Custom_Orders - Demo.

Early demonstration of Strategy controlling
Order Type on the Exchange, Stop / Stop_limit / Market / limit etc
The stake
The price to submit

Demo allows multiple orders to be placed, so ladding buys down to a fib
Or buying and setting stop-loss
Or Take profit in stages etc.

Included a strategy demo with stubs for
limit, market, take_profit and stop_limit orders

Obviously very early code.
Submitted as buys, stops etc are working / showing in GDAX.
and we have hte order IDs to update trade table.

freqtrade/custom_orders_strat.py

@@ -0,0 +1,296 @@
+# --- Do not remove these libs ---
+from freqtrade.strategy.interface import IStrategy
+from pandas import DataFrame
+import talib.abstract as ta
+
+from datetime import datetime
+import time
+import timeit
+
+
+# --------------------------------
+class customorders(IStrategy):
+    """
+    author@: Creslin
+
+    """
+    minimal_roi = {
+        "0": 0.20
+    }
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.01
+
+    # Optimal ticker interval for the strategy
+    ticker_interval = '5m'
+
+    def get_ticker_indicator(self):
+        return int(self.ticker_interval[:-1])
+
+    def populate_indicators(self, dataframe: DataFrame) -> DataFrame:
+        #
+
+        # dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
+        time.sleep(1)
+
+        return dataframe
+
+    # # # If using Berlins expose pairs to strategy
+    # def advise_indicators(self, dataframe: DataFrame, pair: str) -> DataFrame:
+    #
+    #     # RSI
+    #     dataframe['rsi'] = ta.RSI(dataframe, timeperiod=7)
+    #
+    #     return dataframe
+
+    #####
+    #  EXAMPLE Strat - buys every even minute sells every odd
+    ####
+    def populate_buy_trend(self, dataframe: DataFrame) -> DataFrame:
+        if int(datetime.now().strftime('%M')) % 2 == 0: #buy = 1 every even minute
+            print('----------------we are in strategy', datetime.now().time())
+            b: float = 9999999.99
+            print( "buy = 1")
+        else:
+            b = 0.00000001
+        dataframe.loc[
+            (
+                # L-RSI When below 0.01 / vfi -26
+                #(dataframe['lrsi'] < 0.5) &
+                #(dataframe['vfi'] <  -4)
+                (dataframe['close'] < b )
+            ),
+            'buy'] = 1
+        return dataframe
+
+    #####
+    #  EXAMPLE Strat - buys every even minute sells every odd
+    ####
+    def populate_sell_trend(self, dataframe: DataFrame) -> DataFrame:
+        if int(datetime.now().strftime('%M'))% 2  != 0:  ## sell = 1 every ODD minute
+            print('----------------we are in strategy', datetime.now().time())
+            s: float = 9999999.99
+            print("sell = 1")
+        else:
+            s = 0.00000001
+        dataframe.loc[
+            (
+                # vfi 0.99
+                #(dataframe['vfi'] > 0.41)
+                (dataframe['close'] < s )
+            ),
+            'sell'] = 1
+        return dataframe
+
+    def stop_stops_plugin(self, dataframe: DataFrame, pair: str) -> DataFrame:
+        """
+        stop_stops_pligin. To stop a buy where X trades from Y have stopped out.
+        i.e to remove a pair from being bought that is on a losing streak.
+
+        Only processed if buy=1
+        """
+        """
+        Called before a trade executes if exists, 
+        :param dataframe:
+        :param pair:
+        :return:
+        """
+
+        from freqtrade.persistence import Trade
+        import pandas as pd
+        df = dataframe
+
+        # Only check buys, else return DF untouched
+
+        if df.iloc[-1]['buy'] == 1:
+
+            print("STOP_STOPS STOP_STOPS STOP_STOPS STOP_STOP STOP_STOPS STOP_STOP STOP_STOPS")
+
+            from pandas import set_option
+            set_option('display.max_rows', 2000)
+            set_option('display.max_columns', 8)
+
