Merge pull request #13106 from freqtrade/new_release

New release 2026.4

.github/dependabot.yml

@@ -49,11 +49,7 @@ updates:
       patterns:
         - "scipy"
         - "scipy-stubs"
-    gymnasium:
-      patterns:
-        - "gymnasium"
-        - "stable-baselines3"
-        - "sb3-contrib"
+
 - package-ecosystem: "github-actions"
   directory: "/"
   cooldown:

.github/workflows/ci.yml

@@ -68,7 +68,7 @@ jobs:
       run: |
         pytest --random-order --cov=freqtrade --cov=freqtrade_client --cov-config=.coveragerc
 
-    - uses: codecov/codecov-action@e79a6962e0d4c0c17b229090214935d2e33f8354 # v6.0.1
+    - uses: codecov/codecov-action@57e3a136b779b570ffcdbf80b3bdc90e7fab3de2 # v6.0.0
       if: (runner.os == 'Linux' && matrix.python-version == '3.12' && matrix.os == 'ubuntu-24.04')
       with:
         fail_ci_if_error: true
@@ -150,10 +150,7 @@ jobs:
       run: |
         $PSVersionTable
         Get-PSRepository | Format-List *
-        if (-not (Get-PSRepository -Name PSGallery -ErrorAction SilentlyContinue)) {
-          Register-PSRepository -Default
-        }
-        Set-PSRepository PSGallery -InstallationPolicy Trusted
+        Set-PSRepository psgallery -InstallationPolicy trusted
         Install-Module -Name Pester -RequiredVersion 5.7.1 -Confirm:$false -Force -SkipPublisherCheck
         $Error.clear()
         Invoke-Pester -Path "tests" -CI

.github/workflows/pre-commit-types-update.yml

@@ -1,53 +0,0 @@
-name: Pre-commit Types update
-
-on:
-  pull_request:
-    branches:
-      - "develop"
-
-concurrency:
-  group: "${{ github.workflow }}-${{ github.ref }}-${{ github.event_name }}"
-  cancel-in-progress: true
-permissions: {}
-
-jobs:
-  mypy-version-update:
-    name: "Pre-commit mypy type versions update"
-    runs-on: ubuntu-24.04
-    # Only run this job for pull requests created by dependabot[bot]
-    if: >
-      github.event.pull_request.user.login == 'dependabot[bot]' &&
-      github.repository == github.event.pull_request.head.repo.full_name &&
-      github.event_name == 'pull_request' &&
-      startsWith(github.head_ref, 'dependabot/')
-
-    environment:
-      name: dependabot-pulls
-
-    steps:
-    - uses: actions/checkout@de0fac2e4500dabe0009e67214ff5f5447ce83dd # v6.0.2
-      with:
-        persist-credentials: true
-        token: ${{ secrets.REPO_SCOPED_TOKEN_DEP }}
-        ref: ${{ github.head_ref || github.ref }}
-
-    - name: Install uv and Python 🐍
-      uses: astral-sh/setup-uv@08807647e7069bb48b6ef5acd8ec9567f424441b # v8.1.0
-      with:
-        activate-environment: true
-        python-version: "3.13"
-
-    - name: Install PyYAML
-      run: |
-
-    - name: pre-commit dependencies
-      run: |
-        uv pip install $(grep -E "^pyyaml==" requirements-dev.txt)
-        python build_helpers/pre_commit_update.py --update
-
-    - uses: stefanzweifel/git-auto-commit-action@04702edda442b2e678b25b537cec683a1493fcb9 # v7
-      with:
-        commit_message: "chore(deps): Apply pre-commit types update"
-        commit_user_name: Freqtrade Bot
-        commit_user_email: 154552126+freqtrade-bot@users.noreply.github.com
-        commit_author: Freqtrade Bot <154552126+freqtrade-bot@users.noreply.github.com>

.github/workflows/pre-commit-update.yml

@@ -2,7 +2,7 @@ name: Pre-commit auto-update
 
 on:
   schedule:
-    - cron: "13 1 * * 2"
+    - cron: "0 3 * * 2"
   # on demand
   workflow_dispatch:
 

.pre-commit-config.yaml

@@ -15,23 +15,23 @@ repos:
 
