Merge branch 'freqtrade:develop' into develop

docker-compose.yml

@@ -6,6 +6,15 @@ services:
     # image: freqtradeorg/freqtrade:develop
     # Use plotting image
     # image: freqtradeorg/freqtrade:develop_plot
+    # # Enable GPU Image and GPU Resources (only relevant for freqAI)
+    # # Make sure to uncomment the whole deploy section
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: 1
+    #           capabilities: [gpu]
     # Build step - only needed when additional dependencies are needed
     # build:
     #   context: .
@@ -16,7 +25,7 @@ services:
       - "./user_data:/freqtrade/user_data"
     # Expose api on port 8080 (localhost only)
     # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
-    # before enabling this.
+    # for more information.
     ports:
       - "127.0.0.1:8080:8080"
     # Default command used when running `docker compose up`

docker/docker-compose-freqai.yml

@@ -0,0 +1,36 @@
+---
+version: '3'
+services:
+  freqtrade:
+    image: freqtradeorg/freqtrade:stable_freqaitorch
+    # # Enable GPU Image and GPU Resources
+    # # Make sure to uncomment the whole deploy section
+    # deploy:
+    #   resources:
+    #     reservations:
+    #       devices:
+    #         - driver: nvidia
+    #           count: 1
+    #           capabilities: [gpu]
+
+    # Build step - only needed when additional dependencies are needed
+    # build:
+    #   context: .
+    #   dockerfile: "./docker/Dockerfile.custom"
+    restart: unless-stopped
+    container_name: freqtrade
+    volumes:
+      - "./user_data:/freqtrade/user_data"
+    # Expose api on port 8080 (localhost only)
+    # Please read the https://www.freqtrade.io/en/stable/rest-api/ documentation
+    # for more information.
+    ports:
+      - "127.0.0.1:8080:8080"
+    # Default command used when running `docker compose up`
+    command: >
+      trade
+      --logfile /freqtrade/user_data/logs/freqtrade.log
+      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
+      --config /freqtrade/user_data/config.json
+      --freqai-model XGBoostClassifier
+      --strategy SampleStrategy

docs/freqai-configuration.md

@@ -248,9 +248,11 @@ The easiest way to quickly run a pytorch model is with the following command (fo
 freqtrade trade --config config_examples/config_freqai.example.json --strategy FreqaiExampleStrategy --freqaimodel PyTorchMLPRegressor --strategy-path freqtrade/templates 
 ```
 
-!!! note "Installation/docker"
+!!! Note "Installation/docker"
     The PyTorch module requires large packages such as `torch`, which should be explicitly requested during `./setup.sh -i` by answering "y" to the question "Do you also want dependencies for freqai-rl or PyTorch (~700mb additional space required) [y/N]?".
     Users who prefer docker should ensure they use the docker image appended with `_freqaitorch`.
+    We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file.
+    This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
 
 ### Structure
 

docs/freqai-reinforcement-learning.md

@@ -145,94 +145,94 @@ As you begin to modify the strategy and the prediction model, you will quickly r
     The best reward functions are ones that are continuously differentiable, and well scaled. In other words, adding a single large negative penalty to a rare event is not a good idea, and the neural net will not be able to learn that function. Instead, it is better to add a small negative penalty to a common event. This will help the agent learn faster. Not only this, but you can help improve the continuity of your rewards/penalties by having them scale with severity according to some linear/exponential functions. In other words, you'd slowly scale the penalty as the duration of the trade increases. This is better than a single large penalty occuring at a single point in time.
 
 ```python
-    from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
-    from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
+from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
+from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv, Positions
 
 
-    class MyCoolRLModel(ReinforcementLearner):
+class MyCoolRLModel(ReinforcementLearner):
+    """
+    User created RL prediction model.
+
+    Save this file to `freqtrade/user_data/freqaimodels`
+
+    then use 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.
+    """
+    class MyRLEnv(Base5ActionRLEnv):
         """
-        User created RL prediction model.
+        User made custom environment. This class inherits from BaseEnvironment and gym.env.
+        Users can override any functions from those parent classes. Here is an example
+        of a user customized `calculate_reward()` function.
 
