Add "allow_position_stacking" value to config, which allows rebuys of a pair

Add function unlock_reason(str: pair) which removes all PairLocks with reason
Provide demo strategy that allows buying the same pair multiple times

StackingConfig.json

@@ -0,0 +1,89 @@
+
+{
+    "max_open_trades": 12,
+    "stake_currency": "USDT",
+    "stake_amount": 100,
+    "tradable_balance_ratio": 0.99,
+    "fiat_display_currency": "USD",
+    "timeframe": "5m",
+    "dry_run": true,
+    "cancel_open_orders_on_exit": false,
+    "allow_position_stacking": true,
+    "unfilledtimeout": {
+        "buy": 10,
+        "sell": 30,
+        "unit": "minutes"
+    },
+    "bid_strategy": {
+        "price_side": "ask",
+        "ask_last_balance": 0.0,
+        "use_order_book": true,
+        "order_book_top": 1,
+        "check_depth_of_market": {
+            "enabled": false,
+            "bids_to_ask_delta": 1
+        }
+    },
+    "ask_strategy": {
+        "price_side": "bid",
+        "use_order_book": true,
+        "order_book_top": 1
+    },
+    "exchange": {
+        "name": "binance",
+        "key": "",
+        "secret": "",
+        "ccxt_config": {},
+        "ccxt_async_config": {},
+        "pair_whitelist": [
+        ],
+        "pair_blacklist": [
+            "BNB/.*"
+        ]
+    },
+    "pairlists": [
+        {
+            "method": "VolumePairList",
+            "number_assets": 80,
+            "sort_key": "quoteVolume",
+            "min_value": 0,
+            "refresh_period": 1800
+        }
+    ],
+    "edge": {
+        "enabled": false,
+        "process_throttle_secs": 3600,
+        "calculate_since_number_of_days": 7,
+        "allowed_risk": 0.01,
+        "stoploss_range_min": -0.01,
+        "stoploss_range_max": -0.1,
+        "stoploss_range_step": -0.01,
+        "minimum_winrate": 0.60,
+        "minimum_expectancy": 0.20,
+        "min_trade_number": 10,
+        "max_trade_duration_minute": 1440,
+        "remove_pumps": false
+    },
+    "telegram": {
+        "enabled": false,
+        "token": "",
+        "chat_id": ""
+    },
+    "api_server": {
+        "enabled": true,
+        "listen_ip_address": "127.0.0.1",
+        "listen_port": 8080,
+        "verbosity": "error",
+        "enable_openapi": false,
+        "jwt_secret_key": "908cd4469c824f3838bfe56e4120d3a3dbda5294ef583ffc62c82f54d2c1bf58",
+        "CORS_origins": [],
+        "username": "user",
+        "password": "pass"
+    },
+    "bot_name": "freqtrade",
+    "initial_state": "running",
+    "forcebuy_enable": false,
+    "internals": {
+        "process_throttle_secs": 5
+    }
+}

