diff --git a/freqtrade/analyze.py b/freqtrade/analyze.py
index f337887e9..4a2a46a5d 100644
--- a/freqtrade/analyze.py
+++ b/freqtrade/analyze.py
@@ -74,6 +74,8 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
     # Plus Directional Indicator / Movement
     dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
     dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+    dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
     """
     # ROC
     dataframe['roc'] = ta.ROC(dataframe)
@@ -114,13 +116,14 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
     dataframe['blower'] = ta.BBANDS(dataframe, nbdevup=2, nbdevdn=2)['lowerband']
     """
     # Bollinger bands
+    """
     bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
     dataframe['bb_lowerband'] = bollinger['lower']
     dataframe['bb_middleband'] = bollinger['mid']
     dataframe['bb_upperband'] = bollinger['upper']
-    """
 
     # EMA - Exponential Moving Average
+    dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
     dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
     dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
     dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
@@ -210,14 +213,12 @@ def populate_indicators(dataframe: DataFrame) -> DataFrame:
 
     # Chart type
     # ------------------------------------
-    """
     # Heikinashi stategy
     heikinashi = qtpylib.heikinashi(dataframe)
     dataframe['ha_open'] = heikinashi['open']
     dataframe['ha_close'] = heikinashi['close']
     dataframe['ha_high'] = heikinashi['high']
     dataframe['ha_low'] = heikinashi['low']
-    """
 
     return dataframe
 
diff --git a/freqtrade/optimize/hyperopt.py b/freqtrade/optimize/hyperopt.py
index 71ddd33c6..959e64206 100644
--- a/freqtrade/optimize/hyperopt.py
+++ b/freqtrade/optimize/hyperopt.py
@@ -30,18 +30,19 @@ logging.getLogger('hyperopt.tpe').setLevel(logging.WARNING)
 logger = logging.getLogger(__name__)
 
 # set TARGET_TRADES to suit your number concurrent trades so its realistic to 20days of data
-TARGET_TRADES = 1100
+TARGET_TRADES = 600
 TOTAL_TRIES = 0
 _CURRENT_TRIES = 0
 CURRENT_BEST_LOSS = 100
 
 # max average trade duration in minutes
 # if eval ends with higher value, we consider it a failed eval
-MAX_ACCEPTED_TRADE_DURATION = 240
+MAX_ACCEPTED_TRADE_DURATION = 300
 
 # this is expexted avg profit * expected trade count
 # for example 3.5%, 1100 trades, EXPECTED_MAX_PROFIT = 3.85
-EXPECTED_MAX_PROFIT = 3.85
+# check that the reported Σ% values do not exceed this!
+EXPECTED_MAX_PROFIT = 3.0
 
 # Configuration and data used by hyperopt
 PROCESSED = None  # optimize.preprocess(optimize.load_data())
@@ -57,6 +58,10 @@ main._CONF = OPTIMIZE_CONFIG
 
 
 SPACE = {
+    'macd_below_zero': hp.choice('macd_below_zero', [
+        {'enabled': False},
+        {'enabled': True}
+    ]),
     'mfi': hp.choice('mfi', [
         {'enabled': False},
         {'enabled': True, 'value': hp.quniform('mfi-value', 5, 25, 1)}
@@ -95,13 +100,15 @@ SPACE = {
     ]),
     'trigger': hp.choice('trigger', [
         {'type': 'lower_bb'},
+        {'type': 'lower_bb_tema'},
         {'type': 'faststoch10'},
         {'type': 'ao_cross_zero'},
-        {'type': 'ema5_cross_ema10'},
+        {'type': 'ema3_cross_ema10'},
         {'type': 'macd_cross_signal'},
         {'type': 'sar_reversal'},
-        {'type': 'stochf_cross'},
         {'type': 'ht_sine'},
+        {'type': 'heiken_reversal_bull'},
+        {'type': 'di_cross'},
     ]),
     'stoploss': hp.uniform('stoploss', -0.5, -0.02),
 }
@@ -133,10 +140,11 @@ def log_results(results):
 
     if results['loss'] < CURRENT_BEST_LOSS:
         CURRENT_BEST_LOSS = results['loss']
-        logger.info('{:5d}/{}: {}'.format(
+        logger.info('{:5d}/{}: {}. Loss {:.5f}'.format(
             results['current_tries'],
             results['total_tries'],
-            results['result']))
+            results['result'],
+            results['loss']))
     else:
         print('.', end='')
         sys.stdout.flush()
@@ -144,9 +152,9 @@ def log_results(results):
 
