mirror of
https://github.com/freqtrade/freqtrade.git
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add more tests for datakitchen functionalities, add regression tests for freqai_interface train/backtest
This commit is contained in:
robcaulk
parent
9c051958a6
commit
d43c146676
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@ -690,8 +690,6 @@ class FreqaiDataKitchen:
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Append backtest prediction from current backtest period to all previous periods
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"""
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# ones = np.ones(len(predictions))
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# target_mean, target_std = ones * self.data["target_mean"], ones * self.data["target_std"]
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self.append_df = DataFrame()
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for label in self.label_list:
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self.append_df[label] = predictions[label]
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@ -707,13 +705,6 @@ class FreqaiDataKitchen:
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else:
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self.full_df = pd.concat([self.full_df, self.append_df], axis=0)
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# self.full_predictions = np.append(self.full_predictions, predictions)
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# self.full_do_predict = np.append(self.full_do_predict, do_predict)
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# if self.freqai_config.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
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# self.full_DI_values = np.append(self.full_DI_values, self.DI_values)
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# self.full_target_mean = np.append(self.full_target_mean, target_mean)
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# self.full_target_std = np.append(self.full_target_std, target_std)
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return
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def fill_predictions(self, dataframe):
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@ -734,12 +725,6 @@ class FreqaiDataKitchen:
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self.append_df = DataFrame()
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self.full_df = DataFrame()
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# self.full_predictions = np.append(filler, self.full_predictions)
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# self.full_do_predict = np.append(filler, self.full_do_predict)
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# if self.freqai_config.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
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# self.full_DI_values = np.append(filler, self.full_DI_values)
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# self.full_target_mean = np.append(filler, self.full_target_mean)
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# self.full_target_std = np.append(filler, self.full_target_std)
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return
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@ -170,11 +170,10 @@ class IFreqaiModel(ABC):
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gc.collect()
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dk.data = {} # clean the pair specific data between training window sliding
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self.training_timerange = tr_train
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# self.training_timerange_timerange = tr_train
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dataframe_train = dk.slice_dataframe(tr_train, dataframe)
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dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
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trained_timestamp = tr_train # TimeRange.parse_timerange(tr_train)
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trained_timestamp = tr_train
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tr_train_startts_str = datetime.datetime.utcfromtimestamp(tr_train.startts).strftime(
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"%Y-%m-%d %H:%M:%S"
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)
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10
setup.sh
10
setup.sh
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@ -77,7 +77,15 @@ function updateenv() {
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fi
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fi
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${PYTHON} -m pip install --upgrade -r ${REQUIREMENTS} ${REQUIREMENTS_HYPEROPT} ${REQUIREMENTS_PLOT}
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REQUIREMENTS_FREQAI=""
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read -p "Do you want to install dependencies for freqai [y/N]? "
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dev=$REPLY
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if [[ $REPLY =~ ^[Yy]$ ]]
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then
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REQUIREMENTS_FREQAI="-r requirements-freqai.txt"
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fi
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${PYTHON} -m pip install --upgrade -r ${REQUIREMENTS} ${REQUIREMENTS_HYPEROPT} ${REQUIREMENTS_PLOT} ${REQUIREMENTS_FREQAI}
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if [ $? -ne 0 ]; then
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echo "Failed installing dependencies"
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exit 1
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@ -2,9 +2,12 @@ from copy import deepcopy
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from pathlib import Path
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from unittest.mock import MagicMock
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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.resolvers import StrategyResolver
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from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
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from tests.conftest import get_patched_exchange
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# @pytest.fixture(scope="function")
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@ -21,16 +24,17 @@ def freqai_conf(default_conf):
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"freqai": {
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"startup_candles": 10000,
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"purge_old_models": True,
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"train_period_days": 15,
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"backtest_period_days": 7,
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"train_period_days": 5,
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"backtest_period_days": 2,
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"live_retrain_hours": 0,
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"identifier": "uniqe-id7",
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"expiration_hours": 1,
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"identifier": "uniqe-id100",
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"live_trained_timestamp": 0,
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"feature_parameters": {
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"include_timeframes": ["5m"],
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"include_corr_pairlist": ["ADA/BTC", "DASH/BTC"],
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"label_period_candles": 20,
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"include_shifted_candles": 2,
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"include_shifted_candles": 1,
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"DI_threshold": 0.9,
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"weight_factor": 0.9,
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"principal_component_analysis": False,
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@ -40,7 +44,7 @@ def freqai_conf(default_conf):
