Merge remote-tracking branch 'upstream/develop' into Error-Skipping

.github/workflows/ci.yml

@@ -129,7 +129,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
       matrix:
-        os: [ "macos-latest", "macos-13", "macos-14" ]
+        os: [ "macos-12", "macos-13", "macos-14" ]
         python-version: ["3.9", "3.10", "3.11", "3.12"]
         exclude:
           - os: "macos-14"
@@ -414,7 +414,7 @@ jobs:
         pytest --random-order --longrun --durations 20 -n auto
 
 
-  # Notify only once - when CI completes (and after deploy) in case it's successfull
+  # Notify only once - when CI completes (and after deploy) in case it's successful
   notify-complete:
     needs: [
       build-linux,

.pre-commit-config.yaml

@@ -31,7 +31,7 @@ repos:
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.3.5'
+    rev: 'v0.4.1'
     hooks:
       - id: ruff
 
@@ -54,3 +54,10 @@ repos:
           (?x)^(
                 .*\.md
             )$
+
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.6
+    hooks:
+    - id: codespell
+      additional_dependencies:
+        - tomli

build_helpers/pre_commit_update.py

@@ -1,4 +1,4 @@
-# File used in CI to ensure pre-commit dependencies are kept uptodate.
+# File used in CI to ensure pre-commit dependencies are kept up-to-date.
 
 import sys
 from pathlib import Path

docs/advanced-backtesting.md

@@ -36,7 +36,7 @@ freqtrade backtesting-analysis -c <config.json> --analysis-groups 0 1 2 3 4 5
 ```
 
 This command will read from the last backtesting results. The `--analysis-groups` option is
-used to specify the various tabular outputs showing the profit fo each group or trade,
+used to specify the various tabular outputs showing the profit of each group or trade,
 ranging from the simplest (0) to the most detailed per pair, per buy and per sell tag (4):
 
 * 0: overall winrate and profit summary by enter_tag

docs/backtesting.md

@@ -587,7 +587,7 @@ These precision values are based on current exchange limits (as described in the
 
 ## Improved backtest accuracy
 
-One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or viceversa?).
+One big limitation of backtesting is it's inability to know how prices moved intra-candle (was high before close, or vice-versa?).
 So assuming you run backtesting with a 1h timeframe, there will be 4 prices for that candle (Open, High, Low, Close).
 
 While backtesting does take some assumptions (read above) about this - this can never be perfect, and will always be biased in one way or the other.

docs/configuration.md

@@ -547,7 +547,7 @@ is automatically cancelled by the exchange.
 **PO (Post only):**
 
 Post only order. The order is either placed as a maker order, or it is canceled.
-This means the order must be placed on orderbook for at at least time in an unfilled state.
+This means the order must be placed on orderbook for at least time in an unfilled state.
 
 #### time_in_force config
 

docs/developer.md

@@ -261,7 +261,7 @@ For that reason, they must implement the following methods:
 
 The `until` portion should be calculated using the provided `calculate_lock_end()` method.
 
-All Protections should use `"stop_duration"` / `"stop_duration_candles"` to define how long a a pair (or all pairs) should be locked.
+All Protections should use `"stop_duration"` / `"stop_duration_candles"` to define how long a pair (or all pairs) should be locked.
 The content of this is made available as `self._stop_duration` to the each Protection.
 
 If your protection requires a look-back period, please use `"lookback_period"` / `"lockback_period_candles"` to keep all protections aligned.

docs/edge.md

@@ -137,7 +137,7 @@ $$ R = \frac{\text{average_profit}}{\text{average_loss}} = \frac{\mu_{win}}{\mu_
 
 ### Expectancy
 
-By combining the Win Rate $W$ and and the Risk Reward ratio $R$ to create an expectancy ratio $E$. A expectance ratio is the expected return of the investment made in a trade. We can compute the value of $E$ as follows:
+By combining the Win Rate $W$ and the Risk Reward ratio $R$ to create an expectancy ratio $E$. A expectance ratio is the expected return of the investment made in a trade. We can compute the value of $E$ as follows:
 
 $$E = R * W - L$$
 

docs/faq.md

@@ -2,7 +2,7 @@
 
 ## Supported Markets
 
-Freqtrade supports spot trading, as well as (isolated) futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an uptodate list of supported exchanges.
+Freqtrade supports spot trading, as well as (isolated) futures trading for some selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an up-to-date list of supported exchanges.
 
 ### Can my bot open short positions?
 
@@ -14,7 +14,7 @@ In spot markets, you can in some cases use leveraged spot tokens, which reflect
 
 ### Can my bot trade options or futures?
 
-Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an uptodate list of supported exchanges.
+Futures trading is supported for selected exchanges. Please refer to the [documentation start page](index.md#supported-futures-exchanges-experimental) for an up-to-date list of supported exchanges.
 
 ## Beginner Tips & Tricks
 

docs/freqai-parameter-table.md

@@ -31,7 +31,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `feature_parameters` | A dictionary containing the parameters used to engineer the feature set. Details and examples are shown [here](freqai-feature-engineering.md). <br> **Datatype:** Dictionary.
 | `include_timeframes` | A list of timeframes that all indicators in `feature_engineering_expand_*()` will be created for. The list is added as features to the base indicators dataset. <br> **Datatype:** List of timeframes (strings).
 | `include_corr_pairlist` | A list of correlated coins that FreqAI will add as additional features to all `pair_whitelist` coins. All indicators set in `feature_engineering_expand_*()` during feature engineering (see details [here](freqai-feature-engineering.md)) will be created for each correlated coin. The correlated coins features are added to the base indicators dataset. <br> **Datatype:** List of assets (strings).
-| `label_period_candles` | Number of candles into the future that the labels are created for. This is used in `feature_engineering_expand_all()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). You can create custom labels and choose whether to make use of this parameter or not. <br> **Datatype:** Positive integer.
+| `label_period_candles` | Number of candles into the future that the labels are created for. This can be used in `set_freqai_targets()` (see `templates/FreqaiExampleStrategy.py` for detailed usage). This parameter is not necessarily required, you can create custom labels and choose whether to make use of this parameter or not. Please see `templates/FreqaiExampleStrategy.py` to see the example usage. <br> **Datatype:** Positive integer.
 | `include_shifted_candles` | Add features from previous candles to subsequent candles with the intent of adding historical information. If used, FreqAI will duplicate and shift all features from the `include_shifted_candles` previous candles so that the information is available for the subsequent candle. <br> **Datatype:** Positive integer.
 | `weight_factor` | Weight training data points according to their recency (see details [here](freqai-feature-engineering.md#weighting-features-for-temporal-importance)). <br> **Datatype:** Positive float (typically < 1).
 | `indicator_max_period_candles` | **No longer used (#7325)**. Replaced by `startup_candle_count` which is set in the [strategy](freqai-configuration.md#building-a-freqai-strategy). `startup_candle_count` is timeframe independent and defines the maximum *period* used in `feature_engineering_*()` for indicator creation. FreqAI uses this parameter together with the maximum timeframe in `include_time_frames` to calculate how many data points to download such that the first data point does not include a NaN. <br> **Datatype:** Positive integer.
@@ -55,7 +55,7 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 |  |  **Data split parameters within the `freqai.data_split_parameters` sub dictionary**
 | `data_split_parameters` | Include any additional parameters available from scikit-learn `test_train_split()`, which are shown [here](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html) (external website). <br> **Datatype:** Dictionary.
 | `test_size` | The fraction of data that should be used for testing instead of training. <br> **Datatype:** Positive float < 1.
-| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Defaut: `False`.
+| `shuffle` | Shuffle the training data points during training. Typically, to not remove the chronological order of data in time-series forecasting, this is set to `False`. <br> **Datatype:** Boolean. <br> Default: `False`.
 
 ### Model training parameters
 

docs/requirements-docs.txt

@@ -1,6 +1,6 @@
 markdown==3.6
 mkdocs==1.5.3
-mkdocs-material==9.5.17
+mkdocs-material==9.5.18
 mdx_truly_sane_lists==1.3
-pymdown-extensions==10.7.1
+pymdown-extensions==10.8
 jinja2==3.1.3

docs/rest-api.md

@@ -89,7 +89,8 @@ Make sure that the following 2 lines are available in your docker-compose file:
 ```
 
 !!! Danger "Security warning"
-    By using `8080:8080` in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
+    By using `"8080:8080"` (or `"0.0.0.0:8080:8080"`) in the docker port mapping, the API will be available to everyone connecting to the server under the correct port, so others may be able to control your bot.
+    This **may** be safe if you're running the bot in a secure environment (like your home network), but it's not recommended to expose the API to the internet.
 
 ## Rest API
 
@@ -454,7 +455,7 @@ To properly configure your reverse proxy (securely), please consult it's documen
 - **Caddy**: Caddy v2 supports websockets out of the box, see the [documentation](https://caddyserver.com/docs/v2-upgrade#proxy)
 
 !!! Tip "SSL certificates"
-    You can use tools like certbot to setup ssl certificates to access your bot's UI through encrypted connection by using any fo the above reverse proxies.
+    You can use tools like certbot to setup ssl certificates to access your bot's UI through encrypted connection by using any of the above reverse proxies.
     While this will protect your data in transit, we do not recommend to run the freqtrade API outside of your private network (VPN, SSH tunnel).
 
 ### OpenAPI interface

docs/stoploss.md

@@ -240,7 +240,7 @@ When using leverage, the same principle is applied - with stoploss defining the
 
 Therefore, a stoploss of 10% on a 10x trade would trigger on a 1% price move.
 If your stake amount (own capital) was 100$ - this trade would be 1000$ at 10x (after leverage).
-If price moves 1% - you've lost 10$ of your own capital - therfore stoploss will trigger in this case.
+If price moves 1% - you've lost 10$ of your own capital - therefore stoploss will trigger in this case.
 
 Make sure to be aware of this, and avoid using too tight stoploss (at 10x leverage, 10% risk may be too little to allow the trade to "breath" a little).
 

