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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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18
tests/data/test_datahandler.py
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@@ -1,6 +1,7 @@
# pragma pylint: disable=missing-docstring, protected-access, C0103
import re
from datetime import datetime, timezone
from pathlib import Path
from unittest.mock import MagicMock
@@ -154,6 +155,23 @@ def test_jsondatahandler_ohlcv_load(testdatadir, caplog):
assert df.columns.equals(df1.columns)
def test_datahandler_ohlcv_data_min_max(testdatadir):
dh = JsonDataHandler(testdatadir)
min_max = dh.ohlcv_data_min_max('UNITTEST/BTC', '5m', 'spot')
assert len(min_max) == 2
# Empty pair
min_max = dh.ohlcv_data_min_max('UNITTEST/BTC', '8m', 'spot')
assert len(min_max) == 2
assert min_max[0] == datetime.fromtimestamp(0, tz=timezone.utc)
assert min_max[0] == min_max[1]
# Empty pair2
min_max = dh.ohlcv_data_min_max('NOPAIR/XXX', '4m', 'spot')
assert len(min_max) == 2
assert min_max[0] == datetime.fromtimestamp(0, tz=timezone.utc)
assert min_max[0] == min_max[1]
def test_datahandler__check_empty_df(testdatadir, caplog):
dh = JsonDataHandler(testdatadir)
expected_text = r"Price jump in UNITTEST/USDT, 1h, spot between"

32
tests/freqai/test_freqai_backtesting.py
View File

@@ -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
View File

@@ -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
View File

@@ -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

12
tests/optimize/test_backtesting.py
View File

@@ -764,6 +764,7 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
'max_rate': [0.10501, 0.1038888],
'is_open': [False, False],
'enter_tag': [None, None],
"leverage": [1.0, 1.0],
"is_short": [False, False],
'open_timestamp': [1517251200000, 1517283000000],
'close_timestamp': [1517265300000, 1517285400000],
@@ -788,13 +789,14 @@ def test_backtest_one(default_conf, fee, mocker, testdatadir) -> None:
assert len(t['orders']) == 2
ln = data_pair.loc[data_pair["date"] == t["open_date"]]
# Check open trade rate alignes to open rate
assert ln is not None
assert not ln.empty
assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
# check close trade rate alignes to close rate or is between high and low
ln = data_pair.loc[data_pair["date"] == t["close_date"]]
assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
ln1 = data_pair.loc[data_pair["date"] == t["close_date"]]
assert not ln1.empty
assert (round(ln1.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
round(ln1.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln1.iloc[0]["high"], 6))
def test_backtest_timedout_entry_orders(default_conf, fee, mocker, testdatadir) -> None:

1
tests/optimize/test_backtesting_adjust_position.py
View File

@@ -72,6 +72,7 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
'max_rate': [0.10481985, 0.1038888],
'is_open': [False, False],
'enter_tag': [None, None],
'leverage': [1.0, 1.0],
'is_short': [False, False],
'open_timestamp': [1517251200000, 1517283000000],
'close_timestamp': [1517265300000, 1517285400000],

21
tests/plugins/test_pairlist.py
View File

@@ -2,6 +2,8 @@
import logging
import time
from copy import deepcopy
from datetime import timedelta
from unittest.mock import MagicMock, PropertyMock
import pandas as pd
@@ -719,15 +721,26 @@ def test_PerformanceFilter_error(mocker, whitelist_conf, caplog) -> None:
def test_ShuffleFilter_init(mocker, whitelist_conf, caplog) -> None:
whitelist_conf['pairlists'] = [
{"method": "StaticPairList"},
{"method": "ShuffleFilter", "seed": 42}
{"method": "ShuffleFilter", "seed": 43}
]
exchange = get_patched_exchange(mocker, whitelist_conf)
PairListManager(exchange, whitelist_conf)
assert log_has("Backtesting mode detected, applying seed value: 42", caplog)
plm = PairListManager(exchange, whitelist_conf)
assert log_has("Backtesting mode detected, applying seed value: 43", caplog)
with time_machine.travel("2021-09-01 05:01:00 +00:00") as t:
plm.refresh_pairlist()
pl1 = deepcopy(plm.whitelist)
plm.refresh_pairlist()
assert plm.whitelist == pl1
t.shift(timedelta(minutes=10))
plm.refresh_pairlist()
assert plm.whitelist != pl1
caplog.clear()
whitelist_conf['runmode'] = RunMode.DRY_RUN
PairListManager(exchange, whitelist_conf)
plm = PairListManager(exchange, whitelist_conf)
assert not log_has("Backtesting mode detected, applying seed value: 42", caplog)
assert log_has("Live mode detected, not applying seed.", caplog)

