Merge branch 'develop' into feat/stoploss_adjust

2024-11-10 10:21:59 +00:00 · 2023-08-29 07:04:08 +02:00 · 2023-08-29 07:04:08 +02:00 · 1a8b793c0a
commit 1a8b793c0a
parent 5ed5907809 e0e0a25811
96 changed files with 1953 additions and 948 deletions
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@ -10,7 +10,7 @@ updates:
  directory: "/"
  schedule:
    interval: weekly
-  open-pull-requests-limit: 10
+  open-pull-requests-limit: 15
  target-branch: develop

 - package-ecosystem: "github-actions"
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@ -8,7 +8,7 @@ repos:
        # stages: [push]

  - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v1.5.0"
+    rev: "v1.5.1"
    hooks:
      - id: mypy
        exclude: build_helpers
@ -18,7 +18,7 @@ repos:
          - types-requests==2.31.0.2
          - types-tabulate==0.9.0.3
          - types-python-dateutil==2.8.19.14
-          - SQLAlchemy==2.0.19
+          - SQLAlchemy==2.0.20
        # stages: [push]

  - repo: https://github.com/pycqa/isort
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@ -1,8 +1,14 @@
 # .readthedocs.yml
+version: 2

 build:
-  image: latest
+  os: "ubuntu-22.04"
+  tools:
+    python: "3.11"

 python:
-  version: 3.8
-  setup_py_install: false
+  install:
+    - requirements: docs/requirements-docs.txt
+
+mkdocs:
+  configuration: mkdocs.yml
--- a/2
+++ b/2
@ -1,4 +1,4 @@
-FROM python:3.11.4-slim-bullseye as base
+FROM python:3.11.5-slim-bullseye as base

 # Setup env
 ENV LANG C.UTF-8
--- a/build_helpers/TA_Lib-0.4.27-cp310-cp310-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.27-cp310-cp310-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.27-cp311-cp311-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.27-cp311-cp311-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.27-cp38-cp38-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.27-cp38-cp38-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.27-cp39-cp39-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.27-cp39-cp39-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.28-cp310-cp310-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.28-cp310-cp310-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.28-cp311-cp311-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.28-cp311-cp311-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.28-cp38-cp38-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.28-cp38-cp38-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.28-cp39-cp39-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.28-cp39-cp39-win_amd64.whl
--- a/build_helpers/install_ta-lib.sh
+++ b/build_helpers/install_ta-lib.sh
@ -8,8 +8,9 @@ if [ -n "$2" ] || [ ! -f "${INSTALL_LOC}/lib/libta_lib.a" ]; then
  tar zxvf ta-lib-0.4.0-src.tar.gz
  cd ta-lib \
  && sed -i.bak "s|0.00000001|0.000000000000000001 |g" src/ta_func/ta_utility.h \
-  && curl 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.guess' -o config.guess \
-  && curl 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.sub' -o config.sub \
+  && echo "Downloading gcc config.guess and config.sub" \
+  && curl -s 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.guess' -o config.guess \
+  && curl -s 'https://raw.githubusercontent.com/gcc-mirror/gcc/master/config.sub' -o config.sub \
  && ./configure --prefix=${INSTALL_LOC}/ \
  && make
  if [ $? -ne 0 ]; then
--- a/build_helpers/install_windows.ps1
+++ b/build_helpers/install_windows.ps1
@ -5,7 +5,7 @@ python -m pip install --upgrade pip wheel
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"


-pip install --find-links=build_helpers\ TA-Lib
+pip install --find-links=build_helpers\ --prefer-binary  TA-Lib

 pip install -r requirements-dev.txt
 pip install -e .
--- a/build_helpers/pyarrow-13.0.0-cp39-cp39-linux_armv7l.whl
+++ b/build_helpers/pyarrow-13.0.0-cp39-cp39-linux_armv7l.whl
--- a/docs/assets/pycharm_debug.png
+++ b/docs/assets/pycharm_debug.png
--- a/docs/bot-basics.md
+++ b/docs/bot-basics.md
@ -7,7 +7,7 @@ This page provides you some basic concepts on how Freqtrade works and operates.
 * **Strategy**: Your trading strategy, telling the bot what to do.
 * **Trade**: Open position.
 * **Open Order**: Order which is currently placed on the exchange, and is not yet complete.
-* **Pair**: Tradable pair, usually in the format of Base/Quote (e.g. XRP/USDT).
+* **Pair**: Tradable pair, usually in the format of Base/Quote (e.g. `XRP/USDT` for spot, `XRP/USDT:USDT` for futures).
 * **Timeframe**: Candle length to use (e.g. `"5m"`, `"1h"`, ...).
 * **Indicators**: Technical indicators (SMA, EMA, RSI, ...).
 * **Limit order**: Limit orders which execute at the defined limit price or better.
@ -20,6 +20,20 @@ This page provides you some basic concepts on how Freqtrade works and operates.

 All profit calculations of Freqtrade include fees. For Backtesting / Hyperopt / Dry-run modes, the exchange default fee is used (lowest tier on the exchange). For live operations, fees are used as applied by the exchange (this includes BNB rebates etc.).

+## Pair naming
+
+Freqtrade follows the [ccxt naming convention](https://docs.ccxt.com/#/README?id=consistency-of-base-and-quote-currencies) for currencies.
+Using the wrong naming convention in the wrong market will usually result in the bot not recognizing the pair, usually resulting in errors like "this pair is not available".
+
+### Spot pair naming
+
+For spot pairs, naming will be `base/quote` (e.g. `ETH/USDT`).
+
+### Futures pair naming
+
+For futures pairs, naming will be `base/quote:settle` (e.g. `ETH/USDT:USDT`).
+
+
 ## Bot execution logic

 Starting freqtrade in dry-run or live mode (using `freqtrade trade`) will start the bot and start the bot iteration loop.
--- a/docs/bot-usage.md
+++ b/docs/bot-usage.md
@ -3,7 +3,7 @@
 This page explains the different parameters of the bot and how to run it.

 !!! Note
-    If you've used `setup.sh`, don't forget to activate your virtual environment (`source .env/bin/activate`) before running freqtrade commands.
+    If you've used `setup.sh`, don't forget to activate your virtual environment (`source .venv/bin/activate`) before running freqtrade commands.

 !!! Warning "Up-to-date clock"
    The clock on the system running the bot must be accurate, synchronized to a NTP server frequently enough to avoid problems with communication to the exchanges.
--- a/docs/data-analysis.md
+++ b/docs/data-analysis.md
@ -27,7 +27,7 @@ For this to work, first activate your virtual environment and run the following

 ``` bash
 # Activate virtual environment
-source .env/bin/activate
+source .venv/bin/activate

 pip install ipykernel
 ipython kernel install --user --name=freqtrade
--- a/docs/developer.md
+++ b/docs/developer.md
@ -77,7 +77,7 @@ def test_method_to_test(caplog):

 ### Debug configuration

-To debug freqtrade, we recommend VSCode with the following launch configuration (located in `.vscode/launch.json`).
+To debug freqtrade, we recommend VSCode (with the Python extension) with the following launch configuration (located in `.vscode/launch.json`).
 Details will obviously vary between setups - but this should work to get you started.

 ``` json
@ -102,6 +102,19 @@ This method can also be used to debug a strategy, by setting the breakpoints wit

 A similar setup can also be taken for Pycharm - using `freqtrade` as module name, and setting the command line arguments as "parameters".

+??? Tip "Correct venv usage"
+    When using a virtual environment (which you should), make sure that your Editor is using the correct virtual environment to avoid problems or "unknown import" errors.
+
+    #### Vscode
+
+    You can select the correct environment in VSCode with the command "Python: Select Interpreter" - which will show you environments the extension detected.
+    If your environment has not been detected, you can also pick a path manually.
+
+    #### Pycharm
+
+    In pycharm, you can select the appropriate Environment in the "Run/Debug Configurations" window.
+    ![Pycharm debug configuration](assets/pycharm_debug.png)
+
 !!! Note "Startup directory"
    This assumes that you have the repository checked out, and the editor is started at the repository root level (so setup.py is at the top level of your repository).

--- a/docs/edge.md
+++ b/docs/edge.md
@ -2,6 +2,10 @@

 The `Edge Positioning` module uses probability to calculate your win rate and risk reward ratio. It will use these statistics to control your strategy trade entry points, position size and, stoploss.

+!!! Danger "Deprecated functionality"
+    `Edge positioning` (or short Edge) is currently in maintenance mode only (we keep existing functionality alive) and should be considered as deprecated.
+    It will currently not receive new features until either someone stepped forward to take up ownership of that module - or we'll decide to remove edge from freqtrade.
+
 !!! Warning
    When using `Edge positioning` with a dynamic whitelist (VolumePairList), make sure to also use `AgeFilter` and set it to at least `calculate_since_number_of_days` to avoid problems with missing data.

--- a/docs/faq.md
+++ b/docs/faq.md
@ -36,7 +36,7 @@ Running the bot with `freqtrade trade --config config.json` shows the output `fr
 This could be caused by the following reasons:

 * The virtual environment is not active.
-  * Run `source .env/bin/activate` to activate the virtual environment.
+  * Run `source .venv/bin/activate` to activate the virtual environment.
 * The installation did not complete successfully.
  * Please check the [Installation documentation](installation.md).

--- a/docs/freqai-parameter-table.md
+++ b/docs/freqai-parameter-table.md
@ -101,11 +101,11 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 #### trainer_kwargs

 | Parameter    | Description |
-|------------|-------------|
+|--------------|-------------|
 |              |  **Model training parameters within the `freqai.model_training_parameters.model_kwargs` sub dictionary**
-| `max_iters` | The number of training iterations to run. iteration here refers to the number of times we call self.optimizer.step(). used to calculate n_epochs. <br> **Datatype:** int. <br> Default: `100`.
-| `batch_size` | The size of the batches to use during training.. <br> **Datatype:** int. <br> Default: `64`.
-| `max_n_eval_batches` | The maximum number batches to use for evaluation.. <br> **Datatype:** int, optional. <br> Default: `None`.
+| `n_epochs`   | The `n_epochs` parameter is a crucial setting in the PyTorch training loop that determines the number of times the entire training dataset will be used to update the model's parameters. An epoch represents one full pass through the entire training dataset. Overrides `n_steps`. Either `n_epochs` or `n_steps` must be set. <br><br> **Datatype:** int. optional. <br> Default: `10`.
+| `n_steps`    | An alternative way of setting `n_epochs` -  the number of training iterations to run. Iteration here refer to the number of times we call `optimizer.step()`. Ignored if `n_epochs` is set. A simplified version of the function: <br><br> n_epochs = n_steps / (n_obs / batch_size) <br><br> The motivation here is that `n_steps` is easier to optimize and keep stable across different n_obs - the number of data points.  <br> <br> **Datatype:** int. optional. <br> Default: `None`.
+| `batch_size` | The size of the batches to use during training. <br><br> **Datatype:** int. <br> Default: `64`.


 ### Additional parameters
--- a/docs/freqai-reinforcement-learning.md
+++ b/docs/freqai-reinforcement-learning.md
@ -20,7 +20,7 @@ With the current framework, we aim to expose the training environment via the co

 We envision the majority of users focusing their effort on creative design of the `calculate_reward()` function [details here](#creating-a-custom-reward-function), while leaving the rest of the environment untouched. Other users may not touch the environment at all, and they will only play with the configuration settings and the powerful feature engineering that already exists in FreqAI. Meanwhile, we enable advanced users to create their own model classes entirely.

-The framework is built on stable_baselines3 (torch) and OpenAI gym for the base environment class. But generally speaking, the model class is well isolated. Thus, the addition of competing libraries can be easily integrated into the existing framework. For the environment, it is inheriting from `gym.env` which means that it is necessary to write an entirely new environment in order to switch to a different library.
+The framework is built on stable_baselines3 (torch) and OpenAI gym for the base environment class. But generally speaking, the model class is well isolated. Thus, the addition of competing libraries can be easily integrated into the existing framework. For the environment, it is inheriting from `gym.Env` which means that it is necessary to write an entirely new environment in order to switch to a different library.

 ### Important considerations

@ -173,7 +173,7 @@ class MyCoolRLModel(ReinforcementLearner):
    """
    class MyRLEnv(Base5ActionRLEnv):
        """
-        User made custom environment. This class inherits from BaseEnvironment and gym.env.
+        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.

@ -254,7 +254,7 @@ FreqAI also provides a built in episodic summary logger called `self.tensorboard
 ```python
    class MyRLEnv(Base5ActionRLEnv):
        """
-        User made custom environment. This class inherits from BaseEnvironment and gym.env.
+        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.
        """
--- a/docs/hyperopt.md
+++ b/docs/hyperopt.md
@ -31,7 +31,7 @@ The docker-image includes hyperopt dependencies, no further action needed.
 ### Easy installation script (setup.sh) / Manual installation

 ```bash
-source .env/bin/activate
+source .venv/bin/activate
 pip install -r requirements-hyperopt.txt
 ```

--- a/docs/installation.md
+++ b/docs/installation.md
@ -143,11 +143,11 @@ If you are on Debian, Ubuntu or MacOS, freqtrade provides the script to install

 ### Activate your virtual environment

-Each time you open a new terminal, you must run `source .env/bin/activate` to activate your virtual environment.
+Each time you open a new terminal, you must run `source .venv/bin/activate` to activate your virtual environment.

 ```bash
-# then activate your .env
-source ./.env/bin/activate
+# activate virtual environment
+source ./.venv/bin/activate
 ```

 ### Congratulations
@ -172,7 +172,7 @@ With this option, the script will install the bot and most dependencies:
 You will need to have git and python3.8+ installed beforehand for this to work.

 * Mandatory software as: `ta-lib`
-* Setup your virtualenv under `.env/`
+* Setup your virtualenv under `.venv/`

 This option is a combination of installation tasks and `--reset`

@ -225,11 +225,11 @@ rm -rf ./ta-lib*
 You will run freqtrade in separated `virtual environment`

 ```bash
-# create virtualenv in directory /freqtrade/.env
-python3 -m venv .env
+# create virtualenv in directory /freqtrade/.venv
+python3 -m venv .venv

 # run virtualenv
-source .env/bin/activate
+source .venv/bin/activate
 ```

 #### Install python dependencies
@ -286,7 +286,7 @@ cd freqtrade
 #### Freqtrade install: Conda Environment

 ```bash
-conda create --name freqtrade python=3.10
+conda create --name freqtrade python=3.11
 ```

 !!! Note "Creating Conda Environment"
@ -383,7 +383,7 @@ You've made it this far, so you have successfully installed freqtrade.
 freqtrade create-userdir --userdir user_data

 # Step 2 - Create a new configuration file
-freqtrade new-config --config config.json
+freqtrade new-config --config user_data/config.json
 ```

 You are ready to run, read [Bot Configuration](configuration.md), remember to start with `dry_run: True` and verify that everything is working.
@ -393,7 +393,7 @@ To learn how to setup your configuration, please refer to the [Bot Configuration
 ### Start the Bot

 ```bash
-freqtrade trade --config config.json --strategy SampleStrategy
+freqtrade trade --config user_data/config.json --strategy SampleStrategy
 ```

 !!! Warning
@ -411,8 +411,8 @@ If you used (1)`Script` or (2)`Manual` installation, you need to run the bot in
 # if:
 bash: freqtrade: command not found

-# then activate your .env
-source ./.env/bin/activate
+# then activate your virtual environment
+source ./.venv/bin/activate
 ```

 ### MacOS installation error
--- a/docs/leverage.md
+++ b/docs/leverage.md
@ -64,7 +64,7 @@ You will also have to pick a "margin mode" (explanation below) - with freqtrade

 ##### Pair namings

-Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/en/latest/manual.html?#perpetual-swap-perpetual-future).
+Freqtrade follows the [ccxt naming conventions for futures](https://docs.ccxt.com/#/README?id=perpetual-swap-perpetual-future).
 A futures pair will therefore have the naming of `base/quote:settle` (e.g. `ETH/USDT:USDT`).

 ### Margin mode
--- a/docs/requirements-docs.txt
+++ b/docs/requirements-docs.txt
@ -1,6 +1,6 @@
 markdown==3.4.4
 mkdocs==1.5.2
-mkdocs-material==9.1.21
+mkdocs-material==9.2.5
 mdx_truly_sane_lists==1.3
 pymdown-extensions==10.1
 jinja2==3.1.2
--- a/docs/windows_installation.md
+++ b/docs/windows_installation.md
@ -31,8 +31,8 @@ Other versions must be downloaded from the above link.

