Merge remote-tracking branch 'origin/develop' into update-freqai-tf-handling

.github/ISSUE_TEMPLATE/bug_report.md

@@ -20,7 +20,7 @@ Please do not use bug reports to request new features.
   * Operating system: ____
   * Python Version: _____ (`python -V`)
   * CCXT version: _____ (`pip freeze | grep ccxt`)
-  * Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
+  * Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
   
 Note: All issues other than enhancement requests will be closed without further comment if the above template is deleted or not filled out.
 

.github/ISSUE_TEMPLATE/feature_request.md

@@ -18,7 +18,7 @@ Have you search for this feature before requesting it? It's highly likely that a
   * Operating system: ____
   * Python Version: _____ (`python -V`)
   * CCXT version: _____ (`pip freeze | grep ccxt`)
-  * Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
+  * Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
 
 
 ## Describe the enhancement

.github/ISSUE_TEMPLATE/question.md

@@ -18,7 +18,7 @@ Please do not use the question template to report bugs or to request new feature
   * Operating system: ____
   * Python Version: _____ (`python -V`)
   * CCXT version: _____ (`pip freeze | grep ccxt`)
-  * Freqtrade Version: ____ (`freqtrade -V` or `docker-compose run --rm freqtrade -V` for Freqtrade running in docker)
+  * Freqtrade Version: ____ (`freqtrade -V` or `docker compose run --rm freqtrade -V` for Freqtrade running in docker)
   
 ## Your question
 

docs/data-analysis.md

@@ -5,7 +5,7 @@ You can analyze the results of backtests and trading history easily using Jupyte
 ## Quick start with docker
 
 Freqtrade provides a docker-compose file which starts up a jupyter lab server.
-You can run this server using the following command: `docker-compose -f docker/docker-compose-jupyter.yml up`
+You can run this server using the following command: `docker compose -f docker/docker-compose-jupyter.yml up`
 
 This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
 Please use the link that's printed in the console after startup for simplified login.

docs/docker_quickstart.md

@@ -4,20 +4,22 @@ This page explains how to run the bot with Docker. It is not meant to work out o
 
 ## Install Docker
 
-Start by downloading and installing Docker CE for your platform:
+Start by downloading and installing Docker / Docker Desktop for your platform:
 
 * [Mac](https://docs.docker.com/docker-for-mac/install/)
 * [Windows](https://docs.docker.com/docker-for-windows/install/)
 * [Linux](https://docs.docker.com/install/)
 
-To simplify running freqtrade, [`docker-compose`](https://docs.docker.com/compose/install/) should be installed and available to follow the below [docker quick start guide](#docker-quick-start).
+!!! Info "Docker compose install"
+    Freqtrade documentation assumes the use of Docker desktop (or the docker compose plugin).  
+    While the docker-compose standalone installation still works, it will require changing all `docker compose` commands from `docker compose` to `docker-compose` to work (e.g. `docker compose up -d` will become `docker-compose up -d`).
 
-## Freqtrade with docker-compose
+## Freqtrade with docker
 
-Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker-compose file](https://github.com/freqtrade/freqtrade/blob/stable/docker-compose.yml) ready for usage.
+Freqtrade provides an official Docker image on [Dockerhub](https://hub.docker.com/r/freqtradeorg/freqtrade/), as well as a [docker compose file](https://github.com/freqtrade/freqtrade/blob/stable/docker-compose.yml) ready for usage.
 
 !!! Note
-    - The following section assumes that `docker` and `docker-compose` are installed and available to the logged in user.
+    - The following section assumes that `docker` is installed and available to the logged in user.
     - All below commands use relative directories and will have to be executed from the directory containing the `docker-compose.yml` file.
 
 ### Docker quick start
@@ -31,13 +33,13 @@ cd ft_userdata/
 curl https://raw.githubusercontent.com/freqtrade/freqtrade/stable/docker-compose.yml -o docker-compose.yml
 
 # Pull the freqtrade image
-docker-compose pull
+docker compose pull
 
 # Create user directory structure
-docker-compose run --rm freqtrade create-userdir --userdir user_data
+docker compose run --rm freqtrade create-userdir --userdir user_data
 
 # Create configuration - Requires answering interactive questions
-docker-compose run --rm freqtrade new-config --config user_data/config.json
+docker compose run --rm freqtrade new-config --config user_data/config.json
 ```
 
 The above snippet creates a new directory called `ft_userdata`, downloads the latest compose file and pulls the freqtrade image.
@@ -64,7 +66,7 @@ The `SampleStrategy` is run by default.
 Once this is done, you're ready to launch the bot in trading mode (Dry-run or Live-trading, depending on your answer to the corresponding question you made above).
 
 ``` bash
-docker-compose up -d
+docker compose up -d
 ```
 
 !!! Warning "Default configuration"
@@ -84,27 +86,27 @@ You can now access the UI by typing localhost:8080 in your browser.
 
 #### Monitoring the bot
 
-You can check for running instances with `docker-compose ps`.
+You can check for running instances with `docker compose ps`.
 This should list the service `freqtrade` as `running`. If that's not the case, best check the logs (see next point).
 
-#### Docker-compose logs
+#### Docker compose logs
 
 Logs will be written to: `user_data/logs/freqtrade.log`.  
-You can also check the latest log with the command `docker-compose logs -f`.
+You can also check the latest log with the command `docker compose logs -f`.
 
 #### Database
 
 The database will be located at: `user_data/tradesv3.sqlite`
 
-#### Updating freqtrade with docker-compose
+#### Updating freqtrade with docker
 
-Updating freqtrade when using `docker-compose` is as simple as running the following 2 commands:
+Updating freqtrade when using `docker` is as simple as running the following 2 commands:
 
 ``` bash
 # Download the latest image
-docker-compose pull
+docker compose pull
 # Restart the image
-docker-compose up -d
+docker compose up -d
 ```
 
 This will first pull the latest image, and will then restart the container with the just pulled version.
@@ -116,43 +118,43 @@ This will first pull the latest image, and will then restart the container with
 
 Advanced users may edit the docker-compose file further to include all possible options or arguments.
 
-All freqtrade arguments will be available by running `docker-compose run --rm freqtrade <command> <optional arguments>`.
+All freqtrade arguments will be available by running `docker compose run --rm freqtrade <command> <optional arguments>`.
 
-!!! Warning "`docker-compose` for trade commands"
+!!! Warning "`docker compose` for trade commands"
-    Trade commands (`freqtrade trade <...>`) should not be ran via `docker-compose run` - but should use `docker-compose up -d` instead.
+    Trade commands (`freqtrade trade <...>`) should not be ran via `docker compose run` - but should use `docker compose up -d` instead.
     This makes sure that the container is properly started (including port forwardings) and will make sure that the container will restart after a system reboot.
     If you intend to use freqUI, please also ensure to adjust the [configuration accordingly](rest-api.md#configuration-with-docker), otherwise the UI will not be available.
 
-!!! Note "`docker-compose run --rm`"
+!!! Note "`docker compose run --rm`"
     Including `--rm` will remove the container after completion, and is highly recommended for all modes except trading mode (running with `freqtrade trade` command).
 
-??? Note "Using docker without docker-compose"
+??? Note "Using docker without docker"
-    "`docker-compose run --rm`" will require a compose file to be provided.
+    "`docker compose run --rm`" will require a compose file to be provided.
     Some freqtrade commands that don't require authentication such as `list-pairs` can be run with "`docker run --rm`" instead.  
     For example `docker run --rm freqtradeorg/freqtrade:stable list-pairs --exchange binance --quote BTC --print-json`.  
     This can be useful for fetching exchange information to add to your `config.json` without affecting your running containers.
 
-#### Example: Download data with docker-compose
+#### Example: Download data with docker
 
 Download backtesting data for 5 days for the pair ETH/BTC and 1h timeframe from Binance. The data will be stored in the directory `user_data/data/` on the host.
 
