Merge branch 'lev-strat' into lev-freqtradebot

freqtrade/edge/edge_positioning.py

@@ -159,7 +159,8 @@ class Edge:
         logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
                     f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
                     f'({(max_date - min_date).days} days)..')
-        headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
+        #  TODO-lev: Should edge support shorts? needs to be investigated further...
+        headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long']
 
         trades: list = []
         for pair, pair_data in preprocessed.items():
@@ -168,7 +169,12 @@ class Edge:
             pair_data = pair_data.reset_index(drop=True)
 
             df_analyzed = self.strategy.advise_sell(
-                self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
+                dataframe=self.strategy.advise_buy(
+                    dataframe=pair_data,
+                    metadata={'pair': pair}
+                ),
+                metadata={'pair': pair}
+            )[headers].copy()
 
             trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
 
@@ -382,8 +388,8 @@ class Edge:
         return final
 
     def _find_trades_for_stoploss_range(self, df, pair, stoploss_range):
-        buy_column = df['buy'].values
-        sell_column = df['sell'].values
+        buy_column = df['enter_long'].values
+        sell_column = df['exit_long'].values
         date_column = df['date'].values
         ohlc_columns = df[['open', 'high', 'low', 'close']].values
 

freqtrade/enums/__init__.py

@@ -4,6 +4,6 @@ from freqtrade.enums.collateral import Collateral
 from freqtrade.enums.rpcmessagetype import RPCMessageType
 from freqtrade.enums.runmode import NON_UTIL_MODES, OPTIMIZE_MODES, TRADING_MODES, RunMode
 from freqtrade.enums.selltype import SellType
-from freqtrade.enums.signaltype import SignalTagType, SignalType
+from freqtrade.enums.signaltype import SignalDirection, SignalTagType, SignalType
 from freqtrade.enums.state import State
 from freqtrade.enums.tradingmode import TradingMode

freqtrade/enums/signaltype.py

@@ -5,8 +5,10 @@ class SignalType(Enum):
     """
     Enum to distinguish between buy and sell signals
     """
-    BUY = "buy"
-    SELL = "sell"
+    ENTER_LONG = "enter_long"
+    EXIT_LONG = "exit_long"
+    ENTER_SHORT = "enter_short"
+    EXIT_SHORT = "exit_short"
 
 
 class SignalTagType(Enum):
@@ -14,3 +16,9 @@ class SignalTagType(Enum):
     Enum for signal columns
     """
     BUY_TAG = "buy_tag"
+    SHORT_TAG = "short_tag"
+
+
+class SignalDirection(Enum):
+    LONG = 'long'
+    SHORT = 'short'

freqtrade/freqtradebot.py

@@ -420,24 +420,25 @@ class FreqtradeBot(LoggingMixin):
             return False
 
         # running get_signal on historical data fetched
-        (buy, sell, buy_tag) = self.strategy.get_signal(
-            pair,
-            self.strategy.timeframe,
-            analyzed_df
-        )
+        (side, enter_tag) = self.strategy.get_entry_signal(
+            pair, self.strategy.timeframe, analyzed_df
+            )
 
-        if buy and not sell:
+        if side:
             stake_amount = self.wallets.get_trade_stake_amount(pair, self.edge)
 
             bid_check_dom = self.config.get('bid_strategy', {}).get('check_depth_of_market', {})
             if ((bid_check_dom.get('enabled', False)) and
                     (bid_check_dom.get('bids_to_ask_delta', 0) > 0)):
+                # TODO-lev: Does the below need to be adjusted for shorts?
                 if self._check_depth_of_market_buy(pair, bid_check_dom):
-                    return self.execute_entry(pair, stake_amount, buy_tag=buy_tag)
+                    # TODO-lev: pass in "enter" as side.
+
+                    return self.execute_entry(pair, stake_amount, enter_tag=enter_tag)
                 else:
                     return False
 
-            return self.execute_entry(pair, stake_amount, buy_tag=buy_tag)
+            return self.execute_entry(pair, stake_amount, enter_tag=enter_tag)
         else:
             return False
 
@@ -466,7 +467,7 @@ class FreqtradeBot(LoggingMixin):
             return False
 
     def execute_entry(self, pair: str, stake_amount: float, price: Optional[float] = None,
-                      forcebuy: bool = False, buy_tag: Optional[str] = None) -> bool:
+                      forcebuy: bool = False, enter_tag: Optional[str] = None) -> bool:
         """
         Executes a limit buy for the given pair
         :param pair: pair for which we want to create a LIMIT_BUY
@@ -575,7 +576,8 @@ class FreqtradeBot(LoggingMixin):
             exchange=self.exchange.id,
             open_order_id=order_id,
             strategy=self.strategy.get_strategy_name(),
-            buy_tag=buy_tag,
+            # TODO-lev: compatibility layer for buy_tag (!)
+            buy_tag=enter_tag,
             timeframe=timeframe_to_minutes(self.config['timeframe'])
         )
         trade.orders.append(order_obj)
@@ -699,22 +701,22 @@ class FreqtradeBot(LoggingMixin):
 
         logger.debug('Handling %s ...', trade)
 
-        (buy, sell) = (False, False)
+        (enter, exit_) = (False, False)
 
         if (self.config.get('use_sell_signal', True) or
                 self.config.get('ignore_roi_if_buy_signal', False)):
             analyzed_df, _ = self.dataprovider.get_analyzed_dataframe(trade.pair,
                                                                       self.strategy.timeframe)
 