-        Save this file to `freqtrade/user_data/freqaimodels`
-
-        then use 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.
+        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.
         """
-        class MyRLEnv(Base5ActionRLEnv):
-            """
-            User made custom environment. This class inherits from BaseEnvironment and gym.env.
-            Users can override any functions from those parent classes. Here is an example
-            of a user customized `calculate_reward()` function.
+        def calculate_reward(self, action: int) -> float:
+            # first, penalize if the action is not valid
+            if not self._is_valid(action):
+                return -2
+            pnl = self.get_unrealized_profit()
 
-            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.
-            """
-            def calculate_reward(self, action: int) -> float:
-                # first, penalize if the action is not valid
-                if not self._is_valid(action):
-                    return -2
-                pnl = self.get_unrealized_profit()
+            factor = 100
 
-                factor = 100
+            pair = self.pair.replace(':', '')
 
-                pair = self.pair.replace(':', '')
+            # you can use feature values from dataframe
+            # Assumes the shifted RSI indicator has been generated in the strategy.
+            rsi_now = self.raw_features[f"%-rsi-period_10_shift-1_{pair}_"
+                            f"{self.config['timeframe']}"].iloc[self._current_tick]
 
-                # you can use feature values from dataframe
-                # Assumes the shifted RSI indicator has been generated in the strategy.
-                rsi_now = self.raw_features[f"%-rsi-period_10_shift-1_{pair}_"
-                                f"{self.config['timeframe']}"].iloc[self._current_tick]
+            # reward agent for entering trades
+            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+                    and self._position == Positions.Neutral):
+                if rsi_now < 40:
+                    factor = 40 / rsi_now
+                else:
+                    factor = 1
+                return 25 * factor
 
-                # reward agent for entering trades
-                if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
-                        and self._position == Positions.Neutral):
-                    if rsi_now < 40:
-                        factor = 40 / rsi_now
-                    else:
-                        factor = 1
-                    return 25 * factor
-
-                # 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
-                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.
+            # 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
+            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.
 ```
 
-### Using Tensorboard
+## Using Tensorboard
 
 Reinforcement Learning models benefit from tracking training metrics. FreqAI has integrated Tensorboard to allow users to track training and evaluation performance across all coins and across all retrainings. Tensorboard is activated via the following command:
 
@@ -245,32 +245,30 @@ where `unique-id` is the `identifier` set in the `freqai` configuration file. Th
 
 ![tensorboard](assets/tensorboard.jpg)
 
-
-### Custom logging
+## Custom logging
 
 FreqAI also provides a built in episodic summary logger called `self.tensorboard_log` for adding custom information to the Tensorboard log. By default, this function is already called once per step inside the environment to record the agent actions. All values accumulated for all steps in a single episode are reported at the conclusion of each episode, followed by a full reset of all metrics to 0 in preparation for the subsequent episode.
 
-
 `self.tensorboard_log` can also be used anywhere inside the environment, for example, it can be added to the `calculate_reward` function to collect more detailed information about how often various parts of the reward were called:
 
-```py
-        class MyRLEnv(Base5ActionRLEnv):
-            """
-            User made custom environment. This class inherits from BaseEnvironment and gym.env.
-            Users can override any functions from those parent classes. Here is an example
-            of a user customized `calculate_reward()` function.
-            """
-            def calculate_reward(self, action: int) -> float:
-                if not self._is_valid(action):
-                    self.tensorboard_log("invalid")
-                    return -2
+```python
+    class MyRLEnv(Base5ActionRLEnv):
+        """
+        User made custom environment. This class inherits from BaseEnvironment and gym.env.
+        Users can override any functions from those parent classes. Here is an example
+        of a user customized `calculate_reward()` function.
+        """
+        def calculate_reward(self, action: int) -> float:
+            if not self._is_valid(action):
+                self.tensorboard_log("invalid")
+                return -2
 
 ```
 
 !!! Note
     The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)`. In this case the metric values are not incremented.
 
