Merge bb62b0fc5a into ae41ab101a

Invalid pairs were filtered out before this was called in most cases.
in cases where it's not - regular pairlist-filtering provides proper warnings.

docs/configuration.md

@@ -222,7 +222,6 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `exchange.ccxt_async_config` | Additional CCXT parameters passed to the async ccxt instance. Parameters may differ from exchange to exchange  and are documented in the [ccxt documentation](https://docs.ccxt.com/#/README?id=overriding-exchange-properties-upon-instantiation) <br> **Datatype:** Dict
 | `exchange.enable_ws` | Enable the usage of Websockets for the exchange. <br>[More information](#consuming-exchange-websockets).<br>*Defaults to `true`.* <br> **Datatype:** Boolean
 | `exchange.markets_refresh_interval` | The interval in minutes in which markets are reloaded. <br>*Defaults to `60` minutes.* <br> **Datatype:** Positive Integer
-| `exchange.skip_pair_validation` | Skip pairlist validation on startup.<br>*Defaults to `false`*<br> **Datatype:** Boolean
 | `exchange.skip_open_order_update` | Skips open order updates on startup should the exchange cause problems. Only relevant in live conditions.<br>*Defaults to `false`*<br> **Datatype:** Boolean
 | `exchange.unknown_fee_rate` | Fallback value to use when calculating trading fees. This can be useful for exchanges which have fees in non-tradable currencies. The value provided here will be multiplied with the "fee cost".<br>*Defaults to `None`<br> **Datatype:** float
 | `exchange.log_responses` | Log relevant exchange responses. For debug mode only - use with care.<br>*Defaults to `false`*<br> **Datatype:** Boolean

docs/developer.md

@@ -205,7 +205,7 @@ This is called with each iteration of the bot (only if the Pairlist Handler is a
 
 It must return the resulting pairlist (which may then be passed into the chain of Pairlist Handlers).
 
-Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filtering. Use this if you limit your result to a certain number of pairs - so the end-result is not shorter than expected.
+Validations are optional, the parent class exposes a `verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filtering. Use this if you limit your result to a certain number of pairs - so the end-result is not shorter than expected.
 
 #### filter_pairlist
 
@@ -219,7 +219,7 @@ The default implementation in the base class simply calls the `_validate_pair()`
 
 If overridden, it must return the resulting pairlist (which may then be passed into the next Pairlist Handler in the chain).
 
-Validations are optional, the parent class exposes a `_verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filters. Use this if you limit your result to a certain number of pairs - so the end result is not shorter than expected.
+Validations are optional, the parent class exposes a `verify_blacklist(pairlist)` and `_whitelist_for_active_markets(pairlist)` to do default filters. Use this if you limit your result to a certain number of pairs - so the end result is not shorter than expected.
 
 In `VolumePairList`, this implements different methods of sorting, does early validation so only the expected number of pairs is returned.
 

docs/includes/pairlists.md

@@ -55,7 +55,6 @@ It uses configuration from `exchange.pair_whitelist` and `exchange.pair_blacklis
 By default, only currently enabled pairs are allowed.
 To skip pair validation against active markets, set `"allow_inactive": true` within the `StaticPairList` configuration.
 This can be useful for backtesting expired pairs (like quarterly spot-markets).
-This option must be configured along with `exchange.skip_pair_validation` in the exchange configuration.
 
 When used in a "follow-up" position (e.g. after VolumePairlist), all pairs in `'pair_whitelist'` will be added to the end of the pairlist.
 

freqtrade/data/history/history_utils.py

@@ -610,9 +610,6 @@ def download_data_main(config: Config) -> None:
     if "timeframes" not in config:
         config["timeframes"] = DL_DATA_TIMEFRAMES
 
-    # Manual validations of relevant settings
-    if not config["exchange"].get("skip_pair_validation", False):
-        exchange.validate_pairs(expanded_pairs)
     logger.info(
         f"About to download pairs: {expanded_pairs}, "
         f"intervals: {config['timeframes']} to {config['datadir']}"

freqtrade/exchange/exchange.py

@@ -104,7 +104,6 @@ from freqtrade.misc import (
     file_load_json,
     safe_value_fallback2,
 )
-from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.util import dt_from_ts, dt_now
 from freqtrade.util.datetime_helpers import dt_humanize_delta, dt_ts, format_ms_time
 from freqtrade.util.periodic_cache import PeriodicCache
@@ -331,8 +330,6 @@ class Exchange:
 
         # Check if all pairs are available
         self.validate_stakecurrency(config["stake_currency"])
-        if not config["exchange"].get("skip_pair_validation"):
-            self.validate_pairs(config["exchange"]["pair_whitelist"])
         self.validate_ordertypes(config.get("order_types", {}))
         self.validate_order_time_in_force(config.get("order_time_in_force", {}))
         self.validate_trading_mode_and_margin_mode(self.trading_mode, self.margin_mode)
@@ -702,54 +699,6 @@ class Exchange:
                 f"Available currencies are: {', '.join(quote_currencies)}"
             )
 