docs/strategy-advanced.md

@@ -326,4 +326,4 @@ for val in self.buy_ema_short.range:
 dataframe = pd.concat(frames, axis=1)
 ```
 
-Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existant.
+Freqtrade does however also counter this by running `dataframe.copy()` on the dataframe right after the `populate_indicators()` method - so performance implications of this should be low to non-existent.

docs/strategy-customization.md

@@ -551,8 +551,8 @@ for more information.
     
         # Define BTC/STAKE informative pair. A custom formatter may be specified for formatting
         # column names. A callable `fmt(**kwargs) -> str` may be specified, to implement custom
-        # formatting. Available in populate_indicators and other methods as 'rsi_upper'.
+        # formatting. Available in populate_indicators and other methods as 'rsi_upper_1h'.
-        @informative('1h', 'BTC/{stake}', '{column}')
+        @informative('1h', 'BTC/{stake}', '{column}_{timeframe}')
         def populate_indicators_btc_1h_2(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
             dataframe['rsi_upper'] = ta.RSI(dataframe, timeperiod=14)
             return dataframe
@@ -776,7 +776,7 @@ The orderbook structure is aligned with the order structure from [ccxt](https://
 Therefore, using `ob['bids'][0][0]` as demonstrated above will result in using the best bid price. `ob['bids'][0][1]` would look at the amount at this orderbook position.
 
 !!! Warning "Warning about backtesting"
-    The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return uptodate values.
+    The order book is not part of the historic data which means backtesting and hyperopt will not work correctly if this method is used, as the method will return up-to-date values.
 
 ### *ticker(pair)*
 

docs/trade-object.md

@@ -126,7 +126,7 @@ An `Order` object will always be tied to it's corresponding [`Trade`](#trade-obj
 ### Order - Available attributes
 
 an Order object is typically attached to a trade.
-Most properties here can be None as they are dependant on the exchange response.
+Most properties here can be None as they are dependent on the exchange response.
 
 |  Attribute | DataType | Description |
 |------------|-------------|-------------|
@@ -141,7 +141,7 @@ Most properties here can be None as they are dependant on the exchange response.
 `amount` | float | Amount in base currency
 `filled` | float | Filled amount (in base currency)
 `remaining` | float | Remaining amount
-`cost` | float | Cost of the order - usually average * filled (*Exchange dependant on futures, may contain the cost with or without leverage and may be in contracts.*)
+`cost` | float | Cost of the order - usually average * filled (*Exchange dependent on futures, may contain the cost with or without leverage and may be in contracts.*)
 `stake_amount` | float | Stake amount used for this order. *Added in 2023.7.*
 `order_date` | datetime | Order creation date **use `order_date_utc` instead**
 `order_date_utc` | datetime | Order creation date (in UTC)

freqtrade/commands/deploy_commands.py

@@ -16,6 +16,10 @@ from freqtrade.util import render_template, render_template_with_fallback
 logger = logging.getLogger(__name__)
 
 
+# Timeout for requests
+req_timeout = 30
+
+
 def start_create_userdir(args: Dict[str, Any]) -> None:
     """
     Create "user_data" directory to contain user data strategies, hyperopt, ...)
@@ -119,7 +123,7 @@ def download_and_install_ui(dest_folder: Path, dl_url: str, version: str):
     from zipfile import ZipFile
 
     logger.info(f"Downloading {dl_url}")
-    resp = requests.get(dl_url).content
+    resp = requests.get(dl_url, timeout=req_timeout).content
     dest_folder.mkdir(parents=True, exist_ok=True)
     with ZipFile(BytesIO(resp)) as zf:
         for fn in zf.filelist:
@@ -137,7 +141,7 @@ def get_ui_download_url(version: Optional[str] = None) -> Tuple[str, str]:
     base_url = 'https://api.github.com/repos/freqtrade/frequi/'
     # Get base UI Repo path
 
-    resp = requests.get(f"{base_url}releases")
+    resp = requests.get(f"{base_url}releases", timeout=req_timeout)
     resp.raise_for_status()
     r = resp.json()
 
@@ -158,7 +162,7 @@ def get_ui_download_url(version: Optional[str] = None) -> Tuple[str, str]:
     # URL not found - try assets url
     if not dl_url:
         assets = r[0]['assets_url']
-        resp = requests.get(assets)
+        resp = requests.get(assets, timeout=req_timeout)
         r = resp.json()
         dl_url = r[0]['browser_download_url']
 

freqtrade/configuration/configuration.py

@@ -202,7 +202,7 @@ class Configuration:
 
         if self.args.get('show_sensitive'):
             logger.warning(
-                "Sensitive information will be shown in the upcomming output. "
+                "Sensitive information will be shown in the upcoming output. "
                 "Please make sure to never share this output without redacting "
                 "the information yourself.")
 

freqtrade/data/btanalysis.py

@@ -238,6 +238,16 @@ def update_backtest_metadata(filename: Path, strategy: str, content: Dict[str, A
     file_dump_json(get_backtest_metadata_filename(filename), metadata)
 
 
+def get_backtest_market_change(filename: Path, include_ts: bool = True) -> pd.DataFrame:
+    """
+    Read backtest market change file.
+    """
+    df = pd.read_feather(filename)
+    if include_ts:
+        df.loc[:, '__date_ts'] = df.loc[:, 'date'].astype(np.int64) // 1000 // 1000
+    return df
+
+
 def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
                                  min_backtest_date: Optional[datetime] = None) -> Dict[str, Any]:
     """

freqtrade/data/dataprovider.py

@@ -523,7 +523,7 @@ class DataProvider:
         Send custom RPC Notifications from your bot.
         Will not send any bot in modes other than Dry-run or Live.
         :param message: Message to be sent. Must be below 4096.
-        :param always_send: If False, will send the message only once per candle, and surpress
+        :param always_send: If False, will send the message only once per candle, and suppress
                             identical messages.
                             Careful as this can end up spaming your chat.
                             Defaults to False

freqtrade/data/history/datahandlers/idatahandler.py

@@ -302,8 +302,8 @@ class IDataHandler(ABC):
         Rebuild pair name from filename
         Assumes a asset name of max. 7 length to also support BTC-PERP and BTC-PERP:USD names.
         """
-        res = re.sub(r'^(([A-Za-z\d]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, 1)
+        res = re.sub(r'^(([A-Za-z\d]{1,10})|^([A-Za-z\-]{1,6}))(_)', r'\g<1>/', pair, count=1)
-        res = re.sub('_', ':', res, 1)
+        res = re.sub('_', ':', res, count=1)
         return res
 
     def ohlcv_load(self, pair, timeframe: str,

freqtrade/data/metrics.py

@@ -30,8 +30,25 @@ def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close"
     return float(np.mean(tmp_means))
 
 
-def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
+def combine_dataframes_by_column(
-                                 column: str = "close") -> pd.DataFrame:
+        data: Dict[str, pd.DataFrame], column: str = "close") -> pd.DataFrame:
+    """
+    Combine multiple dataframes "column"
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return: DataFrame with the column renamed to the dict key.
+    :raise: ValueError if no data is provided.
+    """
+    if not data:
+        raise ValueError("No data provided.")
+    df_comb = pd.concat([data[pair].set_index('date').rename(
+        {column: pair}, axis=1)[pair] for pair in data], axis=1)
+    return df_comb
+
+
+def combined_dataframes_with_rel_mean(
+        data: Dict[str, pd.DataFrame], fromdt: datetime, todt: datetime,
+        column: str = "close") -> pd.DataFrame:
     """
     Combine multiple dataframes "column"
     :param data: Dict of Dataframes, dict key should be pair.
@@ -40,8 +57,26 @@ def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
         named mean, containing the mean of all pairs.
     :raise: ValueError if no data is provided.
     """
-    df_comb = pd.concat([data[pair].set_index('date').rename(
+    df_comb = combine_dataframes_by_column(data, column)
-        {column: pair}, axis=1)[pair] for pair in data], axis=1)
+    # Trim dataframes to the given timeframe
+    df_comb = df_comb.iloc[(df_comb.index >= fromdt) & (df_comb.index < todt)]
+    df_comb['count'] = df_comb.count(axis=1)
+    df_comb['mean'] = df_comb.mean(axis=1)
+    df_comb['rel_mean'] = df_comb['mean'].pct_change().fillna(0).cumsum()
+    return df_comb[['mean', 'rel_mean', 'count']]
+
+
+def combine_dataframes_with_mean(
+        data: Dict[str, pd.DataFrame], column: str = "close") -> pd.DataFrame:
+    """
+    Combine multiple dataframes "column"
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return: DataFrame with the column renamed to the dict key, and a column
+        named mean, containing the mean of all pairs.
+    :raise: ValueError if no data is provided.
+    """
+    df_comb = combine_dataframes_by_column(data, column)
 
     df_comb['mean'] = df_comb.mean(axis=1)
 

freqtrade/exchange/__init__.py

@@ -25,6 +25,7 @@ from freqtrade.exchange.exchange_utils_timeframe import (timeframe_to_minutes, t
 from freqtrade.exchange.gate import Gate
 from freqtrade.exchange.hitbtc import Hitbtc
 from freqtrade.exchange.htx import Htx
+from freqtrade.exchange.idex import Idex
 from freqtrade.exchange.kraken import Kraken
 from freqtrade.exchange.kucoin import Kucoin
 from freqtrade.exchange.okx import Okx

freqtrade/exchange/bybit.py

@@ -239,7 +239,7 @@ class Bybit(Exchange):
 
         return orders
 
-    def fetch_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def fetch_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         order = super().fetch_order(order_id, pair, params)
         if (
             order.get('status') == 'canceled'

freqtrade/exchange/exchange.py

@@ -44,7 +44,7 @@ from freqtrade.misc import (chunks, deep_merge_dicts, file_dump_json, file_load_
                             safe_value_fallback2)
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.util import dt_from_ts, dt_now
-from freqtrade.util.datetime_helpers import dt_humanize, dt_ts
+from freqtrade.util.datetime_helpers import dt_humanize_delta, dt_ts
 from freqtrade.util.periodic_cache import PeriodicCache
 
 
@@ -239,8 +239,8 @@ class Exchange:
         self.validate_pricing(config['exit_pricing'])
         self.validate_pricing(config['entry_pricing'])
 
-    def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt,
+    def _init_ccxt(self, exchange_config: Dict[str, Any], ccxt_module: CcxtModuleType = ccxt, *,
-                   ccxt_kwargs: Dict = {}) -> ccxt.Exchange:
+                   ccxt_kwargs: Dict) -> ccxt.Exchange:
         """
         Initialize ccxt with given config and return valid
         ccxt instance.
@@ -348,10 +348,13 @@ class Exchange:
         return int(self._ft_has.get('ohlcv_candle_limit_per_timeframe', {}).get(
             timeframe, self._ft_has.get('ohlcv_candle_limit')))
 
-    def get_markets(self, base_currencies: List[str] = [], quote_currencies: List[str] = [],
+    def get_markets(
-                    spot_only: bool = False, margin_only: bool = False, futures_only: bool = False,
+            self,
-                    tradable_only: bool = True,
+            base_currencies: Optional[List[str]] = None,
-                    active_only: bool = False) -> Dict[str, Any]:
+            quote_currencies: Optional[List[str]] = None,
+            spot_only: bool = False, margin_only: bool = False, futures_only: bool = False,
+            tradable_only: bool = True,
+            active_only: bool = False) -> Dict[str, Any]:
         """
         Return exchange ccxt markets, filtered out by base currency and quote currency
         if this was requested in parameters.
@@ -758,7 +761,7 @@ class Exchange:
 
     def price_get_one_pip(self, pair: str, price: float) -> float:
         """
-        Get's the "1 pip" value for this pair.
+        Gets the "1 pip" value for this pair.
         Used in PriceFilter to calculate the 1pip movements.
         """
         precision = self.markets[pair]['precision']['price']
@@ -848,7 +851,7 @@ class Exchange:
     # Dry-run methods
 
     def create_dry_run_order(self, pair: str, ordertype: str, side: str, amount: float,
-                             rate: float, leverage: float, params: Dict = {},
+                             rate: float, leverage: float, params: Optional[Dict] = None,
                              stop_loss: bool = False) -> Dict[str, Any]:
         now = dt_now()
         order_id = f'dry_run_{side}_{pair}_{now.timestamp()}'
@@ -1297,9 +1300,11 @@ class Exchange:
             raise OperationalException(e) from e
 
     @retrier(retries=API_FETCH_ORDER_RETRY_COUNT)
-    def fetch_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def fetch_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         if self._config['dry_run']:
             return self.fetch_dry_run_order(order_id)
+        if params is None:
+            params = {}
         try:
             if not self.exchange_has('fetchOrder'):
                 return self.fetch_order_emulated(order_id, pair, params)
@@ -1321,7 +1326,7 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
-    def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def fetch_stoploss_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         return self.fetch_order(order_id, pair, params)
 
     def fetch_order_or_stoploss_order(self, order_id: str, pair: str,
@@ -1347,7 +1352,7 @@ class Exchange:
                 and order.get('filled') == 0.0)
 
     @retrier
-    def cancel_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def cancel_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         if self._config['dry_run']:
             try:
                 order = self.fetch_dry_run_order(order_id)
@@ -1357,6 +1362,8 @@ class Exchange:
             except InvalidOrderException:
                 return {}
 
+        if params is None:
+            params = {}
         try:
             order = self._api.cancel_order(order_id, pair, params=params)
             self._log_exchange_response('cancel_order', order)
@@ -1373,7 +1380,8 @@ class Exchange:
         except ccxt.BaseError as e:
             raise OperationalException(e) from e
 
-    def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def cancel_stoploss_order(
+            self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         return self.cancel_order(order_id, pair, params)
 
     def is_cancel_order_result_suitable(self, corder) -> bool:
@@ -2000,14 +2008,14 @@ class Exchange:
         logger.debug(
             "one_call: %s msecs (%s)",
             one_call,