-### Choosing a base environment
+## Choosing a base environment
 
 FreqAI provides three base environments, `Base3ActionRLEnvironment`, `Base4ActionEnvironment` and `Base5ActionEnvironment`. As the names imply, the environments are customized for agents that can select from 3, 4 or 5 actions. The `Base3ActionEnvironment` is the simplest, the agent can select from hold, long, or short. This environment can also be used for long-only bots (it automatically follows the `can_short` flag from the strategy), where long is the enter condition and short is the exit condition. Meanwhile, in the `Base4ActionEnvironment`, the agent can enter long, enter short, hold neutral, or exit position. Finally, in the `Base5ActionEnvironment`, the agent has the same actions as Base4, but instead of a single exit action, it separates exit long and exit short. The main changes stemming from the environment selection include:
 

docs/freqai.md

@@ -78,6 +78,9 @@ pip install -r requirements-freqai.txt
 
 If you are using docker, a dedicated tag with FreqAI dependencies is available as `:freqai`. As such - you can replace the image line in your docker compose file with `image: freqtradeorg/freqtrade:develop_freqai`. This image contains the regular FreqAI dependencies. Similar to native installs, Catboost will not be available on ARM based devices.
 
+!!! note "docker-compose-freqai.yml"
+   We do provide an explicit docker-compose file for this in `docker/docker-compose-freqai.yml` - which can be used via `docker compose -f docker/docker-compose-freqai.yml run ...` - or can be copied to replace the original docker file. This docker-compose file also contains a (disabled) section to enable GPU resources within docker containers. This obviously assumes the system has GPU resources available.
+
 ### FreqAI position in open-source machine learning landscape
 
 Forecasting chaotic time-series based systems, such as equity/cryptocurrency markets, requires a broad set of tools geared toward testing a wide range of hypotheses. Fortunately, a recent maturation of robust machine learning libraries (e.g. `scikit-learn`) has opened up a wide range of research possibilities. Scientists from a diverse range of fields can now easily prototype their studies on an abundance of established machine learning algorithms. Similarly, these user-friendly libraries enable "citzen scientists" to use their basic Python skills for data exploration. However, leveraging these machine learning libraries on historical and live chaotic data sources can be logistically difficult and expensive. Additionally, robust data collection, storage, and handling presents a disparate challenge. [`FreqAI`](#freqai) aims to provide a generalized and extensible open-sourced framework geared toward live deployments of adaptive modeling for market forecasting. The `FreqAI` framework is effectively a sandbox for the rich world of open-source machine learning libraries. Inside the `FreqAI` sandbox, users find they can combine a wide variety of third-party libraries to test creative hypotheses on a free live 24/7 chaotic data source - cryptocurrency exchange data. 

freqtrade/constants.py

@@ -693,4 +693,6 @@ BidAsk = Literal['bid', 'ask']
 OBLiteral = Literal['asks', 'bids']
 
 Config = Dict[str, Any]
+# Exchange part of the configuration.
+ExchangeConfig = Dict[str, Any]
 IntOrInf = float

freqtrade/exchange/__init__.py

@@ -1,6 +1,6 @@
 # flake8: noqa: F401
 # isort: off
-from freqtrade.exchange.common import remove_credentials, MAP_EXCHANGE_CHILDCLASS
+from freqtrade.exchange.common import remove_exchange_credentials, MAP_EXCHANGE_CHILDCLASS
 from freqtrade.exchange.exchange import Exchange
 # isort: on
 from freqtrade.exchange.binance import Binance

freqtrade/exchange/common.py

@@ -4,7 +4,7 @@ import time
 from functools import wraps
 from typing import Any, Callable, Optional, TypeVar, cast, overload
 
-from freqtrade.constants import Config
+from freqtrade.constants import ExchangeConfig
 from freqtrade.exceptions import DDosProtection, RetryableOrderError, TemporaryError
 from freqtrade.mixins import LoggingMixin
 