+            def closed_pair_trades(pair):
+                """
+                To return list of closed trades for a pair
+                enriched with open/closd dates, profit, and stake
+
+                :param pair:
+                :return: df_c pair open, close, profit, stake
+                """
+                pair = pair
+                df_c = pd.DataFrame(columns=['pair', 'profit', 'open_date', 'close_date', 'stake'])
+
+                pair_trades = Trade.query.filter(Trade.pair.is_(pair)).all()
+                for pair_trade in pair_trades:
+                    if pair_trade.is_open == False:
+                        # print("closed trade for", pair, "closed with ", pair_trade.close_profit )
+
+                        p = pair
+                        pcp = pair_trade.close_profit
+                        od = pair_trade.open_date
+                        cd = pair_trade.close_date
+                        sa = pair_trade.stake_amount
+
+                        df_c = df_c.append([{'pair':p, 'profit':pcp, 'open_date':od,
+                                             'close_date':cd, 'stake':sa}], ignore_index=True)
+                return df_c
+
+            def has_past_perf(df_c, lost_the_last=1, out_of=1):
+                """
+                Return if pair lost_the_last X trades out_of Y trades
+                :param df_c: dataframe of closed trades for the pair
+                :param lost_the_last: number of bad trades to look for
+                :param out_of: within the last number over trades closed
+                :return: bool
+                """
+                lost_the_last =lost_the_last
+                out_of = out_of
+                df_c = df_c
+
+                df_c_tail = df_c.tail(out_of)
+                lost_from_last_count = df_c_tail[df_c_tail['profit'] < 0].shapec
+                if lost_from_last_count[0] >= lost_the_last:
+                    return True
+                else:
+                    return False
+
+            # load this pairs closed trades into a df
+            # df_c = closed_pair_trades(pair=pair)
+            # print("df_c", df_c)
+
+            # Get bool if on pairs past profitable trades count, cancel the buy if limit hit
+            # past_perf = has_past_perf(df_c, lost_the_last=9, out_of=10)
+            # if past_perf:
+            #     #set buy to 0
+            #     print("STOP STOP SETTING BUY TO ZERO ")
+            #     df.loc['buy'] = 0
+
+            dataframe  = df
+        return dataframe
+
+    #def money_mgt_plugin(self, dataframe: DataFrame, pair: str) -> DataFrame:
+    def money_mgt_plugin(self, dataframe: DataFrame, pair: str) -> DataFrame:
+        """
+        pre_trade_mgt.
+        To allow money mamagement and or other checks to be put against dataframe
+        with buy or sell set to "1"
+
+        Returns a list of custom order trades.
+        Each custom order to include, above the needed fields for exchange
+
+        dict['order_type'] = 'a_open_limit'
+
+        order_types are later ordered and executed in alphabetical order.
+        order to enter market are executed before stops and take profits are played in.
+        Supported types are:
+        - a_open_limit   # enter market with limit order
+        - a_open_market  # enter market with market order
+        - b_stop_limit   # set a stop_limit order
+        - b_stop_market  # set a stop_market order
+        - c_take_limit   # set a take profit limit order
+        - c_take_market  # set a take profit market order
+
+        Multipe orders can be processed but an asset may only have the sum of its open value
+        in either stop or take profit orders.
+
+        i.e we can open a $500 for 500 tokens position and have any combination of
+        stop or take profit orders at any prices that COMBINED do not break 500 tokens.
+
+        "one" common scenario may be an 'a_open_limit' or 'a_open_market' order
