Merge branch 'develop' into feature/hyperliquid-hip3-support

2026-01-20 22:10:35 +00:00 · 2025-12-30 08:46:48 +01:00
parent 511b817e84 70834b6491
commit 0dad2b36ab
47 changed files with 775 additions and 551 deletions
--- a/.github/dependabot.yml
+++ b/.github/dependabot.yml
@@ -28,9 +28,9 @@ updates:
    exclude:
      - ccxt
  schedule:
-    interval: weekly
-    time: "03:00"
-    timezone: "Etc/UTC"
+    interval: "cron"
+    # Monday at 03:00
+    cronjob: "0 3 * * 1"
  open-pull-requests-limit: 15
  target-branch: develop
  groups:
@@ -53,6 +53,13 @@ updates:
  cooldown:
    default-days: 7
  schedule:
-    interval: "weekly"
+    interval: "cron"
+    # Monday at 03:00
+    cronjob: "0 3 * * 1"
  open-pull-requests-limit: 10
  target-branch: develop
+  groups:
+    actions:
+      patterns:
+        # Combine updates for github provided actions
+        - "actions/*"
--- a/.github/workflows/binance-lev-tier-update.yml
+++ b/.github/workflows/binance-lev-tier-update.yml
@@ -34,7 +34,7 @@ jobs:
      run: python build_helpers/binance_update_lev_tiers.py


-    - uses: peter-evans/create-pull-request@84ae59a2cdc2258d6fa0732dd66352dddae2a412 # v7.0.9
+    - uses: peter-evans/create-pull-request@98357b18bf14b5342f975ff684046ec3b2a07725 # v8.0.0
      with:
        token: ${{ secrets.REPO_SCOPED_TOKEN }}
        add-paths: freqtrade/exchange/binance_leverage_tiers.json
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -25,7 +25,7 @@ jobs:
    strategy:
      matrix:
        os: [ "ubuntu-22.04", "ubuntu-24.04", "macos-14", "macos-15" , "windows-2022", "windows-2025" ]
-        python-version: ["3.11", "3.12", "3.13"]
+        python-version: ["3.11", "3.12", "3.13", "3.14"]

    steps:
    - uses: actions/checkout@v6.0.1
@@ -38,7 +38,7 @@ jobs:
        python-version: ${{ matrix.python-version }}

    - name: Install uv
-      uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # v7.1.4
+      uses: astral-sh/setup-uv@681c641aba71e4a1c380be3ab5e12ad51f415867 # v7.1.6
      with:
        activate-environment: true
        enable-cache: true
@@ -74,7 +74,7 @@ jobs:
      run: |
        pytest --random-order --cov=freqtrade --cov=freqtrade_client --cov-config=.coveragerc

-    - uses: codecov/codecov-action@5a1091511ad55cbe89839c7260b706298ca349f7 # v5.5.1
+    - uses: codecov/codecov-action@671740ac38dd9b0130fbe1cec585b89eea48d3de # v5.5.2
      if: (runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-24.04')
      with:
        fail_ci_if_error: true
@@ -87,12 +87,12 @@ jobs:
        rm -rf codecov codecov.SHA256SUM codecov.SHA256SUM.sig

    - name: Run json schema extract
-      # This should be kept before the repository check to ensure that the schema is up-to-date
+      # This must be kept before the repository check to ensure that the schema is up-to-date
      run: |
        python build_helpers/extract_config_json_schema.py

    - name: Run command docs partials extract
-      # This should be kept before the repository check to ensure that the docs are up-to-date
+      # This must be kept before the repository check to ensure that the docs are up-to-date
      if: ${{ (matrix.python-version == '3.13') }}
      run: |
        python build_helpers/create_command_partials.py
@@ -110,7 +110,7 @@ jobs:
        fi

    - name: Check for repository changes - Windows
-      if: ${{ runner.os == 'Windows' && (matrix.python-version != '3.13') }}
+      if: ${{ runner.os == 'Windows' }}
      run: |
        if (git status --porcelain) {
          Write-Host "Repository is dirty, changes detected:"
@@ -159,6 +159,7 @@ jobs:
      shell: powershell
      run: |
        $PSVersionTable
+        Get-PSRepository | Format-List *
        Set-PSRepository psgallery -InstallationPolicy trusted
        Install-Module -Name Pester -RequiredVersion 5.3.1 -Confirm:$false -Force -SkipPublisherCheck
        $Error.clear()
@@ -250,7 +251,7 @@ jobs:
        python-version: "3.12"

    - name: Install uv
-      uses: astral-sh/setup-uv@1e862dfacbd1d6d858c55d9b792c756523627244 # v7.1.4
+      uses: astral-sh/setup-uv@681c641aba71e4a1c380be3ab5e12ad51f415867 # v7.1.6
      with:
        activate-environment: true
        enable-cache: true
@@ -335,7 +336,7 @@ jobs:
        python -m build --sdist --wheel

    - name: Upload artifacts 📦
-      uses: actions/upload-artifact@v5
+      uses: actions/upload-artifact@v6
      with:
        name: freqtrade-build
        path: |
@@ -348,7 +349,7 @@ jobs:
        python -m build --sdist --wheel ft_client

    - name: Upload artifacts 📦
-      uses: actions/upload-artifact@v5
+      uses: actions/upload-artifact@v6
      with:
        name: freqtrade-client-build
        path: |
@@ -372,7 +373,7 @@ jobs:
        persist-credentials: false

    - name: Download artifact  📦
-      uses: actions/download-artifact@v6
+      uses: actions/download-artifact@v7
      with:
        pattern: freqtrade*-build
        path: dist
@@ -401,7 +402,7 @@ jobs:
        persist-credentials: false

    - name: Download artifact  📦
-      uses: actions/download-artifact@v6
+      uses: actions/download-artifact@v7
      with:
        pattern: freqtrade*-build
        path: dist
--- a/.github/workflows/docker-build.yml
+++ b/.github/workflows/docker-build.yml
@@ -40,6 +40,14 @@ jobs:
    - name: Visualize disk usage before build
      run: df -h

+    - name: Cleanup some disk space
+      run: |
+        docker system prune -a --force || true
+        docker builder prune -af || true
+
+    - name: Visualize disk usage after cleanup
+      run: df -h
+
    - name: Set docker tag names
      id: tags
      uses: ./.github/actions/docker-tags
@@ -57,7 +65,7 @@ jobs:

    - name: Set up Docker Buildx
      id: buildx
-      uses: docker/setup-buildx-action@e468171a9de216ec08956ac3ada2f0791b6bd435 #v3.11.1
+      uses: docker/setup-buildx-action@8d2750c68a42422c14e847fe6c8ac0403b4cbd6f #v3.12.0

    - name: Available platforms
      run: echo ${PLATFORMS}
@@ -282,6 +290,7 @@ jobs:
        docker buildx imagetools create \
          --tag ${GHCR_IMAGE_NAME}:${TAG} \
          --tag ${GHCR_IMAGE_NAME}:latest \
+          --tag ${IMAGE_NAME}:latest \
          ${IMAGE_NAME}:${TAG}

    - name: Docker images
--- a/.github/workflows/pre-commit-update.yml
+++ b/.github/workflows/pre-commit-update.yml
@@ -28,7 +28,7 @@ jobs:
    - name: Run auto-update
      run: pre-commit autoupdate

-    - uses: peter-evans/create-pull-request@84ae59a2cdc2258d6fa0732dd66352dddae2a412 # v7.0.9
+    - uses: peter-evans/create-pull-request@98357b18bf14b5342f975ff684046ec3b2a07725 # v8.0.0
      with:
        token: ${{ secrets.REPO_SCOPED_TOKEN }}
        add-paths: .pre-commit-config.yaml
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -31,8 +31,8 @@ repos:
          - types-requests==2.32.4.20250913
          - types-tabulate==0.9.0.20241207
          - types-python-dateutil==2.9.0.20251115
-          - scipy-stubs==1.16.3.2
-          - SQLAlchemy==2.0.44
+          - scipy-stubs==1.16.3.3
+          - SQLAlchemy==2.0.45
        # stages: [push]

  - repo: https://github.com/pycqa/isort
@@ -44,7 +44,7 @@ repos:

  - repo: https://github.com/charliermarsh/ruff-pre-commit
    # Ruff version.
-    rev: 'v0.14.9'
+    rev: 'v0.14.10'
    hooks:
      - id: ruff
      - id: ruff-format
@@ -83,6 +83,6 @@ repos:

  # Ensure github actions remain safe
  - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.18.0
+    rev: v1.19.0
    hooks:
    - id: zizmor
--- a/2
+++ b/2
@@ -1,4 +1,4 @@
-FROM python:3.13.8-slim-bookworm AS base
+FROM python:3.13.11-slim-trixie AS base

 # Setup env
 ENV LANG=C.UTF-8
--- a/build_helpers/create_command_partials.py
+++ b/build_helpers/create_command_partials.py
@@ -1,5 +1,7 @@
+import os
 import subprocess  # noqa: S404, RUF100
 import sys
+from io import StringIO
 from pathlib import Path


