Fix all tests

tests/data/test_entryexitanalysis.py

@@ -69,7 +69,7 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, cap
         '--export', 'signals',
         '--cache', 'none',
         '--strategy-list',
-        'StrategyTestV3',
+        'StrategyTestV3Analysis',
     ]
     args = get_args(args)
     start_backtesting(args)
@@ -85,7 +85,7 @@ def test_backtest_analysis_nomock(default_conf, mocker, caplog, testdatadir, cap
         '--datadir', str(testdatadir),
         '--analysis_groups', '0',
         '--strategy',
-        'StrategyTestV3',
+        'StrategyTestV3Analysis',
     ]
     args = get_args(args)
     start_analysis_entries_exits(args)

tests/rpc/test_rpc_apiserver.py

@@ -1384,12 +1384,16 @@ def test_api_strategies(botclient):
     rc = client_get(client, f"{BASE_URI}/strategies")
 
     assert_response(rc)
+
+    print(rc.json())
+
     assert rc.json() == {'strategies': [
         'HyperoptableStrategy',
         'InformativeDecoratorTest',
         'StrategyTestV2',
         'StrategyTestV3',
-        'StrategyTestV3Futures',
+        'StrategyTestV3Analysis',
+        'StrategyTestV3Futures'
     ]}
 
 

tests/strategy/strats/strategy_test_v3.py

@@ -143,13 +143,13 @@ class StrategyTestV3(IStrategy):
                 (dataframe['adx'] > 65) &
                 (dataframe['plus_di'] > self.buy_plusdi.value)
             ),
-            ['enter_long', 'enter_tag']] = 1, 'enter_tag_long'
+            'enter_long'] = 1
 
         dataframe.loc[
             (
                 qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value)
             ),
-            ['enter_short', 'enter_tag']] = 1, 'enter_tag_short'
+            'enter_short'] = 1
 
         return dataframe
 
@@ -167,13 +167,13 @@ class StrategyTestV3(IStrategy):
                 (dataframe['adx'] > 70) &
                 (dataframe['minus_di'] > self.sell_minusdi.value)
             ),
-            ['exit_long', 'exit_tag']] = 1, 'exit_tag_long'
+            'exit_long'] = 1
 
         dataframe.loc[
             (
                 qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)
             ),
-            ['exit_long', 'exit_tag']] = 1, 'exit_tag_short'
+            'exit_short'] = 1
 