-            (buy, sell, _) = self.strategy.get_signal(
+            (enter, exit_) = self.strategy.get_exit_signal(
                 trade.pair,
                 self.strategy.timeframe,
-                analyzed_df
+                analyzed_df, is_short=trade.is_short
             )
 
-        logger.debug('checking sell')
+        # TODO-lev: side should depend on trade side.
         sell_rate = self.exchange.get_rate(trade.pair, refresh=True, side="sell")
-        if self._check_and_execute_sell(trade, sell_rate, buy, sell):
+        if self._check_and_execute_exit(trade, sell_rate, enter, exit_):
             return True
 
         logger.debug('Found no sell signal for %s.', trade)
@@ -855,19 +857,19 @@ class FreqtradeBot(LoggingMixin):
                     logger.warning(f"Could not create trailing stoploss order "
                                    f"for pair {trade.pair}.")
 
-    def _check_and_execute_sell(self, trade: Trade, sell_rate: float,
-                                buy: bool, sell: bool) -> bool:
+    def _check_and_execute_exit(self, trade: Trade, sell_rate: float,
+                                enter: bool, exit_: bool) -> bool:
         """
-        Check and execute sell
+        Check and execute trade exit
         """
-        should_sell = self.strategy.should_sell(
-            trade, sell_rate, datetime.now(timezone.utc), buy, sell,
+        should_exit: SellCheckTuple = self.strategy.should_exit(
+            trade, sell_rate, datetime.now(timezone.utc), enter=enter, exit_=exit_,
             force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0
         )
 
-        if should_sell.sell_flag:
-            logger.info(f'Executing Sell for {trade.pair}. Reason: {should_sell.sell_type}')
-            self.execute_trade_exit(trade, sell_rate, should_sell)
+        if should_exit.sell_flag:
+            logger.info(f'Exit for {trade.pair} detected. Reason: {should_exit.sell_type}')
+            self.execute_trade_exit(trade, sell_rate, should_exit)
             return True
         return False
 

freqtrade/optimize/backtesting.py

@@ -37,13 +37,16 @@ logger = logging.getLogger(__name__)
 
 # Indexes for backtest tuples
 DATE_IDX = 0
-BUY_IDX = 1
-OPEN_IDX = 2
-CLOSE_IDX = 3
-SELL_IDX = 4
-LOW_IDX = 5
-HIGH_IDX = 6
-BUY_TAG_IDX = 7
+OPEN_IDX = 1
+HIGH_IDX = 2
+LOW_IDX = 3
+CLOSE_IDX = 4
+LONG_IDX = 5
+ELONG_IDX = 6  # Exit long
+SHORT_IDX = 7
+ESHORT_IDX = 8  # Exit short
+BUY_TAG_IDX = 9
+SHORT_TAG_IDX = 10
 
 
 class Backtesting:
@@ -65,8 +68,8 @@ class Backtesting:
         remove_credentials(self.config)
         self.strategylist: List[IStrategy] = []
         self.all_results: Dict[str, Dict] = {}
-
-        self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
+        self._exchange_name = self.config['exchange']['name']
+        self.exchange = ExchangeResolver.load_exchange(self._exchange_name, self.config)
         self.dataprovider = DataProvider(self.config, None)
 
         if self.config.get('strategy_list', None):
@@ -246,7 +249,8 @@ class Backtesting:
         """
         # Every change to this headers list must evaluate further usages of the resulting tuple
         # and eventually change the constants for indexes at the top
-        headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high', 'buy_tag']
+        headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
+                   'enter_short', 'exit_short', 'long_tag', 'short_tag']
         data: Dict = {}
         self.progress.init_step(BacktestState.CONVERT, len(processed))
 
@@ -254,10 +258,18 @@ class Backtesting:
         for pair, pair_data in processed.items():
             self.check_abort()
             self.progress.increment()
+
             if not pair_data.empty:
-                pair_data.loc[:, 'buy'] = 0  # cleanup if buy_signal is exist
-                pair_data.loc[:, 'sell'] = 0  # cleanup if sell_signal is exist
-                pair_data.loc[:, 'buy_tag'] = None  # cleanup if buy_tag is exist
+                # Cleanup from prior runs
+                # TODO-lev: The below is not 100% compatible with the interface compatibility layer
+                if 'enter_long' in pair_data.columns:
+                    pair_data.loc[:, 'enter_long'] = 0
+                pair_data.loc[:, 'enter_short'] = 0
+                if 'exit_long' in pair_data.columns:
+                    pair_data.loc[:, 'exit_long'] = 0
+                pair_data.loc[:, 'exit_short'] = 0
+                pair_data.loc[:, 'long_tag'] = None
+                pair_data.loc[:, 'short_tag'] = None
 
             df_analyzed = self.strategy.advise_sell(
                 self.strategy.advise_buy(pair_data, {'pair': pair}),
@@ -268,9 +280,11 @@ class Backtesting:
                                          startup_candles=self.required_startup)
             # To avoid using data from future, we use buy/sell signals shifted
             # from the previous candle
-            df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
-            df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
-            df_analyzed.loc[:, 'buy_tag'] = df_analyzed.loc[:, 'buy_tag'].shift(1)
+            df_analyzed.loc[:, 'enter_long'] = df_analyzed.loc[:, 'enter_long'].shift(1)
+            df_analyzed.loc[:, 'enter_short'] = df_analyzed.loc[:, 'enter_short'].shift(1)
+            df_analyzed.loc[:, 'exit_long'] = df_analyzed.loc[:, 'exit_long'].shift(1)
+            df_analyzed.loc[:, 'exit_short'] = df_analyzed.loc[:, 'exit_short'].shift(1)
+            df_analyzed.loc[:, 'long_tag'] = df_analyzed.loc[:, 'long_tag'].shift(1)
 