 ``` bash
-docker-compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
+docker compose run --rm freqtrade download-data --pairs ETH/BTC --exchange binance --days 5 -t 1h
 ```
 
 Head over to the [Data Downloading Documentation](data-download.md) for more details on downloading data.
 
-#### Example: Backtest with docker-compose
+#### Example: Backtest with docker
 
 Run backtesting in docker-containers for SampleStrategy and specified timerange of historical data, on 5m timeframe:
 
 ``` bash
-docker-compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
+docker compose run --rm freqtrade backtesting --config user_data/config.json --strategy SampleStrategy --timerange 20190801-20191001 -i 5m
 ```
 
 Head over to the [Backtesting Documentation](backtesting.md) to learn more.
 
-### Additional dependencies with docker-compose
+### Additional dependencies with docker
 
 If your strategy requires dependencies not included in the default image - it will be necessary to build the image on your host.
 For this, please create a Dockerfile containing installation steps for the additional dependencies (have a look at [docker/Dockerfile.custom](https://github.com/freqtrade/freqtrade/blob/develop/docker/Dockerfile.custom) for an example).
@@ -166,15 +168,15 @@ You'll then also need to modify the `docker-compose.yml` file and uncomment the
       dockerfile: "./Dockerfile.<yourextension>"
 ```
 
-You can then run `docker-compose build --pull` to build the docker image, and run it using the commands described above.
+You can then run `docker compose build --pull` to build the docker image, and run it using the commands described above.
 
-### Plotting with docker-compose
+### Plotting with docker
 
 Commands `freqtrade plot-profit` and `freqtrade plot-dataframe` ([Documentation](plotting.md)) are available by changing the image to `*_plot` in your docker-compose.yml file.
 You can then use these commands as follows:
 
 ``` bash
-docker-compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
+docker compose run --rm freqtrade plot-dataframe --strategy AwesomeStrategy -p BTC/ETH --timerange=20180801-20180805
 ```
 
 The output will be stored in the `user_data/plot` directory, and can be opened with any modern browser.
@@ -185,7 +187,7 @@ Freqtrade provides a docker-compose file which starts up a jupyter lab server.
 You can run this server using the following command:
 
 ``` bash
-docker-compose -f docker/docker-compose-jupyter.yml up
+docker compose -f docker/docker-compose-jupyter.yml up
 ```
 
 This will create a docker-container running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
@@ -194,7 +196,7 @@ Please use the link that's printed in the console after startup for simplified l
 Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) up-to-date.
 
 ``` bash
-docker-compose -f docker/docker-compose-jupyter.yml build --no-cache
+docker compose -f docker/docker-compose-jupyter.yml build --no-cache
 ```
 
 ## Troubleshooting

docs/sql_cheatsheet.md

@@ -13,12 +13,12 @@ Feel free to use a visual Database editor like SqliteBrowser if you feel more co
 sudo apt-get install sqlite3
 ```
 
-### Using sqlite3 via docker-compose
+### Using sqlite3 via docker
 
 The freqtrade docker image does contain sqlite3, so you can edit the database without having to install anything on the host system.
 
 ``` bash
-docker-compose exec freqtrade /bin/bash
+docker compose exec freqtrade /bin/bash
 sqlite3 <database-file>.sqlite
 ```
 

docs/strategy_analysis_example.md

@@ -2,12 +2,37 @@
 
 Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.
 The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.
+Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details.
 
 ## Setup
 
+### Change Working directory to repository root
+
 
 ```python
+import os
 from pathlib import Path
+
+# Change directory
+# Modify this cell to insure that the output shows the correct path.
+# Define all paths relative to the project root shown in the cell output
+project_root = "somedir/freqtrade"
+i=0
+try:
+    os.chdirdir(project_root)
+    assert Path('LICENSE').is_file()
+except:
+    while i<4 and (not Path('LICENSE').is_file()):
+        os.chdir(Path(Path.cwd(), '../'))
+        i+=1
+    project_root = Path.cwd()
+print(Path.cwd())
+```
+
+### Configure Freqtrade environment
+
+
+```python
 from freqtrade.configuration import Configuration
 
 # Customize these according to your needs.
@@ -15,14 +40,14 @@ from freqtrade.configuration import Configuration
 # Initialize empty configuration object
 config = Configuration.from_files([])
 # Optionally (recommended), use existing configuration file
-# config = Configuration.from_files(["config.json"])
+# config = Configuration.from_files(["user_data/config.json"])
 
 # Define some constants
 config["timeframe"] = "5m"
 # Name of the strategy class
 config["strategy"] = "SampleStrategy"
 # Location of the data
-data_location = config['datadir']
+data_location = config["datadir"]
 # Pair to analyze - Only use one pair here
 pair = "BTC/USDT"
 ```
@@ -36,12 +61,12 @@ from freqtrade.enums import CandleType
 candles = load_pair_history(datadir=data_location,
                             timeframe=config["timeframe"],
                             pair=pair,
-                            data_format = "hdf5",
+                            data_format = "json",  # Make sure to update this to your data
                             candle_type=CandleType.SPOT,
                             )
 
 # Confirm success
-print("Loaded " + str(len(candles)) + f" rows of data for {pair} from {data_location}")
+print(f"Loaded {len(candles)} rows of data for {pair} from {data_location}")
 candles.head()
 ```
 

docs/updating.md

@@ -6,14 +6,14 @@ To update your freqtrade installation, please use one of the below methods, corr
     Breaking changes / changed behavior will be documented in the changelog that is posted alongside every release.
     For the develop branch, please follow PR's to avoid being surprised by changes.
 