 ``` powershell
 cd \path\freqtrade
-python -m venv .env
-.env\Scripts\activate.ps1
+python -m venv .venv
+.venv\Scripts\activate.ps1
 # optionally install ta-lib from wheel
 # Eventually adjust the below filename to match the downloaded wheel
 pip install --find-links build_helpers\ TA-Lib -U
--- a/freqtrade/init.py
+++ b/freqtrade/init.py
@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2023.8.dev'
+__version__ = '2023.9-dev'

 if 'dev' in __version__:
    from pathlib import Path
--- a/freqtrade/commands/build_config_commands.py
+++ b/freqtrade/commands/build_config_commands.py
@ -10,7 +10,7 @@ from freqtrade.configuration.directory_operations import chown_user_directory
 from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import MAP_EXCHANGE_CHILDCLASS, available_exchanges
-from freqtrade.misc import render_template
+from freqtrade.util import render_template


 logger = logging.getLogger(__name__)
@ -105,7 +105,7 @@ def ask_user_config() -> Dict[str, Any]:
            "type": "select",
            "name": "exchange_name",
            "message": "Select exchange",
-            "choices": lambda x: [
+            "choices": [
                "binance",
                "binanceus",
                "bittrex",
--- a/freqtrade/commands/cli_options.py
+++ b/freqtrade/commands/cli_options.py
@ -441,7 +441,7 @@ AVAILABLE_CLI_OPTIONS = {
    "dataformat_trades": Arg(
        '--data-format-trades',
        help='Storage format for downloaded trades data. (default: `feather`).',
-        choices=constants.AVAILABLE_DATAHANDLERS_TRADES,
+        choices=constants.AVAILABLE_DATAHANDLERS,
    ),
    "show_timerange": Arg(
        '--show-timerange',
--- a/freqtrade/commands/deploy_commands.py
+++ b/freqtrade/commands/deploy_commands.py
@ -10,7 +10,7 @@ from freqtrade.configuration.directory_operations import copy_sample_files, crea
 from freqtrade.constants import USERPATH_STRATEGIES
 from freqtrade.enums import RunMode
 from freqtrade.exceptions import OperationalException
-from freqtrade.misc import render_template, render_template_with_fallback
+from freqtrade.util import render_template, render_template_with_fallback


 logger = logging.getLogger(__name__)
@ -35,6 +35,10 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st
    Deploy new strategy from template to strategy_path
    """
    fallback = 'full'
+    attributes = render_template_with_fallback(
+        templatefile=f"strategy_subtemplates/strategy_attributes_{subtemplate}.j2",
+        templatefallbackfile=f"strategy_subtemplates/strategy_attributes_{fallback}.j2",
+    )
    indicators = render_template_with_fallback(
        templatefile=f"strategy_subtemplates/indicators_{subtemplate}.j2",
        templatefallbackfile=f"strategy_subtemplates/indicators_{fallback}.j2",
@ -58,6 +62,7 @@ def deploy_new_strategy(strategy_name: str, strategy_path: Path, subtemplate: st

    strategy_text = render_template(templatefile='base_strategy.py.j2',
                                    arguments={"strategy": strategy_name,
+                                               "attributes": attributes,
                                               "indicators": indicators,
                                               "buy_trend": buy_trend,
                                               "sell_trend": sell_trend,
--- a/freqtrade/commands/webserver_commands.py
+++ b/freqtrade/commands/webserver_commands.py
@ -7,9 +7,10 @@ def start_webserver(args: Dict[str, Any]) -> None:
    """
    Main entry point for webserver mode
    """
-    from freqtrade.configuration import Configuration
+    from freqtrade.configuration import setup_utils_configuration
    from freqtrade.rpc.api_server import ApiServer

    # Initialize configuration
-    config = Configuration(args, RunMode.WEBSERVER).get_config()
+
+    config = setup_utils_configuration(args, RunMode.WEBSERVER)
    ApiServer(config, standalone=True)
--- a/freqtrade/configuration/config_validation.py
+++ b/freqtrade/configuration/config_validation.py
@ -51,6 +51,8 @@ def validate_config_schema(conf: Dict[str, Any], preliminary: bool = False) -> D
            conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED
        else:
            conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED_FINAL
+    elif conf.get('runmode', RunMode.OTHER) == RunMode.WEBSERVER:
+        conf_schema['required'] = constants.SCHEMA_MINIMAL_WEBSERVER
    else:
        conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
    try:
--- a/freqtrade/configuration/environment_vars.py
+++ b/freqtrade/configuration/environment_vars.py
@ -41,7 +41,7 @@ def flat_vars_to_nested_dict(env_dict: Dict[str, Any], prefix: str) -> Dict[str,
            key = env_var.replace(prefix, '')
            for k in reversed(key.split('__')):
                val = {k.lower(): get_var_typed(val)
-                       if type(val) != dict and k not in no_convert else val}
+                       if not isinstance(val, dict) and k not in no_convert else val}
            relevant_vars = deep_merge_dicts(val, relevant_vars)
    return relevant_vars

--- a/freqtrade/constants.py
+++ b/freqtrade/constants.py
@ -38,8 +38,7 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList', 'ProducerPairList', '
                       'ShuffleFilter', 'SpreadFilter', 'VolatilityFilter']
 AVAILABLE_PROTECTIONS = ['CooldownPeriod',
                         'LowProfitPairs', 'MaxDrawdown', 'StoplossGuard']
-AVAILABLE_DATAHANDLERS_TRADES = ['json', 'jsongz', 'hdf5', 'feather']
-AVAILABLE_DATAHANDLERS = AVAILABLE_DATAHANDLERS_TRADES + ['parquet']
+AVAILABLE_DATAHANDLERS = ['json', 'jsongz', 'hdf5', 'feather', 'parquet']
 BACKTEST_BREAKDOWNS = ['day', 'week', 'month']
 BACKTEST_CACHE_AGE = ['none', 'day', 'week', 'month']
 BACKTEST_CACHE_DEFAULT = 'day'
@ -50,6 +49,15 @@ DEFAULT_DATAFRAME_COLUMNS = ['date', 'open', 'high', 'low', 'close', 'volume']
 # Don't modify sequence of DEFAULT_TRADES_COLUMNS
 # it has wide consequences for stored trades files
 DEFAULT_TRADES_COLUMNS = ['timestamp', 'id', 'type', 'side', 'price', 'amount', 'cost']
+TRADES_DTYPES = {
+    'timestamp': 'int64',
+    'id': 'str',
+    'type': 'str',
+    'side': 'str',
+    'price': 'float64',
+    'amount': 'float64',
+    'cost': 'float64',
+}
 TRADING_MODES = ['spot', 'margin', 'futures']
 MARGIN_MODES = ['cross', 'isolated', '']

@ -450,7 +458,7 @@ CONF_SCHEMA = {
        },
        'dataformat_trades': {
            'type': 'string',
-            'enum': AVAILABLE_DATAHANDLERS_TRADES,
+            'enum': AVAILABLE_DATAHANDLERS,
            'default': 'feather'
        },
        'position_adjustment_enable': {'type': 'boolean'},
@ -667,6 +675,9 @@ SCHEMA_MINIMAL_REQUIRED = [
    'dataformat_ohlcv',
    'dataformat_trades',
 ]
+SCHEMA_MINIMAL_WEBSERVER = SCHEMA_MINIMAL_REQUIRED + [
+    'api_server',
+]

 CANCEL_REASON = {
    "TIMEOUT": "cancelled due to timeout",
--- a/freqtrade/data/converter.py
+++ b/freqtrade/data/converter.py
@ -1,16 +1,15 @@
 """
 Functions to convert data from one format to another
 """
-import itertools
 import logging
-from operator import itemgetter
 from typing import Dict, List

 import numpy as np
 import pandas as pd
 from pandas import DataFrame, to_datetime

-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, Config, TradeList
+from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TRADES_DTYPES,
+                                 Config, TradeList)
 from freqtrade.enums import CandleType, TradingMode


@ -195,15 +194,14 @@ def order_book_to_dataframe(bids: list, asks: list) -> DataFrame:
    return frame


-def trades_remove_duplicates(trades: List[List]) -> List[List]:
+def trades_df_remove_duplicates(trades: pd.DataFrame) -> pd.DataFrame:
    """
-    Removes duplicates from the trades list.
-    Uses itertools.groupby to avoid converting to pandas.
-    Tests show it as being pretty efficient on lists of 4M Lists.
-    :param trades: List of Lists with constants.DEFAULT_TRADES_COLUMNS as columns
-    :return: same format as above, but with duplicates removed
+    Removes duplicates from the trades DataFrame.
+    Uses pandas.DataFrame.drop_duplicates to remove duplicates based on the 'timestamp' column.
+    :param trades: DataFrame with the columns constants.DEFAULT_TRADES_COLUMNS
+    :return: DataFrame with duplicates removed based on the 'timestamp' column
    """
-    return [i for i, _ in itertools.groupby(sorted(trades, key=itemgetter(0)))]
+    return trades.drop_duplicates(subset=['timestamp', 'id'])


 def trades_dict_to_list(trades: List[Dict]) -> TradeList:
@ -215,7 +213,32 @@ def trades_dict_to_list(trades: List[Dict]) -> TradeList:
    return [[t[col] for col in DEFAULT_TRADES_COLUMNS] for t in trades]


-def trades_to_ohlcv(trades: TradeList, timeframe: str) -> DataFrame:
+def trades_convert_types(trades: DataFrame) -> DataFrame:
+    """
+    Convert Trades dtypes and add 'date' column
+    """
+    trades = trades.astype(TRADES_DTYPES)
+    trades['date'] = to_datetime(trades['timestamp'], unit='ms', utc=True)
+    return trades
+
+
+def trades_list_to_df(trades: TradeList, convert: bool = True):
+    """
+    convert trades list to dataframe
+    :param trades: List of Lists with constants.DEFAULT_TRADES_COLUMNS as columns
+    """
+    if not trades:
+        df = DataFrame(columns=DEFAULT_TRADES_COLUMNS)
+    else:
+        df = DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
+
+    if convert:
+        df = trades_convert_types(df)
+
+    return df
+
+
+def trades_to_ohlcv(trades: DataFrame, timeframe: str) -> DataFrame:
    """
    Converts trades list to OHLCV list
    :param trades: List of trades, as returned by ccxt.fetch_trades.
@ -225,12 +248,9 @@ def trades_to_ohlcv(trades: TradeList, timeframe: str) -> DataFrame:
    """
    from freqtrade.exchange import timeframe_to_minutes
    timeframe_minutes = timeframe_to_minutes(timeframe)
-    if not trades:
+    if trades.empty:
        raise ValueError('Trade-list empty.')
-    df = pd.DataFrame(trades, columns=DEFAULT_TRADES_COLUMNS)
-    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms',
-                                     utc=True,)
-    df = df.set_index('timestamp')
+    df = trades.set_index('date', drop=True)

    df_new = df['price'].resample(f'{timeframe_minutes}min').ohlc()
    df_new['volume'] = df['amount'].resample(f'{timeframe_minutes}min').sum()
--- a/freqtrade/data/dataprovider.py
+++ b/freqtrade/data/dataprovider.py
@ -17,7 +17,7 @@ from freqtrade.constants import (FULL_DATAFRAME_THRESHOLD, Config, ListPairsWith
 from freqtrade.data.history import load_pair_history
 from freqtrade.enums import CandleType, RPCMessageType, RunMode
 from freqtrade.exceptions import ExchangeError, OperationalException
-from freqtrade.exchange import Exchange, timeframe_to_seconds
+from freqtrade.exchange import Exchange, timeframe_to_prev_date, timeframe_to_seconds
 from freqtrade.exchange.types import OrderBook
 from freqtrade.misc import append_candles_to_dataframe
 from freqtrade.rpc import RPCManager
@ -46,6 +46,8 @@ class DataProvider:
        self.__rpc = rpc
        self.__cached_pairs: Dict[PairWithTimeframe, Tuple[DataFrame, datetime]] = {}
        self.__slice_index: Optional[int] = None
+        self.__slice_date: Optional[datetime] = None
+
        self.__cached_pairs_backtesting: Dict[PairWithTimeframe, DataFrame] = {}
        self.__producer_pairs_df: Dict[str,
                                       Dict[PairWithTimeframe, Tuple[DataFrame, datetime]]] = {}
@ -64,10 +66,19 @@ class DataProvider:
    def _set_dataframe_max_index(self, limit_index: int):
        """
        Limit analyzed dataframe to max specified index.
+        Only relevant in backtesting.
        :param limit_index: dataframe index.
        """
        self.__slice_index = limit_index

+    def _set_dataframe_max_date(self, limit_date: datetime):
+        """
+        Limit infomrative dataframe to max specified index.
+        Only relevant in backtesting.
+        :param limit_date: "current date"
+        """
+        self.__slice_date = limit_date
+
    def _set_cached_df(
        self,
        pair: str,
@ -284,7 +295,7 @@ class DataProvider:
    def historic_ohlcv(
        self,
        pair: str,
-        timeframe: Optional[str] = None,
+        timeframe: str,
        candle_type: str = ''
    ) -> DataFrame:
        """
@ -307,7 +318,7 @@ class DataProvider:
            timerange.subtract_start(tf_seconds * startup_candles)
            self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
                pair=pair,
-                timeframe=timeframe or self._config['timeframe'],
+                timeframe=timeframe,
                datadir=self._config['datadir'],
                timerange=timerange,
                data_format=self._config['dataformat_ohlcv'],
@ -354,7 +365,13 @@ class DataProvider:
            data = self.ohlcv(pair=pair, timeframe=timeframe, candle_type=candle_type)
        else:
            # Get historical OHLCV data (cached on disk).
+            timeframe = timeframe or self._config['timeframe']
            data = self.historic_ohlcv(pair=pair, timeframe=timeframe, candle_type=candle_type)
+            # Cut date to timeframe-specific date.
+            # This is necessary to prevent lookahead bias in callbacks through informative pairs.
+            if self.__slice_date:
+                cutoff_date = timeframe_to_prev_date(timeframe, self.__slice_date)
+                data = data.loc[data['date'] < cutoff_date]
        if len(data) == 0:
            logger.warning(f"No data found for ({pair}, {timeframe}, {candle_type}).")
        return data
--- a/freqtrade/data/history/featherdatahandler.py
+++ b/freqtrade/data/history/featherdatahandler.py
@ -4,7 +4,7 @@ from typing import Optional
 from pandas import DataFrame, read_feather, to_datetime

 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
 from freqtrade.enums import CandleType

 from .idatahandler import IDataHandler
@ -82,43 +82,41 @@ class FeatherDataHandler(IDataHandler):
        """
        raise NotImplementedError()

-    def trades_store(self, pair: str, data: TradeList) -> None:
+    def _trades_store(self, pair: str, data: DataFrame) -> None:
        """
        Store trades data (list of Dicts) to file
        :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                     column sequence as in DEFAULT_TRADES_COLUMNS
        """
        filename = self._pair_trades_filename(self._datadir, pair)
        self.create_dir_if_needed(filename)
+        data.reset_index(drop=True).to_feather(filename, compression_level=9, compression='lz4')

-        tradesdata = DataFrame(data, columns=DEFAULT_TRADES_COLUMNS)
-        tradesdata.to_feather(filename, compression_level=9, compression='lz4')
-
-    def trades_append(self, pair: str, data: TradeList):
+    def trades_append(self, pair: str, data: DataFrame):
        """
        Append data to existing files
        :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                     column sequence as in DEFAULT_TRADES_COLUMNS
        """
        raise NotImplementedError()

-    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> DataFrame:
        """
        Load a pair from file, either .json.gz or .json
        # TODO: respect timerange ...
        :param pair: Load trades for this pair
        :param timerange: Timerange to load trades for - currently not implemented
-        :return: List of trades
+        :return: Dataframe containing trades
        """
        filename = self._pair_trades_filename(self._datadir, pair)
        if not filename.exists():
-            return []
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)

        tradesdata = read_feather(filename)

-        return tradesdata.values.tolist()
+        return tradesdata

    @classmethod
    def _get_file_extension(cls):
--- a/freqtrade/data/history/hdf5datahandler.py
+++ b/freqtrade/data/history/hdf5datahandler.py
@ -5,7 +5,7 @@ import numpy as np
 import pandas as pd

 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
 from freqtrade.enums import CandleType

 from .idatahandler import IDataHandler
@ -100,42 +100,42 @@ class HDF5DataHandler(IDataHandler):
        """
        raise NotImplementedError()

-    def trades_store(self, pair: str, data: TradeList) -> None:
+    def _trades_store(self, pair: str, data: pd.DataFrame) -> None:
        """
        Store trades data (list of Dicts) to file
        :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                     column sequence as in DEFAULT_TRADES_COLUMNS
        """
        key = self._pair_trades_key(pair)

-        pd.DataFrame(data, columns=DEFAULT_TRADES_COLUMNS).to_hdf(
+        data.to_hdf(
            self._pair_trades_filename(self._datadir, pair), key,
            mode='a', complevel=9, complib='blosc',
            format='table', data_columns=['timestamp']
        )

-    def trades_append(self, pair: str, data: TradeList):
+    def trades_append(self, pair: str, data: pd.DataFrame):
        """
        Append data to existing files
        :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                     column sequence as in DEFAULT_TRADES_COLUMNS
        """
        raise NotImplementedError()

-    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> pd.DataFrame:
        """
        Load a pair from h5 file.
        :param pair: Load trades for this pair
        :param timerange: Timerange to load trades for - currently not implemented
-        :return: List of trades
+        :return: Dataframe containing trades
        """
        key = self._pair_trades_key(pair)
        filename = self._pair_trades_filename(self._datadir, pair)

        if not filename.exists():
-            return []
+            return pd.DataFrame(columns=DEFAULT_TRADES_COLUMNS)
        where = []
        if timerange:
            if timerange.starttype == 'date':
@ -145,7 +145,7 @@ class HDF5DataHandler(IDataHandler):

        trades: pd.DataFrame = pd.read_hdf(filename, key=key, mode="r", where=where)
        trades[['id', 'type']] = trades[['id', 'type']].replace({np.nan: None})
-        return trades.values.tolist()
+        return trades

    @classmethod
    def _get_file_extension(cls):
--- a/freqtrade/data/history/history_utils.py
+++ b/freqtrade/data/history/history_utils.py
@ -10,14 +10,16 @@ from freqtrade.configuration import TimeRange
 from freqtrade.constants import (DATETIME_PRINT_FORMAT, DEFAULT_DATAFRAME_COLUMNS,
                                 DL_DATA_TIMEFRAMES, Config)
 from freqtrade.data.converter import (clean_ohlcv_dataframe, ohlcv_to_dataframe,
-                                      trades_remove_duplicates, trades_to_ohlcv)
+                                      trades_df_remove_duplicates, trades_list_to_df,
+                                      trades_to_ohlcv)
 from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
 from freqtrade.enums import CandleType
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import Exchange
 from freqtrade.plugins.pairlist.pairlist_helpers import dynamic_expand_pairlist
-from freqtrade.util import format_ms_time
+from freqtrade.util import dt_ts, format_ms_time
 from freqtrade.util.binance_mig import migrate_binance_futures_data
+from freqtrade.util.datetime_helpers import dt_now


 logger = logging.getLogger(__name__)
@ -349,24 +351,27 @@ def _download_trades_history(exchange: Exchange,
        # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
        # DEFAULT_TRADES_COLUMNS: 1 -> id

-        if trades and since < trades[0][0]:
+        if not trades.empty and since > 0 and since < trades.iloc[0]['timestamp']:
            # since is before the first trade
-            logger.info(f"Start earlier than available data. Redownloading trades for {pair}...")
-            trades = []
+            logger.info(f"Start ({trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}) earlier than "
+                        f"available data. Redownloading trades for {pair}...")
+            trades = trades_list_to_df([])

-        if not since:
-            since = int((datetime.now() - timedelta(days=new_pairs_days)).timestamp()) * 1000
-
-        from_id = trades[-1][1] if trades else None
-        if trades and since < trades[-1][0]:
+        from_id = trades.iloc[-1]['id'] if not trades.empty else None
+        if not trades.empty and since < trades.iloc[-1]['timestamp']:
            # Reset since to the last available point
            # - 5 seconds (to ensure we're getting all trades)
-            since = trades[-1][0] - (5 * 1000)
+            since = trades.iloc[-1]['timestamp'] - (5 * 1000)
            logger.info(f"Using last trade date -5s - Downloading trades for {pair} "
                        f"since: {format_ms_time(since)}.")