             # Update dataprovider cache
             self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
@@ -351,10 +365,13 @@ class Backtesting:
     def _get_sell_trade_entry_for_candle(self, trade: LocalTrade,
                                          sell_row: Tuple) -> Optional[LocalTrade]:
         sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
-        sell = self.strategy.should_sell(trade, sell_row[OPEN_IDX],  # type: ignore
-                                         sell_candle_time, sell_row[BUY_IDX],
-                                         sell_row[SELL_IDX],
-                                         low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX])
+        enter = sell_row[SHORT_IDX] if trade.is_short else sell_row[LONG_IDX]
+        exit_ = sell_row[ESHORT_IDX] if trade.is_short else sell_row[ELONG_IDX]
+        sell = self.strategy.should_exit(
+            trade, sell_row[OPEN_IDX], sell_candle_time,  # type: ignore
+            enter=enter, exit_=exit_,
+            low=sell_row[LOW_IDX], high=sell_row[HIGH_IDX]
+            )
 
         if sell.sell_flag:
             trade.close_date = sell_candle_time
@@ -390,9 +407,12 @@ class Backtesting:
             if len(detail_data) == 0:
                 # Fall back to "regular" data if no detail data was found for this candle
                 return self._get_sell_trade_entry_for_candle(trade, sell_row)
-            detail_data['buy'] = sell_row[BUY_IDX]
-            detail_data['sell'] = sell_row[SELL_IDX]
-            headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
+            detail_data['enter_long'] = sell_row[LONG_IDX]
+            detail_data['exit_long'] = sell_row[ELONG_IDX]
+            detail_data['enter_short'] = sell_row[SHORT_IDX]
+            detail_data['exit_short'] = sell_row[ESHORT_IDX]
+            headers = ['date', 'open', 'high', 'low', 'close', 'enter_long', 'exit_long',
+                       'enter_short', 'exit_short']
             for det_row in detail_data[headers].values.tolist():
                 res = self._get_sell_trade_entry_for_candle(trade, det_row)
                 if res:
@@ -403,7 +423,7 @@ class Backtesting:
         else:
             return self._get_sell_trade_entry_for_candle(trade, sell_row)
 
-    def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
+    def _enter_trade(self, pair: str, row: List, direction: str) -> Optional[LocalTrade]:
         try:
             stake_amount = self.wallets.get_trade_stake_amount(pair, None)
         except DependencyException:
@@ -442,7 +462,8 @@ class Backtesting:
                 fee_close=self.fee,
                 is_open=True,
                 buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
-                exchange='backtesting',
+                exchange=self._exchange_name,
+                is_short=(direction == 'short'),
             )
             return trade
         return None
@@ -476,6 +497,20 @@ class Backtesting:
         self.rejected_trades += 1
         return False
 
+    def check_for_trade_entry(self, row) -> Optional[str]:
+        enter_long = row[LONG_IDX] == 1
+        exit_long = row[ELONG_IDX] == 1
+        enter_short = row[SHORT_IDX] == 1
+        exit_short = row[ESHORT_IDX] == 1
+
+        if enter_long == 1 and not any([exit_long, enter_short]):
+            # Long
+            return 'long'
+        if enter_short == 1 and not any([exit_short, enter_long]):
+            # Short
+            return 'short'
+        return None
+
     def backtest(self, processed: Dict,
                  start_date: datetime, end_date: datetime,
                  max_open_trades: int = 0, position_stacking: bool = False,
@@ -538,15 +573,15 @@ class Backtesting:
                 # without positionstacking, we can only have one open trade per pair.
                 # max_open_trades must be respected
                 # don't open on the last row
+                trade_dir = self.check_for_trade_entry(row)
                 if (
                     (position_stacking or len(open_trades[pair]) == 0)
                     and self.trade_slot_available(max_open_trades, open_trade_count_start)
                     and tmp != end_date
-                    and row[BUY_IDX] == 1
-                    and row[SELL_IDX] != 1
+                    and trade_dir is not None
                     and not PairLocks.is_pair_locked(pair, row[DATE_IDX])
                 ):
-                    trade = self._enter_trade(pair, row)
+                    trade = self._enter_trade(pair, row, trade_dir)
                     if trade:
                         # TODO: hacky workaround to avoid opening > max_open_trades
                         # This emulates previous behaviour - not sure if this is correct

freqtrade/plot/plotting.py

@@ -386,8 +386,9 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
     )
     fig.add_trace(candles, 1, 1)
 
-    if 'buy' in data.columns:
-        df_buy = data[data['buy'] == 1]
+    # TODO-lev: Needs short equivalent
+    if 'enter_long' in data.columns:
+        df_buy = data[data['enter_long'] == 1]
         if len(df_buy) > 0:
             buys = go.Scatter(
                 x=df_buy.date,
@@ -405,8 +406,8 @@ def generate_candlestick_graph(pair: str, data: pd.DataFrame, trades: pd.DataFra
         else:
             logger.warning("No buy-signals found.")
 