-    def validate_pairs(self, pairs: List[str]) -> None:
-        """
-        Checks if all given pairs are tradable on the current exchange.
-        :param pairs: list of pairs
-        :raise: OperationalException if one pair is not available
-        :return: None
-        """
-
-        if not self.markets:
-            logger.warning("Unable to validate pairs (assuming they are correct).")
-            return
-        extended_pairs = expand_pairlist(pairs, list(self.markets), keep_invalid=True)
-        invalid_pairs = []
-        for pair in extended_pairs:
-            # Note: ccxt has BaseCurrency/QuoteCurrency format for pairs
-            if self.markets and pair not in self.markets:
-                raise OperationalException(
-                    f"Pair {pair} is not available on {self.name} {self.trading_mode}. "
-                    f"Please remove {pair} from your whitelist."
-                )
-
-                # From ccxt Documentation:
-                # markets.info: An associative array of non-common market properties,
-                # including fees, rates, limits and other general market information.
-                # The internal info array is different for each particular market,
-                # its contents depend on the exchange.
-                # It can also be a string or similar ... so we need to verify that first.
-            elif isinstance(self.markets[pair].get("info"), dict) and self.markets[pair].get(
-                "info", {}
-            ).get("prohibitedIn", False):
-                # Warn users about restricted pairs in whitelist.
-                # We cannot determine reliably if Users are affected.
-                logger.warning(
-                    f"Pair {pair} is restricted for some users on this exchange."
-                    f"Please check if you are impacted by this restriction "
-                    f"on the exchange and eventually remove {pair} from your whitelist."
-                )
-            if (
-                self._config["stake_currency"]
-                and self.get_pair_quote_currency(pair) != self._config["stake_currency"]
-            ):
-                invalid_pairs.append(pair)
-        if invalid_pairs:
-            raise OperationalException(
-                f"Stake-currency '{self._config['stake_currency']}' not compatible with "
-                f"pair-whitelist. Please remove the following pairs: {invalid_pairs}"
-            )
-
     def get_valid_pair_combination(self, curr_1: str, curr_2: str) -> str:
         """
         Get valid pair combination of curr_1 and curr_2 by trying both combinations.

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -22,12 +22,18 @@ class Base5ActionRLEnv(BaseEnvironment):
     Base class for a 5 action environment
     """
 
-    def __init__(self, **kwargs):
-        super().__init__(**kwargs)
+    def __init__(self, *args, action_space_type: str = "Discrete", **kwargs):
+        super().__init__(*args, **kwargs)
+        self.action_space_type = action_space_type
         self.actions = Actions
 
     def set_action_space(self):
-        self.action_space = spaces.Discrete(len(Actions))
+            if self.action_space_type == "Discrete":
+                self.action_space = spaces.Discrete(len(Actions))
+            elif self.action_space_type == "Box":
+                self.action_space = spaces.Box(low=-1, high=1, shape=(1,))
+            else:
+                raise ValueError(f"Unknown action space type: {self.action_space_type}")
 
     def step(self, action: int):
         """

freqtrade/freqai/RL/BaseEnvironment.py

@@ -60,6 +60,7 @@ class BaseEnvironment(gym.Env):
         can_short: bool = False,
         pair: str = "",
         df_raw: DataFrame = DataFrame(),
+        action_space_type: str = "Discrete"
     ):
         """
         Initializes the training/eval environment.
@@ -93,6 +94,7 @@ class BaseEnvironment(gym.Env):
         self.tensorboard_metrics: dict = {}
         self.can_short: bool = can_short
         self.live: bool = live
+        self.action_space_type: str = action_space_type
         if not self.live and self.add_state_info:
             raise OperationalException(
                 "`add_state_info` is not available in backtesting. Change "

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -32,7 +32,7 @@ logger = logging.getLogger(__name__)
 
 torch.multiprocessing.set_sharing_strategy("file_system")
 
-SB3_MODELS = ["PPO", "A2C", "DQN"]
+SB3_MODELS = ["PPO", "A2C", "DQN", "DDPG", "TD3"]
 SB3_CONTRIB_MODELS = ["TRPO", "ARS", "RecurrentPPO", "MaskablePPO", "QRDQN"]
 
 

