460 lines
18 KiB
Python
460 lines
18 KiB
Python
import platform
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import shutil
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from pathlib import Path
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from unittest.mock import MagicMock
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import pytest
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from freqtrade.configuration import TimeRange
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from freqtrade.data.dataprovider import DataProvider
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.plugins.pairlistmanager import PairListManager
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from tests.conftest import get_patched_exchange, log_has_re
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from tests.freqai.conftest import get_patched_freqai_strategy
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def is_arm() -> bool:
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machine = platform.machine()
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return "arm" in machine or "aarch64" in machine
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def is_mac() -> bool:
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machine = platform.system()
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return "Darwin" in machine
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@pytest.mark.parametrize('model', [
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'LightGBMRegressor',
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'XGBoostRegressor',
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'CatboostRegressor',
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'ReinforcementLearner',
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'ReinforcementLearner_multiproc',
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'ReinforcementLearner_test_4ac'
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])
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def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
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if is_arm() and model == 'CatboostRegressor':
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pytest.skip("CatBoost is not supported on ARM")
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if is_mac():
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pytest.skip("Reinforcement learning module not available on intel based Mac OS")
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model_save_ext = 'joblib'
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freqai_conf.update({"freqaimodel": model})
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"strategy": "freqai_test_strat"})
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if 'ReinforcementLearner' in model:
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model_save_ext = 'zip'
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freqai_conf.update({"strategy": "freqai_rl_test_strat"})
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freqai_conf["freqai"].update({"model_training_parameters": {
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"learning_rate": 0.00025,
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"gamma": 0.9,
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"verbose": 1
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}})
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freqai_conf["freqai"].update({"model_save_type": 'stable_baselines'})
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freqai_conf["freqai"]["rl_config"] = {
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"train_cycles": 1,
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"thread_count": 2,
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"max_trade_duration_candles": 300,
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"model_type": "PPO",
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"policy_type": "MlpPolicy",
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"max_training_drawdown_pct": 0.5,
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"model_reward_parameters": {
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"rr": 1,
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"profit_aim": 0.02,
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"win_reward_factor": 2
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}}
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if 'test_4ac' in model:
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freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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freqai.dd.pair_dict = MagicMock()
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data_load_timerange = TimeRange.parse_timerange("20180125-20180130")
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new_timerange = TimeRange.parse_timerange("20180127-20180130")
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freqai.extract_data_and_train_model(
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new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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assert Path(freqai.dk.data_path /
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f"{freqai.dk.model_filename}_model.{model_save_ext}").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
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shutil.rmtree(Path(freqai.dk.full_path))
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@pytest.mark.parametrize('model', [
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'LightGBMRegressorMultiTarget',
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'XGBoostRegressorMultiTarget',
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'CatboostRegressorMultiTarget',
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])
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def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
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if is_arm() and model == 'CatboostRegressorMultiTarget':
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pytest.skip("CatBoost is not supported on ARM")
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
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freqai_conf.update({"freqaimodel": model})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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freqai.dd.pair_dict = MagicMock()
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.extract_data_and_train_model(
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new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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assert len(freqai.dk.label_list) == 2
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
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assert len(freqai.dk.data['training_features_list']) == 14
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shutil.rmtree(Path(freqai.dk.full_path))
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@pytest.mark.parametrize('model', [
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'LightGBMClassifier',
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'CatboostClassifier',
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'XGBoostClassifier',
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])
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def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
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if is_arm() and model == 'CatboostClassifier':
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pytest.skip("CatBoost is not supported on ARM")
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freqai_conf.update({"freqaimodel": model})
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freqai_conf.update({"strategy": "freqai_test_classifier"})
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freqai_conf.update({"timerange": "20180110-20180130"})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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freqai.dd.pair_dict = MagicMock()
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
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strategy, freqai.dk, data_load_timerange)
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").exists()
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shutil.rmtree(Path(freqai.dk.full_path))
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@pytest.mark.parametrize('model', [
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'LightGBMRegressor',
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'XGBoostRegressor',
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'CatboostRegressor',
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'ReinforcementLearner',
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'ReinforcementLearner_multiproc',
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'ReinforcementLearner_test_4ac'
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])
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def test_start_backtesting(mocker, freqai_conf):
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freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
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if is_arm() and model == 'CatboostRegressor':
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pytest.skip("CatBoost is not supported on ARM")
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if is_mac():
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pytest.skip("Reinforcement learning module not available on intel based Mac OS")
