increase test coverage for RL and FreqAI

config_examples/config_freqai-rl.example.json

@@ -1,109 +0,0 @@
-{
-    "trading_mode": "futures",
-    "new_pairs_days": 30,
-    "margin_mode": "isolated",
-    "max_open_trades": 8,
-    "stake_currency": "USDT",
-    "stake_amount": 1000,
-    "tradable_balance_ratio": 1,
-    "fiat_display_currency": "USD",
-    "dry_run": true,
-    "timeframe": "5m",
-    "dataformat_ohlcv": "json",
-    "dry_run_wallet": 12000,
-    "cancel_open_orders_on_exit": true,
-    "unfilledtimeout": {
-        "entry": 10,
-        "exit": 30
-    },
-    "exchange": {
-        "name": "binance",
-        "key": "",
-        "secret": "",
-        "ccxt_config": {
-            "enableRateLimit": true
-        },
-        "ccxt_async_config": {
-            "enableRateLimit": true,
-            "rateLimit": 200
-        },
-        "pair_whitelist": [
-            "1INCH/USDT",
-            "AAVE/USDT"
-        ],
-        "pair_blacklist": []
-    },
-    "entry_pricing": {
-        "price_side": "same",
-        "use_order_book": true,
-        "order_book_top": 1,
-        "price_last_balance": 0.0,
-        "check_depth_of_market": {
-            "enabled": false,
-            "bids_to_ask_delta": 1
-        }
-    },
-    "exit_pricing": {
-        "price_side": "other",
-        "use_order_book": true,
-        "order_book_top": 1
-    },
-    "pairlists": [
-        {
-            "method": "StaticPairList"
-        }
-    ],
-    "freqai": {
-        "enabled": true,
-        "model_save_type": "stable_baselines",
-        "conv_width": 4,
-        "purge_old_models": true,
-        "limit_ram_usage": false,
-        "train_period_days": 5,
-        "backtest_period_days": 2,
-        "identifier": "unique-id",
-        "continual_learning": false,
-        "data_kitchen_thread_count": 2,
-        "feature_parameters": {
-            "include_corr_pairlist": [
-                "BTC/USDT",
-                "ETH/USDT"
-            ],
-            "include_timeframes": [
-                "5m",
-                "30m"
-            ],
-            "indicator_max_period_candles": 20,
-            "indicator_periods_candles": [14]
-        },
-        "data_split_parameters": {
-            "test_size": 0.5,
-            "random_state": 1,
-            "shuffle": false
-        },
-        "model_training_parameters": {
-            "learning_rate": 0.00025,
-            "gamma": 0.9,
-            "verbose": 1
-        },
-        "rl_config": {
-            "train_cycles": 6,
-            "thread_count": 4,
-            "max_trade_duration_candles": 300,
-            "model_type": "PPO",
-            "policy_type": "MlpPolicy",
-            "max_training_drawdown_pct": 0.5,
-            "model_reward_parameters": {
-                "rr": 1,
-                "profit_aim": 0.02,
-                "win_reward_factor": 2
-            }
-        }
-    },
-    "bot_name": "RL_test",
-    "force_entry_enable": true,
-    "initial_state": "running",
-    "internals": {
-        "process_throttle_secs": 5
-    }
-}

freqtrade/freqai/data_drawer.py

@@ -602,22 +602,3 @@ class FreqaiDataDrawer:
                         )
 
         return corr_dataframes, base_dataframes
-
-    # to be used if we want to send predictions directly to the follower instead of forcing
-    # follower to load models and inference
-    # def save_model_return_values_to_disk(self) -> None:
-    #     with open(self.full_path / str('model_return_values.json'), "w") as fp:
-    #         json.dump(self.model_return_values, fp, default=self.np_encoder)
-
-    # def load_model_return_values_from_disk(self, dk: FreqaiDataKitchen) -> FreqaiDataKitchen:
-    #     exists = Path(self.full_path / str('model_return_values.json')).resolve().exists()
-    #     if exists:
-    #         with open(self.full_path / str('model_return_values.json'), "r") as fp:
-    #             self.model_return_values = json.load(fp)
-    #     elif not self.follow_mode:
-    #         logger.info("Could not find existing datadrawer, starting from scratch")
-    #     else:
-    #         logger.warning(f'Follower could not find pair_dictionary at {self.full_path} '
-    #                        'sending null values back to strategy')
-
-    #     return exists, dk

tests/freqai/test_freqai_interface.py

@@ -4,13 +4,15 @@ from pathlib import Path
 from unittest.mock import MagicMock
 
 import pytest
-
+from freqtrade.enums import RunMode
 from freqtrade.configuration import TimeRange
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.plugins.pairlistmanager import PairListManager
 from tests.conftest import get_patched_exchange, log_has_re
 from tests.freqai.conftest import get_patched_freqai_strategy
+from freqtrade.persistence import Trade
+from freqtrade.freqai.utils import download_all_data_for_training, get_required_data_timerange
 
 
 def is_arm() -> bool:
@@ -173,29 +175,34 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
     shutil.rmtree(Path(freqai.dk.full_path))
 
