Merge branch 'develop' into pairlocks_direction

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,9 +1,9 @@
-Thank you for sending your pull request. But first, have you included
+<!-- Thank you for sending your pull request. But first, have you included
 unit tests, and is your code PEP8 conformant? [More details](https://github.com/freqtrade/freqtrade/blob/develop/CONTRIBUTING.md)
-
+-->
 ## Summary
 
-Explain in one sentence the goal of this PR
+<!-- Explain in one sentence the goal of this PR -->
 
 Solve the issue: #___
 
@@ -14,4 +14,4 @@ Solve the issue: #___
 
 ## What's new?
 
-*Explain in details what this PR solve or improve. You can include visuals.* 
+<!-- Explain in details what this PR solve or improve. You can include visuals. -->

.github/workflows/ci.yml

@@ -100,7 +100,7 @@ jobs:
 
     - name: Mypy
       run: |
-        mypy freqtrade scripts
+        mypy freqtrade scripts tests
 
     - name: Discord notification
       uses: rjstone/discord-webhook-notify@v1
@@ -255,7 +255,7 @@ jobs:
 
     - name: Mypy
       run: |
-        mypy freqtrade scripts
+        mypy freqtrade scripts tests
 
     - name: Discord notification
       uses: rjstone/discord-webhook-notify@v1
@@ -265,6 +265,21 @@ jobs:
           details: Test Failed
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
+  mypy_version_check:
+    runs-on: ubuntu-20.04
+    steps:
+    - uses: actions/checkout@v3
+
+    - name: Set up Python
+      uses: actions/setup-python@v3
+      with:
+        python-version: 3.9
+
+    - name: pre-commit dependencies
+      run: |
+        pip install pyaml
+        python build_helpers/pre_commit_update.py
+
   docs_check:
     runs-on: ubuntu-20.04
     steps:
@@ -277,7 +292,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v3
       with:
-        python-version: 3.8
+        python-version: 3.9
 
     - name: Documentation build
       run: |
@@ -294,6 +309,9 @@ jobs:
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   cleanup-prior-runs:
+    permissions:
+      actions: write  # for rokroskar/workflow-run-cleanup-action to obtain workflow name & cancel it
+      contents: read  # for rokroskar/workflow-run-cleanup-action to obtain branch
     runs-on: ubuntu-20.04
     steps:
     - name: Cleanup previous runs on this branch
@@ -304,8 +322,12 @@ jobs:
 
   # Notify only once - when CI completes (and after deploy) in case it's successfull
   notify-complete:
-    needs: [ build_linux, build_macos, build_windows, docs_check ]
+    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check ]
     runs-on: ubuntu-20.04
+    # Discord notification can't handle schedule events
+    if: (github.event_name != 'schedule')
+    permissions:
+      repository-projects: read  
     steps:
 
     - name: Check user permission
@@ -325,7 +347,7 @@ jobs:
           webhookUrl: ${{ secrets.DISCORD_WEBHOOK }}
 
   deploy:
-    needs: [ build_linux, build_macos, build_windows, docs_check ]
+    needs: [ build_linux, build_macos, build_windows, docs_check, mypy_version_check ]
     runs-on: ubuntu-20.04
 
     if: (github.event_name == 'push' || github.event_name == 'schedule' || github.event_name == 'release') && github.repository == 'freqtrade/freqtrade'

.pre-commit-config.yaml

@@ -11,6 +11,13 @@ repos:
     rev: "v0.942"
     hooks:
       - id: mypy
+        exclude: build_helpers
+        additional_dependencies:
+          - types-cachetools==5.0.1
+          - types-filelock==3.2.5
+          - types-requests==2.27.20
+          - types-tabulate==0.8.7
+          - types-python-dateutil==2.8.12
         # stages: [push]
 
   - repo: https://github.com/pycqa/isort

README.md

@@ -39,6 +39,14 @@ Please read the [exchange specific notes](docs/exchanges.md) to learn about even
 - [X] [OKX](https://okx.com/) (Former OKEX)
 - [ ] [potentially many others](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
+### Experimentally, freqtrade also supports futures on the following exchanges
+
+- [X] [Binance](https://www.binance.com/)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [OKX](https://okx.com/).
+
+Please make sure to read the [exchange specific notes](docs/exchanges.md), as well as the [trading with leverage](docs/leverage.md) documentation before diving in.
+
 ### Community tested
 
 Exchanges confirmed working by the community:

build_helpers/pre_commit_update.py

@@ -0,0 +1,42 @@
+# File used in CI to ensure pre-commit dependencies are kept uptodate.
+
+import sys
+from pathlib import Path
+
+import yaml
+
+
+pre_commit_file = Path('.pre-commit-config.yaml')
+require_dev = Path('requirements-dev.txt')
+
+with require_dev.open('r') as rfile:
+    requirements = rfile.readlines()
+
+# Extract types only
+type_reqs = [r.strip('\n') for r in requirements if r.startswith('types-')]
+
+with pre_commit_file.open('r') as file:
+    f = yaml.load(file, Loader=yaml.FullLoader)
+
+
+mypy_repo = [repo for repo in f['repos'] if repo['repo']
+             == 'https://github.com/pre-commit/mirrors-mypy']
+
+hooks = mypy_repo[0]['hooks'][0]['additional_dependencies']
+
+errors = []
+for hook in hooks:
+    if hook not in type_reqs:
+        errors.append(f"{hook} is missing in requirements-dev.txt.")
+
+for req in type_reqs:
+    if req not in hooks:
+        errors.append(f"{req} is missing in pre-config file.")
+
+
+if errors:
+    for e in errors:
+        print(e)
+    sys.exit(1)
+
+sys.exit(0)

config_examples/config_binance.example.json

@@ -90,7 +90,7 @@
     },
     "bot_name": "freqtrade",
     "initial_state": "running",
-    "force_enter_enable": false,
+    "force_entry_enable": false,
     "internals": {
         "process_throttle_secs": 5
     }

docs/backtesting.md

@@ -299,6 +299,7 @@ A backtesting result will look like that:
 | Final balance          | 0.01762792 BTC      |
 | Absolute profit        | 0.00762792 BTC      |
 | Total profit %         | 76.2%               |
+| CAGR %                 | 460.87%             |
 | Trades per day         | 3.575               |
 | Avg. stake amount      | 0.001      BTC      |
 | Total trade volume     | 0.429      BTC      |
@@ -388,6 +389,7 @@ It contains some useful key metrics about performance of your strategy on backte
 | Final balance          | 0.01762792 BTC      |
 | Absolute profit        | 0.00762792 BTC      |
 | Total profit %         | 76.2%               |
+| CAGR %                 | 460.87%             |
 | Avg. stake amount      | 0.001      BTC      |
 | Total trade volume     | 0.429      BTC      |
 |                        |                     |

docs/index.md

@@ -51,6 +51,14 @@ Please read the [exchange specific notes](exchanges.md) to learn about eventual,
 - [X] [OKX](https://okx.com/) (Former OKEX)
 - [ ] [potentially many others through <img alt="ccxt" width="30px" src="assets/ccxt-logo.svg" />](https://github.com/ccxt/ccxt/). _(We cannot guarantee they will work)_
 
+### Experimentally, freqtrade also supports futures on the following exchanges:
+
+- [X] [Binance](https://www.binance.com/)
+- [X] [Gate.io](https://www.gate.io/ref/6266643)
+- [X] [OKX](https://okx.com/).
+
+Please make sure to read the [exchange specific notes](exchanges.md), as well as the [trading with leverage](leverage.md) documentation before diving in.
+
 ### Community tested
 
 Exchanges confirmed working by the community:

docs/requirements-docs.txt

@@ -1,5 +1,5 @@
 mkdocs==1.3.0
-mkdocs-material==8.2.9
+mkdocs-material==8.2.10
 mdx_truly_sane_lists==1.2
-pymdown-extensions==9.3
+pymdown-extensions==9.4
 jinja2==3.1.1

docs/strategy-callbacks.md

@@ -7,6 +7,7 @@ Depending on the callback used, they may be called when entering / exiting a tra
 
 Currently available callbacks:
 
+* [`bot_start()`](#bot-start)
 * [`bot_loop_start()`](#bot-loop-start)
 * [`custom_stake_amount()`](#stake-size-management)
 * [`custom_exit()`](#custom-exit-signal)
@@ -21,6 +22,29 @@ Currently available callbacks:
 !!! Tip "Callback calling sequence"
     You can find the callback calling sequence in [bot-basics](bot-basics.md#bot-execution-logic)
 
+## Bot start
+
+A simple callback which is called once when the strategy is loaded.
+This can be used to perform actions that must only be performed once and runs after dataprovider and wallet are set
+
+``` python
+import requests
+
+class AwesomeStrategy(IStrategy):
+
+    # ... populate_* methods
+
+    def bot_start(self, **kwargs) -> None:
+        """
+        Called only once after bot instantiation.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        if self.config['runmode'].value in ('live', 'dry_run'):
+            # Assign this to the class by using self.*
+            # can then be used by populate_* methods
+            self.cust_remote_data = requests.get('https://some_remote_source.example.com')
+
+```
 ## Bot loop start
 
 A simple callback which is called once at the start of every bot throttling iteration (roughly every 5 seconds, unless configured differently).
@@ -122,11 +146,11 @@ See [Dataframe access](strategy-advanced.md#dataframe-access) for more informati
 
 ## Custom stoploss
 
-Called for open trade every throttling iteration (roughly every 5 seconds) until a trade is closed.
+Called for open trade every iteration (roughly every 5 seconds) until a trade is closed.
 
 The usage of the custom stoploss method must be enabled by setting `use_custom_stoploss=True` on the strategy object.
 
-The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss (before this method is called for the first time for a trade).
+The stoploss price can only ever move upwards - if the stoploss value returned from `custom_stoploss` would result in a lower stoploss price than was previously set, it will be ignored. The traditional `stoploss` value serves as an absolute lower level and will be instated as the initial stoploss (before this method is called for the first time for a trade), and is still mandatory.
 
