Merge branch 'develop' into dataformat/feather

2024-11-10 02:12:01 +00:00 · 2023-07-29 20:04:12 +02:00 · 2023-07-29 20:04:12 +02:00 · 6659d26131
commit 6659d26131
parent b593205ad9 15dd8bec1c
86 changed files with 1387 additions and 411 deletions
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@ -461,7 +461,7 @@ jobs:
        python setup.py sdist bdist_wheel

    - name: Publish to PyPI (Test)
-      uses: pypa/gh-action-pypi-publish@v1.8.7
+      uses: pypa/gh-action-pypi-publish@v1.8.8
      if: (github.event_name == 'release')
      with:
        user: __token__
@ -469,7 +469,7 @@ jobs:
        repository_url: https://test.pypi.org/legacy/

    - name: Publish to PyPI
-      uses: pypa/gh-action-pypi-publish@v1.8.7
+      uses: pypa/gh-action-pypi-publish@v1.8.8
      if: (github.event_name == 'release')
      with:
        user: __token__
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@ -13,12 +13,12 @@ repos:
      - id: mypy
        exclude: build_helpers
        additional_dependencies:
-          - types-cachetools==5.3.0.5
+          - types-cachetools==5.3.0.6
          - types-filelock==3.2.7
-          - types-requests==2.31.0.1
-          - types-tabulate==0.9.0.2
-          - types-python-dateutil==2.8.19.13
-          - SQLAlchemy==2.0.18
+          - types-requests==2.31.0.2
+          - types-tabulate==0.9.0.3
+          - types-python-dateutil==2.8.19.14
+          - SQLAlchemy==2.0.19
        # stages: [push]

  - repo: https://github.com/pycqa/isort
--- a/build_helpers/TA_Lib-0.4.26-cp310-cp310-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.26-cp310-cp310-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.26-cp311-cp311-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.26-cp311-cp311-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.26-cp38-cp38-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.26-cp38-cp38-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.26-cp39-cp39-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.26-cp39-cp39-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.27-cp310-cp310-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.27-cp310-cp310-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.27-cp311-cp311-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.27-cp311-cp311-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.27-cp38-cp38-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.27-cp38-cp38-win_amd64.whl
--- a/build_helpers/TA_Lib-0.4.27-cp39-cp39-win_amd64.whl
+++ b/build_helpers/TA_Lib-0.4.27-cp39-cp39-win_amd64.whl
--- a/build_helpers/install_windows.ps1
+++ b/build_helpers/install_windows.ps1
@ -1,21 +1,11 @@
-# Downloads don't work automatically, since the URL is regenerated via javascript.
-# Downloaded from https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib
+# vendored Wheels compiled via https://github.com/xmatthias/ta-lib-python/tree/ta_bundled_040

 python -m pip install --upgrade pip wheel

 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"

-if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.26-cp38-cp38-win_amd64.whl
-}
-if ($pyv -eq '3.9') {
-    pip install build_helpers\TA_Lib-0.4.26-cp39-cp39-win_amd64.whl
-}
-if ($pyv -eq '3.10') {
-    pip install build_helpers\TA_Lib-0.4.26-cp310-cp310-win_amd64.whl
-}
-if ($pyv -eq '3.11') {
-    pip install build_helpers\TA_Lib-0.4.26-cp311-cp311-win_amd64.whl
-}
+
+pip install --find-links=build_helpers\ TA-Lib
+
 pip install -r requirements-dev.txt
 pip install -e .
--- a/build_helpers/pyarrow-12.0.1-cp39-cp39-linux_armv7l.whl
+++ b/build_helpers/pyarrow-12.0.1-cp39-cp39-linux_armv7l.whl
--- a/docker/docker-compose-freqai.yml
+++ b/docker/docker-compose-freqai.yml
@ -32,5 +32,5 @@ services:
      --logfile /freqtrade/user_data/logs/freqtrade.log
      --db-url sqlite:////freqtrade/user_data/tradesv3.sqlite
      --config /freqtrade/user_data/config.json
-      --freqai-model XGBoostClassifier
-      --strategy SampleStrategy
+      --freqaimodel XGBoostRegressor
+      --strategy FreqaiExampleStrategy
--- a/docs/advanced-backtesting.md
+++ b/docs/advanced-backtesting.md
@ -103,6 +103,22 @@ The indicators have to be present in your strategy's main DataFrame (either for
 timeframe or for informative timeframes) otherwise they will simply be ignored in the script
 output.

+There are a range of candle and trade-related fields that are included in the analysis so are 
+automatically accessible by including them on the indicator-list, and these include:
+
+- **open_date     :** trade open datetime
+- **close_date    :** trade close datetime
+- **min_rate      :** minimum price seen throughout the position
+- **max_rate      :** maxiumum price seen throughout the position
+- **open          :** signal candle open price
+- **close         :** signal candle close price
+- **high          :** signal candle high price
+- **low           :** signal candle low price
+- **volume        :** signal candle volumne
+- **profit_ratio  :** trade profit ratio
+- **profit_abs    :** absolute profit return of the trade 
+
+
 ### Filtering the trade output by date

 To show only trades between dates within your backtested timerange, supply the usual `timerange` option in `YYYYMMDD-[YYYYMMDD]` format:
--- a/docs/backtesting.md
+++ b/docs/backtesting.md
@ -305,7 +305,7 @@ A backtesting result will look like that:
 | Sharpe                      | 2.97                |
 | Calmar                      | 6.29                |
 | Profit factor               | 1.11                |
-| Expectancy                  | -0.15               |
+| Expectancy (Ratio)          | -0.15 (-0.05)       |
 | Avg. stake amount           | 0.001      BTC      |
 | Total trade volume          | 0.429      BTC      |
 |                             |                     |
@ -324,6 +324,7 @@ A backtesting result will look like that:
 | Days win/draw/lose          | 12 / 82 / 25        |
 | Avg. Duration Winners       | 4:23:00             |
 | Avg. Duration Loser         | 6:55:00             |
+| Max Consecutive Wins / Loss | 3 / 4               |
 | Rejected Entry signals      | 3089                |
 | Entry/Exit Timeouts         | 0 / 0               |
 | Canceled Trade Entries      | 34                  |
@ -409,7 +410,7 @@ It contains some useful key metrics about performance of your strategy on backte
 | Sharpe                      | 2.97                |
 | Calmar                      | 6.29                |
 | Profit factor               | 1.11                |
-| Expectancy                  | -0.15               |
+| Expectancy (Ratio)          | -0.15 (-0.05)       |
 | Avg. stake amount           | 0.001      BTC      |
 | Total trade volume          | 0.429      BTC      |
 |                             |                     |
@ -428,6 +429,7 @@ It contains some useful key metrics about performance of your strategy on backte
 | Days win/draw/lose          | 12 / 82 / 25        |
 | Avg. Duration Winners       | 4:23:00             |
 | Avg. Duration Loser         | 6:55:00             |
+| Max Consecutive Wins / Loss | 3 / 4               |
 | Rejected Entry signals      | 3089                |
 | Entry/Exit Timeouts         | 0 / 0               |
 | Canceled Trade Entries      | 34                  |
@ -467,6 +469,7 @@ It contains some useful key metrics about performance of your strategy on backte
 - `Best day` / `Worst day`: Best and worst day based on daily profit.
 - `Days win/draw/lose`: Winning / Losing days (draws are usually days without closed trade).
 - `Avg. Duration Winners` / `Avg. Duration Loser`: Average durations for winning and losing trades.
+- `Max Consecutive Wins / Loss`: Maximum consecutive wins/losses in a row.
 - `Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
 - `Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
 - `Canceled Trade Entries`: Number of trades that have been canceled by user request via `adjust_entry_price`.
@ -534,6 +537,7 @@ Since backtesting lacks some detailed information about what happens within a ca
 - ROI
  - exits are compared to high - but the ROI value is used (e.g. ROI = 2%, high=5% - so the exit will be at 2%)
  - exits are never "below the candle", so a ROI of 2% may result in a exit at 2.4% if low was at 2.4% profit
+  - ROI entries which came into effect on the triggering candle (e.g. `120: 0.02` for 1h candles, from `60: 0.05`) will use the candle's open as exit rate
  - Force-exits caused by `<N>=-1` ROI entries use low as exit value, unless N falls on the candle open (e.g. `120: -1` for 1h candles)
 - Stoploss exits happen exactly at stoploss price, even if low was lower, but the loss will be `2 * fees` higher than the stoploss price
 - Stoploss is evaluated before ROI within one candle. So you can often see more trades with the `stoploss` exit reason comparing to the results obtained with the same strategy in the Dry Run/Live Trade modes
--- a/docs/exchanges.md
+++ b/docs/exchanges.md
@ -259,10 +259,17 @@ The configuration parameter `exchange.unknown_fee_rate` can be used to specify t

 Futures trading on bybit is currently supported for USDT markets, and will use isolated futures mode.
 Users with unified accounts (there's no way back) can create a Sub-account which will start as "non-unified", and can therefore use isolated futures.
-On startup, freqtrade will set the position mode to "One-way Mode" for the whole (sub)account. This avoids making this call over and over again (slowing down bot operations), but means that changes to this setting may result in exceptions and errors.
+On startup, freqtrade will set the position mode to "One-way Mode" for the whole (sub)account. This avoids making this call over and over again (slowing down bot operations), but means that changes to this setting may result in exceptions and errors

 As bybit doesn't provide funding rate history, the dry-run calculation is used for live trades as well.

+API Keys for live futures trading (Subaccount on non-unified) must have the following permissions:
+* Read-write
+* Contract - Orders
+* Contract - Positions
+
+We do strongly recommend to limit all API keys to the IP you're going to use it from.
+
 !!! Tip "Stoploss on Exchange"
    Bybit (futures only) supports `stoploss_on_exchange` and uses `stop-loss-limit` orders. It provides great advantages, so we recommend to benefit from it by enabling stoploss on exchange.
    On futures, Bybit supports both `stop-limit` as well as `stop-market` orders. You can use either `"limit"` or `"market"` in the `order_types.stoploss` configuration setting to decide which type to use.
--- a/docs/freqai-feature-engineering.md
+++ b/docs/freqai-feature-engineering.md
@ -261,7 +261,7 @@ class MyFreqaiModel(BaseRegressionModel):
        """
        feature_pipeline = Pipeline([
            ('qt', SKLearnWrapper(QuantileTransformer(output_distribution='normal'))),
-            ('di', ds.DissimilarityIndex(di_threshold=1)
+            ('di', ds.DissimilarityIndex(di_threshold=1))
        ])

        return feature_pipeline
--- a/docs/freqai-parameter-table.md
+++ b/docs/freqai-parameter-table.md
@ -42,7 +42,6 @@ Mandatory parameters are marked as **Required** and have to be set in one of the
 | `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training dataset, as well as from incoming data points. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
 | `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](freqai-feature-engineering.md#identifying-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.
 | `use_DBSCAN_to_remove_outliers` | Cluster data using the DBSCAN algorithm to identify and remove outliers from training and prediction data. See details about how it works [here](freqai-feature-engineering.md#identifying-outliers-with-dbscan). <br> **Datatype:** Boolean. 
-| `inlier_metric_window` | If set, FreqAI adds an `inlier_metric` to the training feature set and set the lookback to be the `inlier_metric_window`, i.e., the number of previous time points to compare the current candle to. Details of how the `inlier_metric` is computed can be found [here](freqai-feature-engineering.md#inlier-metric). <br> **Datatype:** Integer. <br> Default: `0`.
 | `noise_standard_deviation` | If set, FreqAI adds noise to the training features with the aim of preventing overfitting. FreqAI generates random deviates from a gaussian distribution with a standard deviation of `noise_standard_deviation` and adds them to all data points. `noise_standard_deviation` should be kept relative to the normalized space, i.e., between -1 and 1. In other words, since data in FreqAI is always normalized to be between -1 and 1, `noise_standard_deviation: 0.05` would result in 32% of the data being randomly increased/decreased by more than 2.5% (i.e., the percent of data falling within the first standard deviation). <br> **Datatype:** Integer. <br> Default: `0`.
 | `outlier_protection_percentage` | Enable to prevent outlier detection methods from discarding too much data. If more than `outlier_protection_percentage` % of points are detected as outliers by the SVM or DBSCAN, FreqAI will log a warning message and ignore outlier detection, i.e., the original dataset will be kept intact. If the outlier protection is triggered, no predictions will be made based on the training dataset. <br> **Datatype:** Float. <br> Default: `30`.
 | `reverse_train_test_order` | Split the feature dataset (see below) and use the latest data split for training and test on historical split of the data. This allows the model to be trained up to the most recent data point, while avoiding overfitting. However, you should be careful to understand the unorthodox nature of this parameter before employing it. <br> **Datatype:** Boolean. <br> Default: `False` (no reversal).
--- a/docs/includes/pairlists.md
+++ b/docs/includes/pairlists.md
@ -211,7 +211,7 @@ The user is responsible for providing a server or local file that returns a JSON
 ```json
 {
    "pairs": ["XRP/USDT", "ETH/USDT", "LTC/USDT"],
-    "refresh_period": 1800,
+    "refresh_period": 1800
 }
 ```

--- a/docs/includes/showcase.md
+++ b/docs/includes/showcase.md
@ -0,0 +1,11 @@
+This section will highlight a few projects from members of the community.
+!!! Note
+    The projects below are for the most part not maintained by the freqtrade , therefore use your own caution before using them.
+
+- [Example freqtrade strategies](https://github.com/freqtrade/freqtrade-strategies/)
+- [FrequentHippo - Grafana dashboard with dry/live runs and backtests](http://frequenthippo.ddns.net:3000/) (by hippocritical).
+- [Online pairlist generator](https://remotepairlist.com/) (by Blood4rc).
+- [Freqtrade Backtesting Project](https://bt.robot.co.network/) (by Blood4rc).
+- [Freqtrade analysis notebook](https://github.com/froggleston/freqtrade_analysis_notebook) (by Froggleston).
+- [TUI for freqtrade](https://github.com/froggleston/freqtrade-frogtrade9000) (by Froggleston).
+- [Bot Academy](https://botacademy.ddns.net/) (by stash86) - Blog about crypto bot projects.
--- a/docs/index.md
+++ b/docs/index.md
@ -63,6 +63,10 @@ Exchanges confirmed working by the community:
 - [X] [Bitvavo](https://bitvavo.com/)
 - [X] [Kucoin](https://www.kucoin.com/)

