freqtrade_origin/tests/freqai/test_freqai_datakitchen.py
2024-05-13 07:10:24 +02:00

204 lines
7.7 KiB
Python

import shutil
from datetime import datetime, timedelta, timezone
from pathlib import Path
from unittest.mock import MagicMock
import pandas as pd
import pytest
from freqtrade.configuration import TimeRange
from freqtrade.data.dataprovider import DataProvider
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from tests.conftest import get_patched_exchange
from tests.freqai.conftest import (
get_patched_data_kitchen,
get_patched_freqai_strategy,
is_mac,
make_unfiltered_dataframe,
)
@pytest.mark.parametrize(
"timerange, train_period_days, expected_result",
[
("20220101-20220201", 30, "20211202-20220201"),
("20220301-20220401", 15, "20220214-20220401"),
],
)
def test_create_fulltimerange(
timerange, train_period_days, expected_result, freqai_conf, mocker, caplog
):
dk = get_patched_data_kitchen(mocker, freqai_conf)
assert dk.create_fulltimerange(timerange, train_period_days) == expected_result
shutil.rmtree(Path(dk.full_path))
def test_create_fulltimerange_incorrect_backtest_period(mocker, freqai_conf):
dk = get_patched_data_kitchen(mocker, freqai_conf)
with pytest.raises(OperationalException, match=r"backtest_period_days must be an integer"):
dk.create_fulltimerange("20220101-20220201", 0.5)
with pytest.raises(OperationalException, match=r"backtest_period_days must be positive"):
dk.create_fulltimerange("20220101-20220201", -1)
shutil.rmtree(Path(dk.full_path))
@pytest.mark.parametrize(
"timerange, train_period_days, backtest_period_days, expected_result",
[
("20220101-20220201", 30, 7, 9),
("20220101-20220201", 30, 0.5, 120),
("20220101-20220201", 10, 1, 80),
],
)
def test_split_timerange(
mocker, freqai_conf, timerange, train_period_days, backtest_period_days, expected_result
):
freqai_conf.update({"timerange": "20220101-20220401"})
dk = get_patched_data_kitchen(mocker, freqai_conf)
tr_list, bt_list = dk.split_timerange(timerange, train_period_days, backtest_period_days)
assert len(tr_list) == len(bt_list) == expected_result
with pytest.raises(
OperationalException, match=r"train_period_days must be an integer greater than 0."
):
dk.split_timerange("20220101-20220201", -1, 0.5)
shutil.rmtree(Path(dk.full_path))
def test_check_if_model_expired(mocker, freqai_conf):
dk = get_patched_data_kitchen(mocker, freqai_conf)
now = datetime.now(tz=timezone.utc).timestamp()
assert dk.check_if_model_expired(now) is False
now = (datetime.now(tz=timezone.utc) - timedelta(hours=2)).timestamp()
assert dk.check_if_model_expired(now) is True
shutil.rmtree(Path(dk.full_path))
def test_filter_features(mocker, freqai_conf):
freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
freqai.dk.find_features(unfiltered_dataframe)
filtered_df, _labels = freqai.dk.filter_features(
unfiltered_dataframe,
freqai.dk.training_features_list,
freqai.dk.label_list,
training_filter=True,
)
assert len(filtered_df.columns) == 14
def test_make_train_test_datasets(mocker, freqai_conf):
freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
freqai.dk.find_features(unfiltered_dataframe)
features_filtered, labels_filtered = freqai.dk.filter_features(
unfiltered_dataframe,
freqai.dk.training_features_list,
freqai.dk.label_list,
training_filter=True,
)
data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
assert data_dictionary
assert len(data_dictionary) == 7
assert len(data_dictionary["train_features"].index) == 1916
@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"})
if is_mac():
pytest.skip("Mac is confused during this test for unknown reasons")
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
)
model_path = freqai.dk.get_full_models_path(freqai_conf)
assert model_path.is_dir() is True
def test_get_pair_data_for_features_with_prealoaded_data(mocker, freqai_conf):
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.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
_, base_df = freqai.dd.get_base_and_corr_dataframes(timerange, "LTC/BTC", freqai.dk)
df = freqai.dk.get_pair_data_for_features("LTC/BTC", "5m", strategy, base_dataframes=base_df)
assert df is base_df["5m"]
assert not df.empty
def test_get_pair_data_for_features_without_preloaded_data(mocker, freqai_conf):
freqai_conf.update({"timerange": "20180115-20180130"})
freqai_conf["runmode"] = "backtest"
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.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180110-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
base_df = {"5m": pd.DataFrame()}
df = freqai.dk.get_pair_data_for_features("LTC/BTC", "5m", strategy, base_dataframes=base_df)
assert df is not base_df["5m"]
assert not df.empty
assert df.iloc[0]["date"].strftime("%Y-%m-%d %H:%M:%S") == "2018-01-11 23:00:00"
assert df.iloc[-1]["date"].strftime("%Y-%m-%d %H:%M:%S") == "2018-01-30 00:00:00"
def test_populate_features(mocker, freqai_conf):
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.dk = FreqaiDataKitchen(freqai_conf)
timerange = TimeRange.parse_timerange("20180115-20180130")
freqai.dd.load_all_pair_histories(timerange, freqai.dk)
corr_df, base_df = freqai.dd.get_base_and_corr_dataframes(timerange, "LTC/BTC", freqai.dk)
mocker.patch.object(strategy, "feature_engineering_expand_all", return_value=base_df["5m"])
df = freqai.dk.populate_features(
base_df["5m"], "LTC/BTC", strategy, base_dataframes=base_df, corr_dataframes=corr_df
)
strategy.feature_engineering_expand_all.assert_called_once()
pd.testing.assert_frame_equal(
base_df["5m"], strategy.feature_engineering_expand_all.call_args[0][0]
)
assert df.iloc[0]["date"].strftime("%Y-%m-%d %H:%M:%S") == "2018-01-15 00:00:00"