freqtrade/freqai/prediction_models/ReinforcementLearner_DDPG_TD3.py

@@ -0,0 +1,556 @@
+import copy
+import logging
+import gc
+from pathlib import Path
+from typing import Any, Dict, Type, Callable, List, Optional, Union
+
+import numpy as np
+import torch as th
+import pandas as pd
+from pandas import DataFrame
+from gymnasium import spaces
+import matplotlib
+import matplotlib.transforms as mtransforms
+import matplotlib.pyplot as plt
+from stable_baselines3.common.callbacks import BaseCallback, EvalCallback
+from stable_baselines3.common.logger import HParam, Figure
+
+from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+from freqtrade.freqai.RL.BaseEnvironment import BaseEnvironment, BaseActions
+from freqtrade.freqai.RL.BaseReinforcementLearningModel import BaseReinforcementLearningModel
+from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
+
+
+logger = logging.getLogger(__name__)
+
+
+class ReinforcementLearner_DDPG_TD3(BaseReinforcementLearningModel):
+    """
+    Reinforcement Learning Model prediction model for DDPG and TD3.
+
+    Users can inherit from this class to make their own RL model with custom
+    environment/training controls. Define the file as follows:
+
+    ```
+    from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
+
+    class MyCoolRLModel(ReinforcementLearner):
+    ```
+
+    Save the file to `user_data/freqaimodels`, then run it with:
+
+    freqtrade trade --freqaimodel MyCoolRLModel --config config.json --strategy SomeCoolStrat
+
+    Here the users can override any of the functions
+    available in the `IFreqaiModel` inheritance tree. Most importantly for RL, this
+    is where the user overrides `MyRLEnv` (see below), to define custom
+    `calculate_reward()` function, or to override any other parts of the environment.
+
+    This class also allows users to override any other part of the IFreqaiModel tree.
+    For example, the user can override `def fit()` or `def train()` or `def predict()`
+    to take fine-tuned control over these processes.
+
+    Another common override may be `def data_cleaning_predict()` where the user can
+    take fine-tuned control over the data handling pipeline.
+    """
+
+    def __init__(self, **kwargs) -> None:
+        """
+        Model specific config
+        """
+        super().__init__(**kwargs)
+
+        # Enable learning rate linear schedule
+        self.lr_schedule: bool = self.rl_config.get("lr_schedule", False)
+
+        # Enable tensorboard logging
+        self.activate_tensorboard: bool = self.rl_config.get("activate_tensorboard", True)
+        # TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS,
+        # IT WILL RETURN FALSE INFORMATIONS, NEVERTHLESS NOT THREAD SAFE WITH SB3!!!
+
+        # Enable tensorboard rollout plot
+        self.tensorboard_plot: bool = self.rl_config.get("tensorboard_plot", False)
+
+    def get_model_params(self):
+        """
+        Get the model specific parameters
+        """
+        model_params = copy.deepcopy(self.freqai_info["model_training_parameters"])
+        
+        if self.lr_schedule:
+            _lr = model_params.get('learning_rate', 0.0003)
+            model_params["learning_rate"] = linear_schedule(_lr)
+            logger.info(f"Learning rate linear schedule enabled, initial value: {_lr}")
+
+        model_params["policy_kwargs"] = dict(
+        net_arch=dict(vf=self.net_arch, pi=self.net_arch),
+        activation_fn=th.nn.ReLU,
+        optimizer_class=th.optim.Adam
+
+        return model_params
+
+    def get_callbacks(self, eval_freq, data_path) -> list:
+        """
+        Get the model specific callbacks
+        """
+        callbacks = []
+        callbacks.append(self.eval_callback)
+        if self.activate_tensorboard:
+            callbacks.append(CustomTensorboardCallback())
+        if self.tensorboard_plot:
+            callbacks.append(FigureRecorderCallback())
+        return callbacks
+
+    def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
+        """
+        User customizable fit method
+        :param data_dictionary: dict = common data dictionary containing all train/test
+            features/labels/weights.
+        :param dk: FreqaiDatakitchen = data kitchen for current pair.
+        :return:
+        model Any = trained model to be used for inference in dry/live/backtesting
+        """
+        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=self.net_arch)
+
+        if self.activate_tensorboard:
+            tb_path = Path(dk.full_path / "tensorboard" / dk.pair.split('/')[0])
+        else:
+            tb_path = None
+
+        model_params = self.get_model_params()
+        logger.info(f"Params: {model_params}")
+
+        if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
+            model = self.MODELCLASS(self.policy_type, self.train_env,
+                                    tensorboard_log=tb_path,
+                                    **model_params)
+        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, self.tensorboard_callback],
+            callback=self.get_callbacks(len(train_df), str(dk.data_path)),
+            progress_bar=self.rl_config.get("progress_bar", False)
+        )
+
+        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
+
+    MyRLEnv: Type[BaseEnvironment]
+
+    class MyRLEnv(Base5ActionRLEnv):  # type: ignore[no-redef]
+        """
+        User can override any function in BaseRLEnv and gym.Env. Here the user
+        sets a custom reward based on profit and trade duration.
+        """
+        def __init__(self, df, prices, reward_kwargs, window_size=10, starting_point=True, id="boxenv-1", seed=1, config={}, live=False, fee=0.0015, can_short=False, pair="", df_raw=None, action_space_type="Box"):
+            super().__init__(df, prices, reward_kwargs, window_size, starting_point, id, seed, config, live, fee, can_short, pair, df_raw)
+            
+            # Define the action space as a continuous space between -1 and 1 for a single action dimension
+            self.action_space = spaces.Box(low=-1, high=1, shape=(1,), dtype=np.float32)
+            
+            # Define the observation space as before
+            self.observation_space = spaces.Box(
+                low=-np.inf,
+                high=np.inf,
+                shape=(window_size, self.total_features),
+                dtype=np.float32
+            )
+
+        def calculate_reward(self, action: int) -> float:
+            """
+            An example reward function. This is the one function that users will likely
+            wish to inject their own creativity into.
+
+                        Warning!
+            This is function is a showcase of functionality designed to show as many possible
+            environment control features as possible. It is also designed to run quickly
+            on small computers. This is a benchmark, it is *not* for live production.
+
+            :param action: int = The action made by the agent for the current candle.
+            :return:
+            float = the reward to give to the agent for current step (used for optimization
+                of weights in NN)
+            """
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                self.tensorboard_log("invalid", category="actions")
+                return -2
+
+            pnl = self.get_unrealized_profit()
+            factor = 100.
+
+            # reward agent for entering trades
+            if (action == Actions.Long_enter.value
+                    and self._position == Positions.Neutral):