+## Community showcase
+
+--8<-- "includes/showcase.md"
+
 ## Requirements

 ### Hardware requirements
--- a/docs/requirements-docs.txt
+++ b/docs/requirements-docs.txt
@ -1,6 +1,6 @@
 markdown==3.3.7
 mkdocs==1.4.3
-mkdocs-material==9.1.18
+mkdocs-material==9.1.19
 mdx_truly_sane_lists==1.3
-pymdown-extensions==10.0.1
+pymdown-extensions==10.1
 jinja2==3.1.2
--- a/docs/strategy-callbacks.md
+++ b/docs/strategy-callbacks.md
@ -750,7 +750,7 @@ class DigDeeperStrategy(IStrategy):
        # Hope you have a deep wallet!
        try:
            # This returns first order stake size
-            stake_amount = filled_entries[0].cost
+            stake_amount = filled_entries[0].stake_amount
            # This then calculates current safety order size
            stake_amount = stake_amount * (1 + (count_of_entries * 0.25))
            return stake_amount
--- a/docs/telegram-usage.md
+++ b/docs/telegram-usage.md
@ -287,12 +287,17 @@ Return a summary of your profit/loss and performance.
 > **Best Performing:** `PAY/BTC: 50.23%`
 > **Trading volume:** `0.5 BTC`
 > **Profit factor:** `1.04`
+> **Win / Loss:** `102 / 36`
+> **Winrate:** `73.91%`
+> **Expectancy (Ratio):** `4.87 (1.66)`
 > **Max Drawdown:** `9.23% (0.01255 BTC)`

 The relative profit of `1.2%` is the average profit per trade.
 The relative profit of `15.2 Σ%` is be based on the starting capital - so in this case, the starting capital was `0.00485701 * 1.152 = 0.00738 BTC`.
 Starting capital is either taken from the `available_capital` setting, or calculated by using current wallet size - profits.
 Profit Factor is calculated as gross profits / gross losses - and should serve as an overall metric for the strategy.
+Expectancy corresponds to the average return per currency unit at risk, i.e. the winrate and the risk-reward ratio (the average gain of winning trades compared to the average loss of losing trades).
+Expectancy Ratio is expected profit or loss of a subsequent trade based on the performance of all past trades.
 Max drawdown corresponds to the backtesting metric `Absolute Drawdown (Account)` - calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
 Bot started date will refer to the date the bot was first started. For older bots, this will default to the first trade's open date.

--- a/docs/trade-object.md
+++ b/docs/trade-object.md
@ -141,7 +141,8 @@ 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
+`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.*)
+`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)
 `order_fill_date` | datetime |  Order fill date **use `order_fill_utc` instead**
--- a/docs/webhook-config.md
+++ b/docs/webhook-config.md
@ -80,12 +80,18 @@ When using the Form-Encoded or JSON-Encoded configuration you can configure any

 The result would be a POST request with e.g. `Status: running` body and `Content-Type: text/plain` header.