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v2.1.0"
+    rev: "v1.20.2"
     hooks:
       - id: mypy
         exclude: build_helpers
         additional_dependencies:
-          - types-cachetools==7.0.0.20260503
+          - types-cachetools==6.2.0.20260408
           - types-filelock==3.2.7
-          - types-requests==2.33.0.20260508
-          - types-tabulate==0.10.0.20260508
-          - types-python-dateutil==2.9.0.20260508
+          - types-requests==2.33.0.20260408
+          - types-tabulate==0.10.0.20260408
+          - types-python-dateutil==2.9.0.20260408
           - scipy-stubs==1.17.1.4
           - SQLAlchemy==2.0.49
         # stages: [push]
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.15.13'
+    rev: 'v0.15.12'
     hooks:
       - id: ruff
       - id: ruff-format

Dockerfile

@@ -1,4 +1,4 @@
-FROM python:3.14.5-slim-trixie AS base
+FROM python:3.14.3-slim-trixie AS base
 
 # Setup env
 ENV LANG=C.UTF-8

build_helpers/pre_commit_update.py

@@ -1,7 +1,5 @@
 # File used in CI to ensure pre-commit dependencies are kept up-to-date.
 
-import argparse
-import re
 import sys
 from pathlib import Path
 
@@ -12,24 +10,6 @@ pre_commit_file = Path(".pre-commit-config.yaml")
 require_dev = Path("requirements-dev.txt")
 require = Path("requirements.txt")
 
-
-parser = argparse.ArgumentParser()
-parser.add_argument("--update", action="store_true")
-args = parser.parse_args()
-
-
-def replace_dependency_version(pre_commit_text: str, dependency: str) -> tuple[str, bool]:
-    """
-    Regex-based replacement of a dependency version in the pre-commit config file.
-    using regex here ensures we only replace the version of the dependency while
-    keeping the overall file intact.
-    """
-    package_name = dependency.split("==", 1)[0]
-    pattern = re.compile(rf"^(\s*-\s+){re.escape(package_name)}==.*$", re.MULTILINE)
-    updated_text, replacements = pattern.subn(rf"\1{dependency}", pre_commit_text, count=1)
-    return updated_text, replacements > 0 and updated_text != pre_commit_text
-
-
 with require_dev.open("r") as rfile:
     requirements = rfile.readlines()
 
@@ -43,18 +23,6 @@ supported = ("types-", "SQLAlchemy", "scipy-stubs")
 # Only keep the first part of the line up to the first space
 type_reqs = [r.strip("\n").split()[0] for r in requirements if r.startswith(supported)]
 
-with pre_commit_file.open("r") as file:
-    pre_commit_text = file.read()
-
-updated = False
-for req in type_reqs:
-    pre_commit_text, req_updated = replace_dependency_version(pre_commit_text, req)
-    updated = updated or req_updated
-
-if args.update and updated:
-    with pre_commit_file.open("w") as file:
-        file.write(pre_commit_text)
-
 with pre_commit_file.open("r") as file:
     f = yaml.load(file, Loader=yaml.SafeLoader)
 
@@ -72,20 +40,12 @@ for hook in hooks:
 
 for req in type_reqs:
     if req not in hooks:
-        errors.append(f"{req} is missing in pre-commit config file.")
-
-if updated:
-    if args.update:
-        errors.append(".pre-commit-config.yaml was updated to match the requirements files.")
-    else:
-        errors.append(
-            ".pre-commit-config.yaml is outdated. Run build_helpers/pre_commit_update.py --update."
-        )
+        errors.append(f"{req} is missing in pre-config file.")
 
 
 if errors:
     for e in errors:
         print(e)
-    sys.exit(1 if not (args.update and updated) else 0)
+    sys.exit(1)
 
 sys.exit(0)

build_helpers/schema.json

@@ -1063,8 +1063,7 @@
         "jwt_secret_key": {
           "description": "Secret key for JWT authentication.",
           "type": "string",
-          "default": "somethingRandomSomethingRandom123",
-          "minLength": 32
+          "default": "somethingRandomSomethingRandom123"
         },
         "CORS_origins": {
           "description": "List of allowed CORS origins.",