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"indicator_periods_candles": [10],
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},
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"data_split_parameters": {"test_size": 0.33, "random_state": 1},
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"model_training_parameters": {"n_estimators": 1000, "task_type": "CPU"},
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"model_training_parameters": {"n_estimators": 100},
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},
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"config_files": [Path('config_examples', 'config_freqai_futures.example.json')]
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}
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@ -55,7 +59,7 @@ def get_patched_data_kitchen(mocker, freqaiconf):
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return dk
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def get_patched_strategy(mocker, freqaiconf):
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def get_patched_freqai_strategy(mocker, freqaiconf):
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strategy = StrategyResolver.load_strategy(freqaiconf)
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strategy.bot_start()
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@ -66,3 +70,48 @@ def get_patched_freqaimodel(mocker, freqaiconf):
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freqaimodel = FreqaiModelResolver.load_freqaimodel(freqaiconf)
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return freqaimodel
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def get_freqai_live_analyzed_dataframe(mocker, freqaiconf):
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strategy = get_patched_freqai_strategy(mocker, freqaiconf)
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exchange = get_patched_exchange(mocker, freqaiconf)
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strategy.dp = DataProvider(freqaiconf, exchange)
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freqai = strategy.model.bridge
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dk.load_all_pair_histories(timerange)
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strategy.analyze_pair('ADA/BTC', '5m')
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return strategy.dp.get_analyzed_dataframe('ADA/BTC', '5m')
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def get_freqai_analyzed_dataframe(mocker, freqaiconf):
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strategy = get_patched_freqai_strategy(mocker, freqaiconf)
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exchange = get_patched_exchange(mocker, freqaiconf)
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strategy.dp = DataProvider(freqaiconf, exchange)
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strategy.freqai_info = freqaiconf.get("freqai", {})
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freqai = strategy.model.bridge
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dk.load_all_pair_histories(timerange)
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sub_timerange = TimeRange.parse_timerange("20180111-20180114")
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corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
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return freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
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def get_ready_to_train(mocker, freqaiconf):
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strategy = get_patched_freqai_strategy(mocker, freqaiconf)
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exchange = get_patched_exchange(mocker, freqaiconf)
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strategy.dp = DataProvider(freqaiconf, exchange)
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strategy.freqai_info = freqaiconf.get("freqai", {})
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freqai = strategy.model.bridge
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dk.load_all_pair_histories(timerange)
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sub_timerange = TimeRange.parse_timerange("20180111-20180114")
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corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
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return corr_df, base_df, freqai, strategy
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@ -1,95 +0,0 @@
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# from unittest.mock import MagicMock
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# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge
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import copy
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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_drawer import FreqaiDataDrawer
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from freqtrade.exceptions import OperationalException
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from tests.conftest import get_patched_exchange
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from tests.freqai.conftest import freqai_conf, get_patched_data_kitchen, get_patched_strategy
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@pytest.mark.parametrize(
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"timerange, train_period_days, expected_result",
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[
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("20220101-20220201", 30, "20211202-20220201"),
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("20220301-20220401", 15, "20220214-20220401"),
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],
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)
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def test_create_fulltimerange(
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timerange, train_period_days, expected_result, default_conf, mocker, caplog
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):
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dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf)))
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assert dk.create_fulltimerange(timerange, train_period_days) == expected_result
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def test_create_fulltimerange_incorrect_backtest_period(mocker, default_conf):
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dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf)))
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with pytest.raises(OperationalException, match=r"backtest_period_days must be an integer"):
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dk.create_fulltimerange("20220101-20220201", 0.5)
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with pytest.raises(OperationalException, match=r"backtest_period_days must be positive"):
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dk.create_fulltimerange("20220101-20220201", -1)
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def test_split_timerange(mocker, default_conf):
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freqaiconf = freqai_conf(copy.deepcopy(default_conf))
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freqaiconf.update({"timerange": "20220101-20220401"})
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dk = get_patched_data_kitchen(mocker, freqaiconf)
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tr_list, bt_list = dk.split_timerange("20220101-20220201", 30, 7)
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assert len(tr_list) == len(bt_list) == 9
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tr_list, bt_list = dk.split_timerange("20220101-20220201", 30, 0.5)
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assert len(tr_list) == len(bt_list) == 120
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tr_list, bt_list = dk.split_timerange("20220101-20220201", 10, 1)
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assert len(tr_list) == len(bt_list) == 80
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with pytest.raises(
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OperationalException, match=r"train_period_days must be an integer greater than 0."