 The method must return a stoploss value (float / number) as a percentage of the current price.
 E.g. If the `current_rate` is 200 USD, then returning `0.02` will set the stoploss price 2% lower, at 196 USD.
@@ -376,7 +400,7 @@ class AwesomeStrategy(IStrategy):
 
     def custom_exit_price(self, pair: str, trade: Trade,
                           current_time: datetime, proposed_rate: float,
-                          current_profit: float, **kwargs) -> float:
+                          current_profit: float, exit_tag: Optional[str], **kwargs) -> float:
 
         dataframe, last_updated = self.dp.get_analyzed_dataframe(pair=pair,
                                                                 timeframe=self.timeframe)

freqtrade/configuration/config_setup.py

@@ -22,6 +22,6 @@ def setup_utils_configuration(args: Dict[str, Any], method: RunMode) -> Dict[str
 
     # Ensure these modes are using Dry-run
     config['dry_run'] = True
-    validate_config_consistency(config)
+    validate_config_consistency(config, preliminary=True)
 
     return config

freqtrade/configuration/config_validation.py

@@ -39,7 +39,7 @@ def _extend_validator(validator_class):
 FreqtradeValidator = _extend_validator(Draft4Validator)
 
 
-def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
+def validate_config_schema(conf: Dict[str, Any], preliminary: bool = False) -> Dict[str, Any]:
     """
     Validate the configuration follow the Config Schema
     :param conf: Config in JSON format
@@ -49,7 +49,10 @@ def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
     if conf.get('runmode', RunMode.OTHER) in (RunMode.DRY_RUN, RunMode.LIVE):
         conf_schema['required'] = constants.SCHEMA_TRADE_REQUIRED
     elif conf.get('runmode', RunMode.OTHER) in (RunMode.BACKTEST, RunMode.HYPEROPT):
-        conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED
+        if preliminary:
+            conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED
+        else:
+            conf_schema['required'] = constants.SCHEMA_BACKTEST_REQUIRED_FINAL
     else:
         conf_schema['required'] = constants.SCHEMA_MINIMAL_REQUIRED
     try:
@@ -64,7 +67,7 @@ def validate_config_schema(conf: Dict[str, Any]) -> Dict[str, Any]:
         )
 
 
-def validate_config_consistency(conf: Dict[str, Any]) -> None:
+def validate_config_consistency(conf: Dict[str, Any], preliminary: bool = False) -> None:
     """
     Validate the configuration consistency.
     Should be ran after loading both configuration and strategy,
@@ -85,7 +88,7 @@ def validate_config_consistency(conf: Dict[str, Any]) -> None:
 
     # validate configuration before returning
     logger.info('Validating configuration ...')
-    validate_config_schema(conf)
+    validate_config_schema(conf, preliminary=preliminary)
 
 
 def _validate_unlimited_amount(conf: Dict[str, Any]) -> None:

freqtrade/constants.py

@@ -462,6 +462,10 @@ SCHEMA_BACKTEST_REQUIRED = [
     'dataformat_ohlcv',
     'dataformat_trades',
 ]
+SCHEMA_BACKTEST_REQUIRED_FINAL = SCHEMA_BACKTEST_REQUIRED + [
+    'stoploss',
+    'minimal_roi',
+]
 
 SCHEMA_MINIMAL_REQUIRED = [
     'exchange',

freqtrade/data/btanalysis.py

@@ -5,14 +5,15 @@ import logging
 from copy import copy
 from datetime import datetime, timezone
 from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Any, Dict, List, Optional, Union
 
 import numpy as np
 import pandas as pd
 
 from freqtrade.constants import LAST_BT_RESULT_FN
 from freqtrade.exceptions import OperationalException
-from freqtrade.misc import get_backtest_metadata_filename, json_load
+from freqtrade.misc import json_load
+from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
 from freqtrade.persistence import LocalTrade, Trade, init_db
 
 
@@ -399,157 +400,3 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame,
     trades = trades.loc[(trades['open_date'] >= trades_start) &
                         (trades['close_date'] <= trades_stop)]
     return trades
-
-
-def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
-    """
-    Calculate market change based on "column".
-    Calculation is done by taking the first non-null and the last non-null element of each column
-    and calculating the pctchange as "(last - first) / first".
-    Then the results per pair are combined as mean.
-
-    :param data: Dict of Dataframes, dict key should be pair.
-    :param column: Column in the original dataframes to use
-    :return:
-    """
-    tmp_means = []
-    for pair, df in data.items():
-        start = df[column].dropna().iloc[0]
-        end = df[column].dropna().iloc[-1]
-        tmp_means.append((end - start) / start)
-
-    return float(np.mean(tmp_means))
-
-
-def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
-                                 column: str = "close") -> pd.DataFrame:
-    """
-    Combine multiple dataframes "column"
-    :param data: Dict of Dataframes, dict key should be pair.
-    :param column: Column in the original dataframes to use
-    :return: DataFrame with the column renamed to the dict key, and a column
-        named mean, containing the mean of all pairs.
-    :raise: ValueError if no data is provided.
-    """
-    df_comb = pd.concat([data[pair].set_index('date').rename(
-        {column: pair}, axis=1)[pair] for pair in data], axis=1)
-
-    df_comb['mean'] = df_comb.mean(axis=1)
-
-    return df_comb
-
-
-def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
-                      timeframe: str) -> pd.DataFrame:
-    """
-    Adds a column `col_name` with the cumulative profit for the given trades array.
-    :param df: DataFrame with date index
-    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
-    :param col_name: Column name that will be assigned the results
-    :param timeframe: Timeframe used during the operations
-    :return: Returns df with one additional column, col_name, containing the cumulative profit.
-    :raise: ValueError if trade-dataframe was found empty.
-    """
-    if len(trades) == 0:
-        raise ValueError("Trade dataframe empty.")
-    from freqtrade.exchange import timeframe_to_minutes
-    timeframe_minutes = timeframe_to_minutes(timeframe)
-    # Resample to timeframe to make sure trades match candles
-    _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
-                                  )[['profit_abs']].sum()
-    df.loc[:, col_name] = _trades_sum['profit_abs'].cumsum()
-    # Set first value to 0
-    df.loc[df.iloc[0].name, col_name] = 0
-    # FFill to get continuous
-    df[col_name] = df[col_name].ffill()
-    return df
-
-
-def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str
-                          ) -> pd.DataFrame:
-    max_drawdown_df = pd.DataFrame()
-    max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
-    max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
-    max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
-    max_drawdown_df['date'] = profit_results.loc[:, date_col]
-    return max_drawdown_df
-
-
-def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
-                         value_col: str = 'profit_ratio'
-                         ):
-    """
-    Calculate max drawdown and the corresponding close dates
-    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
-    :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
-    :param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
-    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
-             high and low time and high and low value.
-    :raise: ValueError if trade-dataframe was found empty.
-    """
-    if len(trades) == 0:
-        raise ValueError("Trade dataframe empty.")
-    profit_results = trades.sort_values(date_col).reset_index(drop=True)
-    max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
-
-    return max_drawdown_df
-
-
-def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
-                           value_col: str = 'profit_abs', starting_balance: float = 0
-                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
-    """
-    Calculate max drawdown and the corresponding close dates
-    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
-    :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
-    :param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
-    :param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
-    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
-             with absolute max drawdown, high and low time and high and low value,
-             and the relative account drawdown
-    :raise: ValueError if trade-dataframe was found empty.
-    """
-    if len(trades) == 0:
-        raise ValueError("Trade dataframe empty.")
-    profit_results = trades.sort_values(date_col).reset_index(drop=True)
-    max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
-
-    idxmin = max_drawdown_df['drawdown'].idxmin()
-    if idxmin == 0:
-        raise ValueError("No losing trade, therefore no drawdown.")
-    high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
-    low_date = profit_results.loc[idxmin, date_col]
-    high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
-                                   ['high_value'].idxmax(), 'cumulative']
-    low_val = max_drawdown_df.loc[idxmin, 'cumulative']
-    max_drawdown_rel = 0.0
-    if high_val + starting_balance != 0:
-        max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
-
-    return (
-        abs(min(max_drawdown_df['drawdown'])),
-        high_date,
-        low_date,
-        high_val,
-        low_val,
-        max_drawdown_rel
-    )
-
-
-def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
-    """
-    Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
-    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
-    :param starting_balance: Add starting balance to results, to show the wallets high / low points
-    :return: Tuple (float, float) with cumsum of profit_abs
-    :raise: ValueError if trade-dataframe was found empty.
-    """
-    if len(trades) == 0:
-        raise ValueError("Trade dataframe empty.")
-
-    csum_df = pd.DataFrame()
-    csum_df['sum'] = trades['profit_abs'].cumsum()
-    csum_min = csum_df['sum'].min() + starting_balance
-    csum_max = csum_df['sum'].max() + starting_balance
-
-    return csum_min, csum_max

freqtrade/data/metrics.py

@@ -0,0 +1,173 @@
+import logging
+from typing import Dict, Tuple
+
+import numpy as np
+import pandas as pd
+
+
+logger = logging.getLogger(__name__)
+
+
+def calculate_market_change(data: Dict[str, pd.DataFrame], column: str = "close") -> float:
+    """
+    Calculate market change based on "column".
+    Calculation is done by taking the first non-null and the last non-null element of each column
+    and calculating the pctchange as "(last - first) / first".
+    Then the results per pair are combined as mean.
+
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return:
+    """
+    tmp_means = []
+    for pair, df in data.items():
+        start = df[column].dropna().iloc[0]
+        end = df[column].dropna().iloc[-1]
+        tmp_means.append((end - start) / start)
+
+    return float(np.mean(tmp_means))
+
+
+def combine_dataframes_with_mean(data: Dict[str, pd.DataFrame],
+                                 column: str = "close") -> pd.DataFrame:
+    """
+    Combine multiple dataframes "column"
+    :param data: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return: DataFrame with the column renamed to the dict key, and a column
+        named mean, containing the mean of all pairs.
+    :raise: ValueError if no data is provided.
+    """
+    df_comb = pd.concat([data[pair].set_index('date').rename(
+        {column: pair}, axis=1)[pair] for pair in data], axis=1)
+
+    df_comb['mean'] = df_comb.mean(axis=1)
+
+    return df_comb
+
+
+def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
+                      timeframe: str) -> pd.DataFrame:
+    """
+    Adds a column `col_name` with the cumulative profit for the given trades array.
+    :param df: DataFrame with date index
+    :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
+    :param col_name: Column name that will be assigned the results
+    :param timeframe: Timeframe used during the operations
+    :return: Returns df with one additional column, col_name, containing the cumulative profit.
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    from freqtrade.exchange import timeframe_to_minutes
+    timeframe_minutes = timeframe_to_minutes(timeframe)
+    # Resample to timeframe to make sure trades match candles
+    _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_date'
+                                  )[['profit_abs']].sum()
+    df.loc[:, col_name] = _trades_sum['profit_abs'].cumsum()
+    # Set first value to 0
+    df.loc[df.iloc[0].name, col_name] = 0
+    # FFill to get continuous
+    df[col_name] = df[col_name].ffill()
+    return df
+
+
+def _calc_drawdown_series(profit_results: pd.DataFrame, *, date_col: str, value_col: str
+                          ) -> pd.DataFrame:
+    max_drawdown_df = pd.DataFrame()
+    max_drawdown_df['cumulative'] = profit_results[value_col].cumsum()
+    max_drawdown_df['high_value'] = max_drawdown_df['cumulative'].cummax()
+    max_drawdown_df['drawdown'] = max_drawdown_df['cumulative'] - max_drawdown_df['high_value']
+    max_drawdown_df['date'] = profit_results.loc[:, date_col]
+    return max_drawdown_df
+
+
+def calculate_underwater(trades: pd.DataFrame, *, date_col: str = 'close_date',
+                         value_col: str = 'profit_ratio'
+                         ):
+    """
+    Calculate max drawdown and the corresponding close dates
+    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
+    :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
+    :param value_col: Column in DataFrame to use for values (defaults to 'profit_ratio')
+    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue) with absolute max drawdown,
+             high and low time and high and low value.
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    profit_results = trades.sort_values(date_col).reset_index(drop=True)
+    max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
+
+    return max_drawdown_df
+
+
+def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
+                           value_col: str = 'profit_abs', starting_balance: float = 0
+                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float, float]:
+    """
+    Calculate max drawdown and the corresponding close dates
+    :param trades: DataFrame containing trades (requires columns close_date and profit_ratio)
+    :param date_col: Column in DataFrame to use for dates (defaults to 'close_date')
+    :param value_col: Column in DataFrame to use for values (defaults to 'profit_abs')
+    :param starting_balance: Portfolio starting balance - properly calculate relative drawdown.
+    :return: Tuple (float, highdate, lowdate, highvalue, lowvalue, relative_drawdown)
+             with absolute max drawdown, high and low time and high and low value,
+             and the relative account drawdown
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+    profit_results = trades.sort_values(date_col).reset_index(drop=True)
+    max_drawdown_df = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
+
+    idxmin = max_drawdown_df['drawdown'].idxmin()
+    if idxmin == 0:
+        raise ValueError("No losing trade, therefore no drawdown.")
+    high_date = profit_results.loc[max_drawdown_df.iloc[:idxmin]['high_value'].idxmax(), date_col]
+    low_date = profit_results.loc[idxmin, date_col]
+    high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
+                                   ['high_value'].idxmax(), 'cumulative']
+    low_val = max_drawdown_df.loc[idxmin, 'cumulative']
+    max_drawdown_rel = 0.0
+    if high_val + starting_balance != 0:
+        max_drawdown_rel = (high_val - low_val) / (high_val + starting_balance)
+
+    return (
+        abs(min(max_drawdown_df['drawdown'])),
+        high_date,
+        low_date,
+        high_val,
+        low_val,
+        max_drawdown_rel
+    )
+
+
+def calculate_csum(trades: pd.DataFrame, starting_balance: float = 0) -> Tuple[float, float]:
+    """
+    Calculate min/max cumsum of trades, to show if the wallet/stake amount ratio is sane
+    :param trades: DataFrame containing trades (requires columns close_date and profit_percent)
+    :param starting_balance: Add starting balance to results, to show the wallets high / low points
+    :return: Tuple (float, float) with cumsum of profit_abs
+    :raise: ValueError if trade-dataframe was found empty.
+    """
+    if len(trades) == 0:
+        raise ValueError("Trade dataframe empty.")
+
+    csum_df = pd.DataFrame()
+    csum_df['sum'] = trades['profit_abs'].cumsum()
+    csum_min = csum_df['sum'].min() + starting_balance
+    csum_max = csum_df['sum'].max() + starting_balance
+
+    return csum_min, csum_max
+
+
+def calculate_cagr(days_passed: int, starting_balance: float, final_balance: float) -> float:
+    """
+    Calculate CAGR
+    :param days_passed: Days passed between start and ending balance
+    :param starting_balance: Starting balance
+    :param final_balance: Final balance to calculate CAGR against
+    :return: CAGR
+    """
+    return (final_balance / starting_balance) ** (1 / (days_passed / 365)) - 1

