diff --git a/freqtrade/data/btanalysis.py b/freqtrade/data/btanalysis.py
index 828fb78f3..8e851a8e8 100644
--- a/freqtrade/data/btanalysis.py
+++ b/freqtrade/data/btanalysis.py
@@ -383,3 +383,21 @@ def calculate_max_drawdown(trades: pd.DataFrame, *, date_col: str = 'close_date'
     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
+
+
+def calculate_csum(trades: pd.DataFrame) -> 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)
+    :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()
+    csum_max = csum_df['sum'].max()
+
+    return csum_min, csum_max
diff --git a/freqtrade/optimize/optimize_reports.py b/freqtrade/optimize/optimize_reports.py
index 118253e86..88b2028ba 100644
--- a/freqtrade/optimize/optimize_reports.py
+++ b/freqtrade/optimize/optimize_reports.py
@@ -9,7 +9,8 @@ from pandas import DataFrame
 from tabulate import tabulate
 
 from freqtrade.constants import DATETIME_PRINT_FORMAT, LAST_BT_RESULT_FN
-from freqtrade.data.btanalysis import calculate_market_change, calculate_max_drawdown
+from freqtrade.data.btanalysis import (calculate_csum, calculate_market_change,
+                                       calculate_max_drawdown)
 from freqtrade.misc import decimals_per_coin, file_dump_json, round_coin_value
 
 
@@ -324,6 +325,13 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
                 'drawdown_end': drawdown_end,
                 'drawdown_end_ts': drawdown_end.timestamp() * 1000,
             })
+
+            csum_min, csum_max = calculate_csum(results)
+            strat_stats.update({
+                'csum_min': csum_min,
+                'csum_max': csum_max
+            })
+
         except ValueError:
             strat_stats.update({
                 'max_drawdown': 0.0,
@@ -331,6 +339,8 @@ def generate_backtest_stats(btdata: Dict[str, DataFrame],
                 'drawdown_start_ts': 0,
                 'drawdown_end': datetime(1970, 1, 1, tzinfo=timezone.utc),
                 'drawdown_end_ts': 0,
+                'csum_min': 0,
+                'csum_max': 0
             })
 
     strategy_results = generate_strategy_metrics(all_results=all_results)
@@ -439,6 +449,12 @@ def text_table_add_metrics(strat_results: Dict) -> str:
             ('Avg. Duration Winners', f"{strat_results['winner_holding_avg']}"),
             ('Avg. Duration Loser', f"{strat_results['loser_holding_avg']}"),
             ('', ''),  # Empty line to improve readability
+
+            ('Abs Profit Min', round_coin_value(strat_results['csum_min'],
+                                                strat_results['stake_currency'])),
+            ('Abs Profit Max', round_coin_value(strat_results['csum_max'],
+                                                strat_results['stake_currency'])),
+
             ('Max Drawdown', f"{round(strat_results['max_drawdown'] * 100, 2)}%"),
             ('Drawdown Start', strat_results['drawdown_start'].strftime(DATETIME_PRINT_FORMAT)),
             ('Drawdown End', strat_results['drawdown_end'].strftime(DATETIME_PRINT_FORMAT)),