@@ -8,7 +10,20 @@ def _write_partial_file(filename: str, content: str):
        f.write(f"``` output\n{content}\n```\n")


+def _get_help_output(parser) -> str:
+    """Capture the help output from a parser."""
+    output = StringIO()
+    parser.print_help(file=output)
+    return output.getvalue()
+
+
 def extract_command_partials():
+    # Set terminal width to 80 columns for consistent output formatting
+    os.environ["COLUMNS"] = "80"
+
+    # Import Arguments here to avoid circular imports and ensure COLUMNS is set
+    from freqtrade.commands.arguments import Arguments
+
    subcommands = [
        "trade",
        "create-userdir",
@@ -46,16 +61,35 @@ def extract_command_partials():
        "recursive-analysis",
    ]

-    result = subprocess.run(["freqtrade", "--help"], capture_output=True, text=True)
+    # Build the Arguments class to get the parser with all subcommands
+    args = Arguments(None)
+    args._build_subcommands()

-    _write_partial_file("docs/commands/main.md", result.stdout)
+    # Get main help output
+    main_help = _get_help_output(args.parser)
+    _write_partial_file("docs/commands/main.md", main_help)
+
+    # Get subparsers from the main parser
+    # The subparsers are stored in _subparsers._group_actions[0].choices
+    subparsers_action = None
+    for action in args.parser._subparsers._group_actions:
+        if hasattr(action, "choices"):
+            subparsers_action = action
+            break
+
+    if subparsers_action is None:
+        raise RuntimeError("Could not find subparsers in the main parser")

    for command in subcommands:
        print(f"Running for {command}")
-        result = subprocess.run(["freqtrade", command, "--help"], capture_output=True, text=True)
-
-        _write_partial_file(f"docs/commands/{command}.md", result.stdout)
+        if command in subparsers_action.choices:
+            subparser = subparsers_action.choices[command]
+            help_output = _get_help_output(subparser)
+            _write_partial_file(f"docs/commands/{command}.md", help_output)
+        else:
+            print(f"  Warning: subcommand '{command}' not found in parser")

+    # freqtrade-client still uses subprocess as requested
    print("Running for freqtrade-client")
    result_client = subprocess.run(["freqtrade-client", "--show"], capture_output=True, text=True)

--- a/docker/Dockerfile.armhf
+++ b/docker/Dockerfile.armhf
@@ -1,4 +1,4 @@
-FROM python:3.11.13-slim-bookworm AS base
+FROM python:3.11.14-slim-bookworm AS base

 # Setup env
 ENV LANG=C.UTF-8
--- a/docs/commands/backtesting.md
+++ b/docs/commands/backtesting.md
@@ -43,7 +43,9 @@ options:
                        separated.
  --eps, --enable-position-stacking
                        Allow buying the same pair multiple times (position
-                        stacking).
+                        stacking). Only applicable to backtesting and
+                        hyperopt. Results archived by this cannot be
+                        reproduced in dry/live trading.
  --enable-protections, --enableprotections
                        Enable protections for backtesting. Will slow
                        backtesting down by a considerable amount, but will
--- a/docs/commands/hyperopt.md
+++ b/docs/commands/hyperopt.md
@@ -41,7 +41,9 @@ options:
                        functions.
  --eps, --enable-position-stacking
                        Allow buying the same pair multiple times (position
-                        stacking).
+                        stacking). Only applicable to backtesting and
+                        hyperopt. Results archived by this cannot be
+                        reproduced in dry/live trading.
  --enable-protections, --enableprotections
                        Enable protections for backtesting. Will slow
                        backtesting down by a considerable amount, but will
--- a/docs/deprecated.md
+++ b/docs/deprecated.md
@@ -135,3 +135,13 @@ you can verify this with `freqtrade list-data --exchange <yourexchange> --show`.
    Additional arguments to the above commands may be necessary, like configuration files or explicit user_data if they deviate from the default.

 **Hyperliquid** is a special case now - which will no longer require 1h mark data - but will use regular candles instead (this data never existed and is identical to 1h futures candles). As we don't support download-data for hyperliquid (they don't provide historic data) - there won't be actions necessary for hyperliquid users.
+
+## Catboost models in freqAI
+
+CatBoost models have been removed with version 2025.12 and are no longer actively supported.
+If you have existing bots using CatBoost models, you can still use them in your custom models by copy/pasting them from the git history (as linked below) and installing the Catboost library manually.
+We do however recommend switching to other supported model libraries like LightGBM or XGBoost for better support and future compatibility.
+
+* [CatboostRegressor](https://github.com/freqtrade/freqtrade/blob/c6f3b0081927e161a16b116cc47fb663f7831d30/freqtrade/freqai/prediction_models/CatboostRegressor.py)
+* [CatboostClassifier](https://github.com/freqtrade/freqtrade/blob/c6f3b0081927e161a16b116cc47fb663f7831d30/freqtrade/freqai/prediction_models/CatboostClassifier.py)
+* [CatboostClassifierMultiTarget](https://github.com/freqtrade/freqtrade/blob/c6f3b0081927e161a16b116cc47fb663f7831d30/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py)
--- a/docs/developer.md
+++ b/docs/developer.md
@@ -432,7 +432,6 @@ freqtrade download-data --timerange 20250625-20250801 --config tests/testdata/co
 freqtrade backtesting --config tests/testdata/config.tests.usdt.json -s SampleStrategy --userdir user_data_bttest/ --cache none --timerange 20250701-20250801
 ```

-
 ## Continuous integration

 This documents some decisions taken for the CI Pipeline.
@@ -464,10 +463,10 @@ git checkout -b new_release <commitid>
 Determine if crucial bugfixes have been made between this commit and the current state, and eventually cherry-pick these.

 * Merge the release branch (stable) into this branch.
-* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2019.7` for July 2019). Minor versions can be `2019.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
+* Edit `freqtrade/__init__.py` and add the version matching the current date (for example `2025.7` for July 2025). Minor versions can be `2025.7.1` should we need to do a second release that month. Version numbers must follow allowed versions from PEP0440 to avoid failures pushing to pypi.
 * Commit this part.
 * Push that branch to the remote and create a PR against the **stable branch**.
-* Update develop version to next version following the pattern `2019.8-dev`.
+* Update develop version to next version following the pattern `2025.8-dev`.

 ### Create changelog from git commits

--- a/docs/freqai-configuration.md
+++ b/docs/freqai-configuration.md
@@ -200,15 +200,15 @@ If this value is set, FreqAI will initially use the predictions from the trainin

 ## Using different prediction models

-FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `CatBoost`, `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`.
+FreqAI has multiple example prediction model libraries that are ready to be used as is via the flag `--freqaimodel`. These libraries include `LightGBM`, and `XGBoost` regression, classification, and multi-target models, and can be found in `freqai/prediction_models/`.

 Regression and classification models differ in what targets they predict - a regression model will predict a target of continuous values, for example what price BTC will be at tomorrow, whilst a classifier will predict a target of discrete values, for example if the price of BTC will go up tomorrow or not. This means that you have to specify your targets differently depending on which model type you are using (see details [below](#setting-model-targets)).

 All of the aforementioned model libraries implement gradient boosted decision tree algorithms. They all work on the principle of ensemble learning, where predictions from multiple simple learners are combined to get a final prediction that is more stable and generalized. The simple learners in this case are decision trees. Gradient boosting refers to the method of learning, where each simple learner is built in sequence - the subsequent learner is used to improve on the error from the previous learner. If you want to learn more about the different model libraries you can find the information in their respective docs:

-* CatBoost: https://catboost.ai/en/docs/
-* LightGBM: https://lightgbm.readthedocs.io/en/v3.3.2/#
-* XGBoost: https://xgboost.readthedocs.io/en/stable/#
+* LightGBM: <https://lightgbm.readthedocs.io/en/v3.3.2/#>
+* XGBoost: <https://xgboost.readthedocs.io/en/stable/#>
+* CatBoost: <https://catboost.ai/en/docs/> (No longer actively supported since 2025.12)

 There are also numerous online articles describing and comparing the algorithms. Some relatively lightweight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.

@@ -219,7 +219,7 @@ Make sure to use unique names to avoid overriding built-in models.

 #### Regressors

-If you are using a regressor, you need to specify a target that has continuous values. FreqAI includes a variety of regressors, such as the `CatboostRegressor`via the flag `--freqaimodel CatboostRegressor`. An example of how you could set a regression target for predicting the price 100 candles into the future would be
+If you are using a regressor, you need to specify a target that has continuous values. FreqAI includes a variety of regressors, such as the `LightGBMRegressor`via the flag `--freqaimodel LightGBMRegressor`. An example of how you could set a regression target for predicting the price 100 candles into the future would be

 ```python
 df['&s-close_price'] = df['close'].shift(-100)
@@ -229,7 +229,7 @@ If you want to predict multiple targets, you need to define multiple labels usin