-        logger.debug(f"Current Start: {format_ms_time(trades[0][0]) if trades else 'None'}")
-        logger.debug(f"Current End: {format_ms_time(trades[-1][0]) if trades else 'None'}")
+        if not since:
+            since = dt_ts(dt_now() - timedelta(days=new_pairs_days))
+
+        logger.debug("Current Start: %s", 'None' if trades.empty else
+                     f"{trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}")
+        logger.debug("Current End: %s", 'None' if trades.empty else
+                     f"{trades.iloc[-1]['date']:{DATETIME_PRINT_FORMAT}}")
        logger.info(f"Current Amount of trades: {len(trades)}")

        # Default since_ms to 30 days if nothing is given
@ -375,13 +380,16 @@ def _download_trades_history(exchange: Exchange,
                                                  until=until,
                                                  from_id=from_id,
                                                  )
-        trades.extend(new_trades[1])
+        new_trades_df = trades_list_to_df(new_trades[1])
+        trades = concat([trades, new_trades_df], axis=0)
        # Remove duplicates to make sure we're not storing data we don't need
-        trades = trades_remove_duplicates(trades)
+        trades = trades_df_remove_duplicates(trades)
        data_handler.trades_store(pair, data=trades)

-        logger.debug(f"New Start: {format_ms_time(trades[0][0])}")
-        logger.debug(f"New End: {format_ms_time(trades[-1][0])}")
+        logger.debug("New Start: %s", 'None' if trades.empty else
+                     f"{trades.iloc[0]['date']:{DATETIME_PRINT_FORMAT}}")
+        logger.debug("New End: %s", 'None' if trades.empty else
+                     f"{trades.iloc[-1]['date']:{DATETIME_PRINT_FORMAT}}")
        logger.info(f"New Amount of trades: {len(trades)}")
        return True

--- a/freqtrade/data/history/idatahandler.py
+++ b/freqtrade/data/history/idatahandler.py
@ -15,8 +15,9 @@ from pandas import DataFrame

 from freqtrade import misc
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import ListPairsWithTimeframes, TradeList
-from freqtrade.data.converter import clean_ohlcv_dataframe, trades_remove_duplicates, trim_dataframe
+from freqtrade.constants import DEFAULT_TRADES_COLUMNS, ListPairsWithTimeframes
+from freqtrade.data.converter import (clean_ohlcv_dataframe, trades_convert_types,
+                                      trades_df_remove_duplicates, trim_dataframe)
 from freqtrade.enums import CandleType, TradingMode
 from freqtrade.exchange import timeframe_to_seconds

@ -170,32 +171,42 @@ class IDataHandler(ABC):
        return [cls.rebuild_pair_from_filename(match[0]) for match in _tmp if match]

    @abstractmethod
-    def trades_store(self, pair: str, data: TradeList) -> None:
+    def _trades_store(self, pair: str, data: DataFrame) -> None:
        """
        Store trades data (list of Dicts) to file
        :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                     column sequence as in DEFAULT_TRADES_COLUMNS
        """

    @abstractmethod
-    def trades_append(self, pair: str, data: TradeList):
+    def trades_append(self, pair: str, data: DataFrame):
        """
        Append data to existing files
        :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                     column sequence as in DEFAULT_TRADES_COLUMNS
        """

    @abstractmethod
-    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> DataFrame:
        """
        Load a pair from file, either .json.gz or .json
        :param pair: Load trades for this pair
        :param timerange: Timerange to load trades for - currently not implemented
-        :return: List of trades
+        :return: Dataframe containing trades
        """

+    def trades_store(self, pair: str, data: DataFrame) -> None:
+        """
+        Store trades data (list of Dicts) to file
+        :param pair: Pair - used for filename
+        :param data: Dataframe containing trades
+                     column sequence as in DEFAULT_TRADES_COLUMNS
+        """
+        # Filter on expected columns (will remove the actual date column).
+        self._trades_store(pair, data[DEFAULT_TRADES_COLUMNS])
+
    def trades_purge(self, pair: str) -> bool:
        """
        Remove data for this pair
@ -208,7 +219,7 @@ class IDataHandler(ABC):
            return True
        return False

-    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+    def trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> DataFrame:
        """
        Load a pair from file, either .json.gz or .json
        Removes duplicates in the process.
@ -216,7 +227,10 @@ class IDataHandler(ABC):
        :param timerange: Timerange to load trades for - currently not implemented
        :return: List of trades
        """
-        return trades_remove_duplicates(self._trades_load(pair, timerange=timerange))
+        trades = trades_df_remove_duplicates(self._trades_load(pair, timerange=timerange))
+
+        trades = trades_convert_types(trades)
+        return trades

    @classmethod
    def create_dir_if_needed(cls, datadir: Path):
--- a/freqtrade/data/history/jsondatahandler.py
+++ b/freqtrade/data/history/jsondatahandler.py
@ -6,8 +6,8 @@ from pandas import DataFrame, read_json, to_datetime

 from freqtrade import misc
 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
-from freqtrade.data.converter import trades_dict_to_list
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS
+from freqtrade.data.converter import trades_dict_to_list, trades_list_to_df
 from freqtrade.enums import CandleType

 from .idatahandler import IDataHandler
@ -94,45 +94,46 @@ class JsonDataHandler(IDataHandler):
        """
        raise NotImplementedError()

-    def trades_store(self, pair: str, data: TradeList) -> None:
+    def _trades_store(self, pair: str, data: DataFrame) -> None:
        """
        Store trades data (list of Dicts) to file
        :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                     column sequence as in DEFAULT_TRADES_COLUMNS
        """
        filename = self._pair_trades_filename(self._datadir, pair)
-        misc.file_dump_json(filename, data, is_zip=self._use_zip)
+        trades = data.values.tolist()
+        misc.file_dump_json(filename, trades, is_zip=self._use_zip)

-    def trades_append(self, pair: str, data: TradeList):
+    def trades_append(self, pair: str, data: DataFrame):
        """
        Append data to existing files
        :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                     column sequence as in DEFAULT_TRADES_COLUMNS
        """
        raise NotImplementedError()

-    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> TradeList:
+    def _trades_load(self, pair: str, timerange: Optional[TimeRange] = None) -> DataFrame:
        """
        Load a pair from file, either .json.gz or .json
        # TODO: respect timerange ...
        :param pair: Load trades for this pair
        :param timerange: Timerange to load trades for - currently not implemented
-        :return: List of trades
+        :return: Dataframe containing trades
        """
        filename = self._pair_trades_filename(self._datadir, pair)
        tradesdata = misc.file_load_json(filename)

        if not tradesdata:
-            return []
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)

        if isinstance(tradesdata[0], dict):
            # Convert trades dict to list
            logger.info("Old trades format detected - converting")
            tradesdata = trades_dict_to_list(tradesdata)
            pass
-        return tradesdata
+        return trades_list_to_df(tradesdata, convert=False)

    @classmethod
    def _get_file_extension(cls):
--- a/freqtrade/data/history/parquetdatahandler.py
+++ b/freqtrade/data/history/parquetdatahandler.py
@ -4,7 +4,7 @@ from typing import Optional
 from pandas import DataFrame, read_parquet, to_datetime

 from freqtrade.configuration import TimeRange
-from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, TradeList
+from freqtrade.constants import DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TradeList
 from freqtrade.enums import CandleType

 from .idatahandler import IDataHandler
@ -81,25 +81,22 @@ class ParquetDataHandler(IDataHandler):
        """
        raise NotImplementedError()

-    def trades_store(self, pair: str, data: TradeList) -> None:
+    def _trades_store(self, pair: str, data: DataFrame) -> None:
        """
        Store trades data (list of Dicts) to file
        :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                     column sequence as in DEFAULT_TRADES_COLUMNS
        """
-        # filename = self._pair_trades_filename(self._datadir, pair)
+        filename = self._pair_trades_filename(self._datadir, pair)
+        self.create_dir_if_needed(filename)
+        data.reset_index(drop=True).to_parquet(filename)

-        raise NotImplementedError()
-        # array = pa.array(data)
-        # array
-        # feather.write_feather(data, filename)
-
-    def trades_append(self, pair: str, data: TradeList):
+    def trades_append(self, pair: str, data: DataFrame):
        """
        Append data to existing files
        :param pair: Pair - used for filename
-        :param data: List of Lists containing trade data,
+        :param data: Dataframe containing trades
                     column sequence as in DEFAULT_TRADES_COLUMNS
        """
        raise NotImplementedError()
@ -112,14 +109,13 @@ class ParquetDataHandler(IDataHandler):
        :param timerange: Timerange to load trades for - currently not implemented
        :return: List of trades
        """
-        raise NotImplementedError()
-        # filename = self._pair_trades_filename(self._datadir, pair)
-        # tradesdata = misc.file_load_json(filename)
+        filename = self._pair_trades_filename(self._datadir, pair)
+        if not filename.exists():
+            return DataFrame(columns=DEFAULT_TRADES_COLUMNS)

-        # if not tradesdata:
-        #     return []
+        tradesdata = read_parquet(filename)

-        # return tradesdata
+        return tradesdata

    @classmethod
    def _get_file_extension(cls):
--- a/freqtrade/exchange/binance_leverage_tiers.json
+++ b/freqtrade/exchange/binance_leverage_tiers.json
--- a/freqtrade/exchange/exchange.py
+++ b/freqtrade/exchange/exchange.py
@ -5,6 +5,7 @@ Cryptocurrency Exchanges support
 import asyncio
 import inspect
 import logging
+import signal
 from copy import deepcopy
 from datetime import datetime, timedelta, timezone
 from math import floor
@ -2151,7 +2152,7 @@ class Exchange:
            except IndexError:
                logger.exception("Error loading %s. Result was %s.", pair, data)
                return pair, timeframe, candle_type, [], self._ohlcv_partial_candle
-            logger.debug("Done fetching pair %s, interval %s ...", pair, timeframe)
+            logger.debug("Done fetching pair %s, %s interval %s...", pair, candle_type, timeframe)
            return pair, timeframe, candle_type, data, self._ohlcv_partial_candle

        except ccxt.NotSupported as e:
@ -2253,6 +2254,7 @@ class Exchange:
            from_id = t[-1][1]
            trades.extend(t[:-1])
        while True:
+            try:
                t = await self._async_fetch_trades(pair,
                                                   params={self._trades_pagination_arg: from_id})
                if t:
@ -2268,6 +2270,9 @@ class Exchange:
                    from_id = t[-1][1]
                else:
                    break
+            except asyncio.CancelledError:
+                logger.debug("Async operation Interrupted, breaking trades DL loop.")
+                break

        return (pair, trades)

@ -2286,6 +2291,7 @@ class Exchange:
        # DEFAULT_TRADES_COLUMNS: 0 -> timestamp
        # DEFAULT_TRADES_COLUMNS: 1 -> id
        while True:
+            try:
                t = await self._async_fetch_trades(pair, since=since)
                if t:
                    since = t[-1][0]
@ -2297,6 +2303,9 @@ class Exchange:
                        break
                else:
                    break
+            except asyncio.CancelledError:
+                logger.debug("Async operation Interrupted, breaking trades DL loop.")
+                break

        return (pair, trades)

@ -2344,9 +2353,16 @@ class Exchange:
            raise OperationalException("This exchange does not support downloading Trades.")

        with self._loop_lock:
-            return self.loop.run_until_complete(
-                self._async_get_trade_history(pair=pair, since=since,
-                                              until=until, from_id=from_id))
+            task = asyncio.ensure_future(self._async_get_trade_history(
+                pair=pair, since=since, until=until, from_id=from_id))
+
+            for sig in [signal.SIGINT, signal.SIGTERM]:
+                try:
+                    self.loop.add_signal_handler(sig, task.cancel)
+                except NotImplementedError:
+                    # Not all platforms implement signals (e.g. windows)
+                    pass
+            return self.loop.run_until_complete(task)

    @retrier
    def _get_funding_fees_from_exchange(self, pair: str, since: Union[datetime, int]) -> float:
--- a/freqtrade/freqai/RL/BaseEnvironment.py
+++ b/freqtrade/freqai/RL/BaseEnvironment.py
@ -11,6 +11,8 @@ from gymnasium import spaces
 from gymnasium.utils import seeding
 from pandas import DataFrame

+from freqtrade.exceptions import OperationalException
+

 logger = logging.getLogger(__name__)

@ -80,8 +82,9 @@ class BaseEnvironment(gym.Env):
        self.can_short: bool = can_short
        self.live: bool = 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.")
+            raise OperationalException("`add_state_info` is not available in backtesting. Change "
+                                       "parameter to false in your rl_config. See `add_state_info` "
+                                       "docs for more info.")
        self.seed(seed)
        self.reset_env(df, prices, window_size, reward_kwargs, starting_point)

--- a/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
+++ b/freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py
@ -26,9 +26,9 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
            "model_training_parameters" : {
                "learning_rate": 3e-4,
                "trainer_kwargs": {
-                    "max_iters": 5000,
+                    "n_steps": 5000,
                    "batch_size": 64,
-                    "max_n_eval_batches": null,
+                    "n_epochs": null,
                },
                "model_kwargs": {
                    "hidden_dim": 512,
--- a/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
+++ b/freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py
@ -27,9 +27,9 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
            "model_training_parameters" : {
                "learning_rate": 3e-4,
                "trainer_kwargs": {
-                    "max_iters": 5000,
+                    "n_steps": 5000,
                    "batch_size": 64,
-                    "max_n_eval_batches": null,
+                    "n_epochs": null,
                },
                "model_kwargs": {
                    "hidden_dim": 512,
--- a/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
+++ b/freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py
@ -30,9 +30,9 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
            "model_training_parameters" : {
                "learning_rate": 3e-4,
                "trainer_kwargs": {
-                    "max_iters": 5000,
+                    "n_steps": 5000,
                    "batch_size": 64,
-                    "max_n_eval_batches": null
+                    "n_epochs": null
                },
                "model_kwargs": {
                    "hidden_dim": 512,
--- a/freqtrade/freqai/torch/PyTorchDataConvertor.py
+++ b/freqtrade/freqai/torch/PyTorchDataConvertor.py
@ -1,5 +1,4 @@
 from abc import ABC, abstractmethod
-from typing import Optional

 import pandas as pd
 import torch
@ -12,14 +11,14 @@ class PyTorchDataConvertor(ABC):
    """

    @abstractmethod
-    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
+    def convert_x(self, df: pd.DataFrame, device: str) -> torch.Tensor:
        """
        :param df: "*_features" dataframe.
        :param device: The device to use for training (e.g. 'cpu', 'cuda').
        """

    @abstractmethod
-    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
+    def convert_y(self, df: pd.DataFrame, device: str) -> torch.Tensor:
        """
        :param df: "*_labels" dataframe.
        :param device: The device to use for training (e.g. 'cpu', 'cuda').
@ -33,8 +32,8 @@ class DefaultPyTorchDataConvertor(PyTorchDataConvertor):

    def __init__(
            self,
-            target_tensor_type: Optional[torch.dtype] = None,
-            squeeze_target_tensor: bool = False
+            target_tensor_type: torch.dtype = torch.float32,
+            squeeze_target_tensor: bool = False,
    ):
        """
        :param target_tensor_type: type of target tensor, for classification use
@ -45,23 +44,14 @@ class DefaultPyTorchDataConvertor(PyTorchDataConvertor):
        self._target_tensor_type = target_tensor_type
        self._squeeze_target_tensor = squeeze_target_tensor

-    def convert_x(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
-        x = torch.from_numpy(df.values).float()
-        if device:
-            x = x.to(device)
-
+    def convert_x(self, df: pd.DataFrame, device: str) -> torch.Tensor:
+        numpy_arrays = df.values
+        x = torch.tensor(numpy_arrays, device=device, dtype=torch.float32)
        return x

-    def convert_y(self, df: pd.DataFrame, device: Optional[str] = None) -> torch.Tensor:
-        y = torch.from_numpy(df.values)
-
-        if self._target_tensor_type:
-            y = y.to(self._target_tensor_type)
-
+    def convert_y(self, df: pd.DataFrame, device: str) -> torch.Tensor:
+        numpy_arrays = df.values
+        y = torch.tensor(numpy_arrays, device=device, dtype=self._target_tensor_type)
        if self._squeeze_target_tensor:
            y = y.squeeze()
-
-        if device:
-            y = y.to(device)
-
        return y
--- a/freqtrade/freqai/torch/PyTorchModelTrainer.py
+++ b/freqtrade/freqai/torch/PyTorchModelTrainer.py
@ -1,5 +1,4 @@
 import logging
-import math
 from pathlib import Path
 from typing import Any, Dict, List, Optional