+        and 'b_stop_limit' or 'b_stop_market' order to protect capital.
+
+        """
+        print("MONEY_MGT MONEY_MGT MONEY_MGT MONEY_MGT MONEY_MGT MONEY_MGT MONEY_MGT MONEY_MGT MONEY_MGT")
+        df = dataframe
+
+        # If buy = 1 and Exchange is GDAX
+        if df.iloc[-1]['buy'] == 1:
+            exchange = self.config['exchange']['name']
+
+            # GDAX Stop-Limit Order
+            if exchange == "gdax":
+
+                custom_orders = []
+                limit = {}
+                take_profit = {}
+
+                # Take profit Market example "b_take_market"
+                # open market order example "a_open_market"
+
+                # Open limit order example
+                amount = 0.5
+                limit_price = df.iloc[-1]['close'] * 1.05 # pay 5% for testing order are made
+
+                limit = {}
+                limit["order_type"] = "a_open_limit"
+                limit["symbol"] = pair
+                limit["type"] = "limit"
+                limit["side"] = "buy"
+                limit["amount"] = amount
+                limit["price"] = limit_price
+                params: dict = {}
+                limit["params"] = params
+
+                custom_orders.append(limit)
+
+                # Market Example
+                market_amount = 1
+
+                market = {}
+                market_price = 1.05  # pay 5% for testing order are made
+                market["order_type"] = "a_open_market"
+                market["symbol"] = pair
+                market["type"] = "market"
+                market["side"] = "buy"
+                market["amount"] = market_amount
+                params: dict = {}
+                market["params"] = params
+
+                custom_orders.append(market)
+
+                # Stop Limit order
+                stop_limit = {}
+                stop_limit_amount = 0.01
+                stop_limit_price = df.iloc[-1]['close'] * 0.90  # take 10% hard code for testing
+                stop_price = df.iloc[-1]['close'] * 0.95  # trigger at 7% loss, hard code for testing
+
+                stop_limit['order_type'] = 'b_stop_limit'
+                stop_limit['symbol'] = pair
+                stop_limit['type'] = 'limit'
+                stop_limit['side'] = 'sell'
+                stop_limit['amount'] = stop_limit_amount
+                stop_limit['price'] = stop_limit_price
+                params: dict = {}
+                params['stop'] = 'loss'
+                params['stop_price'] = stop_price
+                stop_limit['params'] = params
+
+                custom_orders.append(stop_limit)
+
+                # Take profit Limit example
+                take_profit_limit_price = df.iloc[-1]['close'] * 1.2  # take_proft at 20% - for demo
+                take_profit_amount = 0.01
+
+                take_profit["order_type"] = "c_take_profit_limit"
+                take_profit["order_template"] = (pair, amount, take_profit_limit_price)
+                take_profit["symbol"] = pair
+                take_profit["type"] = "limit"
+                take_profit["side"] = "sell"
+                take_profit["amount"] = take_profit_amount
+                take_profit["price"] = take_profit_limit_price
+                take_profit_params: dict = {}
+                take_profit["params"] = take_profit_params
+
+                # custom_orders.append(take_profit)
+
+                #todo Add library of trade templates for binance, polo, gdax...
+                #todo Code to check have all the fields and sense check
+
+                # Cancel Legacy buy/sell signals if there are custom orders.
+                # Set buy / sell = 0 if we have custom orders
+                dataframe.loc['buy'] = 0
+                dataframe.loc['sell'] = 0
+
+                return dataframe, custom_orders
+
+        return dataframe, None

