reduce freqai testing time by reducing retrain frequency and number of features

tests/freqai/conftest.py

@@ -27,10 +27,9 @@ def freqai_conf(default_conf, tmpdir):
             "timerange": "20180110-20180115",
             "freqai": {
                 "enabled": True,
-                "startup_candles": 10000,
                 "purge_old_models": True,
                 "train_period_days": 2,
-                "backtest_period_days": 2,
+                "backtest_period_days": 10,
                 "live_retrain_hours": 0,
                 "expiration_hours": 1,
                 "identifier": "uniqe-id100",

tests/freqai/test_freqai_interface.py

@@ -192,13 +192,13 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
 @pytest.mark.parametrize(
     "model, num_files, strat",
     [
-        ("LightGBMRegressor", 6, "freqai_test_strat"),
+        ("LightGBMRegressor", 2, "freqai_test_strat"),
-        ("XGBoostRegressor", 6, "freqai_test_strat"),
+        ("XGBoostRegressor", 2, "freqai_test_strat"),
-        ("CatboostRegressor", 6, "freqai_test_strat"),
+        ("CatboostRegressor", 2, "freqai_test_strat"),
-        ("ReinforcementLearner", 7, "freqai_rl_test_strat"),
+        ("ReinforcementLearner", 3, "freqai_rl_test_strat"),
-        ("XGBoostClassifier", 6, "freqai_test_classifier"),
+        ("XGBoostClassifier", 2, "freqai_test_classifier"),
-        ("LightGBMClassifier", 6, "freqai_test_classifier"),
+        ("LightGBMClassifier", 2, "freqai_test_classifier"),
-        ("CatboostClassifier", 6, "freqai_test_classifier")
+        ("CatboostClassifier", 2, "freqai_test_classifier")
     ],
     )
 def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog):
@@ -305,7 +305,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
     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) == 6
+    assert len(model_folders) == 2
 
     # without deleting the existing folder structure, re-run
 
@@ -333,7 +333,7 @@ def test_start_backtesting_from_existing_folder(mocker, freqai_conf, caplog):
 
     path = (freqai.dd.full_path / freqai.dk.backtest_predictions_folder)
     prediction_files = [x for x in path.iterdir() if x.is_file()]
-    assert len(prediction_files) == 5
+    assert len(prediction_files) == 1
 
     shutil.rmtree(Path(freqai.dk.full_path))
 

tests/freqai/test_models/ReinforcementLearner_test_4ac.py

@@ -1,57 +1,19 @@
 import logging
-from pathlib import Path
-from typing import Any, Dict
 
 import numpy as np
-import torch as th
 
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.RL.Base4ActionRLEnv import Actions, Base4ActionRLEnv, Positions
-from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
+from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
 
 
 logger = logging.getLogger(__name__)
 
 
-class ReinforcementLearner_test_4ac(BaseReinforcementLearningModel):
+class ReinforcementLearner_test_4ac(ReinforcementLearner):
     """
     User created Reinforcement Learning Model prediction model.
     """
 
-    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
-
-        train_df = data_dictionary["train_features"]
-        total_timesteps = self.freqai_info["rl_config"]["train_cycles"] * len(train_df)
-
-        policy_kwargs = dict(activation_fn=th.nn.ReLU,
-                             net_arch=[64, 64])
-
-        if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
-            model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
-                                    tensorboard_log=Path(
-                                        dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
-                                    **self.freqai_info['model_training_parameters']
-                                    )
-        else:
-            logger.info('Continual training activated - starting training from previously '
-                        'trained agent.')
-            model = self.dd.model_dictionary[dk.pair]
-            model.set_env(self.train_env)
-
-        model.learn(
-            total_timesteps=int(total_timesteps),
-            callback=self.eval_callback
-        )
-
-        if Path(dk.data_path / "best_model.zip").is_file():
-            logger.info('Callback found a best model.')
-            best_model = self.MODELCLASS.load(dk.data_path / "best_model")
-            return best_model
-
-        logger.info('Couldnt find best model, using final model instead.')
-
-        return model
-
     class MyRLEnv(Base4ActionRLEnv):
         """
         User can override any function in BaseRLEnv and gym.Env. Here the user

tests/strategy/strats/freqai_rl_test_strat.py

@@ -19,19 +19,6 @@ class freqai_rl_test_strat(IStrategy):
 
     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
@@ -50,10 +37,7 @@ class freqai_rl_test_strat(IStrategy):
 
             t = int(t)
             informative[f"%-{pair}rsi-period_{t}"] = ta.RSI(informative, timeperiod=t)
-            informative[f"%-{pair}mfi-period_{t}"] = ta.MFI(informative, timeperiod=t)
-            informative[f"%-{pair}adx-period_{t}"] = ta.ADX(informative, window=t)
 
-        # FIXME: add these outside the user strategy?
         # The following columns are necessary for RL models.
         informative[f"%-{pair}raw_close"] = informative["close"]
         informative[f"%-{pair}raw_open"] = informative["open"]
@@ -79,9 +63,6 @@ class freqai_rl_test_strat(IStrategy):
         # 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
-
             # For RL, there are no direct targets to set. This is filler (neutral)
             # until the agent sends an action.
             df["&-action"] = 0