Merge pull request freqtrade#11953 from freqtrade/drop_3.10

Drop support for python 3.10

Author: xmatthias

.github/workflows/ci.yml

@@ -25,7 +25,7 @@ jobs:
     strategy:
       matrix:
         os: [ "ubuntu-22.04", "ubuntu-24.04" ]
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.11", "3.12", "3.13"]
 
     steps:
     - uses: actions/checkout@v4
@@ -160,7 +160,7 @@ jobs:
     strategy:
       matrix:
         os: [ "macos-14", "macos-15" ]
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.11", "3.12", "3.13"]
 
     steps:
     - uses: actions/checkout@v4
@@ -288,7 +288,7 @@ jobs:
     strategy:
       matrix:
         os: [ windows-latest ]
-        python-version: ["3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.11", "3.12", "3.13"]
 
     steps:
     - uses: actions/checkout@v4

README.md

@@ -64,7 +64,7 @@ Please find the complete documentation on the [freqtrade website](https://www.fr
 
 ## Features
 
-- [x] **Based on Python 3.10+**: For botting on any operating system - Windows, macOS and Linux.
+- [x] **Based on Python 3.11+**: For botting on any operating system - Windows, macOS and Linux.
 - [x] **Persistence**: Persistence is achieved through sqlite.
 - [x] **Dry-run**: Run the bot without paying money.
 - [x] **Backtesting**: Run a simulation of your buy/sell strategy.
@@ -219,7 +219,7 @@ To run this bot we recommend you a cloud instance with a minimum of:
 
 ### Software requirements
 
-- [Python >= 3.10](http://docs.python-guide.org/en/latest/starting/installation/)
+- [Python >= 3.11](http://docs.python-guide.org/en/latest/starting/installation/)
 - [pip](https://pip.pypa.io/en/stable/installing/)
 - [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 - [TA-Lib](https://ta-lib.github.io/ta-lib-python/)

docs/index.md

@@ -87,7 +87,7 @@ To run this bot we recommend you a linux cloud instance with a minimum of:
 
 Alternatively
 
-- Python 3.10+
+- Python 3.11+
 - pip (pip3)
 - git
 - TA-Lib

docs/installation.md

@@ -24,7 +24,7 @@ The easiest way to install and run Freqtrade is to clone the bot Github reposito
     The `stable` branch contains the code of the last release (done usually once per month on an approximately one week old snapshot of the `develop` branch to prevent packaging bugs, so potentially it's more stable).
 
 !!! Note
-    Python3.10 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.  
+    Python3.11 or higher and the corresponding `pip` are assumed to be available. The install-script will warn you and stop if that's not the case. `git` is also needed to clone the Freqtrade repository.  
     Also, python headers (`python<yourversion>-dev` / `python<yourversion>-devel`) must be available for the installation to complete successfully.
 
 !!! Warning "Up-to-date clock"
@@ -42,7 +42,7 @@ These requirements apply to both [Script Installation](#script-installation) and
 
 ### Install guide
 
-* [Python >= 3.10](http://docs.python-guide.org/en/latest/starting/installation/)
+* [Python >= 3.11](http://docs.python-guide.org/en/latest/starting/installation/)
 * [pip](https://pip.pypa.io/en/stable/installing/)
 * [git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
 * [virtualenv](https://virtualenv.pypa.io/en/stable/installation.html) (Recommended)
@@ -54,7 +54,7 @@ We've included/collected install instructions for Ubuntu, MacOS, and Windows. Th
 OS Specific steps are listed first, the common section below is necessary for all systems.
 
 !!! Note
-    Python3.10 or higher and the corresponding pip are assumed to be available.
+    Python3.11 or higher and the corresponding pip are assumed to be available.
 
 === "Debian/Ubuntu"
     #### Install necessary dependencies
@@ -179,7 +179,7 @@ You can as well update, configure and reset the codebase of your bot with `./scr
 ** --install **
 
 With this option, the script will install the bot and most dependencies:
-You will need to have git and python3.10+ installed beforehand for this to work.
+You will need to have git and python3.11+ installed beforehand for this to work.
 