freqtrade/exchange/__init__.py

@@ -91,7 +91,7 @@ class Exchange(object):
                 'secret': exchange_config.get('secret'),
                 'password': exchange_config.get('password'),
                 'uid': exchange_config.get('uid', ''),
-                'enableRateLimit': True,
+                'enableRateLimit': exchange_config.get('ccxt_rate_limit', True),
             })
         except (KeyError, AttributeError):
             raise OperationalException(f'Exchange {name} is not supported')
@@ -185,6 +185,79 @@ class Exchange(object):
             price = ceil(big_price) / pow(10, symbol_prec)
         return price
 
+    def submit_custom_order(self, symbol, type, side, amount, price=None, params={}):
+        pair = symbol
+        rate = price
+        try:
+
+            return self._api.createOrder(symbol, type, side, amount, price, params)
+
+        except ccxt.InsufficientFunds as e:
+            raise DependencyException(
+                f'Insufficient funds to create limit buy order on market {pair}.'
+                f'Tried to buy amount {amount} at rate {rate} (total {rate*amount}).'
+                f'Message: {e}')
+
+        except ccxt.InvalidOrder as e:
+            raise DependencyException(
+                f'Could not create limit buy order on market {pair}.'
+                f'Tried to buy amount {amount} at rate {rate} (total {rate*amount}).'
+                f'Message: {e}')
+
+        except (ccxt.NetworkError, ccxt.ExchangeError) as e:
+            raise TemporaryError(
+                f'Could not place buy order due to {e.__class__.__name__}. Message: {e}')
+        except ccxt.BaseError as e:
+            raise OperationalException(e)
+
+    def customOrders(self, custom_orders):
+        '''
+        To process custom orders.
+        These are a list of dicts of orders created in strategy.
+
+        :param custom_orders:
+        :return:
+        '''
+        sorted_custom_orders = sorted(custom_orders, key=lambda k: k['order_type'])
+
+        for order in sorted_custom_orders:
+            order_type       = order.get('order_type')     if order.get('order_type')     else ""
+            symbol           = order.get('symbol')         if order.get('symbol')         else ""
+            type             = order.get('type')           if order.get('type')           else ""
+            side             = order.get('side')           if order.get('side')           else ""
+            amount           = order.get('amount')         if order.get('amount')         else None
+            price            = order.get('price')          if order.get('price')          else None
+            stop             = order.get('stop')           if order.get('stop')           else ""
+            stop_price       = order.get('stop_price')     if order.get('stop_price')     else None
+            params           = order.get('params')         if order.get('params')         else {}
+
+            # Set the precision for amount and price(rate) as accepted by the exchange
+            if amount:
+                float(amount)
+                amount = self.symbol_amount_prec(symbol, amount)
+                float(amount)
+
+            if price:
+                float(price)
+                price = self.symbol_price_prec(symbol, price)
+                float(price)
+
+            if params.get('stop_price'):
+                stop_price = params['stop_price']
+                float(stop_price)
+                params['stop_price'] = self.symbol_price_prec(symbol, stop_price)
+                float(stop_price)
+
+            # destroy the params dict if its empty, not to pass to CCXT
+            if not params:
+                params = ""
+
+            # create_order(self, symbol, type, side, amount, price=None, params={})
+            print(symbol, type, side, amount, price, params)
+            custom_order_id = self.submit_custom_order(symbol=symbol, type=type, side=side,
+                                                       amount=amount, price=price, params=params)
+            print("custom order_id is", custom_order_id)
+
     def buy(self, pair: str, rate: float, amount: float) -> Dict:
         if self._conf['dry_run']:
             order_id = f'dry_run_buy_{randint(0, 10**6)}'

freqtrade/freqtradebot.py

@@ -330,14 +330,15 @@ class FreqtradeBot(object):
 
         # Pick pair based on buy signals
         for _pair in whitelist:
-            thistory = self.exchange.get_ticker_history(_pair, interval)
-            (buy, sell) = self.strategy.get_signal(_pair, interval, thistory)
+            (buy, sell, custom_orders) = self.strategy.get_signal(self.exchange, _pair, interval)
 
             if buy and not sell:
-                return self.execute_buy(_pair, stake_amount)
+                return self.execute_buy(_pair, stake_amount, custom_orders)
+            if custom_orders:
+                return self.execute_buy(_pair, stake_amount, custom_orders)
         return False
 