freqtrade/exchange/exchange.py

@@ -9,6 +9,7 @@ import logging
 from copy import deepcopy
 from datetime import datetime, timedelta, timezone
 from math import ceil
+from threading import Lock
 from typing import Any, Coroutine, Dict, List, Literal, Optional, Tuple, Union
 
 import arrow
@@ -64,6 +65,7 @@ class Exchange:
         "ohlcv_params": {},
         "ohlcv_candle_limit": 500,
         "ohlcv_partial_candle": True,
+        "ohlcv_require_since": False,
         # Check https://github.com/ccxt/ccxt/issues/10767 for removal of ohlcv_volume_currency
         "ohlcv_volume_currency": "base",  # "base" or "quote"
         "tickers_have_quoteVolume": True,
@@ -95,6 +97,9 @@ class Exchange:
         self._markets: Dict = {}
         self._trading_fees: Dict[str, Any] = {}
         self._leverage_tiers: Dict[str, List[Dict]] = {}
+        # Lock event loop. This is necessary to avoid race-conditions when using force* commands
+        # Due to funding fee fetching.
+        self._loop_lock = Lock()
         self.loop = asyncio.new_event_loop()
         asyncio.set_event_loop(self.loop)
         self._config: Dict = {}
@@ -166,7 +171,7 @@ class Exchange:
         self._api_async = self._init_ccxt(
             exchange_config, ccxt_async, ccxt_kwargs=ccxt_async_config)
 
-        logger.info('Using Exchange "%s"', self.name)
+        logger.info(f'Using Exchange "{self.name}"')
 
         if validate:
             # Check if timeframe is available
@@ -368,6 +373,9 @@ class Exchange:
         return (
             market.get('quote', None) is not None
             and market.get('base', None) is not None
+            and (self.precisionMode != TICK_SIZE
+                 # Too low precision will falsify calculations
+                 or market.get('precision', {}).get('price', None) > 1e-11)
             and ((self.trading_mode == TradingMode.SPOT and self.market_is_spot(market))
                  or (self.trading_mode == TradingMode.MARGIN and self.market_is_margin(market))
                  or (self.trading_mode == TradingMode.FUTURES and self.market_is_future(market)))
@@ -551,7 +559,7 @@ class Exchange:
             # Therefore we also show that.
             raise OperationalException(
                 f"The ccxt library does not provide the list of timeframes "
-                f"for the exchange \"{self.name}\" and this exchange "
+                f"for the exchange {self.name} and this exchange "
                 f"is therefore not supported. ccxt fetchOHLCV: {self.exchange_has('fetchOHLCV')}")
 
         if timeframe and (timeframe not in self.timeframes):
@@ -781,7 +789,9 @@ class Exchange:
                              rate: float, leverage: float, params: Dict = {},
                              stop_loss: bool = False) -> Dict[str, Any]:
         order_id = f'dry_run_{side}_{datetime.now().timestamp()}'
-        _amount = self.amount_to_precision(pair, amount)
+        # Rounding here must respect to contract sizes
+        _amount = self._contracts_to_amount(
+            pair, self.amount_to_precision(pair, self._amount_to_contracts(pair, amount)))
         dry_order: Dict[str, Any] = {
             'id': order_id,
             'symbol': pair,
@@ -1710,7 +1720,8 @@ class Exchange:
     def _build_coroutine(self, pair: str, timeframe: str, candle_type: CandleType,
                          since_ms: Optional[int]) -> Coroutine:
 
-        if not since_ms and self.required_candle_call_count > 1:
+        if (not since_ms
+                and (self._ft_has["ohlcv_require_since"] or self.required_candle_call_count > 1)):
             # Multiple calls for one pair - to get more history
             one_call = timeframe_to_msecs(timeframe) * self.ohlcv_candle_limit(timeframe)
             move_to = one_call * self.required_candle_call_count
@@ -1770,7 +1781,8 @@ class Exchange:
             async def gather_stuff():
                 return await asyncio.gather(*input_coro, return_exceptions=True)
 
-            results = self.loop.run_until_complete(gather_stuff())
+            with self._loop_lock:
+                results = self.loop.run_until_complete(gather_stuff())
 
             for res in results:
                 if isinstance(res, Exception):
@@ -1829,17 +1841,18 @@ class Exchange:
                 pair, timeframe, since_ms, s
             )
             params = deepcopy(self._ft_has.get('ohlcv_params', {}))
+            candle_limit = self.ohlcv_candle_limit(timeframe)
             if candle_type != CandleType.SPOT:
                 params.update({'price': candle_type})
             if candle_type != CandleType.FUNDING_RATE:
                 data = await self._api_async.fetch_ohlcv(
                     pair, timeframe=timeframe, since=since_ms,
-                    limit=self.ohlcv_candle_limit(timeframe), params=params)
+                    limit=candle_limit, params=params)
             else:
                 # Funding rate
                 data = await self._api_async.fetch_funding_rate_history(
                     pair, since=since_ms,
-                    limit=self.ohlcv_candle_limit(timeframe))
+                    limit=candle_limit)
                 # Convert funding rate to candle pattern
                 data = [[x['timestamp'], x['fundingRate'], 0, 0, 0, 0] for x in data]
             # Some exchanges sort OHLCV in ASC order and others in DESC.
@@ -2026,9 +2039,10 @@ class Exchange:
         if not self.exchange_has("fetchTrades"):
             raise OperationalException("This exchange does not support downloading Trades.")
 
-        return self.loop.run_until_complete(
-            self._async_get_trade_history(pair=pair, since=since,
-                                          until=until, from_id=from_id))
+        with self._loop_lock:
+            return self.loop.run_until_complete(
+                self._async_get_trade_history(pair=pair, since=since,
+                                              until=until, from_id=from_id))
 
     @retrier
     def _get_funding_fees_from_exchange(self, pair: str, since: Union[datetime, int]) -> float:

freqtrade/exchange/ftx.py

@@ -20,6 +20,7 @@ class Ftx(Exchange):
     _ft_has: Dict = {
         "stoploss_on_exchange": True,
         "ohlcv_candle_limit": 1500,
+        "ohlcv_require_since": True,
         "ohlcv_volume_currency": "quote",
         "mark_ohlcv_price": "index",
         "mark_ohlcv_timeframe": "1h",

freqtrade/freqtradebot.py

@@ -122,6 +122,8 @@ class FreqtradeBot(LoggingMixin):
                     self._schedule.every().day.at(t).do(update)
         self.last_process = datetime(1970, 1, 1, tzinfo=timezone.utc)
 
+        self.strategy.bot_start()
+
     def notify_status(self, msg: str) -> None:
         """
         Public method for users of this class (worker, etc.) to send notifications
@@ -588,7 +590,6 @@ class FreqtradeBot(LoggingMixin):
         Executes a limit buy for the given pair
         :param pair: pair for which we want to create a LIMIT_BUY
         :param stake_amount: amount of stake-currency for the pair
-        :param leverage: amount of leverage applied to this trade
         :return: True if a buy order is created, false if it fails.
         """
         time_in_force = self.strategy.order_time_in_force['entry']
@@ -667,16 +668,6 @@ class FreqtradeBot(LoggingMixin):
             amount = safe_value_fallback(order, 'filled', 'amount')
             enter_limit_filled_price = safe_value_fallback(order, 'average', 'price')
 
-        # TODO: this might be unnecessary, as we're calling it in update_trade_state.
-        isolated_liq = self.exchange.get_liquidation_price(
-            leverage=leverage,
-            pair=pair,
-            amount=amount,
-            open_rate=enter_limit_filled_price,
-            is_short=is_short
-        )
-        interest_rate = self.exchange.get_interest_rate()
-
         # Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
         fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
         base_currency = self.exchange.get_pair_base_currency(pair)
@@ -705,8 +696,6 @@ class FreqtradeBot(LoggingMixin):
                 timeframe=timeframe_to_minutes(self.config['timeframe']),
                 leverage=leverage,
                 is_short=is_short,
-                interest_rate=interest_rate,
-                liquidation_price=isolated_liq,
                 trading_mode=self.trading_mode,
                 funding_fees=funding_fees
             )
@@ -1376,7 +1365,8 @@ class FreqtradeBot(LoggingMixin):
                                                   default_retval=proposed_limit_rate)(
             pair=trade.pair, trade=trade,
             current_time=datetime.now(timezone.utc),
-            proposed_rate=proposed_limit_rate, current_profit=current_profit)
+            proposed_rate=proposed_limit_rate, current_profit=current_profit,
+            exit_tag=exit_check.exit_reason)
 
         limit = self.get_valid_price(custom_exit_price, proposed_limit_rate)
 

freqtrade/misc.py

@@ -2,13 +2,11 @@
 Various tool function for Freqtrade and scripts
 """
 import gzip
-import hashlib
 import logging
 import re
-from copy import deepcopy
 from datetime import datetime
 from pathlib import Path
-from typing import Any, Iterator, List, Union
+from typing import Any, Iterator, List
 from typing.io import IO
 from urllib.parse import urlparse
 