-## docker-compose
+## docker
 
 !!! Note "Legacy installations using the `master` image"
     We're switching from master to stable for the release Images - please adjust your docker-file and replace `freqtradeorg/freqtrade:master` with `freqtradeorg/freqtrade:stable`
 
 ``` bash
-docker-compose pull
+docker compose pull
-docker-compose up -d
+docker compose up -d
 ```
 
 ## Installation via setup script

docs/utils.md

@@ -652,7 +652,7 @@ Common arguments:
 
 You can also use webserver mode via docker.
 Starting a one-off container requires the configuration of the port explicitly, as ports are not exposed by default.
-You can use `docker-compose run --rm -p 127.0.0.1:8080:8080 freqtrade webserver` to start a one-off container that'll be removed once you stop it. This assumes that port 8080 is still available and no other bot is running on that port.
+You can use `docker compose run --rm -p 127.0.0.1:8080:8080 freqtrade webserver` to start a one-off container that'll be removed once you stop it. This assumes that port 8080 is still available and no other bot is running on that port.
 
 Alternatively, you can reconfigure the docker-compose file to have the command updated:
 
@@ -662,7 +662,7 @@ Alternatively, you can reconfigure the docker-compose file to have the command u
       --config /freqtrade/user_data/config.json
 ```
 
-You can now use `docker-compose up` to start the webserver.
+You can now use `docker compose up` to start the webserver.
 This assumes that the configuration has a webserver enabled and configured for docker (listening port = `0.0.0.0`).
 
 !!! Tip

freqtrade/data/dataprovider.py

@@ -104,13 +104,15 @@ class DataProvider:
     def _emit_df(
         self,
         pair_key: PairWithTimeframe,
-        dataframe: DataFrame
+        dataframe: DataFrame,
+        new_candle: bool
     ) -> None:
         """
         Send this dataframe as an ANALYZED_DF message to RPC
 
         :param pair_key: PairWithTimeframe tuple
-        :param data: Tuple containing the DataFrame and the datetime it was cached
+        :param dataframe: Dataframe to emit
+        :param new_candle: This is a new candle
         """
         if self.__rpc:
             self.__rpc.send_msg(
@@ -123,6 +125,11 @@ class DataProvider:
                     }
                 }
             )
+            if new_candle:
+                self.__rpc.send_msg({
+                        'type': RPCMessageType.NEW_CANDLE,
+                        'data': pair_key,
+                    })
 
     def _add_external_df(
         self,

freqtrade/enums/__init__.py

@@ -6,7 +6,7 @@ from freqtrade.enums.exittype import ExitType
 from freqtrade.enums.hyperoptstate import HyperoptState
 from freqtrade.enums.marginmode import MarginMode
 from freqtrade.enums.ordertypevalue import OrderTypeValues
-from freqtrade.enums.rpcmessagetype import RPCMessageType, RPCRequestType
+from freqtrade.enums.rpcmessagetype import NO_ECHO_MESSAGES, RPCMessageType, RPCRequestType
 from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
 from freqtrade.enums.signaltype import SignalDirection, SignalTagType, SignalType
 from freqtrade.enums.state import State

freqtrade/enums/rpcmessagetype.py

@@ -21,6 +21,7 @@ class RPCMessageType(str, Enum):
 
     WHITELIST = 'whitelist'
     ANALYZED_DF = 'analyzed_df'
+    NEW_CANDLE = 'new_candle'
 
     def __repr__(self):
         return self.value
@@ -35,3 +36,6 @@ class RPCRequestType(str, Enum):
 
     WHITELIST = 'whitelist'
     ANALYZED_DF = 'analyzed_df'
+
+
+NO_ECHO_MESSAGES = (RPCMessageType.ANALYZED_DF, RPCMessageType.WHITELIST, RPCMessageType.NEW_CANDLE)

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -20,6 +20,9 @@ class Base4ActionRLEnv(BaseEnvironment):
     """
     Base class for a 4 action environment
     """
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.actions = Actions
 
     def set_action_space(self):
         self.action_space = spaces.Discrete(len(Actions))
@@ -92,9 +95,12 @@ class Base4ActionRLEnv(BaseEnvironment):
 
         info = dict(
             tick=self._current_tick,
+            action=action,
             total_reward=self.total_reward,
             total_profit=self._total_profit,
-            position=self._position.value
+            position=self._position.value,
+            trade_duration=self.get_trade_duration(),
+            current_profit_pct=self.get_unrealized_profit()
         )
 
         observation = self._get_observation()

freqtrade/freqai/RL/Base5ActionRLEnv.py

@@ -21,6 +21,9 @@ class Base5ActionRLEnv(BaseEnvironment):
     """
     Base class for a 5 action environment
     """
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.actions = Actions
 
     def set_action_space(self):
         self.action_space = spaces.Discrete(len(Actions))
@@ -98,9 +101,12 @@ class Base5ActionRLEnv(BaseEnvironment):
 
         info = dict(
             tick=self._current_tick,
+            action=action,
             total_reward=self.total_reward,
             total_profit=self._total_profit,
-            position=self._position.value
+            position=self._position.value,
+            trade_duration=self.get_trade_duration(),
+            current_profit_pct=self.get_unrealized_profit()
         )
 
         observation = self._get_observation()

freqtrade/freqai/RL/BaseEnvironment.py

@@ -2,7 +2,7 @@ import logging
 import random
 from abc import abstractmethod
 from enum import Enum
-from typing import Optional
+from typing import Optional, Type
 
 import gym
 import numpy as np
@@ -12,11 +12,23 @@ from gym.utils import seeding
 from pandas import DataFrame
 
 from freqtrade.data.dataprovider import DataProvider
+from freqtrade.enums import RunMode
 
 
 logger = logging.getLogger(__name__)
 
 
+class BaseActions(Enum):
+    """
+    Default action space, mostly used for type handling.
+    """
+    Neutral = 0
+    Long_enter = 1
+    Long_exit = 2
+    Short_enter = 3
+    Short_exit = 4
+
+
 class Positions(Enum):
     Short = 0
     Long = 1
@@ -64,6 +76,16 @@ class BaseEnvironment(gym.Env):
         else:
             self.fee = 0.0015
 
+        # set here to default 5Ac, but all children envs can override this
+        self.actions: Type[Enum] = BaseActions
+        self.custom_info: dict = {}
+        self.live: bool = False
+        if dp:
+            self.live = dp.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
+        if not self.live and self.add_state_info:
+            self.add_state_info = False
+            logger.warning("add_state_info is not available in backtesting. Deactivating.")
+
     def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
                   reward_kwargs: dict, starting_point=True):
         """
@@ -118,6 +140,19 @@ class BaseEnvironment(gym.Env):
         return [seed]
 
     def reset(self):
+        """
+        Reset is called at the beginning of every episode
