start adding tests

tests/freqai/conftest.py

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+from copy import deepcopy
+from pathlib import Path
+from unittest.mock import MagicMock
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.resolvers import StrategyResolver
+from freqtrade.resolvers.freqaimodel_resolver import FreqaiModelResolver
+
+
+# @pytest.fixture(scope="function")
+def freqai_conf(default_conf):
+    freqaiconf = deepcopy(default_conf)
+    freqaiconf.update(
+        {
+            "datadir": Path(default_conf["datadir"]),
+            "strategy": "FreqaiExampleStrategy",
+            "strategy-path": "freqtrade/templates",
+            "freqaimodel": "LightGBMPredictionModel",
+            "freqaimodel_path": "freqai/prediction_models",
+            "timerange": "20180110-20180115",
+            "freqai": {
+                "startup_candles": 10000,
+                "purge_old_models": True,
+                "train_period_days": 15,
+                "backtest_period_days": 7,
+                "live_retrain_hours": 0,
+                "identifier": "uniqe-id7",
+                "live_trained_timestamp": 0,
+                "feature_parameters": {
+                    "include_timeframes": ["5m"],
+                    "include_corr_pairlist": ["ADA/BTC", "DASH/BTC"],
+                    "label_period_candles": 20,
+                    "include_shifted_candles": 2,
+                    "DI_threshold": 0.9,
+                    "weight_factor": 0.9,
+                    "principal_component_analysis": False,
+                    "use_SVM_to_remove_outliers": True,
+                    "stratify_training_data": 0,
+                    "indicator_max_period_candles": 10,
+                    "indicator_periods_candles": [10],
+                },
+                "data_split_parameters": {"test_size": 0.33, "random_state": 1},
+                "model_training_parameters": {"n_estimators": 1000, "task_type": "CPU"},
+            },
+            "config_files": [Path('config_examples', 'config_freqai_futures.example.json')]
+        }
+    )
+    freqaiconf['exchange'].update({'pair_whitelist': ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC']})
+    return freqaiconf
+
+
+def get_patched_data_kitchen(mocker, freqaiconf):
+    dd = mocker.patch('freqtrade.freqai.data_drawer', MagicMock())
+    dk = FreqaiDataKitchen(freqaiconf, dd)
+    return dk
+
+
+def get_patched_strategy(mocker, freqaiconf):
+    strategy = StrategyResolver.load_strategy(freqaiconf)
+    strategy.bot_start()
+
+    return strategy
+
+
+def get_patched_freqaimodel(mocker, freqaiconf):
+    freqaimodel = FreqaiModelResolver.load_freqaimodel(freqaiconf)
+
+    return freqaimodel

tests/freqai/test_freqai.py

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+# from unittest.mock import MagicMock
+# from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_edge
+import copy
+
+import pytest
+
+from freqtrade.configuration import TimeRange
+from freqtrade.data.dataprovider import DataProvider
+# from freqtrade.freqai.data_drawer import FreqaiDataDrawer
+from freqtrade.exceptions import OperationalException
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from tests.conftest import get_patched_exchange
+from tests.freqai.conftest import freqai_conf, get_patched_data_kitchen, get_patched_strategy
+
+
+@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, default_conf, mocker, caplog
+):
+    dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_conf)))
+    assert dk.create_fulltimerange(timerange, train_period_days) == expected_result
+
+
+def test_create_fulltimerange_incorrect_backtest_period(mocker, default_conf):
+    dk = get_patched_data_kitchen(mocker, freqai_conf(copy.deepcopy(default_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)
+
+
+def test_split_timerange(mocker, default_conf):
+    freqaiconf = freqai_conf(copy.deepcopy(default_conf))
+    freqaiconf.update({"timerange": "20220101-20220401"})
+    dk = get_patched_data_kitchen(mocker, freqaiconf)
+    tr_list, bt_list = dk.split_timerange("20220101-20220201", 30, 7)
+    assert len(tr_list) == len(bt_list) == 9
+
+    tr_list, bt_list = dk.split_timerange("20220101-20220201", 30, 0.5)
+    assert len(tr_list) == len(bt_list) == 120
+
+    tr_list, bt_list = dk.split_timerange("20220101-20220201", 10, 1)
+    assert len(tr_list) == len(bt_list) == 80
+
+    with pytest.raises(
+        OperationalException, match=r"train_period_days must be an integer greater than 0."
+    ):
+        dk.split_timerange("20220101-20220201", -1, 0.5)
+
+
+def test_update_historic_data(mocker, default_conf):
+    freqaiconf = freqai_conf(copy.deepcopy(default_conf))
+    strategy = get_patched_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)
+    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.update_historic_data(strategy)
+
+    updated_historic_candles = len(freqai.dd.historic_data["ADA/BTC"]["5m"])
+
+    assert updated_historic_candles - historic_candles == candle_difference
+
+
+# def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05'):
+#     np.random.seed(42)
+#     tf_mins = timeframe_to_minutes(timeframe)
+
+#     base = np.random.normal(20, 2, size=size)
+
+#     date = pd.date_range(start, periods=size, freq=f'{tf_mins}min', tz='UTC')
+#     df = pd.DataFrame({
+#         'date': date,
+#         'open': base,
+#         'high': base + np.random.normal(2, 1, size=size),
+#         'low': base - np.random.normal(2, 1, size=size),
+#         'close': base + np.random.normal(0, 1, size=size),
+#         'volume': np.random.normal(200, size=size)
+#     }
+#     )
+#     df = df.dropna()
+#     return df