-Optional parameters are available to enable automatic retries for webhook messages. The `webhook.retries` parameter can be set for the maximum number of retries the webhook request should attempt if it is unsuccessful (i.e. HTTP response status is not 200). By default this is set to `0` which is disabled. An additional `webhook.retry_delay` parameter can be set to specify the time in seconds between retry attempts. By default this is set to `0.1` (i.e. 100ms). Note that increasing the number of retries or retry delay may slow down the trader if there are connectivity issues with the webhook. Example configuration for retries:
+## Additional configurations
+
+The `webhook.retries` parameter can be set for the maximum number of retries the webhook request should attempt if it is unsuccessful (i.e. HTTP response status is not 200). By default this is set to `0` which is disabled. An additional `webhook.retry_delay` parameter can be set to specify the time in seconds between retry attempts. By default this is set to `0.1` (i.e. 100ms). Note that increasing the number of retries or retry delay may slow down the trader if there are connectivity issues with the webhook.
+You can also specify `webhook.timeout` - which defines how long the bot will wait until it assumes the other host as unresponsive (defaults to 10s).
+
+Example configuration for retries:

 ```json
  "webhook": {
        "enabled": true,
        "url": "https://<YOURHOOKURL>",
+        "timeout": 10,
        "retries": 3,
        "retry_delay": 0.2,
        "status": {
@ -109,6 +115,8 @@ Custom messages can be sent to Webhook endpoints via the `self.dp.send_msg()` fu

 Different payloads can be configured for different events. Not all fields are necessary, but you should configure at least one of the dicts, otherwise the webhook will never be called.

+## Webhook Message types
+
 ### Entry

 The fields in `webhook.entry` are filled when the bot executes a long/short. Parameters are filled using string.format.
--- a/freqtrade/init.py
+++ b/freqtrade/init.py
@ -1,5 +1,5 @@
 """ Freqtrade bot """
-__version__ = '2023.7.dev'
+__version__ = '2023.8.dev'

 if 'dev' in __version__:
    from pathlib import Path
--- a/freqtrade/commands/build_config_commands.py
+++ b/freqtrade/commands/build_config_commands.py
@ -5,6 +5,7 @@ from typing import Any, Dict, List

 from questionary import Separator, prompt

+from freqtrade.configuration.detect_environment import running_in_docker
 from freqtrade.configuration.directory_operations import chown_user_directory
 from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
 from freqtrade.exceptions import OperationalException
@ -179,7 +180,7 @@ def ask_user_config() -> Dict[str, Any]:
            "name": "api_server_listen_addr",
            "message": ("Insert Api server Listen Address (0.0.0.0 for docker, "
                        "otherwise best left untouched)"),
-            "default": "127.0.0.1",
+            "default": "127.0.0.1" if not running_in_docker() else "0.0.0.0",
            "when": lambda x: x['api_server']
        },
        {
--- a/freqtrade/configuration/detect_environment.py
+++ b/freqtrade/configuration/detect_environment.py
@ -0,0 +1,8 @@
+import os
+
+
+def running_in_docker() -> bool:
+    """
+    Check if we are running in a docker container
+    """
+    return os.environ.get('FT_APP_ENV') == 'docker'
--- a/freqtrade/configuration/directory_operations.py
+++ b/freqtrade/configuration/directory_operations.py
@ -3,6 +3,7 @@ import shutil
 from pathlib import Path
 from typing import Optional

+from freqtrade.configuration.detect_environment import running_in_docker
 from freqtrade.constants import (USER_DATA_FILES, USERPATH_FREQAIMODELS, USERPATH_HYPEROPTS,
                                 USERPATH_NOTEBOOKS, USERPATH_STRATEGIES, Config)
 from freqtrade.exceptions import OperationalException
@ -30,8 +31,7 @@ def chown_user_directory(directory: Path) -> None:
    Use Sudo to change permissions of the home-directory if necessary
    Only applies when running in docker!
    """
-    import os
-    if os.environ.get('FT_APP_ENV') == 'docker':
+    if running_in_docker():
        try:
            import subprocess
            subprocess.check_output(
--- a/freqtrade/data/btanalysis.py
+++ b/freqtrade/data/btanalysis.py
@ -170,6 +170,7 @@ def load_and_merge_backtest_result(strategy_name: str, filename: Path, results:


 def _get_backtest_files(dirname: Path) -> List[Path]:
+    # Weird glob expression here avoids including .meta.json files.
    return list(reversed(sorted(dirname.glob('backtest-result-*-[0-9][0-9].json'))))


@ -184,7 +185,7 @@ def get_backtest_resultlist(dirname: Path):
            continue
        for s, v in metadata.items():
            results.append({
-                'filename': filename.name,
+                'filename': filename.stem,
                'strategy': s,
                'run_id': v['run_id'],
                'backtest_start_time': v['backtest_start_time'],
@ -193,6 +194,17 @@ def get_backtest_resultlist(dirname: Path):
    return results


+def delete_backtest_result(file_abs: Path):
+    """
+    Delete backtest result file and corresponding metadata file.
+    """
+    # *.meta.json
+    logger.info(f"Deleting backtest result file: {file_abs.name}")
+    file_abs_meta = file_abs.with_suffix('.meta.json')
+    file_abs.unlink()
+    file_abs_meta.unlink()
+
+
 def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, str],
                                 min_backtest_date: Optional[datetime] = None) -> Dict[str, Any]:
    """
@ -211,7 +223,6 @@ def find_existing_backtest_stats(dirname: Union[Path, str], run_ids: Dict[str, s
        'strategy_comparison': [],
    }

-    # Weird glob expression here avoids including .meta.json files.
    for filename in _get_backtest_files(dirname):
        metadata = load_backtest_metadata(filename)
        if not metadata:
--- a/freqtrade/data/converter.py
+++ b/freqtrade/data/converter.py
@ -96,8 +96,14 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
        'volume': 'sum'
    }
    timeframe_minutes = timeframe_to_minutes(timeframe)
+    resample_interval = f'{timeframe_minutes}min'
+    if timeframe_minutes >= 43200 and timeframe_minutes < 525600:
+        # Monthly candles need special treatment to stick to the 1st of the month
+        resample_interval = f'{timeframe}S'
+    elif timeframe_minutes > 43200:
+        resample_interval = timeframe
    # Resample to create "NAN" values
-    df = dataframe.resample(f'{timeframe_minutes}min', on='date').agg(ohlcv_dict)
+    df = dataframe.resample(resample_interval, on='date').agg(ohlcv_dict)

    # Forwardfill close for missing columns
    df['close'] = df['close'].fillna(method='ffill')
@ -122,7 +128,7 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, timeframe: str, pair: str)
    return df


-def trim_dataframe(df: DataFrame, timerange, df_date_col: str = 'date',
+def trim_dataframe(df: DataFrame, timerange, *, df_date_col: str = 'date',
                   startup_candles: int = 0) -> DataFrame:
    """
    Trim dataframe based on given timerange
--- a/freqtrade/data/metrics.py
+++ b/freqtrade/data/metrics.py
@ -194,32 +194,35 @@ def calculate_cagr(days_passed: int, starting_balance: float, final_balance: flo
    return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1


-def calculate_expectancy(trades: pd.DataFrame) -> float:
+def calculate_expectancy(trades: pd.DataFrame) -> Tuple[float, float]:
    """
    Calculate expectancy
    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
-    :return: expectancy
+    :return: expectancy, expectancy_ratio
    """
-    if len(trades) == 0:
-        return 0

-    expectancy = 1
+    expectancy = 0
+    expectancy_ratio = 100

-    profit_sum = trades.loc[trades['profit_abs'] > 0, 'profit_abs'].sum()
-    loss_sum = abs(trades.loc[trades['profit_abs'] < 0, 'profit_abs'].sum())
-    nb_win_trades = len(trades.loc[trades['profit_abs'] > 0])
-    nb_loss_trades = len(trades.loc[trades['profit_abs'] < 0])
+    if len(trades) > 0:
+        winning_trades = trades.loc[trades['profit_abs'] > 0]
+        losing_trades = trades.loc[trades['profit_abs'] < 0]
+        profit_sum = winning_trades['profit_abs'].sum()
+        loss_sum = abs(losing_trades['profit_abs'].sum())
+        nb_win_trades = len(winning_trades)
+        nb_loss_trades = len(losing_trades)

-    if (nb_win_trades > 0) and (nb_loss_trades > 0):
-        average_win = profit_sum / nb_win_trades
-        average_loss = loss_sum / nb_loss_trades
-        risk_reward_ratio = average_win / average_loss
-        winrate = nb_win_trades / len(trades)
-        expectancy = ((1 + risk_reward_ratio) * winrate) - 1
-    elif nb_win_trades == 0:
-        expectancy = 0
+        average_win = (profit_sum / nb_win_trades) if nb_win_trades > 0 else 0
+        average_loss = (loss_sum / nb_loss_trades) if nb_loss_trades > 0 else 0
+        winrate = (nb_win_trades / len(trades))
+        loserate = (nb_loss_trades / len(trades))

-    return expectancy
+        expectancy = (winrate * average_win) - (loserate * average_loss)
+        if (average_loss > 0):
+            risk_reward_ratio = average_win / average_loss
+            expectancy_ratio = ((1 + risk_reward_ratio) * winrate) - 1
+
+    return expectancy, expectancy_ratio


 def calculate_sortino(trades: pd.DataFrame, min_date: datetime, max_date: datetime,
--- a/freqtrade/edge/edge_positioning.py
+++ b/freqtrade/edge/edge_positioning.py
@ -172,13 +172,7 @@ class Edge:
            pair_data = pair_data.sort_values(by=['date'])
            pair_data = pair_data.reset_index(drop=True)

-            df_analyzed = self.strategy.advise_exit(
-                dataframe=self.strategy.advise_entry(
-                    dataframe=pair_data,
-                    metadata={'pair': pair}
-                ),
-                metadata={'pair': pair}
-            )[headers].copy()
+            df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})[headers].copy()

            trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)

--- a/freqtrade/exchange/binance.py
+++ b/freqtrade/exchange/binance.py
@ -34,6 +34,7 @@ class Binance(Exchange):
        "tickers_have_price": False,
        "floor_leverage": True,
        "stop_price_type_field": "workingType",
+        "order_props_in_contracts": ['amount', 'cost', 'filled', 'remaining'],
        "stop_price_type_value_mapping": {
            PriceType.LAST: "CONTRACT_PRICE",
            PriceType.MARK: "MARK_PRICE",
--- a/freqtrade/exchange/binance_leverage_tiers.json
+++ b/freqtrade/exchange/binance_leverage_tiers.json
--- a/freqtrade/exchange/exchange.py
+++ b/freqtrade/exchange/exchange.py
@ -80,9 +80,8 @@ class Exchange:
        "mark_ohlcv_price": "mark",
        "mark_ohlcv_timeframe": "8h",
        "ccxt_futures_name": "swap",
-        "fee_cost_in_contracts": False,  # Fee cost needs contract conversion
        "needs_trading_fees": False,  # use fetch_trading_fees to cache fees
-        "order_props_in_contracts": ['amount', 'cost', 'filled', 'remaining'],
+        "order_props_in_contracts": ['amount', 'filled', 'remaining'],
        # Override createMarketBuyOrderRequiresPrice where ccxt has it wrong
        "marketOrderRequiresPrice": False,
    }
@ -1859,9 +1858,6 @@ class Exchange:
        if fee_curr is None:
            return None
        fee_cost = float(fee['cost'])
-        if self._ft_has['fee_cost_in_contracts']:
-            # Convert cost via "contracts" conversion
-            fee_cost = self._contracts_to_amount(symbol, fee['cost'])

        # Calculate fee based on order details
        if fee_curr == self.get_pair_base_currency(symbol):
--- a/freqtrade/exchange/gate.py
+++ b/freqtrade/exchange/gate.py
@ -33,8 +33,6 @@ class Gate(Exchange):
    _ft_has_futures: Dict = {
        "needs_trading_fees": True,
        "marketOrderRequiresPrice": False,
-        "fee_cost_in_contracts": False,  # Set explicitly to false for clarity
-        "order_props_in_contracts": ['amount', 'filled', 'remaining'],
        "stop_price_type_field": "price_type",
        "stop_price_type_value_mapping": {
            PriceType.LAST: 0,
--- a/freqtrade/exchange/okx.py
+++ b/freqtrade/exchange/okx.py
@ -32,7 +32,6 @@ class Okx(Exchange):
    }
    _ft_has_futures: Dict = {
        "tickers_have_quoteVolume": False,
-        "fee_cost_in_contracts": True,
        "stop_price_type_field": "slTriggerPxType",
        "stop_price_type_value_mapping": {
            PriceType.LAST: "last",
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@ -86,8 +86,6 @@ class IFreqaiModel(ABC):
            logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")

        self.CONV_WIDTH = self.freqai_info.get('conv_width', 1)
-        if self.ft_params.get("inlier_metric_window", 0):
-            self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
        self.class_names: List[str] = []  # used in classification subclasses
        self.pair_it = 0
        self.pair_it_train = 0
@ -676,15 +674,6 @@ class IFreqaiModel(ABC):
        hist_preds_df['close_price'] = strat_df['close']
        hist_preds_df['date_pred'] = strat_df['date']

-        # # for keras type models, the conv_window needs to be prepended so
-        # # viewing is correct in frequi
-        if self.ft_params.get('inlier_metric_window', 0):
-            n_lost_points = self.freqai_info.get('conv_width', 2)
-            zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
-                                 columns=hist_preds_df.columns)
-            self.dd.historic_predictions[pair] = pd.concat(
-                [zeros_df, hist_preds_df], axis=0, ignore_index=True)
-
    def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
        """
        Fit the labels with a gaussian distribution
--- a/freqtrade/freqai/prediction_models/LightGBMClassifier.py
+++ b/freqtrade/freqai/prediction_models/LightGBMClassifier.py
@ -32,8 +32,8 @@ class LightGBMClassifier(BaseClassifierModel):
            eval_set = None
            test_weights = None
        else:
-            eval_set = (data_dictionary["test_features"].to_numpy(),
-                        data_dictionary["test_labels"].to_numpy()[:, 0])
+            eval_set = [(data_dictionary["test_features"].to_numpy(),
+                        data_dictionary["test_labels"].to_numpy()[:, 0])]
            test_weights = data_dictionary["test_weights"]
        X = data_dictionary["train_features"].to_numpy()
        y = data_dictionary["train_labels"].to_numpy()[:, 0]
@ -42,7 +42,6 @@ class LightGBMClassifier(BaseClassifierModel):
        init_model = self.get_init_model(dk.pair)

        model = LGBMClassifier(**self.model_training_parameters)
-
        model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
                  eval_sample_weight=[test_weights], init_model=init_model)

--- a/freqtrade/freqai/prediction_models/LightGBMRegressor.py
+++ b/freqtrade/freqai/prediction_models/LightGBMRegressor.py
@ -32,7 +32,7 @@ class LightGBMRegressor(BaseRegressionModel):
            eval_set = None
            eval_weights = None