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model_save_ext = 'joblib'
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freqai_conf.update({"freqaimodel": model})
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"strategy": "freqai_test_strat"})
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if 'ReinforcementLearner' in model:
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model_save_ext = 'zip'
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freqai_conf.update({"strategy": "freqai_rl_test_strat"})
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freqai_conf["freqai"].update({"model_training_parameters": {
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"learning_rate": 0.00025,
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"gamma": 0.9,
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"verbose": 1
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}})
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freqai_conf["freqai"].update({"model_save_type": 'stable_baselines'})
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freqai_conf["freqai"]["rl_config"] = {
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"train_cycles": 1,
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"thread_count": 2,
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"max_trade_duration_candles": 300,
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"model_type": "PPO",
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"policy_type": "MlpPolicy",
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"max_training_drawdown_pct": 0.5,
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"model_reward_parameters": {
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"rr": 1,
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"profit_aim": 0.02,
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"win_reward_factor": 2
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}}
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if 'test_4ac' in model:
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freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = False
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180110-20180130")
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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metadata = {"pair": "LTC/BTC"}
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freqai.start_backtesting(df, metadata, freqai.dk)
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model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
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assert len(model_folders) == 6
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180120-20180124"})
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freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5})
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freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = False
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180110-20180130")
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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metadata = {"pair": "LTC/BTC"}
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freqai.start_backtesting(df, metadata, freqai.dk)
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model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
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assert len(model_folders) == 9
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
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freqai_conf.update({"timerange": "20180120-20180130"})
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freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = False
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180110-20180130")
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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metadata = {"pair": "ADA/BTC"}
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freqai.start_backtesting(df, metadata, freqai.dk)
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model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
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assert len(model_folders) == 6
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# without deleting the exiting folder structure, re-run
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freqai_conf.update({"timerange": "20180120-20180130"})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = False
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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sub_timerange = TimeRange.parse_timerange("20180110-20180130")
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corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
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df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
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freqai.start_backtesting(df, metadata, freqai.dk)
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assert log_has_re(
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"Found backtesting prediction file ",
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caplog,
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)
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path = (freqai.dd.full_path / freqai.dk.backtest_predictions_folder)
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prediction_files = [x for x in path.iterdir() if x.is_file()]
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assert len(prediction_files) == 5
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_follow_mode(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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metadata = {"pair": "ADA/BTC"}
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freqai.dd.set_pair_dict_info(metadata)
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.extract_data_and_train_model(
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new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
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# start the follower and ask it to predict on existing files
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freqai_conf.get("freqai", {}).update({"follow_mode": "true"})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.live)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
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freqai.start_live(df, metadata, strategy, freqai.dk)
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assert len(freqai.dk.return_dataframe.index) == 5702
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_principal_component_analysis(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
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{"princpial_component_analysis": "true"})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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freqai.dd.pair_dict = MagicMock()
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.extract_data_and_train_model(
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new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl")
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shutil.rmtree(Path(freqai.dk.full_path))
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|
|
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def test_plot_feature_importance(mocker, freqai_conf):
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from freqtrade.freqai.utils import plot_feature_importance
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
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{"princpial_component_analysis": "true"})
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|
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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|
|
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freqai.dd.pair_dict = MagicMock()
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|
|
|
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
|
|
|
freqai.extract_data_and_train_model(
|
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new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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|
|
|
model = freqai.dd.load_data("ADA/BTC", freqai.dk)
|
|
|
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plot_feature_importance(model, "ADA/BTC", freqai.dk)
|
|
|
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}.html")
|
|
|
|
shutil.rmtree(Path(freqai.dk.full_path))
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|
|
|
|
|
@pytest.mark.parametrize('timeframes,corr_pairs', [
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|
(['5m'], ['ADA/BTC', 'DASH/BTC']),
|
|
(['5m'], ['ADA/BTC', 'DASH/BTC', 'ETH/USDT']),
|
|
(['5m', '15m'], ['ADA/BTC', 'DASH/BTC', 'ETH/USDT']),
|
|
])
|
|
def test_freqai_informative_pairs(mocker, freqai_conf, timeframes, corr_pairs):
|
|
freqai_conf['freqai']['feature_parameters'].update({
|
|
'include_timeframes': timeframes,
|
|
'include_corr_pairlist': corr_pairs,
|
|
|
|
})
|
|
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
|
|
exchange = get_patched_exchange(mocker, freqai_conf)
|
|
pairlists = PairListManager(exchange, freqai_conf)
|
|
strategy.dp = DataProvider(freqai_conf, exchange, pairlists)
|
|
pairlist = strategy.dp.current_whitelist()
|
|
|
|
pairs_a = strategy.informative_pairs()
|
|
assert len(pairs_a) == 0
|
|
pairs_b = strategy.gather_informative_pairs()
|
|
# we expect unique pairs * timeframes
|
|
assert len(pairs_b) == len(set(pairlist + corr_pairs)) * len(timeframes)
|