@ -40,23 +39,27 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
            state_dict and model_meta_data saved by self.save() method.
        :param model_meta_data: Additional metadata about the model (optional).
        :param data_convertor: convertor from pd.DataFrame to torch.tensor.
-        :param max_iters: The number of training iterations to run.
-            iteration here refers to the number of times we call
-            self.optimizer.step(). used to calculate n_epochs.
+        :param n_steps: used to calculate n_epochs. The number of training iterations to run.
+            iteration here refers to the number of times optimizer.step() is called.
+            ignored if n_epochs is set.
+        :param n_epochs: The maximum number batches to use for evaluation.
        :param batch_size: The size of the batches to use during training.
-        :param max_n_eval_batches: The maximum number batches to use for evaluation.
        """
        self.model = model
        self.optimizer = optimizer
        self.criterion = criterion
        self.model_meta_data = model_meta_data
        self.device = device
-        self.max_iters: int = kwargs.get("max_iters", 100)
+        self.n_epochs: Optional[int] = kwargs.get("n_epochs", 10)
+        self.n_steps: Optional[int] = kwargs.get("n_steps", None)
+        if self.n_steps is None and not self.n_epochs:
+            raise Exception("Either `n_steps` or `n_epochs` should be set.")
+
        self.batch_size: int = kwargs.get("batch_size", 64)
-        self.max_n_eval_batches: Optional[int] = kwargs.get("max_n_eval_batches", None)
        self.data_convertor = data_convertor
        self.window_size: int = window_size
        self.tb_logger = tb_logger
+        self.test_batch_counter = 0

    def fit(self, data_dictionary: Dict[str, pd.DataFrame], splits: List[str]):
        """
@ -72,55 +75,46 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
           backpropagation.
         - Updates the model's parameters using an optimizer.
        """
-        data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
-        epochs = self.calc_n_epochs(
-            n_obs=len(data_dictionary["train_features"]),
-            batch_size=self.batch_size,
-            n_iters=self.max_iters
-        )
        self.model.train()
-        for epoch in range(1, epochs + 1):
-            for i, batch_data in enumerate(data_loaders_dictionary["train"]):

+        data_loaders_dictionary = self.create_data_loaders_dictionary(data_dictionary, splits)
+        n_obs = len(data_dictionary["train_features"])
+        n_epochs = self.n_epochs or self.calc_n_epochs(n_obs=n_obs)
+        batch_counter = 0
+        for _ in range(n_epochs):
+            for _, batch_data in enumerate(data_loaders_dictionary["train"]):
                xb, yb = batch_data
-                xb.to(self.device)
-                yb.to(self.device)
+                xb = xb.to(self.device)
+                yb = yb.to(self.device)
                yb_pred = self.model(xb)
                loss = self.criterion(yb_pred, yb)

                self.optimizer.zero_grad(set_to_none=True)
                loss.backward()
                self.optimizer.step()
-                self.tb_logger.log_scalar("train_loss", loss.item(), i)
+                self.tb_logger.log_scalar("train_loss", loss.item(), batch_counter)
+                batch_counter += 1

            # evaluation
            if "test" in splits:
-                self.estimate_loss(
-                    data_loaders_dictionary,
-                    self.max_n_eval_batches,
-                    "test"
-                )
+                self.estimate_loss(data_loaders_dictionary, "test")

    @torch.no_grad()
    def estimate_loss(
            self,
            data_loader_dictionary: Dict[str, DataLoader],
-            max_n_eval_batches: Optional[int],
            split: str,
    ) -> None:
        self.model.eval()
-        n_batches = 0
-        for i, batch_data in enumerate(data_loader_dictionary[split]):
-            if max_n_eval_batches and i > max_n_eval_batches:
-                n_batches += 1
-                break
+        for _, batch_data in enumerate(data_loader_dictionary[split]):
            xb, yb = batch_data
-            xb.to(self.device)
-            yb.to(self.device)
+            xb = xb.to(self.device)
+            yb = yb.to(self.device)

            yb_pred = self.model(xb)
            loss = self.criterion(yb_pred, yb)
-            self.tb_logger.log_scalar(f"{split}_loss", loss.item(), i)
+            self.tb_logger.log_scalar(f"{split}_loss", loss.item(), self.test_batch_counter)
+            self.test_batch_counter += 1

        self.model.train()

@ -148,31 +142,30 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):

        return data_loader_dictionary

-    @staticmethod
-    def calc_n_epochs(n_obs: int, batch_size: int, n_iters: int) -> int:
+    def calc_n_epochs(self, n_obs: int) -> int:
        """
        Calculates the number of epochs required to reach the maximum number
        of iterations specified in the model training parameters.

-        the motivation here is that `max_iters` is easier to optimize and keep stable,
+        the motivation here is that `n_steps` is easier to optimize and keep stable,
        across different n_obs - the number of data points.
        """
+        assert isinstance(self.n_steps, int), "Either `n_steps` or `n_epochs` should be set."
+        n_batches = n_obs // self.batch_size
+        n_epochs = min(self.n_steps // n_batches, 1)
+        if n_epochs <= 10:
+            logger.warning(
+                f"Setting low n_epochs: {n_epochs}. "
+                f"Please consider increasing `n_steps` hyper-parameter."
+            )

-        n_batches = math.ceil(n_obs // batch_size)
-        epochs = math.ceil(n_iters // n_batches)
-        if epochs <= 10:
-            logger.warning("User set `max_iters` in such a way that the trainer will only perform "
-                           f" {epochs} epochs. Please consider increasing this value accordingly")
-            if epochs <= 1:
-                logger.warning("Epochs set to 1. Please review your `max_iters` value")
-                epochs = 1
-        return epochs
+        return n_epochs

    def save(self, path: Path):
        """
        - Saving any nn.Module state_dict
        - Saving model_meta_data, this dict should contain any additional data that the
-          user needs to store. e.g class_names for classification models.
+          user needs to store. e.g. class_names for classification models.
        """

        torch.save({
--- a/freqtrade/misc.py
+++ b/freqtrade/misc.py
@ -192,30 +192,6 @@ def plural(num: float, singular: str, plural: Optional[str] = None) -> str:
    return singular if (num == 1 or num == -1) else plural or singular + 's'


-def render_template(templatefile: str, arguments: dict = {}) -> str:
-
-    from jinja2 import Environment, PackageLoader, select_autoescape
-
-    env = Environment(
-        loader=PackageLoader('freqtrade', 'templates'),
-        autoescape=select_autoescape(['html', 'xml'])
-    )
-    template = env.get_template(templatefile)
-    return template.render(**arguments)
-
-
-def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
-                                  arguments: dict = {}) -> str:
-    """
-    Use templatefile if possible, otherwise fall back to templatefallbackfile
-    """
-    from jinja2.exceptions import TemplateNotFound
-    try:
-        return render_template(templatefile, arguments)
-    except TemplateNotFound:
-        return render_template(templatefallbackfile, arguments)
-
-
 def chunks(lst: List[Any], n: int) -> Iterator[List[Any]]:
    """
    Split lst into chunks of the size n.
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@ -369,13 +369,14 @@ class Backtesting:
                # Cleanup from prior runs
                pair_data.drop(HEADERS[5:] + ['buy', 'sell'], axis=1, errors='ignore')
            df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})
-            # Trim startup period from analyzed dataframe
-            df_analyzed = processed[pair] = pair_data = trim_dataframe(
-                df_analyzed, self.timerange, startup_candles=self.required_startup)
            # Update dataprovider cache
            self.dataprovider._set_cached_df(
                pair, self.timeframe, df_analyzed, self.config['candle_type_def'])

+            # Trim startup period from analyzed dataframe
+            df_analyzed = processed[pair] = pair_data = trim_dataframe(
+                df_analyzed, self.timerange, startup_candles=self.required_startup)
+
            # Create a copy of the dataframe before shifting, that way the entry signal/tag
            # remains on the correct candle for callbacks.
            df_analyzed = df_analyzed.copy()
@ -1204,7 +1205,8 @@ class Backtesting:

                row_index += 1
                indexes[pair] = row_index
-                self.dataprovider._set_dataframe_max_index(row_index)
+                self.dataprovider._set_dataframe_max_index(self.required_startup + row_index)
+                self.dataprovider._set_dataframe_max_date(current_time)
                current_detail_time: datetime = row[DATE_IDX].to_pydatetime()
                trade_dir: Optional[LongShort] = self.check_for_trade_entry(row)

@ -1237,12 +1239,14 @@ class Backtesting:
                    is_first = True
                    current_time_det = current_time
                    for det_row in detail_data[HEADERS].values.tolist():
+                        self.dataprovider._set_dataframe_max_date(current_time_det)
                        open_trade_count_start = self.backtest_loop(
                            det_row, pair, current_time_det, end_date,
                            open_trade_count_start, trade_dir, is_first)
                        current_time_det += timedelta(minutes=self.timeframe_detail_min)
                        is_first = False
                else:
+                    self.dataprovider._set_dataframe_max_date(current_time)
                    open_trade_count_start = self.backtest_loop(
                        row, pair, current_time, end_date,
                        open_trade_count_start, trade_dir)
--- a/freqtrade/persistence/trade_model.py
+++ b/freqtrade/persistence/trade_model.py
@ -1043,6 +1043,7 @@ class LocalTrade:
    def select_filled_orders(self, order_side: Optional[str] = None) -> List['Order']:
        """
        Finds filled orders for this order side.
+        Will not return open orders which already partially filled.
        :param order_side: Side of the order (either 'buy', 'sell', or None)
        :return: array of Order objects
        """
--- a/freqtrade/plugins/protections/iprotection.py
+++ b/freqtrade/plugins/protections/iprotection.py
@ -42,7 +42,7 @@ class IProtection(LoggingMixin, ABC):
            self._stop_duration = (tf_in_min * self._stop_duration_candles)
        else:
            self._stop_duration_candles = None
-            self._stop_duration = protection_config.get('stop_duration', 60)
+            self._stop_duration = int(protection_config.get('stop_duration', 60))
        if 'lookback_period_candles' in protection_config:
            self._lookback_period_candles = int(protection_config.get('lookback_period_candles', 1))
            self._lookback_period = tf_in_min * self._lookback_period_candles
--- a/freqtrade/rpc/api_server/api_schemas.py
+++ b/freqtrade/rpc/api_server/api_schemas.py
@ -1,7 +1,7 @@
 from datetime import date, datetime
 from typing import Any, Dict, List, Optional, Union

-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict, RootModel, SerializeAsAny

 from freqtrade.constants import DATETIME_PRINT_FORMAT, IntOrInf
 from freqtrade.enums import MarginMode, OrderTypeValues, SignalDirection, TradingMode
@ -9,9 +9,9 @@ from freqtrade.types import ValidExchangesType


 class ExchangeModePayloadMixin(BaseModel):
-    trading_mode: Optional[TradingMode]
-    margin_mode: Optional[MarginMode]
-    exchange: Optional[str]
+    trading_mode: Optional[TradingMode] = None
+    margin_mode: Optional[MarginMode] = None
+    exchange: Optional[str] = None


 class Ping(BaseModel):
@ -43,11 +43,11 @@ class BackgroundTaskStatus(BaseModel):
    job_category: str
    status: str
    running: bool
-    progress: Optional[float]
+    progress: Optional[float] = None


 class BackgroundTaskResult(BaseModel):
-    error: Optional[str]
+    error: Optional[str] = None
    status: str


@ -60,9 +60,9 @@ class Balance(BaseModel):
    free: float
    balance: float
    used: float
-    bot_owned: Optional[float]
+    bot_owned: Optional[float] = None
    est_stake: float
-    est_stake_bot: Optional[float]
+    est_stake_bot: Optional[float] = None
    stake: str
    # Starting with 2.x
    side: str
@ -141,7 +141,7 @@ class Profit(BaseModel):
    expectancy_ratio: float
    max_drawdown: float
    max_drawdown_abs: float
-    trading_volume: Optional[float]
+    trading_volume: Optional[float] = None
    bot_start_timestamp: int
    bot_start_date: str

@ -173,50 +173,50 @@ class Daily(BaseModel):


 class UnfilledTimeout(BaseModel):
-    entry: Optional[int]
-    exit: Optional[int]
-    unit: Optional[str]
-    exit_timeout_count: Optional[int]
+    entry: Optional[int] = None
+    exit: Optional[int] = None
+    unit: Optional[str] = None
+    exit_timeout_count: Optional[int] = None


 class OrderTypes(BaseModel):
    entry: OrderTypeValues
    exit: OrderTypeValues
-    emergency_exit: Optional[OrderTypeValues]
-    force_exit: Optional[OrderTypeValues]
-    force_entry: Optional[OrderTypeValues]
+    emergency_exit: Optional[OrderTypeValues] = None
+    force_exit: Optional[OrderTypeValues] = None
+    force_entry: Optional[OrderTypeValues] = None
    stoploss: OrderTypeValues
    stoploss_on_exchange: bool
-    stoploss_on_exchange_interval: Optional[int]
+    stoploss_on_exchange_interval: Optional[int] = None


 class ShowConfig(BaseModel):
    version: str
-    strategy_version: Optional[str]
+    strategy_version: Optional[str] = None
    api_version: float
    dry_run: bool
    trading_mode: str
    short_allowed: bool
    stake_currency: str
    stake_amount: str
-    available_capital: Optional[float]
+    available_capital: Optional[float] = None
    stake_currency_decimals: int
    max_open_trades: IntOrInf
    minimal_roi: Dict[str, Any]
-    stoploss: Optional[float]
+    stoploss: Optional[float] = None
    stoploss_on_exchange: bool
-    trailing_stop: Optional[bool]
-    trailing_stop_positive: Optional[float]
-    trailing_stop_positive_offset: Optional[float]
-    trailing_only_offset_is_reached: Optional[bool]
-    unfilledtimeout: Optional[UnfilledTimeout]  # Empty in webserver mode
-    order_types: Optional[OrderTypes]
-    use_custom_stoploss: Optional[bool]
-    timeframe: Optional[str]
+    trailing_stop: Optional[bool] = None
+    trailing_stop_positive: Optional[float] = None
+    trailing_stop_positive_offset: Optional[float] = None
+    trailing_only_offset_is_reached: Optional[bool] = None
+    unfilledtimeout: Optional[UnfilledTimeout] = None  # Empty in webserver mode
+    order_types: Optional[OrderTypes] = None
+    use_custom_stoploss: Optional[bool] = None
+    timeframe: Optional[str] = None
    timeframe_ms: int
    timeframe_min: int
    exchange: str
-    strategy: Optional[str]
+    strategy: Optional[str] = None
    force_entry_enable: bool
    exit_pricing: Dict[str, Any]
    entry_pricing: Dict[str, Any]
@ -231,17 +231,17 @@ class OrderSchema(BaseModel):
    pair: str
    order_id: str
    status: str
-    remaining: Optional[float]
+    remaining: Optional[float] = None
    amount: float
    safe_price: float
    cost: float
-    filled: Optional[float]
+    filled: Optional[float] = None
    ft_order_side: str
    order_type: str
    is_open: bool
-    order_timestamp: Optional[int]
-    order_filled_timestamp: Optional[int]
-    ft_fee_base: Optional[float]
+    order_timestamp: Optional[int] = None
+    order_filled_timestamp: Optional[int] = None
+    ft_fee_base: Optional[float] = None


 class TradeSchema(BaseModel):
@ -255,81 +255,81 @@ class TradeSchema(BaseModel):
    amount: float
    amount_requested: float
    stake_amount: float
-    max_stake_amount: Optional[float]
+    max_stake_amount: Optional[float] = None
    strategy: str
-    enter_tag: Optional[str]
+    enter_tag: Optional[str] = None
    timeframe: int
-    fee_open: Optional[float]
-    fee_open_cost: Optional[float]
-    fee_open_currency: Optional[str]
-    fee_close: Optional[float]
-    fee_close_cost: Optional[float]
-    fee_close_currency: Optional[str]
+    fee_open: Optional[float] = None
+    fee_open_cost: Optional[float] = None
+    fee_open_currency: Optional[str] = None
+    fee_close: Optional[float] = None
+    fee_close_cost: Optional[float] = None
+    fee_close_currency: Optional[str] = None

    open_date: str
    open_timestamp: int
    open_rate: float
-    open_rate_requested: Optional[float]
+    open_rate_requested: Optional[float] = None
    open_trade_value: float

-    close_date: Optional[str]
-    close_timestamp: Optional[int]
-    close_rate: Optional[float]
-    close_rate_requested: Optional[float]
+    close_date: Optional[str] = None
+    close_timestamp: Optional[int] = None
+    close_rate: Optional[float] = None
+    close_rate_requested: Optional[float] = None

-    close_profit: Optional[float]
-    close_profit_pct: Optional[float]
-    close_profit_abs: Optional[float]
+    close_profit: Optional[float] = None
+    close_profit_pct: Optional[float] = None
+    close_profit_abs: Optional[float] = None

-    profit_ratio: Optional[float]
-    profit_pct: Optional[float]
-    profit_abs: Optional[float]
-    profit_fiat: Optional[float]
+    profit_ratio: Optional[float] = None
+    profit_pct: Optional[float] = None
+    profit_abs: Optional[float] = None
+    profit_fiat: Optional[float] = None

    realized_profit: float
-    realized_profit_ratio: Optional[float]
+    realized_profit_ratio: Optional[float] = None

-    exit_reason: Optional[str]
-    exit_order_status: Optional[str]
+    exit_reason: Optional[str] = None
+    exit_order_status: Optional[str] = None

-    stop_loss_abs: Optional[float]
-    stop_loss_ratio: Optional[float]
-    stop_loss_pct: Optional[float]
-    stoploss_order_id: Optional[str]
-    stoploss_last_update: Optional[str]
-    stoploss_last_update_timestamp: Optional[int]
-    initial_stop_loss_abs: Optional[float]
-    initial_stop_loss_ratio: Optional[float]
-    initial_stop_loss_pct: Optional[float]
+    stop_loss_abs: Optional[float] = None
+    stop_loss_ratio: Optional[float] = None
+    stop_loss_pct: Optional[float] = None
+    stoploss_order_id: Optional[str] = None
+    stoploss_last_update: Optional[str] = None
+    stoploss_last_update_timestamp: Optional[int] = None
+    initial_stop_loss_abs: Optional[float] = None
+    initial_stop_loss_ratio: Optional[float] = None
+    initial_stop_loss_pct: Optional[float] = None