docs/freqai-configuration.md

@@ -166,7 +166,7 @@ Below are the values you can expect to include/use inside a typical strategy dat
 | `df['do_predict']` | Indication of an outlier data point. The return value is integer between -2 and 2, which lets you know if the prediction is trustworthy or not. `do_predict==1` means that the prediction is trustworthy. If the Dissimilarity Index (DI, see details [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di)) of the input data point is above the threshold defined in the config, FreqAI will subtract 1 from `do_predict`, resulting in `do_predict==0`. If `use_SVM_to_remove_outliers` is active, the Support Vector Machine (SVM, see details [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm)) may also detect outliers in training and prediction data. In this case, the SVM will also subtract 1 from `do_predict`. If the input data point was considered an outlier by the SVM but not by the DI, or vice versa, the result will be `do_predict==0`. If both the DI and the SVM considers the input data point to be an outlier, the result will be `do_predict==-1`. As with the SVM, if `use_DBSCAN_to_remove_outliers` is active, DBSCAN (see details [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan)) may also detect outliers and subtract 1 from `do_predict`. Hence, if both the SVM and DBSCAN are active and identify a datapoint that was above the DI threshold as an outlier, the result will be `do_predict==-2`. A particular case is when `do_predict == 2`, which means that the model has expired due to exceeding `expired_hours`. <br> **Datatype:** Integer between -2 and 2.
 | `df['DI_values']` | Dissimilarity Index (DI) values are proxies for the level of confidence FreqAI has in the prediction. A lower DI means the prediction is close to the training data, i.e., higher prediction confidence. See details about the DI [here](freqai-feature-engineering.md#identifying-outliers-with-the-dissimilarity-index-di). <br> **Datatype:** Float.
 | `df['%*']` | Any dataframe column prepended with `%` in `feature_engineering_*()` is treated as a training feature. For example, you can include the RSI in the training feature set (similar to in `templates/FreqaiExampleStrategy.py`) by setting `df['%-rsi']`. See more details on how this is done [here](freqai-feature-engineering.md). <br> **Note:** Since the number of features prepended with `%` can multiply very quickly (10s of thousands of features are easily engineered using the multiplictative functionality of, e.g., `include_shifted_candles` and `include_timeframes` as described in the [parameter table](freqai-parameter-table.md)), these features are removed from the dataframe that is returned from FreqAI to the strategy. To keep a particular type of feature for plotting purposes, you would prepend it with `%%` (see details below). <br> **Datatype:** Depends on the feature created by the user.
-| `df['%%*']` | Any dataframe column prepended with `%%` in `feature_engineering_*()` is treated as a training feature, just the same as the above `%` prepend. However, in this case, the features are returned back to the strategy for FreqUI/plot-dataframe plotting and monitoring in Dry/Live/Backtesting <br> **Datatype:** Depends on the feature created by the user. <br>*Please note* that features created in `feature_engineering_expand()` will have automatic FreqAI naming schemas depending on the expansions that you configured (i.e. `include_timeframes`, `include_corr_pairlist`, `indicators_periods_candles`, `include_shifted_candles`). So if you want to plot `%%-rsi` from `feature_engineering_expand_all()`, the final naming scheme for your plotting config would be: `%%-rsi-period_10_ETH/USDT:USDT_1h` for the `rsi` feature with `period=10`, `timeframe=1h`, and `pair=ETH/USDT:USDT` (the `:USDT` is added if you are using futures pairs). It is useful to simply add `print(dataframe.columns)` in your `populate_indicators()` after `self.freqai.start()` to see the full list of available features that are returned to the strategy for plotting purposes.
+| `df['%%*']` | Any dataframe column prepended with `%%` in `feature_engineering_*()` is treated as a training feature, just the same as the above `%` prepend. However, in this case, the features are returned back to the strategy for FreqUI/plot-dataframe plotting and monitoring in Dry/Live/Backtesting <br> **Datatype:** Depends on the feature created by the user. Please note that features created in `feature_engineering_expand()` will have automatic FreqAI naming schemas depending on the expansions that you configured (i.e. `include_timeframes`, `include_corr_pairlist`, `indicators_periods_candles`, `include_shifted_candles`). So if you want to plot `%%-rsi` from `feature_engineering_expand_all()`, the final naming scheme for your plotting config would be: `%%-rsi-period_10_ETH/USDT:USDT_1h` for the `rsi` feature with `period=10`, `timeframe=1h`, and `pair=ETH/USDT:USDT` (the `:USDT` is added if you are using futures pairs). It is useful to simply add `print(dataframe.columns)` in your `populate_indicators()` after `self.freqai.start()` to see the full list of available features that are returned to the strategy for plotting purposes.
 
