lychiyu/freqtrade_origin
1
0
Fork 0
mirror of https://github.com/freqtrade/freqtrade.git synced 2024-11-14 04:03:55 +00:00
Code Issues Actions 1 Packages Projects Releases Wiki Activity
a5f5293bc8
freqtrade_origin/tests/freqai/test_freqai_interface.py
Matthias 02071df8fa
Merge pull request #8692 from freqtrade/feat/outsource-data-pipeline 
Outsource data pipeline handling to improve flexibility
2023-06-18 13:39:36 +02:00

559 lines
23 KiB
Python
Raw Blame History

import logging
import platform
import shutil
import sys
from pathlib import Path
from unittest.mock import MagicMock
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.enums import RunMode
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.utils import download_all_data_for_training, get_required_data_timerange
from freqtrade.optimize.backtesting import Backtesting
from freqtrade.persistence import Trade
from freqtrade.plugins.pairlistmanager import PairListManager
from tests.conftest import EXMS, create_mock_trades, get_patched_exchange, log_has_re
from tests.freqai.conftest import (get_patched_freqai_strategy, is_mac, make_rl_config,
mock_pytorch_mlp_model_training_parameters)
def is_py11() -> bool:
return sys.version_info >= (3, 11)
def is_arm() -> bool:
machine = platform.machine()
return "arm" in machine or "aarch64" in machine
def can_run_model(model: str) -> None:
if is_arm() and "Catboost" in model:
pytest.skip("CatBoost is not supported on ARM.")
is_pytorch_model = 'Reinforcement' in model or 'PyTorch' in model
if is_pytorch_model and is_mac() and not is_arm():
pytest.skip("Reinforcement learning / PyTorch module not available on intel based Mac OS.")
@pytest.mark.parametrize('model, pca, dbscan, float32, can_short, shuffle, buffer, noise', [
('LightGBMRegressor', True, False, True, True, False, 0, 0),
('XGBoostRegressor', False, True, False, True, False, 10, 0.05),
('XGBoostRFRegressor', False, False, False, True, False, 0, 0),
('CatboostRegressor', False, False, False, True, True, 0, 0),
('PyTorchMLPRegressor', False, False, False, False, False, 0, 0),
('PyTorchTransformerRegressor', False, False, False, False, False, 0, 0),
('ReinforcementLearner', False, True, False, True, False, 0, 0),
('ReinforcementLearner_multiproc', False, False, False, True, False, 0, 0),
('ReinforcementLearner_test_3ac', False, False, False, False, False, 0, 0),
('ReinforcementLearner_test_3ac', False, False, False, True, False, 0, 0),
('ReinforcementLearner_test_4ac', False, False, False, True, False, 0, 0),
])
def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model, pca,
dbscan, float32, can_short, shuffle,
buffer, noise):
can_run_model(model)
test_tb = True
if is_mac():
test_tb = False
model_save_ext = 'joblib'
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})
freqai_conf.update({"reduce_df_footprint": float32})
freqai_conf['freqai']['feature_parameters'].update({"shuffle_after_split": shuffle})
freqai_conf['freqai']['feature_parameters'].update({"buffer_train_data_candles": buffer})
freqai_conf['freqai']['feature_parameters'].update({"noise_standard_deviation": noise})
if 'ReinforcementLearner' in model:
model_save_ext = 'zip'
freqai_conf = make_rl_config(freqai_conf)
# test the RL guardrails
freqai_conf['freqai']['feature_parameters'].update({"use_SVM_to_remove_outliers": True})
freqai_conf['freqai']['feature_parameters'].update({"DI_threshold": 2})
freqai_conf['freqai']['data_split_parameters'].update({'shuffle': True})
if 'test_3ac' in model or 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
freqai_conf["freqai"]["rl_config"]["drop_ohlc_from_features"] = True
if 'PyTorch' in model:
model_save_ext = 'zip'
pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp)
if 'Transformer' in model:
# transformer model takes a window, unlike the MLP regressor
freqai_conf.update({"conv_width": 10})
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.activate_tensorboard = test_tb
freqai.can_short = can_short
freqai.dk = FreqaiDataKitchen(freqai_conf)
freqai.dk.live = True
freqai.dk.set_paths('ADA/BTC', 10000)
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("20180125-20180130")
new_timerange = TimeRange.parse_timerange("20180127-20180130")
freqai.dk.set_paths('ADA/BTC', None)
freqai.train_timer("start", "ADA/BTC")
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
freqai.train_timer("stop", "ADA/BTC")
freqai.dd.save_metric_tracker_to_disk()
freqai.dd.save_drawer_to_disk()
assert Path(freqai.dk.full_path / "metric_tracker.json").is_file()
assert Path(freqai.dk.full_path / "pair_dictionary.json").is_file()
assert Path(freqai.dk.data_path /