-    def execute_buy(self, pair: str, stake_amount: float) -> bool:
+    def execute_buy(self, pair: str, stake_amount: float, custom_orders: list) -> bool:
         """
         Executes a limit buy for the given pair
         :param pair: pair for which we want to create a LIMIT_BUY
@@ -352,6 +353,13 @@ class FreqtradeBot(object):
         buy_limit = self.get_target_bid(self.exchange.get_ticker(pair))
 
         min_stake_amount = self._get_min_pair_stake_amount(pair_s, buy_limit)
+
+        # if custom trades are set over_ride stake_amount test
+
+        if custom_orders:
+            # todo condider how or if to do this for limit, market, stop cutom_orders.
+            logger.info("bypassing stake amount test for custom_orders")
+        else:
             if min_stake_amount is not None and min_stake_amount > stake_amount:
                 logger.warning(
                     f'Can\'t open a new trade for {pair_s}: stake amount'
@@ -359,6 +367,14 @@ class FreqtradeBot(object):
                 )
                 return False
 
+        # Call exchange to make order or customOrder
+        if custom_orders:
+            # Call custom_oders in exchange/__init__.py
+            logger.info("Calling customOrders in exchange")
+            order_ids = self.exchange.customOrders(custom_orders)
+
+        else:
+
             amount = stake_amount / buy_limit
 
             order_id = self.exchange.buy(pair, buy_limit, amount)['id']
@@ -393,6 +409,8 @@ class FreqtradeBot(object):
             Trade.session.flush()
             return True
 
+        return False
+
     def process_maybe_execute_buy(self) -> bool:
         """
         Tries to execute a buy trade in a safe way
@@ -497,9 +515,8 @@ class FreqtradeBot(object):
         (buy, sell) = (False, False)
         experimental = self.config.get('experimental', {})
         if experimental.get('use_sell_signal') or experimental.get('ignore_roi_if_buy_signal'):
-            ticker = self.exchange.get_ticker_history(trade.pair, self.strategy.ticker_interval)
-            (buy, sell) = self.strategy.get_signal(trade.pair, self.strategy.ticker_interval,
-                                                   ticker)
+            (buy, sell, custom_orders) = self.strategy.get_signal(self.exchange,
+                                                   trade.pair, self.strategy.ticker_interval)
 
         should_sell = self.strategy.should_sell(trade, current_rate, datetime.utcnow(), buy, sell)
         if should_sell.sell_flag:

freqtrade/strategy/interface.py

@@ -14,6 +14,7 @@ from pandas import DataFrame
 
 from freqtrade import constants
 from freqtrade.exchange.exchange_helpers import parse_ticker_dataframe
+from freqtrade.exchange import Exchange
 from freqtrade.persistence import Trade
 
 logger = logging.getLogger(__name__)
@@ -74,29 +75,29 @@ class IStrategy(ABC):
         self.config = config
 
     @abstractmethod
-    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_indicators(self, dataframe: DataFrame, pair: str) -> DataFrame:
         """
         Populate indicators that will be used in the Buy and Sell strategy
         :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
-        :param metadata: Additional information, like the currently traded pair
+        :param pair: Pair currently analyzed
         :return: a Dataframe with all mandatory indicators for the strategies
         """
 
     @abstractmethod
-    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_buy_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
         """
         Based on TA indicators, populates the buy signal for the given dataframe
         :param dataframe: DataFrame
-        :param metadata: Additional information, like the currently traded pair
+        :param pair: Pair currently analyzed
         :return: DataFrame with buy column
         """
 
     @abstractmethod
-    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def populate_sell_trend(self, dataframe: DataFrame, pair: str) -> DataFrame:
         """
         Based on TA indicators, populates the sell signal for the given dataframe
         :param dataframe: DataFrame
-        :param metadata: Additional information, like the currently traded pair
+        :param pair: Pair currently analyzed
         :return: DataFrame with sell column
         """
 
@@ -106,31 +107,51 @@ class IStrategy(ABC):
         """
         return self.__class__.__name__
 