 ## Setting the `startup_candle_count`
 

docs/requirements-docs.txt

@@ -2,6 +2,6 @@ markdown==3.10.2
 mkdocs==1.6.1
 mkdocs-material==9.7.6
 mdx_truly_sane_lists==1.3
-pymdown-extensions==10.21.3
+pymdown-extensions==10.21.2
 jinja2==3.1.6
 mike==2.2.0

docs/rest-api.md

@@ -202,20 +202,20 @@ All endpoints in the below table need to be prefixed with the base URL of the AP
 | `/blacklist` | GET | Show the current blacklist.
 | `/blacklist` | POST | Adds the specified pair to the blacklist.<br/>*Params:*<br/>- `blacklist` (`str`)
 | `/blacklist` | DELETE | Deletes the specified list of pairs from the blacklist.<br/>*Params:*<br/>- `[pair,pair]` (`list[str]`)
-| `/pair_candles` | GET | Returns dataframe for a pair / timeframe combination while the bot is running.
-| `/pair_candles` | POST | Returns dataframe for a pair / timeframe combination while the bot is running, filtered by a provided list of columns to return.<br/>*Params:*<br/>- `<column_list>` (`list[str]`)
-| `/pair_history` | GET | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy.
-| `/pair_history` | POST | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy, filtered by a provided list of columns to return.<br/>*Params:*<br/>- `<column_list>` (`list[str]`)
-| `/plot_config` | GET | Get plot config from the strategy (or nothing if not configured).
-| `/strategies` | GET | List strategies in strategy directory.
-| `/strategy/<strategy>` | GET | Get specific Strategy content by strategy class name.<br/>*Params:*<br/>- `<strategy>` (`str`)
-| `/available_pairs` | GET | List available backtest data.
+| `/pair_candles` | GET | Returns dataframe for a pair / timeframe combination while the bot is running. **Alpha**
+| `/pair_candles` | POST | Returns dataframe for a pair / timeframe combination while the bot is running, filtered by a provided list of columns to return. **Alpha**<br/>*Params:*<br/>- `<column_list>` (`list[str]`)
+| `/pair_history` | GET | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy. **Alpha**
+| `/pair_history` | POST | Returns an analyzed dataframe for a given timerange, analyzed by a given strategy, filtered by a provided list of columns to return. **Alpha**<br/>*Params:*<br/>- `<column_list>` (`list[str]`)
+| `/plot_config` | GET | Get plot config from the strategy (or nothing if not configured). **Alpha**
+| `/strategies` | GET | List strategies in strategy directory. **Alpha**
+| `/strategy/<strategy>` | GET | Get specific Strategy content by strategy class name. **Alpha**<br/>*Params:*<br/>- `<strategy>` (`str`)
+| `/available_pairs` | GET | List available backtest data. **Alpha**
 | `/version` | GET | Show version.
 | `/sysinfo` | GET | Show information about the system load.
 | `/health` | GET | Show bot health (last bot loop).
 
 !!! Warning "Alpha status"
-    Endpoints labeled with *Alpha status* or *Beta status* above may change at any time without notice.
+    Endpoints labeled with *Alpha status* above may change at any time without notice.
 
 ### Message WebSocket
 

docs/strategy-customization.md

@@ -910,8 +910,6 @@ if self.dp.runmode.value in ('live', 'dry_run'):
 
