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merge develop into feat/freqai-rl-dev

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This commit is contained in:
robcaulk
2022-11-12 10:54:34 +01:00
parent a2843165e1 e6172a68d7
commit 7a4bb040a5
60 changed files with 1314 additions and 264 deletions
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32
tests/freqai/test_freqai_backtesting.py
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@@ -3,8 +3,11 @@ from datetime import datetime, timezone
from pathlib import Path
from unittest.mock import PropertyMock
import pytest
from freqtrade.commands.optimize_commands import setup_optimize_configuration
from freqtrade.enums import RunMode
from freqtrade.exceptions import OperationalException
from freqtrade.optimize.backtesting import Backtesting
from tests.conftest import (CURRENT_TEST_STRATEGY, get_args, log_has_re, patch_exchange,
patched_configuration_load_config_file)
@@ -51,3 +54,32 @@ def test_freqai_backtest_load_data(freqai_conf, mocker, caplog):
assert log_has_re('Increasing startup_candle_count for freqai to.*', caplog)
Backtesting.cleanup()
def test_freqai_backtest_live_models_model_not_found(freqai_conf, mocker, testdatadir, caplog):
patch_exchange(mocker)
now = datetime.now(timezone.utc)
mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
PropertyMock(return_value=['HULUMULU/USDT', 'XRP/USDT']))
mocker.patch('freqtrade.optimize.backtesting.history.load_data')
mocker.patch('freqtrade.optimize.backtesting.history.get_timerange', return_value=(now, now))
freqai_conf["timerange"] = ""
patched_configuration_load_config_file(mocker, freqai_conf)
args = [
'backtesting',
'--config', 'config.json',
'--datadir', str(testdatadir),
'--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
'--timeframe', '5m',
'--freqai-backtest-live-models'
]
args = get_args(args)
bt_config = setup_optimize_configuration(args, RunMode.BACKTEST)
with pytest.raises(OperationalException,
match=r".* Saved models are required to run backtest .*"):
Backtesting(bt_config)
Backtesting.cleanup()

1
tests/freqai/test_freqai_datadrawer.py
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@@ -22,6 +22,7 @@ def test_update_historic_data(mocker, freqai_conf):
historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
candle_difference = dp_candles - historic_candles
freqai.dk.pair = "ADA/BTC"
freqai.dd.update_historic_data(strategy, freqai.dk)
updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])

106
tests/freqai/test_freqai_datakitchen.py
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@@ -1,13 +1,18 @@
import shutil
from datetime import datetime, timedelta, timezone
from pathlib import Path
from unittest.mock import MagicMock
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from tests.conftest import log_has_re
from tests.freqai.conftest import (get_patched_data_kitchen, make_data_dictionary,
make_unfiltered_dataframe)
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.utils import get_timerange_backtest_live_models
from tests.conftest import get_patched_exchange, log_has_re
from tests.freqai.conftest import (get_patched_data_kitchen, get_patched_freqai_strategy,
make_data_dictionary, make_unfiltered_dataframe)
@pytest.mark.parametrize(
@@ -159,3 +164,98 @@ def test_make_train_test_datasets(mocker, freqai_conf):
assert data_dictionary
assert len(data_dictionary) == 7
assert len(data_dictionary['train_features'].index) == 1916
def test_get_pairs_timestamp_validation(mocker, freqai_conf):
exchange = get_patched_exchange(mocker, freqai_conf)
strategy = get_patched_freqai_strategy(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_conf['freqai'].update({"identifier": "invalid_id"})
model_path = freqai.dk.get_full_models_path(freqai_conf)
with pytest.raises(
OperationalException,
match=r'.*required to run backtest with the freqai-backtest-live-models.*'
):
freqai.dk.get_assets_timestamps_training_from_ready_models(model_path)
@pytest.mark.parametrize('model', [
'LightGBMRegressor'
])
def test_get_timerange_from_ready_models(mocker, freqai_conf, model):
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_strat"})
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("20180101-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = MagicMock()
data_load_timerange = TimeRange.parse_timerange("20180101-20180130")
# 1516233600 (2018-01-18 00:00) - Start Training 1
# 1516406400 (2018-01-20 00:00) - End Training 1 (Backtest slice 1)
# 1516579200 (2018-01-22 00:00) - End Training 2 (Backtest slice 2)
# 1516838400 (2018-01-25 00:00) - End Timerange
new_timerange = TimeRange("date", "date", 1516233600, 1516406400)
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
new_timerange = TimeRange("date", "date", 1516406400, 1516579200)
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
model_path = freqai.dk.get_full_models_path(freqai_conf)
(backtesting_timerange,
pairs_end_dates) = freqai.dk.get_timerange_and_assets_end_dates_from_ready_models(
models_path=model_path)
assert len(pairs_end_dates["ADA"]) == 2
assert backtesting_timerange.startts == 1516406400
assert backtesting_timerange.stopts == 1516838400
backtesting_string_timerange = get_timerange_backtest_live_models(freqai_conf)
assert backtesting_string_timerange == '20180120-20180125'
@pytest.mark.parametrize('model', [
'LightGBMRegressor'
])
def test_get_full_model_path(mocker, freqai_conf, model):
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_strat"})
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)
freqai.dd.pair_dict = MagicMock()
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)
model_path = freqai.dk.get_full_models_path(freqai_conf)
assert model_path.is_dir() is True