f"{freqai.dk.model_filename}_model.{model_save_ext}").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()
shutil.rmtree(Path(freqai.dk.full_path))
@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, strat):
can_run_model(model)
freqai_conf.update({"timerange": "20180110-20180130"})
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)
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.dk.live = True
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.dk.set_paths('ADA/BTC', None)
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
assert len(freqai.dk.label_list) == 2
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 len(freqai.dk.data['training_features_list']) == 14
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize('model', [
'LightGBMClassifier',
'CatboostClassifier',
'XGBoostClassifier',
'XGBoostRFClassifier',
'PyTorchMLPClassifier',
])
def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
can_run_model(model)
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"strategy": "freqai_test_classifier"})
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)
freqai.dk.live = True
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.dk.set_paths('ADA/BTC', None)
freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
strategy, freqai.dk, data_load_timerange)
if 'PyTorchMLPClassifier':
pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp)
if freqai.dd.model_type == 'joblib':
model_file_extension = ".joblib"
elif freqai.dd.model_type == "pytorch":
model_file_extension = ".zip"
else:
raise Exception(f"Unsupported model type: {freqai.dd.model_type},"
f" can't assign model_file_extension")
assert Path(freqai.dk.data_path /
f"{freqai.dk.model_filename}_model{model_file_extension}").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_trained_df.pkl").exists()
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize(
"model, num_files, strat",
[
("LightGBMRegressor", 2, "freqai_test_strat"),
("XGBoostRegressor", 2, "freqai_test_strat"),
("CatboostRegressor", 2, "freqai_test_strat"),
("PyTorchMLPRegressor", 2, "freqai_test_strat"),
("PyTorchTransformerRegressor", 2, "freqai_test_strat"),
("ReinforcementLearner", 3, "freqai_rl_test_strat"),
("XGBoostClassifier", 2, "freqai_test_classifier"),
("LightGBMClassifier", 2, "freqai_test_classifier"),
("CatboostClassifier", 2, "freqai_test_classifier"),
("PyTorchMLPClassifier", 2, "freqai_test_classifier")
],
)
def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
can_run_model(model)
test_tb = True
if is_mac():
test_tb = False
freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
freqai_conf['runmode'] = RunMode.BACKTEST
Trade.use_db = False
freqai_conf.update({"freqaimodel": model})
freqai_conf.update({"timerange": "20180120-20180130"})
freqai_conf.update({"strategy": strat})
if 'ReinforcementLearner' in model:
freqai_conf = make_rl_config(freqai_conf)
if 'test_4ac' in model:
freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
if 'PyTorch' in model:
pytorch_mlp_mtp = mock_pytorch_mlp_model_training_parameters()
freqai_conf['freqai']['model_training_parameters'].update(pytorch_mlp_mtp)
if 'Transformer' in model:
# transformer model takes a window, unlike the MLP regressor
freqai_conf.update({"conv_width": 10})
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)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.activate_tensorboard = test_tb
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")
_, 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.dk.set_paths('LTC/BTC', None)
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
Trade.use_db = True
Backtesting.cleanup()
shutil.rmtree(Path(freqai.dk.full_path))
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)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
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")
_, 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, strategy)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == 9
shutil.rmtree(Path(freqai.dk.full_path))
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)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
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("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, strategy)
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
assert len(model_folders) == 2
# without deleting the existing folder structure, re-run
freqai_conf.update({"timerange": "20180120-20180130"})
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 = False
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")
_, 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, strategy)
assert log_has_re(
"Found backtesting prediction file ",
caplog,
)
pair = "ETH/BTC"
