ensure pytest passes

freqtrade/freqai/spice_rack/test_config.json

@@ -0,0 +1,30 @@
+{
+
+    "freqai": {
+        "enabled": true,
+        "purge_old_models": true,
+        "train_period_days": 4,
+        "backtest_period_days": 2,
+        "identifier": "spicy-id",
+        "feature_parameters": {
+            "include_timeframes": [
+                "5m"
+            ],
+            "include_corr_pairlist": ["ADA/BTC", "DASH/BTC"],
+            "label_period_candles": 20,
+            "include_shifted_candles": 1,
+            "DI_threshold": 0.9,
+            "weight_factor": 0.9,
+            "indicator_periods_candles": [
+                10
+            ]
+        },
+        "data_split_parameters": {
+            "test_size": 0,
+            "random_state": 1
+        },
+        "model_training_parameters": {
+            "n_estimators": 800
+        }
+    }
+}

tests/strategy/strats/freqai_test_spice_rack.py

@@ -0,0 +1,115 @@
+import logging
+from functools import reduce
+
+import pandas as pd
+import talib.abstract as ta
+from pandas import DataFrame
+
+from freqtrade.strategy import DecimalParameter, IntParameter, IStrategy, merge_informative_pair
+
+
+logger = logging.getLogger(__name__)
+
+
+class freqai_test_spice_rack(IStrategy):
+    """
+    Test strategy - used for testing freqAI 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 informative_pairs(self):
+        whitelist_pairs = self.dp.current_whitelist()
+        corr_pairs = self.config["freqai"]["feature_parameters"]["include_corr_pairlist"]
+        informative_pairs = []
+        for tf in self.config["freqai"]["feature_parameters"]["include_timeframes"]:
+            for pair in whitelist_pairs:
+                informative_pairs.append((pair, tf))
+            for pair in corr_pairs:
+                if pair in whitelist_pairs:
+                    continue  # avoid duplication
+                informative_pairs.append((pair, tf))
+        return informative_pairs
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        # Example of how to use the freqai.spice_rack. User treats it the same as any
+        # typical talib indicator. They set a new column in their dataframe
+
+        dataframe['dissimilarity_index'] = self.freqai.spice_rack(
+            'DI_values', dataframe, metadata, self)
+        dataframe['maxima'] = self.freqai.spice_rack(
+            '&s-maxima', dataframe, metadata, self)
+        dataframe['minima'] = self.freqai.spice_rack(
+            '&s-minima', dataframe, metadata, self)
+        self.freqai.close_spice_rack()  # user must close the spicerack
+
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        return dataframe
+
+    def populate_entry_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        df.loc[
+            (
+                (df['tema'] > df['tema'].shift(1)) &  # Guard: tema is raising
+                (df['dissimilarity_index'] < 1) &
+                (df['maxima'] > 0.1)
+            ),
+            'enter_long'] = 1
+
+        df.loc[
+            (
+                (df['tema'] < df['tema'].shift(1)) &  # Guard: tema is falling
+                (df['dissimilarity_index'] < 1) &
+                (df['minima'] > 0.1)
+            ),
+            'enter_short'] = 1
+
+        return df
+
+    def populate_exit_trend(self, df: DataFrame, metadata: dict) -> DataFrame:
+
+        df.loc[
+            (
+                (df['tema'] < df['tema'].shift(1)) &  # Guard: tema is falling
+                (df['dissimilarity_index'] < 1) &
+                (df['maxima'] > 0.1)
+            ),
+
+            'exit_long'] = 1
+
+        df.loc[
+            (
+                (df['tema'] > df['tema'].shift(1)) &  # Guard: tema is raising
+                (df['dissimilarity_index'] < 1) &
+                (df['minima'] > 0.1)
+            ),
+            '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) == 9
+    assert len(strategies) == 10
     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) == 10
+    assert len(strategies) == 11
     # 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]) == 9
+    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 None]) == 1
 
     directory = Path(__file__).parent / "strats_nonexistingdir"