@@ -251,34 +249,3 @@ def parse_db_uri_for_logging(uri: str):
         return uri
     pwd = parsed_db_uri.netloc.split(':')[1].split('@')[0]
     return parsed_db_uri.geturl().replace(f':{pwd}@', ':*****@')
-
-
-def get_strategy_run_id(strategy) -> str:
-    """
-    Generate unique identification hash for a backtest run. Identical config and strategy file will
-    always return an identical hash.
-    :param strategy: strategy object.
-    :return: hex string id.
-    """
-    digest = hashlib.sha1()
-    config = deepcopy(strategy.config)
-
-    # Options that have no impact on results of individual backtest.
-    not_important_keys = ('strategy_list', 'original_config', 'telegram', 'api_server')
-    for k in not_important_keys:
-        if k in config:
-            del config[k]
-
-    # Explicitly allow NaN values (e.g. max_open_trades).
-    # as it does not matter for getting the hash.
-    digest.update(rapidjson.dumps(config, default=str,
-                                  number_mode=rapidjson.NM_NAN).encode('utf-8'))
-    with open(strategy.__file__, 'rb') as fp:
-        digest.update(fp.read())
-    return digest.hexdigest().lower()
-
-
-def get_backtest_metadata_filename(filename: Union[Path, str]) -> Path:
-    """Return metadata filename for specified backtest results file."""
-    filename = Path(filename)
-    return filename.parent / Path(f'{filename.stem}.meta{filename.suffix}')

freqtrade/optimize/backtest_caching.py

@@ -0,0 +1,40 @@
+import hashlib
+from copy import deepcopy
+from pathlib import Path
+from typing import Union
+
+import rapidjson
+
+
+def get_strategy_run_id(strategy) -> str:
+    """
+    Generate unique identification hash for a backtest run. Identical config and strategy file will
+    always return an identical hash.
+    :param strategy: strategy object.
+    :return: hex string id.
+    """
+    digest = hashlib.sha1()
+    config = deepcopy(strategy.config)
+
+    # Options that have no impact on results of individual backtest.
+    not_important_keys = ('strategy_list', 'original_config', 'telegram', 'api_server')
+    for k in not_important_keys:
+        if k in config:
+            del config[k]
+
+    # Explicitly allow NaN values (e.g. max_open_trades).
+    # as it does not matter for getting the hash.
+    digest.update(rapidjson.dumps(config, default=str,
+                                  number_mode=rapidjson.NM_NAN).encode('utf-8'))
+    # Include _ft_params_from_file - so changing parameter files cause cache eviction
+    digest.update(rapidjson.dumps(
+        strategy._ft_params_from_file, default=str, number_mode=rapidjson.NM_NAN).encode('utf-8'))
+    with open(strategy.__file__, 'rb') as fp:
+        digest.update(fp.read())
+    return digest.hexdigest().lower()
+
+
+def get_backtest_metadata_filename(filename: Union[Path, str]) -> Path:
+    """Return metadata filename for specified backtest results file."""
+    filename = Path(filename)
+    return filename.parent / Path(f'{filename.stem}.meta{filename.suffix}')

freqtrade/optimize/backtesting.py

@@ -9,6 +9,7 @@ from copy import deepcopy
 from datetime import datetime, timedelta, timezone
 from typing import Any, Dict, List, Optional, Tuple
 
+import pandas as pd
 from numpy import nan
 from pandas import DataFrame
 
@@ -23,8 +24,8 @@ from freqtrade.enums import (BacktestState, CandleType, ExitCheckTuple, ExitType
                              TradingMode)
 from freqtrade.exceptions import DependencyException, OperationalException
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
-from freqtrade.misc import get_strategy_run_id
 from freqtrade.mixins import LoggingMixin
+from freqtrade.optimize.backtest_caching import get_strategy_run_id
 from freqtrade.optimize.bt_progress import BTProgress
 from freqtrade.optimize.optimize_reports import (generate_backtest_stats, show_backtest_results,
                                                  store_backtest_signal_candles,
@@ -53,6 +54,11 @@ ESHORT_IDX = 8  # Exit short
 ENTER_TAG_IDX = 9
 EXIT_TAG_IDX = 10
 
+# Every change to this headers list must evaluate further usages of the resulting tuple
+# and eventually change the constants for indexes at the top
+HEADERS = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
+           'enter_short', 'exit_short', 'enter_tag', 'exit_tag']
+
 
 class Backtesting:
     """
@@ -181,6 +187,7 @@ class Backtesting:
         # since a "perfect" stoploss-exit is assumed anyway
         # And the regular "stoploss" function would not apply to that case
         self.strategy.order_types['stoploss_on_exchange'] = False
+        self.strategy.bot_start()
 
     def _load_protections(self, strategy: IStrategy):
         if self.config.get('enable_protections', False):
@@ -263,10 +270,18 @@ class Backtesting:
                 candle_type=CandleType.from_string(self.exchange._ft_has["mark_ohlcv_price"])
             )
             # Combine data to avoid combining the data per trade.
+            unavailable_pairs = []
             for pair in self.pairlists.whitelist:
+                if pair not in self.exchange._leverage_tiers:
+                    unavailable_pairs.append(pair)
+                    continue
                 self.futures_data[pair] = funding_rates_dict[pair].merge(
                     mark_rates_dict[pair], on='date', how="inner", suffixes=["_fund", "_mark"])
 
+            if unavailable_pairs:
+                raise OperationalException(
+                        f"Pairs {', '.join(unavailable_pairs)} got no leverage tiers available. "
+                        "It is therefore impossible to backtest with this pair at the moment.")
         else:
             self.futures_data = {}
 
@@ -304,10 +319,7 @@ class Backtesting:
         :param processed: a processed dictionary with format {pair, data}, which gets cleared to
         optimize memory usage!
         """
-        # Every change to this headers list must evaluate further usages of the resulting tuple
-        # and eventually change the constants for indexes at the top
-        headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
-                   'enter_short', 'exit_short', 'enter_tag', 'exit_tag']
+
         data: Dict = {}
         self.progress.init_step(BacktestState.CONVERT, len(processed))
 
@@ -319,7 +331,7 @@ class Backtesting:
 
             if not pair_data.empty:
                 # Cleanup from prior runs
-                pair_data.drop(headers[5:] + ['buy', 'sell'], axis=1, errors='ignore')
+                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}),
@@ -338,7 +350,7 @@ class Backtesting:
 
             # To avoid using data from future, we use entry/exit signals shifted
             # from the previous candle
-            for col in headers[5:]:
+            for col in HEADERS[5:]:
                 tag_col = col in ('enter_tag', 'exit_tag')
                 if col in df_analyzed.columns:
                     df_analyzed.loc[:, col] = df_analyzed.loc[:, col].replace(
@@ -350,7 +362,7 @@ class Backtesting:
 
             # Convert from Pandas to list for performance reasons
             # (Looping Pandas is slow.)
-            data[pair] = df_analyzed[headers].values.tolist() if not df_analyzed.empty else []
+            data[pair] = df_analyzed[HEADERS].values.tolist() if not df_analyzed.empty else []
         return data
 
     def _get_close_rate(self, row: Tuple, trade: LocalTrade, exit: ExitCheckTuple,
@@ -514,10 +526,10 @@ class Backtesting:
 
         exit_candle_time: datetime = row[DATE_IDX].to_pydatetime()
         enter = row[SHORT_IDX] if trade.is_short else row[LONG_IDX]
-        exit_ = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
+        exit_sig = row[ESHORT_IDX] if trade.is_short else row[ELONG_IDX]
         exit_ = self.strategy.should_exit(
             trade, row[OPEN_IDX], exit_candle_time,  # type: ignore
-            enter=enter, exit_=exit_,
+            enter=enter, exit_=exit_sig,
             low=row[LOW_IDX], high=row[HIGH_IDX]
         )
 
@@ -539,7 +551,8 @@ class Backtesting:
                                                       default_retval=closerate)(
                         pair=trade.pair, trade=trade,
                         current_time=exit_candle_time,
-                        proposed_rate=closerate, current_profit=current_profit)
+                        proposed_rate=closerate, current_profit=current_profit,
+                        exit_tag=exit_.exit_reason)
                     # We can't place orders lower than current low.
                     # freqtrade does not support this in live, and the order would fill immediately
                     if trade.is_short:
@@ -566,6 +579,7 @@ class Backtesting:
                 len(row) > EXIT_TAG_IDX
                 and row[EXIT_TAG_IDX] is not None
                 and len(row[EXIT_TAG_IDX]) > 0
+                and exit_.exit_type in (ExitType.EXIT_SIGNAL,)
             ):
                 trade.exit_reason = row[EXIT_TAG_IDX]
 
@@ -624,9 +638,7 @@ class Backtesting:
             detail_data.loc[:, 'exit_short'] = row[ESHORT_IDX]
             detail_data.loc[:, 'enter_tag'] = row[ENTER_TAG_IDX]
             detail_data.loc[:, 'exit_tag'] = row[EXIT_TAG_IDX]
-            headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
-                       'enter_short', 'exit_short', 'enter_tag', 'exit_tag']
-            for det_row in detail_data[headers].values.tolist():
+            for det_row in detail_data[HEADERS].values.tolist():
                 res = self._get_exit_trade_entry_for_candle(trade, det_row)
                 if res:
                     return res
@@ -1028,7 +1040,7 @@ class Backtesting:
                               timerange: TimeRange):
         self.progress.init_step(BacktestState.ANALYZE, 0)
 
-        logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
+        logger.info(f"Running backtesting for Strategy {strat.get_strategy_name()}")
         backtest_start_time = datetime.now(timezone.utc)
         self._set_strategy(strat)
 
@@ -1095,7 +1107,7 @@ class Backtesting:
                 for t, v in pairresults.open_date.items():
                     allinds = pairdf.loc[(pairdf['date'] < v)]
                     signal_inds = allinds.iloc[[-1]]
-                    signal_candles_only_df = signal_candles_only_df.append(signal_inds)
+                    signal_candles_only_df = pd.concat([signal_candles_only_df, signal_inds])
 
                 signal_candles_only[pair] = signal_candles_only_df
 

freqtrade/optimize/edge_cli.py

@@ -44,6 +44,7 @@ class EdgeCli:
 
         self.edge._timerange = TimeRange.parse_timerange(None if self.config.get(
             'timerange') is None else str(self.config.get('timerange')))
+        self.strategy.bot_start()
 
     def start(self) -> None:
         result = self.edge.calculate(self.config['exchange']['pair_whitelist'])

freqtrade/optimize/hyperopt.py

@@ -468,6 +468,7 @@ class Hyperopt:
         self.backtesting.exchange._api = None
         self.backtesting.exchange._api_async = None
         self.backtesting.exchange.loop = None  # type: ignore
+        self.backtesting.exchange._loop_lock = None  # type: ignore
         # self.backtesting.exchange = None  # type: ignore
         self.backtesting.pairlists = None  # type: ignore
 

freqtrade/optimize/hyperopt_loss/hyperopt_loss_calmar.py

@@ -10,7 +10,7 @@ from typing import Any, Dict
 
 from pandas import DataFrame
 
-from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.data.metrics import calculate_max_drawdown
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 

freqtrade/optimize/hyperopt_loss/hyperopt_loss_max_drawdown.py

@@ -8,7 +8,7 @@ from datetime import datetime
 
 from pandas import DataFrame
 
-from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.data.metrics import calculate_max_drawdown
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 

freqtrade/optimize/hyperopt_loss/hyperopt_loss_profit_drawdown.py

@@ -9,7 +9,7 @@ individual needs.
 """
 from pandas import DataFrame
 
-from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.data.metrics import calculate_max_drawdown
 from freqtrade.optimize.hyperopt import IHyperOptLoss
 