-    min_rate: Optional[float]
-    max_rate: Optional[float]
-    open_order_id: Optional[str]
+    min_rate: Optional[float] = None
+    max_rate: Optional[float] = None
+    open_order_id: Optional[str] = None
    orders: List[OrderSchema]

-    leverage: Optional[float]
-    interest_rate: Optional[float]
-    liquidation_price: Optional[float]
-    funding_fees: Optional[float]
-    trading_mode: Optional[TradingMode]
+    leverage: Optional[float] = None
+    interest_rate: Optional[float] = None
+    liquidation_price: Optional[float] = None
+    funding_fees: Optional[float] = None
+    trading_mode: Optional[TradingMode] = None

-    amount_precision: Optional[float]
-    price_precision: Optional[float]
-    precision_mode: Optional[int]
+    amount_precision: Optional[float] = None
+    price_precision: Optional[float] = None
+    precision_mode: Optional[int] = None


 class OpenTradeSchema(TradeSchema):
-    stoploss_current_dist: Optional[float]
-    stoploss_current_dist_pct: Optional[float]
-    stoploss_current_dist_ratio: Optional[float]
-    stoploss_entry_dist: Optional[float]
-    stoploss_entry_dist_ratio: Optional[float]
+    stoploss_current_dist: Optional[float] = None
+    stoploss_current_dist_pct: Optional[float] = None
+    stoploss_current_dist_ratio: Optional[float] = None
+    stoploss_entry_dist: Optional[float] = None
+    stoploss_entry_dist_ratio: Optional[float] = None
    current_rate: float
    total_profit_abs: float
-    total_profit_fiat: Optional[float]
-    total_profit_ratio: Optional[float]
+    total_profit_fiat: Optional[float] = None
+    total_profit_ratio: Optional[float] = None

-    open_order: Optional[str]
+    open_order: Optional[str] = None


 class TradeResponse(BaseModel):
@ -339,8 +339,7 @@ class TradeResponse(BaseModel):
    total_trades: int


-class ForceEnterResponse(BaseModel):
-    __root__: Union[TradeSchema, StatusMsg]
+ForceEnterResponse = RootModel[Union[TradeSchema, StatusMsg]]


 class LockModel(BaseModel):
@ -352,7 +351,7 @@ class LockModel(BaseModel):
    lock_timestamp: int
    pair: str
    side: str
-    reason: Optional[str]
+    reason: Optional[str] = None


 class Locks(BaseModel):
@ -361,8 +360,8 @@ class Locks(BaseModel):


 class DeleteLockRequest(BaseModel):
-    pair: Optional[str]
-    lockid: Optional[int]
+    pair: Optional[str] = None
+    lockid: Optional[int] = None


 class Logs(BaseModel):
@ -373,17 +372,17 @@ class Logs(BaseModel):
 class ForceEnterPayload(BaseModel):
    pair: str
    side: SignalDirection = SignalDirection.LONG
-    price: Optional[float]
-    ordertype: Optional[OrderTypeValues]
-    stakeamount: Optional[float]
-    entry_tag: Optional[str]
-    leverage: Optional[float]
+    price: Optional[float] = None
+    ordertype: Optional[OrderTypeValues] = None
+    stakeamount: Optional[float] = None
+    entry_tag: Optional[str] = None
+    leverage: Optional[float] = None


 class ForceExitPayload(BaseModel):
    tradeid: str
-    ordertype: Optional[OrderTypeValues]
-    amount: Optional[float]
+    ordertype: Optional[OrderTypeValues] = None
+    amount: Optional[float] = None


 class BlacklistPayload(BaseModel):
@ -405,7 +404,7 @@ class WhitelistResponse(BaseModel):


 class WhitelistEvaluateResponse(BackgroundTaskResult):
-    result: Optional[WhitelistResponse]
+    result: Optional[WhitelistResponse] = None


 class DeleteTrade(BaseModel):
@ -420,8 +419,7 @@ class PlotConfig_(BaseModel):
    subplots: Dict[str, Any]


-class PlotConfig(BaseModel):
-    __root__: Union[PlotConfig_, Dict]
+PlotConfig = RootModel[Union[PlotConfig_, Dict]]


 class StrategyListResponse(BaseModel):
@ -470,7 +468,7 @@ class PairHistory(BaseModel):
    timeframe: str
    timeframe_ms: int
    columns: List[str]
-    data: List[Any]
+    data: SerializeAsAny[List[Any]]
    length: int
    buy_signals: int
    sell_signals: int
@ -484,11 +482,11 @@ class PairHistory(BaseModel):
    data_start: str
    data_stop: str
    data_stop_ts: int
-
-    class Config:
-        json_encoders = {
+    # TODO[pydantic]: The following keys were removed: `json_encoders`.
+    # Check https://docs.pydantic.dev/dev-v2/migration/#changes-to-config for more information.
+    model_config = ConfigDict(json_encoders={
        datetime: lambda v: v.strftime(DATETIME_PRINT_FORMAT),
-        }
+    })


 class BacktestFreqAIInputs(BaseModel):
@ -497,16 +495,16 @@ class BacktestFreqAIInputs(BaseModel):

 class BacktestRequest(BaseModel):
    strategy: str
-    timeframe: Optional[str]
-    timeframe_detail: Optional[str]
-    timerange: Optional[str]
-    max_open_trades: Optional[IntOrInf]
-    stake_amount: Optional[str]
+    timeframe: Optional[str] = None
+    timeframe_detail: Optional[str] = None
+    timerange: Optional[str] = None
+    max_open_trades: Optional[IntOrInf] = None
+    stake_amount: Optional[Union[str, float]] = None
    enable_protections: bool
-    dry_run_wallet: Optional[float]
-    backtest_cache: Optional[str]
-    freqaimodel: Optional[str]
-    freqai: Optional[BacktestFreqAIInputs]
+    dry_run_wallet: Optional[float] = None
+    backtest_cache: Optional[str] = None
+    freqaimodel: Optional[str] = None
+    freqai: Optional[BacktestFreqAIInputs] = None


 class BacktestResponse(BaseModel):
@ -515,9 +513,9 @@ class BacktestResponse(BaseModel):
    status_msg: str
    step: str
    progress: float
-    trade_count: Optional[float]
+    trade_count: Optional[float] = None
    # TODO: Properly type backtestresult...
-    backtest_result: Optional[Dict[str, Any]]
+    backtest_result: Optional[Dict[str, Any]] = None


 # TODO: This is a copy of BacktestHistoryEntryType
@ -540,5 +538,5 @@ class SysInfo(BaseModel):


 class Health(BaseModel):
-    last_process: Optional[datetime]
-    last_process_ts: Optional[int]
+    last_process: Optional[datetime] = None
+    last_process_ts: Optional[int] = None
--- a/freqtrade/rpc/api_server/api_v1.py
+++ b/freqtrade/rpc/api_server/api_v1.py
@ -175,9 +175,9 @@ def force_entry(payload: ForceEnterPayload, rpc: RPC = Depends(get_rpc)):
                                 leverage=payload.leverage)

    if trade:
-        return ForceEnterResponse.parse_obj(trade.to_json())
+        return ForceEnterResponse.model_validate(trade.to_json())
    else:
-        return ForceEnterResponse.parse_obj(
+        return ForceEnterResponse.model_validate(
            {"status": f"Error entering {payload.side} trade for pair {payload.pair}."})


@ -282,14 +282,14 @@ def plot_config(strategy: Optional[str] = None, config=Depends(get_config),
    if not strategy:
        if not rpc:
            raise RPCException("Strategy is mandatory in webserver mode.")
-        return PlotConfig.parse_obj(rpc._rpc_plot_config())
+        return PlotConfig.model_validate(rpc._rpc_plot_config())
    else:
        config1 = deepcopy(config)
        config1.update({
            'strategy': strategy
        })
    try:
-        return PlotConfig.parse_obj(RPC._rpc_plot_config_with_strategy(config1))
+        return PlotConfig.model_validate(RPC._rpc_plot_config_with_strategy(config1))
    except Exception as e:
        raise HTTPException(status_code=502, detail=str(e))

--- a/freqtrade/rpc/api_server/api_ws.py
+++ b/freqtrade/rpc/api_server/api_ws.py
@ -65,7 +65,7 @@ async def _process_consumer_request(
    """
    # Validate the request, makes sure it matches the schema
    try:
-        websocket_request = WSRequestSchema.parse_obj(request)
+        websocket_request = WSRequestSchema.model_validate(request)
    except ValidationError as e:
        logger.error(f"Invalid request from {channel}: {e}")
        return
@ -94,7 +94,7 @@ async def _process_consumer_request(

        # Format response
        response = WSWhitelistMessage(data=whitelist)
-        await channel.send(response.dict(exclude_none=True))
+        await channel.send(response.model_dump(exclude_none=True))

    elif type_ == RPCRequestType.ANALYZED_DF:
        # Limit the amount of candles per dataframe to 'limit' or 1500
@ -105,7 +105,7 @@ async def _process_consumer_request(
        for message in rpc._ws_request_analyzed_df(limit, pair):
            # Format response
            response = WSAnalyzedDFMessage(data=message)
-            await channel.send(response.dict(exclude_none=True))
+            await channel.send(response.model_dump(exclude_none=True))


@router.websocket("/message/ws")
--- a/freqtrade/rpc/api_server/ws_schemas.py
+++ b/freqtrade/rpc/api_server/ws_schemas.py
@ -2,15 +2,14 @@ from datetime import datetime
 from typing import Any, Dict, List, Optional, TypedDict

 from pandas import DataFrame
-from pydantic import BaseModel
+from pydantic import BaseModel, ConfigDict

 from freqtrade.constants import PairWithTimeframe
 from freqtrade.enums.rpcmessagetype import RPCMessageType, RPCRequestType


 class BaseArbitraryModel(BaseModel):
-    class Config:
-        arbitrary_types_allowed = True
+    model_config = ConfigDict(arbitrary_types_allowed=True)


 class WSRequestSchema(BaseArbitraryModel):
@ -27,9 +26,7 @@ class WSMessageSchemaType(TypedDict):
 class WSMessageSchema(BaseArbitraryModel):
    type: RPCMessageType
    data: Optional[Any] = None
-
-    class Config:
-        extra = 'allow'
+    model_config = ConfigDict(extra='allow')


 # ------------------------------ REQUEST SCHEMAS ----------------------------
--- a/freqtrade/rpc/external_message_consumer.py
+++ b/freqtrade/rpc/external_message_consumer.py
@ -41,7 +41,7 @@ logger = logging.getLogger(__name__)


 def schema_to_dict(schema: Union[WSMessageSchema, WSRequestSchema]):
-    return schema.dict(exclude_none=True)
+    return schema.model_dump(exclude_none=True)


 class ExternalMessageConsumer:
@ -322,7 +322,7 @@ class ExternalMessageConsumer:
        producer_name = producer.get('name', 'default')

        try:
-            producer_message = WSMessageSchema.parse_obj(message)
+            producer_message = WSMessageSchema.model_validate(message)
        except ValidationError as e:
            logger.error(f"Invalid message from `{producer_name}`: {e}")
            return
@ -344,7 +344,7 @@ class ExternalMessageConsumer:
    def _consume_whitelist_message(self, producer_name: str, message: WSMessageSchema):
        try:
            # Validate the message
-            whitelist_message = WSWhitelistMessage.parse_obj(message)
+            whitelist_message = WSWhitelistMessage.model_validate(message.model_dump())
        except ValidationError as e:
            logger.error(f"Invalid message from `{producer_name}`: {e}")
            return
@ -356,7 +356,7 @@ class ExternalMessageConsumer:

    def _consume_analyzed_df_message(self, producer_name: str, message: WSMessageSchema):
        try:
-            df_message = WSAnalyzedDFMessage.parse_obj(message)
+            df_message = WSAnalyzedDFMessage.model_validate(message.model_dump())
        except ValidationError as e:
            logger.error(f"Invalid message from `{producer_name}`: {e}")
            return
--- a/freqtrade/rpc/fiat_convert.py
+++ b/freqtrade/rpc/fiat_convert.py
@ -26,6 +26,7 @@ coingecko_mapping = {
    'sol': 'solana',
    'usdt': 'tether',
    'busd': 'binance-usd',
+    'tusd': 'true-usd',
 }


--- a/freqtrade/templates/base_strategy.py.j2
+++ b/freqtrade/templates/base_strategy.py.j2
@ -78,19 +78,7 @@ class {{ strategy }}(IStrategy):
    buy_rsi = IntParameter(10, 40, default=30, space="buy")
    sell_rsi = IntParameter(60, 90, default=70, space="sell")

-    # Optional order type mapping.
-    order_types = {
-        'entry': 'limit',
-        'exit': 'limit',
-        'stoploss': 'market',
-        'stoploss_on_exchange': False
-    }
-
-    # Optional order time in force.
-    order_time_in_force = {
-        'entry': 'GTC',
-        'exit': 'GTC'
-    }
+    {{ attributes | indent(4) }}
    {{ plot_config | indent(4) }}

    def informative_pairs(self):
--- a/freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2
+++ b/freqtrade/templates/strategy_subtemplates/strategy_attributes_full.j2
@ -0,0 +1,13 @@
+# Optional order type mapping.
+order_types = {
+    'entry': 'limit',
+    'exit': 'limit',
+    'stoploss': 'market',
+    'stoploss_on_exchange': False
+}
+
+# Optional order time in force.
+order_time_in_force = {
+    'entry': 'GTC',
+    'exit': 'GTC'
+}
--- a/freqtrade/templates/strategy_subtemplates/strategy_attributes_minimal.j2
+++ b/freqtrade/templates/strategy_subtemplates/strategy_attributes_minimal.j2
--- a/freqtrade/util/init.py
+++ b/freqtrade/util/init.py
@ -2,6 +2,7 @@ from freqtrade.util.datetime_helpers import (dt_floor_day, dt_from_ts, dt_humani
                                             dt_utc, format_ms_time, shorten_date)
 from freqtrade.util.ft_precise import FtPrecise
 from freqtrade.util.periodic_cache import PeriodicCache
+from freqtrade.util.template_renderer import render_template, render_template_with_fallback  # noqa


 __all__ = [
--- a/freqtrade/util/template_renderer.py
+++ b/freqtrade/util/template_renderer.py
@ -0,0 +1,27 @@
+"""
+Jinja2 rendering utils, used to generate new strategy and configurations.
+"""
+
+
+def render_template(templatefile: str, arguments: dict = {}) -> str:
+
+    from jinja2 import Environment, PackageLoader, select_autoescape
+
+    env = Environment(
+        loader=PackageLoader('freqtrade', 'templates'),
+        autoescape=select_autoescape(['html', 'xml'])
+    )
+    template = env.get_template(templatefile)
+    return template.render(**arguments)
+
+
+def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
+                                  arguments: dict = {}) -> str:
+    """
+    Use templatefile if possible, otherwise fall back to templatefallbackfile
+    """
+    from jinja2.exceptions import TemplateNotFound
+    try:
+        return render_template(templatefile, arguments)
+    except TemplateNotFound:
+        return render_template(templatefallbackfile, arguments)
--- a/pyproject.toml
+++ b/pyproject.toml
@ -63,7 +63,7 @@ ignore = ["freqtrade/vendor/**"]

 [tool.ruff]
 line-length = 100
-extend-exclude = [".env"]
+extend-exclude = [".env", ".venv"]
 target-version = "py38"
 extend-select = [
  "C90",    # mccabe
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@ -7,8 +7,8 @@
 -r docs/requirements-docs.txt

 coveralls==3.3.1
-ruff==0.0.284
-mypy==1.5.0
+ruff==0.0.286
+mypy==1.5.1
 pre-commit==3.3.3
 pytest==7.4.0
 pytest-asyncio==0.21.1
@ -17,10 +17,10 @@ pytest-mock==3.11.1
 pytest-random-order==1.1.0
 isort==5.12.0
 # For datetime mocking
-time-machine==2.11.0
+time-machine==2.12.0

 # Convert jupyter notebooks to markdown documents
-nbconvert==7.7.3
+nbconvert==7.7.4

 # mypy types
 types-cachetools==5.3.0.6
--- a/requirements-freqai-rl.txt
+++ b/requirements-freqai-rl.txt
@ -4,8 +4,8 @@
 # Required for freqai-rl
 torch==2.0.1
 #until these branches will be released we can use this
-gymnasium==0.28.1
-stable_baselines3==2.0.0
+gymnasium==0.29.1
+stable_baselines3==2.1.0
 sb3_contrib>=2.0.0a9
 # Progress bar for stable-baselines3 and sb3-contrib
 tqdm==4.66.1
--- a/requirements-hyperopt.txt
+++ b/requirements-hyperopt.txt
@ -2,7 +2,7 @@
 -r requirements.txt

 # Required for hyperopt
-scipy==1.11.1; python_version >= '3.9'
+scipy==1.11.2; python_version >= '3.9'
 scipy==1.10.1; python_version < '3.9'
 scikit-learn==1.1.3
 scikit-optimize==0.9.0
--- a/requirements-plot.txt
+++ b/requirements-plot.txt
@ -1,4 +1,4 @@
 # Include all requirements to run the bot.
 -r requirements.txt

-plotly==5.16.0
+plotly==5.16.1
--- a/requirements.txt
+++ b/requirements.txt
@ -3,11 +3,11 @@ numpy==1.24.3; python_version <= '3.8'
 pandas==2.0.3
 pandas-ta==0.3.14b