 #### Classifiers

-If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `CatboostClassifier` via the flag `--freqaimodel CatboostClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set
+If you are using a classifier, you need to specify a target that has discrete values. FreqAI includes a variety of classifiers, such as the `LightGBMClassifier` via the flag `--freqaimodel LightGBMClassifier`. If you elects to use a classifier, the classes need to be set using strings. For example, if you want to predict if the price 100 candles into the future goes up or down you would set

 ```python
 df['&s-up_or_down'] = np.where( df["close"].shift(-100) > df["close"], 'up', 'down')
--- a/docs/freqai-parameter-table.md
+++ b/docs/freqai-parameter-table.md
@@ -107,7 +107,6 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `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

 |  Parameter | Description |
@@ -116,3 +115,4 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `freqai.keras` | If the selected model makes use of Keras (typical for TensorFlow-based prediction models), this flag needs to be activated so that the model save/loading follows Keras standards. <br> **Datatype:** Boolean. <br> Default: `False`.
 | `freqai.conv_width` | The width of a neural network input tensor. This replaces the need for shifting candles (`include_shifted_candles`) by feeding in historical data points as the second dimension of the tensor. Technically, this parameter can also be used for regressors, but it only adds computational overhead and does not change the model training/prediction. <br> **Datatype:** Integer. <br> Default: `2`.
 | `freqai.reduce_df_footprint` | Recast all numeric columns to float32/int32, with the objective of reducing ram/disk usage and decreasing train/inference timing. This parameter is set in the main level of the Freqtrade configuration file (not inside FreqAI). <br> **Datatype:** Boolean. <br> Default: `False`.
+| `freqai.override_exchange_check` | Override the exchange check to force FreqAI to use exchanges that may not have enough historic data. Turn this to True if you know your FreqAI model and strategy do not require historical data. <br> **Datatype:** Boolean. <br> Default: `False`.
--- a/docs/includes/release_template.md
+++ b/docs/includes/release_template.md
@@ -1,28 +1,28 @@
-## Highlighted changes
+# Highlighted changes

 - ...

-### How to update
+## How to update

 As always, you can update your bot using one of the following commands:

-#### docker-compose
+### docker-compose

 ```bash
 docker-compose pull
 docker-compose up -d
 ```

-#### Installation via setup script
+### Installation via setup script

-```
+``` bash
 # Deactivate venv and run 
 ./setup.sh --update
 ```