 ### *check_delisting(pair)*
 
-Return Datetime of the pair delisting schedule if any, otherwise return None
-
 ```python
 def custom_exit(self, pair: str, trade: Trade, current_time: datetime, current_rate: float, current_profit: float, **kwargs):
     if self.dp.runmode.value in ('live', 'dry_run'):

freqtrade/__init__.py

@@ -1,6 +1,6 @@
 """Freqtrade bot"""
 
-__version__ = "2026.5-dev"
+__version__ = "2026.4"
 
 if "dev" in __version__:
     from pathlib import Path

freqtrade/commands/list_commands.py

@@ -393,7 +393,7 @@ def start_show_trades(args: dict[str, Any]) -> None:
     tfilter = []
 
     if config.get("trade_ids"):
-        tfilter.append(Trade.id.in_(int(tid) for tid in config["trade_ids"]))
+        tfilter.append(Trade.id.in_(config["trade_ids"]))
 
     trades = Trade.get_trades(tfilter).all()
     logger.info(f"Printing {len(trades)} Trades: ")

freqtrade/config_schema/config_schema.py

@@ -757,7 +757,6 @@ CONF_SCHEMA = {
                     "description": "Secret key for JWT authentication.",
                     "type": "string",
                     "default": "somethingRandomSomethingRandom123",
-                    "minLength": 32,
                 },
                 "CORS_origins": {
                     "description": "List of allowed CORS origins.",

freqtrade/exchange/binance.py

@@ -559,7 +559,7 @@ class Binance(Exchange):
 
 
 class Binanceusdm(Binance):
-    """Binance USDM Exchange
+    """Binacne USDM Exchange
     Same as Binance - only futures trading is supported (via ccxt).
 
     Not actually necessary, binance should be preferred.

freqtrade/misc.py

@@ -214,12 +214,6 @@ def dataframe_to_json(dataframe: pd.DataFrame) -> str:
     :param dataframe: A pandas DataFrame
     :returns: A JSON string of the pandas DataFrame
     """
-    date_columns = dataframe.select_dtypes(include=["datetime", "datetime64", "datetimetz"])
-    # Explicit conversion to ms
-    # This used to be part of to_json, but was deprecated in pandas 3
-    for date_column in date_columns:
-        dataframe[date_column] = date_columns[date_column].dt.as_unit("ms").astype("int64")
-
     return dataframe.to_json(orient="split")
 
 

freqtrade/persistence/trade_model.py

@@ -189,8 +189,8 @@ class Order(ModelBase):
     def __repr__(self):
         return (
             f"Order(id={self.id}, trade={self.ft_trade_id}, order_id={self.order_id}, "
-            f"side={self.side or self.ft_order_side}, filled={self.safe_filled}, "
-            f"price={self.safe_price}, amount={self.amount}, "
+            f"side={self.side}, filled={self.safe_filled}, price={self.safe_price}, "
+            f"amount={self.amount}, "
             f"status={self.status}, date={self.order_date_utc:{DATETIME_PRINT_FORMAT}})"
         )
 
@@ -1248,16 +1248,12 @@ class LocalTrade:
         close_profit_abs = 0.0
         # Reset funding fees
         self.funding_fees = 0.0
-        # Total funding fees - cumulated over all orders
-        total_funding_fees = 0.0
-        # current funding fees - resetting on every exit to be aligned with profit calculation,
-        # as funding fees are part of the profit
-        current_funding_fee = 0.0
+        funding_fees = 0.0
+        ordercount = len(self.orders) - 1
         for i, o in enumerate(self.orders):
             if o.ft_is_open or not o.filled:
                 continue
-            current_funding_fee += o.funding_fee or 0.0
-            total_funding_fees += o.funding_fee or 0.0
+            funding_fees += o.funding_fee or 0.0
             tmp_amount = FtPrecise(o.safe_amount_after_fee)
             tmp_price = FtPrecise(o.safe_price)
 
@@ -1272,8 +1268,11 @@ class LocalTrade:
                     avg_price = current_stake / current_amount
 
             if is_exit:
-                # Intermediate funding fees for profit calculation
-                self.funding_fees = current_funding_fee
+                # Process exits
+                if i == ordercount and is_closing:
+                    # Apply funding fees only to the last closing order
+                    self.funding_fees = funding_fees
+
                 exit_rate = o.safe_price
                 exit_amount = o.safe_amount_after_fee