-ccxt==4.0.59
+ccxt==4.0.76
 cryptography==41.0.3; platform_machine != 'armv7l'
 cryptography==40.0.1; platform_machine == 'armv7l'
 aiohttp==3.8.5
-SQLAlchemy==2.0.19
+SQLAlchemy==2.0.20
 python-telegram-bot==20.4
 # can't be hard-pinned due to telegram-bot pinning httpx with ~
 httpx>=0.24.1
@ -16,7 +16,7 @@ cachetools==5.3.1
 requests==2.31.0
 urllib3==2.0.4
 jsonschema==4.19.0
-TA-Lib==0.4.27
+TA-Lib==0.4.28
 technical==1.4.0
 tabulate==0.9.0
 pycoingecko==3.1.0
@ -25,7 +25,7 @@ tables==3.8.0
 blosc==1.11.1
 joblib==1.3.2
 rich==13.5.2
-pyarrow==12.0.1; platform_machine != 'armv7l'
+pyarrow==13.0.0; platform_machine != 'armv7l'

 # find first, C search in arrays
 py_find_1st==1.1.5
@ -33,14 +33,14 @@ py_find_1st==1.1.5
 # Load ticker files 30% faster
 python-rapidjson==1.10
 # Properly format api responses
-orjson==3.9.4
+orjson==3.9.5

 # Notify systemd
 sdnotify==0.3.2

 # API Server
-fastapi==0.101.0
-pydantic==1.10.11
+fastapi==0.103.0
+pydantic==2.3.0
 uvicorn==0.23.2
 pyjwt==2.8.0
 aiofiles==23.2.1
--- a/setup.py
+++ b/setup.py
@ -97,7 +97,7 @@ setup(
        'rich',
        'pyarrow; platform_machine != "armv7l"',
        'fastapi',
-        'pydantic>=1.8.0,<2.0',
+        'pydantic>=2.2.0',
        'uvicorn',
        'psutil',
        'pyjwt',
--- a/setup.sh
+++ b/setup.sh
@ -11,7 +11,7 @@ function check_installed_pip() {
   ${PYTHON} -m pip > /dev/null
   if [ $? -ne 0 ]; then
        echo_block "Installing Pip for ${PYTHON}"
-        curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py
+        curl https://bootstrap.pypa.io/get-pip.py -s -o get-pip.py
        ${PYTHON} get-pip.py
        rm get-pip.py
   fi
@ -41,12 +41,12 @@ function check_installed_python() {
 }

 function updateenv() {
-    echo_block "Updating your virtual env"
-    if [ ! -f .env/bin/activate ]; then
+    echo_block "Updating your virtual environment"
+    if [ ! -f .venv/bin/activate ]; then
        echo "Something went wrong, no virtual environment found."
        exit 1
    fi
-    source .env/bin/activate
+    source .venv/bin/activate
    SYS_ARCH=$(uname -m)
    echo "pip install in-progress. Please wait..."
    ${PYTHON} -m pip install --upgrade pip wheel setuptools
@ -120,7 +120,7 @@ function updateenv() {

 # Install tab lib
 function install_talib() {
-    if [ -f /usr/local/lib/libta_lib.a ]; then
+    if [ -f /usr/local/lib/libta_lib.a ] || [ -f /usr/local/lib/libta_lib.so ] || [ -f /usr/lib/libta_lib.so ]; then
        echo "ta-lib already installed, skipping"
        return
    fi
@ -186,7 +186,14 @@ function install_redhat() {
 # Upgrade the bot
 function update() {
    git pull
+    if [ -f .env/bin/activate  ]; then
+        # Old environment found - updating to new environment.
+        recreate_environments
+    fi
    updateenv
+    echo "Update completed."
+    echo_block "Don't forget to activate your virtual enviorment with 'source .venv/bin/activate'!"
+
 }

 function check_git_changes() {
@ -199,6 +206,27 @@ function check_git_changes() {
    fi
 }

+function recreate_environments() {
+    if [ -d ".env" ]; then
+        # Remove old virtual env
+        echo "- Deleting your previous virtual env"
+        echo "Warning: Your new environment will be at .venv!"
+        rm -rf .env
+    fi
+    if [ -d ".venv" ]; then
+        echo "- Deleting your previous virtual env"
+        rm -rf .venv
+    fi
+
+    echo
+    ${PYTHON} -m venv .venv
+    if [ $? -ne 0 ]; then
+        echo "Could not create virtual environment. Leaving now"
+        exit 1
+    fi
+
+}
+
 # Reset Develop or Stable branch
 function reset() {
    echo_block "Resetting branch and virtual env"
@ -225,22 +253,13 @@ function reset() {
    else
        echo "Reset ignored because you are not on 'stable' or 'develop'."
    fi
+    recreate_environments

-    if [ -d ".env" ]; then
-        echo "- Deleting your previous virtual env"
-        rm -rf .env
-    fi
-    echo
-    ${PYTHON} -m venv .env
-    if [ $? -ne 0 ]; then
-        echo "Could not create virtual environment. Leaving now"
-        exit 1
-    fi
    updateenv
 }

 function config() {
-    echo_block "Please use 'freqtrade new-config -c config.json' to generate a new configuration file."
+    echo_block "Please use 'freqtrade new-config -c user_data/config.json' to generate a new configuration file."
 }

 function install() {
@ -266,9 +285,9 @@ function install() {
    reset
    config
    echo_block "Run the bot !"
-    echo "You can now use the bot by executing 'source .env/bin/activate; freqtrade <subcommand>'."
-    echo "You can see the list of available bot sub-commands by executing 'source .env/bin/activate; freqtrade --help'."
-    echo "You verify that freqtrade is installed successfully by running 'source .env/bin/activate; freqtrade --version'."
+    echo "You can now use the bot by executing 'source .venv/bin/activate; freqtrade <subcommand>'."
+    echo "You can see the list of available bot sub-commands by executing 'source .venv/bin/activate; freqtrade --help'."
+    echo "You verify that freqtrade is installed successfully by running 'source .venv/bin/activate; freqtrade --version'."
 }

 function plot() {
--- a/tests/conftest.py
+++ b/tests/conftest.py
@ -14,7 +14,7 @@ import pytest

 from freqtrade import constants
 from freqtrade.commands import Arguments
-from freqtrade.data.converter import ohlcv_to_dataframe
+from freqtrade.data.converter import ohlcv_to_dataframe, trades_list_to_df
 from freqtrade.edge import PairInfo
 from freqtrade.enums import CandleType, MarginMode, RunMode, SignalDirection, TradingMode
 from freqtrade.exchange import Exchange
@ -2346,7 +2346,15 @@ def trades_history():
            [1565798399629, '1261813bb30', None, 'buy', 0.019627, 0.244, 0.004788987999999999],
            [1565798399752, '1261813cc31', None, 'sell', 0.019626, 0.011, 0.00021588599999999999],
            [1565798399862, '126181cc332', None, 'sell', 0.019626, 0.011, 0.00021588599999999999],
-            [1565798399872, '1261aa81333', None, 'sell', 0.019626, 0.011, 0.00021588599999999999]]
+            [1565798399862, '126181cc333', None, 'sell', 0.019626, 0.012, 0.00021588599999999999],
+            [1565798399872, '1261aa81334', None, 'sell', 0.019626, 0.011, 0.00021588599999999999]]
+
+
+@pytest.fixture(scope="function")
+def trades_history_df(trades_history):
+    trades = trades_list_to_df(trades_history)
+    trades['date'] = pd.to_datetime(trades['timestamp'], unit='ms', utc=True)
+    return trades


@pytest.fixture(scope="function")
--- a/tests/conftest_trades.py
+++ b/tests/conftest_trades.py
@ -22,15 +22,15 @@ def mock_order_1(is_short: bool):
    return {
        'id': f'1234_{direc(is_short)}',
        'symbol': 'ETH/BTC',
-        'status': 'closed',
+        'status': 'open',
        'side': entry_side(is_short),
        'type': 'limit',
        'price': 0.123,
        'average': 0.123,
        'amount': 123.0,
-        'filled': 123.0,
+        'filled': 50.0,
        'cost': 15.129,
-        'remaining': 0.0,
+        'remaining': 123.0 - 50.0,
    }


@ -103,7 +103,6 @@ def mock_trade_2(fee, is_short: bool):
        close_profit_abs=-0.005584127 if is_short else 0.000584127,
        exchange='binance',
        is_open=False,
-        open_order_id=f'dry_run_sell_{direc(is_short)}_12345',
        strategy='StrategyTestV3',
        timeframe=5,
        enter_tag='TEST1',
@ -412,7 +411,7 @@ def short_trade(fee):
        # close_profit_abs=-0.6925113200000013,
        exchange='binance',
        is_open=True,
-        open_order_id='dry_run_exit_short_12345',
+        open_order_id=None,
        strategy='DefaultStrategy',
        timeframe=5,
        exit_reason='sell_signal',
--- a/tests/conftest_trades_usdt.py
+++ b/tests/conftest_trades_usdt.py
@ -36,13 +36,13 @@ def mock_order_usdt_1_exit(is_short: bool):
    return {
        'id': f'prod_exit_1_{direc(is_short)}',
        'symbol': 'LTC/USDT',
-        'status': 'closed',
+        'status': 'open',
        'side': exit_side(is_short),
        'type': 'limit',
        'price': 8.0,
        'amount': 2.0,
-        'filled': 2.0,
-        'remaining': 0.0,
+        'filled': 0.0,
+        'remaining': 2.0,
    }


@ -96,13 +96,13 @@ def mock_order_usdt_2_exit(is_short: bool):
    return {
        'id': f'12366_{direc(is_short)}',
        'symbol': 'NEO/USDT',
-        'status': 'closed',
+        'status': 'open',
        'side': exit_side(is_short),
        'type': 'limit',
        'price': 2.05,
        'amount': 100.0,
-        'filled': 100.0,
-        'remaining': 0.0,
+        'filled': 0.0,
+        'remaining': 100.0,
    }


@ -378,7 +378,7 @@ def mock_trade_usdt_7(fee, is_short: bool):
        open_date=datetime.now(tz=timezone.utc) - timedelta(minutes=17),
        open_rate=2.0,
        exchange='binance',
-        open_order_id=f'1234_{direc(is_short)}',
+        open_order_id=None,
        strategy='StrategyTestV2',
        timeframe=5,
        is_short=is_short,
--- a/tests/data/test_converter.py
+++ b/tests/data/test_converter.py
@ -4,13 +4,14 @@ from pathlib import Path
 from shutil import copyfile

 import numpy as np
+import pandas as pd
 import pytest

 from freqtrade.configuration.timerange import TimeRange
 from freqtrade.data.converter import (convert_ohlcv_format, convert_trades_format,
                                      ohlcv_fill_up_missing_data, ohlcv_to_dataframe,
-                                      reduce_dataframe_footprint, trades_dict_to_list,
-                                      trades_remove_duplicates, trades_to_ohlcv, trim_dataframe)
+                                      reduce_dataframe_footprint, trades_df_remove_duplicates,
+                                      trades_dict_to_list, trades_to_ohlcv, trim_dataframe)
 from freqtrade.data.history import (get_timerange, load_data, load_pair_history,
                                    validate_backtest_data)
 from freqtrade.data.history.idatahandler import IDataHandler
@ -34,26 +35,21 @@ def test_ohlcv_to_dataframe(ohlcv_history_list, caplog):
    assert log_has('Converting candle (OHLCV) data to dataframe for pair UNITTEST/BTC.', caplog)


-def test_trades_to_ohlcv(ohlcv_history_list, caplog):
+def test_trades_to_ohlcv(trades_history_df, caplog):

    caplog.set_level(logging.DEBUG)
    with pytest.raises(ValueError, match="Trade-list empty."):
-        trades_to_ohlcv([], '1m')
+        trades_to_ohlcv(pd.DataFrame(columns=trades_history_df.columns), '1m')

-    trades = [
-        [1570752011620, "13519807", None, "sell", 0.00141342, 23.0, 0.03250866],
-        [1570752011620, "13519808", None, "sell", 0.00141266, 54.0, 0.07628364],
-        [1570752017964, "13519809", None, "sell", 0.00141266, 8.0, 0.01130128]]
-
-    df = trades_to_ohlcv(trades, '1m')
+    df = trades_to_ohlcv(trades_history_df, '1m')
    assert not df.empty
    assert len(df) == 1
    assert 'open' in df.columns
    assert 'high' in df.columns
    assert 'low' in df.columns
    assert 'close' in df.columns
-    assert df.loc[:, 'high'][0] == 0.00141342
-    assert df.loc[:, 'low'][0] == 0.00141266
+    assert df.loc[:, 'high'][0] == 0.019627
+    assert df.loc[:, 'low'][0] == 0.019626


 def test_ohlcv_fill_up_missing_data(testdatadir, caplog):
@ -302,13 +298,13 @@ def test_trim_dataframe(testdatadir) -> None:
    assert all(data_modify.iloc[0] == data.iloc[25])


-def test_trades_remove_duplicates(trades_history):
-    trades_history1 = trades_history * 3
-    assert len(trades_history1) == len(trades_history) * 3
-    res = trades_remove_duplicates(trades_history1)
-    assert len(res) == len(trades_history)
-    for i, t in enumerate(res):
-        assert t == trades_history[i]
+def test_trades_df_remove_duplicates(trades_history_df):
+    trades_history1 = pd.concat([trades_history_df, trades_history_df, trades_history_df]
+                                ).reset_index(drop=True)
+    assert len(trades_history1) == len(trades_history_df) * 3
+    res = trades_df_remove_duplicates(trades_history1)
+    assert len(res) == len(trades_history_df)
+    assert res.equals(trades_history_df)


 def test_trades_dict_to_list(fetch_trades_result):
--- a/tests/data/test_datahandler.py
+++ b/tests/data/test_datahandler.py
@ -6,7 +6,8 @@ from pathlib import Path
 from unittest.mock import MagicMock

 import pytest
-from pandas import DataFrame
+from pandas import DataFrame, Timestamp
+from pandas.testing import assert_frame_equal

 from freqtrade.configuration import TimeRange
 from freqtrade.constants import AVAILABLE_DATAHANDLERS
@ -117,12 +118,6 @@ def test_datahandler_ohlcv_get_available_data(testdatadir):
    assert set(paircombs) == {('UNITTEST/BTC', '5m', CandleType.SPOT)}


-def test_jsondatahandler_trades_get_pairs(testdatadir):
-    pairs = JsonGzDataHandler.trades_get_pairs(testdatadir)
-    # Convert to set to avoid failures due to sorting
-    assert set(pairs) == {'XRP/ETH', 'XRP/OLD'}
-
-
 def test_jsondatahandler_ohlcv_purge(mocker, testdatadir):
    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
    unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
@ -246,8 +241,10 @@ def test_datahandler__check_empty_df(testdatadir, caplog):
    assert log_has_re(expected_text, caplog)


-@pytest.mark.parametrize('datahandler', ['parquet'])
+# @pytest.mark.parametrize('datahandler', [])
+@pytest.mark.skip("All datahandlers currently support trades data.")
 def test_datahandler_trades_not_supported(datahandler, testdatadir, ):
+    # Currently disabled. Reenable should a new provider not support trades data.
    dh = get_datahandler(testdatadir, datahandler)
    with pytest.raises(NotImplementedError):
        dh.trades_load('UNITTEST/ETH')
@ -266,18 +263,6 @@ def test_jsondatahandler_trades_load(testdatadir, caplog):
    assert log_has(logmsg, caplog)


-def test_jsondatahandler_trades_purge(mocker, testdatadir):
-    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
-    unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
-    dh = JsonGzDataHandler(testdatadir)
-    assert not dh.trades_purge('UNITTEST/NONEXIST')
-    assert unlinkmock.call_count == 0
-
-    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
-    assert dh.trades_purge('UNITTEST/NONEXIST')
-    assert unlinkmock.call_count == 1
-
-
@pytest.mark.parametrize('datahandler', AVAILABLE_DATAHANDLERS)
 def test_datahandler_ohlcv_append(datahandler, testdatadir, ):
    dh = get_datahandler(testdatadir, datahandler)
@ -291,79 +276,48 @@ def test_datahandler_ohlcv_append(datahandler, testdatadir, ):
 def test_datahandler_trades_append(datahandler, testdatadir):
    dh = get_datahandler(testdatadir, datahandler)
    with pytest.raises(NotImplementedError):
-        dh.trades_append('UNITTEST/ETH', [])
+        dh.trades_append('UNITTEST/ETH', DataFrame())


-def test_hdf5datahandler_trades_get_pairs(testdatadir):
-    pairs = HDF5DataHandler.trades_get_pairs(testdatadir)
+@pytest.mark.parametrize('datahandler,expected', [
+    ('jsongz', {'XRP/ETH', 'XRP/OLD'}),
+    ('hdf5',  {'XRP/ETH'}),
+    ('feather', {'XRP/ETH'}),
+    ('parquet', {'XRP/ETH'}),
+])
+def test_datahandler_trades_get_pairs(testdatadir, datahandler, expected):
+
+    pairs = get_datahandlerclass(datahandler).trades_get_pairs(testdatadir)
    # Convert to set to avoid failures due to sorting
-    assert set(pairs) == {'XRP/ETH'}
+    assert set(pairs) == expected


 def test_hdf5datahandler_trades_load(testdatadir):
    dh = get_datahandler(testdatadir, 'hdf5')
    trades = dh.trades_load('XRP/ETH')
-    assert isinstance(trades, list)
+    assert isinstance(trades, DataFrame)

    trades1 = dh.trades_load('UNITTEST/NONEXIST')
-    assert trades1 == []
+    assert isinstance(trades1, DataFrame)
+    assert trades1.empty
    # data goes from 2019-10-11 - 2019-10-13
    timerange = TimeRange.parse_timerange('20191011-20191012')

    trades2 = dh._trades_load('XRP/ETH', timerange)
    assert len(trades) > len(trades2)
    # Check that ID is None (If it's nan, it's wrong)
-    assert trades2[0][2] is None
+    assert trades2.iloc[0]['type'] is None