        else:
-            eval_set = (data_dictionary["test_features"], data_dictionary["test_labels"])
+            eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
            eval_weights = data_dictionary["test_weights"]
        X = data_dictionary["train_features"]
        y = data_dictionary["train_labels"]
--- a/freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
+++ b/freqtrade/freqai/prediction_models/LightGBMRegressorMultiTarget.py
@ -42,10 +42,10 @@ class LightGBMRegressorMultiTarget(BaseRegressionModel):
            eval_weights = [data_dictionary["test_weights"]]
            eval_sets = [(None, None)] * data_dictionary['test_labels'].shape[1]  # type: ignore
            for i in range(data_dictionary['test_labels'].shape[1]):
-                eval_sets[i] = (  # type: ignore
+                eval_sets[i] = [(  # type: ignore
                    data_dictionary["test_features"],
                    data_dictionary["test_labels"].iloc[:, i]
-                )
+                )]

        init_model = self.get_init_model(dk.pair)
        if init_model:
--- a/freqtrade/freqtradebot.py
+++ b/freqtrade/freqtradebot.py
@ -1383,7 +1383,10 @@ class FreqtradeBot(LoggingMixin):
        latest_candle_close_date = timeframe_to_next_date(self.strategy.timeframe,
                                                          latest_candle_open_date)
        # Check if new candle
-        if order_obj and latest_candle_close_date > order_obj.order_date_utc:
+        if (
+            order_obj and order_obj.side == trade.entry_side
+            and latest_candle_close_date > order_obj.order_date_utc
+        ):
            # New candle
            proposed_rate = self.exchange.get_rate(
                trade.pair, side='entry', is_short=trade.is_short, refresh=True)
@ -1939,6 +1942,7 @@ class FreqtradeBot(LoggingMixin):
        """
        Applies the fee to amount (either from Order or from Trades).
        Can eat into dust if more than the required asset is available.
+        In case of trade adjustment orders, trade.amount will not have been adjusted yet.
        Can't happen in Futures mode - where Fees are always in settlement currency,
        never in base currency.
        """
@ -1948,6 +1952,10 @@ class FreqtradeBot(LoggingMixin):
            # check against remaining amount!
            amount_ = trade.amount - amount

+        if trade.nr_of_successful_entries >= 1 and order_obj.ft_order_side == trade.entry_side:
+            # In case of rebuy's, trade.amount doesn't contain the amount of the last entry.
+            amount_ = trade.amount + amount
+
        if fee_abs != 0 and self.wallets.get_free(trade_base_currency) >= amount_:
            # Eat into dust if we own more than base currency
            logger.info(f"Fee amount for {trade} was in base currency - "
@ -1977,7 +1985,11 @@ class FreqtradeBot(LoggingMixin):
        # Init variables
        order_amount = safe_value_fallback(order, 'filled', 'amount')
        # Only run for closed orders
-        if trade.fee_updated(order.get('side', '')) or order['status'] == 'open':
+        if (
+            trade.fee_updated(order.get('side', ''))
+            or order['status'] == 'open'
+            or order_obj.ft_fee_base
+        ):
            return None

        trade_base_currency = self.exchange.get_pair_base_currency(trade.pair)
--- a/freqtrade/misc.py
+++ b/freqtrade/misc.py
@ -116,6 +116,13 @@ def file_load_json(file: Path):
    return pairdata


+def is_file_in_dir(file: Path, directory: Path) -> bool:
+    """
+    Helper function to check if file is in directory.
+    """
+    return file.is_file() and file.parent.samefile(directory)
+
+
 def pair_to_filename(pair: str) -> str:
    for ch in ['/', ' ', '.', '@', '$', '+', ':']:
        pair = pair.replace(ch, '_')
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@ -367,11 +367,7 @@ class Backtesting:
            if not pair_data.empty:
                # Cleanup from prior runs
                pair_data.drop(HEADERS[5:] + ['buy', 'sell'], axis=1, errors='ignore')
-
-            df_analyzed = self.strategy.advise_exit(
-                self.strategy.advise_entry(pair_data, {'pair': pair}),
-                {'pair': pair}
-            ).copy()
+            df_analyzed = self.strategy.ft_advise_signals(pair_data, {'pair': pair})
            # Trim startup period from analyzed dataframe
            df_analyzed = processed[pair] = pair_data = trim_dataframe(
                df_analyzed, self.timerange, startup_candles=self.required_startup)
@ -679,6 +675,7 @@ class Backtesting:
            remaining=amount,
            cost=amount * close_rate,
        )
+        order._trade_bt = trade
        trade.orders.append(order)
        return trade

@ -901,8 +898,9 @@ class Backtesting:
                amount=amount,
                filled=0,
                remaining=amount,
-                cost=stake_amount + trade.fee_open,
+                cost=amount * propose_rate + trade.fee_open,
            )
+            order._trade_bt = trade
            trade.orders.append(order)
            if pos_adjust and self._get_order_filled(order.ft_price, row):
                order.close_bt_order(current_time, trade)
@ -1275,6 +1273,7 @@ class Backtesting:
        preprocessed = self.strategy.advise_all_indicators(data)

        # Trim startup period from analyzed dataframe
+        # This only used to determine if trimming would result in an empty dataframe
        preprocessed_tmp = trim_dataframes(preprocessed, timerange, self.required_startup)

        if not preprocessed_tmp:
--- a/freqtrade/optimize/hyperopt.py
+++ b/freqtrade/optimize/hyperopt.py
@ -446,6 +446,8 @@ class Hyperopt:
        preprocessed = self.backtesting.strategy.advise_all_indicators(data)

        # Trim startup period from analyzed dataframe to get correct dates for output.
+        # This is only used to keep track of min/max date after trimming.
+        # The result is NOT returned from this method, actual trimming happens in backtesting.
        trimmed = trim_dataframes(preprocessed, self.timerange, self.backtesting.required_startup)
        self.min_date, self.max_date = get_timerange(trimmed)
        if not self.market_change:
--- a/freqtrade/optimize/hyperopt_tools.py
+++ b/freqtrade/optimize/hyperopt_tools.py
@ -432,12 +432,10 @@ class HyperoptTools:
            for i in range(len(trials)):
                if trials.loc[i]['is_profit']:
                    for j in range(len(trials.loc[i]) - 3):
-                        trials.iat[i, j] = "{}{}{}".format(Fore.GREEN,
-                                                           str(trials.loc[i][j]), Fore.RESET)
+                        trials.iat[i, j] = f"{Fore.GREEN}{str(trials.loc[i][j])}{Fore.RESET}"
                if trials.loc[i]['is_best'] and highlight_best:
                    for j in range(len(trials.loc[i]) - 3):
-                        trials.iat[i, j] = "{}{}{}".format(Style.BRIGHT,
-                                                           str(trials.loc[i][j]), Style.RESET_ALL)
+                        trials.iat[i, j] = f"{Style.BRIGHT}{str(trials.loc[i][j])}{Style.RESET_ALL}"

        trials = trials.drop(columns=['is_initial_point', 'is_best', 'is_profit', 'is_random'])
        if remove_header > 0:
--- a/freqtrade/optimize/optimize_reports/init.py
+++ b/freqtrade/optimize/optimize_reports/init.py
@ -1,5 +1,6 @@
 # flake8: noqa: F401
 from freqtrade.optimize.optimize_reports.bt_output import (generate_edge_table,
+                                                           generate_wins_draws_losses,
                                                           show_backtest_result,
                                                           show_backtest_results,
                                                           show_sorted_pairlist,
@ -14,5 +15,4 @@ from freqtrade.optimize.optimize_reports.optimize_reports import (
    generate_all_periodic_breakdown_stats, generate_backtest_stats, generate_daily_stats,
    generate_exit_reason_stats, generate_pair_metrics, generate_periodic_breakdown_stats,
    generate_rejected_signals, generate_strategy_comparison, generate_strategy_stats,
-    generate_tag_metrics, generate_trade_signal_candles, generate_trading_stats,
-    generate_wins_draws_losses)
+    generate_tag_metrics, generate_trade_signal_candles, generate_trading_stats)
--- a/freqtrade/optimize/optimize_reports/bt_output.py
+++ b/freqtrade/optimize/optimize_reports/bt_output.py
@ -5,8 +5,7 @@ from tabulate import tabulate

 from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config
 from freqtrade.misc import decimals_per_coin, round_coin_value
-from freqtrade.optimize.optimize_reports.optimize_reports import (generate_periodic_breakdown_stats,
-                                                                  generate_wins_draws_losses)
+from freqtrade.optimize.optimize_reports.optimize_reports import generate_periodic_breakdown_stats


 logger = logging.getLogger(__name__)
@ -30,6 +29,16 @@ def _get_line_header(first_column: str, stake_currency: str,
            'Win  Draw  Loss  Win%']


+def generate_wins_draws_losses(wins, draws, losses):
+    if wins > 0 and losses == 0:
+        wl_ratio = '100'
+    elif wins == 0:
+        wl_ratio = '0'
+    else:
+        wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
+    return f'{wins:>4}  {draws:>4}  {losses:>4}  {wl_ratio:>4}'
+
+
 def text_table_bt_results(pair_results: List[Dict[str, Any]], stake_currency: str) -> str:
    """
    Generates and returns a text table for the given backtest data and the results dataframe
@ -233,8 +242,9 @@ def text_table_add_metrics(strat_results: Dict) -> str:
            ('Calmar', f"{strat_results['calmar']:.2f}" if 'calmar' in strat_results else 'N/A'),
            ('Profit factor', f'{strat_results["profit_factor"]:.2f}' if 'profit_factor'
                              in strat_results else 'N/A'),
-            ('Expectancy', f"{strat_results['expectancy']:.2f}" if 'expectancy'
-                           in strat_results else 'N/A'),
+            ('Expectancy (Ratio)', (
+                f"{strat_results['expectancy']:.2f} ({strat_results['expectancy_ratio']:.2f})" if
+                'expectancy_ratio' in strat_results else 'N/A')),
            ('Trades per day', strat_results['trades_per_day']),
            ('Avg. daily profit %',
             f"{(strat_results['profit_total'] / strat_results['backtest_days']):.2%}"),
@ -260,6 +270,9 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                f"{strat_results['draw_days']} / {strat_results['losing_days']}"),
            ('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
            ('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
+            ('Max Consecutive Wins / Loss',
+             f"{strat_results['max_consecutive_wins']} / {strat_results['max_consecutive_losses']}"
+             if 'max_consecutive_losses' in strat_results else 'N/A'),
            ('Rejected Entry signals', strat_results.get('rejected_signals', 'N/A')),
            ('Entry/Exit Timeouts',
             f"{strat_results.get('timedout_entry_orders', 'N/A')} / "
--- a/freqtrade/optimize/optimize_reports/optimize_reports.py
+++ b/freqtrade/optimize/optimize_reports/optimize_reports.py
@ -1,9 +1,10 @@
 import logging
 from copy import deepcopy
 from datetime import datetime, timedelta, timezone
-from typing import Any, Dict, List, Union
+from typing import Any, Dict, List, Tuple, Union

-from pandas import DataFrame, concat, to_datetime
+import numpy as np
+from pandas import DataFrame, Series, concat, to_datetime

 from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT, IntOrInf
 from freqtrade.data.metrics import (calculate_cagr, calculate_calmar, calculate_csum,
@ -57,16 +58,6 @@ def generate_rejected_signals(preprocessed_df: Dict[str, DataFrame],
    return rejected_candles_only


-def generate_wins_draws_losses(wins, draws, losses):
-    if wins > 0 and losses == 0:
-        wl_ratio = '100'
-    elif wins == 0:
-        wl_ratio = '0'
-    else:
-        wl_ratio = f'{100.0 / (wins + draws + losses) * wins:.1f}' if losses > 0 else '100'
-    return f'{wins:>4}  {draws:>4}  {losses:>4}  {wl_ratio:>4}'
-
-
 def _generate_result_line(result: DataFrame, starting_balance: int, first_column: str) -> Dict:
    """
    Generate one result dict, with "first_column" as key.
@ -97,6 +88,7 @@ def _generate_result_line(result: DataFrame, starting_balance: int, first_column
        'wins': len(result[result['profit_abs'] > 0]),
        'draws': len(result[result['profit_abs'] == 0]),
        'losses': len(result[result['profit_abs'] < 0]),
+        'winrate': len(result[result['profit_abs'] > 0]) / len(result) if len(result) else 0.0,
    }


@ -184,6 +176,7 @@ def generate_exit_reason_stats(max_open_trades: IntOrInf, results: DataFrame) ->
                'wins': len(result[result['profit_abs'] > 0]),
                'draws': len(result[result['profit_abs'] == 0]),
                'losses': len(result[result['profit_abs'] < 0]),
+                'winrate': len(result[result['profit_abs'] > 0]) / count if count else 0.0,
                'profit_mean': profit_mean,
                'profit_mean_pct': round(profit_mean * 100, 2),
                'profit_sum': profit_sum,
@ -238,6 +231,7 @@ def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dic
        wins = sum(day['profit_abs'] > 0)
        draws = sum(day['profit_abs'] == 0)
        loses = sum(day['profit_abs'] < 0)
+        trades = (wins + draws + loses)
        stats.append(
            {
                'date': name.strftime('%d/%m/%Y'),
@ -245,7 +239,8 @@ def generate_periodic_breakdown_stats(trade_list: List, period: str) -> List[Dic
                'profit_abs': profit_abs,
                'wins': wins,
                'draws': draws,
-                'loses': loses
+                'loses': loses,
+                'winrate': wins / trades if trades else 0.0,
            }
        )
    return stats
@ -258,6 +253,23 @@ def generate_all_periodic_breakdown_stats(trade_list: List) -> Dict[str, List]:
    return result


+def calc_streak(dataframe: DataFrame) -> Tuple[int, int]:
+    """
+    Calculate consecutive win and loss streaks
+    :param dataframe: Dataframe containing the trades dataframe, with profit_ratio column
+    :return: Tuple containing consecutive wins and losses
+    """
+
+    df = Series(np.where(dataframe['profit_ratio'] > 0, 'win', 'loss')).to_frame('result')
+    df['streaks'] = df['result'].ne(df['result'].shift()).cumsum().rename('streaks')
+    df['counter'] = df['streaks'].groupby(df['streaks']).cumcount() + 1
+    res = df.groupby(df['result']).max()
+    #
+    cons_wins = int(res.loc['win', 'counter']) if 'win' in res.index else 0
+    cons_losses = int(res.loc['loss', 'counter']) if 'loss' in res.index else 0
+    return cons_wins, cons_losses
+
+
 def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
    """ Generate overall trade statistics """
    if len(results) == 0:
@ -265,9 +277,12 @@ def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
            'wins': 0,