-    def analyze_ticker(self, ticker_history: List[Dict], metadata: dict) -> DataFrame:
+    def analyze_ticker(self, ticker_history: List[Dict], pair: str) -> DataFrame:
         """
         Parses the given ticker history and returns a populated DataFrame
         add several TA indicators and buy signal to it
         :return DataFrame with ticker data and indicator data
+        :return custom_orders list of dicts containing any custom trades from strategy
         """
         dataframe = parse_ticker_dataframe(ticker_history)
-        dataframe = self.advise_indicators(dataframe, metadata)
-        dataframe = self.advise_buy(dataframe, metadata)
-        dataframe = self.advise_sell(dataframe, metadata)
-        return dataframe
+        dataframe = self.advise_indicators(dataframe, pair)
+        dataframe = self.advise_buy(dataframe, pair)
+        dataframe = self.advise_sell(dataframe, pair)
 
-    def get_signal(self, pair: str, interval: str, ticker_hist: List[Dict]) -> Tuple[bool, bool]:
+        ## Strategy Plugin stop_stops_mgt' if in strategy.
+        # Analyzes stop-count of past X from Y trades, cancels BUY if True
+        if hasattr(self, 'stop_stops_plugin'):
+            dataframe = self.stop_stops_plugin(dataframe, pair)
+
+        ## Strategy Plugin 'win_rate' if in strategy.
+        # Calls micro backslap on proposed buy=1 pair. If winrate is poor, cancels BUY
+        if hasattr(self, 'win_rate_plugin'):
+            dataframe = self.win_rate_plugin(dataframe, pair)
+
+        ## Strategy Plugin 'money_mgt' if in strategy.
+        # Money management for any order in buy. Determines stake size, stop-loss, take-profit
+        # Returns dataframe, custom_orders (list of dicts)
+        if hasattr(self, 'money_mgt_plugin'):
+            dataframe, custom_orders = self.money_mgt_plugin(dataframe, pair)
+
+        return dataframe, custom_orders
+
+    def get_signal(self, exchange: Exchange, pair: str, interval: str) -> Tuple[bool, bool]:
         """
         Calculates current signal based several technical analysis indicators
         :param pair: pair in format ANT/BTC
         :param interval: Interval to use (in min)
-        :return: (Buy, Sell) A bool-tuple indicating buy/sell signal
+        :return: (Buy, Sell, custom_orders) A bool-tuple indicating buy/sell signal,
+        and any custom_orders
         """
+        ticker_hist = exchange.get_ticker_history(pair, interval)
         if not ticker_hist:
             logger.warning('Empty ticker history for pair %s', pair)
             return False, False
 
         try:
-            dataframe = self.analyze_ticker(ticker_hist, {'pair': pair})
+            dataframe, custom_orders = self.analyze_ticker(ticker_hist, pair)
         except ValueError as error:
             logger.warning(
                 'Unable to analyze ticker for pair %s: %s',
@@ -155,13 +176,13 @@ class IStrategy(ABC):
         # Check if dataframe is out of date
         signal_date = arrow.get(latest['date'])
         interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
-        if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5))):
-            logger.warning(
-                'Outdated history for pair %s. Last tick is %s minutes old',
-                pair,
-                (arrow.utcnow() - signal_date).seconds // 60
-            )
-            return False, False
+        # if signal_date < (arrow.utcnow().shift(minutes=-(interval_minutes * 2 + 5))):
+        #     logger.warning(
+        #         'Outdated history for pair %s. Last tick is %s minutes old',
+        #         pair,
+        #         (arrow.utcnow() - signal_date).seconds // 60
+        #     )
+        #     return False, False
 
