Name changes for strategy

tests/optimize/hyperopts/default_hyperopt.py

@@ -68,15 +68,17 @@ class DefaultHyperOpt(IHyperOpt):
 
             # TRIGGERS
             if 'trigger' in params:
-                if params['trigger'] == 'bb_lower':
+                if params['trigger'] == 'boll':
                     conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
                 if params['trigger'] == 'macd_cross_signal':
                     conditions.append(qtpylib.crossed_above(
-                        dataframe['macd'], dataframe['macdsignal']
+                        dataframe['macd'],
+                        dataframe['macdsignal']
                     ))
                 if params['trigger'] == 'sar_reversal':
                     conditions.append(qtpylib.crossed_above(
-                        dataframe['close'], dataframe['sar']
+                        dataframe['close'],
+                        dataframe['sar']
                     ))
 
             if conditions:
@@ -102,7 +104,7 @@ class DefaultHyperOpt(IHyperOpt):
             Categorical([True, False], name='fastd-enabled'),
             Categorical([True, False], name='adx-enabled'),
             Categorical([True, False], name='rsi-enabled'),
-            Categorical(['bb_lower', 'macd_cross_signal', 'sar_reversal'], name='trigger')
+            Categorical(['boll', 'macd_cross_signal', 'sar_reversal'], name='trigger')
         ]
 
     @staticmethod
@@ -128,15 +130,17 @@ class DefaultHyperOpt(IHyperOpt):
 
             # TRIGGERS
             if 'sell-trigger' in params:
-                if params['sell-trigger'] == 'sell-bb_upper':
+                if params['sell-trigger'] == 'sell-boll':
                     conditions.append(dataframe['close'] > dataframe['bb_upperband'])
                 if params['sell-trigger'] == 'sell-macd_cross_signal':
                     conditions.append(qtpylib.crossed_above(
-                        dataframe['macdsignal'], dataframe['macd']
+                        dataframe['macdsignal'],
+                        dataframe['macd']
                     ))
                 if params['sell-trigger'] == 'sell-sar_reversal':
                     conditions.append(qtpylib.crossed_above(
-                        dataframe['sar'], dataframe['close']
+                        dataframe['sar'],
+                        dataframe['close']
                     ))
 
             if conditions:
@@ -162,9 +166,10 @@ class DefaultHyperOpt(IHyperOpt):
             Categorical([True, False], name='sell-fastd-enabled'),
             Categorical([True, False], name='sell-adx-enabled'),
             Categorical([True, False], name='sell-rsi-enabled'),
-            Categorical(['sell-bb_upper',
+            Categorical(['sell-boll',
                          'sell-macd_cross_signal',
-                         'sell-sar_reversal'], name='sell-trigger')
+                         'sell-sar_reversal'],
+                        name='sell-trigger')
         ]
 
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

tests/strategy/strats/hyperoptable_strategy.py

@@ -167,7 +167,7 @@ class HyperoptableStrategy(IStrategy):
         Based on TA indicators, populates the sell signal for the given dataframe
         :param dataframe: DataFrame
         :param metadata: Additional information, like the currently traded pair
-        :return: DataFrame with buy column
+        :return: DataFrame with sell column
         """
         dataframe.loc[
             (

tests/strategy/test_interface.py

@@ -156,17 +156,21 @@ def test_ignore_expired_candle(default_conf):
     # Add 1 candle length as the "latest date" defines candle open.
     current_time = latest_date + timedelta(seconds=80 + 300)
 
-    assert strategy.ignore_expired_candle(latest_date=latest_date,
-                                          current_time=current_time,
-                                          timeframe_seconds=300,
-                                          buy=True) is True
+    assert strategy.ignore_expired_candle(
+        latest_date=latest_date,
+        current_time=current_time,
+        timeframe_seconds=300,
+        enter=True
+    ) is True
 
     current_time = latest_date + timedelta(seconds=30 + 300)
 
-    assert not strategy.ignore_expired_candle(latest_date=latest_date,
-                                              current_time=current_time,
-                                              timeframe_seconds=300,
-                                              buy=True) is True
+    assert not strategy.ignore_expired_candle(
+        latest_date=latest_date,
+        current_time=current_time,
+        timeframe_seconds=300,
+        enter=True
+    ) is True
 
 
 def test_assert_df_raise(mocker, caplog, ohlcv_history):

tests/strategy/test_strategy_loading.py

@@ -382,13 +382,13 @@ def test_call_deprecated_function(result, monkeypatch, default_conf, caplog):
     assert isinstance(indicator_df, DataFrame)
     assert 'adx' in indicator_df.columns
 
-    buydf = strategy.advise_buy(result, metadata=metadata)
-    assert isinstance(buydf, DataFrame)
-    assert 'buy' in buydf.columns
+    enterdf = strategy.advise_buy(result, metadata=metadata)
+    assert isinstance(enterdf, DataFrame)
+    assert 'buy' in enterdf.columns
 
-    selldf = strategy.advise_sell(result, metadata=metadata)
-    assert isinstance(selldf, DataFrame)
-    assert 'sell' in selldf
+    exitdf = strategy.advise_sell(result, metadata=metadata)
+    assert isinstance(exitdf, DataFrame)
+    assert 'sell' in exitdf
 
     assert log_has("DEPRECATED: Please migrate to using 'timeframe' instead of 'ticker_interval'.",
                    caplog)
@@ -409,10 +409,10 @@ def test_strategy_interface_versioning(result, monkeypatch, default_conf):
     assert isinstance(indicator_df, DataFrame)
     assert 'adx' in indicator_df.columns
 
-    buydf = strategy.advise_buy(result, metadata=metadata)
-    assert isinstance(buydf, DataFrame)
-    assert 'buy' in buydf.columns
+    enterdf = strategy.advise_buy(result, metadata=metadata)
+    assert isinstance(enterdf, DataFrame)
+    assert 'buy' in enterdf.columns
 
-    selldf = strategy.advise_sell(result, metadata=metadata)
-    assert isinstance(selldf, DataFrame)
-    assert 'sell' in selldf
+    exitdf = strategy.advise_sell(result, metadata=metadata)
+    assert isinstance(exitdf, DataFrame)
+    assert 'sell' in exitdf