 def calculate_loss(total_profit: float, trade_count: int, trade_duration: float):
     """ objective function, returns smaller number for more optimal results """
-    trade_loss = 1 - 0.35 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.2)
+    trade_loss = 1 - 0.25 * exp(-(trade_count - TARGET_TRADES) ** 2 / 10 ** 5.8)
     profit_loss = max(0, 1 - total_profit / EXPECTED_MAX_PROFIT)
-    duration_loss = min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
+    duration_loss = 0.7 + 0.3 * min(trade_duration / MAX_ACCEPTED_TRADE_DURATION, 1)
     return trade_loss + profit_loss + duration_loss
 
 
@@ -190,12 +198,13 @@ def optimizer(params):
 
 def format_results(results: DataFrame):
     return ('{:6d} trades. Avg profit {: 5.2f}%. '
-            'Total profit {: 11.8f} BTC. Avg duration {:5.1f} mins.').format(
+            'Total profit {: 11.8f} BTC ({:.4f}Σ%). Avg duration {:5.1f} mins.').format(
                 len(results.index),
                 results.profit_percent.mean() * 100.0,
                 results.profit_BTC.sum(),
+                results.profit_percent.sum(),
                 results.duration.mean() * 5,
-    )
+            )
 
 
 def buy_strategy_generator(params):
@@ -204,6 +213,8 @@ def buy_strategy_generator(params):
         # GUARDS AND TRENDS
         if params['uptrend_long_ema']['enabled']:
             conditions.append(dataframe['ema50'] > dataframe['ema100'])
+        if params['macd_below_zero']['enabled']:
+            conditions.append(dataframe['macd'] < 0)
         if params['uptrend_short_ema']['enabled']:
             conditions.append(dataframe['ema5'] > dataframe['ema10'])
         if params['mfi']['enabled']:
@@ -224,14 +235,17 @@ def buy_strategy_generator(params):
 
         # TRIGGERS
         triggers = {
-            'lower_bb': dataframe['tema'] <= dataframe['blower'],
+            'lower_bb': (dataframe['close'] < dataframe['bb_lowerband']),
+            'lower_bb_tema': (dataframe['tema'] < dataframe['bb_lowerband']),
             'faststoch10': (crossed_above(dataframe['fastd'], 10.0)),
             'ao_cross_zero': (crossed_above(dataframe['ao'], 0.0)),
-            'ema5_cross_ema10': (crossed_above(dataframe['ema5'], dataframe['ema10'])),
+            'ema3_cross_ema10': (crossed_above(dataframe['ema3'], dataframe['ema10'])),
             'macd_cross_signal': (crossed_above(dataframe['macd'], dataframe['macdsignal'])),
             'sar_reversal': (crossed_above(dataframe['close'], dataframe['sar'])),
-            'stochf_cross': (crossed_above(dataframe['fastk'], dataframe['fastd'])),
             'ht_sine': (crossed_above(dataframe['htleadsine'], dataframe['htsine'])),
+            'heiken_reversal_bull': (crossed_above(dataframe['ha_close'], dataframe['ha_open'])) &
+                                    (dataframe['ha_low'] == dataframe['ha_open']),
+            'di_cross': (crossed_above(dataframe['plus_di'], dataframe['minus_di'])),
         }
         conditions.append(triggers.get(params['trigger']['type']))
 
diff --git a/freqtrade/tests/optimize/test_hyperopt.py b/freqtrade/tests/optimize/test_hyperopt.py
index 3e03d26c0..4bb5c8c0b 100644
--- a/freqtrade/tests/optimize/test_hyperopt.py
+++ b/freqtrade/tests/optimize/test_hyperopt.py
@@ -107,6 +107,7 @@ def test_no_log_if_loss_does_not_improve(mocker):
 
 def test_fmin_best_results(mocker, caplog):
     fmin_result = {
+        "macd_below_zero": 0,
         "adx": 1,
         "adx-value": 15.0,
         "fastd": 1,
@@ -136,7 +137,7 @@ def test_fmin_best_results(mocker, caplog):
         '"adx": {\n        "enabled": true,\n        "value": 15.0\n    },',
         '"green_candle": {\n        "enabled": true\n    },',
         '"mfi": {\n        "enabled": false\n    },',
-        '"trigger": {\n        "type": "ao_cross_zero"\n    },',
+        '"trigger": {\n        "type": "faststoch10"\n    },',
         '"stoploss": -0.1',
     ]