1
tests/rpc/test_rpc_apiserver.py
View File

@@ -1461,6 +1461,7 @@ def test_api_strategies(botclient, tmpdir):
'StrategyTestV3Futures',
'freqai_rl_test_strat',
'freqai_test_classifier',
'freqai_test_multimodel_classifier_strat',
'freqai_test_multimodel_strat',
'freqai_test_strat'
]}

138
tests/strategy/strats/freqai_test_multimodel_classifier_strat.py Normal file
View File

@@ -0,0 +1,138 @@
import logging
from functools import reduce
import numpy as np
import pandas as pd
import talib.abstract as ta
from pandas import DataFrame
from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
logger = logging.getLogger(__name__)
class freqai_test_multimodel_classifier_strat(IStrategy):
"""
Test strategy - used for testing freqAI multimodel functionalities.
DO not use in production.
"""
minimal_roi = {"0": 0.1, "240": -1}
plot_config = {
"main_plot": {},
"subplots": {
"prediction": {"prediction": {"color": "blue"}},
"target_roi": {
"target_roi": {"color": "brown"},
},
"do_predict": {
"do_predict": {"color": "brown"},
},
},
}
process_only_new_candles = True
stoploss = -0.05
use_exit_signal = True
startup_candle_count: int = 300
can_short = False
linear_roi_offset = DecimalParameter(
0.00, 0.02, default=0.005, space="sell", optimize=False, load=True
)
max_roi_time_long = IntParameter(0, 800, default=400, space="sell", optimize=False, load=True)
def populate_any_indicators(
self, pair, df, tf, informative=None, set_generalized_indicators=False
):
coin = pair.split('/')[0]
if informative is None:
informative = self.dp.get_pair_dataframe(pair, tf)
# first loop is automatically duplicating indicators for time periods
for t in self.freqai_info["feature_parameters"]["indicator_periods_candles"]:
t = int(t)
informative[f"%-{coin}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
informative[f"%-{coin}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
informative[f"%-{coin}adx-period_{t}"] = ta.ADX(informative, window=t)
informative[f"%-{coin}pct-change"] = informative["close"].pct_change()
informative[f"%-{coin}raw_volume"] = informative["volume"]
informative[f"%-{coin}raw_price"] = informative["close"]
indicators = [col for col in informative if col.startswith("%")]
# This loop duplicates and shifts all indicators to add a sense of recency to data
for n in range(self.freqai_info["feature_parameters"]["include_shifted_candles"] + 1):
if n == 0:
continue
informative_shift = informative[indicators].shift(n)
informative_shift = informative_shift.add_suffix("_shift-" + str(n))
informative = pd.concat((informative, informative_shift), axis=1)
df = merge_informative_pair(df, informative, self.config["timeframe"], tf, ffill=True)
skip_columns = [
(s + "_" + tf) for s in ["date", "open", "high", "low", "close", "volume"]
]
df = df.drop(columns=skip_columns)
# Add generalized indicators here (because in live, it will call this
# function to populate indicators during training). Notice how we ensure not to
# add them multiple times
if set_generalized_indicators:
df["%-day_of_week"] = (df["date"].dt.dayofweek + 1) / 7
df["%-hour_of_day"] = (df["date"].dt.hour + 1) / 25
# user adds targets here by prepending them with &- (see convention below)
# If user wishes to use multiple targets, a multioutput prediction model
# needs to be used such as templates/CatboostPredictionMultiModel.py
df['&s-up_or_down'] = np.where(df["close"].shift(-50) >
df["close"], 'up', 'down')
df['&s-up_or_down2'] = np.where(df["close"].shift(-50) >
df["close"], 'up2', 'down2')
return df
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
self.freqai_info = self.config["freqai"]
dataframe = self.freqai.start(dataframe, metadata, self)
dataframe["target_roi"] = dataframe["&-s_close_mean"] + dataframe["&-s_close_std"] * 1.25
dataframe["sell_roi"] = dataframe["&-s_close_mean"] - dataframe["&-s_close_std"] * 1.25
return dataframe
def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
enter_long_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"]]
if enter_long_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_long_conditions), ["enter_long", "enter_tag"]
] = (1, "long")
enter_short_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"]]
if enter_short_conditions:
df.loc[
reduce(lambda x, y: x & y, enter_short_conditions), ["enter_short", "enter_tag"]
] = (1, "short")
return df
def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
exit_long_conditions = [df["do_predict"] == 1, df["&-s_close"] < df["sell_roi"] * 0.25]
if exit_long_conditions:
df.loc[reduce(lambda x, y: x & y, exit_long_conditions), "exit_long"] = 1
exit_short_conditions = [df["do_predict"] == 1, df["&-s_close"] > df["target_roi"] * 0.25]
if exit_short_conditions:
df.loc[reduce(lambda x, y: x & y, exit_short_conditions), "exit_short"] = 1
return df