38
tests/freqai/test_freqai_interface.py
View File

@@ -27,16 +27,16 @@ def is_mac() -> bool:
return "Darwin" in machine
@pytest.mark.parametrize('model', [
'LightGBMRegressor',
'XGBoostRegressor',
'XGBoostRFRegressor',
'CatboostRegressor',
'ReinforcementLearner',
'ReinforcementLearner_multiproc',
'ReinforcementLearner_test_4ac'
@pytest.mark.parametrize('model, pca, dbscan', [
('LightGBMRegressor', True, False),
('XGBoostRegressor', False, True),
('XGBoostRFRegressor', False, False),
('CatboostRegressor', False, False),
('ReinforcementLearner', False, False),
('ReinforcementLearner_multiproc', False, False),
('ReinforcementLearner_test_4ac', False, False)
])
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca, dbscan):
if is_arm() and model == 'CatboostRegressor':
pytest.skip("CatBoost is not supported on ARM")
@@ -47,6 +47,8 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_strat"})
freqai_conf['freqai']['feature_parameters'].update({"principal_component_analysis": pca})
freqai_conf['freqai']['feature_parameters'].update({"use_DBSCAN_to_remove_outliers": dbscan})
if 'ReinforcementLearner' in model:
model_save_ext = 'zip'
@@ -89,17 +91,19 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize('model', [
'LightGBMRegressorMultiTarget',
'XGBoostRegressorMultiTarget',
'CatboostRegressorMultiTarget',
@pytest.mark.parametrize('model, strat', [
('LightGBMRegressorMultiTarget', "freqai_test_multimodel_strat"),
('XGBoostRegressorMultiTarget', "freqai_test_multimodel_strat"),
('CatboostRegressorMultiTarget', "freqai_test_multimodel_strat"),
('LightGBMClassifierMultiTarget', "freqai_test_multimodel_classifier_strat"),
('CatboostClassifierMultiTarget', "freqai_test_multimodel_classifier_strat")
])
def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
if is_arm() and model == 'CatboostRegressorMultiTarget':
def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, strat):
if is_arm() and 'Catboost' in model:
pytest.skip("CatBoost is not supported on ARM")
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
freqai_conf.update({"strategy": strat})
freqai_conf.update({"freqaimodel": model})
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
@@ -216,6 +220,7 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
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")
df = freqai.cache_corr_pairlist_dfs(df, freqai.dk)
for i in range(5):
df[f'%-constant_{i}'] = i
# df.loc[:, f'%-constant_{i}'] = i
@@ -362,6 +367,7 @@ def test_follow_mode(mocker, freqai_conf):
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