+        """
+        # custom_info is used for episodic reports and tensorboard logging
+        self.custom_info["Invalid"] = 0
+        self.custom_info["Hold"] = 0
+        self.custom_info["Unknown"] = 0
+        self.custom_info["pnl_factor"] = 0
+        self.custom_info["duration_factor"] = 0
+        self.custom_info["reward_exit"] = 0
+        self.custom_info["reward_hold"] = 0
+        for action in self.actions:
+            self.custom_info[f"{action.name}"] = 0
 
         self._done = False
 
@@ -160,7 +195,7 @@ class BaseEnvironment(gym.Env):
         """
         features_window = self.signal_features[(
             self._current_tick - self.window_size):self._current_tick]
-        if self.add_state_info:
+        if self.add_state_info and self.live:
             features_and_state = DataFrame(np.zeros((len(features_window), 3)),
                                            columns=['current_profit_pct',
                                                     'position',
@@ -271,6 +306,13 @@ class BaseEnvironment(gym.Env):
     def current_price(self) -> float:
         return self.prices.iloc[self._current_tick].open
 
+    def get_actions(self) -> Type[Enum]:
+        """
+        Used by SubprocVecEnv to get actions from
+        initialized env for tensorboard callback
+        """
+        return self.actions
+
     # Keeping around incase we want to start building more complex environment
     # templates in the future.
     # def most_recent_return(self):

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -21,7 +21,8 @@ from freqtrade.exceptions import OperationalException
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
-from freqtrade.freqai.RL.BaseEnvironment import Positions
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions, Positions
+from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
 from freqtrade.persistence import Trade
 
 
@@ -44,8 +45,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             'cpu_count', 1), max(int(self.max_system_threads / 2), 1))
         th.set_num_threads(self.max_threads)
         self.reward_params = self.freqai_info['rl_config']['model_reward_parameters']
-        self.train_env: Union[SubprocVecEnv, gym.Env] = None
+        self.train_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
-        self.eval_env: Union[SubprocVecEnv, gym.Env] = None
+        self.eval_env: Union[SubprocVecEnv, Type[gym.Env]] = gym.Env()
         self.eval_callback: Optional[EvalCallback] = None
         self.model_type = self.freqai_info['rl_config']['model_type']
         self.rl_config = self.freqai_info['rl_config']
@@ -65,6 +66,8 @@ class BaseReinforcementLearningModel(IFreqaiModel):
         self.unset_outlier_removal()
         self.net_arch = self.rl_config.get('net_arch', [128, 128])
         self.dd.model_type = import_str
+        self.tensorboard_callback: TensorboardCallback = \
+            TensorboardCallback(verbose=1, actions=BaseActions)
 
     def unset_outlier_removal(self):
         """
@@ -156,6 +159,9 @@ class BaseReinforcementLearningModel(IFreqaiModel):
                                           render=False, eval_freq=len(train_df),
                                           best_model_save_path=str(dk.data_path))
 
+        actions = self.train_env.get_actions()
+        self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)
+
     @abstractmethod
     def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen, **kwargs):
         """

freqtrade/freqai/RL/TensorboardCallback.py

@@ -0,0 +1,60 @@
+from enum import Enum
+from typing import Any, Dict, Type, Union
+
+from stable_baselines3.common.callbacks import BaseCallback
+from stable_baselines3.common.logger import HParam
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
+
+
+class TensorboardCallback(BaseCallback):
+    """
+    Custom callback for plotting additional values in tensorboard and
+    episodic summary reports.
+    """
+    def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
+        super(TensorboardCallback, self).__init__(verbose)
+        self.model: Any = None
+        self.logger = None  # type: Any
+        self.training_env: BaseEnvironment = None  # type: ignore
+        self.actions: Type[Enum] = actions
+
+    def _on_training_start(self) -> None:
+        hparam_dict = {
+            "algorithm": self.model.__class__.__name__,
+            "learning_rate": self.model.learning_rate,
+            # "gamma": self.model.gamma,
+            # "gae_lambda": self.model.gae_lambda,
+            # "batch_size": self.model.batch_size,
+            # "n_steps": self.model.n_steps,
+        }
+        metric_dict: Dict[str, Union[float, int]] = {
+            "eval/mean_reward": 0,
+            "rollout/ep_rew_mean": 0,
+            "rollout/ep_len_mean": 0,
+            "train/value_loss": 0,
+            "train/explained_variance": 0,
+        }
+        self.logger.record(
+            "hparams",
+            HParam(hparam_dict, metric_dict),
+            exclude=("stdout", "log", "json", "csv"),
+        )
+
+    def _on_step(self) -> bool:
+        custom_info = self.training_env.get_attr("custom_info")[0]
+        self.logger.record("_state/position", self.locals["infos"][0]["position"])
+        self.logger.record("_state/trade_duration", self.locals["infos"][0]["trade_duration"])
+        self.logger.record("_state/current_profit_pct", self.locals["infos"]
+                           [0]["current_profit_pct"])
+        self.logger.record("_reward/total_profit", self.locals["infos"][0]["total_profit"])
+        self.logger.record("_reward/total_reward", self.locals["infos"][0]["total_reward"])
+        self.logger.record_mean("_reward/mean_trade_duration", self.locals["infos"]
+                                [0]["trade_duration"])
+        self.logger.record("_actions/action", self.locals["infos"][0]["action"])
+        self.logger.record("_actions/_Invalid", custom_info["Invalid"])
+        self.logger.record("_actions/_Unknown", custom_info["Unknown"])
+        self.logger.record("_actions/Hold", custom_info["Hold"])
+        for action in self.actions:
+            self.logger.record(f"_actions/{action.name}", custom_info[action.name])
+        return True

freqtrade/freqai/data_kitchen.py

@@ -462,10 +462,10 @@ class FreqaiDataKitchen:
         :param df: Dataframe containing all candles to run the entire backtest. Here
                    it is sliced down to just the present training period.
         """
-
-        df = df.loc[df["date"] >= timerange.startdt, :]
         if not self.live:
-            df = df.loc[df["date"] < timerange.stopdt, :]
+            df = df.loc[(df["date"] >= timerange.startdt) & (df["date"] < timerange.stopdt), :]
+        else:
+            df = df.loc[df["date"] >= timerange.startdt, :]
 
         return df
 

freqtrade/freqai/freqai_interface.py