-            dt_humanize(dt_now() - timedelta(milliseconds=one_call), only_distance=True)
+            dt_humanize_delta(dt_now() - timedelta(milliseconds=one_call))
         )
         input_coroutines = [self._async_get_candle_history(
             pair, timeframe, candle_type, since) for since in
             range(since_ms, until_ms or dt_ts(), one_call)]
 
         data: List = []
-        # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
+        # Chunk requests into batches of 100 to avoid overwhelming ccxt Throttling
         for input_coro in chunks(input_coroutines, 100):
 
             results = await asyncio.gather(*input_coro, return_exceptions=True)
@@ -2124,7 +2132,7 @@ class Exchange:
         Only used in the dataprovider.refresh() method.
         :param pair_list: List of 2 element tuples containing pair, interval to refresh
         :param since_ms: time since when to download, in milliseconds
-        :param cache: Assign result to _klines. Usefull for one-off downloads like for pairlists
+        :param cache: Assign result to _klines. Useful for one-off downloads like for pairlists
         :param drop_incomplete: Control candle dropping.
             Specifying None defaults to _ohlcv_partial_candle
         :return: Dict of [{(pair, timeframe): Dataframe}]
@@ -2135,7 +2143,7 @@ class Exchange:
         input_coroutines, cached_pairs = self._build_ohlcv_dl_jobs(pair_list, since_ms, cache)
 
         results_df = {}
-        # Chunk requests into batches of 100 to avoid overwelming ccxt Throttling
+        # Chunk requests into batches of 100 to avoid overwhelming ccxt Throttling
         for input_coro in chunks(input_coroutines, 100):
             async def gather_stuff():
                 return await asyncio.gather(*input_coro, return_exceptions=True)
@@ -2295,7 +2303,7 @@ class Exchange:
                                   since: Optional[int] = None,
                                   params: Optional[dict] = None) -> Tuple[List[List], Any]:
         """
-        Asyncronously gets trade history using fetch_trades.
+        Asynchronously gets trade history using fetch_trades.
         Handles exchange errors, does one call to the exchange.
         :param pair: Pair to fetch trade data for
         :param since: Since as integer timestamp in milliseconds
@@ -2352,7 +2360,7 @@ class Exchange:
                                           since: Optional[int] = None,
                                           from_id: Optional[str] = None) -> Tuple[str, List[List]]:
         """
-        Asyncronously gets trade history using fetch_trades
+        Asynchronously gets trade history using fetch_trades
         use this when exchange uses id-based iteration (check `self._trades_pagination`)
         :param pair: Pair to fetch trade data for
         :param since: Since as integer timestamp in milliseconds
@@ -2403,7 +2411,7 @@ class Exchange:
     async def _async_get_trade_history_time(self, pair: str, until: int,
                                             since: Optional[int] = None) -> Tuple[str, List[List]]:
         """
-        Asyncronously gets trade history using fetch_trades,
+        Asynchronously gets trade history using fetch_trades,
         when the exchange uses time-based iteration (check `self._trades_pagination`)
         :param pair: Pair to fetch trade data for
         :param since: Since as integer timestamp in milliseconds
@@ -2786,7 +2794,7 @@ class Exchange:
 
     @retrier
     def set_margin_mode(self, pair: str, margin_mode: MarginMode, accept_fail: bool = False,
-                        params: dict = {}):
+                        params: Optional[Dict] = None):
         """
         Set's the margin mode on the exchange to cross or isolated for a specific pair
         :param pair: base/quote currency pair (e.g. "ADA/USDT")
@@ -2795,6 +2803,8 @@ class Exchange:
             # Some exchanges only support one margin_mode type
             return
 
+        if params is None:
+            params = {}
         try:
             res = self._api.set_margin_mode(margin_mode.value, pair, params)
             self._log_exchange_response('set_margin_mode', res)

freqtrade/exchange/gate.py

@@ -79,7 +79,7 @@ class Gate(Exchange):
             # As such, futures orders on gate will not contain a fee, which causes
             # a repeated "update fee" cycle and wrong calculations.
             # Therefore we patch the response with fees if it's not available.
-            # An alternative also contianing fees would be
+            # An alternative also containing fees would be
             # privateFuturesGetSettleAccountBook({"settle": "usdt"})
             pair_fees = self._trading_fees.get(pair, {})
             if pair_fees:
@@ -98,7 +98,7 @@ class Gate(Exchange):
     def get_order_id_conditional(self, order: Dict[str, Any]) -> str:
         return safe_value_fallback2(order, order, 'id_stop', 'id')
 
-    def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def fetch_stoploss_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         order = self.fetch_order(
             order_id=order_id,
             pair=pair,
@@ -119,7 +119,8 @@ class Gate(Exchange):
                 return order1
         return order
 
-    def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def cancel_stoploss_order(
+            self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         return self.cancel_order(
             order_id=order_id,
             pair=pair,

freqtrade/exchange/idex.py

@@ -0,0 +1,19 @@
+""" Idex exchange subclass """
+import logging
+from typing import Dict
+
+from freqtrade.exchange import Exchange
+
+
+logger = logging.getLogger(__name__)
+
+
+class Idex(Exchange):
+    """
+    Idex exchange class. Contains adjustments needed for Freqtrade to work
+    with this exchange.
+    """
+
+    _ft_has: Dict = {
+        "ohlcv_candle_limit": 1000,
+    }

freqtrade/exchange/okx.py

@@ -56,7 +56,7 @@ class Okx(Exchange):
         """
         Exchange ohlcv candle limit
         OKX has the following behaviour:
-        * 300 candles for uptodate data
+        * 300 candles for up-to-date data
         * 100 candles for historic data
         * 100 candles for additional candles (not futures or spot).
         :param timeframe: Timeframe to check
@@ -202,7 +202,7 @@ class Okx(Exchange):
         order['type'] = 'stoploss'
         return order
 
-    def fetch_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def fetch_stoploss_order(self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         if self._config['dry_run']:
             return self.fetch_dry_run_order(order_id)
 
@@ -232,7 +232,8 @@ class Okx(Exchange):
             return safe_value_fallback2(order, order, 'id_stop', 'id')
         return order['id']
 
-    def cancel_stoploss_order(self, order_id: str, pair: str, params: Dict = {}) -> Dict:
+    def cancel_stoploss_order(
+            self, order_id: str, pair: str, params: Optional[Dict] = None) -> Dict:
         params1 = {'stop': True}
         # 'ordType': 'conditional'
         #

freqtrade/freqai/RL/BaseEnvironment.py

@@ -222,7 +222,7 @@ class BaseEnvironment(gym.Env):
     @abstractmethod
     def step(self, action: int):
         """
-        Step depeneds on action types, this must be inherited.
+        Step depends on action types, this must be inherited.
         """
         return
 
@@ -326,7 +326,7 @@ class BaseEnvironment(gym.Env):
 
     def _update_unrealized_total_profit(self):
         """
-        Update the unrealized total profit incase of episode end.
+        Update the unrealized total profit in case of episode end.
         """
         if self._position in (Positions.Long, Positions.Short):
             pnl = self.get_unrealized_profit()
@@ -357,7 +357,7 @@ class BaseEnvironment(gym.Env):
         """
         return self.actions
 
-    # Keeping around incase we want to start building more complex environment
+    # Keeping around in case we want to start building more complex environment
     # templates in the future.
     # def most_recent_return(self):
     #     """

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -311,7 +311,7 @@ class BaseReinforcementLearningModel(IFreqaiModel):
             if not prices_train_old.empty:
                 prices_train = prices_train_old
                 rename_dict = rename_dict_old
-            logger.warning('Reinforcement learning module didnt find the correct raw prices '
+            logger.warning('Reinforcement learning module didn\'t find the correct raw prices '
                            'assigned in feature_engineering_standard(). '
                            'Please assign them with:\n'
                            'dataframe["%-raw_close"] = dataframe["close"]\n'
@@ -458,7 +458,7 @@ def make_env(MyRLEnv: Type[BaseEnvironment], env_id: str, rank: int,
 
     :param env_id: (str) the environment ID
     :param num_env: (int) the number of environment you wish to have in subprocesses
-    :param seed: (int) the inital seed for RNG
+    :param seed: (int) the initial seed for RNG
     :param rank: (int) index of the subprocess
     :param env_info: (dict) all required arguments to instantiate the environment.
     :return: (Callable)

freqtrade/freqai/data_drawer.py

@@ -280,7 +280,7 @@ class FreqaiDataDrawer:
 
         new_pred = pred_df.copy()
         # set new_pred values to nans (we want to signal to user that there was nothing
-        # historically made during downtime. The newest pred will get appeneded later in
+        # historically made during downtime. The newest pred will get appended later in
         # append_model_predictions)
 
         new_pred["date_pred"] = dataframe["date"]

freqtrade/freqai/data_kitchen.py

@@ -612,7 +612,7 @@ class FreqaiDataKitchen:
         pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
 
         for pair in pairs:
-            pair = pair.replace(':', '')  # lightgbm doesnt like colons
+            pair = pair.replace(':', '')  # lightgbm does not like colons
             pair_cols = [col for col in dataframe.columns if col.startswith("%")
                          and f"{pair}_" in col]
 
@@ -638,7 +638,7 @@ class FreqaiDataKitchen:
         pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
         current_pair = current_pair.replace(':', '')
         for pair in pairs:
-            pair = pair.replace(':', '')  # lightgbm doesnt work with colons
+            pair = pair.replace(':', '')  # lightgbm does not work with colons
             if current_pair != pair:
                 dataframe = dataframe.merge(corr_dataframes[pair], how='left', on='date')
 
@@ -841,7 +841,7 @@ class FreqaiDataKitchen:
             f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
             self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
 
-        # incase targets are classifications
+        # in case targets are classifications
         for label in self.unique_class_list:
             self.data["labels_mean"][label], self.data["labels_std"][label] = 0, 0
 

freqtrade/freqai/freqai_interface.py

@@ -221,7 +221,7 @@ class IFreqaiModel(ABC):
             time.sleep(1)
             pair = self.train_queue[0]
 
-            # ensure pair is avaialble in dp
+            # ensure pair is available in dp
             if pair not in strategy.dp.current_whitelist():
                 self.train_queue.popleft()
                 logger.warning(f'{pair} not in current whitelist, removing from train queue.')
@@ -722,9 +722,6 @@ class IFreqaiModel(ABC):
             if self.pair_it == self.total_pairs:
                 logger.info(
                     f'Total time spent inferencing pairlist {self.inference_time:.2f} seconds')
-                if self.inference_time > 0.25 * self.base_tf_seconds:
-                    logger.warning("Inference took over 25% of the candle time. Reduce pairlist to"
-                                   " avoid blinding open trades and degrading performance.")
                 self.pair_it = 0
                 self.inference_time = 0
         return

freqtrade/freqai/prediction_models/PyTorchMLPClassifier.py

@@ -74,7 +74,7 @@ class PyTorchMLPClassifier(BasePyTorchClassifier):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.CrossEntropyLoss()
-        # check if continual_learning is activated, and retreive the model to continue training
+        # check if continual_learning is activated, and retrieve the model to continue training
         trainer = self.get_init_model(dk.pair)
         if trainer is None:
             trainer = PyTorchModelTrainer(

freqtrade/freqai/prediction_models/PyTorchMLPRegressor.py

@@ -69,7 +69,7 @@ class PyTorchMLPRegressor(BasePyTorchRegressor):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.MSELoss()
-        # check if continual_learning is activated, and retreive the model to continue training
+        # check if continual_learning is activated, and retrieve the model to continue training
         trainer = self.get_init_model(dk.pair)
         if trainer is None:
             trainer = PyTorchModelTrainer(

freqtrade/freqai/prediction_models/PyTorchTransformerRegressor.py

@@ -80,7 +80,7 @@ class PyTorchTransformerRegressor(BasePyTorchRegressor):
         model.to(self.device)
         optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
         criterion = torch.nn.MSELoss()
-        # check if continual_learning is activated, and retreive the model to continue training
+        # check if continual_learning is activated, and retrieve the model to continue training
         trainer = self.get_init_model(dk.pair)
         if trainer is None:
             trainer = PyTorchTransformerTrainer(

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -63,6 +63,6 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
                                                                is_masking_supported(self.eval_env)))
 
         # TENSORBOARD CALLBACK DOES NOT RECOMMENDED TO USE WITH MULTIPLE ENVS,
-        # IT WILL RETURN FALSE INFORMATIONS, NEVERTHLESS NOT THREAD SAFE WITH SB3!!!
+        # IT WILL RETURN FALSE INFORMATION, NEVERTHELESS NOT THREAD SAFE WITH SB3!!!
         actions = self.train_env.env_method("get_actions")[0]
         self.tensorboard_callback = TensorboardCallback(verbose=1, actions=actions)

freqtrade/freqai/torch/PyTorchModelTrainer.py

@@ -38,7 +38,7 @@ class PyTorchModelTrainer(PyTorchTrainerInterface):
         :param init_model: A dictionary containing the initial model/optimizer
             state_dict and model_meta_data saved by self.save() method.
         :param model_meta_data: Additional metadata about the model (optional).
-        :param data_convertor: convertor from pd.DataFrame to torch.tensor.
+        :param data_convertor: converter from pd.DataFrame to torch.tensor.
         :param n_steps: used to calculate n_epochs. The number of training iterations to run.