-    if 'sell' in data.columns:
-        df_sell = data[data['sell'] == 1]
+    if 'exit_long' in data.columns:
+        df_sell = data[data['exit_long'] == 1]
         if len(df_sell) > 0:
             sells = go.Scatter(
                 x=df_sell.date,

freqtrade/resolvers/hyperopt_resolver.py

@@ -51,6 +51,7 @@ class HyperOptResolver(IResolver):
         if not hasattr(hyperopt, 'populate_sell_trend'):
             logger.info("Hyperopt class does not provide populate_sell_trend() method. "
                         "Using populate_sell_trend from the strategy.")
+        # TODO-lev: Short equivelents?
         return hyperopt
 
 

freqtrade/strategy/interface.py

@@ -13,7 +13,7 @@ from pandas import DataFrame
 
 from freqtrade.constants import ListPairsWithTimeframes
 from freqtrade.data.dataprovider import DataProvider
-from freqtrade.enums import SellType, SignalTagType, SignalType
+from freqtrade.enums import SellType, SignalDirection, SignalTagType, SignalType
 from freqtrade.exceptions import OperationalException, StrategyError
 from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
 from freqtrade.exchange.exchange import timeframe_to_next_date
@@ -166,7 +166,7 @@ class IStrategy(ABC, HyperStrategyMixin):
 
     def check_buy_timeout(self, pair: str, trade: Trade, order: dict, **kwargs) -> bool:
         """
-        Check buy enter timeout function callback.
+        Check buy timeout function callback.
         This method can be used to override the enter-timeout.
         It is called whenever a limit buy/short order has been created,
         and is not yet fully filled.
@@ -209,6 +209,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         pass
 
+    # TODO-lev: add side
     def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
                             time_in_force: str, current_time: datetime, **kwargs) -> bool:
         """
@@ -343,6 +344,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         return None
 
+    # TODO-lev: add side
     def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
                             proposed_stake: float, min_stake: float, max_stake: float,
                             **kwargs) -> float:
@@ -461,7 +463,10 @@ class IStrategy(ABC, HyperStrategyMixin):
             logger.debug("Skipping TA Analysis for already analyzed candle")
             dataframe['buy'] = 0
             dataframe['sell'] = 0
+            dataframe['enter_short'] = 0
+            dataframe['exit_short'] = 0
             dataframe['buy_tag'] = None
+            dataframe['short_tag'] = None
 
         # Other Defs in strategy that want to be called every loop here
         # twitter_sell = self.watch_twitter_feed(dataframe, metadata)
@@ -521,6 +526,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         if dataframe is None:
             message = "No dataframe returned (return statement missing?)."
         elif 'buy' not in dataframe:
+            # TODO-lev: Something?
             message = "Buy column not set."
         elif df_len != len(dataframe):
             message = message_template.format("length")
@@ -534,25 +540,22 @@ class IStrategy(ABC, HyperStrategyMixin):
             else:
                 raise StrategyError(message)
 
-    def get_signal(
+    def get_latest_candle(
         self,
         pair: str,
         timeframe: str,
-        dataframe: DataFrame
-    ) -> Tuple[bool, bool, Optional[str]]:
+        dataframe: DataFrame,
+    ) -> Tuple[Optional[DataFrame], Optional[arrow.Arrow]]:
         """
-        Calculates current signal based based on the buy/short or sell/exit_short
-        columns of the dataframe.
-        Used by Bot to get the signal to buy, sell, short, or exit_short
+        Get the latest candle. Used only during real mode
         :param pair: pair in format ANT/BTC
         :param timeframe: timeframe to use
         :param dataframe: Analyzed dataframe to get signal from.
-        :return: (Buy, Sell)/(Short, Exit_short) A bool-tuple indicating
-        (buy/sell)/(short/exit_short) signal
+        :return: (None, None) or (Dataframe, latest_date) - corresponding to the last candle
         """
         if not isinstance(dataframe, DataFrame) or dataframe.empty:
             logger.warning(f'Empty candle (OHLCV) data for pair {pair}')
-            return False, False, None
+            return None, None
 
         latest_date = dataframe['date'].max()
         latest = dataframe.loc[dataframe['date'] == latest_date].iloc[-1]
@@ -567,27 +570,89 @@ class IStrategy(ABC, HyperStrategyMixin):
                 'Outdated history for pair %s. Last tick is %s minutes old',
                 pair, int((arrow.utcnow() - latest_date).total_seconds() // 60)
             )
-            return False, False, None
+            return None, None
+        return latest, latest_date
 
-        enter = latest[SignalType.BUY.value] == 1
+    def get_exit_signal(
+        self,
+        pair: str,
+        timeframe: str,
+        dataframe: DataFrame,
+        is_short: bool = None
+    ) -> Tuple[bool, bool]:
+        """
+        Calculates current exit signal based based on the buy/short or sell/exit_short
+        columns of the dataframe.
+        Used by Bot to get the signal to exit.
+        depending on is_short, looks at "short" or "long" columns.
+        :param pair: pair in format ANT/BTC
+        :param timeframe: timeframe to use
+        :param dataframe: Analyzed dataframe to get signal from.
+        :param is_short: Indicating existing trade direction.
+        :return: (enter, exit) A bool-tuple with enter / exit values.
+        """
+        latest, latest_date = self.get_latest_candle(pair, timeframe, dataframe)
+        if latest is None:
+            return False, False
 