    # unfiltered load has trades before starttime
-    assert len([t for t in trades if t[0] < timerange.startts * 1000]) >= 0
+
+    assert len(trades.loc[trades['timestamp'] < timerange.startts * 1000]) >= 0
    # filtered list does not have trades before starttime
-    assert len([t for t in trades2 if t[0] < timerange.startts * 1000]) == 0
+    assert len(trades2.loc[trades2['timestamp'] < timerange.startts * 1000]) == 0
    # unfiltered load has trades after endtime
-    assert len([t for t in trades if t[0] > timerange.stopts * 1000]) > 0
+    assert len(trades.loc[trades['timestamp'] > timerange.stopts * 1000]) >= 0
    # filtered list does not have trades after endtime
-    assert len([t for t in trades2 if t[0] > timerange.stopts * 1000]) == 0
-
-
-def test_hdf5datahandler_trades_store(testdatadir, tmpdir):
-    tmpdir1 = Path(tmpdir)
-    dh = get_datahandler(testdatadir, 'hdf5')
-    trades = dh.trades_load('XRP/ETH')
-
-    dh1 = get_datahandler(tmpdir1, 'hdf5')
-    dh1.trades_store('XRP/NEW', trades)
-    file = tmpdir1 / 'XRP_NEW-trades.h5'
-    assert file.is_file()
-    # Load trades back
-    trades_new = dh1.trades_load('XRP/NEW')
-
-    assert len(trades_new) == len(trades)
-    assert trades[0][0] == trades_new[0][0]
-    assert trades[0][1] == trades_new[0][1]
-    # assert trades[0][2] == trades_new[0][2]  # This is nan - so comparison does not make sense
-    assert trades[0][3] == trades_new[0][3]
-    assert trades[0][4] == trades_new[0][4]
-    assert trades[0][5] == trades_new[0][5]
-    assert trades[0][6] == trades_new[0][6]
-    assert trades[-1][0] == trades_new[-1][0]
-    assert trades[-1][1] == trades_new[-1][1]
-    # assert trades[-1][2] == trades_new[-1][2]  # This is nan - so comparison does not make sense
-    assert trades[-1][3] == trades_new[-1][3]
-    assert trades[-1][4] == trades_new[-1][4]
-    assert trades[-1][5] == trades_new[-1][5]
-    assert trades[-1][6] == trades_new[-1][6]
-
-
-def test_hdf5datahandler_trades_purge(mocker, testdatadir):
-    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
-    unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
-    dh = get_datahandler(testdatadir, 'hdf5')
-    assert not dh.trades_purge('UNITTEST/NONEXIST')
-    assert unlinkmock.call_count == 0
-
-    mocker.patch.object(Path, "exists", MagicMock(return_value=True))
-    assert dh.trades_purge('UNITTEST/NONEXIST')
-    assert unlinkmock.call_count == 1
+    assert len(trades2.loc[trades2['timestamp'] > timerange.stopts * 1000]) == 0
+    # assert len([t for t in trades2 if t[0] > timerange.stopts * 1000]) == 0


@pytest.mark.parametrize('pair,timeframe,candle_type,candle_append,startdt,enddt', [
@ -490,50 +444,42 @@ def test_hdf5datahandler_ohlcv_purge(mocker, testdatadir):
    assert unlinkmock.call_count == 2


-def test_featherdatahandler_trades_load(testdatadir):
-    dh = get_datahandler(testdatadir, 'feather')
+@pytest.mark.parametrize('datahandler', ['jsongz', 'hdf5', 'feather', 'parquet'])
+def test_datahandler_trades_load(testdatadir, datahandler):
+    dh = get_datahandler(testdatadir, datahandler)
    trades = dh.trades_load('XRP/ETH')
-    assert isinstance(trades, list)
-    assert trades[0][0] == 1570752011620
-    assert trades[-1][-1] == 0.1986231
+    assert isinstance(trades, DataFrame)
+    assert trades.iloc[0]['timestamp'] == 1570752011620
+    assert trades.iloc[0]['date'] == Timestamp('2019-10-11 00:00:11.620000+0000')
+    assert trades.iloc[-1]['cost'] == 0.1986231

    trades1 = dh.trades_load('UNITTEST/NONEXIST')
-    assert trades1 == []
+    assert isinstance(trades, DataFrame)
+    assert trades1.empty


-def test_featherdatahandler_trades_store(testdatadir, tmpdir):
+@pytest.mark.parametrize('datahandler', ['jsongz', 'hdf5', 'feather', 'parquet'])
+def test_datahandler_trades_store(testdatadir, tmpdir, datahandler):
    tmpdir1 = Path(tmpdir)
-    dh = get_datahandler(testdatadir, 'feather')
+    dh = get_datahandler(testdatadir, datahandler)
    trades = dh.trades_load('XRP/ETH')

-    dh1 = get_datahandler(tmpdir1, 'feather')
+    dh1 = get_datahandler(tmpdir1, datahandler)
    dh1.trades_store('XRP/NEW', trades)
-    file = tmpdir1 / 'XRP_NEW-trades.feather'
+
+    file = tmpdir1 / f'XRP_NEW-trades.{dh1._get_file_extension()}'
    assert file.is_file()
    # Load trades back
    trades_new = dh1.trades_load('XRP/NEW')
-
+    assert_frame_equal(trades, trades_new, check_exact=True)
    assert len(trades_new) == len(trades)
-    assert trades[0][0] == trades_new[0][0]
-    assert trades[0][1] == trades_new[0][1]
-    # assert trades[0][2] == trades_new[0][2]  # This is nan - so comparison does not make sense
-    assert trades[0][3] == trades_new[0][3]
-    assert trades[0][4] == trades_new[0][4]
-    assert trades[0][5] == trades_new[0][5]
-    assert trades[0][6] == trades_new[0][6]
-    assert trades[-1][0] == trades_new[-1][0]
-    assert trades[-1][1] == trades_new[-1][1]
-    # assert trades[-1][2] == trades_new[-1][2]  # This is nan - so comparison does not make sense
-    assert trades[-1][3] == trades_new[-1][3]
-    assert trades[-1][4] == trades_new[-1][4]
-    assert trades[-1][5] == trades_new[-1][5]
-    assert trades[-1][6] == trades_new[-1][6]


-def test_featherdatahandler_trades_purge(mocker, testdatadir):
+@pytest.mark.parametrize('datahandler', ['jsongz', 'hdf5', 'feather', 'parquet'])
+def test_datahandler_trades_purge(mocker, testdatadir, datahandler):
    mocker.patch.object(Path, "exists", MagicMock(return_value=False))
    unlinkmock = mocker.patch.object(Path, "unlink", MagicMock())
-    dh = get_datahandler(testdatadir, 'feather')
+    dh = get_datahandler(testdatadir, datahandler)
    assert not dh.trades_purge('UNITTEST/NONEXIST')
    assert unlinkmock.call_count == 0

--- a/tests/data/test_dataprovider.py
+++ b/tests/data/test_dataprovider.py
@ -129,9 +129,14 @@ def test_get_pair_dataframe(mocker, default_conf, ohlcv_history, candle_type):
    default_conf["runmode"] = RunMode.BACKTEST
    dp = DataProvider(default_conf, exchange)
    assert dp.runmode == RunMode.BACKTEST
-    assert isinstance(dp.get_pair_dataframe(
-        "UNITTEST/BTC", timeframe, candle_type=candle_type), DataFrame)
-    # assert dp.get_pair_dataframe("NONESENSE/AAA", timeframe).empty
+    df = dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type)
+    assert isinstance(df, DataFrame)
+    assert len(df) == 3  # ohlcv_history mock has just 3 rows
+
+    dp._set_dataframe_max_date(ohlcv_history.iloc[-1]['date'])
+    df = dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type)
+    assert isinstance(df, DataFrame)
+    assert len(df) == 2  # ohlcv_history is limited to 2 rows now


 def test_available_pairs(mocker, default_conf, ohlcv_history):
@ -259,7 +264,7 @@ def test_orderbook(mocker, default_conf, order_book_l2):
    assert order_book_l2.call_args_list[0][0][0] == 'ETH/BTC'
    assert order_book_l2.call_args_list[0][0][1] >= 5

-    assert type(res) is dict
+    assert isinstance(res, dict)
    assert 'bids' in res
    assert 'asks' in res

@ -272,7 +277,7 @@ def test_market(mocker, default_conf, markets):
    dp = DataProvider(default_conf, exchange)
    res = dp.market('ETH/BTC')

-    assert type(res) is dict
+    assert isinstance(res, dict)
    assert 'symbol' in res
    assert res['symbol'] == 'ETH/BTC'

@ -286,7 +291,7 @@ def test_ticker(mocker, default_conf, tickers):
    exchange = get_patched_exchange(mocker, default_conf)
    dp = DataProvider(default_conf, exchange)
    res = dp.ticker('ETH/BTC')
-    assert type(res) is dict
+    assert isinstance(res, dict)
    assert 'symbol' in res
    assert res['symbol'] == 'ETH/BTC'

--- a/tests/data/test_history.py
+++ b/tests/data/test_history.py
@ -3,6 +3,7 @@
 import json
 import logging
 import uuid
+from datetime import timedelta
 from pathlib import Path
 from shutil import copyfile
 from unittest.mock import MagicMock, PropertyMock
@ -26,7 +27,7 @@ from freqtrade.enums import CandleType
 from freqtrade.exchange import timeframe_to_minutes
 from freqtrade.misc import file_dump_json
 from freqtrade.resolvers import StrategyResolver
-from freqtrade.util import dt_utc
+from freqtrade.util import dt_ts, dt_utc
 from tests.conftest import (CURRENT_TEST_STRATEGY, EXMS, get_patched_exchange, log_has, log_has_re,
                            patch_exchange)

@ -569,7 +570,10 @@ def test_refresh_backtest_trades_data(mocker, default_conf, markets, caplog, tes


 def test_download_trades_history(trades_history, mocker, default_conf, testdatadir, caplog,
-                                 tmpdir) -> None:
+                                 tmpdir, time_machine) -> None:
+    start_dt = dt_utc(2023, 1, 1)
+    time_machine.move_to(start_dt, tick=False)
+
    tmpdir1 = Path(tmpdir)
    ght_mock = MagicMock(side_effect=lambda pair, *args, **kwargs: (pair, trades_history))
    mocker.patch(f'{EXMS}.get_historic_trades', ght_mock)
@ -581,8 +585,13 @@ def test_download_trades_history(trades_history, mocker, default_conf, testdatad

    assert _download_trades_history(data_handler=data_handler, exchange=exchange,
                                    pair='ETH/BTC')
-    assert log_has("New Amount of trades: 5", caplog)
+    assert log_has("Current Amount of trades: 0", caplog)
+    assert log_has("New Amount of trades: 6", caplog)
+    assert ght_mock.call_count == 1
+    # Default "since" - 30 days before current day.
+    assert ght_mock.call_args_list[0][1]['since'] == dt_ts(start_dt - timedelta(days=30))
    assert file1.is_file()
+    caplog.clear()

    ght_mock.reset_mock()
    since_time = int(trades_history[-3][0] // 1000)
@ -599,6 +608,7 @@ def test_download_trades_history(trades_history, mocker, default_conf, testdatad
    file1.unlink()

    mocker.patch(f'{EXMS}.get_historic_trades', MagicMock(side_effect=ValueError))
+    caplog.clear()

    assert not _download_trades_history(data_handler=data_handler, exchange=exchange,
                                        pair='ETH/BTC')
@ -620,7 +630,7 @@ def test_download_trades_history(trades_history, mocker, default_conf, testdatad

    assert int(ght_mock.call_args_list[0][1]['since'] // 1000) == since_time
    assert ght_mock.call_args_list[0][1]['from_id'] is None
-    assert log_has_re(r'Start earlier than available data. Redownloading trades for.*', caplog)
+    assert log_has_re(r'Start .* earlier than available data. Redownloading trades for.*', caplog)
    _clean_test_file(file2)


@ -651,10 +661,10 @@ def test_convert_trades_to_ohlcv(testdatadir, tmpdir, caplog):

    assert_frame_equal(dfbak_1m, df_1m, check_exact=True)
    assert_frame_equal(dfbak_5m, df_5m, check_exact=True)
-
-    assert not log_has('Could not convert NoDatapair to OHLCV.', caplog)
+    msg = 'Could not convert NoDatapair to OHLCV.'
+    assert not log_has(msg, caplog)

    convert_trades_to_ohlcv(['NoDatapair'], timeframes=['1m', '5m'],
                            data_format_trades='jsongz',
                            datadir=tmpdir1, timerange=tr, erase=True)
-    assert log_has('Could not convert NoDatapair to OHLCV.', caplog)
+    assert log_has(msg, caplog)
--- a/tests/exchange/test_exchange.py
+++ b/tests/exchange/test_exchange.py
@ -2470,7 +2470,7 @@ def test_refresh_latest_ohlcv_inv_result(default_conf, mocker, caplog):
    assert exchange._klines
    assert exchange._api_async.fetch_ohlcv.call_count == 2

-    assert type(res) is dict
+    assert isinstance(res, dict)
    assert len(res) == 1
    # Test that each is in list at least once as order is not guaranteed
    assert log_has("Error loading ETH/BTC. Result was [[]].", caplog)
@ -2854,7 +2854,7 @@ async def test__async_fetch_trades(default_conf, mocker, caplog, exchange_name,

    pair = 'ETH/BTC'
    res = await exchange._async_fetch_trades(pair, since=None, params=None)
-    assert type(res) is list
+    assert isinstance(res, list)
    assert isinstance(res[0], list)
    assert isinstance(res[1], list)

@ -2954,9 +2954,9 @@ async def test__async_get_trade_history_id(default_conf, mocker, exchange_name,
    ret = await exchange._async_get_trade_history_id(pair,
                                                     since=fetch_trades_result[0]['timestamp'],
                                                     until=fetch_trades_result[-1]['timestamp'] - 1)
-    assert type(ret) is tuple
+    assert isinstance(ret, tuple)
    assert ret[0] == pair
-    assert type(ret[1]) is list
+    assert isinstance(ret[1], list)
    assert len(ret[1]) == len(fetch_trades_result)
    assert exchange._api_async.fetch_trades.call_count == 3
    fetch_trades_cal = exchange._api_async.fetch_trades.call_args_list
@ -2992,9 +2992,9 @@ async def test__async_get_trade_history_time(default_conf, mocker, caplog, excha
        pair,
        since=fetch_trades_result[0]['timestamp'],
        until=fetch_trades_result[-1]['timestamp'] - 1)
-    assert type(ret) is tuple
+    assert isinstance(ret, tuple)
    assert ret[0] == pair
-    assert type(ret[1]) is list
+    assert isinstance(ret[1], list)
    assert len(ret[1]) == len(fetch_trades_result)
    assert exchange._api_async.fetch_trades.call_count == 2
    fetch_trades_cal = exchange._api_async.fetch_trades.call_args_list
@ -3028,9 +3028,9 @@ async def test__async_get_trade_history_time_empty(default_conf, mocker, caplog,
    pair = 'ETH/BTC'
    ret = await exchange._async_get_trade_history_time(pair, since=trades_history[0][0],
                                                       until=trades_history[-1][0] - 1)
-    assert type(ret) is tuple
+    assert isinstance(ret, tuple)
    assert ret[0] == pair
-    assert type(ret[1]) is list
+    assert isinstance(ret[1], list)
    assert len(ret[1]) == len(trades_history) - 1
    assert exchange._async_fetch_trades.call_count == 2
    fetch_trades_cal = exchange._async_fetch_trades.call_args_list
--- a/tests/freqai/conftest.py
+++ b/tests/freqai/conftest.py
@ -97,9 +97,9 @@ def mock_pytorch_mlp_model_training_parameters() -> Dict[str, Any]:
    return {
            "learning_rate": 3e-4,
            "trainer_kwargs": {
-                "max_iters": 1,
+                "n_steps": None,
                "batch_size": 64,
-                "max_n_eval_batches": 1,
+                "n_epochs": 1,
            },
            "model_kwargs": {
                "hidden_dim": 32,
--- a/tests/optimize/test_backtesting.py
+++ b/tests/optimize/test_backtesting.py
@ -20,7 +20,7 @@ from freqtrade.data.dataprovider import DataProvider
 from freqtrade.data.history import get_timerange
 from freqtrade.enums import CandleType, ExitType, RunMode
 from freqtrade.exceptions import DependencyException, OperationalException
-from freqtrade.exchange.exchange import timeframe_to_next_date
+from freqtrade.exchange import timeframe_to_next_date, timeframe_to_prev_date
 from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename, get_strategy_run_id
 from freqtrade.optimize.backtesting import Backtesting
 from freqtrade.persistence import LocalTrade, Trade
@ -1122,10 +1122,10 @@ def test_backtest_dataprovider_analyzed_df(default_conf, fee, mocker, testdatadi
    processed = backtesting.strategy.advise_all_indicators(data)
    min_date, max_date = get_timerange(processed)

-    global count
    count = 0

    def tmp_confirm_entry(pair, current_time, **kwargs):
+        nonlocal count
        dp = backtesting.strategy.dp
        df, _ = dp.get_analyzed_dataframe(pair, backtesting.strategy.timeframe)
        current_candle = df.iloc[-1].squeeze()
@ -1135,8 +1135,13 @@ def test_backtest_dataprovider_analyzed_df(default_conf, fee, mocker, testdatadi
        assert candle_date == current_time
        # These asserts don't properly raise as they are nested,
        # therefore we increment count and assert for that.
-        global count
-        count = count + 1
+        df = dp.get_pair_dataframe(pair, backtesting.strategy.timeframe)
+        prior_time = timeframe_to_prev_date(backtesting.strategy.timeframe,
+                                            candle_date - timedelta(seconds=1))
+        assert prior_time == df.iloc[-1].squeeze()['date']
+        assert df.iloc[-1].squeeze()['date'] < current_time
+
+        count += 1

    backtesting.strategy.confirm_trade_entry = tmp_confirm_entry
    backtesting.backtest(
@ -1354,11 +1359,11 @@ def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair, testdatadir)