@@ -282,10 +282,10 @@ class IFreqaiModel(ABC):
             train_it += 1
             total_trains = len(dk.backtesting_timeranges)
             self.training_timerange = tr_train
-            dataframe_train = dk.slice_dataframe(tr_train, dataframe)
+            len_backtest_df = len(dataframe.loc[(dataframe["date"] >= tr_backtest.startdt) & (
-            dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
+                                  dataframe["date"] < tr_backtest.stopdt), :])
 
-            if not self.ensure_data_exists(dataframe_backtest, tr_backtest, pair):
+            if not self.ensure_data_exists(len_backtest_df, tr_backtest, pair):
                 continue
 
             self.log_backtesting_progress(tr_train, pair, train_it, total_trains)
@@ -298,13 +298,15 @@ class IFreqaiModel(ABC):
 
             dk.set_new_model_names(pair, timestamp_model_id)
 
-            if dk.check_if_backtest_prediction_is_valid(len(dataframe_backtest)):
+            if dk.check_if_backtest_prediction_is_valid(len_backtest_df):
                 self.dd.load_metadata(dk)
-                dk.find_features(dataframe_train)
+                dk.find_features(dataframe)
                 self.check_if_feature_list_matches_strategy(dk)
                 append_df = dk.get_backtesting_prediction()
                 dk.append_predictions(append_df)
             else:
+                dataframe_train = dk.slice_dataframe(tr_train, dataframe)
+                dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
                 if not self.model_exists(dk):
                     dk.find_features(dataframe_train)
                     dk.find_labels(dataframe_train)
@@ -804,16 +806,16 @@ class IFreqaiModel(ABC):
             self.pair_it = 1
             self.current_candle = self.dd.current_candle
 
-    def ensure_data_exists(self, dataframe_backtest: DataFrame,
+    def ensure_data_exists(self, len_dataframe_backtest: int,
                            tr_backtest: TimeRange, pair: str) -> bool:
         """
         Check if the dataframe is empty, if not, report useful information to user.
-        :param dataframe_backtest: the backtesting dataframe, maybe empty.
+        :param len_dataframe_backtest: the len of backtesting dataframe
         :param tr_backtest: current backtesting timerange.
         :param pair: current pair
         :return: if the data exists or not
         """
-        if self.config.get("freqai_backtest_live_models", False) and len(dataframe_backtest) == 0:
+        if self.config.get("freqai_backtest_live_models", False) and len_dataframe_backtest == 0:
             logger.info(f"No data found for pair {pair} from "
                         f"from { tr_backtest.start_fmt} to {tr_backtest.stop_fmt}. "
                         "Probably more than one training within the same candle period.")

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -71,7 +71,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
 
         model.learn(
             total_timesteps=int(total_timesteps),
-            callback=self.eval_callback
+            callback=[self.eval_callback, self.tensorboard_callback]
         )
 
         if Path(dk.data_path / "best_model.zip").is_file():
@@ -100,17 +100,24 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             """
             # first, penalize if the action is not valid
             if not self._is_valid(action):
+                self.custom_info["Invalid"] += 1
                 return -2
 
             pnl = self.get_unrealized_profit()
             factor = 100.
 
             # reward agent for entering trades
-            if (action in (Actions.Long_enter.value, Actions.Short_enter.value)
+            if (action == Actions.Long_enter.value
                     and self._position == Positions.Neutral):
+                self.custom_info[f"{Actions.Long_enter.name}"] += 1
+                return 25
+            if (action == Actions.Short_enter.value
+                    and self._position == Positions.Neutral):
+                self.custom_info[f"{Actions.Short_enter.name}"] += 1
                 return 25
             # discourage agent from not entering trades
             if action == Actions.Neutral.value and self._position == Positions.Neutral:
+                self.custom_info[f"{Actions.Neutral.name}"] += 1
                 return -1
 
             max_trade_duration = self.rl_config.get('max_trade_duration_candles', 300)
@@ -124,18 +131,22 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             # discourage sitting in position
             if (self._position in (Positions.Short, Positions.Long) and
                     action == Actions.Neutral.value):
+                self.custom_info["Hold"] += 1
                 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)
+                self.custom_info[f"{Actions.Long_exit.name}"] += 1
                 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)
+                self.custom_info[f"{Actions.Short_exit.name}"] += 1
                 return float(pnl * factor)
 
+            self.custom_info["Unknown"] += 1
             return 0.

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -1,7 +1,6 @@
 import logging
-from typing import Any, Dict  # , Tuple
+from typing import Any, Dict
 
-# import numpy.typing as npt
 from pandas import DataFrame
 from stable_baselines3.common.callbacks import EvalCallback
 from stable_baselines3.common.vec_env import SubprocVecEnv
@@ -9,6 +8,7 @@ from stable_baselines3.common.vec_env import SubprocVecEnv
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
+from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
 
 
 logger = logging.getLogger(__name__)
@@ -49,3 +49,6 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
         self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
                                           render=False, eval_freq=len(train_df),
                                           best_model_save_path=str(dk.data_path))
+
+        actions = self.train_env.env_method("get_actions")[0]
+        self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)

freqtrade/plugins/pairlist/VolumePairList.py

@@ -218,7 +218,7 @@ class VolumePairList(IPairList):
                 else:
                     filtered_tickers[i]['quoteVolume'] = 0
         else:
-            # Tickers mode - filter based on incomming pairlist.
+            # Tickers mode - filter based on incoming pairlist.
             filtered_tickers = [v for k, v in tickers.items() if k in pairlist]
 
         if self._min_value > 0:

freqtrade/rpc/api_server/api_v1.py

@@ -37,7 +37,8 @@ logger = logging.getLogger(__name__)
 # 2.16: Additional daily metrics
 # 2.17: Forceentry - leverage, partial force_exit
 # 2.20: Add websocket endpoints
-API_VERSION = 2.20
+# 2.21: Add new_candle messagetype
+API_VERSION = 2.21
 