-        exit = False
-        if SignalType.SELL.value in latest:
-            exit = latest[SignalType.SELL.value] == 1
+        if is_short:
+            enter = latest.get(SignalType.ENTER_SHORT.value, 0) == 1
+            exit_ = latest.get(SignalType.EXIT_SHORT.value, 0) == 1
+        else:
+            enter = latest[SignalType.ENTER_LONG.value] == 1
+            exit_ = latest.get(SignalType.EXIT_LONG.value, 0) == 1
 
-        buy_tag = latest.get(SignalTagType.BUY_TAG.value, None)
+        logger.debug(f"exit-trigger: {latest['date']} (pair={pair}) "
+                     f"enter={enter} exit={exit_}")
+
+        return enter, exit_
+
+    def get_entry_signal(
+        self,
+        pair: str,
+        timeframe: str,
+        dataframe: DataFrame,
+    ) -> Tuple[Optional[SignalDirection], Optional[str]]:
+        """
+        Calculates current entry signal based based on the buy/short or sell/exit_short
+        columns of the dataframe.
+        Used by Bot to get the signal to buy, sell, short, or exit_short
+        :param pair: pair in format ANT/BTC
+        :param timeframe: timeframe to use
+        :param dataframe: Analyzed dataframe to get signal from.
+        :return: (SignalDirection, entry_tag)
+        """
+        latest, latest_date = self.get_latest_candle(pair, timeframe, dataframe)
+        if latest is None or latest_date is None:
+            return None, None
+
+        enter_long = latest[SignalType.ENTER_LONG.value] == 1
+        exit_long = latest.get(SignalType.EXIT_LONG.value, 0) == 1
+        enter_short = latest.get(SignalType.ENTER_SHORT.value, 0) == 1
+        exit_short = latest.get(SignalType.EXIT_SHORT.value, 0) == 1
+
+        enter_signal: Optional[SignalDirection] = None
+        enter_tag_value: Optional[str] = None
+        if enter_long == 1 and not any([exit_long, enter_short]):
+            enter_signal = SignalDirection.LONG
+            enter_tag_value = latest.get(SignalTagType.BUY_TAG.value, None)
+        if enter_short == 1 and not any([exit_short, enter_long]):
+            enter_signal = SignalDirection.SHORT
+            enter_tag_value = latest.get(SignalTagType.SHORT_TAG.value, None)
 
-        logger.debug('trigger: %s (pair=%s) buy=%s sell=%s',
-                     latest['date'], pair, str(enter), str(exit))
         timeframe_seconds = timeframe_to_seconds(timeframe)
+
         if self.ignore_expired_candle(
-            latest_date=latest_date,
+            latest_date=latest_date.datetime,
             current_time=datetime.now(timezone.utc),
             timeframe_seconds=timeframe_seconds,
-            enter=enter
+            enter=bool(enter_signal)
         ):
-            return False, exit, buy_tag
-        return enter, exit, buy_tag
+            return None, enter_tag_value
+
+        logger.debug(f"entry trigger: {latest['date']} (pair={pair}) "
+                     f"enter={enter_long} enter_tag_value={enter_tag_value}")
+        return enter_signal, enter_tag_value
 
     def ignore_expired_candle(
         self,
@@ -602,8 +667,9 @@ class IStrategy(ABC, HyperStrategyMixin):
         else:
             return False
 
-    def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
-                    sell: bool, low: float = None, high: float = None,
+    def should_exit(self, trade: Trade, rate: float, date: datetime, *,
+                    enter: bool, exit_: bool,
+                    low: float = None, high: float = None,
                     force_stoploss: float = 0) -> SellCheckTuple:
         """
         This function evaluates if one of the conditions required to trigger a sell/exit_short
@@ -613,6 +679,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         :param force_stoploss: Externally provided stoploss
         :return: True if trade should be exited, False otherwise
         """
+
         current_rate = rate
         current_profit = trade.calc_profit_ratio(current_rate)
 
@@ -627,7 +694,7 @@ class IStrategy(ABC, HyperStrategyMixin):
         current_profit = trade.calc_profit_ratio(current_rate)
 
         # if enter signal and ignore_roi is set, we don't need to evaluate min_roi.
-        roi_reached = (not (buy and self.ignore_roi_if_buy_signal)
+        roi_reached = (not (enter and self.ignore_roi_if_buy_signal)
                        and self.min_roi_reached(trade=trade, current_profit=current_profit,
                                                 current_time=date))
 
@@ -640,10 +707,11 @@ class IStrategy(ABC, HyperStrategyMixin):
         if (self.sell_profit_only and current_profit <= self.sell_profit_offset):
             # sell_profit_only and profit doesn't reach the offset - ignore sell signal
             pass
-        elif self.use_sell_signal and not buy:
-            if sell:
+        elif self.use_sell_signal and not enter:
+            if exit_:
                 sell_signal = SellType.SELL_SIGNAL
             else:
+                trade_type = "exit_short" if trade.is_short else "sell"
                 custom_reason = strategy_safe_wrapper(self.custom_sell, default_retval=False)(
                     pair=trade.pair, trade=trade, current_time=date, current_rate=current_rate,
                     current_profit=current_profit)
@@ -651,9 +719,9 @@ class IStrategy(ABC, HyperStrategyMixin):
                     sell_signal = SellType.CUSTOM_SELL
                     if isinstance(custom_reason, str):
                         if len(custom_reason) > CUSTOM_SELL_MAX_LENGTH:
-                            logger.warning(f'Custom sell reason returned from custom_sell is too '
-                                           f'long and was trimmed to {CUSTOM_SELL_MAX_LENGTH} '
-                                           f'characters.')
+                            logger.warning(f'Custom {trade_type} reason returned from '
+                                           f'custom_{trade_type} is too long and was trimmed'
+                                           f'to {CUSTOM_SELL_MAX_LENGTH} characters.')
                             custom_reason = custom_reason[:CUSTOM_SELL_MAX_LENGTH]
                     else:
                         custom_reason = None
@@ -699,7 +767,12 @@ class IStrategy(ABC, HyperStrategyMixin):
         # Initiate stoploss with open_rate. Does nothing if stoploss is already set.
         trade.adjust_stop_loss(trade.open_rate, stop_loss_value, initial=True)
 