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):
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dk.split_timerange("20220101-20220201", -1, 0.5)
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def test_update_historic_data(mocker, default_conf):
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freqaiconf = freqai_conf(copy.deepcopy(default_conf))
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strategy = get_patched_strategy(mocker, freqaiconf)
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exchange = get_patched_exchange(mocker, freqaiconf)
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strategy.dp = DataProvider(freqaiconf, exchange)
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freqai = strategy.model.bridge
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dk.load_all_pair_histories(timerange)
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historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
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dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
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candle_difference = dp_candles - historic_candles
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freqai.dk.update_historic_data(strategy)
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updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
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assert updated_historic_candles - historic_candles == candle_difference
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# def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05'):
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# np.random.seed(42)
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# tf_mins = timeframe_to_minutes(timeframe)
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# base = np.random.normal(20, 2, size=size)
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# date = pd.date_range(start, periods=size, freq=f'{tf_mins}min', tz='UTC')
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# df = pd.DataFrame({
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# 'date': date,
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# 'open': base,
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# 'high': base + np.random.normal(2, 1, size=size),
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# 'low': base - np.random.normal(2, 1, size=size),
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# 'close': base + np.random.normal(0, 1, size=size),
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# 'volume': np.random.normal(200, size=size)
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# }
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# )
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# df = df.dropna()
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# return df
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167
tests/freqai/test_freqai_datakitchen.py
Normal file
167
tests/freqai/test_freqai_datakitchen.py
Normal file
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@ -0,0 +1,167 @@
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# from unittest.mock import MagicMock
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# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge
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import copy
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import datetime
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import shutil
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from pathlib import Path
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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_drawer import FreqaiDataDrawer
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from freqtrade.exceptions import OperationalException
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from tests.conftest import get_patched_exchange
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from tests.freqai.conftest import freqai_conf, get_patched_data_kitchen, get_patched_freqai_strategy
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@pytest.mark.parametrize(
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"timerange, train_period_days, expected_result",
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[
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("20220101-20220201", 30, "20211202-20220201"),
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("20220301-20220401", 15, "20220214-20220401"),
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],
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)
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def test_create_fulltimerange(
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timerange, train_period_days, expected_result, default_conf, mocker, caplog
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):
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dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf)))
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assert dk.create_fulltimerange(timerange, train_period_days) == expected_result
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shutil.rmtree(Path(dk.full_path))
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def test_create_fulltimerange_incorrect_backtest_period(mocker, default_conf):
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dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf)))
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with pytest.raises(OperationalException, match=r"backtest_period_days must be an integer"):
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dk.create_fulltimerange("20220101-20220201", 0.5)
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with pytest.raises(OperationalException, match=r"backtest_period_days must be positive"):
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dk.create_fulltimerange("20220101-20220201", -1)
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shutil.rmtree(Path(dk.full_path))
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@pytest.mark.parametrize(
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"timerange, train_period_days, backtest_period_days, expected_result",
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[
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("20220101-20220201", 30, 7, 9),
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("20220101-20220201", 30, 0.5, 120),
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("20220101-20220201", 10, 1, 80),
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],
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)
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def test_split_timerange(
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mocker, default_conf, timerange, train_period_days, backtest_period_days, expected_result
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):
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freqaiconf = freqai_conf(copy.deepcopy(default_conf))
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freqaiconf.update({"timerange": "20220101-20220401"})
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dk = get_patched_data_kitchen(mocker, freqaiconf)
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tr_list, bt_list = dk.split_timerange(timerange, train_period_days, backtest_period_days)
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assert len(tr_list) == len(bt_list) == expected_result
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with pytest.raises(
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OperationalException, match=r"train_period_days must be an integer greater than 0."