         return dataframe
 

tests/strategy/strats/strategy_test_v3_analysis.py

@@ -0,0 +1,195 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+from datetime import datetime
+
+import talib.abstract as ta
+from pandas import DataFrame
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.persistence import Trade
+from freqtrade.strategy import (BooleanParameter, DecimalParameter, IntParameter, IStrategy,
+                                RealParameter)
+
+
+class StrategyTestV3Analysis(IStrategy):
+    """
+    Strategy used by tests freqtrade bot.
+    Please do not modify this strategy, it's  intended for internal use only.
+    Please look at the SampleStrategy in the user_data/strategy directory
+    or strategy repository https://github.com/freqtrade/freqtrade-strategies
+    for samples and inspiration.
+    """
+    INTERFACE_VERSION = 3
+
+    # Minimal ROI designed for the strategy
+    minimal_roi = {
+        "40": 0.0,
+        "30": 0.01,
+        "20": 0.02,
+        "0": 0.04
+    }
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.10
+
+    # Optimal timeframe for the strategy
+    timeframe = '5m'
+
+    # Optional order type mapping
+    order_types = {
+        'entry': 'limit',
+        'exit': 'limit',
+        'stoploss': 'limit',
+        'stoploss_on_exchange': False
+    }
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
+    # Optional time in force for orders
+    order_time_in_force = {
+        'entry': 'gtc',
+        'exit': 'gtc',
+    }
+
+    buy_params = {
+        'buy_rsi': 35,
+        # Intentionally not specified, so "default" is tested
+        # 'buy_plusdi': 0.4
+    }
+
+    sell_params = {
+        'sell_rsi': 74,
+        'sell_minusdi': 0.4
+    }
+
+    buy_rsi = IntParameter([0, 50], default=30, space='buy')
+    buy_plusdi = RealParameter(low=0, high=1, default=0.5, space='buy')
+    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell')
+    sell_minusdi = DecimalParameter(low=0, high=1, default=0.5001, decimals=3, space='sell',
+                                    load=False)
+    protection_enabled = BooleanParameter(default=True)
+    protection_cooldown_lookback = IntParameter([0, 50], default=30)
+
+    # TODO: Can this work with protection tests? (replace HyperoptableStrategy implicitly ... )
+    # @property
+    # def protections(self):
+    #     prot = []
+    #     if self.protection_enabled.value:
+    #         prot.append({
+    #             "method": "CooldownPeriod",
+    #             "stop_duration_candles": self.protection_cooldown_lookback.value
+    #         })
+    #     return prot
+
+    bot_started = False
+
+    def bot_start(self):
+        self.bot_started = True
+
+    def informative_pairs(self):
+
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        # Momentum Indicator
+        # ------------------------------------
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # Minus Directional Indicator / Movement
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # Plus Directional Indicator / Movement
+        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # 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['ema10'] = ta.EMA(dataframe, timeperiod=10)
+
+        return dataframe
+
+    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+
+        dataframe.loc[
+            (
+                (dataframe['rsi'] < self.buy_rsi.value) &
+                (dataframe['fastd'] < 35) &
+                (dataframe['adx'] > 30) &
+                (dataframe['plus_di'] > self.buy_plusdi.value)
+            ) |
+            (
+                (dataframe['adx'] > 65) &
+                (dataframe['plus_di'] > self.buy_plusdi.value)
+            ),
+            ['enter_long', 'enter_tag']] = 1, 'enter_tag_long'
+
+        dataframe.loc[
+            (
+                qtpylib.crossed_below(dataframe['rsi'], self.sell_rsi.value)
+            ),
+            ['enter_short', 'enter_tag']] = 1, 'enter_tag_short'
+
+        return dataframe
+
+    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        dataframe.loc[
+            (
+                (
+                    (qtpylib.crossed_above(dataframe['rsi'], self.sell_rsi.value)) |
+                    (qtpylib.crossed_above(dataframe['fastd'], 70))
+                ) &
+                (dataframe['adx'] > 10) &
+                (dataframe['minus_di'] > 0)
+            ) |
+            (
+                (dataframe['adx'] > 70) &
+                (dataframe['minus_di'] > self.sell_minusdi.value)
+            ),
+            ['exit_long', 'exit_tag']] = 1, 'exit_tag_long'
+
+        dataframe.loc[
+            (
+                qtpylib.crossed_above(dataframe['rsi'], self.buy_rsi.value)
+            ),
+            ['exit_long', 'exit_tag']] = 1, 'exit_tag_short'
+
+        return dataframe
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                 proposed_leverage: float, max_leverage: float, side: str,
+                 **kwargs) -> float:
+        # Return 3.0 in all cases.
+        # Bot-logic must make sure it's an allowed leverage and eventually adjust accordingly.
+
+        return 3.0
+
+    def adjust_trade_position(self, trade: Trade, current_time: datetime, current_rate: float,
+                              current_profit: float, min_stake: float, max_stake: float, **kwargs):
+
+        if current_profit < -0.0075:
+            orders = trade.select_filled_orders(trade.entry_side)
+            return round(orders[0].cost, 0)
+
+        return None

tests/strategy/test_strategy_loading.py

@@ -34,7 +34,7 @@ def test_search_all_strategies_no_failed():
     directory = Path(__file__).parent / "strats"
     strategies = StrategyResolver.search_all_objects(directory, enum_failed=False)
     assert isinstance(strategies, list)
-    assert len(strategies) == 5
+    assert len(strategies) == 6
     assert isinstance(strategies[0], dict)
 
 
@@ -42,10 +42,10 @@ def test_search_all_strategies_with_failed():
     directory = Path(__file__).parent / "strats"
     strategies = StrategyResolver.search_all_objects(directory, enum_failed=True)
     assert isinstance(strategies, list)
-    assert len(strategies) == 6
+    assert len(strategies) == 7
     # with enum_failed=True search_all_objects() shall find 2 good strategies
     # and 1 which fails to load
-    assert len([x for x in strategies if x['class'] is not None]) == 5
+    assert len([x for x in strategies if x['class'] is not None]) == 6
     assert len([x for x in strategies if x['class'] is None]) == 1