 * Mandatory software as: `ta-lib`
 * Setup your virtualenv under `.venv/`

docs/windows_installation.md

@@ -5,7 +5,7 @@ We **strongly** recommend that Windows users use [Docker](docker_quickstart.md)
 If that is not possible, try using the Windows Linux subsystem (WSL) - for which the Ubuntu instructions should work.
 Otherwise, please follow the instructions below.
 
-All instructions assume that python 3.10+ is installed and available.
+All instructions assume that python 3.11+ is installed and available.
 
 ## Clone the git repository
 
@@ -42,7 +42,7 @@ cd freqtrade
 
 Install ta-lib according to the [ta-lib documentation](https://github.com/TA-Lib/ta-lib-python#windows).
 
-As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), Freqtrade provides these dependencies (in the binary wheel format) for the latest 3 Python versions (3.10, 3.11, 3.12 and 3.13) and for 64bit Windows.
+As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), Freqtrade provides these dependencies (in the binary wheel format) for the latest 3 Python versions (3.11, 3.12 and 3.13) and for 64bit Windows.
 These Wheels are also used by CI running on windows, and are therefore tested together with freqtrade.
 
 Other versions must be downloaded from the above link.

freqtrade/__main__.py

@@ -3,7 +3,7 @@
 __main__.py for Freqtrade
 To launch Freqtrade as a module
 
-> python -m freqtrade (with Python >= 3.10)
+> python -m freqtrade (with Python >= 3.11)
 """
 
 from freqtrade import main

freqtrade/configuration/timerange.py

@@ -4,9 +4,8 @@
 
 import logging
 import re
-from datetime import datetime, timezone
-
-from typing_extensions import Self
+from datetime import UTC, datetime
+from typing import Self
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 from freqtrade.exceptions import ConfigurationError
@@ -151,9 +150,7 @@ def parse_timerange(cls, text: str | None) -> Self:
                     starts = rvals[index]
                     if stype[0] == "date" and len(starts) == 8:
                         start = int(
-                            datetime.strptime(starts, "%Y%m%d")
-                            .replace(tzinfo=timezone.utc)
-                            .timestamp()
+                            datetime.strptime(starts, "%Y%m%d").replace(tzinfo=UTC).timestamp()
                         )
                     elif len(starts) == 13:
                         start = int(starts) // 1000
@@ -164,9 +161,7 @@ def parse_timerange(cls, text: str | None) -> Self:
                     stops = rvals[index]
                     if stype[1] == "date" and len(stops) == 8:
                         stop = int(
-                            datetime.strptime(stops, "%Y%m%d")
-                            .replace(tzinfo=timezone.utc)
-                            .timestamp()
+                            datetime.strptime(stops, "%Y%m%d").replace(tzinfo=UTC).timestamp()
                         )
                     elif len(stops) == 13:
                         stop = int(stops) // 1000

freqtrade/data/btanalysis/bt_fileutils.py

@@ -5,7 +5,7 @@
 import logging
 import zipfile
 from copy import copy
-from datetime import datetime, timezone
+from datetime import UTC, datetime
 from io import BytesIO, StringIO
 from pathlib import Path
 from typing import Any, Literal
@@ -324,7 +324,7 @@ def find_existing_backtest_stats(
 
             if min_backtest_date is not None:
                 backtest_date = strategy_metadata["backtest_start_time"]
-                backtest_date = datetime.fromtimestamp(backtest_date, tz=timezone.utc)
+                backtest_date = datetime.fromtimestamp(backtest_date, tz=UTC)
                 if backtest_date < min_backtest_date:
                     # Do not use a cached result for this strategy as first result is too old.
                     del run_ids[strategy_name]

freqtrade/data/dataprovider.py

@@ -7,7 +7,7 @@
 
 import logging
 from collections import deque
-from datetime import datetime, timezone
+from datetime import UTC, datetime
 from typing import Any
 
 from pandas import DataFrame, Timedelta, Timestamp, to_timedelta
@@ -98,7 +98,7 @@ def _set_cached_df(
         :param candle_type: Any of the enum CandleType (must match trading mode!)
         """
         pair_key = (pair, timeframe, candle_type)
-        self.__cached_pairs[pair_key] = (dataframe, datetime.now(timezone.utc))
+        self.__cached_pairs[pair_key] = (dataframe, datetime.now(UTC))
 