                 prof = self.calculate_profit(exit_rate, exit_amount, float(avg_price))
@@ -1282,12 +1281,10 @@ class LocalTrade:
                     # This needs to be calculated based on the last occurring exit to be aligned
                     # with realized_profit.
                     close_profit = (close_profit_abs / total_stake) * self.leverage
-                current_funding_fee = 0.0
             else:
                 total_stake += self._calc_open_trade_value(tmp_amount, price)
                 max_stake_amount += tmp_amount * price
-        # Assign cumulated funding fees after all orders have been processed
-        self.funding_fees = total_funding_fees
+        self.funding_fees = funding_fees
         self.max_stake_amount = float(max_stake_amount) / (self.leverage or 1.0)
 
         if close_profit:

freqtrade/plugins/pairlist/MarketCapPairList.py

@@ -38,7 +38,7 @@ class MarketCapPairList(IPairList):
         self._max_rank = self._pairlistconfig.get("max_rank", 30)
         self._refresh_period = self._pairlistconfig.get("refresh_period", 86400)
         self._categories = self._pairlistconfig.get("categories", [])
-        self._marketcap_cache: FtTTLCache = FtTTLCache(maxsize=2, ttl=self._refresh_period)
+        self._marketcap_cache: FtTTLCache = FtTTLCache(maxsize=1, ttl=self._refresh_period)
 
         _coingecko_config = self._config.get("coingecko", {})
 

freqtrade/resolvers/strategy_resolver.py

@@ -51,7 +51,7 @@ class StrategyResolver(IResolver):
         strategy: IStrategy = StrategyResolver._load_strategy(
             strategy_name, config=config, extra_dir=config.get("strategy_path")
         )
-        strategy.ft_set_special_params_from_file()
+        strategy.ft_load_params_from_file()
         # Set attributes
         # Check if we need to override configuration
         # (Attribute name, default, subkey)

freqtrade/rpc/api_server/api_auth.py

@@ -15,6 +15,7 @@ from freqtrade.rpc.api_server.deps import get_api_config
 logger = logging.getLogger(__name__)
 
 ALGORITHM = "HS256"
+__DEFAULT_JWT = "somethingRandomSomethingRandom123"
 
 router_login = APIRouter()
 

freqtrade/rpc/rpc.py

@@ -796,7 +796,7 @@ class RPC:
         results = results.rename({"timestamp": "date"}, axis=1)
         results.loc[:, "__date_ts"] = results.loc[:, "date"].dt.as_unit("ms").astype("int64")
         # Exclude non-bot managed for now
-        results_filtered = results.loc[results["bot_managed"].astype(bool)]
+        results_filtered = results.loc[results["bot_managed"]]
 
         results_final = (
             results_filtered.groupby(["date", "__date_ts"])
@@ -1548,7 +1548,8 @@ class RPC:
 
             # band-aid until this is fixed:
             # https://github.com/pandas-dev/pandas/issues/45836
-            date_columns = dataframe.select_dtypes(include=["datetime", "datetime64", "datetimetz"])
+            datetime_types = ["datetime", "datetime64", "datetimetz"]
+            date_columns = dataframe.select_dtypes(include=datetime_types)
             for date_column in date_columns:
                 # replace NaT with `None`
                 dataframe[date_column] = dataframe[date_column].astype(object).replace({NaT: None})
@@ -1694,11 +1695,8 @@ class RPC:
                     else dt_ts(dt_now() - timedelta(days=30)),
                     is_new_pair=True,  # history is never available - so always treat as new pair
                     candle_type=config.get("candle_type_def", CandleType.SPOT),
-                    until_ms=timerange_parsed.stopts * 1000 if timerange_parsed.stopts else None,
+                    until_ms=timerange_parsed.stopts,
                 )
-                if timerange_parsed.stopts and len(data) > 1:
-                    # trim last candle if it is newer than the stop time
-                    data = data.loc[data["date"] <= timerange_parsed.stopdt]
             else:
                 _data = load_data(
                     datadir=config["datadir"],

freqtrade/strategy/hyper.py

@@ -51,10 +51,9 @@ class HyperStrategyMixin:
             for par in self._ft_hyper_params[space].values():
                 yield par.name, par
 
-    def ft_set_special_params_from_file(self) -> None:
+    def ft_load_params_from_file(self) -> None:
         """