-#### Plain native installation
+### Plain native installation

-```
+``` bash
 git pull
 pip install -U -r requirements.txt
 ```
--- a/docs/requirements-docs.txt
+++ b/docs/requirements-docs.txt
@@ -1,7 +1,7 @@
 markdown==3.10
 mkdocs==1.6.1
-mkdocs-material==9.7.0
+mkdocs-material==9.7.1
 mdx_truly_sane_lists==1.3
-pymdown-extensions==10.18
+pymdown-extensions==10.19.1
 jinja2==3.1.6
 mike==2.1.3
--- a/freqtrade/init.py
+++ b/freqtrade/init.py
@@ -1,6 +1,6 @@
 """Freqtrade bot"""

-__version__ = "2025.12-dev"
+__version__ = "2026.1-dev"

 if "dev" in __version__:
    from pathlib import Path
--- a/freqtrade/commands/cli_options.py
+++ b/freqtrade/commands/cli_options.py
@@ -180,7 +180,11 @@ AVAILABLE_CLI_OPTIONS = {
    "position_stacking": Arg(
        "--eps",
        "--enable-position-stacking",
-        help="Allow buying the same pair multiple times (position stacking).",
+        help=(
+            "Allow buying the same pair multiple times (position stacking). "
+            "Only applicable to backtesting and hyperopt. "
+            "Results archived by this cannot be reproduced in dry/live trading."
+        ),
        action="store_true",
        default=False,
    ),
--- a/freqtrade/exchange/binance_leverage_tiers.json
+++ b/freqtrade/exchange/binance_leverage_tiers.json
@@ -36764,15 +36764,15 @@
      "symbol": "FXS/USDT:USDT",
      "currency": "USDT",
      "minNotional": 0.0,
-      "maxNotional": 5000.0,
-      "maintenanceMarginRate": 0.01,
-      "maxLeverage": 75.0,
+      "maxNotional": 10000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
      "info": {
        "bracket": "1",
-        "initialLeverage": "75",
-        "notionalCap": "5000",
+        "initialLeverage": "20",
+        "notionalCap": "10000",
        "notionalFloor": "0",
-        "maintMarginRatio": "0.01",
+        "maintMarginRatio": "0.025",
        "cum": "0.0"
      }
    },
@@ -36780,153 +36780,102 @@
      "tier": 2.0,
      "symbol": "FXS/USDT:USDT",
      "currency": "USDT",
-      "minNotional": 5000.0,
-      "maxNotional": 10000.0,
-      "maintenanceMarginRate": 0.015,
-      "maxLeverage": 50.0,
+      "minNotional": 10000.0,
+      "maxNotional": 20000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
      "info": {
        "bracket": "2",
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-        "notionalCap": "10000",
-        "notionalFloor": "5000",
-        "maintMarginRatio": "0.015",
-        "cum": "25.0"
+        "initialLeverage": "10",
+        "notionalCap": "20000",
+        "notionalFloor": "10000",
+        "maintMarginRatio": "0.05",
+        "cum": "250.0"
      }
    },
    {
      "tier": 3.0,
      "symbol": "FXS/USDT:USDT",
      "currency": "USDT",
-      "minNotional": 10000.0,
-      "maxNotional": 25000.0,
-      "maintenanceMarginRate": 0.02,
-      "maxLeverage": 25.0,
+      "minNotional": 20000.0,
+      "maxNotional": 60000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
      "info": {
        "bracket": "3",
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-        "notionalCap": "25000",
-        "notionalFloor": "10000",
-        "maintMarginRatio": "0.02",
-        "cum": "75.0"
+        "initialLeverage": "5",
+        "notionalCap": "60000",
+        "notionalFloor": "20000",
+        "maintMarginRatio": "0.1",
+        "cum": "1250.0"
      }
    },
    {
      "tier": 4.0,
      "symbol": "FXS/USDT:USDT",
      "currency": "USDT",
-      "minNotional": 25000.0,
-      "maxNotional": 50000.0,
-      "maintenanceMarginRate": 0.025,
-      "maxLeverage": 20.0,
+      "minNotional": 60000.0,
+      "maxNotional": 150000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 4.0,
      "info": {
        "bracket": "4",
-        "initialLeverage": "20",
-        "notionalCap": "50000",
-        "notionalFloor": "25000",
-        "maintMarginRatio": "0.025",
-        "cum": "200.0"
+        "initialLeverage": "4",
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      }
    },
    {
      "tier": 5.0,
      "symbol": "FXS/USDT:USDT",
      "currency": "USDT",
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-      "maxNotional": 125000.0,
-      "maintenanceMarginRate": 0.05,
-      "maxLeverage": 10.0,
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+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1667,
+      "maxLeverage": 3.0,
      "info": {
        "bracket": "5",
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-        "notionalCap": "125000",
-        "notionalFloor": "50000",
-        "maintMarginRatio": "0.05",
-        "cum": "1450.0"
+        "initialLeverage": "3",
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      }
    },
    {
      "tier": 6.0,
      "symbol": "FXS/USDT:USDT",
      "currency": "USDT",
-      "minNotional": 125000.0,
-      "maxNotional": 250000.0,
-      "maintenanceMarginRate": 0.1,
-      "maxLeverage": 5.0,
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+      "maxNotional": 2500000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
      "info": {
        "bracket": "6",
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-        "notionalCap": "250000",
-        "notionalFloor": "125000",
-        "maintMarginRatio": "0.1",
-        "cum": "7700.0"
+        "initialLeverage": "2",
+        "notionalCap": "2500000",
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      }
    },
    {
      "tier": 7.0,
      "symbol": "FXS/USDT:USDT",
      "currency": "USDT",
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-      "maxNotional": 500000.0,
-      "maintenanceMarginRate": 0.125,
-      "maxLeverage": 4.0,
-      "info": {
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-        "initialLeverage": "4",
-        "notionalCap": "500000",
-        "notionalFloor": "250000",
-        "maintMarginRatio": "0.125",
-        "cum": "13950.0"
-      }
-    },
-    {
-      "tier": 8.0,
-      "symbol": "FXS/USDT:USDT",
-      "currency": "USDT",
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-      "maxNotional": 1000000.0,
-      "maintenanceMarginRate": 0.1667,
-      "maxLeverage": 3.0,
-      "info": {
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-        "initialLeverage": "3",
-        "notionalCap": "1000000",
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-        "cum": "34800.0"
-      }
-    },
-    {
-      "tier": 9.0,
-      "symbol": "FXS/USDT:USDT",
-      "currency": "USDT",
-      "minNotional": 1000000.0,
-      "maxNotional": 7500000.0,
-      "maintenanceMarginRate": 0.25,
-      "maxLeverage": 2.0,
-      "info": {
-        "bracket": "9",
-        "initialLeverage": "2",
-        "notionalCap": "7500000",
-        "notionalFloor": "1000000",
-        "maintMarginRatio": "0.25",
-        "cum": "118100.0"
-      }
-    },
-    {
-      "tier": 10.0,
-      "symbol": "FXS/USDT:USDT",
-      "currency": "USDT",
-      "minNotional": 7500000.0,
-      "maxNotional": 12500000.0,
+      "minNotional": 2500000.0,
+      "maxNotional": 5000000.0,
      "maintenanceMarginRate": 0.5,
      "maxLeverage": 1.0,
      "info": {
-        "bracket": "10",
+        "bracket": "7",
        "initialLeverage": "1",
-        "notionalCap": "12500000",
-        "notionalFloor": "7500000",
+        "notionalCap": "5000000",
+        "notionalFloor": "2500000",
        "maintMarginRatio": "0.5",
-        "cum": "1993100.0"
+        "cum": "654830.0"
      }
    }
  ],
@@ -39393,6 +39342,127 @@
      }
    }
  ],
+  "GUA/USDT:USDT": [
+    {
+      "tier": 1.0,
+      "symbol": "GUA/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "20",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.025",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "symbol": "GUA/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 5000.0,
+      "maxNotional": 10000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "10",
+        "notionalCap": "10000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.05",
+        "cum": "125.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "symbol": "GUA/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 10000.0,
+      "maxNotional": 50000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "5",
+        "notionalCap": "50000",
+        "notionalFloor": "10000",
+        "maintMarginRatio": "0.1",
+        "cum": "625.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "symbol": "GUA/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 50000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 4.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "4",
+        "notionalCap": "100000",
+        "notionalFloor": "50000",
+        "maintMarginRatio": "0.125",
+        "cum": "1875.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "symbol": "GUA/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1667,
+      "maxLeverage": 3.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "3",
+        "notionalCap": "250000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.1667",
+        "cum": "6045.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "symbol": "GUA/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 250000.0,
+      "maxNotional": 500000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "2",
+        "notionalCap": "500000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.25",
+        "cum": "26870.0"
+      }
+    },
+    {
+      "tier": 7.0,
+      "symbol": "GUA/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 500000.0,
+      "maxNotional": 800000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "7",
+        "initialLeverage": "1",
+        "notionalCap": "800000",
+        "notionalFloor": "500000",
+        "maintMarginRatio": "0.5",
+        "cum": "151870.0"
+      }
+    }
+  ],
  "GUN/USDT:USDT": [
    {
      "tier": 1.0,
@@ -45062,6 +45132,144 @@
      }
    }
  ],
+  "IR/USDT:USDT": [
+    {
+      "tier": 1.0,
+      "symbol": "IR/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.02,
+      "maxLeverage": 40.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "40",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.02",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "symbol": "IR/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 5000.0,
+      "maxNotional": 10000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "20",
+        "notionalCap": "10000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.025",