            'losses': 0,
            'draws': 0,
+            'winrate': 0,
            'holding_avg': timedelta(),
            'winner_holding_avg': timedelta(),
            'loser_holding_avg': timedelta(),
+            'max_consecutive_wins': 0,
+            'max_consecutive_losses': 0,
        }

    winning_trades = results.loc[results['profit_ratio'] > 0]
@ -280,17 +295,21 @@ def generate_trading_stats(results: DataFrame) -> Dict[str, Any]:
                          if not winning_trades.empty else timedelta())
    loser_holding_avg = (timedelta(minutes=round(losing_trades['trade_duration'].mean()))
                         if not losing_trades.empty else timedelta())
+    winstreak, loss_streak = calc_streak(results)

    return {
        'wins': len(winning_trades),
        'losses': len(losing_trades),
        'draws': len(draw_trades),
+        'winrate': len(winning_trades) / len(results) if len(results) else 0.0,
        'holding_avg': holding_avg,
        'holding_avg_s': holding_avg.total_seconds(),
        'winner_holding_avg': winner_holding_avg,
        'winner_holding_avg_s': winner_holding_avg.total_seconds(),
        'loser_holding_avg': loser_holding_avg,
        'loser_holding_avg_s': loser_holding_avg.total_seconds(),
+        'max_consecutive_wins': winstreak,
+        'max_consecutive_losses': loss_streak,
    }


@ -383,6 +402,7 @@ def generate_strategy_stats(pairlist: List[str],
    losing_profit = results.loc[results['profit_abs'] < 0, 'profit_abs'].sum()
    profit_factor = winning_profit / abs(losing_profit) if losing_profit else 0.0

+    expectancy, expectancy_ratio = calculate_expectancy(results)
    backtest_days = (max_date - min_date).days or 1
    strat_stats = {
        'trades': results.to_dict(orient='records'),
@ -408,7 +428,8 @@ def generate_strategy_stats(pairlist: List[str],
        'profit_total_long_abs': results.loc[~results['is_short'], 'profit_abs'].sum(),
        'profit_total_short_abs': results.loc[results['is_short'], 'profit_abs'].sum(),
        'cagr': calculate_cagr(backtest_days, start_balance, content['final_balance']),
-        'expectancy': calculate_expectancy(results),
+        'expectancy': expectancy,
+        'expectancy_ratio': expectancy_ratio,
        'sortino': calculate_sortino(results, min_date, max_date, start_balance),
        'sharpe': calculate_sharpe(results, min_date, max_date, start_balance),
        'calmar': calculate_calmar(results, min_date, max_date, start_balance),
--- a/freqtrade/persistence/trade_model.py
+++ b/freqtrade/persistence/trade_model.py
@ -38,6 +38,7 @@ class Order(ModelBase):
    Mirrors CCXT Order structure
    """
    __tablename__ = 'orders'
+    __allow_unmapped__ = True
    session: ClassVar[SessionType]

    # Uniqueness should be ensured over pair, order_id
@ -47,7 +48,8 @@ class Order(ModelBase):
    id: Mapped[int] = mapped_column(Integer, primary_key=True)
    ft_trade_id: Mapped[int] = mapped_column(Integer, ForeignKey('trades.id'), index=True)

-    trade: Mapped["Trade"] = relationship("Trade", back_populates="orders")
+    _trade_live: Mapped["Trade"] = relationship("Trade", back_populates="orders")
+    _trade_bt: "LocalTrade" = None  # type: ignore

    # order_side can only be 'buy', 'sell' or 'stoploss'
    ft_order_side: Mapped[str] = mapped_column(String(25), nullable=False)
@ -119,6 +121,15 @@ class Order(ModelBase):
    def safe_amount_after_fee(self) -> float:
        return self.safe_filled - self.safe_fee_base

+    @property
+    def trade(self) -> "LocalTrade":
+        return self._trade_bt or self._trade_live
+
+    @property
+    def stake_amount(self) -> float:
+        """ Amount in stake currency used for this order"""
+        return self.safe_amount * self.safe_price / self.trade.leverage
+
    def __repr__(self):

        return (f"Order(id={self.id}, trade={self.ft_trade_id}, order_id={self.order_id}, "
@ -1299,9 +1310,12 @@ class Trade(ModelBase, LocalTrade):
        Float(), nullable=True, default=None)  # type: ignore

    def __init__(self, **kwargs):
+        from_json = kwargs.pop('__FROM_JSON', None)
        super().__init__(**kwargs)
-        self.realized_profit = 0
-        self.recalc_open_trade_value()
+        if not from_json:
+            # Skip recalculation when loading from json
+            self.realized_profit = 0
+            self.recalc_open_trade_value()

    @validates('enter_tag', 'exit_reason')
    def validate_string_len(self, key, value):
@ -1655,6 +1669,7 @@ class Trade(ModelBase, LocalTrade):
        import rapidjson
        data = rapidjson.loads(json_str)
        trade = cls(
+            __FROM_JSON=True,
            id=data["trade_id"],
            pair=data["pair"],
            base_currency=data["base_currency"],
--- a/freqtrade/plot/plotting.py
+++ b/freqtrade/plot/plotting.py
@ -84,7 +84,7 @@ def init_plotscript(config, markets: List, startup_candles: int = 0):
    except ValueError as e:
        raise OperationalException(e) from e
    if not trades.empty:
-        trades = trim_dataframe(trades, timerange, 'open_date')
+        trades = trim_dataframe(trades, timerange, df_date_col='open_date')

    return {"ohlcv": data,
            "trades": trades,
--- a/freqtrade/plugins/pairlist/RemotePairList.py
+++ b/freqtrade/plugins/pairlist/RemotePairList.py
@ -3,15 +3,16 @@ Remote PairList provider

 Provides pair list fetched from a remote source
 """
-import json
 import logging
 from pathlib import Path
 from typing import Any, Dict, List, Tuple

+import rapidjson
 import requests
 from cachetools import TTLCache

 from freqtrade import __version__
+from freqtrade.configuration.load_config import CONFIG_PARSE_MODE
 from freqtrade.constants import Config
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange.types import Tickers
@ -236,7 +237,7 @@ class RemotePairList(IPairList):
                if file_path.exists():
                    with file_path.open() as json_file:
                        # Load the JSON data into a dictionary
-                        jsonparse = json.load(json_file)
+                        jsonparse = rapidjson.load(json_file, parse_mode=CONFIG_PARSE_MODE)

                        try:
                            pairlist = self.process_json(jsonparse)
--- a/freqtrade/rpc/api_server/api_backtest.py
+++ b/freqtrade/rpc/api_server/api_backtest.py
@ -2,6 +2,7 @@ import asyncio
 import logging
 from copy import deepcopy
 from datetime import datetime
+from pathlib import Path
 from typing import Any, Dict, List

 from fastapi import APIRouter, BackgroundTasks, Depends
@ -9,11 +10,12 @@ from fastapi.exceptions import HTTPException

 from freqtrade.configuration.config_validation import validate_config_consistency
 from freqtrade.constants import Config
-from freqtrade.data.btanalysis import get_backtest_resultlist, load_and_merge_backtest_result
+from freqtrade.data.btanalysis import (delete_backtest_result, get_backtest_resultlist,
+                                       load_and_merge_backtest_result)
 from freqtrade.enums import BacktestState
 from freqtrade.exceptions import DependencyException, OperationalException
 from freqtrade.exchange.common import remove_exchange_credentials
-from freqtrade.misc import deep_merge_dicts
+from freqtrade.misc import deep_merge_dicts, is_file_in_dir
 from freqtrade.rpc.api_server.api_schemas import (BacktestHistoryEntry, BacktestRequest,
                                                  BacktestResponse)
 from freqtrade.rpc.api_server.deps import get_config
@ -245,13 +247,16 @@ def api_backtest_history(config=Depends(get_config)):
            tags=['webserver', 'backtest'])
 def api_backtest_history_result(filename: str, strategy: str, config=Depends(get_config)):
    # Get backtest result history, read from metadata files
-    fn = config['user_data_dir'] / 'backtest_results' / filename
+    bt_results_base: Path = config['user_data_dir'] / 'backtest_results'
+    fn = (bt_results_base / filename).with_suffix('.json')
+
    results: Dict[str, Any] = {
        'metadata': {},
        'strategy': {},
        'strategy_comparison': [],
    }
-
+    if not is_file_in_dir(fn, bt_results_base):
+        raise HTTPException(status_code=404, detail="File not found.")
    load_and_merge_backtest_result(strategy, fn, results)
    return {
        "status": "ended",
@ -261,3 +266,17 @@ def api_backtest_history_result(filename: str, strategy: str, config=Depends(get
        "status_msg": "Historic result",
        "backtest_result": results,
    }
+
+
+@router.delete('/backtest/history/{file}', response_model=List[BacktestHistoryEntry],
+               tags=['webserver', 'backtest'])
+def api_delete_backtest_history_entry(file: str, config=Depends(get_config)):
+    # Get backtest result history, read from metadata files
+    bt_results_base: Path = config['user_data_dir'] / 'backtest_results'
+    file_abs = (bt_results_base / file).with_suffix('.json')
+    # 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.")
+
+    delete_backtest_result(file_abs)
+    return get_backtest_resultlist(config['user_data_dir'] / 'backtest_results')
--- a/freqtrade/rpc/api_server/api_schemas.py
+++ b/freqtrade/rpc/api_server/api_schemas.py
@ -136,6 +136,9 @@ class Profit(BaseModel):
    winning_trades: int
    losing_trades: int
    profit_factor: float
+    winrate: float
+    expectancy: float
+    expectancy_ratio: float
    max_drawdown: float
    max_drawdown_abs: float
    trading_volume: Optional[float]
--- a/freqtrade/rpc/api_server/api_v1.py
+++ b/freqtrade/rpc/api_server/api_v1.py
@ -49,7 +49,8 @@ logger = logging.getLogger(__name__)
 # 2.28: Switch reload endpoint to Post
 # 2.29: Add /exchanges endpoint
 # 2.30: new /pairlists endpoint
-API_VERSION = 2.30
+# 2.31: new /backtest/history/ delete endpoint
+API_VERSION = 2.31

 # Public API, requires no auth.
 router_public = APIRouter()
@ -268,7 +269,10 @@ def pair_history(pair: str, timeframe: str, timerange: str, strategy: str,
        'timerange': timerange,
        'freqaimodel': freqaimodel if freqaimodel else config.get('freqaimodel'),
    })
-    return RPC._rpc_analysed_history_full(config, pair, timeframe, exchange)
+    try:
+        return RPC._rpc_analysed_history_full(config, pair, timeframe, exchange)
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=str(e))


@router.get('/plot_config', response_model=PlotConfig, tags=['candle data'])
@ -283,7 +287,10 @@ def plot_config(strategy: Optional[str] = None, config=Depends(get_config),
        config1.update({
            'strategy': strategy
        })
+    try:
        return PlotConfig.parse_obj(RPC._rpc_plot_config_with_strategy(config1))
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=str(e))


@router.get('/strategies', response_model=StrategyListResponse, tags=['strategy'])
@ -308,7 +315,8 @@ def get_strategy(strategy: str, config=Depends(get_config)):
                                                       extra_dir=config_.get('strategy_path'))
    except OperationalException:
        raise HTTPException(status_code=404, detail='Strategy not found')
-
+    except Exception as e:
+        raise HTTPException(status_code=502, detail=str(e))
    return {
        'strategy': strategy_obj.get_strategy_name(),
        'code': strategy_obj.__source__,
--- a/freqtrade/rpc/api_server/web_ui.py
+++ b/freqtrade/rpc/api_server/web_ui.py
@ -30,7 +30,7 @@ async def ui_version():
    }


-def is_relative_to(path, base) -> bool:
+def is_relative_to(path: Path, base: Path) -> bool:
    # Helper function simulating behaviour of is_relative_to, which was only added in python 3.9
    try:
        path.relative_to(base)
--- a/freqtrade/rpc/fiat_convert.py
+++ b/freqtrade/rpc/fiat_convert.py
@ -25,6 +25,7 @@ coingecko_mapping = {
    'bnb': 'binancecoin',
    'sol': 'solana',
    'usdt': 'tether',
+    'busd': 'binance-usd',
 }


--- a/freqtrade/rpc/rpc.py
+++ b/freqtrade/rpc/rpc.py
@ -18,7 +18,7 @@ from freqtrade import __version__
 from freqtrade.configuration.timerange import TimeRange
 from freqtrade.constants import CANCEL_REASON, DATETIME_PRINT_FORMAT, Config
 from freqtrade.data.history import load_data
-from freqtrade.data.metrics import calculate_max_drawdown
+from freqtrade.data.metrics import calculate_expectancy, calculate_max_drawdown
 from freqtrade.enums import (CandleType, ExitCheckTuple, ExitType, MarketDirection, SignalDirection,
                             State, TradingMode)
 from freqtrade.exceptions import ExchangeError, PricingError
@ -494,6 +494,8 @@ class RPC:
            profit_all_coin.append(profit_abs)
            profit_all_ratio.append(profit_ratio)

+        closed_trade_count = len([t for t in trades if not t.is_open])
+
        best_pair = Trade.get_best_pair(start_date)
        trading_volume = Trade.get_trading_volume(start_date)

@ -521,9 +523,14 @@ class RPC:

        profit_factor = winning_profit / abs(losing_profit) if losing_profit else float('inf')

+        winrate = (winning_trades / closed_trade_count) if closed_trade_count > 0 else 0
+
        trades_df = DataFrame([{'close_date': trade.close_date.strftime(DATETIME_PRINT_FORMAT),
                                'profit_abs': trade.close_profit_abs}
                               for trade in trades if not trade.is_open and trade.close_date])
+
+        expectancy, expectancy_ratio = calculate_expectancy(trades_df)
+
        max_drawdown_abs = 0.0
        max_drawdown = 0.0
        if len(trades_df) > 0:
@ -562,7 +569,7 @@ class RPC:
            'profit_all_percent': round(profit_all_ratio_fromstart * 100, 2),
            'profit_all_fiat': profit_all_fiat,
            'trade_count': len(trades),
-            'closed_trade_count': len([t for t in trades if not t.is_open]),
+            'closed_trade_count': closed_trade_count,
            'first_trade_date': first_date.strftime(DATETIME_PRINT_FORMAT) if first_date else '',
            'first_trade_humanized': dt_humanize(first_date) if first_date else '',
            'first_trade_timestamp': int(first_date.timestamp() * 1000) if first_date else 0,
@ -576,6 +583,9 @@ class RPC:
            'winning_trades': winning_trades,
            'losing_trades': losing_trades,
            'profit_factor': profit_factor,
+            'winrate': winrate,
+            'expectancy': expectancy,
+            'expectancy_ratio': expectancy_ratio,
            'max_drawdown': max_drawdown,
            'max_drawdown_abs': max_drawdown_abs,
            'trading_volume': trading_volume,
@ -1169,8 +1179,8 @@ class RPC:
        """ Analyzed dataframe in Dict form """

        _data, last_analyzed = self.__rpc_analysed_dataframe_raw(pair, timeframe, limit)
-        return self._convert_dataframe_to_dict(self._freqtrade.config['strategy'],
-                                               pair, timeframe, _data, last_analyzed)
+        return RPC._convert_dataframe_to_dict(self._freqtrade.config['strategy'],
+                                              pair, timeframe, _data, last_analyzed)

    def __rpc_analysed_dataframe_raw(
        self,
@ -1240,27 +1250,34 @@ class RPC:
                                   exchange) -> Dict[str, Any]:
        timerange_parsed = TimeRange.parse_timerange(config.get('timerange'))

+        from freqtrade.data.converter import trim_dataframe
+        from freqtrade.data.dataprovider import DataProvider
+        from freqtrade.resolvers.strategy_resolver import StrategyResolver
+
+        strategy = StrategyResolver.load_strategy(config)
+        startup_candles = strategy.startup_candle_count
+
        _data = load_data(
            datadir=config["datadir"],
            pairs=[pair],
            timeframe=timeframe,
            timerange=timerange_parsed,
            data_format=config['dataformat_ohlcv'],
-            candle_type=config.get('candle_type_def', CandleType.SPOT)
+            candle_type=config.get('candle_type_def', CandleType.SPOT),
+            startup_candles=startup_candles,
        )
        if pair not in _data:
            raise RPCException(
                f"No data for {pair}, {timeframe} in {config.get('timerange')} found.")
-        from freqtrade.data.dataprovider import DataProvider
-        from freqtrade.resolvers.strategy_resolver import StrategyResolver
-        strategy = StrategyResolver.load_strategy(config)
+
        strategy.dp = DataProvider(config, exchange=exchange, pairlists=None)
        strategy.ft_bot_start()

        df_analyzed = strategy.analyze_ticker(_data[pair], {'pair': pair})
+        df_analyzed = trim_dataframe(df_analyzed, timerange_parsed, startup_candles=startup_candles)

        return RPC._convert_dataframe_to_dict(strategy.get_strategy_name(), pair, timeframe,
-                                              df_analyzed, dt_now())
+                                              df_analyzed.copy(), dt_now())

    def _rpc_plot_config(self) -> Dict[str, Any]:
        if (self._freqtrade.strategy.plot_config and
--- a/freqtrade/rpc/telegram.py
+++ b/freqtrade/rpc/telegram.py
@ -849,6 +849,10 @@ class Telegram(RPCHandler):
        avg_duration = stats['avg_duration']
        best_pair = stats['best_pair']
        best_pair_profit_ratio = stats['best_pair_profit_ratio']
+        winrate = stats['winrate']
+        expectancy = stats['expectancy']
+        expectancy_ratio = stats['expectancy_ratio']
+
        if stats['trade_count'] == 0:
            markdown_msg = f"No trades yet.\n*Bot started:* `{stats['bot_start_date']}`"
        else:
@ -873,7 +877,9 @@ class Telegram(RPCHandler):
                f"*{'First Trade opened' if not timescale else 'Showing Profit since'}:* "
                f"`{first_trade_date}`\n"
                f"*Latest Trade opened:* `{latest_trade_date}`\n"
-                f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`"
+                f"*Win / Loss:* `{stats['winning_trades']} / {stats['losing_trades']}`\n"
+                f"*Winrate:* `{winrate:.2%}`\n"
+                f"*Expectancy (Ratio):* `{expectancy:.2f} ({expectancy_ratio:.2f})`"
            )
            if stats['closed_trade_count'] > 0:
                markdown_msg += (
--- a/freqtrade/rpc/webhook.py
+++ b/freqtrade/rpc/webhook.py
@ -34,6 +34,7 @@ class Webhook(RPCHandler):
        self._format = self._config['webhook'].get('format', 'form')
        self._retries = self._config['webhook'].get('retries', 0)
        self._retry_delay = self._config['webhook'].get('retry_delay', 0.1)
+        self._timeout = self._config['webhook'].get('timeout', 10)

    def cleanup(self) -> None:
        """
@ -107,12 +108,13 @@ class Webhook(RPCHandler):

            try:
                if self._format == 'form':
-                    response = post(self._url, data=payload)
+                    response = post(self._url, data=payload, timeout=self._timeout)
                elif self._format == 'json':
-                    response = post(self._url, json=payload)
+                    response = post(self._url, json=payload, timeout=self._timeout)
                elif self._format == 'raw':
                    response = post(self._url, data=payload['data'],
-                                    headers={'Content-Type': 'text/plain'})
+                                    headers={'Content-Type': 'text/plain'},
+                                    timeout=self._timeout)
                else:
                    raise NotImplementedError(f'Unknown format: {self._format}')

--- a/freqtrade/strategy/interface.py
+++ b/freqtrade/strategy/interface.py
@ -825,6 +825,7 @@ class IStrategy(ABC, HyperStrategyMixin):
        """
        Parses the given candle (OHLCV) data and returns a populated DataFrame
        add several TA indicators and entry order signal to it
+        Should only be used in live.
        :param dataframe: Dataframe containing data from exchange
        :param metadata: Metadata dictionary with additional data (e.g. 'pair')
        :return: DataFrame of candle (OHLCV) data with indicator data and signals added
@ -1321,6 +1322,20 @@ class IStrategy(ABC, HyperStrategyMixin):
        return {pair: self.advise_indicators(pair_data.copy(), {'pair': pair}).copy()
                for pair, pair_data in data.items()}

+    def ft_advise_signals(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Call advise_entry and advise_exit and return the resulting dataframe.
+        :param dataframe: Dataframe containing data from exchange, as well as pre-calculated
+                          indicators
+        :param metadata: Metadata dictionary with additional data (e.g. 'pair')
+        :return: DataFrame of candle (OHLCV) data with indicator data and signals added
+
+        """
+
+        dataframe = self.advise_entry(dataframe, metadata)
+        dataframe = self.advise_exit(dataframe, metadata)
+        return dataframe
+
    def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        """
        Populate indicators that will be used in the Buy, Sell, short, exit_short strategy
--- a/freqtrade/wallets.py
+++ b/freqtrade/wallets.py
@ -84,6 +84,7 @@ class Wallets:
            tot_profit = Trade.get_total_closed_profit()
        else:
            tot_profit = LocalTrade.total_profit
+        tot_profit += sum(trade.realized_profit for trade in open_trades)
        tot_in_trades = sum(trade.stake_amount for trade in open_trades)
        used_stake = 0.0

--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@ -7,11 +7,11 @@
 -r docs/requirements-docs.txt

 coveralls==3.3.1
-ruff==0.0.277
+ruff==0.0.280
 mypy==1.4.1
 pre-commit==3.3.3
 pytest==7.4.0
-pytest-asyncio==0.21.0
+pytest-asyncio==0.21.1
 pytest-cov==4.1.0
 pytest-mock==3.11.1
 pytest-random-order==1.1.0
@ -20,11 +20,11 @@ isort==5.12.0
 time-machine==2.11.0

 # Convert jupyter notebooks to markdown documents
-nbconvert==7.6.0
+nbconvert==7.7.2

 # mypy types
-types-cachetools==5.3.0.5
+types-cachetools==5.3.0.6
 types-filelock==3.2.7
-types-requests==2.31.0.1
-types-tabulate==0.9.0.2
-types-python-dateutil==2.8.19.13
+types-requests==2.31.0.2
+types-tabulate==0.9.0.3
+types-python-dateutil==2.8.19.14
--- a/requirements-freqai.txt
+++ b/requirements-freqai.txt
@ -6,7 +6,7 @@
 scikit-learn==1.1.3
 joblib==1.3.1
 catboost==1.2; 'arm' not in platform_machine
-lightgbm==3.3.5
+lightgbm==4.0.0
 xgboost==1.7.6
 tensorboard==2.13.0
 datasieve==0.1.7
--- a/requirements.txt
+++ b/requirements.txt
@ -3,20 +3,20 @@ numpy==1.24.3; python_version <= '3.8'
 pandas==2.0.3
 pandas-ta==0.3.14b

-ccxt==4.0.17
-cryptography==41.0.1; platform_machine != 'armv7l'
+ccxt==4.0.36
+cryptography==41.0.2; platform_machine != 'armv7l'
 cryptography==40.0.1; platform_machine == 'armv7l'
-aiohttp==3.8.4
-SQLAlchemy==2.0.18
+aiohttp==3.8.5
+SQLAlchemy==2.0.19
 python-telegram-bot==20.4
 # can't be hard-pinned due to telegram-bot pinning httpx with ~
 httpx>=0.24.1
 arrow==1.2.3
 cachetools==5.3.1
 requests==2.31.0
-urllib3==2.0.3
-jsonschema==4.18.0
-TA-Lib==0.4.26
+urllib3==2.0.4
+jsonschema==4.18.4
+TA-Lib==0.4.27
 technical==1.4.0
 tabulate==0.9.0
 pycoingecko==3.1.0
@ -25,7 +25,7 @@ tables==3.8.0
 blosc==1.11.1
 joblib==1.3.1
 rich==13.4.2
-pyarrow==12.0.0; platform_machine != 'armv7l'
+pyarrow==12.0.1; platform_machine != 'armv7l'

 # find first, C search in arrays
 py_find_1st==1.1.5
@ -40,9 +40,9 @@ sdnotify==0.3.2

 # API Server
 fastapi==0.100.0
-pydantic==1.10.9
-uvicorn==0.22.0
-pyjwt==2.7.0
+pydantic==1.10.11
+uvicorn==0.23.1
+pyjwt==2.8.0
 aiofiles==23.1.0
 psutil==5.9.5

--- a/setup.py
+++ b/setup.py
@ -97,7 +97,7 @@ setup(
        'rich',
        'pyarrow; platform_machine != "armv7l"',
        'fastapi',
-        'pydantic>=1.8.0',
+        'pydantic>=1.8.0,<2.0',
        'uvicorn',
        'psutil',
        'pyjwt',
--- a/tests/conftest.py
+++ b/tests/conftest.py
@ -3002,85 +3002,85 @@ def mark_ohlcv():
 def funding_rate_history_hourly():
    return [
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": -0.000008,
            "timestamp": 1630454400000,
            "datetime": "2021-09-01T00:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": -0.000004,
            "timestamp": 1630458000000,
            "datetime": "2021-09-01T01:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": 0.000012,
            "timestamp": 1630461600000,
            "datetime": "2021-09-01T02:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": -0.000003,
            "timestamp": 1630465200000,
            "datetime": "2021-09-01T03:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": -0.000007,
            "timestamp": 1630468800000,
            "datetime": "2021-09-01T04:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": 0.000003,
            "timestamp": 1630472400000,
            "datetime": "2021-09-01T05:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": 0.000019,
            "timestamp": 1630476000000,
            "datetime": "2021-09-01T06:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": 0.000003,
            "timestamp": 1630479600000,
            "datetime": "2021-09-01T07:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": -0.000003,
            "timestamp": 1630483200000,
            "datetime": "2021-09-01T08:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": 0,
            "timestamp": 1630486800000,
            "datetime": "2021-09-01T09:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": 0.000013,
            "timestamp": 1630490400000,
            "datetime": "2021-09-01T10:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": 0.000077,
            "timestamp": 1630494000000,
            "datetime": "2021-09-01T11:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": 0.000072,
            "timestamp": 1630497600000,
            "datetime": "2021-09-01T12:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": 0.000097,
            "timestamp": 1630501200000,
            "datetime": "2021-09-01T13:00:00.000Z"
@ -3092,13 +3092,13 @@ def funding_rate_history_hourly():
 def funding_rate_history_octohourly():
    return [
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": -0.000008,
            "timestamp": 1630454400000,
            "datetime": "2021-09-01T00:00:00.000Z"
        },
        {
-            "symbol": "ADA/USDT",
+            "symbol": "ADA/USDT:USDT",
            "fundingRate": -0.000003,
            "timestamp": 1630483200000,
            "datetime": "2021-09-01T08:00:00.000Z"
--- a/tests/data/test_btanalysis.py
+++ b/tests/data/test_btanalysis.py
@ -343,12 +343,24 @@ def test_calculate_expectancy(testdatadir):
    filename = testdatadir / "backtest_results/backtest-result.json"
    bt_data = load_backtest_data(filename)

-    expectancy = calculate_expectancy(DataFrame())
+    expectancy, expectancy_ratio = calculate_expectancy(DataFrame())
    assert expectancy == 0.0
+    assert expectancy_ratio == 100

-    expectancy = calculate_expectancy(bt_data)
+    expectancy, expectancy_ratio = calculate_expectancy(bt_data)
    assert isinstance(expectancy, float)
-    assert pytest.approx(expectancy) == 0.07151374226574791
+    assert isinstance(expectancy_ratio, float)
+    assert pytest.approx(expectancy) == 5.820687070932315e-06
+    assert pytest.approx(expectancy_ratio) == 0.07151374226574791
+
+    data = {
+        'profit_abs': [100, 200, 50, -150, 300, -100, 80, -30]
+    }
+    df = DataFrame(data)
+    expectancy, expectancy_ratio = calculate_expectancy(df)
+
+    assert pytest.approx(expectancy) == 56.25
+    assert pytest.approx(expectancy_ratio) == 0.60267857


 def test_calculate_sortino(testdatadir):
--- a/tests/data/test_converter.py
+++ b/tests/data/test_converter.py
@ -135,6 +135,73 @@ def test_ohlcv_fill_up_missing_data2(caplog):
                      f"{len(data)} - after: {len(data2)}.*", caplog)


+def test_ohlcv_to_dataframe_1M():
+
+    # Monthly ticks from 2019-09-01 to 2023-07-01
+    ticks = [
+        [1567296000000, 8042.08, 10475.54, 7700.67, 8041.96, 608742.1109999999],
+        [1569888000000, 8285.31, 10408.48, 7172.76, 9150.0, 2439561.887],
+        [1572566400000, 9149.88, 9550.0, 6510.19, 7542.93, 4042674.725],
+        [1575158400000, 7541.08, 7800.0, 6427.0, 7189.0, 4063882.296],
+        [1577836800000, 7189.43, 9599.0, 6863.44, 9364.51, 5165281.358],
+        [1580515200000, 9364.5, 10540.0, 8450.0, 8531.98, 4581788.124],
+        [1583020800000, 8532.5, 9204.0, 3621.81, 6407.1, 10859497.479],
+        [1585699200000, 6407.1, 9479.77, 6140.0, 8624.76, 11276526.968],
+        [1588291200000, 8623.61, 10080.0, 7940.0, 9446.43, 12469561.02],
+        [1590969600000, 9446.49, 10497.25, 8816.4, 9138.87, 6684044.201],
+        [1593561600000, 9138.88, 11488.0, 8900.0, 11343.68, 5709327.926],
+        [1596240000000, 11343.67, 12499.42, 10490.0, 11658.11, 6746487.129],
+        [1598918400000, 11658.11, 12061.07, 9808.58, 10773.0, 6442697.051],
+        [1601510400000, 10773.0, 14140.0, 10371.03, 13783.73, 7404103.004],
+        [1604188800000, 13783.73, 19944.0, 13195.0, 19720.0, 12328272.549],
+        [1606780800000, 19722.09, 29376.7, 17555.0, 28951.68, 10067314.24],
+        [1609459200000, 28948.19, 42125.51, 27800.0, 33126.21, 12408873.079],
+        [1612137600000, 33125.11, 58472.14, 32322.47, 45163.36, 8784474.482],
+        [1614556800000, 45162.64, 61950.0, 44972.49, 58807.24, 9459821.267],
+        [1617235200000, 58810.99, 64986.11, 46930.43, 57684.16, 7895051.389],
+        [1619827200000, 57688.29, 59654.0, 28688.0, 37243.38, 16790964.443],
+        [1622505600000, 37244.36, 41413.0, 28780.01, 35031.39, 23474519.886],
+        [1625097600000, 35031.39, 48168.6, 29242.24, 41448.11, 16932491.175],
+        [1627776000000, 41448.1, 50600.0, 37291.0, 47150.32, 13645800.254],
+        [1630454400000, 47150.32, 52950.0, 39503.58, 43796.57, 10734742.869],
+        [1633046400000, 43799.49, 67150.0, 43260.01, 61348.61, 9111112.847],
+        [1635724800000, 61347.14, 69198.7, 53245.0, 56975.0, 7111424.463],
+        [1638316800000, 56978.06, 59100.0, 40888.89, 46210.56, 8404449.024],
+        [1640995200000, 46210.57, 48000.0, 32853.83, 38439.04, 11047479.277],
+        [1643673600000, 38439.04, 45847.5, 34303.7, 43155.0, 10910339.91],
+        [1646092800000, 43155.0, 48200.0, 37134.0, 45506.0, 10459721.586],
+        [1648771200000, 45505.9, 47448.0, 37550.0, 37614.5, 8463568.862],