82
tests/test_configuration.py
View File

@@ -1538,3 +1538,85 @@ def test_flat_vars_to_nested_dict(caplog):
assert log_has("Loading variable 'FREQTRADE__EXCHANGE__SOME_SETTING'", caplog)
assert not log_has("Loading variable 'NOT_RELEVANT'", caplog)
def test_setup_hyperopt_freqai(mocker, default_conf, caplog) -> None:
patched_configuration_load_config_file(mocker, default_conf)
mocker.patch(
'freqtrade.configuration.configuration.create_datadir',
lambda c, x: x
)
mocker.patch(
'freqtrade.configuration.configuration.create_userdata_dir',
lambda x, *args, **kwargs: Path(x)
)
arglist = [
'hyperopt',
'--config', 'config.json',
'--strategy', CURRENT_TEST_STRATEGY,
'--timerange', '20220801-20220805',
"--freqaimodel",
"LightGBMRegressorMultiTarget",
"--analyze-per-epoch"
]
args = Arguments(arglist).get_parsed_arg()
configuration = Configuration(args)
config = configuration.get_config()
config['freqai'] = {
"enabled": True
}
with pytest.raises(
OperationalException, match=r".*analyze-per-epoch parameter is not supported.*"
):
validate_config_consistency(config)
def test_setup_freqai_backtesting(mocker, default_conf, caplog) -> None:
patched_configuration_load_config_file(mocker, default_conf)
mocker.patch(
'freqtrade.configuration.configuration.create_datadir',
lambda c, x: x
)
mocker.patch(
'freqtrade.configuration.configuration.create_userdata_dir',
lambda x, *args, **kwargs: Path(x)
)
arglist = [
'backtesting',
'--config', 'config.json',
'--strategy', CURRENT_TEST_STRATEGY,
'--timerange', '20220801-20220805',
"--freqaimodel",
"LightGBMRegressorMultiTarget",
"--freqai-backtest-live-models"
]
args = Arguments(arglist).get_parsed_arg()
configuration = Configuration(args)
config = configuration.get_config()
config['runmode'] = RunMode.BACKTEST
with pytest.raises(
OperationalException, match=r".*--freqai-backtest-live-models parameter is only.*"
):
validate_config_consistency(config)
conf = deepcopy(config)
conf['freqai'] = {
"enabled": True
}
with pytest.raises(
OperationalException, match=r".* timerange parameter is not supported with .*"
):
validate_config_consistency(conf)
conf['timerange'] = None
conf['freqai_backtest_live_models'] = False
with pytest.raises(
OperationalException, match=r".* pass --timerange if you intend to use FreqAI .*"
):
validate_config_consistency(conf)

4
tests/test_freqtradebot.py
View File

@@ -5305,7 +5305,7 @@ def test_get_valid_price(mocker, default_conf_usdt) -> None:
])
def test_update_funding_fees_schedule(mocker, default_conf, trading_mode, calls, time_machine,
t1, t2):
time_machine.move_to(f"{t1} +00:00")
time_machine.move_to(f"{t1} +00:00", tick=False)
patch_RPCManager(mocker)
patch_exchange(mocker)
@@ -5314,7 +5314,7 @@ def test_update_funding_fees_schedule(mocker, default_conf, trading_mode, calls,
default_conf['margin_mode'] = 'isolated'
freqtrade = get_patched_freqtradebot(mocker, default_conf)
time_machine.move_to(f"{t2} +00:00")
time_machine.move_to(f"{t2} +00:00", tick=False)
# Check schedule jobs in debugging with freqtrade._schedule.jobs
freqtrade._schedule.run_pending()

10
tests/test_worker.py
View File

@@ -113,6 +113,16 @@ def test_throttle_sleep_time(mocker, default_conf, caplog) -> None:
# 300 (5m) - 60 (1m - see set time above) - 5 (duration of throttled_func) = 235
assert 235.2 < sleep_mock.call_args[0][0] < 235.6
t.move_to("2022-09-01 05:04:51 +00:00")
sleep_mock.reset_mock()
# Offset of 5s, so we hit the sweet-spot between "candle" and "candle offset"
# Which should not get a throttle iteration to avoid late candle fetching
assert worker._throttle(throttled_func, throttle_secs=10, timeframe='5m',
timeframe_offset=5, x=1.2) == 42
assert sleep_mock.call_count == 1
# Time is slightly bigger than throttle secs due to the high timeframe offset.
assert 11.1 < sleep_mock.call_args[0][0] < 13.2
def test_throttle_with_assets(mocker, default_conf) -> None:
def throttled_func(nb_assets=-1):