@@ -89,18 +89,18 @@ EXCHANGE_HAS_OPTIONAL = [
 ]
 
 
-def remove_credentials(config: Config) -> None:
+def remove_exchange_credentials(exchange_config: ExchangeConfig, dry_run: bool) -> None:
     """
     Removes exchange keys from the configuration and specifies dry-run
     Used for backtesting / hyperopt / edge and utils.
     Modifies the input dict!
     """
-    if config.get('dry_run', False):
-        config['exchange']['key'] = ''
-        config['exchange']['apiKey'] = ''
-        config['exchange']['secret'] = ''
-        config['exchange']['password'] = ''
-        config['exchange']['uid'] = ''
+    if dry_run:
+        exchange_config['key'] = ''
+        exchange_config['apiKey'] = ''
+        exchange_config['secret'] = ''
+        exchange_config['password'] = ''
+        exchange_config['uid'] = ''
 
 
 def calculate_backoff(retrycount, max_retries):

freqtrade/exchange/exchange.py

@@ -20,16 +20,16 @@ from dateutil import parser
 from pandas import DataFrame, concat
 
 from freqtrade.constants import (DEFAULT_AMOUNT_RESERVE_PERCENT, NON_OPEN_EXCHANGE_STATES, BidAsk,
-                                 BuySell, Config, EntryExit, ListPairsWithTimeframes, MakerTaker,
-                                 OBLiteral, PairWithTimeframe)
+                                 BuySell, Config, EntryExit, ExchangeConfig,
+                                 ListPairsWithTimeframes, MakerTaker, OBLiteral, PairWithTimeframe)
 from freqtrade.data.converter import clean_ohlcv_dataframe, ohlcv_to_dataframe, trades_dict_to_list
 from freqtrade.enums import OPTIMIZE_MODES, CandleType, MarginMode, TradingMode
 from freqtrade.enums.pricetype import PriceType
 from freqtrade.exceptions import (DDosProtection, ExchangeError, InsufficientFundsError,
                                   InvalidOrderException, OperationalException, PricingError,
                                   RetryableOrderError, TemporaryError)
-from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, remove_credentials, retrier,
-                                       retrier_async)
+from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, remove_exchange_credentials,
+                                       retrier, retrier_async)
 from freqtrade.exchange.exchange_utils import (ROUND, ROUND_DOWN, ROUND_UP, CcxtModuleType,
                                                amount_to_contract_precision, amount_to_contracts,
                                                amount_to_precision, contracts_to_amount,
@@ -92,8 +92,8 @@ class Exchange:
         # TradingMode.SPOT always supported and not required in this list
     ]
 
-    def __init__(self, config: Config, *, validate: bool = True,
-                 load_leverage_tiers: bool = False) -> None:
+    def __init__(self, config: Config, *, exchange_config: Optional[ExchangeConfig] = None,
+                 validate: bool = True, load_leverage_tiers: bool = False) -> None:
         """
         Initializes this module with the given config,
         it does basic validation whether the specified exchange and pairs are valid.
@@ -131,13 +131,13 @@ class Exchange:
 
         # Holds all open sell orders for dry_run
         self._dry_run_open_orders: Dict[str, Any] = {}
-        remove_credentials(config)
 
         if config['dry_run']:
             logger.info('Instance is running with dry_run enabled')
         logger.info(f"Using CCXT {ccxt.__version__}")
-        exchange_config = config['exchange']
-        self.log_responses = exchange_config.get('log_responses', False)
+        exchange_conf: Dict[str, Any] = exchange_config if exchange_config else config['exchange']
+        remove_exchange_credentials(exchange_conf, config.get('dry_run', False))
+        self.log_responses = exchange_conf.get('log_responses', False)
 