+        "cum": "25.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "symbol": "IR/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 10000.0,
+      "maxNotional": 20000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "10",
+        "notionalCap": "20000",
+        "notionalFloor": "10000",
+        "maintMarginRatio": "0.05",
+        "cum": "275.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "symbol": "IR/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 20000.0,
+      "maxNotional": 50000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "5",
+        "notionalCap": "50000",
+        "notionalFloor": "20000",
+        "maintMarginRatio": "0.1",
+        "cum": "1275.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "symbol": "IR/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 50000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 4.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "4",
+        "notionalCap": "100000",
+        "notionalFloor": "50000",
+        "maintMarginRatio": "0.125",
+        "cum": "2525.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "symbol": "IR/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1667,
+      "maxLeverage": 3.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "3",
+        "notionalCap": "250000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.1667",
+        "cum": "6695.0"
+      }
+    },
+    {
+      "tier": 7.0,
+      "symbol": "IR/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 250000.0,
+      "maxNotional": 2500000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "7",
+        "initialLeverage": "2",
+        "notionalCap": "2500000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.25",
+        "cum": "27520.0"
+      }
+    },
+    {
+      "tier": 8.0,
+      "symbol": "IR/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 2500000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "8",
+        "initialLeverage": "1",
+        "notionalCap": "5000000",
+        "notionalFloor": "2500000",
+        "maintMarginRatio": "0.5",
+        "cum": "652520.0"
+      }
+    }
+  ],
  "IRYS/USDT:USDT": [
    {
      "tier": 1.0,
@@ -50238,10 +50446,10 @@
      "minNotional": 0.0,
      "maxNotional": 5000.0,
      "maintenanceMarginRate": 0.015,
-      "maxLeverage": 10.0,
+      "maxLeverage": 50.0,
      "info": {
        "bracket": "1",
-        "initialLeverage": "10",
+        "initialLeverage": "50",
        "notionalCap": "5000",
        "notionalFloor": "0",
        "maintMarginRatio": "0.015",
@@ -50253,13 +50461,13 @@
      "symbol": "LIT/USDT:USDT",
      "currency": "USDT",
      "minNotional": 5000.0,
-      "maxNotional": 20000.0,
+      "maxNotional": 10000.0,
      "maintenanceMarginRate": 0.02,
-      "maxLeverage": 8.0,
+      "maxLeverage": 25.0,
      "info": {
        "bracket": "2",
-        "initialLeverage": "8",
-        "notionalCap": "20000",
+        "initialLeverage": "25",
+        "notionalCap": "10000",
        "notionalFloor": "5000",
        "maintMarginRatio": "0.02",
        "cum": "25.0"
@@ -50269,17 +50477,17 @@
      "tier": 3.0,
      "symbol": "LIT/USDT:USDT",
      "currency": "USDT",
-      "minNotional": 20000.0,
+      "minNotional": 10000.0,
      "maxNotional": 25000.0,
      "maintenanceMarginRate": 0.025,
-      "maxLeverage": 6.0,
+      "maxLeverage": 20.0,
      "info": {
        "bracket": "3",
-        "initialLeverage": "6",
+        "initialLeverage": "20",
        "notionalCap": "25000",
-        "notionalFloor": "20000",
+        "notionalFloor": "10000",
        "maintMarginRatio": "0.025",
-        "cum": "125.0"
+        "cum": "75.0"
      }
    },
    {
@@ -50287,84 +50495,101 @@
      "symbol": "LIT/USDT:USDT",
      "currency": "USDT",
      "minNotional": 25000.0,
-      "maxNotional": 200000.0,
+      "maxNotional": 62500.0,
      "maintenanceMarginRate": 0.05,
-      "maxLeverage": 5.0,
+      "maxLeverage": 10.0,
      "info": {
        "bracket": "4",
-        "initialLeverage": "5",
-        "notionalCap": "200000",
+        "initialLeverage": "10",
+        "notionalCap": "62500",
        "notionalFloor": "25000",
        "maintMarginRatio": "0.05",
-        "cum": "750.0"
+        "cum": "700.0"
      }
    },
    {
      "tier": 5.0,
      "symbol": "LIT/USDT:USDT",
      "currency": "USDT",
-      "minNotional": 200000.0,
-      "maxNotional": 400000.0,
+      "minNotional": 62500.0,
+      "maxNotional": 125000.0,
      "maintenanceMarginRate": 0.1,
-      "maxLeverage": 4.0,
+      "maxLeverage": 5.0,
      "info": {
        "bracket": "5",
-        "initialLeverage": "4",
-        "notionalCap": "400000",
-        "notionalFloor": "200000",
+        "initialLeverage": "5",
+        "notionalCap": "125000",
+        "notionalFloor": "62500",
        "maintMarginRatio": "0.1",
-        "cum": "10750.0"
+        "cum": "3825.0"
      }
    },
    {
      "tier": 6.0,
      "symbol": "LIT/USDT:USDT",
      "currency": "USDT",
-      "minNotional": 400000.0,
-      "maxNotional": 500000.0,
+      "minNotional": 125000.0,
+      "maxNotional": 250000.0,
      "maintenanceMarginRate": 0.125,
-      "maxLeverage": 3.0,
+      "maxLeverage": 4.0,
      "info": {
        "bracket": "6",
-        "initialLeverage": "3",
-        "notionalCap": "500000",
-        "notionalFloor": "400000",
+        "initialLeverage": "4",
+        "notionalCap": "250000",
+        "notionalFloor": "125000",
        "maintMarginRatio": "0.125",
-        "cum": "20750.0"
+        "cum": "6950.0"
      }
    },
    {
      "tier": 7.0,
      "symbol": "LIT/USDT:USDT",
      "currency": "USDT",
-      "minNotional": 500000.0,
-      "maxNotional": 520000.0,
-      "maintenanceMarginRate": 0.25,
-      "maxLeverage": 2.0,
+      "minNotional": 250000.0,
+      "maxNotional": 500000.0,
+      "maintenanceMarginRate": 0.1667,
+      "maxLeverage": 3.0,
      "info": {
        "bracket": "7",
-        "initialLeverage": "2",
-        "notionalCap": "520000",
-        "notionalFloor": "500000",
-        "maintMarginRatio": "0.25",
-        "cum": "83250.0"
+        "initialLeverage": "3",
+        "notionalCap": "500000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.1667",
+        "cum": "17375.0"
      }
    },
    {
      "tier": 8.0,
      "symbol": "LIT/USDT:USDT",
      "currency": "USDT",
-      "minNotional": 520000.0,
-      "maxNotional": 550000.0,
+      "minNotional": 500000.0,
+      "maxNotional": 7500000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "8",
+        "initialLeverage": "2",
+        "notionalCap": "7500000",
+        "notionalFloor": "500000",
+        "maintMarginRatio": "0.25",
+        "cum": "59025.0"
+      }
+    },
+    {
+      "tier": 9.0,
+      "symbol": "LIT/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 7500000.0,
+      "maxNotional": 12500000.0,
      "maintenanceMarginRate": 0.5,
      "maxLeverage": 1.0,
      "info": {
-        "bracket": "8",
+        "bracket": "9",
        "initialLeverage": "1",
-        "notionalCap": "550000",
-        "notionalFloor": "520000",
+        "notionalCap": "12500000",
+        "notionalFloor": "7500000",
        "maintMarginRatio": "0.5",
-        "cum": "213250.0"
+        "cum": "1934025.0"
      }
    }
  ],
@@ -95892,6 +96117,144 @@
      }
    }
  ],
+  "ZKP/USDT:USDT": [
+    {
+      "tier": 1.0,
+      "symbol": "ZKP/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 0.0,
+      "maxNotional": 5000.0,
+      "maintenanceMarginRate": 0.02,
+      "maxLeverage": 40.0,
+      "info": {
+        "bracket": "1",
+        "initialLeverage": "40",
+        "notionalCap": "5000",
+        "notionalFloor": "0",
+        "maintMarginRatio": "0.02",
+        "cum": "0.0"
+      }
+    },
+    {
+      "tier": 2.0,
+      "symbol": "ZKP/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 5000.0,
+      "maxNotional": 10000.0,
+      "maintenanceMarginRate": 0.025,
+      "maxLeverage": 20.0,
+      "info": {
+        "bracket": "2",
+        "initialLeverage": "20",
+        "notionalCap": "10000",
+        "notionalFloor": "5000",
+        "maintMarginRatio": "0.025",
+        "cum": "25.0"
+      }
+    },
+    {
+      "tier": 3.0,
+      "symbol": "ZKP/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 10000.0,
+      "maxNotional": 20000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
+      "info": {
+        "bracket": "3",
+        "initialLeverage": "10",
+        "notionalCap": "20000",
+        "notionalFloor": "10000",
+        "maintMarginRatio": "0.05",
+        "cum": "275.0"
+      }
+    },
+    {
+      "tier": 4.0,
+      "symbol": "ZKP/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 20000.0,
+      "maxNotional": 50000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
+      "info": {
+        "bracket": "4",
+        "initialLeverage": "5",
+        "notionalCap": "50000",
+        "notionalFloor": "20000",
+        "maintMarginRatio": "0.1",
+        "cum": "1275.0"
+      }
+    },
+    {
+      "tier": 5.0,
+      "symbol": "ZKP/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 50000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 4.0,
+      "info": {
+        "bracket": "5",
+        "initialLeverage": "4",
+        "notionalCap": "100000",
+        "notionalFloor": "50000",
+        "maintMarginRatio": "0.125",
+        "cum": "2525.0"
+      }
+    },
+    {
+      "tier": 6.0,
+      "symbol": "ZKP/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.1667,
+      "maxLeverage": 3.0,
+      "info": {
+        "bracket": "6",
+        "initialLeverage": "3",
+        "notionalCap": "250000",
+        "notionalFloor": "100000",
+        "maintMarginRatio": "0.1667",
+        "cum": "6695.0"
+      }
+    },
+    {
+      "tier": 7.0,
+      "symbol": "ZKP/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 250000.0,
+      "maxNotional": 2500000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": "7",
+        "initialLeverage": "2",
+        "notionalCap": "2500000",
+        "notionalFloor": "250000",
+        "maintMarginRatio": "0.25",
+        "cum": "27520.0"
+      }
+    },
+    {
+      "tier": 8.0,
+      "symbol": "ZKP/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 2500000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.5,
+      "maxLeverage": 1.0,
+      "info": {
+        "bracket": "8",
+        "initialLeverage": "1",
+        "notionalCap": "5000000",
+        "notionalFloor": "2500000",