 

freqtrade/optimize/optimize_reports.py

@@ -9,10 +9,10 @@ from pandas import DataFrame, to_datetime
 from tabulate import tabulate
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN, UNLIMITED_STAKE_AMOUNT
-from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
-                                       calculate_max_drawdown)
-from freqtrade.misc import (decimals_per_coin, file_dump_joblib, file_dump_json,
-                            get_backtest_metadata_filename, round_coin_value)
+from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
+                                    calculate_max_drawdown)
+from freqtrade.misc import decimals_per_coin, file_dump_joblib, file_dump_json, round_coin_value
+from freqtrade.optimize.backtest_caching import get_backtest_metadata_filename
 
 
 logger = logging.getLogger(__name__)
@@ -446,6 +446,7 @@ def generate_strategy_stats(pairlist: List[str],
         'profit_total_abs': results['profit_abs'].sum(),
         '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']),
         'backtest_start': min_date.strftime(DATETIME_PRINT_FORMAT),
         'backtest_start_ts': int(min_date.timestamp() * 1000),
         'backtest_end': max_date.strftime(DATETIME_PRINT_FORMAT),
@@ -746,6 +747,7 @@ def text_table_add_metrics(strat_results: Dict) -> str:
             ('Absolute profit ', round_coin_value(strat_results['profit_total_abs'],
                                                   strat_results['stake_currency'])),
             ('Total profit %', f"{strat_results['profit_total']:.2%}"),
+            ('CAGR %', f"{strat_results['cagr']:.2%}" if 'cagr' 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%}"),

freqtrade/persistence/models.py

@@ -429,12 +429,10 @@ class LocalTrade():
 
     def __repr__(self):
         open_since = self.open_date.strftime(DATETIME_PRINT_FORMAT) if self.is_open else 'closed'
-        leverage = self.leverage or 1.0
-        is_short = self.is_short or False
 
         return (
             f'Trade(id={self.id}, pair={self.pair}, amount={self.amount:.8f}, '
-            f'is_short={is_short}, leverage={leverage}, '
+            f'is_short={self.is_short or False}, leverage={self.leverage or 1.0}, '
             f'open_rate={self.open_rate:.8f}, open_since={open_since})'
         )
 

freqtrade/plot/plotting.py

@@ -5,12 +5,13 @@ from typing import Any, Dict, List, Optional
 import pandas as pd
 
 from freqtrade.configuration import TimeRange
-from freqtrade.data.btanalysis import (analyze_trade_parallelism, calculate_max_drawdown,
-                                       calculate_underwater, combine_dataframes_with_mean,
-                                       create_cum_profit, extract_trades_of_period, load_trades)
+from freqtrade.data.btanalysis import (analyze_trade_parallelism, extract_trades_of_period,
+                                       load_trades)
 from freqtrade.data.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
 from freqtrade.data.history import get_timerange, load_data
+from freqtrade.data.metrics import (calculate_max_drawdown, calculate_underwater,
+                                    combine_dataframes_with_mean, create_cum_profit)
 from freqtrade.enums import CandleType
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_prev_date, timeframe_to_seconds
@@ -610,6 +611,7 @@ def load_and_plot_trades(config: Dict[str, Any]):
 
     exchange = ExchangeResolver.load_exchange(config['exchange']['name'], config)
     IStrategy.dp = DataProvider(config, exchange)
+    strategy.bot_start()
     plot_elements = init_plotscript(config, list(exchange.markets), strategy.startup_candle_count)
     timerange = plot_elements['timerange']
     trades = plot_elements['trades']

freqtrade/plugins/protections/max_drawdown_protection.py

@@ -6,7 +6,7 @@ from typing import Any, Dict, Optional
 import pandas as pd
 
 from freqtrade.constants import LongShort
-from freqtrade.data.btanalysis import calculate_max_drawdown
+from freqtrade.data.metrics import calculate_max_drawdown
 from freqtrade.persistence import Trade
 from freqtrade.plugins.protections import IProtection, ProtectionReturn
 

freqtrade/resolvers/hyperopt_resolver.py

@@ -23,7 +23,7 @@ class HyperOptLossResolver(IResolver):
     object_type = IHyperOptLoss
     object_type_str = "HyperoptLoss"
     user_subdir = USERPATH_HYPEROPTS
-    initial_search_path = Path(__file__).parent.parent.joinpath('optimize').resolve()
+    initial_search_path = Path(__file__).parent.parent.joinpath('optimize/hyperopt_loss').resolve()
 
     @staticmethod
     def load_hyperoptloss(config: Dict) -> IHyperOptLoss:

freqtrade/resolvers/strategy_resolver.py

@@ -217,15 +217,19 @@ class StrategyResolver(IResolver):
                 raise OperationalException(
                     "`populate_exit_trend` or `populate_sell_trend` must be implemented.")
 
-            strategy._populate_fun_len = len(getfullargspec(strategy.populate_indicators).args)
-            strategy._buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)
-            strategy._sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args)
+            _populate_fun_len = len(getfullargspec(strategy.populate_indicators).args)
+            _buy_fun_len = len(getfullargspec(strategy.populate_buy_trend).args)
+            _sell_fun_len = len(getfullargspec(strategy.populate_sell_trend).args)
             if any(x == 2 for x in [
-                strategy._populate_fun_len,
-                strategy._buy_fun_len,
-                strategy._sell_fun_len
+                _populate_fun_len,
+                _buy_fun_len,
+                _sell_fun_len
             ]):
-                strategy.INTERFACE_VERSION = 1
+                raise OperationalException(
+                    "Strategy Interface v1 is no longer supported. "
+                    "Please update your strategy to implement "
+                    "`populate_indicators`, `populate_entry_trend` and `populate_exit_trend` "
+                    "with the metadata argument. ")
         return strategy
 
     @staticmethod

freqtrade/rpc/api_server/api_backtest.py

@@ -84,6 +84,7 @@ async def api_start_backtest(bt_settings: BacktestRequest, background_tasks: Bac
             lastconfig['enable_protections'] = btconfig.get('enable_protections')
             lastconfig['dry_run_wallet'] = btconfig.get('dry_run_wallet')
 
+            ApiServer._bt.strategylist = [strat]
             ApiServer._bt.results = {}
             ApiServer._bt.load_prior_backtest()
 

freqtrade/rpc/api_server/webserver.py

@@ -2,7 +2,7 @@ import logging
 from ipaddress import IPv4Address
 from typing import Any, Dict
 
-import rapidjson
+import orjson
 import uvicorn
 from fastapi import Depends, FastAPI
 from fastapi.middleware.cors import CORSMiddleware
@@ -24,7 +24,7 @@ class FTJSONResponse(JSONResponse):
         Use rapidjson for responses
         Handles NaN and Inf / -Inf in a javascript way by default.
         """
-        return rapidjson.dumps(content).encode("utf-8")
+        return orjson.dumps(content, option=orjson.OPT_SERIALIZE_NUMPY)
 
 
 class ApiServer(RPCHandler):

freqtrade/rpc/telegram.py

@@ -943,7 +943,7 @@ class Telegram(RPCHandler):
         else:
             fiat_currency = self._config.get('fiat_display_currency', '')
             try:
-                statlist, head, fiat_profit_sum = self._rpc._rpc_status_table(
+                statlist, _, _ = self._rpc._rpc_status_table(
                     self._config['stake_currency'], fiat_currency)
             except RPCException:
                 self._send_msg(msg='No open trade found.')

freqtrade/strategy/informative_decorator.py

@@ -23,7 +23,7 @@ class InformativeData:
 def informative(timeframe: str, asset: str = '',
                 fmt: Optional[Union[str, Callable[[Any], str]]] = None,
                 *,
-                candle_type: Optional[CandleType] = None,
+                candle_type: Optional[Union[CandleType, str]] = None,
                 ffill: bool = True) -> Callable[[PopulateIndicators], PopulateIndicators]:
     """
     A decorator for populate_indicators_Nn(self, dataframe, metadata), allowing these functions to

freqtrade/strategy/interface.py

@@ -3,7 +3,6 @@ IStrategy interface
 This module defines the interface to apply for strategies
 """
 import logging
-import warnings
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta, timezone
 from typing import Dict, List, Optional, Tuple, Union
@@ -44,14 +43,11 @@ class IStrategy(ABC, HyperStrategyMixin):
     """
     # Strategy interface version
     # Default to version 2
-    # Version 1 is the initial interface without metadata dict
+    # Version 1 is the initial interface without metadata dict - deprecated and no longer supported.
     # Version 2 populate_* include metadata dict
     # Version 3 - First version with short and leverage support
     INTERFACE_VERSION: int = 3
 
-    _populate_fun_len: int = 0
-    _buy_fun_len: int = 0
-    _sell_fun_len: int = 0
     _ft_params_from_file: Dict
     # associated minimal roi
     minimal_roi: Dict = {}
@@ -114,7 +110,7 @@ class IStrategy(ABC, HyperStrategyMixin):
     # Class level variables (intentional) containing
     # the dataprovider (dp) (access to other candles, historic data, ...)
     # and wallets - access to the current balance.
-    dp: Optional[DataProvider]
+    dp: DataProvider
     wallets: Optional[Wallets] = None
     # Filled from configuration
     stake_currency: str
@@ -197,6 +193,13 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         return self.populate_sell_trend(dataframe, metadata)
 
+    def bot_start(self, **kwargs) -> None:
+        """
+        Called only once after bot instantiation.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        """
+        pass
+
     def bot_loop_start(self, **kwargs) -> None:
         """
         Called at the start of the bot iteration (one loop).
@@ -359,7 +362,7 @@ class IStrategy(ABC, HyperStrategyMixin):
 
     def custom_exit_price(self, pair: str, trade: Trade,
                           current_time: datetime, proposed_rate: float,
-                          current_profit: float, **kwargs) -> float:
+                          current_profit: float, exit_tag: Optional[str], **kwargs) -> float:
         """
         Custom exit price logic, returning the new exit price.
 
@@ -372,6 +375,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param current_time: datetime object, containing the current datetime
         :param proposed_rate: Rate, calculated based on pricing settings in exit_pricing.
         :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param exit_tag: Exit reason.
         :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
         :return float: New exit price value if provided
         """
@@ -1092,12 +1096,7 @@ class IStrategy(ABC, HyperStrategyMixin):
             dataframe = _create_and_merge_informative_pair(
                 self, dataframe, metadata, inf_data, populate_fn)
 
-        if self._populate_fun_len == 2:
-            warnings.warn("deprecated - check out the Sample strategy to see "
-                          "the current function headers!", DeprecationWarning)
-            return self.populate_indicators(dataframe)  # type: ignore
-        else:
-            return self.populate_indicators(dataframe, metadata)
+        return self.populate_indicators(dataframe, metadata)
 
     def advise_entry(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
@@ -1111,12 +1110,7 @@ class IStrategy(ABC, HyperStrategyMixin):
 
         logger.debug(f"Populating enter signals for pair {metadata.get('pair')}.")
 
-        if self._buy_fun_len == 2:
-            warnings.warn("deprecated - check out the Sample strategy to see "
-                          "the current function headers!", DeprecationWarning)
-            df = self.populate_buy_trend(dataframe)  # type: ignore
-        else:
-            df = self.populate_entry_trend(dataframe, metadata)
+        df = self.populate_entry_trend(dataframe, metadata)
         if 'enter_long' not in df.columns:
             df = df.rename({'buy': 'enter_long', 'buy_tag': 'enter_tag'}, axis='columns')
 
@@ -1131,14 +1125,8 @@ class IStrategy(ABC, HyperStrategyMixin):
             currently traded pair
         :return: DataFrame with exit column
         """
-
         logger.debug(f"Populating exit signals for pair {metadata.get('pair')}.")
-        if self._sell_fun_len == 2:
-            warnings.warn("deprecated - check out the Sample strategy to see "
-                          "the current function headers!", DeprecationWarning)
-            df = self.populate_sell_trend(dataframe)  # type: ignore
-        else:
-            df = self.populate_exit_trend(dataframe, metadata)
+        df = self.populate_exit_trend(dataframe, metadata)
         if 'exit_long' not in df.columns:
             df = df.rename({'sell': 'exit_long'}, axis='columns')
         return df

freqtrade/templates/subtemplates/strategy_methods_advanced.j2

@@ -32,7 +32,7 @@ def custom_entry_price(self, pair: str, current_time: 'datetime', proposed_rate:
 
 def custom_exit_price(self, pair: str, trade: 'Trade',
                       current_time: 'datetime', proposed_rate: float,
-                      current_profit: float, **kwargs) -> float:
+                      current_profit: float, exit_tag: Optional[str], **kwargs) -> float:
     """
     Custom exit price logic, returning the new exit price.
 
@@ -45,6 +45,7 @@ def custom_exit_price(self, pair: str, trade: 'Trade',
     :param current_time: datetime object, containing the current datetime
     :param proposed_rate: Rate, calculated based on pricing settings in exit_pricing.
     :param current_profit: Current profit (as ratio), calculated based on current_rate.
+    :param exit_tag: Exit reason.
     :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
     :return float: New exit price value if provided
     """

pyproject.toml

@@ -23,7 +23,7 @@ exclude = '''
 line_length = 100
 multi_line_output=0
 lines_after_imports=2
-skip_glob = ["**/.env*", "**/env/*", "**/.venv/*", "**/docs/*"]
+skip_glob = ["**/.env*", "**/env/*", "**/.venv/*", "**/docs/*", "**/user_data/*"]
 
 [tool.pytest.ini_options]
 asyncio_mode = "auto"

requirements-dev.txt

@@ -9,7 +9,7 @@ flake8==4.0.1
 flake8-tidy-imports==4.6.0
 mypy==0.942
 pre-commit==2.18.1
-pytest==7.1.1
+pytest==7.1.2
 pytest-asyncio==0.18.3
 pytest-cov==3.0.0
 pytest-mock==3.7.0
@@ -24,8 +24,6 @@ nbconvert==6.5.0
 # mypy types
 types-cachetools==5.0.1
 types-filelock==3.2.5
-types-requests==2.27.19
+types-requests==2.27.20
 types-tabulate==0.8.7
-
-# Extensions to datetime library
-types-python-dateutil==2.8.11
+types-python-dateutil==2.8.12

requirements.txt

@@ -2,7 +2,7 @@ numpy==1.22.3
 pandas==1.4.2
 pandas-ta==0.3.14b
 
-ccxt==1.79.81
+ccxt==1.80.61
 # Pin cryptography for now due to rust build errors with piwheels
 cryptography==36.0.2
 aiohttp==3.8.1
@@ -27,6 +27,8 @@ py_find_1st==1.1.5
 
 # Load ticker files 30% faster
 python-rapidjson==1.6
+# Properly format api responses
+orjson==3.6.8
 
 # Notify systemd
 sdnotify==0.3.2

setup.cfg

@@ -53,6 +53,10 @@ exclude =
 [mypy]
 ignore_missing_imports = True
 warn_unused_ignores = True
+exclude = (?x)(
+    ^build_helpers\.py$
+    )
+
 
 [mypy-tests.*]
 ignore_errors = True

setup.py

@@ -57,6 +57,7 @@ setup(
         'pycoingecko',
         'py_find_1st',
         'python-rapidjson',
+        'orjson',
         'sdnotify',
         'colorama',
         'jinja2',

tests/commands/test_commands.py

@@ -859,8 +859,8 @@ def test_start_list_strategies(capsys):
     # pargs['config'] = None
     start_list_strategies(pargs)
     captured = capsys.readouterr()
-    assert "TestStrategyLegacyV1" in captured.out
-    assert "legacy_strategy_v1.py" not in captured.out
+    assert "StrategyTestV2" in captured.out
+    assert "strategy_test_v2.py" not in captured.out
     assert CURRENT_TEST_STRATEGY in captured.out
 
     # Test regular output
@@ -874,8 +874,8 @@ def test_start_list_strategies(capsys):
     # pargs['config'] = None
     start_list_strategies(pargs)
     captured = capsys.readouterr()
-    assert "TestStrategyLegacyV1" in captured.out
-    assert "legacy_strategy_v1.py" in captured.out
+    assert "StrategyTestV2" in captured.out
+    assert "strategy_test_v2.py" in captured.out
     assert CURRENT_TEST_STRATEGY in captured.out
 
     # Test color output
@@ -888,8 +888,8 @@ def test_start_list_strategies(capsys):
     # pargs['config'] = None
     start_list_strategies(pargs)
     captured = capsys.readouterr()
-    assert "TestStrategyLegacyV1" in captured.out
-    assert "legacy_strategy_v1.py" in captured.out
+    assert "StrategyTestV2" in captured.out
+    assert "strategy_test_v2.py" in captured.out
     assert CURRENT_TEST_STRATEGY in captured.out
     assert "LOAD FAILED" in captured.out
     # Recursive
@@ -907,8 +907,8 @@ def test_start_list_strategies(capsys):
     # pargs['config'] = None
     start_list_strategies(pargs)
     captured = capsys.readouterr()
-    assert "TestStrategyLegacyV1" in captured.out
-    assert "legacy_strategy_v1.py" in captured.out
+    assert "StrategyTestV2" in captured.out
+    assert "strategy_test_v2.py" in captured.out
     assert "StrategyTestV2" in captured.out
     assert "TestStrategyNoImplements" in captured.out
     assert str(Path("broken_strats/broken_futures_strategies.py")) in captured.out

tests/data/test_btanalysis.py

@@ -8,14 +8,14 @@ from pandas import DataFrame, DateOffset, Timestamp, to_datetime
 
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import LAST_BT_RESULT_FN
-from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelism, calculate_csum,
-                                       calculate_market_change, calculate_max_drawdown,
-                                       calculate_underwater, combine_dataframes_with_mean,
-                                       create_cum_profit, extract_trades_of_period,
-                                       get_latest_backtest_filename, get_latest_hyperopt_file,
-                                       load_backtest_data, load_backtest_metadata, load_trades,
-                                       load_trades_from_db)
+from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, analyze_trade_parallelism,
+                                       extract_trades_of_period, get_latest_backtest_filename,
+                                       get_latest_hyperopt_file, load_backtest_data,
+                                       load_backtest_metadata, load_trades, load_trades_from_db)
 from freqtrade.data.history import load_data, load_pair_history
+from freqtrade.data.metrics import (calculate_cagr, calculate_csum, calculate_market_change,
+                                    calculate_max_drawdown, calculate_underwater,
+                                    combine_dataframes_with_mean, create_cum_profit)
 from freqtrade.exceptions import OperationalException
 from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
 from tests.conftest_trades import MOCK_TRADE_COUNT
@@ -336,6 +336,19 @@ def test_calculate_csum(testdatadir):
         csum_min, csum_max = calculate_csum(DataFrame())
 
 
+@pytest.mark.parametrize('start,end,days, expected', [
+    (64900, 176000, 3 * 365, 0.3945),
+    (64900, 176000, 365, 1.7119),
+    (1000, 1000, 365, 0.0),
+    (1000, 1500, 365, 0.5),
+    (1000, 1500, 100, 3.3927),  # sub year
+    (0.01000000, 0.01762792, 120, 4.6087),  # sub year BTC values
+])
+def test_calculate_cagr(start, end, days, expected):
+
+    assert round(calculate_cagr(days, start, end), 4) == expected
+
+
 def test_calculate_max_drawdown2():
     values = [0.011580, 0.010048, 0.011340, 0.012161, 0.010416, 0.010009, 0.020024,
               -0.024662, -0.022350, 0.020496, -0.029859, -0.030511, 0.010041, 0.010872,

tests/exchange/test_exchange.py

@@ -909,7 +909,7 @@ def test_validate_timeframes_emulated_ohlcv_1(default_conf, mocker):
     mocker.patch('freqtrade.exchange.Exchange.validate_stakecurrency')
     with pytest.raises(OperationalException,
                        match=r'The ccxt library does not provide the list of timeframes '
-                             r'for the exchange ".*" and this exchange '
+                             r'for the exchange .* and this exchange '
                              r'is therefore not supported. *'):
         Exchange(default_conf)
 
@@ -930,7 +930,7 @@ def test_validate_timeframes_emulated_ohlcvi_2(default_conf, mocker):
     mocker.patch('freqtrade.exchange.Exchange.validate_stakecurrency')
     with pytest.raises(OperationalException,
                        match=r'The ccxt library does not provide the list of timeframes '
-                             r'for the exchange ".*" and this exchange '
+                             r'for the exchange .* and this exchange '
                              r'is therefore not supported. *'):
         Exchange(default_conf)
 

tests/optimize/test_backtesting.py

@@ -22,7 +22,7 @@ from freqtrade.data.history import get_timerange
 from freqtrade.enums import ExitType, RunMode
 from freqtrade.exceptions import DependencyException, OperationalException
 from freqtrade.exchange.exchange import timeframe_to_next_date
-from freqtrade.misc import get_strategy_run_id
+from freqtrade.optimize.backtest_caching import get_strategy_run_id
 from freqtrade.optimize.backtesting import Backtesting
 from freqtrade.persistence import LocalTrade
 from freqtrade.resolvers import StrategyResolver
@@ -312,6 +312,7 @@ def test_backtesting_init(mocker, default_conf, order_types) -> None:
     get_fee.assert_called()
     assert backtesting.fee == 0.5
     assert not backtesting.strategy.order_types["stoploss_on_exchange"]
+    assert backtesting.strategy.bot_started is True
 
 
 def test_backtesting_init_no_timeframe(mocker, default_conf, caplog) -> None:
@@ -500,7 +501,7 @@ def test_backtesting_pairlist_list(default_conf, mocker, caplog, testdatadir, ti
     Backtesting(default_conf)
 
     # Multiple strategies
-    default_conf['strategy_list'] = [CURRENT_TEST_STRATEGY, 'TestStrategyLegacyV1']
+    default_conf['strategy_list'] = [CURRENT_TEST_STRATEGY, 'StrategyTestV2']
     with pytest.raises(OperationalException,
                        match='PrecisionFilter not allowed for backtesting multiple strategies.'):
         Backtesting(default_conf)
@@ -1198,7 +1199,7 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
         '--disable-max-market-positions',
         '--strategy-list',
         CURRENT_TEST_STRATEGY,
-        'TestStrategyLegacyV1',
+        'StrategyTestV2',
     ]
     args = get_args(args)
     start_backtesting(args)
@@ -1221,14 +1222,13 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
         'up to 2017-11-14 22:58:00 (0 days).',
         'Parameter --enable-position-stacking detected ...',
         f'Running backtesting for Strategy {CURRENT_TEST_STRATEGY}',
-        'Running backtesting for Strategy TestStrategyLegacyV1',
+        'Running backtesting for Strategy StrategyTestV2',
     ]
 
     for line in exists:
         assert log_has(line, caplog)
 
