add tests for CatboostClassifier

tests/freqai/test_freqai_interface.py

@@ -75,17 +75,20 @@ def test_extract_data_and_train_model_Standard(mocker, freqai_conf, model):
     shutil.rmtree(Path(freqai.dk.full_path))
 
 
-@pytest.mark.parametrize('model', [
+@pytest.mark.parametrize('model, strat', [
-    'LightGBMRegressorMultiTarget',
+    ('LightGBMRegressorMultiTarget', "freqai_test_multimodel_strat"),
-    'XGBoostRegressorMultiTarget',
+    ('XGBoostRegressorMultiTarget', "freqai_test_multimodel_strat"),
-    'CatboostRegressorMultiTarget',
+    ('CatboostRegressorMultiTarget', "freqai_test_multimodel_strat"),
+    # ('LightGBMClassifierMultiTarget', "freqai_test_multimodel_classifier_strat"),
+    # ('XGBoostClassifierMultiTarget', "freqai_test_multimodel_classifier_strat"),
+    ('CatboostClassifierMultiTarget', "freqai_test_multimodel_classifier_strat")
     ])
-def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model):
+def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, strat):
-    if is_arm() and model == 'CatboostRegressorMultiTarget':
+    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)

tests/rpc/test_rpc_apiserver.py

@@ -1460,6 +1460,7 @@ def test_api_strategies(botclient, tmpdir):
         'StrategyTestV3CustomEntryPrice',
         'StrategyTestV3Futures',
         'freqai_test_classifier',
+        'freqai_test_multimodel_classifier_strat',
         'freqai_test_multimodel_strat',
         'freqai_test_strat'
     ]}

tests/strategy/strats/freqai_test_multimodel_classifier_strat.py

@@ -0,0 +1,138 @@
+import logging
+from functools import reduce
+
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+import numpy as np
+
+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

tests/strategy/test_strategy_loading.py

@@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
     directory = Path(__file__).parent / "strats"
     strategies = StrategyResolver._search_all_objects(directory, enum_failed=False)
     assert isinstance(strategies, list)
-    assert len(strategies) == 10
+    assert len(strategies) == 11
     assert isinstance(strategies[0], dict)
 
 
@@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
     directory = Path(__file__).parent / "strats"
     strategies = StrategyResolver._search_all_objects(directory, enum_failed=True)
     assert isinstance(strategies, list)
-    assert len(strategies) == 11
+    assert len(strategies) == 12
     # with enum_failed=True search_all_objects() shall find 2 good strategies
     # and 1 which fails to load
-    assert len([x for x in strategies if x['class'] is not None]) == 10
+    assert len([x for x in strategies if x['class'] is not None]) == 11
 
     assert len([x for x in strategies if x['class'] is None]) == 1