             iteration here refers to the number of times optimizer.step() is called.
             ignored if n_epochs is set.

freqtrade/freqai/utils.py

@@ -178,7 +178,7 @@ def record_params(config: Dict[str, Any], full_path: Path) -> None:
 
 def get_timerange_backtest_live_models(config: Config) -> str:
     """
-    Returns a formated timerange for backtest live/ready models
+    Returns a formatted timerange for backtest live/ready models
     :param config: Configuration dictionary
 
     :return: a string timerange (format example: '20220801-20220822')

freqtrade/freqtradebot.py

@@ -37,6 +37,7 @@ from freqtrade.rpc.rpc_types import (ProfitLossStr, RPCCancelMsg, RPCEntryMsg, R
                                      RPCExitMsg, RPCProtectionMsg)
 from freqtrade.strategy.interface import IStrategy
 from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
+from freqtrade.util import MeasureTime
 from freqtrade.util.migrations import migrate_binance_futures_names
 from freqtrade.wallets import Wallets
 
@@ -64,7 +65,7 @@ class FreqtradeBot(LoggingMixin):
         # Init objects
         self.config = config
         exchange_config: ExchangeConfig = deepcopy(config['exchange'])
-        # Remove credentials from original exchange config to avoid accidental credentail exposure
+        # Remove credentials from original exchange config to avoid accidental credential exposure
         remove_exchange_credentials(config['exchange'], True)
 
         self.strategy: IStrategy = StrategyResolver.load_strategy(self.config)
@@ -117,7 +118,8 @@ class FreqtradeBot(LoggingMixin):
 
         # Protect exit-logic from forcesell and vice versa
         self._exit_lock = Lock()
-        LoggingMixin.__init__(self, logger, timeframe_to_seconds(self.strategy.timeframe))
+        timeframe_secs = timeframe_to_seconds(self.strategy.timeframe)
+        LoggingMixin.__init__(self, logger, timeframe_secs)
 
         self._schedule = Scheduler()
 
@@ -139,6 +141,16 @@ class FreqtradeBot(LoggingMixin):
         # Initialize protections AFTER bot start - otherwise parameters are not loaded.
         self.protections = ProtectionManager(self.config, self.strategy.protections)
 
+        def log_took_too_long(duration: float, time_limit: float):
+            logger.warning(
+                f"Strategy analysis took {duration:.2f}, which is 25% of the timeframe. "
+                "This can lead to delayed orders and missed signals."
+                "Consider either reducing the amount of work your strategy performs "
+                "or reduce the amount of pairs in the Pairlist."
+            )
+
+        self._measure_execution = MeasureTime(log_took_too_long, timeframe_secs * 0.25)
+
     def notify_status(self, msg: str, msg_type=RPCMessageType.STATUS) -> None:
         """
         Public method for users of this class (worker, etc.) to send notifications
@@ -175,7 +187,7 @@ class FreqtradeBot(LoggingMixin):
         try:
             Trade.commit()
         except Exception:
-            # Exeptions here will be happening if the db disappeared.
+            # Exceptions here will be happening if the db disappeared.
             # At which point we can no longer commit anyway.
             pass
 
@@ -223,10 +235,11 @@ class FreqtradeBot(LoggingMixin):
         strategy_safe_wrapper(self.strategy.bot_loop_start, supress_error=True)(
             current_time=datetime.now(timezone.utc))
 
-        self.strategy.analyze(self.active_pair_whitelist)
+        with self._measure_execution:
+            self.strategy.analyze(self.active_pair_whitelist)
 
         with self._exit_lock:
-            # Check for exchange cancelations, timeouts and user requested replace
+            # Check for exchange cancellations, timeouts and user requested replace
             self.manage_open_orders()
 
         # Protect from collisions with force_exit.
@@ -277,7 +290,7 @@ class FreqtradeBot(LoggingMixin):
             }
             self.rpc.send_msg(msg)
 
-    def _refresh_active_whitelist(self, trades: List[Trade] = []) -> List[str]:
+    def _refresh_active_whitelist(self, trades: Optional[List[Trade]] = None) -> List[str]:
         """
         Refresh active whitelist from pairlist or edge and extend it with
         pairs that have open trades.
@@ -449,6 +462,7 @@ class FreqtradeBot(LoggingMixin):
                 trade.pair, trade.open_date_utc - timedelta(seconds=10))
             prev_exit_reason = trade.exit_reason
             prev_trade_state = trade.is_open
+            prev_trade_amount = trade.amount
             for order in orders:
                 trade_order = [o for o in trade.orders if o.order_id == order['id']]
 
@@ -480,6 +494,26 @@ class FreqtradeBot(LoggingMixin):
                                         send_msg=prev_trade_state != trade.is_open)
             else:
                 trade.exit_reason = prev_exit_reason
+                total = self.wallets.get_total(trade.base_currency)
+                if total < trade.amount:
+                    if total > trade.amount * 0.98:
+                        logger.warning(
+                            f"{trade} has a total of {trade.amount} {trade.base_currency}, "
+                            f"but the Wallet shows a total of {total} {trade.base_currency}. "
+                            f"Adjusting trade amount to {total}."
+                            "This may however lead to further issues."
+                        )
+                        trade.amount = total
+                    else:
+                        logger.warning(
+                            f"{trade} has a total of {trade.amount} {trade.base_currency}, "
+                            f"but the Wallet shows a total of {total} {trade.base_currency}. "
+                            "Refusing to adjust as the difference is too large."
+                            "This may however lead to further issues."
+                        )
+                if prev_trade_amount != trade.amount:
+                    # Cancel stoploss on exchange if the amount changed
+                    trade = self.cancel_stoploss_on_exchange(trade)
             Trade.commit()
 
         except ExchangeError:
@@ -1290,12 +1324,12 @@ class FreqtradeBot(LoggingMixin):
 
     def manage_trade_stoploss_orders(self, trade: Trade, stoploss_orders: List[Dict]):
         """
-        Perform required actions acording to existing stoploss orders of trade
+        Perform required actions according to existing stoploss orders of trade
         :param trade: Corresponding Trade
         :param stoploss_orders: Current on exchange stoploss orders
         :return: None
         """
-        # If all stoploss orderd are canceled for some reason we add it again
+        # If all stoploss ordered are canceled for some reason we add it again
         canceled_sl_orders = [o for o in stoploss_orders
                               if o['status'] in ('canceled', 'cancelled')]
         if (
@@ -1935,21 +1969,23 @@ class FreqtradeBot(LoggingMixin):
 
         trade.update_trade(order_obj, not send_msg)
 
-        trade = self._update_trade_after_fill(trade, order_obj)
+        trade = self._update_trade_after_fill(trade, order_obj, send_msg)
         Trade.commit()
 
         self.order_close_notify(trade, order_obj, stoploss_order, send_msg)
 
         return False
 
-    def _update_trade_after_fill(self, trade: Trade, order: Order) -> Trade:
+    def _update_trade_after_fill(self, trade: Trade, order: Order, send_msg: bool) -> Trade:
         if order.status in constants.NON_OPEN_EXCHANGE_STATES:
             strategy_safe_wrapper(
                 self.strategy.order_filled, default_retval=None)(
                 pair=trade.pair, trade=trade, order=order, current_time=datetime.now(timezone.utc))
             # If a entry order was closed, force update on stoploss on exchange
             if order.ft_order_side == trade.entry_side:
-                trade = self.cancel_stoploss_on_exchange(trade)
+                if send_msg:
+                    # Don't cancel stoploss in recovery modes immediately
+                    trade = self.cancel_stoploss_on_exchange(trade)
                 if not self.edge:
                     # TODO: should shorting/leverage be supported by Edge,
                     # then this will need to be fixed.

freqtrade/optimize/backtesting.py

@@ -19,6 +19,7 @@ from freqtrade.data import history
 from freqtrade.data.btanalysis import find_existing_backtest_stats, trade_list_to_dataframe
 from freqtrade.data.converter import trim_dataframe, trim_dataframes
 from freqtrade.data.dataprovider import DataProvider
+from freqtrade.data.metrics import combined_dataframes_with_rel_mean
 from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType, RunMode,
                              TradingMode)
 from freqtrade.exceptions import DependencyException, OperationalException
@@ -296,7 +297,7 @@ class Backtesting:
                 candle_type=CandleType.FUNDING_RATE
             )
 
-            # For simplicity, assign to CandleType.Mark (might contian index candles!)
+            # For simplicity, assign to CandleType.Mark (might contain index candles!)
             mark_rates_dict = history.load_data(
                 datadir=self.config['datadir'],
                 pairs=self.pairlists.whitelist,
@@ -1216,7 +1217,7 @@ class Backtesting:
         :return: DataFrame with trades (results of backtesting)
         """
         self.prepare_backtest(self.enable_protections)
-        # Ensure wallets are uptodate (important for --strategy-list)
+        # Ensure wallets are up-to-date (important for --strategy-list)
         self.wallets.update()
         # Use dict of lists with data for performance
         # (looping lists is a lot faster than pandas DataFrames)
@@ -1394,7 +1395,7 @@ class Backtesting:
         """
         Run backtesting end-to-end
         """
-        data: Dict[str, Any] = {}
+        data: Dict[str, DataFrame] = {}
 
         data, timerange = self.load_bt_data()
         self.load_bt_data_detail()
@@ -1421,7 +1422,9 @@ class Backtesting:
                 self.results = results
             dt_appendix = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
             if self.config.get('export', 'none') in ('trades', 'signals'):
-                store_backtest_stats(self.config['exportfilename'], self.results, dt_appendix)
+                combined_res = combined_dataframes_with_rel_mean(data, min_date, max_date)
+                store_backtest_stats(self.config['exportfilename'], self.results, dt_appendix,
+                                     market_change_data=combined_res)
 
             if (self.config.get('export', 'none') == 'signals' and
                     self.dataprovider.runmode == RunMode.BACKTEST):

freqtrade/optimize/hyperopt_tools.py

@@ -237,8 +237,10 @@ class HyperoptTools:
                 result_dict.update(all_space_params)
 
     @staticmethod
-    def _params_pretty_print(params, space: str, header: str, non_optimized={}) -> None:
+    def _params_pretty_print(
-        if space in params or space in non_optimized:
+            params, space: str, header: str, non_optimized: Optional[Dict] = None) -> None:
+
+        if space in params or (non_optimized and space in non_optimized):
             space_params = HyperoptTools._space_params(params, space, 5)
             no_params = HyperoptTools._space_params(non_optimized, space, 5)
             appendix = ''

freqtrade/optimize/optimize_reports/bt_output.py

@@ -278,7 +278,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
 
 
 def show_backtest_result(strategy: str, results: Dict[str, Any], stake_currency: str,
-                         backtest_breakdown=[]):
+                         backtest_breakdown: List[str]):
     """
     Print results for one strategy
     """

freqtrade/optimize/optimize_reports/bt_storage.py

@@ -1,6 +1,8 @@
 import logging
 from pathlib import Path
-from typing import Dict
+from typing import Dict, Optional
+
+from pandas import DataFrame
 
 from freqtrade.constants import LAST_BT_RESULT_FN
 from freqtrade.misc import file_dump_joblib, file_dump_json
@@ -11,8 +13,26 @@ from freqtrade.types import BacktestResultType
 logger = logging.getLogger(__name__)
 
 
+def _generate_filename(recordfilename: Path, appendix: str, suffix: str) -> Path:
+    """
+    Generates a filename based on the provided parameters.
+    :param recordfilename: Path object, which can either be a filename or a directory.
+    :param appendix: use for the filename. e.g. backtest-result-<datetime>
+    :param suffix: Suffix to use for the file, e.g. .json, .pkl
+    :return: Generated filename as a Path object
+    """
+    if recordfilename.is_dir():
+        filename = (recordfilename / f'backtest-result-{appendix}').with_suffix(suffix)
+    else:
+        filename = Path.joinpath(
+            recordfilename.parent, f'{recordfilename.stem}-{appendix}'
+        ).with_suffix(suffix)
+    return filename
+
+
 def store_backtest_stats(
-        recordfilename: Path, stats: BacktestResultType, dtappendix: str) -> Path:
+        recordfilename: Path, stats: BacktestResultType, dtappendix: str, *,
+        market_change_data: Optional[DataFrame] = None) -> Path:
     """
     Stores backtest results
     :param recordfilename: Path object, which can either be a filename or a directory.
@@ -21,12 +41,7 @@ def store_backtest_stats(
     :param stats: Dataframe containing the backtesting statistics
     :param dtappendix: Datetime to use for the filename
     """
-    if recordfilename.is_dir():
+    filename = _generate_filename(recordfilename, dtappendix, '.json')
-        filename = (recordfilename / f'backtest-result-{dtappendix}.json')
-    else:
-        filename = Path.joinpath(
-            recordfilename.parent, f'{recordfilename.stem}-{dtappendix}'
-        ).with_suffix(recordfilename.suffix)
 
     # Store metadata separately.
     file_dump_json(get_backtest_metadata_filename(filename), stats['metadata'])
@@ -41,6 +56,11 @@ def store_backtest_stats(
     latest_filename = Path.joinpath(filename.parent, LAST_BT_RESULT_FN)
     file_dump_json(latest_filename, {'latest_backtest': str(filename.name)})
 
+    if market_change_data is not None:
+        filename_mc = _generate_filename(recordfilename, f"{dtappendix}_market_change", '.feather')
+        market_change_data.reset_index().to_feather(
+            filename_mc, compression_level=9, compression='lz4')
+
     return filename
 
 
@@ -57,12 +77,7 @@ def _store_backtest_analysis_data(
     :param dtappendix: Datetime to use for the filename
     :param name: Name to use for the file, e.g. signals, rejected
     """
-    if recordfilename.is_dir():
+    filename = _generate_filename(recordfilename, f"{dtappendix}_{name}", '.pkl')
-        filename = (recordfilename / f'backtest-result-{dtappendix}_{name}.pkl')
-    else:
-        filename = Path.joinpath(
-            recordfilename.parent, f'{recordfilename.stem}-{dtappendix}_{name}.pkl'
-        )
 
     file_dump_joblib(filename, data)
 

freqtrade/persistence/custom_data.py

@@ -18,7 +18,7 @@ class _CustomData(ModelBase):
     """
     CustomData database model
     Keeps records of metadata as key/value store
-    for trades or global persistant values
+    for trades or global persistent values
     One to many relationship with Trades:
       - One trade can have many metadata entries
       - One metadata entry can only be associated with one Trade

freqtrade/persistence/trade_model.py

@@ -847,7 +847,7 @@ class LocalTrade:
                 isclose(order.safe_amount_after_fee, amount_tr, abs_tol=MATH_CLOSE_PREC)
                 or (not recalculating and order.safe_amount_after_fee > amount_tr)
             ):
-                # When recalculating a trade, only comming out to 0 can force a close
+                # When recalculating a trade, only coming out to 0 can force a close
                 self.close(order.safe_price)
             else:
                 self.recalc_trade_from_orders()
@@ -1125,7 +1125,7 @@ class LocalTrade:
                 prof = self.calculate_profit(exit_rate, exit_amount, float(avg_price))
                 close_profit_abs += prof.profit_abs
                 if total_stake > 0:
-                    # This needs to be calculated based on the last occuring exit to be aligned
+                    # 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
             else:
@@ -1538,7 +1538,7 @@ class Trade(ModelBase, LocalTrade):
     amount: Mapped[float] = mapped_column(Float())  # type: ignore
     amount_requested: Mapped[Optional[float]] = mapped_column(Float())  # type: ignore
     open_date: Mapped[datetime] = mapped_column(
-        nullable=False, default=datetime.utcnow)  # type: ignore
+        nullable=False, default=datetime.now)  # type: ignore
     close_date: Mapped[Optional[datetime]] = mapped_column()  # type: ignore
     # absolute value of the stop loss
     stop_loss: Mapped[float] = mapped_column(Float(), nullable=True, default=0.0)  # type: ignore

freqtrade/plot/plotting.py

@@ -440,12 +440,12 @@ def create_scatter(
 
 def generate_candlestick_graph(
         pair: str, data: pd.DataFrame, trades: Optional[pd.DataFrame] = None, *,
-        indicators1: List[str] = [], indicators2: List[str] = [],
+        indicators1: Optional[List[str]] = None, indicators2: Optional[List[str]] = None,
-        plot_config: Dict[str, Dict] = {},
+        plot_config: Optional[Dict[str, Dict]] = None,
         ) -> go.Figure:
     """
     Generate the graph from the data generated by Backtesting or from DB
-    Volume will always be ploted in row2, so Row 1 and 3 are to our disposal for custom indicators
+    Volume will always be plotted in row2, so Row 1 and 3 are to our disposal for custom indicators
     :param pair: Pair to Display on the graph
     :param data: OHLCV DataFrame containing indicators and entry/exit signals
     :param trades: All trades created
@@ -454,7 +454,11 @@ def generate_candlestick_graph(
     :param plot_config: Dict of Dicts containing advanced plot configuration
     :return: Plotly figure
     """
-    plot_config = create_plotconfig(indicators1, indicators2, plot_config)
+    plot_config = create_plotconfig(
+        indicators1 or [],
+        indicators2 or [],
+        plot_config or {},
+    )
     rows = 2 + len(plot_config['subplots'])
     row_widths = [1 for _ in plot_config['subplots']]
     # Define the graph
@@ -673,7 +677,7 @@ def plot_profit(config: Config) -> None:
     """
     Plots the total profit for all pairs.
     Note, the profit calculation isn't realistic.
-    But should be somewhat proportional, and therefor useful
+    But should be somewhat proportional, and therefore useful
     in helping out to find a good algorithm.
     """
     if 'timeframe' not in config:

freqtrade/plugins/pairlist/MarketCapPairList.py

@@ -38,7 +38,7 @@ class MarketCapPairList(IPairList):
         self._refresh_period = self._pairlistconfig.get('refresh_period', 86400)
         self._marketcap_cache: TTLCache = TTLCache(maxsize=1, ttl=self._refresh_period)
         self._def_candletype = self._config['candle_type_def']
-        self._coingekko: CoinGeckoAPI = CoinGeckoAPI()
+        self._coingecko: CoinGeckoAPI = CoinGeckoAPI()
 
         if self._max_rank > 250:
             raise OperationalException(
@@ -127,7 +127,7 @@ class MarketCapPairList(IPairList):
         marketcap_list = self._marketcap_cache.get('marketcap')
 
         if marketcap_list is None:
-            data = self._coingekko.get_coins_markets(vs_currency='usd', order='market_cap_desc',
+            data = self._coingecko.get_coins_markets(vs_currency='usd', order='market_cap_desc',
                                                      per_page='250', page='1', sparkline='false',
                                                      locale='en')
             if data:

freqtrade/plugins/pairlist/PriceFilter.py

@@ -101,7 +101,7 @@ class PriceFilter(IPairList):
 
     def _validate_pair(self, pair: str, ticker: Optional[Ticker]) -> bool:
         """
-        Check if if one price-step (pip) is > than a certain barrier.
+        Check if one price-step (pip) is > than a certain barrier.
         :param pair: Pair that's currently validated
         :param ticker: ticker dict as returned from ccxt.fetch_ticker
         :return: True if the pair can stay, false if it should be removed

freqtrade/plugins/pairlist/RemotePairList.py

@@ -116,7 +116,7 @@ class RemotePairList(IPairList):
                 "default": "filter",
                 "options": ["filter", "append"],
                 "description": "Processing mode",
-                "help": "Append pairs to incomming pairlist or filter them?",
+                "help": "Append pairs to incoming pairlist or filter them?",
             },
             **IPairList.refresh_period_parameter(),
             "keep_pairlist_on_failure": {

freqtrade/plugins/pairlist/VolumePairList.py

@@ -65,7 +65,7 @@ class VolumePairList(IPairList):
         self._tf_in_min = timeframe_to_minutes(self._lookback_timeframe)
         _tf_in_sec = self._tf_in_min * 60
 
-        # wether to use range lookback or not
+        # whether to use range lookback or not
         self._use_range = (self._tf_in_min > 0) & (self._lookback_period > 0)
 
         if self._use_range & (self._refresh_period < _tf_in_sec):

freqtrade/plugins/protections/iprotection.py

@@ -110,7 +110,7 @@ class IProtection(LoggingMixin, ABC):
         Get lock end time
         """
         max_date: datetime = max([trade.close_date for trade in trades if trade.close_date])
-        # comming from Database, tzinfo is not set.
+        # coming from Database, tzinfo is not set.
         if max_date.tzinfo is None:
             max_date = max_date.replace(tzinfo=timezone.utc)
 

freqtrade/resolvers/iresolver.py

@@ -47,7 +47,7 @@ class IResolver:
 
     @classmethod
     def build_search_paths(cls, config: Config, user_subdir: Optional[str] = None,
-                           extra_dirs: List[str] = []) -> List[Path]:
+                           extra_dirs: Optional[List[str]] = None) -> List[Path]:
 
         abs_paths: List[Path] = []
         if cls.initial_search_path:
@@ -57,8 +57,9 @@ class IResolver:
             abs_paths.insert(0, config['user_data_dir'].joinpath(user_subdir))
 
         # Add extra directory to the top of the search paths
-        for dir in extra_dirs:
+        if extra_dirs:
-            abs_paths.insert(0, Path(dir).resolve())
+            for dir in extra_dirs:
+                abs_paths.insert(0, Path(dir).resolve())
 
         if cls.extra_path and (extra := config.get(cls.extra_path)):
             abs_paths.insert(0, Path(extra).resolve())
@@ -139,7 +140,7 @@ class IResolver:
 
     @classmethod
     def _load_object(cls, paths: List[Path], *, object_name: str, add_source: bool = False,
-                     kwargs: dict = {}) -> Optional[Any]:
+                     kwargs: Dict) -> Optional[Any]:
         """
         Try to load object from path list.
         """
@@ -163,7 +164,7 @@ class IResolver:
     def load_object(cls, object_name: str, config: Config, *, kwargs: dict,
                     extra_dir: Optional[str] = None) -> Any:
         """
-        Search and loads the specified object as configured in hte child class.
+        Search and loads the specified object as configured in the child class.
         :param object_name: name of the module to import
         :param config: configuration dictionary
         :param extra_dir: additional directory to search for the given pairlist

freqtrade/rpc/api_server/api_auth.py

@@ -26,6 +26,7 @@ def verify_auth(api_config, username: str, password: str):
 
 
 httpbasic = HTTPBasic(auto_error=False)
+security = HTTPBasic()
 oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token", auto_error=False)
 
 
@@ -117,7 +118,7 @@ def http_basic_or_jwt_token(form_data: HTTPBasicCredentials = Depends(httpbasic)
 
 
 @router_login.post('/token/login', response_model=AccessAndRefreshToken)
-def token_login(form_data: HTTPBasicCredentials = Depends(HTTPBasic()),
+def token_login(form_data: HTTPBasicCredentials = Depends(security),
                 api_config=Depends(get_api_config)):
 
     if verify_auth(api_config, form_data.username, form_data.password):

freqtrade/rpc/api_server/api_backtest.py

@@ -10,15 +10,16 @@ from fastapi.exceptions import HTTPException
 
 from freqtrade.configuration.config_validation import validate_config_consistency
 from freqtrade.constants import Config
-from freqtrade.data.btanalysis import (delete_backtest_result, get_backtest_result,
+from freqtrade.data.btanalysis import (delete_backtest_result, get_backtest_market_change,
-                                       get_backtest_resultlist, load_and_merge_backtest_result,
+                                       get_backtest_result, get_backtest_resultlist,
-                                       update_backtest_metadata)
+                                       load_and_merge_backtest_result, update_backtest_metadata)
 from freqtrade.enums import BacktestState
 from freqtrade.exceptions import ConfigurationError, DependencyException, OperationalException
 from freqtrade.exchange.common import remove_exchange_credentials
 from freqtrade.misc import deep_merge_dicts, is_file_in_dir
-from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestMetadataUpdate,
+from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestMarketChange,
-                                                  BacktestRequest, BacktestResponse)
+                                                  BacktestMetadataUpdate, BacktestRequest,
+                                                  BacktestResponse)
 from freqtrade.rpc.api_server.deps import get_config
 from freqtrade.rpc.api_server.webserver_bgwork import ApiBG
 from freqtrade.rpc.rpc import RPCException
@@ -32,8 +33,10 @@ router = APIRouter()
 
 
 def __run_backtest_bg(btconfig: Config):
+    from freqtrade.data.metrics import combined_dataframes_with_rel_mean
     from freqtrade.optimize.optimize_reports import generate_backtest_stats, store_backtest_stats
     from freqtrade.resolvers import StrategyResolver
+
     asyncio.set_event_loop(asyncio.new_event_loop())
     try:
         # Reload strategy
@@ -89,11 +92,14 @@ def __run_backtest_bg(btconfig: Config):
                 min_date=min_date, max_date=max_date)
 
         if btconfig.get('export', 'none') == 'trades':
+            combined_res = combined_dataframes_with_rel_mean(ApiBG.bt['data'], min_date, max_date)
             fn = store_backtest_stats(
-                btconfig['exportfilename'], ApiBG.bt['bt'].results,
+                btconfig['exportfilename'],
-                datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+                ApiBG.bt['bt'].results,
+                datetime.now().strftime("%Y-%m-%d_%H-%M-%S"),
+                market_change_data=combined_res
                 )
-            ApiBG.bt['bt'].results['metadata'][strategy_name]['filename'] = str(fn.name)
+            ApiBG.bt['bt'].results['metadata'][strategy_name]['filename'] = str(fn.stem)
             ApiBG.bt['bt'].results['metadata'][strategy_name]['strategy'] = strategy_name
 
         logger.info("Backtest finished.")
@@ -308,3 +314,20 @@ def api_update_backtest_history_entry(file: str, body: BacktestMetadataUpdate,
         raise HTTPException(status_code=400, detail=str(e))
 
     return get_backtest_result(file_abs)
+
+
+@router.get('/backtest/history/{file}/market_change', response_model=BacktestMarketChange,
+            tags=['webserver', 'backtest'])
+def api_get_backtest_market_change(file: str, config=Depends(get_config)):
+    bt_results_base: Path = config['user_data_dir'] / 'backtest_results'
+    file_abs = (bt_results_base / f"{file}_market_change").with_suffix('.feather')
+    # Ensure file is in backtest_results directory
+    if not is_file_in_dir(file_abs, bt_results_base):
+        raise HTTPException(status_code=404, detail="File not found.")
+    df = get_backtest_market_change(file_abs)
+
+    return {
+        'columns': df.columns.tolist(),
+        'data': df.values.tolist(),
+        'length': len(df),
+    }

freqtrade/rpc/api_server/api_schemas.py

@@ -558,6 +558,12 @@ class BacktestMetadataUpdate(BaseModel):
     notes: str = ''
 
 
+class BacktestMarketChange(BaseModel):
+    columns: List[str]
+    length: int
+    data: List[List[Any]]
+
+
 class SysInfo(BaseModel):
     cpu_pct: List[float]
     ram_pct: float

freqtrade/rpc/api_server/ws/channel.py

@@ -152,7 +152,7 @@ class WebSocketChannel:
         """
         return self._closed.is_set()
 
-    def set_subscriptions(self, subscriptions: List[str] = []) -> None:
+    def set_subscriptions(self, subscriptions: List[str]) -> None:
         """
         Set which subscriptions this channel is subscribed to
 

freqtrade/rpc/external_message_consumer.py

@@ -237,7 +237,7 @@ class ExternalMessageConsumer:
                 continue
 
             except Exception as e:
-                # An unforseen error has occurred, log and continue
+                # An unforeseen error has occurred, log and continue
                 logger.error("Unexpected error has occurred:")
                 logger.exception(e)
                 await asyncio.sleep(self.sleep_time)
@@ -387,7 +387,7 @@ class ExternalMessageConsumer:
             )
 
         if not did_append:
-            # We want an overlap in candles incase some data has changed
+            # We want an overlap in candles in case some data has changed
             n_missing += 1
             # Set to None for all candles if we missed a full df's worth of candles
             n_missing = n_missing if n_missing < FULL_DATAFRAME_THRESHOLD else 1500

