prediction_from_signal = PricePredictionFromSignalRegression()
out = price_prediction_from_signal.fit_transform(simulated_market_data)
assert isinstance(out, pd.DataFrame)
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_specific_rule():
"""Checks using SMA."""
from infertrade.data.simulate_data import simulated_market_data_4_years_gen
data = simulated_market_data_4_years_gen()
sma_signal = Api.calculate_signal(data, "SMA")
assert isinstance(sma_signal, pd.DataFrame)
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_one_external():
"""Example of signal generation from time series via simple function"""
adapted_aroon = ta_adaptor(AroonIndicator, "aroon_down", window=1)
signal_transformer = scikit_signal_factory(adapted_aroon)
signal_transformer.fit_transform(simulated_market_data_4_years_gen())
def test_readme_example_one():
"""Example of signal generation from time series via simple function"""
signal_transformer = scikit_signal_factory(normalised_close)
signal_transformer.fit_transform(simulated_market_data_4_years_gen())
def test_readme_example_two():
"""Example of position calculation from simple position function"""
position_transformer = scikit_allocation_factory(constant_allocation_size)
position_transformer.fit_transform(simulated_market_data_4_years_gen())
def test_market_data_4_years():
"""Creates a small amount of simulated market data for testing."""
return simulated_market_data_4_years_gen()
See the License for the specific language governing permissions and
limitations under the License.
Created by: Thomas Oliver
Created date: 25th March 2021
"""
import pandas as pd
import pytest
from infertrade.PandasEnum import PandasEnum
from infertrade.api import Api
from infertrade.data.simulate_data import simulated_market_data_4_years_gen
api_instance = Api()
test_dfs = [simulated_market_data_4_years_gen(), simulated_market_data_4_years_gen()]
def test_get_available_algorithms():
"""Checks can get algorithm list and that returned algorithms can supply all expected properties."""
list_of_algos = Api.available_algorithms()
assert isinstance(list_of_algos, list)
for ii_algo_name in list_of_algos:
assert isinstance(ii_algo_name, str)
assert Api.return_algorithm_category(ii_algo_name) in Api.algorithm_categories()
assert Api.determine_package_of_algorithm(ii_algo_name) in Api.available_packages()
inputs = Api.required_inputs_for_algorithm(ii_algo_name)
assert isinstance(inputs, list)
for ii_required_input in inputs:
assert isinstance(ii_required_input, str)
See the License for the specific language governing permissions and
limitations under the License.
Created by: Thomas Oliver
Created date: 25th March 2021
"""
import pandas as pd
from infertrade.PandasEnum import PandasEnum
from infertrade.api import Api
from infertrade.data.simulate_data import simulated_market_data_4_years_gen
api_instance = Api()
test_dfs = [
simulated_market_data_4_years_gen(),
simulated_market_data_4_years_gen()
]
def test_get_available_algorithms():
"""Checks can get algorithm list and that returned algorithms can supply all expected properties."""
list_of_algos = Api.available_algorithms()
assert isinstance(list_of_algos, list)
for ii_algo_name in list_of_algos:
assert isinstance(ii_algo_name, str)
assert Api.return_algorithm_category(
ii_algo_name) in Api.algorithm_categories()
assert Api.determine_package_of_algorithm(
ii_algo_name) in Api.available_packages()
