fix save/reload functionality for stablebaselines

config_examples/config_reinforcementlearning_example.json

@@ -1,110 +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",
-        "purge_old_models": true,
-        "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": {
-        "model_save_type": "stable_baselines",
-        "conv_width": 10,
-        "follow_mode": false,
-        "purge_old_models": true,
-        "expiration_hours": 1,
-        "train_period_days": 10,
-        "backtest_period_days": 2,
-        "identifier": "test_rl10",
-        "feature_parameters": {
-            "include_corr_pairlist": [
-                "BTC/USDT",
-                "ETH/USDT"
-            ],
-            "include_timeframes": [
-                "15m",
-                "30m"
-            ],
-            "label_period_candles": 80,
-            "include_shifted_candles": 0,
-            "DI_threshold": 0,
-            "weight_factor": 0.9,
-            "principal_component_analysis": false,
-            "use_SVM_to_remove_outliers": false,
-            "svm_params": {"shuffle": true, "nu": 0.1},
-            "stratify_training_data": 0,
-            "indicator_max_period_candles": 10,
-            "indicator_periods_candles": [5]
-        },
-        "data_split_parameters": {
-            "test_size": 0.5,
-            "random_state": 1,
-            "shuffle": false
-        },
-        "model_training_parameters": {
-            "n_steps": 2048,
-            "ent_coef": 0.005,
-            "learning_rate": 0.000025,
-            "batch_size": 256, 
-            "eval_cycles" : 5,
-            "train_cycles" : 15
-        },
-        "model_reward_parameters": {
-            "rr": 1,
-            "profit_aim": 0.01
-        }
-    },
-    "bot_name": "RL_test",
-    "force_entry_enable": true,
-    "initial_state": "running",
-    "internals": {
-        "process_throttle_secs": 5
-    }
-}

freqtrade/freqai/data_drawer.py

@@ -395,7 +395,7 @@ class FreqaiDataDrawer:
             dump(model, save_path / f"{dk.model_filename}_model.joblib")
         elif model_type == 'keras':
             model.save(save_path / f"{dk.model_filename}_model.h5")
-        elif model_type == 'stable_baselines':
+        elif 'stable_baselines' in model_type:
             model.save(save_path / f"{dk.model_filename}_model.zip")
 
         if dk.svm_model is not None:
@@ -473,10 +473,10 @@ class FreqaiDataDrawer:
             model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
         elif model_type == 'stable_baselines_ppo':
             from stable_baselines3.ppo.ppo import PPO
-            model = PPO.load(dk.data_path / f"{dk.model_filename}_model.zip")
+            model = PPO.load(dk.data_path / f"{dk.model_filename}_model")
         elif model_type == 'stable_baselines_dqn':
             from stable_baselines3 import DQN
-            model = DQN.load(dk.data_path / f"{dk.model_filename}_model.zip")
+            model = DQN.load(dk.data_path / f"{dk.model_filename}_model")
 
         if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
             dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")