 # Public API, requires no auth.
 router_public = APIRouter()

freqtrade/rpc/rpc_manager.py

@@ -6,7 +6,7 @@ from collections import deque
 from typing import Any, Dict, List
 
 from freqtrade.constants import Config
-from freqtrade.enums import RPCMessageType
+from freqtrade.enums import NO_ECHO_MESSAGES, RPCMessageType
 from freqtrade.rpc import RPC, RPCHandler
 
 
@@ -67,7 +67,7 @@ class RPCManager:
             'status': 'stopping bot'
         }
         """
-        if msg.get('type') not in (RPCMessageType.ANALYZED_DF, RPCMessageType.WHITELIST):
+        if msg.get('type') not in NO_ECHO_MESSAGES:
             logger.info('Sending rpc message: %s', msg)
         if 'pair' in msg:
             msg.update({

freqtrade/rpc/webhook.py

@@ -68,6 +68,7 @@ class Webhook(RPCHandler):
                 RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
                 RPCMessageType.WHITELIST,
                 RPCMessageType.ANALYZED_DF,
+                RPCMessageType.NEW_CANDLE,
                 RPCMessageType.STRATEGY_MSG):
             # Don't fail for non-implemented types
             return None

freqtrade/strategy/interface.py

@@ -739,10 +739,10 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         pair = str(metadata.get('pair'))
 
+        new_candle = self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']
         # Test if seen this pair and last candle before.
         # always run if process_only_new_candles is set to false
-        if (not self.process_only_new_candles or
+        if not self.process_only_new_candles or new_candle:
-                self._last_candle_seen_per_pair.get(pair, None) != dataframe.iloc[-1]['date']):
 
             # Defs that only make change on new candle data.
             dataframe = self.analyze_ticker(dataframe, metadata)
@@ -751,7 +751,7 @@ class IStrategy(ABC, HyperStrategyMixin):
 
             candle_type = self.config.get('candle_type_def', CandleType.SPOT)
             self.dp._set_cached_df(pair, self.timeframe, dataframe, candle_type=candle_type)
-            self.dp._emit_df((pair, self.timeframe, candle_type), dataframe)
+            self.dp._emit_df((pair, self.timeframe, candle_type), dataframe, new_candle)
 
         else:
             logger.debug("Skipping TA Analysis for already analyzed candle")

freqtrade/templates/strategy_analysis_example.ipynb

@@ -7,14 +7,17 @@
     "# Strategy analysis example\n",
     "\n",
     "Debugging a strategy can be time-consuming. Freqtrade offers helper functions to visualize raw data.\n",
-    "The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location."
+    "The following assumes you work with SampleStrategy, data for 5m timeframe from Binance and have downloaded them into the data directory in the default location.\n",
+    "Please follow the [documentation](https://www.freqtrade.io/en/stable/data-download/) for more details."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Setup"
+    "## Setup\n",
+    "\n",
+    "### Change Working directory to repository root"
    ]
   },
   {
@@ -23,7 +26,38 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import os\n",
     "from pathlib import Path\n",
+    "\n",
+    "# Change directory\n",
+    "# Modify this cell to insure that the output shows the correct path.\n",
+    "# Define all paths relative to the project root shown in the cell output\n",
+    "project_root = \"somedir/freqtrade\"\n",
+    "i=0\n",
+    "try:\n",
+    "    os.chdirdir(project_root)\n",
+    "    assert Path('LICENSE').is_file()\n",
+    "except:\n",
+    "    while i<4 and (not Path('LICENSE').is_file()):\n",
+    "        os.chdir(Path(Path.cwd(), '../'))\n",
+    "        i+=1\n",
+    "    project_root = Path.cwd()\n",
+    "print(Path.cwd())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Configure Freqtrade environment"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "from freqtrade.configuration import Configuration\n",
     "\n",
     "# Customize these according to your needs.\n",
@@ -31,14 +65,14 @@
     "# Initialize empty configuration object\n",
     "config = Configuration.from_files([])\n",
     "# Optionally (recommended), use existing configuration file\n",
-    "# config = Configuration.from_files([\"config.json\"])\n",
+    "# config = Configuration.from_files([\"user_data/config.json\"])\n",
     "\n",
     "# Define some constants\n",
     "config[\"timeframe\"] = \"5m\"\n",
     "# Name of the strategy class\n",
     "config[\"strategy\"] = \"SampleStrategy\"\n",
     "# Location of the data\n",
-    "data_location = config['datadir']\n",
+    "data_location = config[\"datadir\"]\n",
     "# Pair to analyze - Only use one pair here\n",
     "pair = \"BTC/USDT\""
    ]
@@ -56,12 +90,12 @@
     "candles = load_pair_history(datadir=data_location,\n",
     "                            timeframe=config[\"timeframe\"],\n",
     "                            pair=pair,\n",
-    "                            data_format = \"hdf5\",\n",
+    "                            data_format = \"json\",  # Make sure to update this to your data\n",
     "                            candle_type=CandleType.SPOT,\n",
     "                            )\n",
     "\n",
     "# Confirm success\n",
-    "print(\"Loaded \" + str(len(candles)) + f\" rows of data for {pair} from {data_location}\")\n",
+    "print(f\"Loaded {len(candles)} rows of data for {pair} from {data_location}\")\n",
     "candles.head()"
    ]
   },
@@ -365,7 +399,7 @@
  "metadata": {
   "file_extension": ".py",
   "kernelspec": {
-   "display_name": "Python 3.9.7 64-bit ('trade_397')",
+   "display_name": "Python 3.9.7 64-bit",
    "language": "python",
    "name": "python3"
   },

tests/data/test_dataprovider.py

@@ -207,12 +207,18 @@ def test_emit_df(mocker, default_conf, ohlcv_history):
     assert send_mock.call_count == 0
 
     # Rpc is added, we call emit, should call send_msg
-    dataprovider._emit_df(pair, ohlcv_history)
+    dataprovider._emit_df(pair, ohlcv_history, False)
     assert send_mock.call_count == 1
 
+    send_mock.reset_mock()
+    dataprovider._emit_df(pair, ohlcv_history, True)
+    assert send_mock.call_count == 2
+
+    send_mock.reset_mock()
+
     # No rpc added, emit called, should not call send_msg
-    dataprovider_no_rpc._emit_df(pair, ohlcv_history)
+    dataprovider_no_rpc._emit_df(pair, ohlcv_history, False)
-    assert send_mock.call_count == 1
+    assert send_mock.call_count == 0
 
 
 def test_refresh(mocker, default_conf, ohlcv_history):

tests/exchange/test_ccxt_compat.py

@@ -224,8 +224,13 @@ class TestCCXTExchange():
         for val in [1, 2, 5, 25, 100]:
             l2 = exchange.fetch_l2_order_book(pair, val)
             if not l2_limit_range or val in l2_limit_range:
-                assert len(l2['asks']) == val
+                if val > 50:
-                assert len(l2['bids']) == val
+                    # Orderbooks are not always this deep.