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):
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dk.split_timerange("20220101-20220201", -1, 0.5)
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shutil.rmtree(Path(dk.full_path))
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def test_update_historic_data(mocker, default_conf):
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freqaiconf = freqai_conf(copy.deepcopy(default_conf))
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strategy = get_patched_freqai_strategy(mocker, freqaiconf)
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exchange = get_patched_exchange(mocker, freqaiconf)
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strategy.dp = DataProvider(freqaiconf, exchange)
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freqai = strategy.model.bridge
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
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timerange = TimeRange.parse_timerange("20180110-20180114")
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freqai.dk.load_all_pair_histories(timerange)
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historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
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dp_candles = len(strategy.dp.get_pair_dataframe("ADA/BTC", "5m"))
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candle_difference = dp_candles - historic_candles
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freqai.dk.update_historic_data(strategy)
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updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
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assert updated_historic_candles - historic_candles == candle_difference
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shutil.rmtree(Path(freqai.dk.full_path))
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@pytest.mark.parametrize(
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"timestamp, expected",
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[
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(datetime.datetime.now(tz=datetime.timezone.utc).timestamp() - 7200, True),
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(datetime.datetime.now(tz=datetime.timezone.utc).timestamp(), False),
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],
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)
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def test_check_if_model_expired(mocker, default_conf, timestamp, expected):
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freqaiconf = freqai_conf(copy.deepcopy(default_conf))
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dk = get_patched_data_kitchen(mocker, freqaiconf)
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assert dk.check_if_model_expired(timestamp) == expected
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shutil.rmtree(Path(dk.full_path))
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|
||||
def test_load_all_pairs_histories(mocker, default_conf):
|
||||
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
freqai = strategy.model.bridge
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
|
||||
assert len(freqai.dd.historic_data.keys()) == len(
|
||||
freqaiconf.get("exchange", {}).get("pair_whitelist")
|
||||
)
|
||||
assert len(freqai.dd.historic_data["ADA/BTC"]) == len(
|
||||
freqaiconf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
|
||||
)
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_get_base_and_corr_dataframes(mocker, default_conf):
|
||||
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
freqai = strategy.model.bridge
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
|
||||
num_tfs = len(
|
||||
freqaiconf.get("freqai", {}).get("feature_parameters", {}).get("include_timeframes")
|
||||
)
|
||||
|
||||
assert len(base_df.keys()) == num_tfs
|
||||
|
||||
assert len(corr_df.keys()) == len(
|
||||
freqaiconf.get("freqai", {}).get("feature_parameters", {}).get("include_corr_pairlist")
|
||||
)
|
||||
|
||||
assert len(corr_df["ADA/BTC"].keys()) == num_tfs
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_use_strategy_to_populate_indicators(mocker, default_conf):
|
||||
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
strategy.freqai_info = freqaiconf.get("freqai", {})
|
||||
freqai = strategy.model.bridge
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180114")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
sub_timerange = TimeRange.parse_timerange("20180111-20180114")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, 'LTC/BTC')
|
||||
|
||||
assert len(df.columns) == 90
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
183
tests/freqai/test_freqai_interface.py
Normal file
183
tests/freqai/test_freqai_interface.py
Normal file
|
|
@ -0,0 +1,183 @@
|
|||
# from unittest.mock import MagicMock
|
||||
# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge
|
||||
import copy
|
||||
import platform
|
||||
import shutil
|
||||
from pathlib import Path
|
||||