+        "maintMarginRatio": "0.5",
+        "cum": "652520.0"
+      }
+    }
+  ],
  "ZORA/USDT:USDT": [
    {
      "tier": 1.0,
--- a/freqtrade/exchange/exchange.py
+++ b/freqtrade/exchange/exchange.py
@@ -709,7 +709,7 @@ class Exchange:
            self._markets = self._api_async.markets
            self._api.set_markets_from_exchange(self._api_async)
            # Assign options array, as it contains some temporary information from the exchange.
-            # TODO: investigate with ccxt if it's safe to remove `.options`
+            # ccxt does not implicitly copy options over in set_markets_from_exchange
            self._api.options = self._api_async.options
            if self._exchange_ws:
                # Set markets to avoid reloading on websocket api
@@ -879,19 +879,20 @@ class Exchange:
                # Only allow 5 calls per pair to somewhat limit the impact
                raise ConfigurationError(
                    f"This strategy requires {startup_candles} candles to start, "
-                    "which is more than 5x "
+                    f"which is more than 5x ({candle_limit * 5 - 1} candles) "
                    f"the amount of candles {self.name} provides for {timeframe}."
                )
        elif required_candle_call_count > 1:
            raise ConfigurationError(
-                f"This strategy requires {startup_candles} candles to start, which is more than "
+                f"This strategy requires {startup_candles} candles to start, "
+                f"which is more than ({candle_limit - 1} candles) "
                f"the amount of candles {self.name} provides for {timeframe}."
            )
        if required_candle_call_count > 1:
            logger.warning(
                f"Using {required_candle_call_count} calls to get OHLCV. "
                f"This can result in slower operations for the bot. Please check "
-                f"if you really need {startup_candles} candles for your strategy"
+                f"if you really need {startup_candles} candles for your strategy."
            )
        return required_candle_call_count

--- a/freqtrade/freqai/base_models/BaseClassifierModel.py
+++ b/freqtrade/freqai/base_models/BaseClassifierModel.py
@@ -18,7 +18,7 @@ class BaseClassifierModel(IFreqaiModel):
    """
    Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
    User *must* inherit from this class and set fit(). See example scripts
-    such as prediction_models/CatboostClassifier.py for guidance.
+    such as prediction_models/XGBoostClassifier.py for guidance.
    """

    def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
--- a/freqtrade/freqai/base_models/BaseRegressionModel.py
+++ b/freqtrade/freqai/base_models/BaseRegressionModel.py
@@ -18,7 +18,7 @@ class BaseRegressionModel(IFreqaiModel):
    """
    Base class for regression type models (e.g. Catboost, LightGBM, XGboost etc.).
    User *must* inherit from this class and set fit(). See example scripts
-    such as prediction_models/CatboostRegressor.py for guidance.
+    such as prediction_models/XGBoostRegressor.py for guidance.
    """

    def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -948,7 +948,7 @@ class IFreqaiModel(ABC):
        return dk

    # Following methods which are overridden by user made prediction models.
-    # See freqai/prediction_models/CatboostPredictionModel.py for an example.
+    # See freqai/prediction_models/XGBoostRegressor.py for an example.

    @abstractmethod
    def train(self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs) -> Any:
@@ -964,7 +964,7 @@ class IFreqaiModel(ABC):
    def fit(self, data_dictionary: dict[str, Any], dk: FreqaiDataKitchen, **kwargs) -> Any:
        """
        Most regressors use the same function names and arguments e.g. user
-        can drop in LGBMRegressor in place of CatBoostRegressor and all data
+        can drop in LGBMRegressor in place of XGBoostRegressor and all data
        management will be properly handled by Freqai.
        :param data_dictionary: Dict = the dictionary constructed by DataHandler to hold
                                all the training and test data/labels.
--- a/freqtrade/freqai/prediction_models/CatboostClassifier.py
+++ b/freqtrade/freqai/prediction_models/CatboostClassifier.py
@@ -1,61 +0,0 @@
-import logging
-from pathlib import Path
-from typing import Any
-
-from catboost import CatBoostClassifier, Pool
-
-from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-
-
-logger = logging.getLogger(__name__)
-
-
-class CatboostClassifier(BaseClassifierModel):
-    """
-    User created prediction model. The class inherits IFreqaiModel, which
-    means it has full access to all Frequency AI functionality. Typically,
-    users would use this to override the common `fit()`, `train()`, or
-    `predict()` methods to add their custom data handling tools or change
-    various aspects of the training that cannot be configured via the
-    top level config.json file.
-    """
-
-    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
-        """
-        User sets up the training and test data to fit their desired model here
-        :param data_dictionary: the dictionary holding all data for train, test,
-            labels, weights
-        :param dk: The datakitchen object for the current coin/model
-        """
-
-        train_data = Pool(
-            data=data_dictionary["train_features"],
-            label=data_dictionary["train_labels"],
-            weight=data_dictionary["train_weights"],
-        )
-        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
-            test_data = None
-        else:
-            test_data = Pool(
-                data=data_dictionary["test_features"],
-                label=data_dictionary["test_labels"],
-                weight=data_dictionary["test_weights"],
-            )
-
-        cbr = CatBoostClassifier(
-            allow_writing_files=True,
-            loss_function="MultiClass",
-            train_dir=Path(dk.data_path),
-            **self.model_training_parameters,
-        )
-
-        init_model = self.get_init_model(dk.pair)
-
-        cbr.fit(
-            X=train_data,
-            eval_set=test_data,
-            init_model=init_model,
-        )
-
-        return cbr
--- a/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py
+++ b/freqtrade/freqai/prediction_models/CatboostClassifierMultiTarget.py
@@ -1,79 +0,0 @@
-import logging
-from pathlib import Path
-from typing import Any
-
-from catboost import CatBoostClassifier, Pool
-
-from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
-from freqtrade.freqai.base_models.FreqaiMultiOutputClassifier import FreqaiMultiOutputClassifier
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-
-
-logger = logging.getLogger(__name__)
-
-
-class CatboostClassifierMultiTarget(BaseClassifierModel):
-    """
-    User created prediction model. The class inherits IFreqaiModel, which
-    means it has full access to all Frequency AI functionality. Typically,
-    users would use this to override the common `fit()`, `train()`, or
-    `predict()` methods to add their custom data handling tools or change
-    various aspects of the training that cannot be configured via the
-    top level config.json file.
-    """
-
-    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
-        """
-        User sets up the training and test data to fit their desired model here
-        :param data_dictionary: the dictionary holding all data for train, test,
-            labels, weights
-        :param dk: The datakitchen object for the current coin/model
-        """
-
-        cbc = CatBoostClassifier(
-            allow_writing_files=True,
-            loss_function="MultiClass",
-            train_dir=Path(dk.data_path),
-            **self.model_training_parameters,
-        )
-
-        X = data_dictionary["train_features"]
-        y = data_dictionary["train_labels"]
-
-        sample_weight = data_dictionary["train_weights"]
-
-        eval_sets = [None] * y.shape[1]
-
-        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
-            eval_sets = [None] * data_dictionary["test_labels"].shape[1]
-
-            for i in range(data_dictionary["test_labels"].shape[1]):
-                eval_sets[i] = Pool(
-                    data=data_dictionary["test_features"],
-                    label=data_dictionary["test_labels"].iloc[:, i],
-                    weight=data_dictionary["test_weights"],
-                )
-
-        init_model = self.get_init_model(dk.pair)
-
-        if init_model:
-            init_models = init_model.estimators_
-        else:
-            init_models = [None] * y.shape[1]
-
-        fit_params = []
-        for i in range(len(eval_sets)):
-            fit_params.append(
-                {
-                    "eval_set": eval_sets[i],
-                    "init_model": init_models[i],
-                }
-            )
-
-        model = FreqaiMultiOutputClassifier(estimator=cbc)
-        thread_training = self.freqai_info.get("multitarget_parallel_training", False)
-        if thread_training:
-            model.n_jobs = y.shape[1]
-        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
-
-        return model
--- a/freqtrade/freqai/prediction_models/CatboostRegressor.py
+++ b/freqtrade/freqai/prediction_models/CatboostRegressor.py
@@ -1,60 +0,0 @@
-import logging
-from pathlib import Path
-from typing import Any
-
-from catboost import CatBoostRegressor, Pool
-
-from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-
-
-logger = logging.getLogger(__name__)
-
-
-class CatboostRegressor(BaseRegressionModel):
-    """
-    User created prediction model. The class inherits IFreqaiModel, which
-    means it has full access to all Frequency AI functionality. Typically,
-    users would use this to override the common `fit()`, `train()`, or
-    `predict()` methods to add their custom data handling tools or change
-    various aspects of the training that cannot be configured via the
-    top level config.json file.
-    """
-
-    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
-        """
-        User sets up the training and test data to fit their desired model here
-        :param data_dictionary: the dictionary holding all data for train, test,
-            labels, weights
-        :param dk: The datakitchen object for the current coin/model
-        """
-
-        train_data = Pool(
-            data=data_dictionary["train_features"],