freqtrade/rpc/fiat_convert.py

@@ -39,7 +39,7 @@ class CryptoToFiatConverter(LoggingMixin):
     This object is also a Singleton
     """
     __instance = None
-    _coingekko: CoinGeckoAPI = None
+    _coingecko: CoinGeckoAPI = None
     _coinlistings: List[Dict] = []
     _backoff: float = 0.0
 
@@ -52,9 +52,9 @@ class CryptoToFiatConverter(LoggingMixin):
             try:
                 # Limit retires to 1 (0 and 1)
                 # otherwise we risk bot impact if coingecko is down.
-                CryptoToFiatConverter._coingekko = CoinGeckoAPI(retries=1)
+                CryptoToFiatConverter._coingecko = CoinGeckoAPI(retries=1)
             except BaseException:
-                CryptoToFiatConverter._coingekko = None
+                CryptoToFiatConverter._coingecko = None
         return CryptoToFiatConverter.__instance
 
     def __init__(self) -> None:
@@ -67,7 +67,7 @@ class CryptoToFiatConverter(LoggingMixin):
     def _load_cryptomap(self) -> None:
         try:
             # Use list-comprehension to ensure we get a list.
-            self._coinlistings = [x for x in self._coingekko.get_coins_list()]
+            self._coinlistings = [x for x in self._coingecko.get_coins_list()]
         except RequestException as request_exception:
             if "429" in str(request_exception):
                 logger.warning(
@@ -84,7 +84,7 @@ class CryptoToFiatConverter(LoggingMixin):
             logger.error(
                 f"Could not load FIAT Cryptocurrency map for the following problem: {exception}")
 
-    def _get_gekko_id(self, crypto_symbol):
+    def _get_gecko_id(self, crypto_symbol):
         if not self._coinlistings:
             if self._backoff <= datetime.now().timestamp():
                 self._load_cryptomap()
@@ -180,9 +180,9 @@ class CryptoToFiatConverter(LoggingMixin):
         if crypto_symbol == fiat_symbol:
             return 1.0
 
-        _gekko_id = self._get_gekko_id(crypto_symbol)
+        _gecko_id = self._get_gecko_id(crypto_symbol)
 
-        if not _gekko_id:
+        if not _gecko_id:
             # return 0 for unsupported stake currencies (fiat-convert should not break the bot)
             self.log_once(
                 f"unsupported crypto-symbol {crypto_symbol.upper()} - returning 0.0",
@@ -191,10 +191,10 @@ class CryptoToFiatConverter(LoggingMixin):
 
         try:
             return float(
-                self._coingekko.get_price(
+                self._coingecko.get_price(
-                    ids=_gekko_id,
+                    ids=_gecko_id,
                     vs_currencies=fiat_symbol
-                )[_gekko_id][fiat_symbol]
+                )[_gecko_id][fiat_symbol]
             )
         except Exception as exception:
             logger.error("Error in _find_price: %s", exception)

freqtrade/rpc/rpc.py

@@ -30,8 +30,8 @@ from freqtrade.persistence.models import PairLock
 from freqtrade.plugins.pairlist.pairlist_helpers import expand_pairlist
 from freqtrade.rpc.fiat_convert import CryptoToFiatConverter
 from freqtrade.rpc.rpc_types import RPCSendMsg
-from freqtrade.util import (decimals_per_coin, dt_humanize, dt_now, dt_ts_def, format_date,
+from freqtrade.util import decimals_per_coin, dt_now, dt_ts_def, format_date, shorten_date
-                            shorten_date)
+from freqtrade.util.datetime_helpers import dt_humanize_delta
 from freqtrade.wallets import PositionWallet, Wallet
 
 
@@ -155,7 +155,7 @@ class RPC:
         }
         return val
 
-    def _rpc_trade_status(self, trade_ids: List[int] = []) -> List[Dict[str, Any]]:
+    def _rpc_trade_status(self, trade_ids: Optional[List[int]] = None) -> List[Dict[str, Any]]:
         """
         Below follows the RPC backend it is prefixed with rpc_ to raise awareness that it is
         a remotely exposed function
@@ -301,13 +301,13 @@ class RPC:
                     for oo in trade.open_orders
                 ]
 
-                # exemple: '*.**.**' trying to enter, exit and exit with 3 different orders
+                # example: '*.**.**' trying to enter, exit and exit with 3 different orders
                 active_attempt_side_symbols_str = '.'.join(active_attempt_side_symbols)
 
                 detail_trade = [
                     f'{trade.id} {direction_str}',
                     trade.pair + active_attempt_side_symbols_str,
-                    shorten_date(dt_humanize(trade.open_date, only_distance=True)),
+                    shorten_date(dt_humanize_delta(trade.open_date_utc)),
                     profit_str
                 ]
 
@@ -460,8 +460,11 @@ class RPC:
 
     def _rpc_trade_statistics(
             self, stake_currency: str, fiat_display_currency: str,
-            start_date: datetime = datetime.fromtimestamp(0)) -> Dict[str, Any]:
+            start_date: Optional[datetime] = None) -> Dict[str, Any]:
         """ Returns cumulative profit statistics """
+
+        start_date = datetime.fromtimestamp(0) if start_date is None else start_date
+
         trade_filter = ((Trade.is_open.is_(False) & (Trade.close_date >= start_date)) |
                         Trade.is_open.is_(True))
         trades: Sequence[Trade] = Trade.session.scalars(Trade.get_trades_query(
@@ -596,10 +599,10 @@ class RPC:
             'trade_count': len(trades),
             'closed_trade_count': closed_trade_count,
             'first_trade_date': format_date(first_date),
-            'first_trade_humanized': dt_humanize(first_date) if first_date else '',
+            'first_trade_humanized': dt_humanize_delta(first_date) if first_date else '',
             'first_trade_timestamp': dt_ts_def(first_date, 0),
             'latest_trade_date': format_date(last_date),
-            'latest_trade_humanized': dt_humanize(last_date) if last_date else '',
+            'latest_trade_humanized': dt_humanize_delta(last_date) if last_date else '',
             'latest_trade_timestamp': dt_ts_def(last_date, 0),
             'avg_duration': str(timedelta(seconds=sum(durations) / num)).split('.')[0],
             'best_pair': best_pair[0] if best_pair else '',

freqtrade/rpc/telegram.py

@@ -33,7 +33,7 @@ from freqtrade.misc import chunks, plural
 from freqtrade.persistence import Trade
 from freqtrade.rpc import RPC, RPCException, RPCHandler
 from freqtrade.rpc.rpc_types import RPCEntryMsg, RPCExitMsg, RPCOrderMsg, RPCSendMsg
-from freqtrade.util import dt_humanize, fmt_coin, format_date, round_value
+from freqtrade.util import dt_from_ts, dt_humanize_delta, fmt_coin, format_date, round_value
 
 
 MAX_MESSAGE_LENGTH = MessageLimit.MAX_TEXT_LENGTH
@@ -488,7 +488,7 @@ class Telegram(RPCHandler):
         elif msg['type'] == RPCMessageType.WARNING:
             message = f"\N{WARNING SIGN} *Warning:* `{msg['status']}`"
         elif msg['type'] == RPCMessageType.EXCEPTION:
-            # Errors will contain exceptions, which are wrapped in tripple ticks.
+            # Errors will contain exceptions, which are wrapped in triple ticks.
             message = f"\N{WARNING SIGN} *ERROR:* \n {msg['status']}"
 
         elif msg['type'] == RPCMessageType.STARTUP:
@@ -573,8 +573,7 @@ class Telegram(RPCHandler):
                 # TODO: This calculation ignores fees.
                 price_to_1st_entry = ((cur_entry_average - first_avg) / first_avg)
                 if is_open:
-                    lines.append("({})".format(dt_humanize(order["order_filled_date"],
+                    lines.append("({})".format(dt_humanize_delta(order["order_filled_date"])))
-                                                           granularity=["day", "hour", "minute"])))
                 lines.append(f"*Amount:* {round_value(cur_entry_amount, 8)} "
                              f"({fmt_coin(order['cost'], quote_currency)})")
                 lines.append(f"*Average {wording} Price:* {round_value(cur_entry_average, 8)} "
@@ -657,7 +656,7 @@ class Telegram(RPCHandler):
         position_adjust = self._config.get('position_adjustment_enable', False)
         max_entries = self._config.get('max_entry_position_adjustment', -1)
         for r in results:
-            r['open_date_hum'] = dt_humanize(r['open_date'])
+            r['open_date_hum'] = dt_humanize_delta(r['open_date'])
             r['num_entries'] = len([o for o in r['orders'] if o['ft_is_entry']])
             r['num_exits'] = len([o for o in r['orders'] if not o['ft_is_entry']
                                  and not o['ft_order_side'] == 'stoploss'])
@@ -1289,7 +1288,7 @@ class Telegram(RPCHandler):
             nrecent
         )
         trades_tab = tabulate(
-            [[dt_humanize(trade['close_date']),
+            [[dt_humanize_delta(dt_from_ts(trade['close_timestamp'])),
                 trade['pair'] + " (#" + str(trade['trade_id']) + ")",
                 f"{(trade['close_profit']):.2%} ({trade['close_profit_abs']})"]
                 for trade in trades['trades']],
@@ -1549,7 +1548,7 @@ class Telegram(RPCHandler):
 
     async def send_blacklist_msg(self, blacklist: Dict):
         errmsgs = []
-        for pair, error in blacklist['errors'].items():
+        for _, error in blacklist['errors'].items():
             errmsgs.append(f"Error: {error['error_msg']}")
         if errmsgs:
             await self._send_msg('\n'.join(errmsgs))

freqtrade/strategy/informative_decorator.py

@@ -64,7 +64,7 @@ def informative(timeframe: str, asset: str = '',
     def decorator(fn: PopulateIndicators):
         informative_pairs = getattr(fn, '_ft_informative', [])
         informative_pairs.append(InformativeData(_asset, _timeframe, _fmt, _ffill, _candle_type))
-        setattr(fn, '_ft_informative', informative_pairs)
+        setattr(fn, '_ft_informative', informative_pairs)  # noqa: B010
         return fn
     return decorator
 

freqtrade/strategy/strategy_helper.py

@@ -78,7 +78,7 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
     # all indicators on the informative sample MUST be calculated before this point
     if ffill:
         # https://pandas.pydata.org/docs/user_guide/merging.html#timeseries-friendly-merging
-        # merge_ordered - ffill method is 2.5x faster than seperate ffill()
+        # merge_ordered - ffill method is 2.5x faster than separate ffill()
         dataframe = pd.merge_ordered(dataframe, informative, fill_method="ffill", left_on='date',
                                      right_on=date_merge, how='left')
     else:

freqtrade/templates/strategy_subtemplates/strategy_methods_advanced.j2

@@ -3,7 +3,7 @@ def bot_loop_start(self, current_time: datetime, **kwargs) -> None:
     """
     Called at the start of the bot iteration (one loop).
     Might be used to perform pair-independent tasks
-    (e.g. gather some remote ressource for comparison)
+    (e.g. gather some remote resource for comparison)
 
     For full documentation please go to https://www.freqtrade.io/en/latest/strategy-advanced/
 

freqtrade/util/__init__.py

@@ -1,8 +1,9 @@
-from freqtrade.util.datetime_helpers import (dt_floor_day, dt_from_ts, dt_humanize, dt_now, dt_ts,
+from freqtrade.util.datetime_helpers import (dt_floor_day, dt_from_ts, dt_humanize_delta, dt_now,
-                                             dt_ts_def, dt_ts_none, dt_utc, format_date,
+                                             dt_ts, dt_ts_def, dt_ts_none, dt_utc, format_date,
                                              format_ms_time, shorten_date)
 from freqtrade.util.formatters import decimals_per_coin, fmt_coin, round_value
 from freqtrade.util.ft_precise import FtPrecise
+from freqtrade.util.measure_time import MeasureTime
 from freqtrade.util.periodic_cache import PeriodicCache
 from freqtrade.util.template_renderer import render_template, render_template_with_fallback  # noqa
 
@@ -10,7 +11,7 @@ from freqtrade.util.template_renderer import render_template, render_template_wi
 __all__ = [
     'dt_floor_day',
     'dt_from_ts',
-    'dt_humanize',
+    'dt_humanize_delta',
     'dt_now',
     'dt_ts',
     'dt_ts_def',
@@ -24,4 +25,5 @@ __all__ = [
     'decimals_per_coin',
     'round_value',
     'fmt_coin',
+    'MeasureTime',
 ]

freqtrade/util/datetime_helpers.py

@@ -1,8 +1,8 @@
 import re
 from datetime import datetime, timezone
-from typing import Optional
+from typing import Optional, Union
 
-import arrow
+import humanize
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT
 
@@ -76,13 +76,11 @@ def shorten_date(_date: str) -> str:
     return new_date
 
 
-def dt_humanize(dt: datetime, **kwargs) -> str:
+def dt_humanize_delta(dt: datetime):
     """
-    Return a humanized string for the given datetime.
+    Return a humanized string for the given timedelta.
-    :param dt: datetime to humanize
-    :param kwargs: kwargs to pass to arrow's humanize()
     """
-    return arrow.get(dt).humanize(**kwargs)
+    return humanize.naturaltime(dt)
 
 
 def format_date(date: Optional[datetime]) -> str:
@@ -96,9 +94,9 @@ def format_date(date: Optional[datetime]) -> str:
     return ''
 
 
-def format_ms_time(date: int) -> str:
+def format_ms_time(date: Union[int, float]) -> str:
     """
     convert MS date to readable format.
     : epoch-string in ms
     """
-    return datetime.fromtimestamp(date / 1000.0).strftime('%Y-%m-%dT%H:%M:%S')
+    return dt_from_ts(date).strftime('%Y-%m-%dT%H:%M:%S')

freqtrade/util/measure_time.py

@@ -0,0 +1,43 @@
+import logging
+import time
+from typing import Callable
+
+from cachetools import TTLCache
+
+
+logger = logging.getLogger(__name__)
+
+
+class MeasureTime:
+    """
+    Measure the time of a block of code and call a callback if the time limit is exceeded.
+    """
+    def __init__(
+            self, callback: Callable[[float, float], None], time_limit: float, ttl: int = 3600 * 4):
+        """
+        :param callback: The callback to call if the time limit is exceeded.
+            This callback will be called once every "ttl" seconds,
+            with the parameters "duration" (in seconds) and
+            "time limit" - representing the passed in time limit.
+        :param time_limit: The time limit in seconds.
+        :param ttl: The time to live of the cache in seconds.
+            defaults to 4 hours.
+        """
+        self._callback = callback
+        self._time_limit = time_limit
+        self.__cache: TTLCache = TTLCache(maxsize=1, ttl=ttl)
+
+    def __enter__(self):
+        self._start = time.time()
+
+    def __exit__(self, *args):
+        end = time.time()
+        if self.__cache.get('value'):
+            return
+        duration = end - self._start
+
+        if duration < self._time_limit:
+            return
+        self._callback(duration, self._time_limit)
+
+        self.__cache['value'] = True

freqtrade/util/template_renderer.py

@@ -3,7 +3,10 @@ Jinja2 rendering utils, used to generate new strategy and configurations.
 """
 
 
-def render_template(templatefile: str, arguments: dict = {}) -> str:
+from typing import Dict, Optional
+
+