     # For multiple producers we will want to merge the pairlists instead of overwriting
     def _set_producer_pairs(self, pairlist: list[str], producer_name: str = "default"):
@@ -131,7 +131,7 @@ def _emit_df(self, pair_key: PairWithTimeframe, dataframe: DataFrame, new_candle
                 "data": {
                     "key": pair_key,
                     "df": dataframe.tail(1),
-                    "la": datetime.now(timezone.utc),
+                    "la": datetime.now(UTC),
                 },
             }
             self.__rpc.send_msg(msg)
@@ -164,7 +164,7 @@ def _replace_external_df(
         if producer_name not in self.__producer_pairs_df:
             self.__producer_pairs_df[producer_name] = {}
 
-        _last_analyzed = datetime.now(timezone.utc) if not last_analyzed else last_analyzed
+        _last_analyzed = datetime.now(UTC) if not last_analyzed else last_analyzed
 
         self.__producer_pairs_df[producer_name][pair_key] = (dataframe, _last_analyzed)
         logger.debug(f"External DataFrame for {pair_key} from {producer_name} added.")
@@ -275,12 +275,12 @@ def get_producer_df(
         # If we have no data from this Producer yet
         if producer_name not in self.__producer_pairs_df:
             # We don't have this data yet, return empty DataFrame and datetime (01-01-1970)
-            return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+            return (DataFrame(), datetime.fromtimestamp(0, tz=UTC))
 
         # If we do have data from that Producer, but no data on this pair_key
         if pair_key not in self.__producer_pairs_df[producer_name]:
             # We don't have this data yet, return empty DataFrame and datetime (01-01-1970)
-            return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+            return (DataFrame(), datetime.fromtimestamp(0, tz=UTC))
 
         # We have it, return this data
         df, la = self.__producer_pairs_df[producer_name][pair_key]
@@ -396,10 +396,10 @@ def get_analyzed_dataframe(self, pair: str, timeframe: str) -> tuple[DataFrame,
                 if (max_index := self.__slice_index.get(pair)) is not None:
                     df = df.iloc[max(0, max_index - MAX_DATAFRAME_CANDLES) : max_index]
                 else:
-                    return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+                    return (DataFrame(), datetime.fromtimestamp(0, tz=UTC))
             return df, date
         else:
-            return (DataFrame(), datetime.fromtimestamp(0, tz=timezone.utc))
+            return (DataFrame(), datetime.fromtimestamp(0, tz=UTC))
 
     @property
     def runmode(self) -> RunMode:

freqtrade/data/history/datahandlers/idatahandler.py

@@ -8,7 +8,7 @@
 import re
 from abc import ABC, abstractmethod
 from copy import deepcopy
-from datetime import datetime, timezone
+from datetime import UTC, datetime
 from pathlib import Path
 
 from pandas import DataFrame, to_datetime
@@ -118,8 +118,8 @@ def ohlcv_data_min_max(
         df = self._ohlcv_load(pair, timeframe, None, candle_type)
         if df.empty:
             return (
-                datetime.fromtimestamp(0, tz=timezone.utc),
-                datetime.fromtimestamp(0, tz=timezone.utc),
+                datetime.fromtimestamp(0, tz=UTC),
+                datetime.fromtimestamp(0, tz=UTC),
                 0,
             )
         return df.iloc[0]["date"].to_pydatetime(), df.iloc[-1]["date"].to_pydatetime(), len(df)
@@ -201,8 +201,8 @@ def trades_data_min_max(
         df = self._trades_load(pair, trading_mode)
         if df.empty:
             return (
-                datetime.fromtimestamp(0, tz=timezone.utc),
-                datetime.fromtimestamp(0, tz=timezone.utc),
+                datetime.fromtimestamp(0, tz=UTC),
+                datetime.fromtimestamp(0, tz=UTC),
                 0,
             )
         return (