    # Cached data correctly removed amounts
    offset = 1 if tres == 0 else 0
-    removed_candles = len(data[pair]) - offset - backtesting.strategy.startup_candle_count
+    removed_candles = len(data[pair]) - offset
    assert len(backtesting.dataprovider.get_analyzed_dataframe(pair, '5m')[0]) == removed_candles
    assert len(
        backtesting.dataprovider.get_analyzed_dataframe('NXT/BTC', '5m')[0]
-    ) == len(data['NXT/BTC']) - 1 - backtesting.strategy.startup_candle_count
+    ) == len(data['NXT/BTC']) - 1

    backtesting.strategy.max_open_trades = 1
    backtesting.config.update({'max_open_trades': 1})
--- a/tests/persistence/test_persistence.py
+++ b/tests/persistence/test_persistence.py
@ -1989,9 +1989,9 @@ def test_select_order(fee, is_short):

    # Open buy order, no sell order
    order = trades[0].select_order(trades[0].entry_side, True)
-    assert order is None
-    order = trades[0].select_order(trades[0].entry_side, False)
    assert order is not None
+    order = trades[0].select_order(trades[0].entry_side, False)
+    assert order is None
    order = trades[0].select_order(trades[0].exit_side, None)
    assert order is None

@ -2462,7 +2462,16 @@ def test_select_filled_orders(fee):

    # Closed buy order, no sell order
    orders = trades[0].select_filled_orders('buy')
+    assert isinstance(orders, list)
+    assert len(orders) == 0
+
+    orders = trades[0].select_filled_orders('sell')
    assert orders is not None
+    assert len(orders) == 0
+
+    # closed buy order, and closed sell order
+    orders = trades[1].select_filled_orders('buy')
+    assert isinstance(orders, list)
    assert len(orders) == 1
    order = orders[0]
    assert order.amount > 0
@ -2470,33 +2479,25 @@ def test_select_filled_orders(fee):
    assert order.side == 'buy'
    assert order.ft_order_side == 'buy'
    assert order.status == 'closed'
-    orders = trades[0].select_filled_orders('sell')
-    assert orders is not None
-    assert len(orders) == 0
-
-    # closed buy order, and closed sell order
-    orders = trades[1].select_filled_orders('buy')
-    assert orders is not None
-    assert len(orders) == 1

    orders = trades[1].select_filled_orders('sell')
-    assert orders is not None
+    assert isinstance(orders, list)
    assert len(orders) == 1

    # Has open buy order
    orders = trades[3].select_filled_orders('buy')
-    assert orders is not None
+    assert isinstance(orders, list)
    assert len(orders) == 0
    orders = trades[3].select_filled_orders('sell')
-    assert orders is not None
+    assert isinstance(orders, list)
    assert len(orders) == 0

    # Open sell order
    orders = trades[4].select_filled_orders('buy')
-    assert orders is not None
+    assert isinstance(orders, list)
    assert len(orders) == 1
    orders = trades[4].select_filled_orders('sell')
-    assert orders is not None
+    assert isinstance(orders, list)
    assert len(orders) == 0


--- a/tests/plugins/test_pairlist.py
+++ b/tests/plugins/test_pairlist.py
@ -553,7 +553,7 @@ def test_VolumePairList_whitelist_gen(mocker, whitelist_conf, shitcoinmarkets, t
        assert isinstance(whitelist, list)

        # Verify length of pairlist matches (used for ShuffleFilter without seed)
-        if type(whitelist_result) is list:
+        if isinstance(whitelist_result, list):
            assert whitelist == whitelist_result
        else:
            len(whitelist) == whitelist_result
--- a/tests/rpc/test_rpc.py
+++ b/tests/rpc/test_rpc.py
@ -363,9 +363,8 @@ def test_rpc_delete_trade(mocker, default_conf, fee, markets, caplog, is_short):

    res = rpc._rpc_delete('2')
    assert isinstance(res, dict)
-    assert cancel_mock.call_count == 1
    assert stoploss_mock.call_count == 1
-    assert res['cancel_order_count'] == 2
+    assert res['cancel_order_count'] == 1

    stoploss_mock = mocker.patch(f'{EXMS}.cancel_stoploss_order', side_effect=InvalidOrderException)

--- a/tests/rpc/test_rpc_apiserver.py
+++ b/tests/rpc/test_rpc_apiserver.py
@ -706,7 +706,7 @@ def test_api_delete_trade(botclient, mocker, fee, markets, is_short):
    assert len(trades) - 1 == len(Trade.session.scalars(select(Trade)).all())
    rc = client_delete(client, f"{BASE_URI}/trades/2")
    assert_response(rc)
-    assert rc.json()['result_msg'] == 'Deleted trade 2. Closed 2 open orders.'
+    assert rc.json()['result_msg'] == 'Deleted trade 2. Closed 1 open orders.'
    assert len(trades) - 2 == len(Trade.session.scalars(select(Trade)).all())
    assert stoploss_mock.call_count == 1

@ -841,7 +841,7 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
         'profit_closed_percent_sum': -1.5, 'profit_closed_ratio': -6.739057628404269e-06,
         'profit_closed_percent': -0.0, 'winning_trades': 0, 'losing_trades': 2,
         'profit_factor': 0.0, 'winrate': 0.0, 'expectancy': -0.0033695635,
-         'expectancy_ratio': -1.0, 'trading_volume': 91.074,
+         'expectancy_ratio': -1.0, 'trading_volume': 75.945,
         }
    ),
    (
@ -857,7 +857,7 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
         'profit_closed_percent_sum': 1.5, 'profit_closed_ratio': 7.391275897987988e-07,
         'profit_closed_percent': 0.0, 'winning_trades': 2, 'losing_trades': 0,
         'profit_factor': None, 'winrate': 1.0, 'expectancy': 0.0003695635,
-         'expectancy_ratio': 100, 'trading_volume': 91.074,
+         'expectancy_ratio': 100, 'trading_volume': 75.945,
         }
    ),
    (
@ -874,7 +874,7 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
         'profit_closed_percent': -0.0, 'winning_trades': 1, 'losing_trades': 1,
         'profit_factor': 0.02775724835771106, 'winrate': 0.5,
         'expectancy': -0.0027145635000000003, 'expectancy_ratio': -0.48612137582114445,
-         'trading_volume': 91.074,
+         'trading_volume': 75.945,
         }
    )
 ])
@ -1125,7 +1125,7 @@ def test_api_status(botclient, mocker, ticker, fee, markets, is_short,
    assert_response(rc)
    resp_values = rc.json()
    assert len(resp_values) == 4
-    assert resp_values[0]['profit_abs'] is None
+    assert resp_values[0]['profit_abs'] == 0.0


 def test_api_version(botclient):
@ -1429,12 +1429,12 @@ def test_api_pair_candles(botclient, ohlcv_history):
    assert len(rc.json()['data']) == amount

    assert (rc.json()['data'] ==
-            [['2017-11-26 08:50:00', 8.794e-05, 8.948e-05, 8.794e-05, 8.88e-05, 0.0877869,
+            [['2017-11-26T08:50:00Z', 8.794e-05, 8.948e-05, 8.794e-05, 8.88e-05, 0.0877869,
              None, 0, 0, 0, 0, 1511686200000, None, None, None, None],
-             ['2017-11-26 08:55:00', 8.88e-05, 8.942e-05, 8.88e-05,
+             ['2017-11-26T08:55:00Z', 8.88e-05, 8.942e-05, 8.88e-05,
                 8.893e-05, 0.05874751, 8.886500000000001e-05, 1, 0, 0, 0, 1511686500000, 8.893e-05,
                 None, None, None],
-             ['2017-11-26 09:00:00', 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05,
+             ['2017-11-26T09:00:00Z', 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05,
                 0.7039405, 8.885e-05, 0, 0, 0, 0, 1511686800000, None, None, None, None]

             ])
@ -1448,13 +1448,13 @@ def test_api_pair_candles(botclient, ohlcv_history):
                    f"{BASE_URI}/pair_candles?limit={amount}&pair=XRP%2FBTC&timeframe={timeframe}")
    assert_response(rc)
    assert (rc.json()['data'] ==
-            [['2017-11-26 08:50:00', 8.794e-05, 8.948e-05, 8.794e-05, 8.88e-05, 0.0877869,
+            [['2017-11-26T08:50:00Z', 8.794e-05, 8.948e-05, 8.794e-05, 8.88e-05, 0.0877869,
              None, 0, None, 0, 0, None, 1511686200000, None, None, None, None],
-             ['2017-11-26 08:55:00', 8.88e-05, 8.942e-05, 8.88e-05,
-                 8.893e-05, 0.05874751, 8.886500000000001e-05, 1, 0.0, 0, 0, '2017-11-26 08:55:00',
+             ['2017-11-26T08:55:00Z', 8.88e-05, 8.942e-05, 8.88e-05,
+                 8.893e-05, 0.05874751, 8.886500000000001e-05, 1, 0.0, 0, 0, '2017-11-26T08:55:00Z',
                 1511686500000, 8.893e-05, None, None, None],
-             ['2017-11-26 09:00:00', 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05,
-                 0.7039405, 8.885e-05, 0, 0.0, 0, 0, '2017-11-26 09:00:00', 1511686800000,
+             ['2017-11-26T09:00:00Z', 8.891e-05, 8.893e-05, 8.875e-05, 8.877e-05,
+                 0.7039405, 8.885e-05, 0, 0.0, 0, 0, '2017-11-26T09:00:00Z', 1511686800000,
                 None, None, None, None]
             ])

@ -1511,7 +1511,7 @@ def test_api_pair_history(botclient, mocker):
    date_col_idx = [idx for idx, c in enumerate(result['columns']) if c == 'date'][0]
    rsi_col_idx = [idx for idx, c in enumerate(result['columns']) if c == 'rsi'][0]

-    assert data[0][date_col_idx] == '2018-01-11 00:00:00'
+    assert data[0][date_col_idx] == '2018-01-11T00:00:00Z'
    assert data[0][rsi_col_idx] is not None
    assert data[0][rsi_col_idx] > 0
    assert lfm.call_count == 1
--- a/tests/rpc/test_rpc_telegram.py
+++ b/tests/rpc/test_rpc_telegram.py
@ -316,7 +316,7 @@ async def test_telegram_status_multi_entry(default_conf, update, mocker, fee) ->

    create_mock_trades(fee)
    trades = Trade.get_open_trades()
-    trade = trades[0]
+    trade = trades[3]
    # Average may be empty on some exchanges
    trade.orders[0].average = 0
    trade.orders.append(Order(
@ -344,9 +344,9 @@ async def test_telegram_status_multi_entry(default_conf, update, mocker, fee) ->

    await telegram._status(update=update, context=MagicMock())
    assert msg_mock.call_count == 4
-    msg = msg_mock.call_args_list[0][0][0]
+    msg = msg_mock.call_args_list[3][0][0]
    assert re.search(r'Number of Entries.*2', msg)
-    assert re.search(r'Number of Exits.*0', msg)
+    assert re.search(r'Number of Exits.*1', msg)
    assert re.search(r'Average Entry Price', msg)
    assert re.search(r'Order filled', msg)
    assert re.search(r'Close Date:', msg) is None
--- a/tests/test_arguments.py
+++ b/tests/test_arguments.py
@ -133,7 +133,7 @@ def test_parse_args_backtesting_custom() -> None:
    assert call_args['command'] == 'backtesting'
    assert call_args['func'] is not None
    assert call_args['timeframe'] == '1m'
-    assert type(call_args['strategy_list']) is list
+    assert isinstance(call_args['strategy_list'], list)
    assert len(call_args['strategy_list']) == 2


--- a/tests/test_freqtradebot.py
+++ b/tests/test_freqtradebot.py
@ -28,9 +28,9 @@ from tests.conftest import (EXMS, create_mock_trades, create_mock_trades_usdt,
                            get_patched_freqtradebot, get_patched_worker, log_has, log_has_re,
                            patch_edge, patch_exchange, patch_get_signal, patch_wallet,
                            patch_whitelist)
-from tests.conftest_trades import (MOCK_TRADE_COUNT, entry_side, exit_side, mock_order_1,
-                                   mock_order_2, mock_order_2_sell, mock_order_3, mock_order_3_sell,
-                                   mock_order_4, mock_order_5_stoploss, mock_order_6_sell)
+from tests.conftest_trades import (MOCK_TRADE_COUNT, entry_side, exit_side, mock_order_2,
+                                   mock_order_2_sell, mock_order_3, mock_order_3_sell, mock_order_4,
+                                   mock_order_5_stoploss, mock_order_6_sell)
 from tests.conftest_trades_usdt import mock_trade_usdt_4


@ -5329,8 +5329,8 @@ def test_sync_wallet_dry_run(mocker, default_conf_usdt, ticker_usdt, fee, limit_

@pytest.mark.usefixtures("init_persistence")
@pytest.mark.parametrize("is_short,buy_calls,sell_calls", [
-    (False, 1, 2),
-    (True, 1, 2),
+    (False, 1, 1),
+    (True, 1, 1),
 ])
 def test_cancel_all_open_orders(mocker, default_conf_usdt, fee, limit_order, limit_order_open,
                                is_short, buy_calls, sell_calls):
@ -5387,7 +5387,7 @@ def test_startup_update_open_orders(mocker, default_conf_usdt, fee, caplog, is_s
    freqtrade.config['dry_run'] = False
    freqtrade.startup_update_open_orders()

-    assert len(Order.get_open_orders()) == 3
+    assert len(Order.get_open_orders()) == 4
    matching_buy_order = mock_order_4(is_short=is_short)
    matching_buy_order.update({
        'status': 'closed',
@ -5395,7 +5395,7 @@ def test_startup_update_open_orders(mocker, default_conf_usdt, fee, caplog, is_s
    mocker.patch(f'{EXMS}.fetch_order', return_value=matching_buy_order)
    freqtrade.startup_update_open_orders()
    # Only stoploss and sell orders are kept open
-    assert len(Order.get_open_orders()) == 2
+    assert len(Order.get_open_orders()) == 3

    caplog.clear()
    mocker.patch(f'{EXMS}.fetch_order', side_effect=ExchangeError)
@ -5407,7 +5407,7 @@ def test_startup_update_open_orders(mocker, default_conf_usdt, fee, caplog, is_s
    # Orders which are no longer found after X days should be assumed as canceled.
    freqtrade.startup_update_open_orders()
    assert log_has_re(r"Order is older than \d days.*", caplog)
-    assert hto_mock.call_count == 2
+    assert hto_mock.call_count == 3
    assert hto_mock.call_args_list[0][0][0]['status'] == 'canceled'
    assert hto_mock.call_args_list[1][0][0]['status'] == 'canceled'

@ -5451,7 +5451,6 @@ def test_update_trades_without_assigned_fees(mocker, default_conf_usdt, fee, is_
                 side_effect=[
                     patch_with_fee(mock_order_2_sell(is_short=is_short)),
                     patch_with_fee(mock_order_3_sell(is_short=is_short)),
-                     patch_with_fee(mock_order_1(is_short=is_short)),
                     patch_with_fee(mock_order_2(is_short=is_short)),
                     patch_with_fee(mock_order_3(is_short=is_short)),
                     patch_with_fee(mock_order_4(is_short=is_short)),
@ -5561,14 +5560,15 @@ def test_handle_insufficient_funds(mocker, default_conf_usdt, fee, is_short, cap
    caplog.clear()

    # No open order
-    trade = trades[0]
+    trade = trades[1]
    reset_open_orders(trade)
    assert trade.open_order_id is None
    assert trade.stoploss_order_id is None

    freqtrade.handle_insufficient_funds(trade)
-    order = mock_order_1(is_short=is_short)
-    assert log_has_re(r"Order Order(.*order_id=" + order['id'] + ".*) is no longer open.", caplog)
+    order = trade.orders[0]
+    assert log_has_re(r"Order Order(.*order_id=" + order.order_id + ".*) is no longer open.",
+                      caplog)
    assert mock_fo.call_count == 0
    assert mock_uts.call_count == 0
    # No change to orderid - as update_trade_state is mocked
--- a/tests/test_misc.py
+++ b/tests/test_misc.py
@ -9,8 +9,7 @@ import pytest

 from freqtrade.misc import (dataframe_to_json, decimals_per_coin, deep_merge_dicts, file_dump_json,
                            file_load_json, is_file_in_dir, json_to_dataframe, pair_to_filename,
-                            parse_db_uri_for_logging, plural, render_template,
-                            render_template_with_fallback, round_coin_value, safe_value_fallback,
+                            parse_db_uri_for_logging, plural, round_coin_value, safe_value_fallback,
                            safe_value_fallback2)


@ -177,20 +176,6 @@ def test_plural() -> None:
    assert plural(-1.5, "ox", "oxen") == "oxen"


-def test_render_template_fallback(mocker):
-    from jinja2.exceptions import TemplateNotFound
-    with pytest.raises(TemplateNotFound):
-        val = render_template(
-            templatefile='subtemplates/indicators_does-not-exist.j2',)
-
-    val = render_template_with_fallback(
-        templatefile='strategy_subtemplates/indicators_does-not-exist.j2',
-        templatefallbackfile='strategy_subtemplates/indicators_minimal.j2',
-    )
-    assert isinstance(val, str)
-    assert 'if self.dp' in val
-
-
@pytest.mark.parametrize('conn_url,expected', [
    ("postgresql+psycopg2://scott123:scott123@host:1245/dbname",
     "postgresql+psycopg2://scott123:*****@host:1245/dbname"),
--- a/tests/testdata/XRP_ETH-trades.parquet
+++ b/tests/testdata/XRP_ETH-trades.parquet
--- a/tests/utils/test_rendering_utils.py
+++ b/tests/utils/test_rendering_utils.py
@ -0,0 +1,17 @@
+import pytest
+
+from freqtrade.util import render_template, render_template_with_fallback
+
+
+def test_render_template_fallback():
+    from jinja2.exceptions import TemplateNotFound
+    with pytest.raises(TemplateNotFound):
+        val = render_template(
+            templatefile='subtemplates/indicators_does-not-exist.j2',)
+
+    val = render_template_with_fallback(
+        templatefile='strategy_subtemplates/indicators_does-not-exist.j2',
+        templatefallbackfile='strategy_subtemplates/indicators_minimal.j2',
+    )
+    assert isinstance(val, str)
+    assert 'if self.dp' in val