+def render_template(templatefile: str, arguments: Dict) -> str:
 
     from jinja2 import Environment, PackageLoader, select_autoescape
 
@@ -16,11 +19,13 @@ def render_template(templatefile: str, arguments: dict = {}) -> str:
 
 
 def render_template_with_fallback(templatefile: str, templatefallbackfile: str,
-                                  arguments: dict = {}) -> str:
+                                  arguments: Optional[Dict] = None) -> str:
     """
     Use templatefile if possible, otherwise fall back to templatefallbackfile
     """
     from jinja2.exceptions import TemplateNotFound
+    if arguments is None:
+        arguments = {}
     try:
         return render_template(templatefile, arguments)
     except TemplateNotFound:

freqtrade/wallets.py

@@ -306,7 +306,7 @@ class Wallets:
         :raise: DependencyException if the available stake amount is too low
         """
         stake_amount: float
-        # Ensure wallets are uptodate.
+        # Ensure wallets are up-to-date.
         if update:
             self.update()
         val_tied_up = Trade.total_open_trades_stakes()

freqtrade/worker.py

@@ -137,7 +137,7 @@ class Worker:
         Throttles the given callable that it
         takes at least `min_secs` to finish execution.
         :param func: Any callable
-        :param throttle_secs: throttling interation execution time limit in seconds
+        :param throttle_secs: throttling iteration execution time limit in seconds
         :param timeframe: ensure iteration is executed at the beginning of the next candle.
         :param timeframe_offset: offset in seconds to apply to the next candle time.
         :return: Any (result of execution of func)

ft_client/freqtrade_client/ft_client.py

@@ -67,7 +67,7 @@ def print_commands():
     # Print dynamic help for the different commands using the commands doc-strings
     client = FtRestClient(None)
     print("Possible commands:\n")
-    for x, y in inspect.getmembers(client):
+    for x, _ in inspect.getmembers(client):
         if not x.startswith('_'):
             doc = re.sub(':return:.*', '', getattr(client, x).__doc__, flags=re.MULTILINE).rstrip()
             print(f"{x}\n\t{doc}\n")

pyproject.toml

@@ -122,6 +122,7 @@ target-version = "py38"
 # Exclude UP036 as it's causing the "exit if < 3.9" to fail.
 extend-select = [
   "C90",    # mccabe
+  # "B",    # bugbear
   # "N",    # pep8-naming
   "F",      # pyflakes
   "E",      # pycodestyle
@@ -129,6 +130,7 @@ extend-select = [
   "UP",     # pyupgrade
   "TID",    # flake8-tidy-imports
   # "EXE",  # flake8-executable
+  # "C4",     # flake8-comprehensions
   "YTT",    # flake8-2020
   # "S",    # flake8-bandit
   # "DTZ",  # flake8-datetimez
@@ -141,6 +143,7 @@ extend-ignore = [
   "E241",  # Multiple spaces after comma
   "E272",  # Multiple spaces before keyword
   "E221",  # Multiple spaces before operator
+  "B007",  # Loop control variable not used
 ]
 
 [tool.ruff.lint.mccabe]
@@ -149,6 +152,10 @@ max-complexity = 12
 [tool.ruff.lint.per-file-ignores]
 "tests/*" = ["S"]
 
+[tool.ruff.lint.flake8-bugbear]
+# Allow default arguments like, e.g., `data: List[str] = fastapi.Query(None)`.
+extend-immutable-calls = ["fastapi.Depends", "fastapi.Query"]
+
 [tool.flake8]
 # Default from https://flake8.pycqa.org/en/latest/user/options.html#cmdoption-flake8-ignore
 # minus E226
@@ -163,3 +170,7 @@ exclude = [
     ".venv",
     ".env",
 ]
+
+[tool.codespell]
+ignore-words-list = "coo,fo,strat,zar,selectin"
+skip="*.svg,./user_data,./freqtrade/rpc/api_server/ui/installed"

requirements-dev.txt

@@ -7,7 +7,7 @@
 -r docs/requirements-docs.txt
 
 coveralls==3.3.1
-ruff==0.3.7
+ruff==0.4.1
 mypy==1.9.0
 pre-commit==3.7.0
 pytest==8.1.1

requirements-freqai.txt

@@ -5,7 +5,7 @@
 # Required for freqai
 scikit-learn==1.4.2
 joblib==1.4.0
-catboost==1.2.3; 'arm' not in platform_machine
+catboost==1.2.5; 'arm' not in platform_machine
 lightgbm==4.3.0
 xgboost==2.0.3
 tensorboard==2.16.2

requirements-plot.txt

@@ -1,4 +1,4 @@
 # Include all requirements to run the bot.
 -r requirements.txt
 
-plotly==5.20.0
+plotly==5.21.0

requirements.txt

@@ -2,14 +2,14 @@ numpy==1.26.4
 pandas==2.2.2
 pandas-ta==0.3.14b
 
-ccxt==4.2.97
+ccxt==4.3.4
 cryptography==42.0.5
-aiohttp==3.9.4
+aiohttp==3.9.5
 SQLAlchemy==2.0.29
-python-telegram-bot==21.1
+python-telegram-bot==21.1.1
 # can't be hard-pinned due to telegram-bot pinning httpx with ~
 httpx>=0.24.1
-arrow==1.3.0
+humanize==4.9.0
 cachetools==5.3.3
 requests==2.31.0
 urllib3==2.2.1
@@ -22,7 +22,7 @@ jinja2==3.1.3
 tables==3.9.1
 joblib==1.4.0
 rich==13.7.1
-pyarrow==15.0.2; platform_machine != 'armv7l'
+pyarrow==16.0.0; platform_machine != 'armv7l'
 
 # find first, C search in arrays
 py_find_1st==1.1.6
@@ -30,13 +30,13 @@ py_find_1st==1.1.6
 # Load ticker files 30% faster
 python-rapidjson==1.16
 # Properly format api responses
-orjson==3.10.0
+orjson==3.10.1
 
 # Notify systemd
 sdnotify==0.3.2
 
 # API Server
-fastapi==0.110.1
+fastapi==0.110.2
 pydantic==2.7.0
 uvicorn==0.29.0
 pyjwt==2.8.0

scripts/ws_client.py

@@ -191,7 +191,7 @@ class ClientProtocol:
             self.logger.info("Empty DataFrame")
 
     async def _handle_default(self, name, type, data):
-        self.logger.info("Unkown message of type {type} received...")
+        self.logger.info("Unknown message of type {type} received...")
         self.logger.info(data)
 
 
@@ -201,7 +201,7 @@ async def create_client(
         token,
         scheme='ws',
         name='default',
-        protocol=ClientProtocol(),
+        protocol=None,
         sleep_time=10,
         ping_timeout=10,
         wait_timeout=30,
@@ -216,6 +216,8 @@ async def create_client(
     :param name: The name of the producer
     :param **kwargs: Any extra kwargs passed to websockets.connect
     """
+    if not protocol:
+        protocol = ClientProtocol()
 
     while 1:
         try:
@@ -277,7 +279,7 @@ async def create_client(
             continue
 
         except Exception as e:
-            # An unforseen error has occurred, log and try reconnecting again
+            # An unforeseen error has occurred, log and try reconnecting again
             logger.error("Unexpected error has occurred:")
             logger.exception(e)
 

setup.py

@@ -73,7 +73,7 @@ setup(
         'ccxt>=4.2.47',
         'SQLAlchemy>=2.0.6',
         'python-telegram-bot>=20.1',
-        'arrow>=1.0.0',
+        'humanize>=4.0.0',
         'cachetools',
         'requests',
         'httpx>=0.24.1',

tests/commands/test_commands.py

@@ -1609,4 +1609,4 @@ def test_start_show_config(capsys, caplog):
     assert "Your combined configuration is:" in captured.out
     assert '"max_open_trades":' in captured.out
     assert '"secret": "REDACTED"' not in captured.out
-    assert log_has_re(r'Sensitive information will be shown in the upcomming output.*', caplog)
+    assert log_has_re(r'Sensitive information will be shown in the upcoming output.*', caplog)

tests/conftest.py

@@ -49,10 +49,10 @@ def pytest_addoption(parser):
 
 def pytest_configure(config):
     config.addinivalue_line(
-        "markers", "longrun: mark test that is running slowly and should not be run regularily"
+        "markers", "longrun: mark test that is running slowly and should not be run regularly"
     )
     if not config.option.longrun:
-        setattr(config.option, 'markexpr', 'not longrun')
+        config.option.markexpr = 'not longrun'
 
 
 class FixtureScheduler(LoadScopeScheduling):
@@ -490,10 +490,10 @@ def user_dir(mocker, tmp_path) -> Path:
 
 
 @pytest.fixture(autouse=True)
-def patch_coingekko(mocker) -> None:
+def patch_coingecko(mocker) -> None:
     """
-    Mocker to coingekko to speed up tests
+    Mocker to coingecko to speed up tests
-    :param mocker: mocker to patch coingekko class
+    :param mocker: mocker to patch coingecko class
     :return: None
     """
 

tests/data/test_btanalysis.py

@@ -16,7 +16,7 @@ from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_
                                     calculate_expectancy, calculate_market_change,
                                     calculate_max_drawdown, calculate_sharpe, calculate_sortino,
                                     calculate_underwater, combine_dataframes_with_mean,
-                                    create_cum_profit)
+                                    combined_dataframes_with_rel_mean, create_cum_profit)
 from freqtrade.exceptions import OperationalException
 from freqtrade.util import dt_utc
 from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
@@ -251,10 +251,29 @@ def test_combine_dataframes_with_mean(testdatadir):
     assert "mean" in df.columns
 
 
+def test_combined_dataframes_with_rel_mean(testdatadir):
+    pairs = ["ETH/BTC", "ADA/BTC"]
+    data = load_data(datadir=testdatadir, pairs=pairs, timeframe='5m')
+    df = combined_dataframes_with_rel_mean(
+        data,
+        datetime(2018, 1, 12, tzinfo=timezone.utc),
+        datetime(2018, 1, 28, tzinfo=timezone.utc)
+    )
+    assert isinstance(df, DataFrame)
+    assert "ETH/BTC" not in df.columns
+    assert "ADA/BTC" not in df.columns
+    assert "mean" in df.columns
+    assert "rel_mean" in df.columns
+    assert "count" in df.columns
+    assert df.iloc[0]['count'] == 2
+    assert df.iloc[-1]['count'] == 2
+    assert len(df) < len(data['ETH/BTC'])
+
+
 def test_combine_dataframes_with_mean_no_data(testdatadir):
     pairs = ["ETH/BTC", "ADA/BTC"]
     data = load_data(datadir=testdatadir, pairs=pairs, timeframe='6m')
-    with pytest.raises(ValueError, match=r"No objects to concatenate"):
+    with pytest.raises(ValueError, match=r"No data provided\."):
         combine_dataframes_with_mean(data)
 
 
@@ -463,12 +482,12 @@ def test_calculate_max_drawdown2():
     assert drawdown == 0.043965
 
 
-@pytest.mark.parametrize('profits,relative,highd,lowd,result,result_rel', [
+@pytest.mark.parametrize('profits,relative,highd,lowdays,result,result_rel', [
     ([0.0, -500.0, 500.0, 10000.0, -1000.0], False, 3, 4, 1000.0, 0.090909),
     ([0.0, -500.0, 500.0, 10000.0, -1000.0], True, 0, 1, 500.0, 0.5),
 
 ])
-def test_calculate_max_drawdown_abs(profits, relative, highd, lowd, result, result_rel):
+def test_calculate_max_drawdown_abs(profits, relative, highd, lowdays, result, result_rel):
     """
     Test case from issue https://github.com/freqtrade/freqtrade/issues/6655
     [1000, 500,  1000, 11000, 10000] # absolute results
@@ -488,7 +507,7 @@ def test_calculate_max_drawdown_abs(profits, relative, highd, lowd, result, resu
     assert isinstance(drawdown, float)
     assert isinstance(drawdown_rel, float)
     assert hdate == init_date + timedelta(days=highd)
-    assert ldate == init_date + timedelta(days=lowd)
+    assert ldate == init_date + timedelta(days=lowdays)
 
     # High must be before low
     assert hdate < ldate

tests/data/test_datahandler.py

@@ -251,7 +251,7 @@ def test_datahandler__check_empty_df(testdatadir, caplog):
 # @pytest.mark.parametrize('datahandler', [])
 @pytest.mark.skip("All datahandlers currently support trades data.")
 def test_datahandler_trades_not_supported(datahandler, testdatadir, ):
-    # Currently disabled. Reenable should a new provider not support trades data.
+    # Currently disabled. Re-enable should a new provider not support trades data.
     dh = get_datahandler(testdatadir, datahandler)
     with pytest.raises(NotImplementedError):
         dh.trades_load('UNITTEST/ETH')

tests/data/test_dataprovider.py

@@ -30,7 +30,7 @@ def test_dp_ohlcv(mocker, default_conf, ohlcv_history, candle_type):
     assert dp.ohlcv("UNITTEST/BTC", timeframe, candle_type=candletype) is not ohlcv_history
     assert dp.ohlcv("UNITTEST/BTC", timeframe, copy=False, candle_type=candletype) is ohlcv_history
     assert not dp.ohlcv("UNITTEST/BTC", timeframe, candle_type=candletype).empty
-    assert dp.ohlcv("NONESENSE/AAA", timeframe, candle_type=candletype).empty
+    assert dp.ohlcv("NONSENSE/AAA", timeframe, candle_type=candletype).empty
 
     # Test with and without parameter
     assert dp.ohlcv(
@@ -114,7 +114,7 @@ def test_get_pair_dataframe(mocker, default_conf, ohlcv_history, candle_type):
     assert dp.get_pair_dataframe("UNITTEST/BTC", timeframe,
                                  candle_type=candle_type) is not ohlcv_history
     assert not dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type).empty
-    assert dp.get_pair_dataframe("NONESENSE/AAA", timeframe, candle_type=candle_type).empty
+    assert dp.get_pair_dataframe("NONSENSE/AAA", timeframe, candle_type=candle_type).empty
 
     # Test with and without parameter
     assert dp.get_pair_dataframe("UNITTEST/BTC", timeframe, candle_type=candle_type)\
@@ -125,7 +125,7 @@ def test_get_pair_dataframe(mocker, default_conf, ohlcv_history, candle_type):
     assert dp.runmode == RunMode.LIVE
     assert isinstance(dp.get_pair_dataframe(
         "UNITTEST/BTC", timeframe, candle_type=candle_type), DataFrame)
-    assert dp.get_pair_dataframe("NONESENSE/AAA", timeframe, candle_type=candle_type).empty
+    assert dp.get_pair_dataframe("NONSENSE/AAA", timeframe, candle_type=candle_type).empty
 
     historymock = MagicMock(return_value=ohlcv_history)
     mocker.patch("freqtrade.data.dataprovider.load_pair_history", historymock)

tests/edge/test_edge.py

@@ -226,8 +226,10 @@ def test_edge_heartbeat_calculate(mocker, edge_conf):
     assert edge.calculate(edge_conf['exchange']['pair_whitelist']) is False
 
 
-def mocked_load_data(datadir, pairs=[], timeframe='0m',
+def mocked_load_data(datadir, pairs=None, timeframe='0m',
                      timerange=None, *args, **kwargs):
+    if pairs is None:
+        pairs = []
     hz = 0.1
     base = 0.001
 

tests/exchange/test_exchange.py

@@ -3830,7 +3830,7 @@ def test_ohlcv_candle_limit(default_conf, mocker, exchange_name):
     [
         ("BTC/USDT", 'BTC', 'USDT', "binance", True, False, False, 'spot', {}, True),
         ("USDT/BTC", 'USDT', 'BTC', "binance", True, False, False, 'spot', {}, True),
-        # No seperating /
+        # No separating /
         ("BTCUSDT", 'BTC', 'USDT', "binance", True, False, False, 'spot', {}, True),
         ("BTCUSDT", None, "USDT", "binance", True, False, False, 'spot', {}, False),
         ("USDT/BTC", "BTC", None, "binance", True, False, False, 'spot', {}, False),
@@ -4346,7 +4346,7 @@ def test_combine_funding_and_mark(
     ('binance', 0, 2, "2021-09-01 00:00:01", "2021-09-01 08:00:00",  30.0, -0.00091409999),
     ('binance', 0, 2, "2021-08-31 23:58:00", "2021-09-01 08:00:00",  30.0, -0.00091409999),
     ('binance', 0, 2, "2021-09-01 00:10:01", "2021-09-01 08:00:00",  30.0, -0.0002493),
-    # TODO: Uncoment once _calculate_funding_fees can pas time_in_ratio to exchange._get_funding_fee
+    # TODO: Uncomment once _calculate_funding_fees can pass time_in_ratio to exchange.
     # ('kraken', "2021-09-01 00:00:00", "2021-09-01 08:00:00",  30.0, -0.0014937),