+                    assert val - 5 < len(l2['asks']) <= val
+                    assert val - 5 < len(l2['bids']) <= val
+                else:
+                    assert len(l2['asks']) == val
+                    assert len(l2['bids']) == val
             else:
                 next_limit = exchange.get_next_limit_in_list(
                     val, l2_limit_range, l2_limit_range_required)

tests/freqai/test_freqai_interface.py

@@ -237,7 +237,6 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
     df = freqai.cache_corr_pairlist_dfs(df, freqai.dk)
     for i in range(5):
         df[f'%-constant_{i}'] = i
-        # df.loc[:, f'%-constant_{i}'] = i
 
     metadata = {"pair": "LTC/BTC"}
     freqai.start_backtesting(df, metadata, freqai.dk)

tests/rpc/test_rpc_apiserver.py

@@ -588,7 +588,7 @@ def test_api_show_config(botclient):
     assert 'unfilledtimeout' in response
     assert 'version' in response
     assert 'api_version' in response
-    assert 2.1 <= response['api_version'] <= 2.2
+    assert 2.1 <= response['api_version'] < 3.0
 
 
 def test_api_daily(botclient, mocker, ticker, fee, markets):

tests/rpc/test_rpc_telegram.py

@@ -12,6 +12,7 @@ from unittest.mock import ANY, MagicMock
 
 import arrow
 import pytest
+import time_machine
 from pandas import DataFrame
 from telegram import Chat, Message, ReplyKeyboardMarkup, Update
 from telegram.error import BadRequest, NetworkError, TelegramError
@@ -1906,119 +1907,120 @@ def test_send_msg_entry_fill_notification(default_conf, mocker, message_type, en
 
 def test_send_msg_sell_notification(default_conf, mocker) -> None:
 
-    telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
+    with time_machine.travel("2022-09-01 05:00:00 +00:00", tick=False):
+        telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
 
-    old_convamount = telegram._rpc._fiat_converter.convert_amount
+        old_convamount = telegram._rpc._fiat_converter.convert_amount
-    telegram._rpc._fiat_converter.convert_amount = lambda a, b, c: -24.812
+        telegram._rpc._fiat_converter.convert_amount = lambda a, b, c: -24.812
-    telegram.send_msg({
+        telegram.send_msg({
-        'type': RPCMessageType.EXIT,
+            'type': RPCMessageType.EXIT,
-        'trade_id': 1,
+            'trade_id': 1,
-        'exchange': 'Binance',
+            'exchange': 'Binance',
-        'pair': 'KEY/ETH',
+            'pair': 'KEY/ETH',
-        'leverage': 1.0,
+            'leverage': 1.0,
-        'direction': 'Long',
+            'direction': 'Long',
-        'gain': 'loss',
+            'gain': 'loss',
-        'order_rate': 3.201e-05,
+            'order_rate': 3.201e-05,
-        'amount': 1333.3333333333335,
+            'amount': 1333.3333333333335,
-        'order_type': 'market',
+            'order_type': 'market',
-        'open_rate': 7.5e-05,
+            'open_rate': 7.5e-05,
-        'current_rate': 3.201e-05,
+            'current_rate': 3.201e-05,
-        'profit_amount': -0.05746268,
+            'profit_amount': -0.05746268,
-        'profit_ratio': -0.57405275,
+            'profit_ratio': -0.57405275,
-        'stake_currency': 'ETH',
+            'stake_currency': 'ETH',
-        'fiat_currency': 'USD',
+            'fiat_currency': 'USD',
-        'enter_tag': 'buy_signal1',
+            'enter_tag': 'buy_signal1',
-        'exit_reason': ExitType.STOP_LOSS.value,
+            'exit_reason': ExitType.STOP_LOSS.value,
-        'open_date': arrow.utcnow().shift(hours=-1),
+            'open_date': arrow.utcnow().shift(hours=-1),
-        'close_date': arrow.utcnow(),
+            'close_date': arrow.utcnow(),
-    })
+        })
-    assert msg_mock.call_args[0][0] == (
+        assert msg_mock.call_args[0][0] == (
-        '\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
+            '\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
-        '*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
+            '*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
-        '*Enter Tag:* `buy_signal1`\n'
+            '*Enter Tag:* `buy_signal1`\n'
-        '*Exit Reason:* `stop_loss`\n'
+            '*Exit Reason:* `stop_loss`\n'
-        '*Direction:* `Long`\n'
+            '*Direction:* `Long`\n'
-        '*Amount:* `1333.33333333`\n'
+            '*Amount:* `1333.33333333`\n'
-        '*Open Rate:* `0.00007500`\n'
+            '*Open Rate:* `0.00007500`\n'
-        '*Current Rate:* `0.00003201`\n'
+            '*Current Rate:* `0.00003201`\n'
-        '*Exit Rate:* `0.00003201`\n'
+            '*Exit Rate:* `0.00003201`\n'
-        '*Duration:* `1:00:00 (60.0 min)`'
+            '*Duration:* `1:00:00 (60.0 min)`'
-    )
-
-    msg_mock.reset_mock()
-    telegram.send_msg({
-        'type': RPCMessageType.EXIT,
-        'trade_id': 1,
-        'exchange': 'Binance',
-        'pair': 'KEY/ETH',
-        'direction': 'Long',
-        'gain': 'loss',
-        'order_rate': 3.201e-05,
-        'amount': 1333.3333333333335,
-        'order_type': 'market',
-        'open_rate': 7.5e-05,
-        'current_rate': 3.201e-05,
-        'cumulative_profit': -0.15746268,
-        'profit_amount': -0.05746268,
-        'profit_ratio': -0.57405275,
-        'stake_currency': 'ETH',
-        'fiat_currency': 'USD',
-        'enter_tag': 'buy_signal1',
-        'exit_reason': ExitType.STOP_LOSS.value,
-        'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
-        'close_date': arrow.utcnow(),
-        'stake_amount': 0.01,
-        'sub_trade': True,
-    })
-    assert msg_mock.call_args[0][0] == (
-        '\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
-        '*Unrealized Sub Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
-        '*Cumulative Profit:* (`-0.15746268 ETH / -24.812 USD`)\n'
-        '*Enter Tag:* `buy_signal1`\n'
-        '*Exit Reason:* `stop_loss`\n'
-        '*Direction:* `Long`\n'
-        '*Amount:* `1333.33333333`\n'
-        '*Open Rate:* `0.00007500`\n'
-        '*Current Rate:* `0.00003201`\n'
-        '*Exit Rate:* `0.00003201`\n'
-        '*Remaining:* `(0.01 ETH, -24.812 USD)`'
         )
 
-    msg_mock.reset_mock()
+        msg_mock.reset_mock()
-    telegram.send_msg({
+        telegram.send_msg({
-        'type': RPCMessageType.EXIT,
+            'type': RPCMessageType.EXIT,
-        'trade_id': 1,
+            'trade_id': 1,
-        'exchange': 'Binance',
+            'exchange': 'Binance',
-        'pair': 'KEY/ETH',
+            'pair': 'KEY/ETH',
-        'direction': 'Long',
+            'direction': 'Long',
-        'gain': 'loss',
+            'gain': 'loss',
-        'order_rate': 3.201e-05,
+            'order_rate': 3.201e-05,
-        'amount': 1333.3333333333335,
+            'amount': 1333.3333333333335,
-        'order_type': 'market',
+            'order_type': 'market',
-        'open_rate': 7.5e-05,