+        [1651363200000, 37614.4, 40071.7, 26631.0, 31797.8, 14463715.774],
+        [1654041600000, 31797.9, 31986.1, 17593.2, 19923.5, 20710810.306],
+        [1656633600000, 19923.3, 24700.0, 18780.1, 23290.1, 20582518.513],
+        [1659312000000, 23290.1, 25200.0, 19508.0, 20041.5, 17221921.557],
+        [1661990400000, 20041.4, 22850.0, 18084.3, 19411.7, 21935261.414],
+        [1664582400000, 19411.6, 21088.0, 17917.8, 20482.0, 16625843.584],
+        [1667260800000, 20482.1, 21473.7, 15443.2, 17153.3, 18460614.013],
+        [1669852800000, 17153.4, 18400.0, 16210.0, 16537.6, 9702408.711],
+        [1672531200000, 16537.5, 23962.7, 16488.0, 23119.4, 14732180.645],
+        [1675209600000, 23119.5, 25347.6, 21338.0, 23129.6, 15025197.415],
+        [1677628800000, 23129.7, 29184.8, 19521.6, 28454.9, 23317458.541],
+        [1680307200000, 28454.8, 31059.0, 26919.3, 29223.0, 14654208.219],
+        [1682899200000, 29223.0, 29840.0, 25751.0, 27201.1, 13328157.284],
+        [1685577600000, 27201.1, 31500.0, 24777.0, 30460.2, 14099299.273],
+        [1688169600000, 30460.2, 31850.0, 28830.0, 29338.8, 8760361.377]
+    ]
+
+    data = ohlcv_to_dataframe(ticks, '1M', pair="UNITTEST/USDT",
+                              fill_missing=False, drop_incomplete=False)
+    assert len(data) == len(ticks)
+    assert data.iloc[0]['date'].strftime('%Y-%m-%d') == '2019-09-01'
+    assert data.iloc[-1]['date'].strftime('%Y-%m-%d') == '2023-07-01'
+
+    # Test with filling missing data
+    data = ohlcv_to_dataframe(ticks, '1M', pair="UNITTEST/USDT",
+                              fill_missing=True, drop_incomplete=False)
+    assert len(data) == len(ticks)
+    assert data.iloc[0]['date'].strftime('%Y-%m-%d') == '2019-09-01'
+    assert data.iloc[-1]['date'].strftime('%Y-%m-%d') == '2023-07-01'
+
+
 def test_ohlcv_drop_incomplete(caplog):
    timeframe = '1d'
    ticks = [
--- a/tests/exchange/test_ccxt_compat.py
+++ b/tests/exchange/test_ccxt_compat.py
@ -544,6 +544,8 @@ class TestCCXTExchange:
        if exchangename in ('bittrex'):
            # For some weired reason, this test returns random lengths for bittrex.
            pytest.skip("Exchange doesn't provide stable ohlcv history")
+        if exchangename in ('bitvavo'):
+            pytest.skip("Exchange Downtime ")

        if not exc._ft_has['ohlcv_has_history']:
            pytest.skip("Exchange does not support candle history")
--- a/tests/exchange/test_exchange.py
+++ b/tests/exchange/test_exchange.py
@ -4343,11 +4343,11 @@ def test__fetch_and_calculate_funding_fees(
    ex = get_patched_exchange(mocker, default_conf, api_mock, id=exchange)
    mocker.patch(f'{EXMS}.timeframes', PropertyMock(return_value=['1h', '4h', '8h']))
    funding_fees = ex._fetch_and_calculate_funding_fees(
-        pair='ADA/USDT', amount=amount, is_short=True, open_date=d1, close_date=d2)
+        pair='ADA/USDT:USDT', amount=amount, is_short=True, open_date=d1, close_date=d2)
    assert pytest.approx(funding_fees) == expected_fees
    # Fees for Longs are inverted
    funding_fees = ex._fetch_and_calculate_funding_fees(
-        pair='ADA/USDT', amount=amount, is_short=False, open_date=d1, close_date=d2)
+        pair='ADA/USDT:USDT', amount=amount, is_short=False, open_date=d1, close_date=d2)
    assert pytest.approx(funding_fees) == -expected_fees

    # Return empty "refresh_latest"
@ -4355,7 +4355,7 @@ def test__fetch_and_calculate_funding_fees(
    ex = get_patched_exchange(mocker, default_conf, api_mock, id=exchange)
    with pytest.raises(ExchangeError, match="Could not find funding rates."):
        ex._fetch_and_calculate_funding_fees(
-            pair='ADA/USDT', amount=amount, is_short=False, open_date=d1, close_date=d2)
+            pair='ADA/USDT:USDT', amount=amount, is_short=False, open_date=d1, close_date=d2)


@pytest.mark.parametrize('exchange,expected_fees', [
@ -5424,7 +5424,7 @@ def test_stoploss_contract_size(mocker, default_conf, contract_size, order_amoun

    assert api_mock.create_order.call_args_list[0][1]['amount'] == order_amount
    assert order['amount'] == 100
-    assert order['cost'] == 100
+    assert order['cost'] == order_amount
    assert order['filled'] == 100
    assert order['remaining'] == 100

--- a/tests/optimize/test_backtesting.py
+++ b/tests/optimize/test_backtesting.py
@ -601,6 +601,9 @@ def test_backtest__enter_trade_futures(default_conf_usdt, fee, mocker) -> None:

    trade = backtesting._enter_trade(pair, row=row, direction='short')
    assert pytest.approx(trade.liquidation_price) == 0.11787191
+    assert pytest.approx(trade.orders[0].cost) == (
+        trade.stake_amount * trade.leverage + trade.fee_open)
+    assert pytest.approx(trade.orders[-1].stake_amount) == trade.stake_amount

    # Stake-amount too high!
    mocker.patch(f"{EXMS}.get_min_pair_stake_amount", return_value=600.0)
--- a/tests/optimize/test_optimize_reports.py
+++ b/tests/optimize/test_optimize_reports.py
@ -23,7 +23,8 @@ from freqtrade.optimize.optimize_reports import (generate_backtest_stats, genera
                                                 store_backtest_analysis_results,
                                                 store_backtest_stats, text_table_bt_results,
                                                 text_table_exit_reason, text_table_strategy)
-from freqtrade.optimize.optimize_reports.optimize_reports import _get_resample_from_period
+from freqtrade.optimize.optimize_reports.optimize_reports import (_get_resample_from_period,
+                                                                  calc_streak)
 from freqtrade.resolvers.strategy_resolver import StrategyResolver
 from freqtrade.util import dt_ts
 from freqtrade.util.datetime_helpers import dt_from_ts, dt_utc
@ -348,6 +349,32 @@ def test_generate_trading_stats(testdatadir):
    assert res['losses'] == 0


+def test_calc_streak(testdatadir):
+    df = pd.DataFrame({
+            'profit_ratio': [0.05, -0.02, -0.03, -0.05, 0.01, 0.02, 0.03, 0.04, -0.02, -0.03],
+        })
+    # 4 consecutive wins, 3 consecutive losses
+    res = calc_streak(df)
+    assert res == (4, 3)
+    assert isinstance(res[0], int)
+    assert isinstance(res[1], int)
+
+    # invert situation
+    df1 = df.copy()
+    df1['profit_ratio'] = df1['profit_ratio'] * -1
+    assert calc_streak(df1) == (3, 4)
+
+    df_empty = pd.DataFrame({
+            'profit_ratio': [],
+    })
+    assert df_empty.empty
+    assert calc_streak(df_empty) == (0, 0)
+
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_data(filename)
+    assert calc_streak(bt_data) == (7, 18)
+
+
 def test_text_table_exit_reason():

    results = pd.DataFrame(
--- a/tests/persistence/test_persistence.py
+++ b/tests/persistence/test_persistence.py
@ -563,14 +563,14 @@ def test_calc_open_close_trade_price(
    trade.open_order_id = f'something-{is_short}-{lev}-{exchange}'

    oobj = Order.parse_from_ccxt_object(entry_order, 'ADA/USDT', trade.entry_side)
-    oobj.trade = trade
+    oobj._trade_live = trade
    oobj.update_from_ccxt_object(entry_order)
    trade.update_trade(oobj)

    trade.funding_fees = funding_fees

    oobj = Order.parse_from_ccxt_object(exit_order, 'ADA/USDT', trade.exit_side)
-    oobj.trade = trade
+    oobj._trade_live = trade
    oobj.update_from_ccxt_object(exit_order)
    trade.update_trade(oobj)

--- a/tests/persistence/test_trade_fromjson.py
+++ b/tests/persistence/test_trade_fromjson.py
@ -179,6 +179,7 @@ def test_trade_fromjson():
    assert trade.open_date_utc == datetime(2022, 10, 18, 9, 12, 42, tzinfo=timezone.utc)
    assert isinstance(trade.open_date, datetime)
    assert trade.exit_reason == 'no longer good'
+    assert trade.realized_profit == 2.76315361

    assert len(trade.orders) == 5
    last_o = trade.orders[-1]
--- a/tests/plugins/test_remotepairlist.py
+++ b/tests/plugins/test_remotepairlist.py
@ -35,7 +35,7 @@ def test_gen_pairlist_with_local_file(mocker, rpl_config):
    mock_file_path.exists.return_value = True

    jsonparse = json.loads(mock_file.read.return_value)
-    mocker.patch('freqtrade.plugins.pairlist.RemotePairList.json.load', return_value=jsonparse)
+    mocker.patch('freqtrade.plugins.pairlist.RemotePairList.rapidjson.load', return_value=jsonparse)

    rpl_config['pairlists'] = [
        {
--- a/tests/rpc/test_rpc.py
+++ b/tests/rpc/test_rpc.py
@ -402,6 +402,8 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
    assert res['first_trade_timestamp'] == 0
    assert res['latest_trade_date'] == ''
    assert res['latest_trade_timestamp'] == 0
+    assert res['expectancy'] == 0
+    assert res['expectancy_ratio'] == 100

    # Create some test data
    create_mock_trades_usdt(fee)
@ -413,6 +415,9 @@ def test_rpc_trade_statistics(default_conf_usdt, ticker, fee, mocker) -> None:
    assert pytest.approx(stats['profit_all_coin']) == -77.45964918
    assert pytest.approx(stats['profit_all_percent_mean']) == -57.86
    assert pytest.approx(stats['profit_all_fiat']) == -85.205614098
+    assert pytest.approx(stats['winrate']) == 0.666666667
+    assert pytest.approx(stats['expectancy']) == 0.913333333
+    assert pytest.approx(stats['expectancy_ratio']) == 0.223308883
    assert stats['trade_count'] == 7
    assert stats['first_trade_humanized'] == '2 days ago'
    assert stats['latest_trade_humanized'] == '17 minutes ago'
--- a/tests/rpc/test_rpc_apiserver.py
+++ b/tests/rpc/test_rpc_apiserver.py
@ -829,7 +829,8 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
         'profit_closed_percent_mean': -0.75, 'profit_closed_ratio_sum': -0.015,
         'profit_closed_percent_sum': -1.5, 'profit_closed_ratio': -6.739057628404269e-06,
         'profit_closed_percent': -0.0, 'winning_trades': 0, 'losing_trades': 2,
-         'profit_factor': 0.0, 'trading_volume': 91.074,
+         'profit_factor': 0.0, 'winrate': 0.0, 'expectancy': -0.0033695635,
+         'expectancy_ratio': -1.0, 'trading_volume': 91.074,
         }
    ),
    (
@ -844,7 +845,8 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
         'profit_closed_percent_mean': 0.75, 'profit_closed_ratio_sum': 0.015,
         'profit_closed_percent_sum': 1.5, 'profit_closed_ratio': 7.391275897987988e-07,
         'profit_closed_percent': 0.0, 'winning_trades': 2, 'losing_trades': 0,
-         'profit_factor': None, 'trading_volume': 91.074,
+         'profit_factor': None, 'winrate': 1.0, 'expectancy': 0.0003695635,
+         'expectancy_ratio': 100, 'trading_volume': 91.074,
         }
    ),
    (
@ -859,7 +861,9 @@ def test_api_edge_disabled(botclient, mocker, ticker, fee, markets):
         'profit_closed_percent_mean': 0.25, 'profit_closed_ratio_sum': 0.005,
         'profit_closed_percent_sum': 0.5, 'profit_closed_ratio': -5.429078808526421e-06,
         'profit_closed_percent': -0.0, 'winning_trades': 1, 'losing_trades': 1,
-         'profit_factor': 0.02775724835771106, 'trading_volume': 91.074,
+         'profit_factor': 0.02775724835771106, 'winrate': 0.5,
+         'expectancy': -0.0027145635000000003, 'expectancy_ratio': -0.48612137582114445,
+         'trading_volume': 91.074,
         }
    )
 ])
@ -916,6 +920,9 @@ def test_api_profit(botclient, mocker, ticker, fee, markets, is_short, expected)
        'winning_trades': expected['winning_trades'],
        'losing_trades': expected['losing_trades'],
        'profit_factor': expected['profit_factor'],
+        'winrate': expected['winrate'],
+        'expectancy': expected['expectancy'],
+        'expectancy_ratio': expected['expectancy_ratio'],
        'max_drawdown': ANY,
        'max_drawdown_abs': ANY,
        'trading_volume': expected['trading_volume'],
@ -1469,30 +1476,47 @@ def test_api_pair_history(botclient, mocker):
                    "&timerange=20180111-20180112")
    assert_response(rc, 422)

+    # Invalid strategy
+    rc = client_get(client,
+                    f"{BASE_URI}/pair_history?pair=UNITTEST%2FBTC&timeframe={timeframe}"
+                    "&timerange=20180111-20180112&strategy={CURRENT_TEST_STRATEGY}11")
+    assert_response(rc, 502)
+
    # Working
    rc = client_get(client,
                    f"{BASE_URI}/pair_history?pair=UNITTEST%2FBTC&timeframe={timeframe}"
                    f"&timerange=20180111-20180112&strategy={CURRENT_TEST_STRATEGY}")
    assert_response(rc, 200)
-    assert rc.json()['length'] == 289
-    assert len(rc.json()['data']) == rc.json()['length']
-    assert 'columns' in rc.json()
-    assert 'data' in rc.json()
+    result = rc.json()
+    assert result['length'] == 289
+    assert len(result['data']) == result['length']
+    assert 'columns' in result
+    assert 'data' in result
+    data = result['data']
+    assert len(data) == 289
+    # analyed DF has 28 columns
+    assert len(result['columns']) == 28
+    assert len(data[0]) == 28
+    date_col_idx = [idx for idx, c in enumerate(result['columns']) if c == 'date'][0]
+    rsi_col_idx = [idx for idx, c in enumerate(result['columns']) if c == 'rsi'][0]
+
+    assert data[0][date_col_idx] == '2018-01-11 00:00:00'
+    assert data[0][rsi_col_idx] is not None
+    assert data[0][rsi_col_idx] > 0
    assert lfm.call_count == 1
-    assert rc.json()['pair'] == 'UNITTEST/BTC'
-    assert rc.json()['strategy'] == CURRENT_TEST_STRATEGY
-    assert rc.json()['data_start'] == '2018-01-11 00:00:00+00:00'
-    assert rc.json()['data_start_ts'] == 1515628800000
-    assert rc.json()['data_stop'] == '2018-01-12 00:00:00+00:00'
-    assert rc.json()['data_stop_ts'] == 1515715200000
+    assert result['pair'] == 'UNITTEST/BTC'
+    assert result['strategy'] == CURRENT_TEST_STRATEGY
+    assert result['data_start'] == '2018-01-11 00:00:00+00:00'
+    assert result['data_start_ts'] == 1515628800000
+    assert result['data_stop'] == '2018-01-12 00:00:00+00:00'
+    assert result['data_stop_ts'] == 1515715200000

    # No data found
    rc = client_get(client,
                    f"{BASE_URI}/pair_history?pair=UNITTEST%2FBTC&timeframe={timeframe}"
                    f"&timerange=20200111-20200112&strategy={CURRENT_TEST_STRATEGY}")
    assert_response(rc, 502)
-    assert rc.json()['error'] == ("Error querying /api/v1/pair_history: "
-                                  "No data for UNITTEST/BTC, 5m in 20200111-20200112 found.")
+    assert rc.json()['detail'] == ("No data for UNITTEST/BTC, 5m in 20200111-20200112 found.")


 def test_api_plot_config(botclient, mocker):
@ -1529,6 +1553,10 @@ def test_api_plot_config(botclient, mocker):
    assert_response(rc)
    assert rc.json()['subplots'] == {}

+    rc = client_get(client, f"{BASE_URI}/plot_config?strategy=NotAStrategy")
+    assert_response(rc, 502)
+    assert rc.json()['detail'] is not None
+
    mocker.patch('freqtrade.rpc.api_server.api_v1.get_rpc_optional', return_value=None)

    rc = client_get(client, f"{BASE_URI}/plot_config")
@ -1981,7 +2009,7 @@ def test_api_backtest_history(botclient, mocker, testdatadir):
    result = rc.json()
    assert len(result) == 3
    fn = result[0]['filename']
-    assert fn == "backtest-result_multistrat.json"
+    assert fn == "backtest-result_multistrat"
    strategy = result[0]['strategy']