+                return 25
+            if (action == Actions.Short_enter.value
+                    and self._position == Positions.Neutral):
+                return 25
+            # discourage agent from not entering trades
+            if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                return -1
+
+            max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
+            trade_duration = self._current_tick - self._last_trade_tick  # type: ignore
+
+            if trade_duration <= max_trade_duration:
+                factor *= 1.5
+            elif trade_duration > max_trade_duration:
+                factor *= 0.5
+
+            # discourage sitting in position
+            if (self._position in (Positions.Short, Positions.Long) and
+                    action == Actions.Neutral.value):
+                return -1 * trade_duration / max_trade_duration
+
+            # close long
+            if action == Actions.Long_exit.value and self._position == Positions.Long:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
+                return float(pnl * factor)
+
+            # close short
+            if action == Actions.Short_exit.value and self._position == Positions.Short:
+                if pnl > self.profit_aim * self.rr:
+                    factor *= self.rl_config["model_reward_parameters"].get("win_reward_factor", 2)
+                return float(pnl * factor)
+
+            return 0.
+
+        def step(self, action):
+            """
+            Logic for a single step (incrementing one candle in time)
+            by the agent
+            :param: action: int = the action type that the agent plans
+                to take for the current step.
+            :returns:
+                observation = current state of environment
+                step_reward = the reward from `calculate_reward()`
+                _done = if the agent "died" or if the candles finished
+                info = dict passed back to openai gym lib
+            """
+            
+            # Ensure action is within the range [-1, 1]
+            action = np.clip(action, -1, 1)
+            
+            # Apply noise for exploration
+            self.noise_std = 0.3  # Standard deviation for exploration noise
+            noise = np.random.normal(0, self.noise_std, size=action.shape)
+            action = np.tanh(action + noise)  # Ensure action is within -1 to 1
+    
+            # Map the continuous action to one of the five discrete actions
+            discrete_action = self._map_continuous_to_discrete(action)
+            
+            #print(f"{self._current_tick} Action!!!: {action}")
+            #print(f"{self._current_tick} Discrete Action!!!: {discrete_action}")
+
+            self._done = False
+            self._current_tick += 1
+    
+            if self._current_tick == self._end_tick:
+                self._done = True
+    
+            self._update_unrealized_total_profit()
+            step_reward = self.calculate_reward(discrete_action)
+            self.total_reward += step_reward
+            
+            self.tensorboard_log(self.actions._member_names_[discrete_action], category="actions")
+    
+            trade_type = None
+            if self.is_tradesignal(discrete_action):
+    
+                if discrete_action == Actions.Neutral.value:
+                    self._position = Positions.Neutral
+                    trade_type = "neutral"
+                    self._last_trade_tick = None
+                elif discrete_action == Actions.Long_enter.value:
+                    self._position = Positions.Long
+                    trade_type = "enter_long"
+                    self._last_trade_tick = self._current_tick
+                elif discrete_action == Actions.Short_enter.value:
+                    self._position = Positions.Short
+                    trade_type = "enter_short"
+                    self._last_trade_tick = self._current_tick
+                elif discrete_action == Actions.Long_exit.value:
+                    self._update_total_profit()
+                    self._position = Positions.Neutral
+                    trade_type = "exit_long"
+                    self._last_trade_tick = None
+                elif discrete_action == Actions.Short_exit.value:
+                    self._update_total_profit()
+                    self._position = Positions.Neutral
+                    trade_type = "exit_short"
+                    self._last_trade_tick = None
+                else:
+                    print("case not defined")
+    
+                if trade_type is not None:
+                    self.trade_history.append(
+                        {"price": self.current_price(), "index": self._current_tick,
+                         "type": trade_type, "profit": self.get_unrealized_profit()})
+    
+            if (self._total_profit < self.max_drawdown or
+                    self._total_unrealized_profit < self.max_drawdown):
+                self._done = True
+    
+            self._position_history.append(self._position)
+    
+            info = dict(
+                tick=self._current_tick,
+                action=discrete_action,
+                total_reward=self.total_reward,
+                total_profit=self._total_profit,
+                position=self._position.value,
+                trade_duration=self.get_trade_duration(),
+                current_profit_pct=self.get_unrealized_profit()
+            )
+    
+            observation = self._get_observation()
+            # user can play with time if they want
+            truncated = False
+    
+            self._update_history(info)
+    
+            return observation, step_reward, self._done, truncated, info
+    
+        def _map_continuous_to_discrete(self, action):
+            """
+            Map the continuous action (a value between -1 and 1) to one of the discrete actions.
+            """
+            action_value = action[0]  # Extract the single continuous action value
+            
+            # Define the number of discrete actions
+            num_discrete_actions = 5
+            
+            # Calculate the step size for each interval
+            step_size = 2 / num_discrete_actions  # (2 because range is from -1 to 1)
+            
+            # Generate the boundaries dynamically
+            boundaries = th.linspace(-1 + step_size, 1 - step_size, steps=num_discrete_actions - 1)
+            
+            # Find the bucket index for the action value
+            bucket_index = th.bucketize(th.tensor(action_value), boundaries, right=True)
+            
+            # Map the bucket index to discrete actions
+            discrete_actions = [
+                BaseActions.Neutral,
+                BaseActions.Long_enter,
+                BaseActions.Long_exit,
+                BaseActions.Short_enter,
+                BaseActions.Short_exit
+            ]
+            
+            return discrete_actions[bucket_index].value
+
+        def get_rollout_history(self) -> DataFrame:
+            """
+            Get environment data from the first to the last trade
+            """
+            _history_df = pd.DataFrame.from_dict(self.history)
+            _trade_history_df = pd.DataFrame.from_dict(self.trade_history)
+            _rollout_history = _history_df.merge(_trade_history_df, left_on="tick", right_on="index", how="left")
+            
+            _price_history = self.prices.iloc[_rollout_history.tick].copy().reset_index()
+
+            history = pd.merge(