 
-@pytest.mark.filterwarnings("ignore:deprecated")
 def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdatadir, capsys):
     default_conf.update({
         "use_exit_signal": True,
@@ -1310,7 +1310,7 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
         '--breakdown', 'day',
         '--strategy-list',
         CURRENT_TEST_STRATEGY,
-        'TestStrategyLegacyV1',
+        'StrategyTestV2',
     ]
     args = get_args(args)
     start_backtesting(args)
@@ -1327,7 +1327,7 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
         'up to 2017-11-14 22:58:00 (0 days).',
         'Parameter --enable-position-stacking detected ...',
         f'Running backtesting for Strategy {CURRENT_TEST_STRATEGY}',
-        'Running backtesting for Strategy TestStrategyLegacyV1',
+        'Running backtesting for Strategy StrategyTestV2',
     ]
 
     for line in exists:
@@ -1342,6 +1342,39 @@ def test_backtest_start_multi_strat_nomock(default_conf, mocker, caplog, testdat
     assert 'STRATEGY SUMMARY' in captured.out
 
 
+@pytest.mark.filterwarnings("ignore:deprecated")
+def test_backtest_start_futures_noliq(default_conf_usdt, mocker,
+                                      caplog, testdatadir, capsys):
+    # Tests detail-data loading
+    default_conf_usdt.update({
+        "trading_mode": "futures",
+        "margin_mode": "isolated",
+        "use_exit_signal": True,
+        "exit_profit_only": False,
+        "exit_profit_offset": 0.0,
+        "ignore_roi_if_entry_signal": False,
+        "strategy": CURRENT_TEST_STRATEGY,
+    })
+    patch_exchange(mocker)
+
+    mocker.patch('freqtrade.plugins.pairlistmanager.PairListManager.whitelist',
+                 PropertyMock(return_value=['HULUMULU/USDT', 'XRP/USDT']))
+    # mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest', backtestmock)
+
+    patched_configuration_load_config_file(mocker, default_conf_usdt)
+
+    args = [
+        'backtesting',
+        '--config', 'config.json',
+        '--datadir', str(testdatadir),
+        '--strategy-path', str(Path(__file__).parents[1] / 'strategy/strats'),
+        '--timeframe', '1h',
+    ]
+    args = get_args(args)
+    with pytest.raises(OperationalException, match=r"Pairs .* got no leverage tiers available\."):
+        start_backtesting(args)
+
+
 @pytest.mark.filterwarnings("ignore:deprecated")
 def test_backtest_start_nomock_futures(default_conf_usdt, mocker,
                                        caplog, testdatadir, capsys):
@@ -1592,7 +1625,7 @@ def test_backtest_start_multi_strat_caching(default_conf, mocker, caplog, testda
         min_backtest_date = now - timedelta(weeks=4)
     load_backtest_metadata = MagicMock(return_value={
         'StrategyTestV2': {'run_id': '1', 'backtest_start_time': now.timestamp()},
-        'TestStrategyLegacyV1': {'run_id': run_id, 'backtest_start_time': start_time.timestamp()}
+        'StrategyTestV3': {'run_id': run_id, 'backtest_start_time': start_time.timestamp()}
     })
     load_backtest_stats = MagicMock(side_effect=[
         {
@@ -1601,9 +1634,9 @@ def test_backtest_start_multi_strat_caching(default_conf, mocker, caplog, testda
             'strategy_comparison': [{'key': 'StrategyTestV2'}]
         },
         {
-            'metadata': {'TestStrategyLegacyV1': {'run_id': '2'}},
-            'strategy': {'TestStrategyLegacyV1': {}},
-            'strategy_comparison': [{'key': 'TestStrategyLegacyV1'}]
+            'metadata': {'StrategyTestV3': {'run_id': '2'}},
+            'strategy': {'StrategyTestV3': {}},
+            'strategy_comparison': [{'key': 'StrategyTestV3'}]
         }
     ])
     mocker.patch('pathlib.Path.glob', return_value=[
@@ -1627,7 +1660,7 @@ def test_backtest_start_multi_strat_caching(default_conf, mocker, caplog, testda
         '--cache', cache,
         '--strategy-list',
         'StrategyTestV2',
-        'TestStrategyLegacyV1',
+        'StrategyTestV3',
     ]
     args = get_args(args)
     start_backtesting(args)
@@ -1649,7 +1682,7 @@ def test_backtest_start_multi_strat_caching(default_conf, mocker, caplog, testda
         assert backtestmock.call_count == 2
         exists = [
             'Running backtesting for Strategy StrategyTestV2',
-            'Running backtesting for Strategy TestStrategyLegacyV1',
+            'Running backtesting for Strategy StrategyTestV3',
             'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
             'Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:58:00 (0 days).',
         ]
@@ -1657,12 +1690,12 @@ def test_backtest_start_multi_strat_caching(default_conf, mocker, caplog, testda
         assert backtestmock.call_count == 0
         exists = [
             'Reusing result of previous backtest for StrategyTestV2',
-            'Reusing result of previous backtest for TestStrategyLegacyV1',
+            'Reusing result of previous backtest for StrategyTestV3',
         ]
     else:
         exists = [
             'Reusing result of previous backtest for StrategyTestV2',
-            'Running backtesting for Strategy TestStrategyLegacyV1',
+            'Running backtesting for Strategy StrategyTestV3',
             'Ignoring max_open_trades (--disable-max-market-positions was used) ...',
             'Backtesting with data from 2017-11-14 21:17:00 up to 2017-11-14 22:58:00 (0 days).',
         ]

tests/optimize/test_edge_cli.py

@@ -94,6 +94,7 @@ def test_edge_init(mocker, edge_conf) -> None:
     assert edge_cli.config == edge_conf
     assert edge_cli.config['stake_amount'] == 'unlimited'
     assert callable(edge_cli.edge.calculate)
+    assert edge_cli.strategy.bot_started is True
 
 
 def test_edge_init_fee(mocker, edge_conf) -> None:

tests/optimize/test_hyperoptloss.py

@@ -4,7 +4,7 @@ from unittest.mock import MagicMock
 import pytest
 
 from freqtrade.exceptions import OperationalException
-from freqtrade.optimize.hyperopt_loss_short_trade_dur import ShortTradeDurHyperOptLoss
+from freqtrade.optimize.hyperopt_loss.hyperopt_loss_short_trade_dur import ShortTradeDurHyperOptLoss
 from freqtrade.resolvers.hyperopt_resolver import HyperOptLossResolver
 
 

tests/rpc/test_rpc_apiserver.py

@@ -13,7 +13,6 @@ import uvicorn
 from fastapi import FastAPI
 from fastapi.exceptions import HTTPException
 from fastapi.testclient import TestClient
-from numpy import isnan
 from requests.auth import _basic_auth_str
 
 from freqtrade.__init__ import __version__
@@ -985,7 +984,7 @@ def test_api_status(botclient, mocker, ticker, fee, markets, is_short,
     assert_response(rc)
     resp_values = rc.json()
     assert len(resp_values) == 4
-    assert isnan(resp_values[0]['profit_abs'])
+    assert resp_values[0]['profit_abs'] is None
 
 
 def test_api_version(botclient):
@@ -1389,7 +1388,6 @@ def test_api_strategies(botclient):
         'StrategyTestV2',
         'StrategyTestV3',
         'StrategyTestV3Futures',
-        'TestStrategyLegacyV1',
     ]}
 
 
@@ -1485,7 +1483,7 @@ def test_api_backtesting(botclient, mocker, fee, caplog, tmpdir):
     assert not result['running']
     assert result['status_msg'] == 'Backtest reset'
     ftbot.config['export'] = 'trades'
-    ftbot.config['backtest_cache'] = 'none'
+    ftbot.config['backtest_cache'] = 'day'
     ftbot.config['user_data_dir'] = Path(tmpdir)
     ftbot.config['exportfilename'] = Path(tmpdir) / "backtest_results"
     ftbot.config['exportfilename'].mkdir()
@@ -1558,19 +1556,19 @@ def test_api_backtesting(botclient, mocker, fee, caplog, tmpdir):
 
     ApiServer._bgtask_running = False
 
-    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest_one_strategy',
-                 side_effect=DependencyException())
-    rc = client_post(client, f"{BASE_URI}/backtest", data=json.dumps(data))
-    assert log_has("Backtesting caused an error: ", caplog)
-
-    ftbot.config['backtest_cache'] = 'day'
-
     # Rerun backtest (should get previous result)
     rc = client_post(client, f"{BASE_URI}/backtest", data=json.dumps(data))
     assert_response(rc)
     result = rc.json()
     assert log_has_re('Reusing result of previous backtest.*', caplog)
 
+    data['stake_amount'] = 101
+
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.backtest_one_strategy',
+                 side_effect=DependencyException())
+    rc = client_post(client, f"{BASE_URI}/backtest", data=json.dumps(data))
+    assert log_has("Backtesting caused an error: ", caplog)
+
     # Delete backtesting to avoid leakage since the backtest-object may stick around.
     rc = client_delete(client, f"{BASE_URI}/backtest")
     assert_response(rc)

tests/strategy/strats/broken_strats/legacy_strategy_v1.py

@@ -0,0 +1,30 @@
+# type: ignore
+from pandas import DataFrame
+
+from freqtrade.strategy import IStrategy
+
+
+# Dummy strategy - no longer loads but raises an exception.
+class TestStrategyLegacyV1(IStrategy):
+
+    minimal_roi = {
+        "40": 0.0,
+        "30": 0.01,
+        "20": 0.02,
+        "0": 0.04
+    }
+    stoploss = -0.10
+
+    timeframe = '5m'
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+
+        return dataframe

tests/strategy/strats/legacy_strategy_v1.py

@@ -1,85 +0,0 @@
-
-# --- Do not remove these libs ---
-# Add your lib to import here
-import talib.abstract as ta
-from pandas import DataFrame
-
-from freqtrade.strategy import IStrategy
-
-
-# --------------------------------
-
-# This class is a sample. Feel free to customize it.
-class TestStrategyLegacyV1(IStrategy):
-    """
-    This is a test strategy using the legacy function headers, which will be
-    removed in a future update.
-    Please do not use this as a template, but refer to user_data/strategy/sample_strategy.py
-    for a uptodate version of this template.
-    """
-
-    # Minimal ROI designed for the strategy.
-    # This attribute will be overridden if the config file contains "minimal_roi"
-    minimal_roi = {
-        "40": 0.0,
-        "30": 0.01,
-        "20": 0.02,
-        "0": 0.04
-    }
-
-    # Optimal stoploss designed for the strategy
-    # This attribute will be overridden if the config file contains "stoploss"
-    stoploss = -0.10
-
-    timeframe = '5m'
-
-    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Adds several different TA indicators to the given DataFrame
-
-        Performance Note: For the best performance be frugal on the number of indicators
-        you are using. Let uncomment only the indicator you are using in your strategies
-        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
-        """
-
-        # Momentum Indicator
-        # ------------------------------------
-
-        # ADX
-        dataframe['adx'] = ta.ADX(dataframe)
-
-        # TEMA - Triple Exponential Moving Average
-        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
-
-        return dataframe
-
-    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Based on TA indicators, populates the buy signal for the given dataframe
-        :param dataframe: DataFrame
-        :return: DataFrame with buy column
-        """
-        dataframe.loc[
-            (
-                (dataframe['adx'] > 30) &
-                (dataframe['tema'] > dataframe['tema'].shift(1)) &
-                (dataframe['volume'] > 0)
-            ),
-            'buy'] = 1
-
-        return dataframe
-
-    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
-        """
-        Based on TA indicators, populates the sell signal for the given dataframe
-        :param dataframe: DataFrame
-        :return: DataFrame with buy column
-        """
-        dataframe.loc[
-            (
-                (dataframe['adx'] > 70) &
-                (dataframe['tema'] < dataframe['tema'].shift(1)) &
-                (dataframe['volume'] > 0)
-            ),
-            'sell'] = 1
-        return dataframe

tests/strategy/strats/strategy_test_v2.py

@@ -56,19 +56,6 @@ class StrategyTestV2(IStrategy):
     # By default this strategy does not use Position Adjustments
     position_adjustment_enable = False
 
-    def informative_pairs(self):
-        """
-        Define additional, informative pair/interval combinations to be cached from the exchange.
-        These pair/interval combinations are non-tradeable, unless they are part
-        of the whitelist as well.
-        For more information, please consult the documentation
-        :return: List of tuples in the format (pair, interval)
-            Sample: return [("ETH/USDT", "5m"),
-                            ("BTC/USDT", "15m"),
-                            ]
-        """
-        return []
-
     def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
         Adds several different TA indicators to the given DataFrame

tests/strategy/strats/strategy_test_v3.py

@@ -82,6 +82,11 @@ class StrategyTestV3(IStrategy):
     #         })
     #     return prot
 
+    bot_started = False
+
+    def bot_start(self):
+        self.bot_started = True
+
     def informative_pairs(self):
 
         return []

tests/strategy/test_interface.py

@@ -686,7 +686,7 @@ def test_is_pair_locked(default_conf):
 
 
 def test_is_informative_pairs_callback(default_conf):
-    default_conf.update({'strategy': 'TestStrategyLegacyV1'})
+    default_conf.update({'strategy': 'StrategyTestV2'})
     strategy = StrategyResolver.load_strategy(default_conf)
     # Should return empty
     # Uses fallback to base implementation

tests/strategy/test_strategy_loading.py

@@ -1,6 +1,5 @@
 # pragma pylint: disable=missing-docstring, protected-access, C0103
 import logging
-import warnings
 from base64 import urlsafe_b64encode
 from pathlib import Path
 
@@ -35,7 +34,7 @@ def test_search_all_strategies_no_failed():
     directory = Path(__file__).parent / "strats"
     strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
     assert isinstance(strategies, list)
-    assert len(strategies) == 6
+    assert len(strategies) == 5
     assert isinstance(strategies[0], dict)
 
 
@@ -43,10 +42,10 @@ def test_search_all_strategies_with_failed():
     directory = Path(__file__).parent / "strats"
     strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
     assert isinstance(strategies, list)
-    assert len(strategies) == 7
+    assert len(strategies) == 6
     # with enum_failed=True search_all_objects() shall find 2 good strategies
     # and 1 which fails to load
-    assert len([x for x in strategies if x['class'] is not None]) == 6
+    assert len([x for x in strategies if x['class'] is not None]) == 5
     assert len([x for x in strategies if x['class'] is None]) == 1
 
 
@@ -100,7 +99,7 @@ def test_load_strategy_noname(default_conf):
 
 
 @pytest.mark.filterwarnings("ignore:deprecated")
-@pytest.mark.parametrize('strategy_name', ['StrategyTestV2', 'TestStrategyLegacyV1'])
+@pytest.mark.parametrize('strategy_name', ['StrategyTestV2'])
 def test_strategy_pre_v3(result, default_conf, strategy_name):
     default_conf.update({'strategy': strategy_name})
 
@@ -346,40 +345,6 @@ def test_strategy_override_use_exit_profit_only(caplog, default_conf):
     assert log_has("Override strategy 'exit_profit_only' with value in config file: True.", caplog)
 
 
-@pytest.mark.filterwarnings("ignore:deprecated")
-def test_deprecate_populate_indicators(result, default_conf):
-    default_location = Path(__file__).parent / "strats"
-    default_conf.update({'strategy': 'TestStrategyLegacyV1',
-                         'strategy_path': default_location})
-    strategy = StrategyResolver.load_strategy(default_conf)
-    with warnings.catch_warnings(record=True) as w:
-        # Cause all warnings to always be triggered.
-        warnings.simplefilter("always")
-        indicators = strategy.advise_indicators(result, {'pair': 'ETH/BTC'})
-        assert len(w) == 1
-        assert issubclass(w[-1].category, DeprecationWarning)
-        assert "deprecated - check out the Sample strategy to see the current function headers!" \
-            in str(w[-1].message)
-
-    with warnings.catch_warnings(record=True) as w:
-        # Cause all warnings to always be triggered.
-        warnings.simplefilter("always")
-        strategy.advise_entry(indicators, {'pair': 'ETH/BTC'})
-        assert len(w) == 1
-        assert issubclass(w[-1].category, DeprecationWarning)
-        assert "deprecated - check out the Sample strategy to see the current function headers!" \
-            in str(w[-1].message)
-
-    with warnings.catch_warnings(record=True) as w:
-        # Cause all warnings to always be triggered.
-        warnings.simplefilter("always")
-        strategy.advise_exit(indicators, {'pair': 'ETH_BTC'})
-        assert len(w) == 1
-        assert issubclass(w[-1].category, DeprecationWarning)
-        assert "deprecated - check out the Sample strategy to see the current function headers!" \
-            in str(w[-1].message)
-
-
 @pytest.mark.filterwarnings("ignore:deprecated")
 def test_missing_implements(default_conf, caplog):
 
@@ -438,33 +403,14 @@ def test_missing_implements(default_conf, caplog):
         StrategyResolver.load_strategy(default_conf)
 
 
-@pytest.mark.filterwarnings("ignore:deprecated")
-def test_call_deprecated_function(result, default_conf, caplog):
-    default_location = Path(__file__).parent / "strats"
+def test_call_deprecated_function(default_conf):
+    default_location = Path(__file__).parent / "strats/broken_strats/"
     del default_conf['timeframe']
     default_conf.update({'strategy': 'TestStrategyLegacyV1',
                          'strategy_path': default_location})
-    strategy = StrategyResolver.load_strategy(default_conf)
-    metadata = {'pair': 'ETH/BTC'}
-
-    # Make sure we are using a legacy function
-    assert strategy._populate_fun_len == 2
-    assert strategy._buy_fun_len == 2
-    assert strategy._sell_fun_len == 2
-    assert strategy.INTERFACE_VERSION == 1
-    assert strategy.timeframe == '5m'
-
-    indicator_df = strategy.advise_indicators(result, metadata=metadata)
-    assert isinstance(indicator_df, DataFrame)
-    assert 'adx' in indicator_df.columns
-
-    enterdf = strategy.advise_entry(result, metadata=metadata)
-    assert isinstance(enterdf, DataFrame)
-    assert 'enter_long' in enterdf.columns
-
-    exitdf = strategy.advise_exit(result, metadata=metadata)
-    assert isinstance(exitdf, DataFrame)
-    assert 'exit_long' in exitdf
+    with pytest.raises(OperationalException,
+                       match=r"Strategy Interface v1 is no longer supported.*"):
+        StrategyResolver.load_strategy(default_conf)
 
 
 def test_strategy_interface_versioning(result, default_conf):
@@ -472,10 +418,6 @@ def test_strategy_interface_versioning(result, default_conf):
     strategy = StrategyResolver.load_strategy(default_conf)
     metadata = {'pair': 'ETH/BTC'}
 
-    # Make sure we are using a legacy function
-    assert strategy._populate_fun_len == 3
-    assert strategy._buy_fun_len == 3
-    assert strategy._sell_fun_len == 3
     assert strategy.INTERFACE_VERSION == 2
 
     indicator_df = strategy.advise_indicators(result, metadata=metadata)

tests/test_freqtradebot.py

@@ -718,12 +718,12 @@ def test_process_informative_pairs_added(default_conf_usdt, ticker_usdt, mocker)
     (True, 'spot', 'gateio', None, 0.0, None),
     (False, 'spot', 'okx', None, 0.0, None),
     (True, 'spot', 'okx', None, 0.0, None),
-    (True, 'futures', 'binance', 'isolated', 0.0, 11.89108910891089),
-    (False, 'futures', 'binance', 'isolated', 0.0, 8.070707070707071),
+    (True, 'futures', 'binance', 'isolated', 0.0, 11.88151815181518),
+    (False, 'futures', 'binance', 'isolated', 0.0, 8.080471380471382),
     (True, 'futures', 'gateio', 'isolated', 0.0, 11.87413417771621),
     (False, 'futures', 'gateio', 'isolated', 0.0, 8.085708510208207),
-    (True, 'futures', 'binance', 'isolated', 0.05, 11.796534653465345),
-    (False, 'futures', 'binance', 'isolated', 0.05, 8.167171717171717),
+    (True, 'futures', 'binance', 'isolated', 0.05, 11.7874422442244),
+    (False, 'futures', 'binance', 'isolated', 0.05, 8.17644781144781),
     (True, 'futures', 'gateio', 'isolated', 0.05, 11.7804274688304),
     (False, 'futures', 'gateio', 'isolated', 0.05, 8.181423084697796),
     (True, 'futures', 'okx', 'isolated', 0.0, 11.87413417771621),
@@ -846,6 +846,7 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order,
     assert trade.open_order_id is None
     assert trade.open_rate == 10
     assert trade.stake_amount == round(order['price'] * order['filled'] / leverage, 8)
+    assert pytest.approx(trade.liquidation_price) == liq_price
 
     # In case of rejected or expired order and partially filled
     order['status'] = 'expired'
@@ -933,8 +934,6 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order,
     assert trade.open_rate_requested == 10
 
     # In case of custom entry price not float type
-    freqtrade.exchange.get_maintenance_ratio_and_amt = MagicMock(return_value=(0.01, 0.01))
-    freqtrade.exchange.name = exchange_name
     order['status'] = 'open'
     order['id'] = '5568'
     freqtrade.strategy.custom_entry_price = lambda **kwargs: "string price"
@@ -947,7 +946,6 @@ def test_execute_entry(mocker, default_conf_usdt, fee, limit_order,
     trade.is_short = is_short
     assert trade
     assert trade.open_rate_requested == 10
-    assert trade.liquidation_price == liq_price
 
     # In case of too high stake amount
 
@@ -3222,7 +3220,7 @@ def test_execute_trade_exit_custom_exit_price(
     freqtrade.execute_trade_exit(
         trade=trade,
         limit=ticker_usdt_sell_up()['ask' if is_short else 'bid'],
-        exit_check=ExitCheckTuple(exit_type=ExitType.EXIT_SIGNAL)
+        exit_check=ExitCheckTuple(exit_type=ExitType.EXIT_SIGNAL, exit_reason='foo')
     )
 
     # Sell price must be different to default bid price
@@ -3250,8 +3248,8 @@ def test_execute_trade_exit_custom_exit_price(
         'profit_ratio': profit_ratio,
         'stake_currency': 'USDT',
         'fiat_currency': 'USD',
-        'sell_reason': ExitType.EXIT_SIGNAL.value,
-        'exit_reason': ExitType.EXIT_SIGNAL.value,
+        'sell_reason': 'foo',
+        'exit_reason': 'foo',
         'open_date': ANY,
         'close_date': ANY,
         'close_rate': ANY,

tests/test_plotting.py

@@ -10,7 +10,8 @@ from plotly.subplots import make_subplots
 from freqtrade.commands import start_plot_dataframe, start_plot_profit
 from freqtrade.configuration import TimeRange
 from freqtrade.data import history
-from freqtrade.data.btanalysis import create_cum_profit, load_backtest_data
+from freqtrade.data.btanalysis import load_backtest_data
+from freqtrade.data.metrics import create_cum_profit
 from freqtrade.exceptions import OperationalException
 from freqtrade.plot.plotting import (add_areas, add_indicators, add_profit, create_plotconfig,
                                      generate_candlestick_graph, generate_plot_filename,