     # ('kraken', "2021-09-01 00:00:15", "2021-09-01 08:00:00",  30.0, -0.0008289),
     # ('kraken', "2021-09-01 01:00:14", "2021-09-01 08:00:00",  30.0, -0.0008289),
@@ -4358,7 +4358,7 @@ def test_combine_funding_and_mark(
     ('gate', 0, 2, "2021-09-01 00:00:00", "2021-09-01 12:00:00",  30.0, -0.0009140999),
     ('gate', 1, 2, "2021-09-01 00:00:01", "2021-09-01 08:00:00",  30.0, -0.0002493),
     ('binance', 0,  2, "2021-09-01 00:00:00", "2021-09-01 08:00:00",  50.0, -0.0015235),
-    # TODO: Uncoment once _calculate_funding_fees can pas time_in_ratio to exchange._get_funding_fee
+    # TODO: Uncomment once _calculate_funding_fees can pass time_in_ratio to exchange.
     # ('kraken', "2021-09-01 00:00:00", "2021-09-01 08:00:00",  50.0, -0.0024895),
 ])
 def test__fetch_and_calculate_funding_fees(
@@ -5133,7 +5133,7 @@ def test_get_maintenance_ratio_and_amt(
     mocker.patch(f'{EXMS}.exchange_has', return_value=True)
     exchange = get_patched_exchange(mocker, default_conf, api_mock)
     exchange._leverage_tiers = leverage_tiers
-    exchange.get_maintenance_ratio_and_amt(pair, value) == (mmr, maintAmt)
+    assert exchange.get_maintenance_ratio_and_amt(pair, value) == (mmr, maintAmt)
 
 
 def test_get_max_leverage_futures(default_conf, mocker, leverage_tiers):

tests/exchange/test_okx.py

@@ -472,7 +472,7 @@ def test_load_leverage_tiers_okx(default_conf, mocker, markets, tmp_path, caplog
     exchange.load_leverage_tiers()
     assert not log_has(logmsg, caplog)
 
-    api_mock.fetch_market_leverage_tiers.call_count == 0
+    assert api_mock.fetch_market_leverage_tiers.call_count == 0
     # 2 day passes ...
     time_machine.move_to(datetime.now() + timedelta(weeks=5))
     exchange.load_leverage_tiers()
@@ -500,7 +500,7 @@ def test__set_leverage_okx(mocker, default_conf):
         'posSide': 'net'}
     api_mock.set_leverage = MagicMock(side_effect=ccxt.NetworkError())
     exchange._lev_prep('BTC/USDT:USDT', 3.2, 'buy')
-    api_mock.fetch_leverage.call_count == 1
+    assert api_mock.fetch_leverage.call_count == 1
 
     api_mock.fetch_leverage = MagicMock(side_effect=ccxt.NetworkError())
     ccxt_exceptionhandlers(

tests/freqai/test_freqai_backtesting.py

@@ -133,6 +133,6 @@ def test_freqai_backtest_consistent_timerange(mocker, freqai_conf):
     backtesting = Backtesting(deepcopy(freqai_conf))
     backtesting.start()
 
-    gbs.call_args[1]['min_date'] == datetime(2021, 11, 20, 0, 0, tzinfo=timezone.utc)
+    assert gbs.call_args[1]['min_date'] == datetime(2021, 11, 20, 0, 0, tzinfo=timezone.utc)
-    gbs.call_args[1]['max_date'] == datetime(2021, 11, 21, 0, 0, tzinfo=timezone.utc)
+    assert gbs.call_args[1]['max_date'] == datetime(2021, 11, 21, 0, 0, tzinfo=timezone.utc)
     Backtesting.cleanup()

tests/freqai/test_freqai_datadrawer.py

@@ -143,7 +143,7 @@ def test_get_timerange_from_backtesting_live_df_pred_not_found(mocker, freqai_co
 def test_set_initial_return_values(mocker, freqai_conf):
     """
     Simple test of the set initial return values that ensures
-    we are concatening and ffilling values properly.
+    we are concatenating and ffilling values properly.
     """
 
     strategy = get_patched_freqai_strategy(mocker, freqai_conf)

tests/freqai/test_freqai_interface.py

@@ -403,7 +403,7 @@ def test_backtesting_fit_live_predictions(mocker, freqai_conf, caplog):
     freqai.dk.get_unique_classes_from_labels(df)
     freqai.dk.pair = "ADA/BTC"
     freqai.dk.full_df = df.fillna(0)
-    freqai.dk.full_df
+
     assert "&-s_close_mean" not in freqai.dk.full_df.columns
     assert "&-s_close_std" not in freqai.dk.full_df.columns
     freqai.backtesting_fit_live_predictions(freqai.dk)

tests/freqtradebot/test_freqtradebot.py

@@ -285,7 +285,7 @@ def test_edge_overrides_stoploss(limit_order, fee, caplog, mocker,
             'last': enter_price * buy_price_mult,
     })
 
-    # stoploss shoud be hit
+    # stoploss should be hit
     assert freqtrade.handle_trade(trade) is not ignore_strat_sl
     if not ignore_strat_sl:
         assert log_has_re('Exit for NEO/BTC detected. Reason: stop_loss.*', caplog)
@@ -1398,7 +1398,7 @@ def test_update_trade_state_sell(
     assert order.status == 'open'
     freqtrade.update_trade_state(trade, trade.open_orders_ids[-1], l_order)
     assert trade.amount == l_order['amount']
-    # Wallet needs to be updated after closing a limit-sell order to reenable buying
+    # Wallet needs to be updated after closing a limit-sell order to re-enable buying
     assert wallet_mock.call_count == 1
     assert not trade.is_open
     # Order is updated by update_trade_state
@@ -3122,7 +3122,7 @@ def test_exit_profit_only(
     if profit_only:
         assert freqtrade.handle_trade(trade) is False
         # Custom-exit is called
-        freqtrade.strategy.custom_exit.call_count == 1
+        assert freqtrade.strategy.custom_exit.call_count == 1
 
     patch_get_signal(freqtrade, enter_long=False, exit_short=is_short, exit_long=not is_short)
     assert freqtrade.handle_trade(trade) is handle_first
@@ -3240,7 +3240,7 @@ def test_locked_pairs(default_conf_usdt, ticker_usdt, fee,
     )
     trade.close(ticker_usdt_sell_down()['bid'])
     assert freqtrade.strategy.is_pair_locked(trade.pair, side='*')
-    # Boths sides are locked
+    # Both sides are locked
     assert freqtrade.strategy.is_pair_locked(trade.pair, side='long')
     assert freqtrade.strategy.is_pair_locked(trade.pair, side='short')
 
@@ -4558,6 +4558,67 @@ def test_handle_onexchange_order(mocker, default_conf_usdt, limit_order, is_shor
     assert trade.exit_reason == ExitType.SOLD_ON_EXCHANGE.value
 
 
+@pytest.mark.usefixtures("init_persistence")
+@pytest.mark.parametrize("is_short", [False, True])
+@pytest.mark.parametrize("factor,adjusts", [
+    (0.99, True),
+    (0.97, False),
+])
+def test_handle_onexchange_order_changed_amount(
+    mocker, default_conf_usdt, limit_order, is_short, caplog,
+    factor, adjusts,
+):
+    default_conf_usdt['dry_run'] = False
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    mock_uts = mocker.spy(freqtrade, 'update_trade_state')
+
+    entry_order = limit_order[entry_side(is_short)]
+    mock_fo = mocker.patch(f'{EXMS}.fetch_orders', return_value=[
+        entry_order,
+    ])
+
+    trade = Trade(
+        pair='ETH/USDT',
+        fee_open=0.001,
+        base_currency='ETH',
+        fee_close=0.001,
+        open_rate=entry_order['price'],
+        open_date=dt_now(),
+        stake_amount=entry_order['cost'],
+        amount=entry_order['amount'],
+        exchange="binance",
+        is_short=is_short,
+        leverage=1,
+    )
+    freqtrade.wallets = MagicMock()
+    freqtrade.wallets.get_total = MagicMock(return_value=entry_order['amount'] * factor)
+
+    trade.orders.append(Order.parse_from_ccxt_object(
+        entry_order, 'ADA/USDT', entry_side(is_short))
+    )
+    Trade.session.add(trade)
+
+    # assert trade.amount > entry_order['amount']
+
+    freqtrade.handle_onexchange_order(trade)
+    assert mock_uts.call_count == 1
+    assert mock_fo.call_count == 1
+
+    trade = Trade.session.scalars(select(Trade)).first()
+
+    assert log_has_re(r'.*has a total of .* but the Wallet shows.*', caplog)
+    if adjusts:
+        # Trade amount is updated
+        assert trade.amount == entry_order['amount'] * factor
+        assert log_has_re(r'.*Adjusting trade amount to.*', caplog)
+    else:
+        assert log_has_re(r'.*Refusing to adjust as the difference.*', caplog)
+        assert trade.amount == entry_order['amount']
+
+    assert len(trade.orders) == 1
+    assert trade.is_open is True
+
+
 @pytest.mark.usefixtures("init_persistence")
 @pytest.mark.parametrize("is_short", [False, True])
 def test_handle_onexchange_order_exit(mocker, default_conf_usdt, limit_order, is_short, caplog):
@@ -4829,7 +4890,7 @@ def test_update_funding_fees(
     freqtrade.execute_entry('ETH/USDT', 123, is_short=is_short)
     freqtrade.execute_entry('LTC/USDT', 2.0, is_short=is_short)
     freqtrade.execute_entry('XRP/USDT', 123, is_short=is_short)
-    multipl = 1 if is_short else -1
+    multiple = 1 if is_short else -1
     trades = Trade.get_open_trades()
     assert len(trades) == 3
     for trade in trades:
@@ -4847,7 +4908,7 @@ def test_update_funding_fees(
             assert trade.funding_fees == pytest.approx(sum(
                 trade.amount *
                 mark_prices[trade.pair].iloc[1:2]['open'] *
-                funding_rates[trade.pair].iloc[1:2]['open'] * multipl
+                funding_rates[trade.pair].iloc[1:2]['open'] * multiple
             ))
 
     else:
@@ -4859,7 +4920,7 @@ def test_update_funding_fees(
             trade.amount *
             mark_prices[trade.pair].iloc[1:2]['open'] *
             funding_rates[trade.pair].iloc[1:2]['open'] *
-            multipl
+            multiple
         ))
 
 

tests/optimize/test_backtest_detail.py

@@ -107,7 +107,7 @@ tc5 = BTContainer(data=[
     trades=[BTrade(exit_reason=ExitType.ROI, open_tick=1, close_tick=3)]
 )
 
-# Test 6: Drops 3% / Recovers 6% Positive / Closes 1% positve, Stop-Loss triggers 2% Loss
+# Test 6: Drops 3% / Recovers 6% Positive / Closes 1% positive, Stop-Loss triggers 2% Loss
 # stop-loss: 2% ROI: 5%
 tc6 = BTContainer(data=[
     # D   O     H     L     C    V    EL XL ES Xs  BT
@@ -121,7 +121,7 @@ tc6 = BTContainer(data=[
     trades=[BTrade(exit_reason=ExitType.STOP_LOSS, open_tick=1, close_tick=2)]
 )
 
-# Test 7: 6% Positive / 1% Negative / Close 1% Positve, ROI Triggers 3% Gain
+# Test 7: 6% Positive / 1% Negative / Close 1% Positive, ROI Triggers 3% Gain
 # stop-loss: 2% ROI: 3%
 tc7 = BTContainer(data=[
     # D   O     H     L     C    V    EL XL ES Xs  BT

tests/optimize/test_backtesting_adjust_position.py

@@ -87,9 +87,9 @@ def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) ->
 
     for _, t in results.iterrows():
         ln = data_pair.loc[data_pair["date"] == t["open_date"]]
-        # Check open trade rate alignes to open rate
+        # Check open trade rate aligns to open rate
         assert ln is not None
-        # check close trade rate alignes to close rate or is between high and low
+        # check close trade rate aligns to close rate or is between high and low
         ln = data_pair.loc[data_pair["date"] == t["close_date"]]
         assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
                 round(ln.iloc[0]["low"], 6) < round(

tests/optimize/test_hyperopt.py

@@ -901,6 +901,7 @@ def test_in_strategy_auto_hyperopt(mocker, hyperopt_conf, tmp_path, fee) -> None
         hyperopt.get_optimizer([], 2)
 
 
+@pytest.mark.filterwarnings("ignore::DeprecationWarning")
 def test_in_strategy_auto_hyperopt_with_parallel(mocker, hyperopt_conf, tmp_path, fee) -> None:
     mocker.patch(f'{EXMS}.validate_config', MagicMock())
     mocker.patch(f'{EXMS}.get_fee', fee)

tests/optimize/test_hyperopt_tools.py

@@ -172,8 +172,8 @@ def test__pprint_dict():
 }"""
 
 
-def test_get_strategy_filename(default_conf):
+def test_get_strategy_filename(default_conf, tmp_path):
-
+    default_conf['user_data_dir'] = tmp_path
     x = HyperoptTools.get_strategy_filename(default_conf, 'StrategyTestV3')
     assert isinstance(x, Path)
     assert x == Path(__file__).parents[1] / 'strategy/strats/strategy_test_v3.py'
@@ -233,6 +233,7 @@ def test_export_params(tmp_path):
 
 def test_try_export_params(default_conf, tmp_path, caplog, mocker):
     default_conf['disableparamexport'] = False
+    default_conf['user_data_dir'] = tmp_path
     export_mock = mocker.patch("freqtrade.optimize.hyperopt_tools.HyperoptTools.export_params")
 
     filename = tmp_path / f"{CURRENT_TEST_STRATEGY}.json"

tests/optimize/test_lookahead_analysis.py

@@ -14,7 +14,8 @@ from tests.conftest import EXMS, get_args, log_has_re, patch_exchange
 
 
 @pytest.fixture
-def lookahead_conf(default_conf_usdt):
+def lookahead_conf(default_conf_usdt, tmp_path):
+    default_conf_usdt['user_data_dir'] = tmp_path
     default_conf_usdt['minimum_trade_amount'] = 10
     default_conf_usdt['targeted_trade_amount'] = 20
     default_conf_usdt['timerange'] = '20220101-20220501'
@@ -152,7 +153,7 @@ def test_lookahead_helper_text_table_lookahead_analysis_instances(lookahead_conf
     assert data[0][2].__contains__('too few trades')
     assert len(data[0]) == 3
 
-    # now check for an error which occured after enough trades
+    # now check for an error which occurred after enough trades
     analysis.total_signals = 12
     analysis.false_entry_signals = 11
     analysis.false_exit_signals = 10

tests/optimize/test_optimize_reports.py

@@ -129,7 +129,7 @@ def test_generate_backtest_stats(default_conf, testdatadir, tmp_path):
     assert strat_stats['backtest_start'] == min_date.strftime(DATETIME_PRINT_FORMAT)
     assert strat_stats['backtest_end'] == max_date.strftime(DATETIME_PRINT_FORMAT)
     assert strat_stats['total_trades'] == len(results['DefStrat']['results'])
-    # Above sample had no loosing trade
+    # Above sample had no losing trade
     assert strat_stats['max_drawdown_account'] == 0.0
 
     # Retry with losing trade
@@ -229,6 +229,28 @@ def test_store_backtest_stats(testdatadir, mocker):
     assert str(dump_mock.call_args_list[0][0][0]).startswith(str(testdatadir / 'testresult'))
 
 
+def test_store_backtest_stats_real(tmp_path):
+    data = {'metadata': {}, 'strategy': {}, 'strategy_comparison': []}
+    store_backtest_stats(tmp_path, data, '2022_01_01_15_05_13')
+
+    assert (tmp_path / 'backtest-result-2022_01_01_15_05_13.json').is_file()
+    assert (tmp_path / 'backtest-result-2022_01_01_15_05_13.meta.json').is_file()
+    assert not (tmp_path / 'backtest-result-2022_01_01_15_05_13_market_change.feather').is_file()
+    assert (tmp_path / LAST_BT_RESULT_FN).is_file()
+    fn = get_latest_backtest_filename(tmp_path)
+    assert fn == 'backtest-result-2022_01_01_15_05_13.json'
+
+    store_backtest_stats(tmp_path, data, '2024_01_01_15_05_25', market_change_data=pd.DataFrame())
+    assert (tmp_path / 'backtest-result-2024_01_01_15_05_25.json').is_file()
+    assert (tmp_path / 'backtest-result-2024_01_01_15_05_25.meta.json').is_file()
+    assert (tmp_path / 'backtest-result-2024_01_01_15_05_25_market_change.feather').is_file()
+    assert (tmp_path / LAST_BT_RESULT_FN).is_file()
+
+    # Last file reference should be updated
+    fn = get_latest_backtest_filename(tmp_path)
+    assert fn == 'backtest-result-2024_01_01_15_05_25.json'
+
+
 def test_store_backtest_candles(testdatadir, mocker):
 
     dump_mock = mocker.patch(

tests/optimize/test_recursive_analysis.py

@@ -14,7 +14,8 @@ from tests.conftest import get_args, log_has_re, patch_exchange
 
 
 @pytest.fixture
-def recursive_conf(default_conf_usdt):
+def recursive_conf(default_conf_usdt, tmp_path):
+    default_conf_usdt['user_data_dir'] = tmp_path
     default_conf_usdt['timerange'] = '20220101-20220501'
 
     default_conf_usdt['strategy_path'] = str(