+                _rollout_history,
+                _price_history,
+                left_index=True, right_index=True
+            )
+            return history
+
+        def get_rollout_plot(self):
+            """
+            Plot trades and environment data
+            """
+            def transform_y_offset(ax, offset):
+                return mtransforms.offset_copy(ax.transData, fig=fig, x=0, y=offset, units="inches")
+
+            def plot_markers(ax, ticks, marker, color, size, offset):
+                ax.plot(ticks, marker=marker, color=color, markersize=size, fillstyle="full",
+                        transform=transform_y_offset(ax, offset), linestyle="none")
+
+            plt.style.use("dark_background")
+            fig, axs = plt.subplots(
+                nrows=5, ncols=1,
+                figsize=(16, 9),
+                height_ratios=[6, 1, 1, 1, 1],
+                sharex=True
+            )
+
+            # Return empty fig if no trades
+            if len(self.trade_history) == 0:
+                return fig
+
+            history = self.get_rollout_history()
+            enter_long_prices = history.loc[history["type"] == "enter_long"]["price"]
+            enter_short_prices = history.loc[history["type"] == "enter_short"]["price"]
+            exit_long_prices = history.loc[history["type"] == "exit_long"]["price"]
+            exit_short_prices = history.loc[history["type"] == "exit_short"]["price"]
+
+            axs[0].plot(history["open"], linewidth=1, color="#c28ce3")
+            plot_markers(axs[0], enter_long_prices, "^", "#4ae747", 5, -0.05)
+            plot_markers(axs[0], enter_short_prices, "v", "#f53580", 5, 0.05)
+            plot_markers(axs[0], exit_long_prices, "o", "#4ae747", 3, 0)
+            plot_markers(axs[0], exit_short_prices, "o", "#f53580", 3, 0)
+
+            axs[1].set_ylabel("pnl")
+            axs[1].plot(history["current_profit_pct"], linewidth=1, color="#a29db9")
+            axs[1].axhline(y=0, label='0', alpha=0.33)
+            axs[2].set_ylabel("duration")
+            axs[2].plot(history["trade_duration"], linewidth=1, color="#a29db9")
+            axs[3].set_ylabel("total_reward")
+            axs[3].plot(history["total_reward"], linewidth=1, color="#a29db9")
+            axs[3].axhline(y=0, label='0', alpha=0.33)
+            axs[4].set_ylabel("total_profit")
+            axs[4].set_xlabel("tick")
+            axs[4].plot(history["total_profit"], linewidth=1, color="#a29db9")
+            axs[4].axhline(y=1, label='1', alpha=0.33)
+
+            for _ax in axs:
+                for _border in ["top", "right", "bottom", "left"]:
+                    _ax.spines[_border].set_color("#5b5e4b")
+
+            fig.suptitle(
+                "Total Reward: %.6f" % self.total_reward + " ~ " +
+                "Total Profit: %.6f" % self._total_profit
+            )
+            fig.tight_layout()
+
+            return fig
+
+        def close(self) -> None:
+            gc.collect()
+            th.cuda.empty_cache()
+
+def linear_schedule(initial_value: float) -> Callable[[float], float]:
+    def func(progress_remaining: float) -> float:
+        return progress_remaining * initial_value
+    return func
+
+class CustomTensorboardCallback(TensorboardCallback):
+    """
+    Tensorboard callback
+    """
+
+    def _on_training_start(self) -> None:
+        _lr = self.model.learning_rate
+        
+        if self.model.__class__.__name__ == "DDPG":
+            hparam_dict = {
+                "algorithm": self.model.__class__.__name__,
+                "buffer_size": self.model.buffer_size,
+                "learning_rate": _lr if isinstance(_lr, float) else "lr_schedule",
+                "learning_starts": self.model.learning_starts,
+                "batch_size": self.model.batch_size,
+                "tau": self.model.tau,
+                "gamma": self.model.gamma,
+                "train_freq": self.model.train_freq,
+                "gradient_steps": self.model.gradient_steps,
+            }
+
+        elif self.model.__class__.__name__ == "TD3":
+            hparam_dict = {
+                "algorithm": self.model.__class__.__name__,
+                "learning_rate": _lr if isinstance(_lr, float) else "lr_schedule",
+                "buffer_size": self.model.buffer_size,
+                "learning_starts": self.model.learning_starts,
+                "batch_size": self.model.batch_size,
+                "tau": self.model.tau,
+                "gamma": self.model.gamma,
+                "train_freq": self.model.train_freq,
+                "gradient_steps": self.model.gradient_steps,
+                "policy_delay": self.model.policy_delay,
+                "target_policy_noise": self.model.target_policy_noise,
+                "target_noise_clip": self.model.target_noise_clip,
+            }
+            
+        else:
+            hparam_dict = {
+                "algorithm": self.model.__class__.__name__,
+                "learning_rate": _lr if isinstance(_lr, float) else "lr_schedule",
+                "gamma": self.model.gamma,
+                "gae_lambda": self.model.gae_lambda,
+                "n_steps": self.model.n_steps,
+                "batch_size": self.model.batch_size,
+            }
+
+        # Convert hparam_dict values to str if they are not of type int, float, str, bool, or torch.Tensor
+        hparam_dict = {k: (str(v) if not isinstance(v, (int, float, str, bool, th.Tensor)) else v) for k, v in hparam_dict.items()}
+        
+        metric_dict = {
+            "eval/mean_reward": 0,
+            "rollout/ep_rew_mean": 0,
+            "rollout/ep_len_mean": 0,
+            "info/total_profit": 1,
+            "info/trades_count": 0,
+            "info/trade_duration": 0,
+        }
+
+        self.logger.record(
+            "hparams",
+            HParam(hparam_dict, metric_dict),
+            exclude=("stdout", "log", "json", "csv"),
+        )   
+    
+    def _on_step(self) -> bool:
+
+        local_info = self.locals["infos"][0]
+        if self.training_env is None:
+            return True
+
+        tensorboard_metrics = self.training_env.env_method("get_wrapper_attr", "tensorboard_metrics")[0]
+
+        for metric in local_info:
+            if metric not in ["episode", "terminal_observation", "TimeLimit.truncated"]:
+                self.logger.record(f"info/{metric}", local_info[metric])
+
+        for category in tensorboard_metrics:
+            for metric in tensorboard_metrics[category]:
+                self.logger.record(f"{category}/{metric}", tensorboard_metrics[category][metric])
+
+        return True
+    
+class FigureRecorderCallback(BaseCallback):
+    """
+    Tensorboard figures callback
+    """
+
+    def __init__(self, verbose=0):
+        super().__init__(verbose)
+
+    def _on_step(self) -> bool:
+        return True
+
+    def _on_rollout_end(self):
+        try:
+            # Access the rollout plot directly from the base environment
+            figures = [env.unwrapped.get_rollout_plot() for env in self.training_env.envs]
+        except AttributeError:
+            # If the above fails, try getting it from the wrappers
+            figures = self.training_env.env_method("get_wrapper_attr", "get_rollout_plot")
+
+        for i, fig in enumerate(figures):
+            self.logger.record(
+                f"rollout/env_{i}",
+                Figure(fig, close=True),
+                exclude=("stdout", "log", "json", "csv")
+            )
+            plt.close(fig)
+        return True