    rc = client_get(client, f"{BASE_URI}/backtest/history/result?filename={fn}&strategy={strategy}")
    assert_response(rc)
@ -1995,6 +2023,34 @@ def test_api_backtest_history(botclient, mocker, testdatadir):
    assert result2['backtest_result']['strategy'][strategy]


+def test_api_delete_backtest_history_entry(botclient, mocker, tmp_path: Path):
+    ftbot, client = botclient
+
+    # Create a temporary directory and file
+    bt_results_base = tmp_path / "backtest_results"
+    bt_results_base.mkdir()
+    file_path = bt_results_base / "test.json"
+    file_path.touch()
+    meta_path = file_path.with_suffix('.meta.json')
+    meta_path.touch()
+
+    rc = client_delete(client, f"{BASE_URI}/backtest/history/randomFile.json")
+    assert_response(rc, 503)
+    assert rc.json()['detail'] == 'Bot is not in the correct state.'
+
+    ftbot.config['user_data_dir'] = tmp_path
+    ftbot.config['runmode'] = RunMode.WEBSERVER
+    rc = client_delete(client, f"{BASE_URI}/backtest/history/randomFile.json")
+    assert rc.status_code == 404
+    assert rc.json()['detail'] == 'File not found.'
+
+    rc = client_delete(client, f"{BASE_URI}/backtest/history/{file_path.name}")
+    assert rc.status_code == 200
+
+    assert not file_path.exists()
+    assert not meta_path.exists()
+
+
 def test_health(botclient):
    ftbot, client = botclient

--- a/tests/rpc/test_rpc_telegram.py
+++ b/tests/rpc/test_rpc_telegram.py
@ -799,6 +799,8 @@ async def test_telegram_profit_handle(
    assert '*Best Performing:* `ETH/USDT: 9.45%`' in msg_mock.call_args_list[-1][0][0]
    assert '*Max Drawdown:*' in msg_mock.call_args_list[-1][0][0]
    assert '*Profit factor:*' in msg_mock.call_args_list[-1][0][0]
+    assert '*Winrate:*' in msg_mock.call_args_list[-1][0][0]
+    assert '*Expectancy (Ratio):*' in msg_mock.call_args_list[-1][0][0]
    assert '*Trading volume:* `126 USDT`' in msg_mock.call_args_list[-1][0][0]


--- a/tests/rpc/test_rpc_webhook.py
+++ b/tests/rpc/test_rpc_webhook.py
@ -381,7 +381,7 @@ def test__send_msg(default_conf, mocker, caplog):
    webhook._send_msg(msg)

    assert post.call_count == 1
-    assert post.call_args[1] == {'data': msg}
+    assert post.call_args[1] == {'data': msg, 'timeout': 10}
    assert post.call_args[0] == (default_conf['webhook']['url'], )

    post = MagicMock(side_effect=RequestException)
@ -399,7 +399,7 @@ def test__send_msg_with_json_format(default_conf, mocker, caplog):
    mocker.patch("freqtrade.rpc.webhook.post", post)
    webhook._send_msg(msg)

-    assert post.call_args[1] == {'json': msg}
+    assert post.call_args[1] == {'json': msg, 'timeout': 10}


 def test__send_msg_with_raw_format(default_conf, mocker, caplog):
@ -411,7 +411,11 @@ def test__send_msg_with_raw_format(default_conf, mocker, caplog):
    mocker.patch("freqtrade.rpc.webhook.post", post)
    webhook._send_msg(msg)

-    assert post.call_args[1] == {'data': msg['data'], 'headers': {'Content-Type': 'text/plain'}}
+    assert post.call_args[1] == {
+        'data': msg['data'],
+        'headers': {'Content-Type': 'text/plain'},
+        'timeout': 10
+    }


 def test_send_msg_discord(default_conf, mocker):
--- a/tests/test_freqtradebot.py
+++ b/tests/test_freqtradebot.py
@ -2793,7 +2793,7 @@ def test_manage_open_orders_entry(

    freqtrade.strategy.check_entry_timeout = MagicMock(return_value=False)
    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=1234)
-    # check it does cancel buy orders over the time limit
+    # check it does cancel entry orders over the time limit
    freqtrade.manage_open_orders()
    assert cancel_order_mock.call_count == 1
    assert rpc_mock.call_count == 2
@ -2801,7 +2801,7 @@ def test_manage_open_orders_entry(
        select(Trade).filter(Trade.open_order_id.is_(open_trade.open_order_id))).all()
    nb_trades = len(trades)
    assert nb_trades == 0
-    # Custom user buy-timeout is never called
+    # Custom user entry-timeout is never called
    assert freqtrade.strategy.check_entry_timeout.call_count == 0
    # Entry adjustment is never called
    assert freqtrade.strategy.adjust_entry_price.call_count == 0
@ -5023,7 +5023,7 @@ def test_get_real_amount_in_point(default_conf_usdt, buy_order_fee, fee, mocker,
    (8.0, 0.1, 8.0, None),
    (8.0, 0.1, 7.9, 0.1),
 ])
-def test_apply_fee_conditional(default_conf_usdt, fee, mocker,
+def test_apply_fee_conditional(default_conf_usdt, fee, mocker, caplog,
                               amount, fee_abs, wallet, amount_exp):
    walletmock = mocker.patch('freqtrade.wallets.Wallets.update')
    mocker.patch('freqtrade.wallets.Wallets.get_free', return_value=wallet)
@ -5048,6 +5048,60 @@ def test_apply_fee_conditional(default_conf_usdt, fee, mocker,
    # Amount is kept as is
    assert freqtrade.apply_fee_conditional(trade, 'LTC', amount, fee_abs, order) == amount_exp
    assert walletmock.call_count == 1
+    if fee_abs != 0 and amount_exp is None:
+        assert log_has_re(r"Fee amount.*Eating.*dust\.", caplog)
+
+
+@pytest.mark.parametrize('amount,fee_abs,wallet,amount_exp', [
+    (8.0, 0.0, 16, None),
+    (8.0, 0.0, 0, None),
+    (8.0, 0.1, 8, 0.1),
+    (8.0, 0.1, 20, None),
+    (8.0, 0.1, 16.0, None),
+    (8.0, 0.1, 7.9, 0.1),
+    (8.0, 0.1, 12, 0.1),
+    (8.0, 0.1, 15.9, 0.1),
+])
+def test_apply_fee_conditional_multibuy(default_conf_usdt, fee, mocker, caplog,
+                                        amount, fee_abs, wallet, amount_exp):
+    walletmock = mocker.patch('freqtrade.wallets.Wallets.update')
+    mocker.patch('freqtrade.wallets.Wallets.get_free', return_value=wallet)
+    trade = Trade(
+        pair='LTC/ETH',
+        amount=amount,
+        exchange='binance',
+        open_rate=0.245441,
+        fee_open=fee.return_value,
+        fee_close=fee.return_value,
+        open_order_id="123456"
+    )
+    # One closed order
+    order = Order(
+        ft_order_side='buy',
+        order_id='10',
+        ft_pair=trade.pair,
+        ft_is_open=False,
+        filled=amount,
+        status="closed"
+    )
+    trade.orders.append(order)
+    # Add additional order - this should NOT eat into dust unless the wallet was bigger already.
+    order1 = Order(
+        ft_order_side='buy',
+        order_id='100',
+        ft_pair=trade.pair,
+        ft_is_open=True,
+    )
+    trade.orders.append(order1)
+
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+
+    walletmock.reset_mock()
+    # The new trade amount will be 2x amount - fee / wallet will have to be adapted to this.
+    assert freqtrade.apply_fee_conditional(trade, 'LTC', amount, fee_abs, order1) == amount_exp
+    assert walletmock.call_count == 1
+    if fee_abs != 0 and amount_exp is None:
+        assert log_has_re(r"Fee amount.*Eating.*dust\.", caplog)


@pytest.mark.parametrize("delta, is_high_delta", [
--- a/tests/test_integration.py
+++ b/tests/test_integration.py
@ -429,6 +429,7 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, leverage, fee, mocker)
    assert pytest.approx(trade.stop_loss) == 1.99 * (1 - 0.1 / leverage)
    assert pytest.approx(trade.initial_stop_loss) == 1.96 * (1 - 0.1 / leverage)
    assert trade.initial_stop_loss_pct == -0.1
+    assert pytest.approx(trade.orders[-1].stake_amount) == trade.stake_amount

    # 2nd order - not filling
    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=120)
@ -473,13 +474,38 @@ def test_dca_order_adjust(default_conf_usdt, ticker_usdt, leverage, fee, mocker)
    assert pytest.approx(trade.orders[1].amount) == 30.150753768 * leverage
    assert pytest.approx(trade.orders[-1].amount) == 61.538461232 * leverage

+    # Full exit
+    mocker.patch(f'{EXMS}._dry_is_price_crossed', return_value=False)
+    freqtrade.strategy.custom_exit = MagicMock(return_value='Exit now')
+    freqtrade.strategy.adjust_entry_price = MagicMock(return_value=2.02)
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 5
+    assert trade.orders[-1].side == trade.exit_side
+    assert trade.orders[-1].status == 'open'
+    assert trade.orders[-1].price == 2.02
+    assert pytest.approx(trade.amount) == 91.689215 * leverage
+    assert pytest.approx(trade.orders[-1].amount) == 91.689215 * leverage
+    assert freqtrade.strategy.adjust_entry_price.call_count == 0
+    # Process again, should not adjust entry price
+    freqtrade.process()
+    trade = Trade.get_trades().first()
+    assert len(trade.orders) == 5
+    assert trade.orders[-1].status == 'open'
+    assert trade.orders[-1].price == 2.02
+    # Adjust entry price cannot be called - this is an exit order
+    assert freqtrade.strategy.adjust_entry_price.call_count == 0
+

@pytest.mark.parametrize('leverage', [1, 2])
 def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog, leverage) -> None:
    default_conf_usdt['position_adjustment_enable'] = True
-
+    spot = leverage == 1
+    if not spot:
+        default_conf_usdt['trading_mode'] = 'futures'
+        default_conf_usdt['margin_mode'] = 'isolated'
    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
-    freqtrade.trading_mode = TradingMode.FUTURES
+    assert freqtrade.trading_mode == TradingMode.FUTURES if not spot else TradingMode.SPOT
    mocker.patch.multiple(
        EXMS,
        fetch_ticker=ticker_usdt,
@ -487,8 +513,11 @@ def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog, levera
        amount_to_precision=lambda s, x, y: y,
        price_to_precision=lambda s, x, y: y,
        get_min_pair_stake_amount=MagicMock(return_value=10),
+        get_funding_fees=MagicMock(return_value=0),
    )
    mocker.patch(f"{EXMS}.get_max_leverage", return_value=10)
+    starting_amount = freqtrade.wallets.get_total('USDT')
+    assert starting_amount == 1000

    patch_get_signal(freqtrade)
    freqtrade.strategy.leverage = MagicMock(return_value=leverage)
@ -498,8 +527,14 @@ def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog, levera
    trade = Trade.get_trades().first()
    assert len(trade.orders) == 1
    assert pytest.approx(trade.stake_amount) == 60
+    assert trade.leverage == leverage
    assert pytest.approx(trade.amount) == 30.0 * leverage
    assert trade.open_rate == 2.0
+    assert pytest.approx(freqtrade.wallets.get_free('USDT')) == starting_amount - 60
+    if spot:
+        assert pytest.approx(freqtrade.wallets.get_total('USDT')) == starting_amount - 60
+    else:
+        assert freqtrade.wallets.get_total('USDT') == starting_amount

    # Too small size
    freqtrade.strategy.adjust_trade_position = MagicMock(return_value=-59)
@ -521,6 +556,15 @@ def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog, levera
    assert pytest.approx(trade.amount) == 20.099 * leverage
    assert trade.open_rate == 2.0
    assert trade.is_open
+    assert trade.realized_profit > 0.098 * leverage
+    expected_profit = starting_amount - 40.1980 + trade.realized_profit
+    assert pytest.approx(freqtrade.wallets.get_free('USDT')) == expected_profit
+
+    if spot:
+        assert pytest.approx(freqtrade.wallets.get_total('USDT')) == expected_profit
+    else:
+        # total won't change in futures mode, only free / used will.
+        assert freqtrade.wallets.get_total('USDT') == starting_amount + trade.realized_profit
    caplog.clear()

    # Sell more than what we got (we got ~20 coins left)
@ -545,3 +589,10 @@ def test_dca_exiting(default_conf_usdt, ticker_usdt, fee, mocker, caplog, levera
    assert pytest.approx(trade.stake_amount) == 40.198
    assert trade.is_open
    assert log_has_re('Amount to exit is 0.0 due to exchange limits - not exiting.', caplog)
+    expected_profit = starting_amount - 40.1980 + trade.realized_profit
+    assert pytest.approx(freqtrade.wallets.get_free('USDT')) == expected_profit
+    if spot:
+        assert pytest.approx(freqtrade.wallets.get_total('USDT')) == expected_profit
+    else:
+        # total won't change in futures mode, only free / used will.
+        assert freqtrade.wallets.get_total('USDT') == starting_amount + trade.realized_profit
--- a/tests/test_misc.py
+++ b/tests/test_misc.py
@ -8,7 +8,7 @@ import pandas as pd
 import pytest

 from freqtrade.misc import (dataframe_to_json, decimals_per_coin, deep_merge_dicts, file_dump_json,
-                            file_load_json, json_to_dataframe, pair_to_filename,
+                            file_load_json, is_file_in_dir, json_to_dataframe, pair_to_filename,
                            parse_db_uri_for_logging, plural, render_template,
                            render_template_with_fallback, round_coin_value, safe_value_fallback,
                            safe_value_fallback2)
@ -64,6 +64,24 @@ def test_file_load_json(mocker, testdatadir) -> None:
    assert ret


+def test_is_file_in_dir(tmp_path):
+
+    # Create a temporary directory and file
+    dir_path = tmp_path / "subdir"
+    dir_path.mkdir()
+    file_path = dir_path / "test.txt"
+    file_path.touch()
+
+    # Test that the function returns True when the file is in the directory
+    assert is_file_in_dir(file_path, dir_path) is True
+
+    # Test that the function returns False when the file is not in the directory
+    assert is_file_in_dir(file_path, tmp_path) is False
+
+    file_path2 = tmp_path / "../../test2.txt"
+    assert is_file_in_dir(file_path2, tmp_path) is False
+
+
@pytest.mark.parametrize("pair,expected_result", [
    ("ETH/BTC", 'ETH_BTC'),
    ("ETH/USDT", 'ETH_USDT'),
--- a/tests/test_wallets.py
+++ b/tests/test_wallets.py
@ -8,7 +8,8 @@ from sqlalchemy import select
 from freqtrade.constants import UNLIMITED_STAKE_AMOUNT
 from freqtrade.exceptions import DependencyException
 from freqtrade.persistence import Trade
-from tests.conftest import EXMS, create_mock_trades, get_patched_freqtradebot, patch_wallet
+from tests.conftest import (EXMS, create_mock_trades, create_mock_trades_usdt,
+                            get_patched_freqtradebot, patch_wallet)


 def test_sync_wallet_at_boot(mocker, default_conf):
@ -341,6 +342,33 @@ def test_sync_wallet_futures_live(mocker, default_conf):
    assert 'ETH/USDT:USDT' not in freqtrade.wallets._positions


+def test_sync_wallet_dry(mocker, default_conf_usdt, fee):
+    default_conf_usdt['dry_run'] = True
+    freqtrade = get_patched_freqtradebot(mocker, default_conf_usdt)
+    assert len(freqtrade.wallets._wallets) == 1
+    assert len(freqtrade.wallets._positions) == 0
+    assert freqtrade.wallets.get_total('USDT') == 1000
+
+    create_mock_trades_usdt(fee, is_short=None)
+
+    freqtrade.wallets.update()
+
+    assert len(freqtrade.wallets._wallets) == 5
+    assert len(freqtrade.wallets._positions) == 0
+    bal = freqtrade.wallets.get_all_balances()
+    assert bal['NEO'].total == 10
+    assert bal['XRP'].total == 10
+    assert bal['LTC'].total == 2
+    assert bal['USDT'].total == 922.74
+
+    assert freqtrade.wallets.get_starting_balance() == default_conf_usdt['dry_run_wallet']
+    total = freqtrade.wallets.get_total('LTC')
+    free = freqtrade.wallets.get_free('LTC')
+    used = freqtrade.wallets.get_used('LTC')
+    assert free != 0
+    assert free + used == total
+
+
 def test_sync_wallet_futures_dry(mocker, default_conf, fee):
    default_conf['dry_run'] = True
    default_conf['trading_mode'] = 'futures'