-            label=data_dictionary["train_labels"],
-            weight=data_dictionary["train_weights"],
-        )
-        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
-            test_data = None
-        else:
-            test_data = Pool(
-                data=data_dictionary["test_features"],
-                label=data_dictionary["test_labels"],
-                weight=data_dictionary["test_weights"],
-            )
-
-        init_model = self.get_init_model(dk.pair)
-
-        model = CatBoostRegressor(
-            allow_writing_files=True,
-            train_dir=Path(dk.data_path),
-            **self.model_training_parameters,
-        )
-
-        model.fit(
-            X=train_data,
-            eval_set=test_data,
-            init_model=init_model,
-        )
-
-        return model
--- a/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
+++ b/freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
@@ -1,78 +0,0 @@
-import logging
-from pathlib import Path
-from typing import Any
-
-from catboost import CatBoostRegressor, Pool
-
-from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
-from freqtrade.freqai.base_models.FreqaiMultiOutputRegressor import FreqaiMultiOutputRegressor
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-
-
-logger = logging.getLogger(__name__)
-
-
-class CatboostRegressorMultiTarget(BaseRegressionModel):
-    """
-    User created prediction model. The class inherits IFreqaiModel, which
-    means it has full access to all Frequency AI functionality. Typically,
-    users would use this to override the common `fit()`, `train()`, or
-    `predict()` methods to add their custom data handling tools or change
-    various aspects of the training that cannot be configured via the
-    top level config.json file.
-    """
-
-    def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
-        """
-        User sets up the training and test data to fit their desired model here
-        :param data_dictionary: the dictionary holding all data for train, test,
-            labels, weights
-        :param dk: The datakitchen object for the current coin/model
-        """
-
-        cbr = CatBoostRegressor(
-            allow_writing_files=True,
-            train_dir=Path(dk.data_path),
-            **self.model_training_parameters,
-        )
-
-        X = data_dictionary["train_features"]
-        y = data_dictionary["train_labels"]
-
-        sample_weight = data_dictionary["train_weights"]
-
-        eval_sets = [None] * y.shape[1]
-
-        if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) != 0:
-            eval_sets = [None] * data_dictionary["test_labels"].shape[1]
-
-            for i in range(data_dictionary["test_labels"].shape[1]):
-                eval_sets[i] = Pool(
-                    data=data_dictionary["test_features"],
-                    label=data_dictionary["test_labels"].iloc[:, i],
-                    weight=data_dictionary["test_weights"],
-                )
-
-        init_model = self.get_init_model(dk.pair)
-
-        if init_model:
-            init_models = init_model.estimators_
-        else:
-            init_models = [None] * y.shape[1]
-
-        fit_params = []
-        for i in range(len(eval_sets)):
-            fit_params.append(
-                {
-                    "eval_set": eval_sets[i],
-                    "init_model": init_models[i],
-                }
-            )
-
-        model = FreqaiMultiOutputRegressor(estimator=cbr)
-        thread_training = self.freqai_info.get("multitarget_parallel_training", False)
-        if thread_training:
-            model.n_jobs = y.shape[1]
-        model.fit(X=X, y=y, sample_weight=sample_weight, fit_params=fit_params)
-
-        return model
--- a/freqtrade/freqai/utils.py
+++ b/freqtrade/freqai/utils.py
@@ -97,7 +97,7 @@ def plot_feature_importance(
    """
    Plot Best and worst features by importance for a single sub-train.
    :param model: Any = A model which was `fit` using a common library
-                        such as catboost or lightgbm
+                        such as XGBoost or lightgbm
    :param pair: str = pair e.g. BTC/USD
    :param dk: FreqaiDataKitchen = non-persistent data container for current coin/loop
    :param count_max: int = the amount of features to be loaded per column
@@ -115,6 +115,8 @@ def plot_feature_importance(
    for label in models:
        mdl = models[label]
        if "catboost.core" in str(mdl.__class__):
+            # CatBoost is no longer actively supported since 2025.12
+            # However users can still use it in their custom models
            feature_importance = mdl.get_feature_importance()
        elif "lightgbm.sklearn" in str(mdl.__class__):
            feature_importance = mdl.feature_importances_
--- a/freqtrade/rpc/api_server/api_backtest.py
+++ b/freqtrade/rpc/api_server/api_backtest.py
@@ -52,29 +52,25 @@ def __run_backtest_bg(btconfig: Config):
        lastconfig = ApiBG.bt["last_config"]
        strat = StrategyResolver.load_strategy(btconfig)
        validate_config_consistency(btconfig)
-
-        if (
-            not ApiBG.bt["bt"]
-            or lastconfig.get("timeframe") != strat.timeframe
+        time_settings_changed = (
+            lastconfig.get("timeframe") != strat.timeframe
            or lastconfig.get("timeframe_detail") != btconfig.get("timeframe_detail")
            or lastconfig.get("timerange") != btconfig["timerange"]
-        ):
+        )
+
+        if not ApiBG.bt["bt"] or time_settings_changed:
            from freqtrade.optimize.backtesting import Backtesting

            ApiBG.bt["bt"] = Backtesting(btconfig)
        else:
            ApiBG.bt["bt"].config = deep_merge_dicts(btconfig, ApiBG.bt["bt"].config)
            ApiBG.bt["bt"].init_backtest()
-        # Only reload data if timeframe changed.
-        if (
-            not ApiBG.bt["data"]
-            or not ApiBG.bt["timerange"]
-            or lastconfig.get("timeframe") != strat.timeframe
-            or lastconfig.get("timerange") != btconfig["timerange"]
-        ):
+        # Only reload data if timerange is open or settings changed
+        if not ApiBG.bt["data"] or not ApiBG.bt["timerange"] or time_settings_changed:
            ApiBG.bt["data"], ApiBG.bt["timerange"] = ApiBG.bt["bt"].load_bt_data()

        lastconfig["timerange"] = btconfig["timerange"]
+        lastconfig["timeframe_detail"] = btconfig.get("timeframe_detail")
        lastconfig["timeframe"] = strat.timeframe
        lastconfig["enable_protections"] = btconfig.get("enable_protections")
        lastconfig["dry_run_wallet"] = btconfig.get("dry_run_wallet")
--- a/freqtrade/templates/FreqaiExampleHybridStrategy.py
+++ b/freqtrade/templates/FreqaiExampleHybridStrategy.py
@@ -20,7 +20,7 @@ class FreqaiExampleHybridStrategy(IStrategy):
    Launching this strategy would be:

    freqtrade trade --strategy FreqaiExampleHybridStrategy --strategy-path freqtrade/templates
-    --freqaimodel CatboostClassifier --config config_examples/config_freqai.example.json
+    --freqaimodel XGBoostClassifier --config config_examples/config_freqai.example.json

    or the user simply adds this to their config:

--- a/freqtrade/templates/FreqaiExampleStrategy.py
+++ b/freqtrade/templates/FreqaiExampleStrategy.py
@@ -205,8 +205,8 @@ class FreqaiExampleStrategy(IStrategy):
        # If user wishes to use multiple targets, they can add more by
        # appending more columns with '&'. User should keep in mind that multi targets
        # requires a multioutput prediction model such as
-        # freqai/prediction_models/CatboostRegressorMultiTarget.py,
-        # freqtrade trade --freqaimodel CatboostRegressorMultiTarget
+        # freqai/prediction_models/LightGBMClassifierMultiTarget.py,
+        # freqtrade trade --freqaimodel LightGBMClassifierMultiTarget

        # df["&-s_range"] = (
        #     df["close"]
--- a/ft_client/freqtrade_client/init.py
+++ b/ft_client/freqtrade_client/init.py
@@ -1,7 +1,7 @@
 from freqtrade_client.ft_rest_client import FtRestClient


-__version__ = "2025.12-dev"
+__version__ = "2026.1-dev"

 if "dev" in __version__:
    from pathlib import Path
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,6 +22,7 @@ classifiers = [
    "Programming Language :: Python :: 3.11",
    "Programming Language :: Python :: 3.12",
    "Programming Language :: Python :: 3.13",
+    "Programming Language :: Python :: 3.14",
    "Operating System :: MacOS",
    "Operating System :: Unix",
    "Topic :: Office/Business :: Financial :: Investment",
@@ -85,7 +86,6 @@ hyperopt = [
 freqai = [
  "scikit-learn",
  "joblib",
-  "catboost; platform_machine != 'arm'",
  "lightgbm",
  "xgboost",
  "tensorboard",
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -6,9 +6,9 @@
 -r requirements-freqai-rl.txt
 -r docs/requirements-docs.txt

-ruff==0.14.8
-mypy==1.19.0
-pre-commit==4.5.0
+ruff==0.14.10
+mypy==1.19.1
+pre-commit==4.5.1
 pytest==9.0.2
 pytest-asyncio==1.3.0
 pytest-cov==7.0.0
@@ -18,15 +18,16 @@ pytest-timeout==2.4.0
 pytest-xdist==3.8.0
 isort==7.0.0
 # For datetime mocking
-time-machine==3.1.0
+time-machine==3.2.0

 # Convert jupyter notebooks to markdown documents
 nbconvert==7.16.6

 # mypy types
-scipy-stubs==1.16.3.2  # keep in sync with `scipy` in `requirements-hyperopt.txt`
+scipy-stubs==1.16.3.3  # keep in sync with `scipy` in `requirements-hyperopt.txt`
 types-cachetools==6.2.0.20251022
 types-filelock==3.2.7
 types-requests==2.32.4.20250913
 types-tabulate==0.9.0.20241207
 types-python-dateutil==2.9.0.20251115
+pip-audit==2.10.0
--- a/requirements-freqai-rl.txt
+++ b/requirements-freqai-rl.txt
@@ -3,7 +3,7 @@

 # Required for freqai-rl
 torch==2.9.1; sys_platform != 'darwin' or platform_machine != 'x86_64'
-gymnasium==1.2.2
+gymnasium==1.2.3
 # SB3 >=2.5.0 depends on torch 2.3.0 - which implies it dropped support x86 macos
 stable_baselines3==2.7.1; sys_platform != 'darwin' or platform_machine != 'x86_64'
 sb3_contrib>=2.2.1; sys_platform != 'darwin' or platform_machine != 'x86_64'
--- a/requirements-freqai.txt
+++ b/requirements-freqai.txt
@@ -3,9 +3,8 @@
 -r requirements-plot.txt

 # Required for freqai
-scikit-learn==1.7.2
-joblib==1.5.2
-catboost==1.2.8; 'arm' not in platform_machine
+scikit-learn==1.8.0
+joblib==1.5.3
 lightgbm==4.6.0
 xgboost==3.1.2
 tensorboard==2.20.0
--- a/requirements-hyperopt.txt
+++ b/requirements-hyperopt.txt
@@ -3,7 +3,7 @@

 # Required for hyperopt
 scipy==1.16.3