freqtrade/plugins/pairlist/StaticPairList.py

@@ -61,14 +61,15 @@ class StaticPairList(IPairList):
         :param tickers: Tickers (from exchange.get_tickers). May be cached.
         :return: List of pairs
         """
+        wl = self.verify_whitelist(
+            self._config["exchange"]["pair_whitelist"], logger.info, keep_invalid=True
+        )
         if self._allow_inactive:
-            return self.verify_whitelist(
-                self._config["exchange"]["pair_whitelist"], logger.info, keep_invalid=True
-            )
+            return wl
         else:
-            return self._whitelist_for_active_markets(
-                self.verify_whitelist(self._config["exchange"]["pair_whitelist"], logger.info)
-            )
+            # Avoid implicit filtering of "verify_whitelist" to keep
+            # proper warnings in the log
+            return self._whitelist_for_active_markets(wl)
 
     def filter_pairlist(self, pairlist: List[str], tickers: Tickers) -> List[str]:
         """

freqtrade/plugins/pairlist/pairlist_helpers.py

@@ -28,6 +28,7 @@ def expand_pairlist(
             except re.error as err:
                 raise ValueError(f"Wildcard error in {pair_wc}, {err}")
 
+        # Remove wildcard pairs that didn't have a match.
         result = [element for element in result if re.fullmatch(r"^[A-Za-z0-9:/-]+$", element)]
 
     else:

tests/exchange/test_exchange.py

@@ -255,7 +255,6 @@ def test_init_exception(default_conf, mocker):
 def test_exchange_resolver(default_conf, mocker, caplog):
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=MagicMock()))
     mocker.patch(f"{EXMS}._load_async_markets")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_timeframes")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
     mocker.patch(f"{EXMS}.validate_pricing")
@@ -555,7 +554,6 @@ def test_get_min_pair_stake_amount_real_data(mocker, default_conf) -> None:
 
 def test__load_async_markets(default_conf, mocker, caplog):
     mocker.patch(f"{EXMS}._init_ccxt")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_timeframes")
     mocker.patch(f"{EXMS}.reload_markets")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
@@ -584,7 +582,6 @@ def test__load_markets(default_conf, mocker, caplog):
     api_mock = MagicMock()
     api_mock.load_markets = get_mock_coro(side_effect=ccxt.BaseError("SomeError"))
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_timeframes")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
     mocker.patch(f"{EXMS}.validate_pricing")
@@ -684,7 +681,6 @@ def test_validate_stakecurrency(default_conf, stake_currency, mocker, caplog):
         }
     )
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_timeframes")
     mocker.patch(f"{EXMS}.validate_pricing")
     Exchange(default_conf)
@@ -702,7 +698,6 @@ def test_validate_stakecurrency_error(default_conf, mocker, caplog):
         }
     )
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_timeframes")
     with pytest.raises(
         ConfigurationError,
@@ -755,147 +750,6 @@ def test_get_pair_base_currency(default_conf, mocker, pair, expected):
     assert ex.get_pair_base_currency(pair) == expected
 