-        Sets special parameters (stoploss, roi, trailing, max_open_trades) from the
-        previously loaded file.
+        Load Parameters from parameter file
         Should/must run before config values are loaded in strategy_resolver.
         """
         if self._ft_params_from_file:
@@ -97,7 +96,7 @@ class HyperStrategyMixin:
             params_values = deep_merge_dicts(
                 self._ft_params_from_file.get(space, {}), getattr(self, f"{space}_params", {})
             )
-            self._ft_set_param(self._ft_hyper_params[space], params_values, space, hyperopt)
+            self._ft_load_params(self._ft_hyper_params[space], params_values, space, hyperopt)
 
     def load_params_from_file(self) -> dict:
         filename_str = getattr(self, "__file__", "")
@@ -119,15 +118,12 @@ class HyperStrategyMixin:
 
         return {}
 
-    def _ft_set_param(
+    def _ft_load_params(
         self, params: SpaceParams, param_values: dict, space: str, hyperopt: bool = False
     ) -> None:
         """
         Set optimizable parameter values.
         :param params: Dictionary with new parameter values.
-        :param param_values: Dictionary with values to set.
-        :param space: The space to which the parameters belong.
-        :param hyperopt: Flag indicating if we are in hyperopt mode.
         """
         if not param_values:
             logger.info(f"No params for {space} found, using default values.")

ft_client/freqtrade_client/__init__.py

@@ -1,7 +1,7 @@
 from freqtrade_client.ft_rest_client import FtRestClient
 
 
-__version__ = "2026.5-dev"
+__version__ = "2026.4"
 
 if "dev" in __version__:
     from pathlib import Path

pyproject.toml

@@ -222,7 +222,6 @@ exclude-newer = "1 week"
 
 [tool.uv.exclude-newer-package]
 ccxt = false
-pymdown-extensions = "6 days"
 
 [tool.ruff]
 line-length = 100

requirements-dev.txt

@@ -6,9 +6,9 @@
 -r requirements-freqai-rl.txt
 -r docs/requirements-docs.txt
 
-ruff==0.15.13
-mypy==2.1.0
-pre-commit==4.6.0
+ruff==0.15.11
+mypy==1.20.1
+pre-commit==4.5.1
 pytest==9.0.3
 pytest-asyncio==1.3.0
 pytest-cov==7.1.0
@@ -24,13 +24,13 @@ nbconvert==7.17.1
 
 # mypy types
 scipy-stubs==1.17.1.4  # keep in sync with `scipy` in `requirements-hyperopt.txt`
-types-cachetools==7.0.0.20260503
+types-cachetools==6.2.0.20260408
 types-filelock==3.2.7
-types-requests==2.33.0.20260508
-types-tabulate==0.10.0.20260508
-types-python-dateutil==2.9.0.20260508
+types-requests==2.33.0.20260408
+types-tabulate==0.10.0.20260408
+types-python-dateutil==2.9.0.20260408
 pip-audit==2.10.0
 # For build step in CI
-build==1.5.0
+build==1.4.3
 # For pre-commit-update check
 pyyaml==6.0.3

requirements-freqai-rl.txt

@@ -5,7 +5,7 @@
 torch==2.11.0; sys_platform != 'darwin' or platform_machine != 'x86_64'
 gymnasium==1.2.3
 # SB3 >=2.5.0 depends on torch 2.3.0 - which implies it dropped support x86 macos
-stable-baselines3==2.8.0; sys_platform != 'darwin' or platform_machine != 'x86_64'
-sb3-contrib==2.8.0; sys_platform != 'darwin' or platform_machine != 'x86_64'
+stable_baselines3==2.8.0; sys_platform != 'darwin' or platform_machine != 'x86_64'
+sb3_contrib==2.8.0; sys_platform != 'darwin' or platform_machine != 'x86_64'
 # Progress bar for stable-baselines3 and sb3-contrib
 tqdm==4.67.3

requirements.txt

@@ -1,4 +1,4 @@
-numpy==2.4.5
+numpy==2.4.4
 pandas==3.0.2
 bottleneck==1.6.0
 numexpr==2.14.1
@@ -7,39 +7,39 @@ ft-pandas-ta==0.3.16
 ta-lib==0.6.8
 technical==1.6.0
 
-ccxt==4.5.54
-cryptography==48.0.0
+ccxt==4.5.50
+cryptography==46.0.7
 aiohttp==3.13.5
 SQLAlchemy==2.0.49
 python-telegram-bot==22.7
 # can't be hard-pinned due to telegram-bot pinning httpx with ~
 httpx>=0.24.1
 humanize==4.15.0
-cachetools==7.1.2
+cachetools==7.0.5
 requests==2.33.1
-urllib3==2.7.0
-certifi==2026.4.22
+urllib3==2.6.3
+certifi==2026.2.25
 jsonschema==4.26.0
 tabulate==0.10.0
 pycoingecko==3.2.0
 jinja2==3.1.6
 joblib==1.5.3
 rich==15.0.0
-pyarrow==24.0.0; platform_machine != 'armv7l'
+pyarrow==23.0.1; platform_machine != 'armv7l'
 
 
 # Load ticker files 30% faster
 python-rapidjson==1.23
 # Properly format api responses
-orjson==3.11.9
+orjson==3.11.8
 
 # Notify systemd
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.136.1
-pydantic==2.13.4
-uvicorn==0.46.0