 
-@pytest.mark.parametrize('model', [
+@pytest.mark.parametrize(
-    'LightGBMRegressor',
+    "model, num_files, strat",
-    'XGBoostRegressor',
+    [
-    'CatboostRegressor',
+        ("LightGBMRegressor", 6, "freqai_test_strat"),
-    'ReinforcementLearner'
+        ("XGBoostRegressor", 6, "freqai_test_strat"),
-    ])
+        ("CatboostRegressor", 6, "freqai_test_strat"),
-def test_start_backtesting(mocker, freqai_conf, model):
+        ("ReinforcementLearner", 7, "freqai_rl_test_strat"),
+        ("XGBoostClassifier", 6, "freqai_test_classifier"),
+        ("LightGBMClassifier", 6, "freqai_test_classifier"),
+        ("CatboostClassifier", 6, "freqai_test_classifier")
+    ],
+    )
+def test_start_backtesting(mocker, freqai_conf, model, num_files, strat):
     freqai_conf.get("freqai", {}).update({"save_backtest_models": True})
-
+    freqai_conf['runmode'] = RunMode.BACKTEST
-    if is_arm() and model == 'CatboostRegressor':
+    Trade.use_db = False
+    if is_arm() and "Catboost" in model:
         pytest.skip("CatBoost is not supported on ARM")
 
     if is_mac():
         pytest.skip("Reinforcement learning module not available on intel based Mac OS")
 
-    model_save_ext = 'joblib'
     freqai_conf.update({"freqaimodel": model})
-    freqai_conf.update({"timerange": "20180110-20180130"})
+    freqai_conf.update({"timerange": "20180120-20180130"})
-    freqai_conf.update({"strategy": "freqai_test_strat"})
+    freqai_conf.update({"strategy": strat})
 
     if 'ReinforcementLearner' in model:
-        model_save_ext = 'zip'
+
-        freqai_conf.update({"strategy": "freqai_rl_test_strat"})
         freqai_conf["freqai"].update({"model_training_parameters": {
             "learning_rate": 0.00025,
             "gamma": 0.9,
@@ -218,7 +225,6 @@ def test_start_backtesting(mocker, freqai_conf, model):
     if 'test_4ac' in model:
         freqai_conf["freqaimodel_path"] = str(Path(__file__).parents[1] / "freqai" / "test_models")
 
-    
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)
     exchange = get_patched_exchange(mocker, freqai_conf)
     strategy.dp = DataProvider(freqai_conf, exchange)
@@ -237,7 +243,7 @@ def test_start_backtesting(mocker, freqai_conf, model):
     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) == num_files
 
     shutil.rmtree(Path(freqai.dk.full_path))
 
@@ -455,3 +461,40 @@ def test_freqai_informative_pairs(mocker, freqai_conf, timeframes, corr_pairs):
     pairs_b = strategy.gather_informative_pairs()
     # we expect unique pairs * timeframes
     assert len(pairs_b) == len(set(pairlist + corr_pairs)) * len(timeframes)
+
+
+def test_start_set_train_queue(mocker, freqai_conf, caplog):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    pairlist = PairListManager(exchange, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
+    strategy.freqai_info = freqai_conf.get("freqai", {})
+    freqai = strategy.freqai
+    freqai.live = False
+
+    freqai.train_queue = freqai._set_train_queue()
+
+    assert log_has_re(
+        "Set fresh train queue from whitelist.",
+        caplog,
+    )
+
+
+def test_get_required_data_timerange(mocker, freqai_conf):
+    time_range = get_required_data_timerange(freqai_conf)
+    assert (time_range.stopts - time_range.startts) == 177300
+
+
+def test_download_all_data_for_training(mocker, freqai_conf, caplog, tmpdir):
+    strategy = get_patched_freqai_strategy(mocker, freqai_conf)
+    exchange = get_patched_exchange(mocker, freqai_conf)
+    pairlist = PairListManager(exchange, freqai_conf)
+    strategy.dp = DataProvider(freqai_conf, exchange, pairlist)
+    freqai_conf['pairs'] = freqai_conf['exchange']['pair_whitelist']
+    freqai_conf['datadir'] = Path(tmpdir)
+    download_all_data_for_training(strategy.dp, freqai_conf)
+
+    assert log_has_re(
+        "Downloading",
+        caplog,
+    )