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merge develop into feat/shuffle_after_split

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robcaulk
2023-02-16 18:46:01 +01:00
parent c9bc91c75b a2e1389943
commit b6a741b421
203 changed files with 12376 additions and 8494 deletions
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tests/freqai/test_freqai_interface.py
+36 -83
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@@ -27,21 +27,24 @@ def is_mac() -> bool:
return "Darwin" in machine
@pytest.mark.parametrize('model, pca, dbscan, float32, shuffle', [
('LightGBMRegressor', True, False, True, False),
('XGBoostRegressor', False, True, False, False),
('XGBoostRFRegressor', False, False, False, False),
('CatboostRegressor', False, False, False, True),
('ReinforcementLearner', False, True, False, False),
('ReinforcementLearner_multiproc', False, False, False, False),
('ReinforcementLearner_test_4ac', False, False, False, False)
@pytest.mark.parametrize('model, pca, dbscan, float32, can_short, shuffle', [
('LightGBMRegressor', True, False, True, True, False),
('XGBoostRegressor', False, True, False, True, False),
('XGBoostRFRegressor', False, False, False, True, False),
('CatboostRegressor', False, False, False, True, True),
('ReinforcementLearner', False, True, False, True, False),
('ReinforcementLearner_multiproc', False, False, False, True, False),
('ReinforcementLearner_test_3ac', False, False, False, False, False),
('ReinforcementLearner_test_3ac', False, False, False, True, False),
('ReinforcementLearner_test_4ac', False, False, False, True, False)
])
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
dbscan, float32, shuffle):
dbscan, float32, can_short, shuffle):
if is_arm() and model == 'CatboostRegressor':
pytest.skip("CatBoost is not supported on ARM")
if is_mac() and 'Reinforcement' in model:
if is_mac() and not is_arm() and 'Reinforcement' in model:
pytest.skip("Reinforcement learning module not available on intel based Mac OS")
model_save_ext = 'joblib'
@@ -60,9 +63,6 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
if 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
if 'ReinforcementLearner' in model:
model_save_ext = 'zip'
freqai_conf = make_rl_config(freqai_conf)
@@ -70,7 +70,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
if 'test_4ac' in model:
if 'test_3ac' in model or 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
@@ -79,6 +79,7 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.can_short = can_short
freqai.dk = FreqaiDataKitchen(freqai_conf)
freqai.dk.set_paths('ADA/BTC', 10000)
timerange = TimeRange.parse_timerange("20180110-20180130")
@@ -223,6 +224,9 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
if 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
{"indicator_periods_candles": [2]})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@@ -233,15 +237,14 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = base_df[freqai_conf["timeframe"]]
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
df = freqai.cache_corr_pairlist_dfs(df, freqai.dk)
for i in range(5):
df[f'%-constant_{i}'] = i
metadata = {"pair": "LTC/BTC"}
freqai.start_backtesting(df, metadata, freqai.dk)
freqai.start_backtesting(df, metadata, freqai.dk, strategy)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == num_files
@@ -262,6 +265,8 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180120-20180124"})
freqai_conf.get("freqai", {}).update({"backtest_period_days": 0.5})
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
{"indicator_periods_candles": [2]})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@@ -272,12 +277,11 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = base_df[freqai_conf["timeframe"]]
metadata = {"pair": "LTC/BTC"}
freqai.start_backtesting(df, metadata, freqai.dk)
freqai.start_backtesting(df, metadata, freqai.dk, strategy)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == 9
@@ -288,6 +292,8 @@ def test_start_backtesting_subdaily_backtest_period(mocker, freqai_conf):
def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
freqai_conf.update({"timerange": "20180120-20180130"})
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
{"indicator_periods_candles": [2]})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
@@ -297,15 +303,14 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
sub_timerange = TimeRange.parse_timerange("20180101-20180130")
_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = base_df[freqai_conf["timeframe"]]
pair = "ADA/BTC"
metadata = {"pair": pair}
freqai.dk.pair = pair
freqai.start_backtesting(df, metadata, freqai.dk)
freqai.start_backtesting(df, metadata, freqai.dk, strategy)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == 2
@@ -323,14 +328,13 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
_, base_df = freqai.dd.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC", freqai.dk)
df = base_df[freqai_conf["timeframe"]]
pair = "ADA/BTC"
metadata = {"pair": pair}
freqai.dk.pair = pair
freqai.start_backtesting(df, metadata, freqai.dk)
freqai.start_backtesting(df, metadata, freqai.dk, strategy)
assert log_has_re(
"Found backtesting prediction file ",
@@ -340,7 +344,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
pair = "ETH/BTC"
metadata = {"pair": pair}
freqai.dk.pair = pair
freqai.start_backtesting(df, metadata, freqai.dk)
freqai.start_backtesting(df, metadata, freqai.dk, strategy)
path = (freqai.dd.full_path / freqai.dk.backtest_predictions_folder)
prediction_files = [x for x in path.iterdir() if x.is_file()]
@@ -374,57 +378,6 @@ def test_backtesting_fit_live_predictions(mocker, freqai_conf, caplog):
shutil.rmtree(Path(freqai.dk.full_path))
def test_follow_mode(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
metadata = {"pair": "ADA/BTC"}
freqai.dd.set_pair_dict_info(metadata)
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").is_file()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_svm_model.joblib").is_file()
# start the follower and ask it to predict on existing files
freqai_conf.get("freqai", {}).update({"follow_mode": "true"})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = True
freqai.dk = FreqaiDataKitchen(freqai_conf, freqai.live)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
freqai.dk.pair = "ADA/BTC"
freqai.start_live(df, metadata, strategy, freqai.dk)
assert len(freqai.dk.return_dataframe.index) == 5702
shutil.rmtree(Path(freqai.dk.full_path))
def test_principal_component_analysis(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(