         # Leverage properties
         self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
@@ -152,8 +152,8 @@ class Exchange:
         self._ft_has = deep_merge_dicts(self._ft_has, deepcopy(self._ft_has_default))
         if self.trading_mode == TradingMode.FUTURES:
             self._ft_has = deep_merge_dicts(self._ft_has_futures, self._ft_has)
-        if exchange_config.get('_ft_has_params'):
-            self._ft_has = deep_merge_dicts(exchange_config.get('_ft_has_params'),
+        if exchange_conf.get('_ft_has_params'):
+            self._ft_has = deep_merge_dicts(exchange_conf.get('_ft_has_params'),
                                             self._ft_has)
             logger.info("Overriding exchange._ft_has with config params, result: %s", self._ft_has)
 
@@ -165,18 +165,18 @@ class Exchange:
 
         # Initialize ccxt objects
         ccxt_config = self._ccxt_config
-        ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}), ccxt_config)
-        ccxt_config = deep_merge_dicts(exchange_config.get('ccxt_sync_config', {}), ccxt_config)
+        ccxt_config = deep_merge_dicts(exchange_conf.get('ccxt_config', {}), ccxt_config)
+        ccxt_config = deep_merge_dicts(exchange_conf.get('ccxt_sync_config', {}), ccxt_config)
 
-        self._api = self._init_ccxt(exchange_config, ccxt_kwargs=ccxt_config)
+        self._api = self._init_ccxt(exchange_conf, ccxt_kwargs=ccxt_config)
 
         ccxt_async_config = self._ccxt_config
-        ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_config', {}),
+        ccxt_async_config = deep_merge_dicts(exchange_conf.get('ccxt_config', {}),
                                              ccxt_async_config)
-        ccxt_async_config = deep_merge_dicts(exchange_config.get('ccxt_async_config', {}),
+        ccxt_async_config = deep_merge_dicts(exchange_conf.get('ccxt_async_config', {}),
                                              ccxt_async_config)
         self._api_async = self._init_ccxt(
-            exchange_config, ccxt_async, ccxt_kwargs=ccxt_async_config)
+            exchange_conf, ccxt_async, ccxt_kwargs=ccxt_async_config)
 
         logger.info(f'Using Exchange "{self.name}"')
         self.required_candle_call_count = 1
@@ -189,7 +189,7 @@ class Exchange:
                 self._startup_candle_count, config.get('timeframe', ''))
 
         # Converts the interval provided in minutes in config to seconds
-        self.markets_refresh_interval: int = exchange_config.get(
+        self.markets_refresh_interval: int = exchange_conf.get(
             "markets_refresh_interval", 60) * 60
 
         if self.trading_mode != TradingMode.SPOT and load_leverage_tiers:

freqtrade/freqai/base_models/BasePyTorchRegressor.py

@@ -47,4 +47,5 @@ class BasePyTorchRegressor(BasePyTorchModel):
         self.model.model.eval()
         y = self.model.model(x)
         pred_df = DataFrame(y.detach().tolist(), columns=[dk.label_list[0]])
+        pred_df = dk.denormalize_labels_from_metadata(pred_df)
         return (pred_df, dk.do_predict)

freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py

@@ -119,11 +119,11 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
         x = x.unsqueeze(0)
         # create empty torch tensor
         self.model.model.eval()
-        yb = torch.empty(0)
+        yb = torch.empty(0).to(self.device)
         if x.shape[1] > 1:
             ws = self.window_size
             for i in range(0, x.shape[1] - ws):
-                xb = x[:, i:i + ws, :]
+                xb = x[:, i:i + ws, :].to(self.device)
                 y = self.model.model(xb)
                 yb = torch.cat((yb, y), dim=0)
         else:

freqtrade/freqtradebot.py

@@ -13,7 +13,7 @@ from schedule import Scheduler
 
 from freqtrade import constants
 from freqtrade.configuration import validate_config_consistency
-from freqtrade.constants import BuySell, Config, LongShort
+from freqtrade.constants import BuySell, Config, ExchangeConfig, LongShort
 from freqtrade.data.converter import order_book_to_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.edge import Edge
@@ -23,6 +23,7 @@ from freqtrade.exceptions import (DependencyException, ExchangeError, Insufficie
                                   InvalidOrderException, PricingError)
 from freqtrade.exchange import (ROUND_DOWN, ROUND_UP, timeframe_to_minutes, timeframe_to_next_date,
                                 timeframe_to_seconds)
+from freqtrade.exchange.common import remove_exchange_credentials
 from freqtrade.misc import safe_value_fallback, safe_value_fallback2
 from freqtrade.mixins import LoggingMixin
 from freqtrade.persistence import Order, PairLocks, Trade, init_db
@@ -63,6 +64,9 @@ class FreqtradeBot(LoggingMixin):
 