metadata = {"pair": pair}
freqai.dk.pair = pair
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()]
assert len(prediction_files) == 2
shutil.rmtree(Path(freqai.dk.full_path))
def test_backtesting_fit_live_predictions(mocker, freqai_conf, caplog):
freqai_conf.get("freqai", {}).update({"fit_live_predictions_candles": 10})
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 = False
freqai.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180128-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
sub_timerange = TimeRange.parse_timerange("20180129-20180130")
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")
df = strategy.set_freqai_targets(df.copy(), metadata={"pair": "LTC/BTC"})
df = freqai.dk.remove_special_chars_from_feature_names(df)
freqai.dk.get_unique_classes_from_labels(df)
freqai.dk.pair = "ADA/BTC"
freqai.dk.full_df = df.fillna(0)
freqai.dk.full_df
assert "&-s_close_mean" not in freqai.dk.full_df.columns
assert "&-s_close_std" not in freqai.dk.full_df.columns
freqai.backtesting_fit_live_predictions(freqai.dk)
assert "&-s_close_mean" in freqai.dk.full_df.columns
assert "&-s_close_std" in freqai.dk.full_df.columns
shutil.rmtree(Path(freqai.dk.full_path))
def test_plot_feature_importance(mocker, freqai_conf):
from freqtrade.freqai.utils import plot_feature_importance
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.get("freqai", {}).get("feature_parameters", {}).update(
{"princpial_component_analysis": "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.dk.live = True
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
freqai.dd.pair_dict = {"ADA/BTC": {"model_filename": "fake_name",
"trained_timestamp": 1, "data_path": "", "extras": {}}}
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
new_timerange = TimeRange.parse_timerange("20180120-20180130")
freqai.dk.set_paths('ADA/BTC', None)
freqai.extract_data_and_train_model(
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
model = freqai.dd.load_data("ADA/BTC", freqai.dk)
plot_feature_importance(model, "ADA/BTC", freqai.dk)
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}.html")
shutil.rmtree(Path(freqai.dk.full_path))
@pytest.mark.parametrize('timeframes,corr_pairs', [
(['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)
def test_start_set_train_queue(mocker, freqai_conf, caplog):
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
pairlist = PairListManager(exchange, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.live = False
freqai.train_queue = freqai._set_train_queue()
assert log_has_re(
"Set fresh train queue from whitelist.",
caplog,
)
def test_get_required_data_timerange(mocker, freqai_conf):
time_range = get_required_data_timerange(freqai_conf)
assert (time_range.stopts - time_range.startts) == 177300
def test_download_all_data_for_training(mocker, freqai_conf, caplog, tmpdir):
caplog.set_level(logging.DEBUG)
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
pairlist = PairListManager(exchange, freqai_conf)
strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
freqai_conf['pairs'] = freqai_conf['exchange']['pair_whitelist']
freqai_conf['datadir'] = Path(tmpdir)
download_all_data_for_training(strategy.dp, freqai_conf)
assert log_has_re(
"Downloading",
caplog,
)
@pytest.mark.usefixtures("init_persistence")
@pytest.mark.parametrize('dp_exists', [(False), (True)])
def test_get_state_info(mocker, freqai_conf, dp_exists, caplog, tickers):
if is_mac():
pytest.skip("Reinforcement learning module not available on intel based Mac OS")
if is_py11():
pytest.skip("Reinforcement learning currently not available on python 3.11.")
freqai_conf.update({"freqaimodel": "ReinforcementLearner"})
freqai_conf.update({"timerange": "20180110-20180130"})
freqai_conf.update({"strategy": "freqai_rl_test_strat"})
freqai_conf = make_rl_config(freqai_conf)
freqai_conf['entry_pricing']['price_side'] = 'same'
freqai_conf['exit_pricing']['price_side'] = 'same'
strategy = get_patched_freqai_strategy(mocker, freqai_conf)
exchange = get_patched_exchange(mocker, freqai_conf)
ticker_mock = MagicMock(return_value=tickers()['ETH/BTC'])
mocker.patch(f"{EXMS}.fetch_ticker", ticker_mock)
strategy.dp = DataProvider(freqai_conf, exchange)
if not dp_exists:
strategy.dp._exchange = None
strategy.freqai_info = freqai_conf.get("freqai", {})
freqai = strategy.freqai
freqai.data_provider = strategy.dp
freqai.live = True
Trade.use_db = True
create_mock_trades(MagicMock(return_value=0.0025), False, True)
freqai.get_state_info("ADA/BTC")
freqai.get_state_info("ETH/BTC")
if not dp_exists:
assert log_has_re(
"No exchange available",
caplog,
)
Reference in New Issue View Git Blame Copy Permalink