from unittest.mock import MagicMock
|
||||
|
||||
from freqtrade.configuration import TimeRange
|
||||
from freqtrade.data.dataprovider import DataProvider
|
||||
# from freqtrade.freqai.data_drawer import FreqaiDataDrawer
|
||||
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
||||
from tests.conftest import get_patched_exchange, log_has
|
||||
from tests.freqai.conftest import freqai_conf, get_patched_freqai_strategy
|
||||
|
||||
|
||||
def test_train_model_in_series_LightGBM(mocker, default_conf):
|
||||
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
|
||||
freqaiconf.update({"timerange": "20180110-20180130"})
|
||||
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
strategy.freqai_info = freqaiconf.get("freqai", {})
|
||||
freqai = strategy.model.bridge
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert (
|
||||
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_model.joblib"))
|
||||
.resolve()
|
||||
.exists()
|
||||
)
|
||||
assert (
|
||||
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_metadata.json"))
|
||||
.resolve()
|
||||
.exists()
|
||||
)
|
||||
assert (
|
||||
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_trained_df.pkl"))
|
||||
.resolve()
|
||||
.exists()
|
||||
)
|
||||
assert (
|
||||
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_svm_model.joblib"))
|
||||
.resolve()
|
||||
.exists()
|
||||
)
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
# Catboost not available for ARM architecture. using platform lib to check processor type
|
||||
if "arm" not in platform.uname()[-1]:
|
||||
|
||||
def test_train_model_in_series_Catboost(mocker, default_conf):
|
||||
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
|
||||
freqaiconf.update({"timerange": "20180110-20180130"})
|
||||
freqaiconf.update({"freqaimodel": "CatboostPredictionModel"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
strategy.freqai_info = freqaiconf.get("freqai", {})
|
||||
freqai = strategy.model.bridge
|
||||
freqai.live = True
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
|
||||
freqai.dd.pair_dict = MagicMock()
|
||||
|
||||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(
|
||||
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange
|
||||
)
|
||||
|
||||
assert (
|
||||
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_model.joblib"))
|
||||
.resolve()
|
||||
.exists()
|
||||
)
|
||||
assert (
|
||||
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_metadata.json"))
|
||||
.resolve()
|
||||
.exists()
|
||||
)
|
||||
assert (
|
||||
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_trained_df.pkl"))
|
||||
.resolve()
|
||||
.exists()
|
||||
)
|
||||
assert (
|
||||
Path(freqai.dk.data_path / str(freqai.dk.model_filename + "_svm_model.joblib"))
|
||||
.resolve()
|
||||
.exists()
|
||||
)
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_start_backtesting(mocker, default_conf):
|
||||
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
|
||||
freqaiconf.update({"timerange": "20180120-20180130"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
strategy.freqai_info = freqaiconf.get("freqai", {})
|
||||
freqai = strategy.model.bridge
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
|
||||
metadata = {"pair": "ADA/BTC"}
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
|
||||
|
||||
assert len(model_folders) == 5
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
|
||||
|
||||
def test_start_backtesting_from_existing_folder(mocker, default_conf, caplog):
|
||||
freqaiconf = freqai_conf(copy.deepcopy(default_conf))
|
||||
freqaiconf.update({"timerange": "20180120-20180130"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
strategy.freqai_info = freqaiconf.get("freqai", {})
|
||||
freqai = strategy.model.bridge
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
|
||||
metadata = {"pair": "ADA/BTC"}
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
|
||||
|
||||
assert len(model_folders) == 5
|
||||
|
||||
# without deleting the exiting folder structure, re-run
|
||||
|
||||
freqaiconf.update({"timerange": "20180120-20180130"})
|
||||
strategy = get_patched_freqai_strategy(mocker, freqaiconf)
|
||||
exchange = get_patched_exchange(mocker, freqaiconf)
|
||||
strategy.dp = DataProvider(freqaiconf, exchange)
|
||||
strategy.freqai_info = freqaiconf.get("freqai", {})
|
||||
freqai = strategy.model.bridge
|
||||
freqai.live = False
|
||||
freqai.dk = FreqaiDataKitchen(freqaiconf, freqai.dd)
|
||||
timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
freqai.dk.load_all_pair_histories(timerange)
|
||||
sub_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
corr_df, base_df = freqai.dk.get_base_and_corr_dataframes(sub_timerange, "LTC/BTC")
|
||||
|
||||
df = freqai.dk.use_strategy_to_populate_indicators(strategy, corr_df, base_df, "LTC/BTC")
|
||||
freqai.start_backtesting(df, metadata, freqai.dk)
|
||||
assert log_has(
|
||||
"Found model at user_data/models/uniqe-id100/sub-train-ADA1517097600/cb_ada_1517097600",
|
||||
caplog,
|
||||
)
|
||||
|
||||
shutil.rmtree(Path(freqai.dk.full_path))
|
||||
Loading…
Reference in New Issue
Block a user