-        if self.use_custom_stoploss and trade.stop_loss < (low or current_rate):
+        dir_correct = (trade.stop_loss < (low or current_rate)
+                       if not trade.is_short else
+                       trade.stop_loss > (high or current_rate)
+                       )
+
+        if self.use_custom_stoploss and dir_correct:
             stop_loss_value = strategy_safe_wrapper(self.custom_stoploss, default_retval=None
                                                     )(pair=trade.pair, trade=trade,
                                                       current_time=current_time,
@@ -717,6 +790,7 @@ class IStrategy(ABC, HyperStrategyMixin):
             sl_offset = self.trailing_stop_positive_offset
 
             # Make sure current_profit is calculated using high for backtesting.
+            # TODO-lev: Check this function - high / low usage must be inversed for short trades!
             high_profit = current_profit if not high else trade.calc_profit_ratio(high)
 
             # Don't update stoploss if trailing_only_offset_is_reached is true.
@@ -825,7 +899,11 @@ class IStrategy(ABC, HyperStrategyMixin):
                           "the current function headers!", DeprecationWarning)
             return self.populate_buy_trend(dataframe)  # type: ignore
         else:
-            return self.populate_buy_trend(dataframe, metadata)
+            df = self.populate_buy_trend(dataframe, metadata)
+            if 'enter_long' not in df.columns:
+                df = df.rename({'buy': 'enter_long', 'buy_tag': 'long_tag'}, axis='columns')
+
+            return df
 
     def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
         """
@@ -843,4 +921,24 @@ class IStrategy(ABC, HyperStrategyMixin):
                           "the current function headers!", DeprecationWarning)
             return self.populate_sell_trend(dataframe)  # type: ignore
         else:
-            return self.populate_sell_trend(dataframe, metadata)
+            df = self.populate_sell_trend(dataframe, metadata)
+            if 'exit_long' not in df.columns:
+                df = df.rename({'sell': 'exit_long'}, axis='columns')
+            return df
+
+    def leverage(self, pair: str, current_time: datetime, current_rate: float,
+                 proposed_leverage: float, max_leverage: float, side: str,
+                 **kwargs) -> float:
+        """
+        Customize leverage for each new trade. This method is not called when edge module is
+        enabled.
+
+        :param pair: Pair that's currently analyzed
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
+        :param proposed_leverage: A leverage proposed by the bot.
+        :param max_leverage: Max leverage allowed on this pair
+        :param side: 'long' or 'short' - indicating the direction of the proposed trade
+        :return: A leverage amount, which is between 1.0 and max_leverage.
+        """
+        return 1.0

freqtrade/strategy/strategy_helper.py

@@ -1,5 +1,6 @@
 import pandas as pd
 
+from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes
 
 
@@ -58,7 +59,11 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
     return dataframe
 
 
-def stoploss_from_open(open_relative_stop: float, current_profit: float) -> float:
+def stoploss_from_open(
+    open_relative_stop: float,
+    current_profit: float,
+    for_short: bool = False
+) -> float:
     """
 
     Given the current profit, and a desired stop loss value relative to the open price,
@@ -79,7 +84,16 @@ def stoploss_from_open(open_relative_stop: float, current_profit: float) -> floa
     if current_profit == -1:
         return 1
 
-    stoploss = 1-((1+open_relative_stop)/(1+current_profit))
+    if for_short is True:
+        # TODO-lev: How would this be calculated for short
+        raise OperationalException(
+            "Freqtrade hasn't figured out how to calculated stoploss on shorts")
+        # stoploss = 1-((1+open_relative_stop)/(1+current_profit))
+    else:
+        stoploss = 1-((1+open_relative_stop)/(1+current_profit))
 
     # negative stoploss values indicate the requested stop price is higher than the current price
-    return max(stoploss, 0.0)
+    if for_short:
+        return min(stoploss, 0.0)
+    else:
+        return max(stoploss, 0.0)