         # Init objects
         self.config = config
+        exchange_config: ExchangeConfig = deepcopy(config['exchange'])
+        # Remove credentials from original exchange config to avoid accidental credentail exposure
+        remove_exchange_credentials(config['exchange'], True)
 
         self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
 
@@ -70,7 +74,7 @@ class FreqtradeBot(LoggingMixin):
         validate_config_consistency(config)
 
         self.exchange = ExchangeResolver.load_exchange(
-            self.config, load_leverage_tiers=True)
+            self.config, exchange_config=exchange_config, load_leverage_tiers=True)
 
         init_db(self.config['db_url'])
 

freqtrade/resolvers/exchange_resolver.py

@@ -2,9 +2,10 @@
 This module loads custom exchanges
 """
 import logging
+from typing import Optional
 
 import freqtrade.exchange as exchanges
-from freqtrade.constants import Config
+from freqtrade.constants import Config, ExchangeConfig
 from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, Exchange
 from freqtrade.resolvers import IResolver
 
@@ -19,8 +20,8 @@ class ExchangeResolver(IResolver):
     object_type = Exchange
 
     @staticmethod
-    def load_exchange(config: Config, validate: bool = True,
-                      load_leverage_tiers: bool = False) -> Exchange:
+    def load_exchange(config: Config, *, exchange_config: Optional[ExchangeConfig] = None,
+                      validate: bool = True, load_leverage_tiers: bool = False) -> Exchange:
         """
         Load the custom class from config parameter
         :param exchange_name: name of the Exchange to load
@@ -37,13 +38,14 @@ class ExchangeResolver(IResolver):
                 kwargs={
                     'config': config,
                     'validate': validate,
+                    'exchange_config': exchange_config,
                     'load_leverage_tiers': load_leverage_tiers}
             )
         except ImportError:
             logger.info(
                 f"No {exchange_name} specific subclass found. Using the generic class instead.")
         if not exchange:
-            exchange = Exchange(config, validate=validate)
+            exchange = Exchange(config, validate=validate, exchange_config=exchange_config,)
         return exchange
 
     @staticmethod

freqtrade/rpc/api_server/api_backtest.py

@@ -11,6 +11,7 @@ from freqtrade.configuration.config_validation import validate_config_consistenc
 from freqtrade.data.btanalysis import get_backtest_resultlist, load_and_merge_backtest_result
 from freqtrade.enums import BacktestState
 from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.exchange.common import remove_exchange_credentials
 from freqtrade.misc import deep_merge_dicts
 from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestRequest,
                                                   BacktestResponse)
@@ -38,6 +39,7 @@ async def api_start_backtest(  # noqa: C901
         raise HTTPException(status_code=500, detail="base64 encoded strategies are not allowed.")
 
     btconfig = deepcopy(config)
+    remove_exchange_credentials(btconfig['exchange'], True)
     settings = dict(bt_settings)
     if settings.get('freqai', None) is not None:
         settings['freqai'] = dict(settings['freqai'])

tests/exchange/test_exchange.py

@@ -20,7 +20,7 @@ from freqtrade.exchange import (Binance, Bittrex, Exchange, Kraken, amount_to_pr
                                 timeframe_to_minutes, timeframe_to_msecs, timeframe_to_next_date,
                                 timeframe_to_prev_date, timeframe_to_seconds)
 from freqtrade.exchange.common import (API_FETCH_ORDER_RETRY_COUNT, API_RETRY_COUNT,
-                                       calculate_backoff, remove_credentials)
+                                       calculate_backoff, remove_exchange_credentials)
 from freqtrade.exchange.exchange import amount_to_contract_precision
 from freqtrade.resolvers.exchange_resolver import ExchangeResolver
 from tests.conftest import (EXMS, generate_test_data_raw, get_mock_coro, get_patched_exchange,
@@ -137,16 +137,14 @@ def test_init(default_conf, mocker, caplog):
     assert log_has('Instance is running with dry_run enabled', caplog)
 