+            'open_rate': 7.5e-05,
-        'current_rate': 3.201e-05,
+            'current_rate': 3.201e-05,
-        'profit_amount': -0.05746268,
+            'cumulative_profit': -0.15746268,
-        'profit_ratio': -0.57405275,
+            'profit_amount': -0.05746268,
-        'stake_currency': 'ETH',
+            'profit_ratio': -0.57405275,
-        'enter_tag': 'buy_signal1',
+            'stake_currency': 'ETH',
-        'exit_reason': ExitType.STOP_LOSS.value,
+            'fiat_currency': 'USD',
-        'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
+            'enter_tag': 'buy_signal1',
-        'close_date': arrow.utcnow(),
+            'exit_reason': ExitType.STOP_LOSS.value,
-    })
+            'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
-    assert msg_mock.call_args[0][0] == (
+            'close_date': arrow.utcnow(),
-        '\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
+            'stake_amount': 0.01,
-        '*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
+            'sub_trade': True,
-        '*Enter Tag:* `buy_signal1`\n'
+        })
-        '*Exit Reason:* `stop_loss`\n'
+        assert msg_mock.call_args[0][0] == (
-        '*Direction:* `Long`\n'
+            '\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
-        '*Amount:* `1333.33333333`\n'
+            '*Unrealized Sub Profit:* `-57.41% (loss: -0.05746268 ETH / -24.812 USD)`\n'
-        '*Open Rate:* `0.00007500`\n'
+            '*Cumulative Profit:* (`-0.15746268 ETH / -24.812 USD`)\n'
-        '*Current Rate:* `0.00003201`\n'
+            '*Enter Tag:* `buy_signal1`\n'
-        '*Exit Rate:* `0.00003201`\n'
+            '*Exit Reason:* `stop_loss`\n'
-        '*Duration:* `1 day, 2:30:00 (1590.0 min)`'
+            '*Direction:* `Long`\n'
-    )
+            '*Amount:* `1333.33333333`\n'
-    # Reset singleton function to avoid random breaks
+            '*Open Rate:* `0.00007500`\n'
-    telegram._rpc._fiat_converter.convert_amount = old_convamount
+            '*Current Rate:* `0.00003201`\n'
+            '*Exit Rate:* `0.00003201`\n'
+            '*Remaining:* `(0.01 ETH, -24.812 USD)`'
+            )
+
+        msg_mock.reset_mock()
+        telegram.send_msg({
+            'type': RPCMessageType.EXIT,
+            'trade_id': 1,
+            'exchange': 'Binance',
+            'pair': 'KEY/ETH',
+            'direction': 'Long',
+            'gain': 'loss',
+            'order_rate': 3.201e-05,
+            'amount': 1333.3333333333335,
+            'order_type': 'market',
+            'open_rate': 7.5e-05,
+            'current_rate': 3.201e-05,
+            'profit_amount': -0.05746268,
+            'profit_ratio': -0.57405275,
+            'stake_currency': 'ETH',
+            'enter_tag': 'buy_signal1',
+            'exit_reason': ExitType.STOP_LOSS.value,
+            'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
+            'close_date': arrow.utcnow(),
+        })
+        assert msg_mock.call_args[0][0] == (
+            '\N{WARNING SIGN} *Binance (dry):* Exiting KEY/ETH (#1)\n'
+            '*Unrealized Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
+            '*Enter Tag:* `buy_signal1`\n'
+            '*Exit Reason:* `stop_loss`\n'
+            '*Direction:* `Long`\n'
+            '*Amount:* `1333.33333333`\n'
+            '*Open Rate:* `0.00007500`\n'
+            '*Current Rate:* `0.00003201`\n'
+            '*Exit Rate:* `0.00003201`\n'
+            '*Duration:* `1 day, 2:30:00 (1590.0 min)`'
+        )
+        # Reset singleton function to avoid random breaks
+        telegram._rpc._fiat_converter.convert_amount = old_convamount
 
 
 def test_send_msg_sell_cancel_notification(default_conf, mocker) -> None:
@@ -2065,41 +2067,42 @@ def test_send_msg_sell_fill_notification(default_conf, mocker, direction,
     default_conf['telegram']['notification_settings']['exit_fill'] = 'on'
     telegram, _, msg_mock = get_telegram_testobject(mocker, default_conf)
 
-    telegram.send_msg({
+    with time_machine.travel("2022-09-01 05:00:00 +00:00", tick=False):
-        'type': RPCMessageType.EXIT_FILL,
+        telegram.send_msg({
-        'trade_id': 1,
+            'type': RPCMessageType.EXIT_FILL,
-        'exchange': 'Binance',
+            'trade_id': 1,
-        'pair': 'KEY/ETH',
+            'exchange': 'Binance',
-        'leverage': leverage,
+            'pair': 'KEY/ETH',
-        'direction': direction,
+            'leverage': leverage,
-        'gain': 'loss',
+            'direction': direction,
-        'limit': 3.201e-05,
+            'gain': 'loss',
-        'amount': 1333.3333333333335,
+            'limit': 3.201e-05,
-        'order_type': 'market',
+            'amount': 1333.3333333333335,
-        'open_rate': 7.5e-05,
+            'order_type': 'market',
-        'close_rate': 3.201e-05,
+            'open_rate': 7.5e-05,
-        'profit_amount': -0.05746268,
+            'close_rate': 3.201e-05,
-        'profit_ratio': -0.57405275,
+            'profit_amount': -0.05746268,
-        'stake_currency': 'ETH',
+            'profit_ratio': -0.57405275,
-        'enter_tag': enter_signal,
+            'stake_currency': 'ETH',
-        'exit_reason': ExitType.STOP_LOSS.value,
+            'enter_tag': enter_signal,
-        'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
+            'exit_reason': ExitType.STOP_LOSS.value,
-        'close_date': arrow.utcnow(),
+            'open_date': arrow.utcnow().shift(days=-1, hours=-2, minutes=-30),
-    })
+            'close_date': arrow.utcnow(),
+        })
 
-    leverage_text = f'*Leverage:* `{leverage}`\n' if leverage and leverage != 1.0 else ''
+        leverage_text = f'*Leverage:* `{leverage}`\n' if leverage and leverage != 1.0 else ''
-    assert msg_mock.call_args[0][0] == (
+        assert msg_mock.call_args[0][0] == (
-        '\N{WARNING SIGN} *Binance (dry):* Exited KEY/ETH (#1)\n'
+            '\N{WARNING SIGN} *Binance (dry):* Exited KEY/ETH (#1)\n'
-        '*Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
+            '*Profit:* `-57.41% (loss: -0.05746268 ETH)`\n'
-        f'*Enter Tag:* `{enter_signal}`\n'
+            f'*Enter Tag:* `{enter_signal}`\n'
-        '*Exit Reason:* `stop_loss`\n'
+            '*Exit Reason:* `stop_loss`\n'
-        f"*Direction:* `{direction}`\n"
+            f"*Direction:* `{direction}`\n"
-        f"{leverage_text}"
+            f"{leverage_text}"
-        '*Amount:* `1333.33333333`\n'
+            '*Amount:* `1333.33333333`\n'
-        '*Open Rate:* `0.00007500`\n'
+            '*Open Rate:* `0.00007500`\n'
-        '*Exit Rate:* `0.00003201`\n'
+            '*Exit Rate:* `0.00003201`\n'
-        '*Duration:* `1 day, 2:30:00 (1590.0 min)`'
+            '*Duration:* `1 day, 2:30:00 (1590.0 min)`'
-    )
+        )
 
 
 def test_send_msg_status_notification(default_conf, mocker) -> None: