Use absolute drawdown calc

freqtrade/data/btanalysis.py

@@ -360,13 +360,14 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
 
 def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date',
                            value_col: str = 'profit_ratio'
-                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp]:
+                           ) -> Tuple[float, pd.Timestamp, pd.Timestamp, 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_ratio')
-    :return: Tuple (float, highdate, lowdate) with absolute max drawdown, high and low time
+    :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:
@@ -382,7 +383,10 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
         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]
-    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date
+    high_val = max_drawdown_df.loc[max_drawdown_df.iloc[:idxmin]
+                                   ['high_value'].idxmax(), 'cumulative']
+    low_val = max_drawdown_df.loc[idxmin, 'cumulative']
+    return abs(min(max_drawdown_df['drawdown'])), high_date, low_date, high_val, low_val
 
 
 def calculate_csum(trades: pd.DataFrame) -> Tuple[float, float]:

freqtrade/optimize/optimize_reports.py

@@ -322,14 +322,20 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
         result['strategy'][strategy] = strat_stats
 
         try:
-            max_drawdown, drawdown_start, drawdown_end = calculate_max_drawdown(
+            max_drawdown, _, _, _, _ = calculate_max_drawdown(
                 results, value_col='profit_ratio')
+            drawdown_abs, drawdown_start, drawdown_end, high_val, low_val = calculate_max_drawdown(
+                results, value_col='profit_abs')
             strat_stats.update({
                 'max_drawdown': max_drawdown,
+                'max_drawdown_abs': drawdown_abs,
                 'drawdown_start': drawdown_start,
                 'drawdown_start_ts': drawdown_start.timestamp() * 1000,
                 'drawdown_end': drawdown_end,
                 'drawdown_end_ts': drawdown_end.timestamp() * 1000,
+
+                'max_drawdown_low': low_val,
+                'max_drawdown_high': high_val,
             })
 
             csum_min, csum_max = calculate_csum(results)
@@ -341,6 +347,9 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
         except ValueError:
             strat_stats.update({
                 'max_drawdown': 0.0,
+                'max_drawdown_abs': 0.0,
+                'max_drawdown_low': 0.0,
+                'max_drawdown_high': 0.0,
                 'drawdown_start': datetime(1970, 1, 1, tzinfo=timezone.utc),
                 'drawdown_start_ts': 0,
                 'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
@@ -471,6 +480,12 @@ def text_table_add_metrics(strat_results: Dict) -> str:
                                                 strat_results['stake_currency'])),
 
             ('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
+            ('Max Drawdown', round_coin_value(strat_results['max_drawdown_abs'],
+                                              strat_results['stake_currency'])),
+            ('Max Drawdown high', round_coin_value(strat_results['max_drawdown_high'],
+                                                   strat_results['stake_currency'])),
+            ('Max Drawdown low', round_coin_value(strat_results['max_drawdown_low'],
+                                                  strat_results['stake_currency'])),
             ('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
             ('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),
             ('Market change', f"{round(strat_results['market_change'] * 100, 2)}%"),

freqtrade/plot/plotting.py

@@ -145,7 +145,7 @@ def add_max_drawdown(fig, row, trades: pd.DataFrame, df_comb: pd.DataFrame,
     Add scatter points indicating max drawdown
     """
     try:
-        max_drawdown, highdate, lowdate = calculate_max_drawdown(trades)
+        max_drawdown, highdate, lowdate, _, _ = calculate_max_drawdown(trades)
 
         drawdown = go.Scatter(
             x=[highdate, lowdate],

freqtrade/plugins/protections/max_drawdown_protection.py

@@ -55,7 +55,7 @@ class MaxDrawdown(IProtection):
 
         # Drawdown is always positive
         try:
-            drawdown, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
+            drawdown, _, _, _, _ = calculate_max_drawdown(trades_df, value_col='close_profit')
         except ValueError:
             return False, None, None
 

tests/data/test_btanalysis.py

@@ -274,15 +274,17 @@ def test_create_cum_profit1(testdatadir):
 def test_calculate_max_drawdown(testdatadir):
     filename = testdatadir / "backtest-result_test.json"
     bt_data = load_backtest_data(filename)
-    drawdown, h, low = calculate_max_drawdown(bt_data)
+    drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(bt_data)
     assert isinstance(drawdown, float)
     assert pytest.approx(drawdown) == 0.21142322
-    assert isinstance(h, Timestamp)
+    assert isinstance(hdate, Timestamp)
-    assert isinstance(low, Timestamp)
+    assert isinstance(lowdate, Timestamp)
-    assert h == Timestamp('2018-01-24 14:25:00', tz='UTC')
+    assert isinstance(hval, float)
-    assert low == Timestamp('2018-01-30 04:45:00', tz='UTC')
+    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')
     with pytest.raises(ValueError, match='Trade dataframe empty.'):
-        drawdown, h, low = calculate_max_drawdown(DataFrame())
+        drawdown, hdate, lowdate, hval, lval = calculate_max_drawdown(DataFrame())
 
 
 def test_calculate_csum(testdatadir):
@@ -310,13 +312,16 @@ def test_calculate_max_drawdown2():
     # sort by profit and reset index
     df = df.sort_values('profit').reset_index(drop=True)
     df1 = df.copy()
-    drawdown, h, low = calculate_max_drawdown(df, date_col='open_date', value_col='profit')
+    drawdown, hdate, ldate, hval, lval = calculate_max_drawdown(
+        df, date_col='open_date', value_col='profit')
     # Ensure df has not been altered.
     assert df.equals(df1)
 
     assert isinstance(drawdown, float)
     # High must be before low
-    assert h < low
+    assert hdate < ldate
+    # High value must be higher than low value
+    assert hval > lval
     assert drawdown == 0.091755
 
     df = DataFrame(zip(values[:5], dates[:5]), columns=['profit', 'open_date'])