 
-def test_validate_pairs(default_conf, mocker):
-    api_mock = MagicMock()
-    id_mock = PropertyMock(return_value="test_exchange")
-    type(api_mock).id = id_mock
-
-    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
-    mocker.patch(f"{EXMS}.validate_timeframes")
-    mocker.patch(
-        f"{EXMS}._load_async_markets",
-        return_value={
-            "ETH/BTC": {"quote": "BTC"},
-            "LTC/BTC": {"quote": "BTC"},
-            "XRP/BTC": {"quote": "BTC"},
-            "NEO/BTC": {"quote": "BTC"},
-        },
-    )
-    mocker.patch(f"{EXMS}.validate_stakecurrency")
-    mocker.patch(f"{EXMS}.validate_pricing")
-    # test exchange.validate_pairs directly
-    # No assert - but this should not fail (!)
-    Exchange(default_conf)
-
-
-def test_validate_pairs_not_available(default_conf, mocker):
-    api_mock = MagicMock()
-    type(api_mock).markets = PropertyMock(
-        return_value={"XRP/BTC": {"inactive": True, "base": "XRP", "quote": "BTC"}}
-    )
-    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
-    mocker.patch(f"{EXMS}.validate_timeframes")
-    mocker.patch(f"{EXMS}.validate_stakecurrency")
-    mocker.patch(f"{EXMS}._load_async_markets")
-
-    with pytest.raises(OperationalException, match=r"not available"):
-        Exchange(default_conf)
-
-
-def test_validate_pairs_exception(default_conf, mocker, caplog):
-    caplog.set_level(logging.INFO)
-    api_mock = MagicMock()
-    mocker.patch(f"{EXMS}.name", PropertyMock(return_value="Binance"))
-
-    type(api_mock).markets = PropertyMock(return_value={})
-    mocker.patch(f"{EXMS}._init_ccxt", api_mock)
-    mocker.patch(f"{EXMS}.validate_timeframes")
-    mocker.patch(f"{EXMS}.validate_stakecurrency")
-    mocker.patch(f"{EXMS}.validate_pricing")
-    mocker.patch(f"{EXMS}._load_async_markets")
-
-    with pytest.raises(OperationalException, match=r"Pair ETH/BTC is not available on Binance"):
-        Exchange(default_conf)
-
-    mocker.patch(f"{EXMS}.markets", PropertyMock(return_value={}))
-    Exchange(default_conf)
-    assert log_has("Unable to validate pairs (assuming they are correct).", caplog)
-
-
-def test_validate_pairs_restricted(default_conf, mocker, caplog):
-    api_mock = MagicMock()
-    type(api_mock).load_markets = get_mock_coro(
-        return_value={
-            "ETH/BTC": {"quote": "BTC"},
-            "LTC/BTC": {"quote": "BTC"},
-            "XRP/BTC": {"quote": "BTC", "info": {"prohibitedIn": ["US"]}},
-            "NEO/BTC": {"quote": "BTC", "info": "TestString"},  # info can also be a string ...
-        }
-    )
-    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
-    mocker.patch(f"{EXMS}.validate_timeframes")
-    mocker.patch(f"{EXMS}.validate_pricing")
-    mocker.patch(f"{EXMS}.validate_stakecurrency")
-
-    Exchange(default_conf)
-    assert log_has(
-        "Pair XRP/BTC is restricted for some users on this exchange."
-        "Please check if you are impacted by this restriction "
-        "on the exchange and eventually remove XRP/BTC from your whitelist.",
-        caplog,
-    )
-
-
-def test_validate_pairs_stakecompatibility(default_conf, mocker):
-    api_mock = MagicMock()
-    type(api_mock).load_markets = get_mock_coro(
-        return_value={
-            "ETH/BTC": {"quote": "BTC"},
-            "LTC/BTC": {"quote": "BTC"},
-            "XRP/BTC": {"quote": "BTC"},
-            "NEO/BTC": {"quote": "BTC"},
-            "HELLO-WORLD": {"quote": "BTC"},
-        }
-    )
-    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
-    mocker.patch(f"{EXMS}.validate_timeframes")
-    mocker.patch(f"{EXMS}.validate_stakecurrency")
-    mocker.patch(f"{EXMS}.validate_pricing")
-
-    Exchange(default_conf)
-
-
-def test_validate_pairs_stakecompatibility_downloaddata(default_conf, mocker):
-    api_mock = MagicMock()
-    default_conf["stake_currency"] = ""
-    type(api_mock).load_markets = get_mock_coro(
-        return_value={
-            "ETH/BTC": {"quote": "BTC"},
-            "LTC/BTC": {"quote": "BTC"},
-            "XRP/BTC": {"quote": "BTC"},
-            "NEO/BTC": {"quote": "BTC"},
-            "HELLO-WORLD": {"quote": "BTC"},
-        }
-    )
-    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
-    mocker.patch(f"{EXMS}.validate_timeframes")
-    mocker.patch(f"{EXMS}.validate_stakecurrency")
-    mocker.patch(f"{EXMS}.validate_pricing")
-
-    Exchange(default_conf)
-    assert type(api_mock).load_markets.call_count == 1
-
-
-def test_validate_pairs_stakecompatibility_fail(default_conf, mocker):
-    default_conf["exchange"]["pair_whitelist"].append("HELLO-WORLD")
-    api_mock = MagicMock()
-    type(api_mock).load_markets = get_mock_coro(
-        return_value={
-            "ETH/BTC": {"quote": "BTC"},
-            "LTC/BTC": {"quote": "BTC"},
-            "XRP/BTC": {"quote": "BTC"},
-            "NEO/BTC": {"quote": "BTC"},
-            "HELLO-WORLD": {"quote": "USDT"},
-        }
-    )
-    mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
-    mocker.patch(f"{EXMS}.validate_timeframes")
-    mocker.patch(f"{EXMS}.validate_stakecurrency")
-
-    with pytest.raises(OperationalException, match=r"Stake-currency 'BTC' not compatible with.*"):
-        Exchange(default_conf)
-
-
 @pytest.mark.parametrize("timeframe", [("5m"), ("1m"), ("15m"), ("1h")])
 def test_validate_timeframes(default_conf, mocker, timeframe):
     default_conf["timeframe"] = timeframe
@@ -907,7 +761,6 @@ def test_validate_timeframes(default_conf, mocker, timeframe):
 
