Merge branch 'develop' into feat/short

build_helpers/install_windows.ps1

@@ -6,16 +6,16 @@ python -m pip install --upgrade pip wheel
 $pyv = python -c "import sys; print(f'{sys.version_info.major}.{sys.version_info.minor}')"
 
 if ($pyv -eq '3.7') {
-    pip install build_helpers\TA_Lib-0.4.22-cp37-cp37m-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.23-cp37-cp37m-win_amd64.whl
 }
 if ($pyv -eq '3.8') {
-    pip install build_helpers\TA_Lib-0.4.22-cp38-cp38-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.23-cp38-cp38-win_amd64.whl
 }
 if ($pyv -eq '3.9') {
-    pip install build_helpers\TA_Lib-0.4.22-cp39-cp39-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.23-cp39-cp39-win_amd64.whl
 }
 if ($pyv -eq '3.10') {
-    pip install build_helpers\TA_Lib-0.4.22-cp310-cp310-win_amd64.whl
+    pip install build_helpers\TA_Lib-0.4.23-cp310-cp310-win_amd64.whl
 }
 pip install -r requirements-dev.txt
 pip install -e .

config_examples/config_full.example.json

@@ -18,6 +18,7 @@
     "sell_profit_only": false,
     "sell_profit_offset": 0.0,
     "ignore_roi_if_buy_signal": false,
+    "ignore_buying_expired_candle_after": 300,
     "minimal_roi": {
         "40":  0.0,
         "30":  0.01,

docs/deprecated.md

@@ -15,8 +15,8 @@ This command line option was deprecated in 2019.7-dev (develop branch) and remov
 
 ### The **--dynamic-whitelist** command line option
 
-This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch)
-and in freqtrade 2019.7.
+This command line option was deprecated in 2018 and removed freqtrade 2019.6-dev (develop branch) and in freqtrade 2019.7.
+Please refer to [pairlists](plugins.md#pairlists-and-pairlist-handlers) instead.
 
 ### the `--live` command line option
 

docs/plotting.md

@@ -283,6 +283,8 @@ The `plot-profit` subcommand shows an interactive graph with three plots:
 * The summarized profit made by backtesting.
 Note that this is not the real-world profit, but more of an estimate.
 * Profit for each individual pair.
+* Parallelism of trades.
+* Underwater (Periods of drawdown).
 
 The first graph is good to get a grip of how the overall market progresses.
 
@@ -292,6 +294,8 @@ This graph will also highlight the start (and end) of the Max drawdown period.
 
 The third graph can be useful to spot outliers, events in pairs that cause profit spikes.
 
+The forth graph can help you analyze trade parallelism, showing how often max_open_trades have been maxed out.
+
 Possible options for the `freqtrade plot-profit` subcommand:
 
 ```

docs/requirements-docs.txt

@@ -1,4 +1,4 @@
 mkdocs==1.2.3
-mkdocs-material==8.1.3
+mkdocs-material==8.1.4
 mdx_truly_sane_lists==1.2
 pymdown-extensions==9.1

docs/strategy-advanced.md

@@ -222,9 +222,9 @@ should be rewritten to
 ```python
 frames = [dataframe]
 for val in self.buy_ema_short.range:
-    frames.append({
+    frames.append(DataFrame({
         f'ema_short_{val}': ta.EMA(dataframe, timeperiod=val)
-    })
+    }))
 
 # Append columns to existing dataframe
 merged_frame = pd.concat(frames, axis=1)

docs/windows_installation.md

@@ -23,7 +23,7 @@ git clone https://github.com/freqtrade/freqtrade.git
 
 Install ta-lib according to the [ta-lib documentation](https://github.com/mrjbq7/ta-lib#windows).
 
-As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib‑0.4.22‑cp38‑cp38‑win_amd64.whl` (make sure to use the version matching your python version).
+As compiling from source on windows has heavy dependencies (requires a partial visual studio installation), there is also a repository of unofficial pre-compiled windows Wheels [here](https://www.lfd.uci.edu/~gohlke/pythonlibs/#ta-lib), which need to be downloaded and installed using `pip install TA_Lib-0.4.23-cp38-cp38-win_amd64.whl` (make sure to use the version matching your python version).
 
 Freqtrade provides these dependencies for the latest 3 Python versions (3.7, 3.8, 3.9 and 3.10) and for 64bit Windows.
 Other versions must be downloaded from the above link.

freqtrade/data/btanalysis.py

@@ -364,6 +364,36 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     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_ratio'
                            ) -> Tuple[float, pd.Timestamp, pd.Timestamp, float, float]:
@@ -379,10 +409,7 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
     if len(trades) == 0:
         raise ValueError("Trade dataframe empty.")
     profit_results = trades.sort_values(date_col).reset_index(drop=True)
-    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 = _calc_drawdown_series(profit_results, date_col=date_col, value_col=value_col)
 
     idxmin = max_drawdown_df['drawdown'].idxmin()
     if idxmin == 0:

freqtrade/optimize/bt_progress.py

@@ -12,7 +12,7 @@ class BTProgress:
     def init_step(self, action: BacktestState, max_steps: float):
         self._action = action
         self._max_steps = max_steps
-        self._proress = 0
+        self._progress = 0
 
     def set_new_value(self, new_value: float):
         self._progress = new_value

freqtrade/optimize/hyperopt_tools.py

@@ -299,8 +299,7 @@ class HyperoptTools():
                 f"Objective: {results['loss']:.5f}")
 
     @staticmethod
-    def prepare_trials_columns(trials: pd.DataFrame, legacy_mode: bool,
-                               has_drawdown: bool) -> pd.DataFrame:
+    def prepare_trials_columns(trials: pd.DataFrame, has_drawdown: bool) -> pd.DataFrame:
         trials['Best'] = ''
 
         if 'results_metrics.winsdrawslosses' not in trials.columns:
@@ -312,26 +311,17 @@ class HyperoptTools():
             trials['results_metrics.max_drawdown_abs'] = None
             trials['results_metrics.max_drawdown'] = None
 
-        if not legacy_mode:
-            # New mode, using backtest result for metrics
-            trials['results_metrics.winsdrawslosses'] = trials.apply(
-                lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
-                          f"{x['results_metrics.losses']:>4}", axis=1)
-            trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
-                             'results_metrics.winsdrawslosses',
-                             'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
-                             'results_metrics.profit_total', 'results_metrics.holding_avg',
-                             'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
-                             'loss', 'is_initial_point', 'is_best']]
+        # New mode, using backtest result for metrics
+        trials['results_metrics.winsdrawslosses'] = trials.apply(
+            lambda x: f"{x['results_metrics.wins']} {x['results_metrics.draws']:>4} "
+                      f"{x['results_metrics.losses']:>4}", axis=1)
 
-        else:
-            # Legacy mode
-            trials = trials[['Best', 'current_epoch', 'results_metrics.trade_count',
-                             'results_metrics.winsdrawslosses', 'results_metrics.avg_profit',
-                             'results_metrics.total_profit', 'results_metrics.profit',
-                             'results_metrics.duration', 'results_metrics.max_drawdown',
-                             'results_metrics.max_drawdown_abs', 'loss', 'is_initial_point',
-                             'is_best']]
+        trials = trials[['Best', 'current_epoch', 'results_metrics.total_trades',
+                         'results_metrics.winsdrawslosses',
+                         'results_metrics.profit_mean', 'results_metrics.profit_total_abs',
+                         'results_metrics.profit_total', 'results_metrics.holding_avg',
+                         'results_metrics.max_drawdown', 'results_metrics.max_drawdown_abs',
+                         'loss', 'is_initial_point', 'is_best']]
 
         trials.columns = ['Best', 'Epoch', 'Trades', ' Win Draw Loss', 'Avg profit',
                           'Total profit', 'Profit', 'Avg duration', 'Max Drawdown',
@@ -351,10 +341,9 @@ class HyperoptTools():
         tabulate.PRESERVE_WHITESPACE = True
         trials = json_normalize(results, max_level=1)
 
-        legacy_mode = 'results_metrics.total_trades' not in trials
         has_drawdown = 'results_metrics.max_drawdown_abs' in trials.columns
 
-        trials = HyperoptTools.prepare_trials_columns(trials, legacy_mode, has_drawdown)
+        trials = HyperoptTools.prepare_trials_columns(trials, has_drawdown)
 
         trials['is_profit'] = False
         trials.loc[trials['is_initial_point'], 'Best'] = '*     '
@@ -362,12 +351,12 @@ class HyperoptTools():
         trials.loc[trials['is_initial_point'] & trials['is_best'], 'Best'] = '* Best'
         trials.loc[trials['Total profit'] > 0, 'is_profit'] = True
         trials['Trades'] = trials['Trades'].astype(str)
-        perc_multi = 1 if legacy_mode else 100
+        # perc_multi = 1 if legacy_mode else 100
         trials['Epoch'] = trials['Epoch'].apply(
             lambda x: '{}/{}'.format(str(x).rjust(len(str(total_epochs)), ' '), total_epochs)
         )
         trials['Avg profit'] = trials['Avg profit'].apply(
-            lambda x: f'{x * perc_multi:,.2f}%'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
+            lambda x: f'{x:,.2%}'.rjust(7, ' ') if not isna(x) else "--".rjust(7, ' ')
         )
         trials['Avg duration'] = trials['Avg duration'].apply(
             lambda x: f'{x:,.1f} m'.rjust(7, ' ') if isinstance(x, float) else f"{x}"
@@ -383,7 +372,7 @@ class HyperoptTools():
             trials['Max Drawdown'] = trials.apply(
                 lambda x: '{} {}'.format(
                     round_coin_value(x['max_drawdown_abs'], stake_currency),
-                    '({:,.2f}%)'.format(x['Max Drawdown'] * perc_multi).rjust(10, ' ')
+                    f"({x['Max Drawdown']:,.2%})".rjust(10, ' ')
                 ).rjust(25 + len(stake_currency))
                 if x['Max Drawdown'] != 0.0 else '--'.rjust(25 + len(stake_currency)),
                 axis=1
@@ -396,7 +385,7 @@ class HyperoptTools():
         trials['Profit'] = trials.apply(
             lambda x: '{} {}'.format(
                 round_coin_value(x['Total profit'], stake_currency),
-                '({:,.2f}%)'.format(x['Profit'] * perc_multi).rjust(10, ' ')
+                f"({x['Profit']:,.2%})".rjust(10, ' ')
             ).rjust(25+len(stake_currency))
             if x['Total profit'] != 0.0 else '--'.rjust(25+len(stake_currency)),
             axis=1

freqtrade/plot/plotting.py

@@ -5,7 +5,8 @@ from typing import Any, Dict, List
 import pandas as pd
 
 from freqtrade.configuration import TimeRange
-from freqtrade.data.btanalysis import (calculate_max_drawdown, combine_dataframes_with_mean,
+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.converter import trim_dataframe
 from freqtrade.data.dataprovider import DataProvider
@@ -185,6 +186,48 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
     return fig
 
 
+def add_underwater(fig, row, trades: pd.DataFrame) -> make_subplots:
+    """
+    Add underwater plot
+    """
+    try:
+        underwater = calculate_underwater(trades, value_col="profit_abs")
+
+        underwater = go.Scatter(
+            x=underwater['date'],
+            y=underwater['drawdown'],
+            name="Underwater Plot",
+            fill='tozeroy',
+            fillcolor='#cc362b',
+            line={'color': '#cc362b'},
+        )
+        fig.add_trace(underwater, row, 1)
+    except ValueError:
+        logger.warning("No trades found - not plotting underwater plot")
+    return fig
+
+
+def add_parallelism(fig, row, trades: pd.DataFrame, timeframe: str) -> make_subplots:
+    """
+    Add Chart showing trade parallelism
+    """
+    try:
+        result = analyze_trade_parallelism(trades, timeframe)
+
+        drawdown = go.Scatter(
+            x=result.index,
+            y=result['open_trades'],
+            name="Parallel trades",
+            fill='tozeroy',
+            fillcolor='#242222',
+            line={'color': '#242222'},
+        )
+        fig.add_trace(drawdown, row, 1)
+    except ValueError:
+        logger.warning("No trades found - not plotting Parallelism.")
+    return fig
+
+
 def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
     """
     Add trades to "fig"
@@ -483,20 +526,30 @@ def generate_profit_graph(pairs: str, data: Dict[str, pd.DataFrame],
         name='Avg close price',
     )
 
-    fig = make_subplots(rows=3, cols=1, shared_xaxes=True,
-                        row_width=[1, 1, 1],
+    fig = make_subplots(rows=5, cols=1, shared_xaxes=True,
+                        row_heights=[1, 1, 1, 0.5, 1],
                         vertical_spacing=0.05,
-                        subplot_titles=["AVG Close Price", "Combined Profit", "Profit per pair"])
+                        subplot_titles=[
+                            "AVG Close Price",
+                            "Combined Profit",
+                            "Profit per pair",
+                            "Parallelism",
+                            "Underwater",
+                            ])
     fig['layout'].update(title="Freqtrade Profit plot")
     fig['layout']['yaxis1'].update(title='Price')
     fig['layout']['yaxis2'].update(title=f'Profit {stake_currency}')
     fig['layout']['yaxis3'].update(title=f'Profit {stake_currency}')
+    fig['layout']['yaxis4'].update(title='Trade count')
+    fig['layout']['yaxis5'].update(title='Underwater Plot')
     fig['layout']['xaxis']['rangeslider'].update(visible=False)
     fig.update_layout(modebar_add=["v1hovermode", "toggleSpikeLines"])
 
     fig.add_trace(avgclose, 1, 1)
     fig = add_profit(fig, 2, df_comb, 'cum_profit', 'Profit')
     fig = add_max_drawdown(fig, 2, trades, df_comb, timeframe)
+    fig = add_parallelism(fig, 4, trades, timeframe)
+    fig = add_underwater(fig, 5, trades)
 
     for pair in pairs:
         profit_col = f'cum_profit_{pair}'

requirements-dev.txt

@@ -20,10 +20,10 @@ time-machine==2.5.0
 nbconvert==6.3.0
 
 # mypy types
-types-cachetools==4.2.6
+types-cachetools==4.2.7
 types-filelock==3.2.1
-types-requests==2.26.2
-types-tabulate==0.8.3
+types-requests==2.26.3
+types-tabulate==0.8.4
 
 # Extensions to datetime library
 types-python-dateutil==2.8.4

requirements-hyperopt.txt

@@ -7,5 +7,4 @@ scikit-learn==1.0.2
 scikit-optimize==0.9.0
 filelock==3.4.2
 joblib==1.1.0
-psutil==5.8.0
 progressbar2==3.55.0

requirements.txt

@@ -3,7 +3,7 @@ numpy==1.22.0; python_version > '3.7'
 pandas==1.3.5
 pandas-ta==0.3.14b
 
-ccxt==1.65.25
+ccxt==1.66.20
 # Pin cryptography for now due to rust build errors with piwheels
 cryptography==36.0.1
 aiohttp==3.8.1
@@ -13,8 +13,8 @@ arrow==1.2.1
 cachetools==4.2.2
 requests==2.26.0
 urllib3==1.26.7
-jsonschema==4.3.2
-TA-Lib==0.4.22
+jsonschema==4.3.3
+TA-Lib==0.4.23
 technical==1.3.0
 tabulate==0.8.9
 pycoingecko==2.2.0
@@ -36,7 +36,7 @@ fastapi==0.70.1
 uvicorn==0.16.0
 pyjwt==2.3.0
 aiofiles==0.8.0
-psutil==5.8.0
+psutil==5.9.0
 
 # Support for colorized terminal output
 colorama==0.4.4

tests/data/test_btanalysis.py

@@ -11,10 +11,10 @@ from freqtrade.constants import LAST_BT_RESULT_FN
 from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, BT_DATA_COLUMNS_MID, BT_DATA_COLUMNS_OLD,
                                        analyze_trade_parallelism, calculate_csum,
                                        calculate_market_change, calculate_max_drawdown,
-                                       combine_dataframes_with_mean, create_cum_profit,
-                                       extract_trades_of_period, get_latest_backtest_filename,
-                                       get_latest_hyperopt_file, load_backtest_data, load_trades,
-                                       load_trades_from_db)
+                                       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_trades, load_trades_from_db)
 from freqtrade.data.history import load_data, load_pair_history
 from tests.conftest import CURRENT_TEST_STRATEGY, create_mock_trades
 from tests.conftest_trades import MOCK_TRADE_COUNT
@@ -292,9 +292,16 @@ def test_calculate_max_drawdown(testdatadir):
     assert isinstance(lval, float)
     assert hdate == Timestamp('2018-01-24 14:25:00', tz='UTC')
     assert lowdate == Timestamp('2018-01-30 04:45:00', tz='UTC')
+
+    underwater = calculate_underwater(bt_data)
+    assert isinstance(underwater, DataFrame)
+
     with pytest.raises(ValueError, match='Trade dataframe empty.'):
         drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(DataFrame())
 
+    with pytest.raises(ValueError, match='Trade dataframe empty.'):
+        calculate_underwater(DataFrame())
+
 
 def test_calculate_csum(testdatadir):
     filename = testdatadir / "backtest-result_test.json"

tests/optimize/test_hyperopt.py

@@ -1,5 +1,5 @@
 # pragma pylint: disable=missing-docstring,W0212,C0103
-from datetime import datetime
+from datetime import datetime, timedelta
 from pathlib import Path
 from unittest.mock import ANY, MagicMock
 
@@ -22,6 +22,29 @@ from tests.conftest import (CURRENT_TEST_STRATEGY, get_args, log_has, log_has_re
                             patched_configuration_load_config_file)
 
 
+def generate_result_metrics():
+    return {
+            'trade_count': 1,
+            'total_trades': 1,
+            'avg_profit': 0.1,
+            'total_profit': 0.001,
+            'profit': 0.01,
+            'duration': 20.0,
+            'wins': 1,
+            'draws': 0,
+            'losses': 0,
+            'profit_mean': 0.01,
+            'profit_total_abs': 0.001,
+            'profit_total': 0.01,
+            'holding_avg': timedelta(minutes=20),
+            'max_drawdown': 0.001,
+            'max_drawdown_abs': 0.001,
+            'loss': 0.001,
+            'is_initial_point': 0.001,
+            'is_best': 1,
+        }
+
+
 def test_setup_hyperopt_configuration_without_arguments(mocker, default_conf, caplog) -> None:
     patched_configuration_load_config_file(mocker, default_conf)
 
@@ -222,14 +245,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
     hyperopt.print_results(
         {
             'loss': 1,
-            'results_metrics':
-                {
-                    'trade_count': 1,
-                    'avg_profit': 0.1,
-                    'total_profit': 0.001,
-                    'profit': 1.0,
-                    'duration': 20.0
-                },
+            'results_metrics': generate_result_metrics(),
             'total_profit': 0,
             'current_epoch': 2,  # This starts from 1 (in a human-friendly manner)
             'is_initial_point': False,
@@ -238,7 +254,7 @@ def test_log_results_if_loss_improves(hyperopt, capsys) -> None:
     )
     out, err = capsys.readouterr()
     assert all(x in out
-               for x in ["Best", "2/2", " 1", "0.10%", "0.00100000 BTC    (1.00%)", "20.0 m"])
+               for x in ["Best", "2/2", " 1", "0.10%", "0.00100000 BTC    (1.00%)", "00:20:00"])
 
 
 def test_no_log_if_loss_does_not_improve(hyperopt, caplog) -> None:
@@ -295,14 +311,7 @@ def test_start_calls_optimizer(mocker, hyperopt_conf, capsys) -> None:
         MagicMock(return_value=[{
             'loss': 1, 'results_explanation': 'foo result',
             'params': {'buy': {}, 'sell': {}, 'roi': {}, 'stoploss': 0.0},
-            'results_metrics':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            },
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -530,14 +539,7 @@ def test_print_json_spaces_all(mocker, hyperopt_conf, capsys) -> None:
                 'roi': {}, 'stoploss': {'stoploss': None},
                 'trailing': {'trailing_stop': None}
             },
-            'results_metrics':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -586,14 +588,7 @@ def test_print_json_spaces_default(mocker, hyperopt_conf, capsys) -> None:
                 'sell': {'sell-mfi-value': None},
                 'roi': {}, 'stoploss': {'stoploss': None}
             },
-            'results_metrics':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -631,14 +626,7 @@ def test_print_json_spaces_roi_stoploss(mocker, hyperopt_conf, capsys) -> None:
         MagicMock(return_value=[{
             'loss': 1, 'results_explanation': 'foo result', 'params': {},
             'params_details': {'roi': {}, 'stoploss': {'stoploss': None}},
-            'results_metrics':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -678,14 +666,7 @@ def test_simplified_interface_roi_stoploss(mocker, hyperopt_conf, capsys) -> Non
         'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
         MagicMock(return_value=[{
             'loss': 1, 'results_explanation': 'foo result', 'params': {'stoploss': 0.0},
-            'results_metrics':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -758,14 +739,7 @@ def test_simplified_interface_buy(mocker, hyperopt_conf, capsys) -> None:
         'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
         MagicMock(return_value=[{
             'loss': 1, 'results_explanation': 'foo result', 'params': {},
-            'results_metrics':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)
@@ -807,14 +781,7 @@ def test_simplified_interface_sell(mocker, hyperopt_conf, capsys) -> None:
         'freqtrade.optimize.hyperopt.Hyperopt.run_optimizer_parallel',
         MagicMock(return_value=[{
             'loss': 1, 'results_explanation': 'foo result', 'params': {},
-            'results_metrics':
-            {
-                'trade_count': 1,
-                'avg_profit': 0.1,
-                'total_profit': 0.001,
-                'profit': 1.0,
-                'duration': 20.0
-            }
+            'results_metrics': generate_result_metrics(),
         }])
     )
     patch_exchange(mocker)

tests/test_plotting.py

@@ -336,15 +336,20 @@ def test_generate_profit_graph(testdatadir):
     assert fig.layout.yaxis3.title.text == "Profit BTC"
 
     figure = fig.layout.figure
-    assert len(figure.data) == 5
+    assert len(figure.data) == 7
 
     avgclose = find_trace_in_fig_data(figure.data, "Avg close price")
     assert isinstance(avgclose, go.Scatter)
 
     profit = find_trace_in_fig_data(figure.data, "Profit")
     assert isinstance(profit, go.Scatter)
-    profit = find_trace_in_fig_data(figure.data, "Max drawdown 10.45%")
-    assert isinstance(profit, go.Scatter)
+    drawdown = find_trace_in_fig_data(figure.data, "Max drawdown 10.45%")
+    assert isinstance(drawdown, go.Scatter)
+    parallel = find_trace_in_fig_data(figure.data, "Parallel trades")
+    assert isinstance(parallel, go.Scatter)
+
+    underwater = find_trace_in_fig_data(figure.data, "Underwater Plot")
+    assert isinstance(underwater, go.Scatter)
 
     for pair in pairs:
         profit_pair = find_trace_in_fig_data(figure.data, f"Profit {pair}")