-scikit-learn==1.7.2
+scikit-learn==1.8.0
 filelock==3.20.1
 optuna==4.6.0
 cmaes==0.12.0
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,4 @@
-numpy==2.3.5
+numpy==2.4.0
 pandas==2.3.3
 bottleneck==1.6.0
 numexpr==2.14.1
@@ -7,23 +7,23 @@ ft-pandas-ta==0.3.16
 ta-lib==0.6.8
 technical==1.5.3

-ccxt==4.5.27
+ccxt==4.5.30
 cryptography==46.0.3
 aiohttp==3.13.2
-SQLAlchemy==2.0.44
+SQLAlchemy==2.0.45
 python-telegram-bot==22.5
 # can't be hard-pinned due to telegram-bot pinning httpx with ~
 httpx>=0.24.1
-humanize==4.14.0
-cachetools==6.2.2
+humanize==4.15.0
+cachetools==6.2.4
 requests==2.32.5
-urllib3==2.6.0
+urllib3==2.6.2
 certifi==2025.11.12
 jsonschema==4.25.1
 tabulate==0.9.0
 pycoingecko==3.2.0
 jinja2==3.1.6
-joblib==1.5.2
+joblib==1.5.3
 rich==14.2.0
 pyarrow==22.0.0; platform_machine != 'armv7l'

@@ -37,9 +37,9 @@ orjson==3.11.5
 sdnotify==0.3.2

 # API Server
-fastapi==0.124.0
+fastapi==0.127.0
 pydantic==2.12.5
-uvicorn==0.38.0
+uvicorn==0.40.0
 pyjwt==2.10.1
 aiofiles==25.1.0
 psutil==7.1.3
--- a/tests/data/test_converter_orderflow.py
+++ b/tests/data/test_converter_orderflow.py
@@ -39,12 +39,6 @@ def populate_dataframe_with_trades_trades(testdatadir):
    return pd.read_feather(testdatadir / "orderflow/populate_dataframe_with_trades_TRADES.feather")


-@pytest.fixture
-def candles(testdatadir):
-    # TODO: this fixture isn't really necessary and could be removed
-    return pd.read_json(testdatadir / "orderflow/candles.json").copy()
-
-
@pytest.fixture
 def public_trades_list(testdatadir):
    return read_csv(testdatadir / "orderflow/public_trades_list.csv").copy()
@@ -293,7 +287,7 @@ def test_public_trades_trades_mock_populate_dataframe_with_trades__check_trades(
    assert t["price"] == 234.72


-def test_public_trades_put_volume_profile_into_ohlcv_candles(public_trades_list_simple, candles):
+def test_public_trades_put_volume_profile_into_ohlcv_candles(public_trades_list_simple):
    """
    Tests the integration of volume profile data into OHLCV candles.

@@ -412,13 +406,11 @@ def test_public_trades_config_max_trades(


 def test_public_trades_testdata_sanity(
-    candles,
    public_trades_list,
    public_trades_list_simple,
    populate_dataframe_with_trades_dataframe,
    populate_dataframe_with_trades_trades,
 ):
-    assert 10999 == len(candles)
    assert 1000 == len(public_trades_list)
    assert 999 == len(populate_dataframe_with_trades_dataframe)
    assert 293532 == len(populate_dataframe_with_trades_trades)
--- a/tests/exchange/test_exchange.py
+++ b/tests/exchange/test_exchange.py
@@ -1012,7 +1012,7 @@ def test_validate_required_startup_candles(default_conf, mocker, caplog):
    ex._ft_has["ohlcv_has_history"] = False
    with pytest.raises(
        OperationalException,
-        match=r"This strategy requires 2500.*, " r"which is more than the amount.*",
+        match=r"This strategy requires 2500.*, " r"which is more than .* the amount",
    ):
        ex.validate_required_startup_candles(2500, "5m")

--- a/tests/exchange_online/conftest.py
+++ b/tests/exchange_online/conftest.py
@@ -515,7 +515,6 @@ EXCHANGES = {
        ],
    },
    "hyperliquid": {
-        # TODO: Should be UBTC/USDC - probably needs a fix in ccxt
        "pair": "BTC/USDC",
        "stake_currency": "USDC",
        "hasQuoteVolume": False,
--- a/tests/freqai/test_freqai_interface.py
+++ b/tests/freqai/test_freqai_interface.py
@@ -31,9 +31,6 @@ from tests.freqai.conftest import (
 def can_run_model(model: str) -> None:
    is_pytorch_model = "Reinforcement" in model or "PyTorch" in model

-    if is_arm() and "Catboost" in model:
-        pytest.skip("CatBoost is not supported on ARM.")
-
    if is_pytorch_model and is_mac():
        pytest.skip("Reinforcement learning / PyTorch module not available on intel based Mac OS.")

@@ -44,7 +41,6 @@ def can_run_model(model: str) -> None:
        ("LightGBMRegressor", True, False, True, True, False, 0, 0),
        ("XGBoostRegressor", False, True, False, True, False, 10, 0.05),
        ("XGBoostRFRegressor", False, False, False, True, False, 0, 0),
-        ("CatboostRegressor", False, False, False, True, True, 0, 0),
        ("PyTorchMLPRegressor", False, False, False, False, False, 0, 0),
        ("PyTorchTransformerRegressor", False, False, False, False, False, 0, 0),
        ("ReinforcementLearner", False, True, False, True, False, 0, 0),
@@ -138,9 +134,7 @@ def test_extract_data_and_train_model_Standard(
    [
        ("LightGBMRegressorMultiTarget", "freqai_test_multimodel_strat"),
        ("XGBoostRegressorMultiTarget", "freqai_test_multimodel_strat"),
-        ("CatboostRegressorMultiTarget", "freqai_test_multimodel_strat"),
        ("LightGBMClassifierMultiTarget", "freqai_test_multimodel_classifier_strat"),
-        ("CatboostClassifierMultiTarget", "freqai_test_multimodel_classifier_strat"),
    ],
 )
@pytest.mark.filterwarnings(r"ignore:.*__sklearn_tags__.*:DeprecationWarning")
@@ -184,7 +178,6 @@ def test_extract_data_and_train_model_MultiTargets(mocker, freqai_conf, model, s
    "model",
    [
        "LightGBMClassifier",
-        "CatboostClassifier",
        "XGBoostClassifier",
        "XGBoostRFClassifier",
        "SKLearnRandomForestClassifier",
@@ -246,13 +239,11 @@ def test_extract_data_and_train_model_Classifiers(mocker, freqai_conf, model):
    [
        ("LightGBMRegressor", 2, "freqai_test_strat"),
        ("XGBoostRegressor", 2, "freqai_test_strat"),
-        ("CatboostRegressor", 2, "freqai_test_strat"),
        ("PyTorchMLPRegressor", 2, "freqai_test_strat"),
        ("PyTorchTransformerRegressor", 2, "freqai_test_strat"),
        ("ReinforcementLearner", 3, "freqai_rl_test_strat"),
        ("XGBoostClassifier", 2, "freqai_test_classifier"),
        ("LightGBMClassifier", 2, "freqai_test_classifier"),
-        ("CatboostClassifier", 2, "freqai_test_classifier"),
        ("PyTorchMLPClassifier", 2, "freqai_test_classifier"),
    ],
 )
--- a/tests/strategy/test_strategy_parameters.py
+++ b/tests/strategy/test_strategy_parameters.py
@@ -64,7 +64,6 @@ def test_hyperopt_real_parameter():

 def test_hyperopt_decimal_parameter():
    HyperoptStateContainer.set_state(HyperoptState.INDICATORS)
-    # TODO: Check for get_space??
    from freqtrade.optimize.space import SKDecimal

    with pytest.raises(OperationalException, match=r"DecimalParameter space must be.*"):
--- a/tests/test_pip_audit.py
+++ b/tests/test_pip_audit.py
@@ -0,0 +1,92 @@
+"""
+Run pip audit to check for known security vulnerabilities in installed packages.
+Original Idea and base for this implementation by Michael Kennedy's blog:
+https://mkennedy.codes/posts/python-supply-chain-security-made-easy/
+"""
+
+import subprocess
+import sys
+from pathlib import Path
+
+import pytest
+
+
+def test_pip_audit_no_vulnerabilities():
+    """
+    Run pip-audit to check for known security vulnerabilities.
+
+    This test will fail if any vulnerabilities are detected in the installed packages.
+
+    Note: CVE-2025-53000 (nbconvert Windows vulnerability) is ignored as it only affects
+    Windows platforms and is a known acceptable risk for this project.
+    """
+    # Get the project root directory
+    project_root = Path(__file__).parent.parent
+    command = [
+        sys.executable,
+        "-m",
+        "pip_audit",
+        # "--format=json",
+        "--progress-spinner=off",
+        "--ignore-vuln",
+        "CVE-2025-53000",
+        "--skip-editable",
+    ]
+
+    # Run pip-audit with JSON output for easier parsing
+    try:
+        result = subprocess.run(
+            command,
+            cwd=project_root,
+            capture_output=True,
+            text=True,
+            timeout=120,  # 2 minute timeout
+        )
+    except subprocess.TimeoutExpired:
+        pytest.fail("pip-audit command timed out after 120 seconds")
+    except FileNotFoundError:
+        pytest.fail("pip-audit not installed or not accessible")
+
+    # Check if pip-audit found any vulnerabilities
+    if result.returncode != 0:
+        # pip-audit returns non-zero when vulnerabilities are found
+        error_output = result.stdout + "\n" + result.stderr
+
+        # Check if it's an actual vulnerability vs an error
+        if "vulnerabilities found" in error_output.lower() or '"dependencies"' in result.stdout:
+            pytest.fail(
+                f"pip-audit detected security vulnerabilities!\n\n"
+                f"Output:\n{result.stdout}\n\n"
+                f"Please review and update vulnerable packages.\n"
+                f"Run manually with: {' '.join(command)}"
+            )
+        else:
+            # Some other error occurred
+            pytest.fail(
+                f"pip-audit failed to run properly:\n\nReturn code: {result.returncode}\n"
+                f"Output: {error_output}\n"
+            )
+
+    # Success - no vulnerabilities found
+    assert result.returncode == 0, "pip-audit should return 0 when no vulnerabilities are found"
+
+
+def test_pip_audit_runs_successfully():
+    """
+    Verify that pip-audit can run successfully (even if vulnerabilities are found).
+
+    This is a smoke test to ensure pip-audit is properly installed and functional.
+    """
+    try:
+        result = subprocess.run(
+            [sys.executable, "-m", "pip_audit", "--version"],
+            capture_output=True,
+            text=True,
+            timeout=10,
+        )
+        assert result.returncode == 0, f"pip-audit --version failed: {result.stderr}"
+        assert "pip-audit" in result.stdout.lower(), "pip-audit version output unexpected"
+    except FileNotFoundError:
+        pytest.fail("pip-audit not installed")
+    except subprocess.TimeoutExpired:
+        pytest.fail("pip-audit --version timed out")
--- a/tests/testdata/orderflow/candles.json
+++ b/tests/testdata/orderflow/candles.json
--- a/tests/util/test_datetime_helpers.py
+++ b/tests/util/test_datetime_helpers.py
@@ -82,7 +82,7 @@ def test_dt_humanize() -> None:
    assert dt_humanize_delta(dt_now() - timedelta(minutes=50)) == "50 minutes ago"
    assert dt_humanize_delta(dt_now() - timedelta(hours=16)) == "16 hours ago"
    assert dt_humanize_delta(dt_now() - timedelta(hours=16, minutes=30)) == "16 hours ago"
-    assert dt_humanize_delta(dt_now() - timedelta(days=16, hours=10, minutes=25)) == "16 days ago"
+    assert dt_humanize_delta(dt_now() - timedelta(days=16, hours=10, minutes=25)) == "a month ago"
    assert dt_humanize_delta(dt_now() - timedelta(minutes=50)) == "50 minutes ago"