freqtrade/templates/sample_short_strategy.py

@@ -0,0 +1,379 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+# isort: skip_file
+# --- Do not remove these libs ---
+import numpy as np  # noqa
+import pandas as pd  # noqa
+from pandas import DataFrame
+
+from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
+                                IStrategy, IntParameter)
+
+# --------------------------------
+# Add your lib to import here
+import talib.abstract as ta
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+
+
+# This class is a sample. Feel free to customize it.
+class SampleStrategy(IStrategy):
+    """
+    This is a sample strategy to inspire you.
+    More information in https://www.freqtrade.io/en/latest/strategy-customization/
+
+    You can:
+        :return: a Dataframe with all mandatory indicators for the strategies
+    - Rename the class name (Do not forget to update class_name)
+    - Add any methods you want to build your strategy
+    - Add any lib you need to build your strategy
+
+    You must keep:
+    - the lib in the section "Do not remove these libs"
+    - the methods: populate_indicators, populate_buy_trend, populate_sell_trend
+    You should keep:
+    - timeframe, minimal_roi, stoploss, trailing_*
+    """
+    # Strategy interface version - allow new iterations of the strategy interface.
+    # Check the documentation or the Sample strategy to get the latest version.
+    INTERFACE_VERSION = 2
+
+    # Minimal ROI designed for the strategy.
+    # This attribute will be overridden if the config file contains "minimal_roi".
+    minimal_roi = {
+        "60": 0.01,
+        "30": 0.02,
+        "0": 0.04
+    }
+
+    # Optimal stoploss designed for the strategy.
+    # This attribute will be overridden if the config file contains "stoploss".
+    stoploss = -0.10
+
+    # Trailing stoploss
+    trailing_stop = False
+    # trailing_only_offset_is_reached = False
+    # trailing_stop_positive = 0.01
+    # trailing_stop_positive_offset = 0.0  # Disabled / not configured
+
+    # Hyperoptable parameters
+    short_rsi = IntParameter(low=51, high=100, default=70, space='sell', optimize=True, load=True)
+    exit_short_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
+
+    # Optimal timeframe for the strategy.
+    timeframe = '5m'
+
+    # Run "populate_indicators()" only for new candle.
+    process_only_new_candles = False
+
+    # These values can be overridden in the "ask_strategy" section in the config.
+    use_sell_signal = True
+    sell_profit_only = False
+    ignore_roi_if_buy_signal = False
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 30
+
+    # Optional order type mapping.
+    order_types = {
+        'buy': 'limit',
+        'sell': 'limit',
+        'stoploss': 'market',
+        'stoploss_on_exchange': False
+    }
+
+    # Optional order time in force.
+    order_time_in_force = {
+        'buy': 'gtc',
+        'sell': 'gtc'
+    }
+
+    plot_config = {
+        'main_plot': {
+            'tema': {},
+            'sar': {'color': 'white'},
+        },
+        'subplots': {
+            "MACD": {
+                'macd': {'color': 'blue'},
+                'macdsignal': {'color': 'orange'},
+            },
+            "RSI": {
+                'rsi': {'color': 'red'},
+            }
+        }
+    }
+
+    def informative_pairs(self):
+        """
+        Define additional, informative pair/interval combinations to be cached from the exchange.
+        These pair/interval combinations are non-tradeable, unless they are part
+        of the whitelist as well.
+        For more information, please consult the documentation
+        :return: List of tuples in the format (pair, interval)
+            Sample: return [("ETH/USDT", "5m"),
+                            ("BTC/USDT", "15m"),
+                            ]
+        """
+        return []
+
+    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Adds several different TA indicators to the given DataFrame
+
+        Performance Note: For the best performance be frugal on the number of indicators
+        you are using. Let uncomment only the indicator you are using in your strategies
+        or your hyperopt configuration, otherwise you will waste your memory and CPU usage.
+        :param dataframe: Dataframe with data from the exchange
+        :param metadata: Additional information, like the currently traded pair
+        :return: a Dataframe with all mandatory indicators for the strategies
+        """
+
+        # Momentum Indicators
+        # ------------------------------------
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+
+        # # Plus Directional Indicator / Movement
+        # dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
+        # dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+
+        # # Minus Directional Indicator / Movement
+        # dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
+        # dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # # Aroon, Aroon Oscillator
+        # aroon = ta.AROON(dataframe)
+        # dataframe['aroonup'] = aroon['aroonup']
+        # dataframe['aroondown'] = aroon['aroondown']
+        # dataframe['aroonosc'] = ta.AROONOSC(dataframe)
+
+        # # Awesome Oscillator
+        # dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
+
+        # # Keltner Channel
+        # keltner = qtpylib.keltner_channel(dataframe)
+        # dataframe["kc_upperband"] = keltner["upper"]
+        # dataframe["kc_lowerband"] = keltner["lower"]
+        # dataframe["kc_middleband"] = keltner["mid"]
+        # dataframe["kc_percent"] = (
+        #     (dataframe["close"] - dataframe["kc_lowerband"]) /
+        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"])
+        # )
+        # dataframe["kc_width"] = (
+        #     (dataframe["kc_upperband"] - dataframe["kc_lowerband"]) / dataframe["kc_middleband"]
+        # )
+
+        # # Ultimate Oscillator
+        # dataframe['uo'] = ta.ULTOSC(dataframe)
+
+        # # Commodity Channel Index: values [Oversold:-100, Overbought:100]
+        # dataframe['cci'] = ta.CCI(dataframe)
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # # Inverse Fisher transform on RSI: values [-1.0, 1.0] (https://goo.gl/2JGGoy)
+        # rsi = 0.1 * (dataframe['rsi'] - 50)
+        # dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
+
+        # # Inverse Fisher transform on RSI normalized: values [0.0, 100.0] (https://goo.gl/2JGGoy)
+        # dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
+
+        # # Stochastic Slow
+        # stoch = ta.STOCH(dataframe)
+        # dataframe['slowd'] = stoch['slowd']
+        # dataframe['slowk'] = stoch['slowk']
+
+        # Stochastic Fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # # Stochastic RSI
+        # Please read https://github.com/freqtrade/freqtrade/issues/2961 before using this.
+        # STOCHRSI is NOT aligned with tradingview, which may result in non-expected results.
+        # stoch_rsi = ta.STOCHRSI(dataframe)
+        # dataframe['fastd_rsi'] = stoch_rsi['fastd']
+        # dataframe['fastk_rsi'] = stoch_rsi['fastk']