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
     mocker.patch(f"{EXMS}.reload_markets")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
     mocker.patch(f"{EXMS}.validate_pricing")
     Exchange(default_conf)
@@ -925,7 +778,6 @@ def test_validate_timeframes_failed(default_conf, mocker):
 
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
     mocker.patch(f"{EXMS}.reload_markets")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
     mocker.patch(f"{EXMS}.validate_pricing")
     with pytest.raises(
@@ -955,7 +807,6 @@ def test_validate_timeframes_emulated_ohlcv_1(default_conf, mocker):
 
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
     mocker.patch(f"{EXMS}.reload_markets")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
     with pytest.raises(
         OperationalException,
@@ -977,7 +828,6 @@ def test_validate_timeframes_emulated_ohlcvi_2(default_conf, mocker):
 
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
     mocker.patch(f"{EXMS}.reload_markets")
-    mocker.patch(f"{EXMS}.validate_pairs", MagicMock())
     mocker.patch(f"{EXMS}.validate_stakecurrency")
     with pytest.raises(
         OperationalException,
@@ -999,7 +849,6 @@ def test_validate_timeframes_not_in_config(default_conf, mocker):
 
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
     mocker.patch(f"{EXMS}.reload_markets")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
     mocker.patch(f"{EXMS}.validate_pricing")
     mocker.patch(f"{EXMS}.validate_required_startup_candles")
@@ -1016,7 +865,6 @@ def test_validate_pricing(default_conf, mocker):
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
     mocker.patch(f"{EXMS}.reload_markets")
     mocker.patch(f"{EXMS}.validate_trading_mode_and_margin_mode")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_timeframes")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
     mocker.patch(f"{EXMS}.name", "Binance")
@@ -1051,7 +899,6 @@ def test_validate_ordertypes(default_conf, mocker):
     type(api_mock).has = PropertyMock(return_value={"createMarketOrder": True})
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
     mocker.patch(f"{EXMS}.reload_markets")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_timeframes")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
     mocker.patch(f"{EXMS}.validate_pricing")
@@ -1110,7 +957,6 @@ def test_validate_ordertypes_stop_advanced(default_conf, mocker, exchange_name,
     type(api_mock).has = PropertyMock(return_value={"createMarketOrder": True})
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
     mocker.patch(f"{EXMS}.reload_markets")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_timeframes")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
     mocker.patch(f"{EXMS}.validate_pricing")
@@ -1135,7 +981,6 @@ def test_validate_order_types_not_in_config(default_conf, mocker):
     api_mock = MagicMock()
     mocker.patch(f"{EXMS}._init_ccxt", MagicMock(return_value=api_mock))
     mocker.patch(f"{EXMS}.reload_markets")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_timeframes")
     mocker.patch(f"{EXMS}.validate_pricing")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
@@ -1151,7 +996,6 @@ def test_validate_required_startup_candles(default_conf, mocker, caplog):
     mocker.patch(f"{EXMS}._init_ccxt", api_mock)
     mocker.patch(f"{EXMS}.validate_timeframes")
     mocker.patch(f"{EXMS}._load_async_markets")
-    mocker.patch(f"{EXMS}.validate_pairs")
     mocker.patch(f"{EXMS}.validate_pricing")
     mocker.patch(f"{EXMS}.validate_stakecurrency")
 
@@ -4185,7 +4029,6 @@ def test_merge_ft_has_dict(default_conf, mocker):
         EXMS,
         _init_ccxt=MagicMock(return_value=MagicMock()),
         _load_async_markets=MagicMock(),
-        validate_pairs=MagicMock(),
         validate_timeframes=MagicMock(),
         validate_stakecurrency=MagicMock(),
         validate_pricing=MagicMock(),
@@ -4220,7 +4063,6 @@ def test_get_valid_pair_combination(default_conf, mocker, markets):
         EXMS,
         _init_ccxt=MagicMock(return_value=MagicMock()),
         _load_async_markets=MagicMock(),
-        validate_pairs=MagicMock(),
         validate_timeframes=MagicMock(),
         validate_pricing=MagicMock(),
         markets=PropertyMock(return_value=markets),
@@ -4500,7 +4342,6 @@ def test_get_markets(
         EXMS,
         _init_ccxt=MagicMock(return_value=MagicMock()),
         _load_async_markets=MagicMock(),
-        validate_pairs=MagicMock(),
         validate_timeframes=MagicMock(),
         validate_pricing=MagicMock(),
         markets=PropertyMock(return_value=markets_static),

tests/freqtradebot/test_freqtradebot.py

@@ -2204,7 +2204,6 @@ def test_manage_open_orders_buy_exception(
     patch_exchange(mocker)
     mocker.patch.multiple(
         EXMS,
-        validate_pairs=MagicMock(),
         fetch_ticker=ticker_usdt,
         fetch_order=MagicMock(side_effect=ExchangeError),
         cancel_order=cancel_order_mock,