freqtrade_origin/tests/freqai/test_freqai_interface.py

182 lines
6.9 KiB
Python

# 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_kitchen import FreqaiDataKitchen
from tests.conftest import get_patched_exchange, log_has_re
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))
# FIXME: hits segfault
# @pytest.mark.skipif("arm" in platform.uname()[-1], reason="no ARM..")
# 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_re(
"Found model at ",
caplog,
)
shutil.rmtree(Path(freqai.dk.full_path))