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # MFI
+        dataframe['mfi'] = ta.MFI(dataframe)
+
+        # # ROC
+        # dataframe['roc'] = ta.ROC(dataframe)
+
+        # Overlap Studies
+        # ------------------------------------
+
+        # 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']
+        dataframe["bb_percent"] = (
+            (dataframe["close"] - dataframe["bb_lowerband"]) /
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"])
+        )
+        dataframe["bb_width"] = (
+            (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
+        )
+
+        # Bollinger Bands - Weighted (EMA based instead of SMA)
+        # weighted_bollinger = qtpylib.weighted_bollinger_bands(
+        #     qtpylib.typical_price(dataframe), window=20, stds=2
+        # )
+        # dataframe["wbb_upperband"] = weighted_bollinger["upper"]
+        # dataframe["wbb_lowerband"] = weighted_bollinger["lower"]
+        # dataframe["wbb_middleband"] = weighted_bollinger["mid"]
+        # dataframe["wbb_percent"] = (
+        #     (dataframe["close"] - dataframe["wbb_lowerband"]) /
+        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"])
+        # )
+        # dataframe["wbb_width"] = (
+        #     (dataframe["wbb_upperband"] - dataframe["wbb_lowerband"]) /
+        #     dataframe["wbb_middleband"]
+        # )
+
+        # # 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['ema21'] = ta.EMA(dataframe, timeperiod=21)
+        # dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
+        # dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+
+        # # SMA - Simple Moving Average
+        # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
+        # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
+        # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
+        # dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
+        # dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
+        # dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
+
+        # Parabolic SAR
+        dataframe['sar'] = ta.SAR(dataframe)
+
+        # TEMA - Triple Exponential Moving Average
+        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
+
+        # Cycle Indicator
+        # ------------------------------------
+        # Hilbert Transform Indicator - SineWave
+        hilbert = ta.HT_SINE(dataframe)
+        dataframe['htsine'] = hilbert['sine']
+        dataframe['htleadsine'] = hilbert['leadsine']
+
+        # Pattern Recognition - Bullish candlestick patterns
+        # ------------------------------------
+        # # Hammer: values [0, 100]
+        # dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
+        # # Inverted Hammer: values [0, 100]
+        # dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
+        # # Dragonfly Doji: values [0, 100]
+        # dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
+        # # Piercing Line: values [0, 100]
+        # dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe) # values [0, 100]
+        # # Morningstar: values [0, 100]
+        # dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe) # values [0, 100]
+        # # Three White Soldiers: values [0, 100]
+        # dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe) # values [0, 100]
+
+        # Pattern Recognition - Bearish candlestick patterns
+        # ------------------------------------
+        # # Hanging Man: values [0, 100]
+        # dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
+        # # Shooting Star: values [0, 100]
+        # dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
+        # # Gravestone Doji: values [0, 100]
+        # dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
+        # # Dark Cloud Cover: values [0, 100]
+        # dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
+        # # Evening Doji Star: values [0, 100]
+        # dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
+        # # Evening Star: values [0, 100]
+        # dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
+
+        # Pattern Recognition - Bullish/Bearish candlestick patterns
+        # ------------------------------------
+        # # Three Line Strike: values [0, -100, 100]
+        # dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
+        # # Spinning Top: values [0, -100, 100]
+        # dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe) # values [0, -100, 100]
+        # # Engulfing: values [0, -100, 100]
+        # dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe) # values [0, -100, 100]
+        # # Harami: values [0, -100, 100]
+        # dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe) # values [0, -100, 100]
+        # # Three Outside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe) # values [0, -100, 100]
+        # # Three Inside Up/Down: values [0, -100, 100]
+        # dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe) # values [0, -100, 100]
+
+        # # Chart type
+        # # ------------------------------------
+        # # Heikin Ashi Strategy
+        # heikinashi = qtpylib.heikinashi(dataframe)
+        # dataframe['ha_open'] = heikinashi['open']
+        # dataframe['ha_close'] = heikinashi['close']
+        # dataframe['ha_high'] = heikinashi['high']
+        # dataframe['ha_low'] = heikinashi['low']
+
+        # Retrieve best bid and best ask from the orderbook
+        # ------------------------------------
+        """
+        # first check if dataprovider is available
+        if self.dp:
+            if self.dp.runmode.value in ('live', 'dry_run'):
+                ob = self.dp.orderbook(metadata['pair'], 1)
+                dataframe['best_bid'] = ob['bids'][0][0]
+                dataframe['best_ask'] = ob['asks'][0][0]
+        """
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the buy signal for the given dataframe
+        :param dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+
+        dataframe.loc[
+            (
+                # Signal: RSI crosses above 70
+                (qtpylib.crossed_above(dataframe['rsi'], self.short_rsi.value)) &
+                (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
+                (dataframe['tema'] < dataframe['tema'].shift(1)) &  # Guard: tema is falling
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'enter_short'] = 1
+
+        return dataframe
+
+    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+        """
+        Based on TA indicators, populates the sell signal for the given dataframe
+        :param dataframe: DataFrame populated with indicators
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with sell column
+        """
+
+        dataframe.loc[
+            (
+                # Signal: RSI crosses above 30
+                (qtpylib.crossed_above(dataframe['rsi'], self.exit_short_rsi.value)) &
+                # Guard: tema below BB middle
+                (dataframe['tema'] <= dataframe['bb_middleband']) &
+                (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'exit_short'] = 1
+
+        return dataframe