Merge branch 'develop' into feat/short

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}'