 
-def test_remove_credentials(default_conf, caplog) -> None:
+def test_remove_exchange_credentials(default_conf) -> None:
     conf = deepcopy(default_conf)
-    conf['dry_run'] = False
-    remove_credentials(conf)
+    remove_exchange_credentials(conf['exchange'], False)
 
     assert conf['exchange']['key'] != ''
     assert conf['exchange']['secret'] != ''
 
-    conf['dry_run'] = True
-    remove_credentials(conf)
+    remove_exchange_credentials(conf['exchange'], True)
     assert conf['exchange']['key'] == ''
     assert conf['exchange']['secret'] == ''
     assert conf['exchange']['password'] == ''

tests/test_freqtradebot.py

@@ -121,7 +121,7 @@ def test_order_dict(default_conf_usdt, mocker, runmode, caplog) -> None:
 
     freqtrade = FreqtradeBot(conf)
     if runmode == RunMode.LIVE:
-        assert not log_has_re(".*stoploss_on_exchange .* dry-run", caplog)
+        assert not log_has_re(r".*stoploss_on_exchange .* dry-run", caplog)
     assert freqtrade.strategy.order_types['stoploss_on_exchange']
 
     caplog.clear()
@@ -136,7 +136,7 @@ def test_order_dict(default_conf_usdt, mocker, runmode, caplog) -> None:
     }
     freqtrade = FreqtradeBot(conf)
     assert not freqtrade.strategy.order_types['stoploss_on_exchange']
-    assert not log_has_re(".*stoploss_on_exchange .* dry-run", caplog)
+    assert not log_has_re(r".*stoploss_on_exchange .* dry-run", caplog)
 
 
 def test_get_trade_stake_amount(default_conf_usdt, mocker) -> None:
@@ -149,6 +149,34 @@ def test_get_trade_stake_amount(default_conf_usdt, mocker) -> None:
     assert result == default_conf_usdt['stake_amount']
 
 
+@pytest.mark.parametrize('runmode', [
+    RunMode.DRY_RUN,
+    RunMode.LIVE
+])
+def test_load_strategy_no_keys(default_conf_usdt, mocker, runmode, caplog) -> None:
+    patch_RPCManager(mocker)
+    patch_exchange(mocker)
+    conf = deepcopy(default_conf_usdt)
+    conf['runmode'] = runmode
+    erm = mocker.patch('freqtrade.freqtradebot.ExchangeResolver.load_exchange')
+
+    freqtrade = FreqtradeBot(conf)
+    strategy_config = freqtrade.strategy.config
+    assert id(strategy_config['exchange']) == id(conf['exchange'])
+    # Keys have been removed and are not passed to the exchange
+    assert strategy_config['exchange']['key'] == ''
+    assert strategy_config['exchange']['secret'] == ''
+
+    assert erm.call_count == 1
+    ex_conf = erm.call_args_list[0][1]['exchange_config']
+    assert id(ex_conf) != id(conf['exchange'])
+    # Keys are still present
+    assert ex_conf['key'] != ''
+    assert ex_conf['key'] == default_conf_usdt['exchange']['key']
+    assert ex_conf['secret'] != ''
+    assert ex_conf['secret'] == default_conf_usdt['exchange']['secret']
+
+
 @pytest.mark.parametrize("amend_last,wallet,max_open,lsamr,expected", [
                         (False, 120, 2, 0.5, [60, None]),
                         (True, 120, 2, 0.5, [60, 58.8]),