diff --git a/freqtrade/tests/test_hyperopt.py b/freqtrade/tests/test_hyperopt.py
index c09901f88..9a9df78e9 100644
--- a/freqtrade/tests/test_hyperopt.py
+++ b/freqtrade/tests/test_hyperopt.py
@@ -35,44 +35,6 @@ def conf():
         "stoploss": -0.05
     }
 
-
-def backtest2(conf, pairs, mocker, buy_strategy):
-    trades = []
-    mocker.patch.dict('freqtrade.main._CONF', conf)
-    for pair in pairs:
-        with open('freqtrade/tests/testdata/'+pair+'.json') as data_file:
-            data = json.load(data_file)
-
-            mocker.patch('freqtrade.analyze.get_ticker_history', return_value=data)
-            mocker.patch('arrow.utcnow', return_value=arrow.get('2017-08-20T14:50:00'))
-            mocker.patch('freqtrade.analyze.populate_buy_trend', side_effect=buy_strategy)
-            ticker = analyze_ticker(pair)
-            # for each buy point
-            for index, row in ticker[ticker.buy == 1].iterrows():
-                trade = Trade(
-                    open_rate=row['close'],
-                    open_date=arrow.get(row['date']).datetime,
-                    amount=1,
-                )
-                # calculate win/lose forwards from buy point
-                for index2, row2 in ticker[index:].iterrows():
-                    if should_sell(trade, row2['close'], arrow.get(row2['date']).datetime):
-                        current_profit = (row2['close'] - trade.open_rate) / trade.open_rate
-
-                        trades.append((pair, current_profit, index2 - index))
-                        break
-
-    labels = ['currency', 'profit', 'duration']
-    results = DataFrame.from_records(trades, columns=labels)
-
-    print_results(results)
-
-    # set the value below to suit your number concurrent trades so its realistic to 20days of data
-    TARGET_TRADES = 1200
-    if results.profit.sum() == 0 or results.profit.mean() == 0:
-        return 49999999999 # avoid division by zero, return huge value to discard result
-    return abs(len(results.index) - 1200.1) / (results.profit.sum() ** 2) * results.duration.mean() # the smaller the better
-
 def buy_strategy_generator(params):
     print(params)
     def populate_buy_trend(dataframe: DataFrame) -> DataFrame:
@@ -114,19 +76,18 @@ def buy_strategy_generator(params):
 
 @pytest.mark.skipif(not os.environ.get('BACKTEST', False), reason="BACKTEST not set")
 def test_hyperopt(conf, pairs, mocker):
-
-    # def optimizer(params):
-        # return backtest2(conf, pairs, mocker, buy_strategy_generator(params))
-
     def optimizer(params):
         buy_strategy = buy_strategy_generator(params)
         mocker.patch('freqtrade.analyze.populate_buy_trend', side_effect=buy_strategy)
         results = backtest(conf, pairs, mocker)
 
         print_results(results)
-        if len(results.index) < 800: # require at least 800 trades
-            return 100000 # return large number to "ignore" this result
-        return results.duration.mean() ** 3 / results.profit.sum() / results.profit.mean() # the smaller the better
+
+        # set the value below to suit your number concurrent trades so its realistic to 20days of data
+        TARGET_TRADES = 1200
+        if results.profit.sum() == 0 or results.profit.mean() == 0:
+            return 49999999999 # avoid division by zero, return huge value to discard result
+        return abs(len(results.index) - 1200.1) / (results.profit.sum() ** 2) * results.duration.mean() # the smaller the better
 
     space = {
         'mfi': hp.choice('mfi', [