+fastapi==0.136.0
+pydantic==2.13.2
+uvicorn==0.44.0
 pyjwt==2.12.1
 aiofiles==25.1.0
 psutil==7.2.2
@@ -49,7 +49,7 @@ questionary==2.1.1
 prompt-toolkit==3.0.52
 # Extensions to datetime library
 python-dateutil==2.9.0.post0
-pytz==2026.2
+pytz==2026.1.post1
 
 #Futures
 schedule==1.2.2
@@ -59,4 +59,4 @@ websockets==16.0
 janus==2.0.0
 
 ast-comments==1.3.0
-packaging==26.2
+packaging==26.1

scripts/ws_client.py

@@ -190,7 +190,7 @@ class ClientProtocol:
             self.logger.info("Empty DataFrame")
 
     async def _handle_default(self, name, msgtype, data):
-        self.logger.info(f"Unknown message of type {msgtype} received...")
+        self.logger.info("Unknown message of type {msgtype} received...")
         self.logger.info(data)
 
 

tests/exchange_online/test_ccxt_compat.py

@@ -163,11 +163,9 @@ class TestCCXTExchange:
             pytest.skip(f"No sample Balances available for exchange {exchangename}")
 
     def test_ccxt_fetch_tickers(self, exchange: EXCHANGE_FIXTURE_TYPE):
-        exch, exchangename, exchange_params = exchange
+        exch, _, exchange_params = exchange
         pair = exchange_params["pair"]
-        if exchangename == "okx":
-            # TODO: re-enable test once ccxt has this fixed.
-            pytest.skip("OKX fetch_tickers is currently broken, skipping test")
+
         tickers = exch.get_tickers()
         assert pair in tickers
         assert "ask" in tickers[pair]

tests/rpc/test_rpc_apiserver.py

@@ -1471,44 +1471,6 @@ def test_api_historic_balance(botclient, mocker, ticker, fee, markets, is_short)
     assert "total_quote" in resp1["columns"]
 
 
-def test_api_historic_balance_int_bot_managed(botclient, mocker):
-    """
-    read_sql may return the wallet_history `bot_managed` column as an
-    integer (e.g. MySQL/MariaDB TINYINT)
-    """
-    _, client = botclient
-
-    # Single row: with an int64 `bot_managed`
-    one_row = pd.DataFrame(
-        {
-            "timestamp": pd.to_datetime(["2024-01-01"]),
-            "total_quote": [100.0],
-            "bot_managed": [1],
-        }
-    ).astype({"bot_managed": "int64"})
-    mocker.patch("freqtrade.rpc.rpc.read_sql", return_value=one_row)
-    rc = client_get(client, f"{BASE_URI}/historic_balance")
-    assert_response(rc, 200)
-    assert rc.json()["length"] == 1
-    assert rc.json()["data"][0][0] == "2024-01-01T00:00:00"
-    assert rc.json()["data"][0][2] == 100.0
-
-    # Mixed rows: the non-bot-managed row (bot_managed=0) must be excluded.
-    two_rows = pd.DataFrame(
-        {
-            "timestamp": pd.to_datetime(["2024-01-01", "2024-01-02"]),
-            "total_quote": [100.0, 200.0],
-            "bot_managed": [0, 1],
-        }
-    ).astype({"bot_managed": "int64"})
-    mocker.patch("freqtrade.rpc.rpc.read_sql", return_value=two_rows)
-    rc = client_get(client, f"{BASE_URI}/historic_balance")
-    assert_response(rc, 200)
-    assert rc.json()["length"] == 1
-    assert rc.json()["data"][0][0] == "2024-01-02T00:00:00"
-    assert rc.json()["data"][0][2] == 200.0
-
-
 def test_api_performance(botclient, fee):
     ftbot, client = botclient
     patch_get_signal(ftbot)

tests/strategy/test_strategy_loading.py

@@ -531,7 +531,7 @@ def test_strategy_interface_versioning(dataframe_1m, default_conf):
     assert "exit_long" in exitdf
 
 
-def test_strategy_ft_set_special_params_from_file(mocker, default_conf):
+def test_strategy_ft_load_params_from_file(mocker, default_conf):
     default_conf.update({"strategy": "StrategyTestV2"})
     del default_conf["max_open_trades"]
     mocker.patch(

tests/test_misc.py

@@ -230,7 +230,7 @@ def test_deep_merge_dicts():
 def test_dataframe_json(ohlcv_history):
     from pandas.testing import assert_frame_equal
 
-    json = dataframe_to_json(ohlcv_history.copy())
+    json = dataframe_to_json(ohlcv_history)
     dataframe = json_to_dataframe(json)
 
     assert list(ohlcv_history.columns) == list(dataframe.columns)
@@ -238,6 +238,6 @@ def test_dataframe_json(ohlcv_history):
 
     assert_frame_equal(ohlcv_history, dataframe)
     ohlcv_history.at[1, "date"] = pd.NaT
-    json = dataframe_to_json(ohlcv_history.copy())
+    json = dataframe_to_json(ohlcv_history)
 
     dataframe = json_to_dataframe(json)