         (buy, sell) = latest[SignalType.BUY.value] == 1, latest[SignalType.SELL.value] == 1
         logger.debug(
@@ -171,7 +192,7 @@ class IStrategy(ABC):
             str(buy),
             str(sell)
         )
-        return buy, sell
+        return buy, sell, custom_orders
 
     def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
                     sell: bool) -> SellCheckTuple:
@@ -212,7 +233,6 @@ class IStrategy(ABC):
         """
         Based on current profit of the trade and configured (trailing) stoploss,
         decides to sell or not
-        :param current_profit: current profit in percent
         """
 
         trailing_stop = self.config.get('trailing_stop', False)
@@ -240,15 +260,12 @@ class IStrategy(ABC):
             # check if we have a special stop loss for positive condition
             # and if profit is positive
             stop_loss_value = self.stoploss
-            sl_offset = self.config.get('trailing_stop_positive_offset', 0.0)
-
-            if 'trailing_stop_positive' in self.config and current_profit > sl_offset:
+            if 'trailing_stop_positive' in self.config and current_profit > 0:
 
                 # Ignore mypy error check in configuration that this is a float
                 stop_loss_value = self.config.get('trailing_stop_positive')  # type: ignore
                 logger.debug(f"using positive stop loss mode: {stop_loss_value} "
-                             f"with offset {sl_offset:.4g} "
-                             f"since we have profit {current_profit:.4f}%")
+                             f"since we have profit {current_profit}")
 
             trade.adjust_stop_loss(current_rate, stop_loss_value)
 
@@ -275,15 +292,15 @@ class IStrategy(ABC):
         """
         Creates a dataframe and populates indicators for given ticker data
         """
-        return {pair: self.advise_indicators(parse_ticker_dataframe(pair_data), {'pair': pair})
+        return {pair: self.advise_indicators(parse_ticker_dataframe(pair_data), pair)
                 for pair, pair_data in tickerdata.items()}
 
-    def advise_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def advise_indicators(self, dataframe: DataFrame, pair: str) -> DataFrame:
         """
         Populate indicators that will be used in the Buy and Sell strategy
         This method should not be overridden.
         :param dataframe: Raw data from the exchange and parsed by parse_ticker_dataframe()
-        :param metadata: Additional information, like the currently traded pair
+        :param pair: The currently traded pair
         :return: a Dataframe with all mandatory indicators for the strategies
         """
         if self._populate_fun_len == 2:
@@ -291,14 +308,14 @@ class IStrategy(ABC):
                           "the current function headers!", DeprecationWarning)
             return self.populate_indicators(dataframe)  # type: ignore
         else:
-            return self.populate_indicators(dataframe, metadata)
+            return self.populate_indicators(dataframe, pair)
 
-    def advise_buy(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def advise_buy(self, dataframe: DataFrame, pair: str) -> DataFrame:
         """
         Based on TA indicators, populates the buy signal for the given dataframe
         This method should not be overridden.
         :param dataframe: DataFrame
-        :param pair: Additional information, like the currently traded pair
+        :param pair: The currently traded pair
         :return: DataFrame with buy column
         """
         if self._buy_fun_len == 2:
@@ -306,14 +323,14 @@ class IStrategy(ABC):
                           "the current function headers!", DeprecationWarning)
             return self.populate_buy_trend(dataframe)  # type: ignore
         else:
-            return self.populate_buy_trend(dataframe, metadata)
+            return self.populate_buy_trend(dataframe, pair)
 
-    def advise_sell(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
+    def advise_sell(self, dataframe: DataFrame, pair: str) -> DataFrame:
         """
         Based on TA indicators, populates the sell signal for the given dataframe
         This method should not be overridden.
         :param dataframe: DataFrame
-        :param pair: Additional information, like the currently traded pair
+        :param pair: The currently traded pair
         :return: DataFrame with sell column
         """
         if self._sell_fun_len == 2:
@@ -321,4 +338,4 @@ class IStrategy(ABC):
                           "the current function headers!", DeprecationWarning)
             return self.populate_sell_trend(dataframe)  # type: ignore
         else:
-            return self.populate_sell_trend(dataframe, metadata)
+            return self.populate_sell_trend(dataframe, pair)