StackingDemo.py

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+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+# flake8: noqa: F401
+
+# --- 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
+
+from freqtrade.persistence import Trade
+from datetime import datetime,timezone,timedelta
+
+"""
+ Warning:
+This is still work in progress, so there is no warranty that everything works as intended, 
+it is possible that this strategy results in huge losses or doesn't even work at all.
+Make sure to only run this in dry_mode so you don't lose any money.
+
+"""
+
+class StackingDemo(IStrategy):
+    """
+    This is the default strategy template with added functions for trade stacking / buying the same positions multiple times.
+    It should function like this:
+        Find good buys using indicators. 
+        When a new buy occurs the strategy will enable rebuys of the pair like this:
+            self.custom_info[metadata["pair"]]["rebuy"] = 1
+        Then, if the price should drop after the last buy within the timerange of rebuy_time_limit_hours,
+        the same pair will be purchased again. This is intended to help with reducing possible losses.
+        If the price only goes up after the first buy, the strategy won't buy this pair again, and after the time limit is over,
+        look for other pairs to buy.
+        For selling there is this flag:
+            self.custom_info[metadata["pair"]]["resell"] = 1
+        which should simply sell all trades of this pair until none are left.
+
+    You can set how many pairs you want to trade and how many trades you want to allow for a pair, 
+    but you must make sure to set max_open_trades to the produce of max_open_pairs and max_open_trades in your configuration file.
+    Also allow_position_stacking has to be set to true in the configuration file.
+
+    For backtesting make sure to provide --enable-position-stacking as an argument in the command line.
+    Backtesting will be slow.
+    Hyperopt was not tested.
+    
+    # run the bot:
+    freqtrade trade -c StackingConfig.json -s StackingDemo --db-url sqlite:///tradesv3_StackingDemo_dry-run.sqlite --dry-run
+    """
+    # 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
+
+    # how many pairs to trade / trades per pair if allow_position_stacking is enabled
+    max_open_pairs, max_trades_per_pair = 4, 3
+    # make sure to have this value in your config file
+    max_open_trades = max_open_pairs * max_trades_per_pair
+
+    # debugging
+    print_trades = True
+
+    # specify for how long to want to allow rebuys of this pair
+    rebuy_time_limit_hours = 2
+
+    # store additional information needed for this strategy:
+    custom_info = {}
+    custom_num_open_pairs = {}
+
+    # 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": 0.001
+    }
+
+    # 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
+
+    # 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': 'market',
+        'sell': 'market',
+        'stoploss': 'market',
+        'stoploss_on_exchange': False
+    }
+
+    # Optional order time in force.
+    order_time_in_force = {
+        'buy': 'gtc',
+        'sell': 'gtc'
+    }
+    
+    plot_config = {
+        # Main plot indicators (Moving averages, ...)
+        'main_plot': {
+            'tema': {},
+            'sar': {'color': 'white'},
+        },
+        'subplots': {
+            # Subplots - each dict defines one additional plot
+            "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
+        """
+
+        # STACKING STUFF
+
+        # confirm config
+        self.max_trades_per_pair = self.config['max_open_trades'] / self.max_open_pairs
+        if not self.config["allow_position_stacking"]:
+            self.max_trades_per_pair = 1
+
+        # store number of open pairs
+        self.custom_num_open_pairs = {"num_open_pairs": 0}
+
+        # Store custom information for this pair:
+        if not metadata["pair"] in self.custom_info:
+            self.custom_info[metadata["pair"]] = {}
+
+        if not "rebuy" in self.custom_info[metadata["pair"]]:
+            # number of trades for this pair
+            self.custom_info[metadata["pair"]]["num_trades"] = 0
+            # use rebuy/resell as buy-/sell- indicators
+            self.custom_info[metadata["pair"]]["rebuy"] = 0
+            self.custom_info[metadata["pair"]]["resell"] = 0
+            # store latest open_date for this pair
+            self.custom_info[metadata["pair"]]["last_open_date"] = datetime.now(timezone.utc) - timedelta(days=100)
+            # stare the value of the latest open price for this pair
+            self.custom_info[metadata["pair"]]["latest_open_rate"] = 0
+
+        # INDICATORS
+
+        # 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[
+            (
+                (
+#                    (qtpylib.crossed_above(dataframe['rsi'], 30)) &  # Signal: RSI crosses above 30
+#                    (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
+#                    (dataframe['tema'] > dataframe['tema'].shift(1)) |  # Guard: tema is raising
+                    (dataframe['close'] < dataframe['close'].shift(1)) |
+                    # use either buy signal or rebuy flag to trigger a buy
+                    (self.custom_info[metadata["pair"]]["rebuy"] == 1)
+                ) &
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'buy'] = 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 buy column
+        """
+        dataframe.loc[
+            (
+                (
+#                    (qtpylib.crossed_above(dataframe['rsi'], 70)) &  # Signal: RSI crosses above 70
+#                    (dataframe['tema'] > dataframe['bb_middleband']) &  # Guard: tema above BB middle
+#                    (dataframe['tema'] < dataframe['tema'].shift(1)) |  # Guard: tema is falling
+                    # use either sell signal or resell flag to trigger a sell
+                    (dataframe['close'] > dataframe['close'].shift(1)) |
+                    (self.custom_info[metadata["pair"]]["resell"] == 1)
+                ) &
+                (dataframe['volume'] > 0)  # Make sure Volume is not 0
+            ),
+            'sell'] = 1
+        return dataframe
+
+    # use_custom_sell = True
+
+    def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
+                    current_profit: float, **kwargs) -> 'Optional[Union[str, bool]]':
+        """
+        Custom sell signal logic indicating that specified position should be sold. Returning a
+        string or True from this method is equal to setting sell signal on a candle at specified
+        time. This method is not called when sell signal is set.
+
+        This method should be overridden to create sell signals that depend on trade parameters. For
+        example you could implement a sell relative to the candle when the trade was opened,
+        or a custom 1:2 risk-reward ROI.
+
+        Custom sell reason max length is 64. Exceeding characters will be removed.
+
+        :param pair: Pair that's currently analyzed
+        :param trade: trade object.
+        :param current_time: datetime object, containing the current datetime
+        :param current_rate: Rate, calculated based on pricing settings in ask_strategy.
+        :param current_profit: Current profit (as ratio), calculated based on current_rate.
+        :param **kwargs: Ensure to keep this here so updates to this won't break your strategy.
+        :return: To execute sell, return a string with custom sell reason or True. Otherwise return
+        None or False.
+        """
+        # if self.custom_info[pair]["resell"] == 1:
+        #    return 'resell'
+        return None
+
+    def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float,
+                            time_in_force: str, current_time: 'datetime', **kwargs) -> bool:
+        return_statement = True
+
+        if self.config['allow_position_stacking']:
+            return_statement = self.check_open_trades(pair, rate, current_time)
+
+        # debugging
+        if return_statement and self.print_trades:
+            # use str.join() for speed
+            out = (current_time.strftime("%c"), " Bought: ", pair, ", rate: ", str(rate), ", rebuy: ", str(self.custom_info[pair]["rebuy"]), ", trades: ", str(self.custom_info[pair]["num_trades"]))
+            print("".join(out))
+
+        return return_statement
+
+    def confirm_trade_exit(self, pair: str, trade: 'Trade', order_type: str, amount: float,
+                           rate: float, time_in_force: str, sell_reason: str,
+                           current_time: 'datetime', **kwargs) -> bool:
+
+        if self.config["allow_position_stacking"]:
+
+            # unlock open pairs limit after every sell
+            self.unlock_reason('Open pairs limit')
+
+            # unlock open pairs limit after last item is sold
+            if self.custom_info[pair]["num_trades"] == 1:
+                # decrement open_pairs_count by 1 if last item is sold
+                self.custom_num_open_pairs["num_open_pairs"]-=1
+                self.custom_info[pair]["resell"] = 0
+                # reset rate
+                self.custom_info[pair]["latest_open_rate"] = 0.0
+                self.unlock_reason('Trades per pair limit')
+
+            # change dataframe to produce sell signal after a sell
+            if self.custom_info[pair]["num_trades"] >= self.max_trades_per_pair:
+                self.custom_info[pair]["resell"] = 1
+
+            # decrement number of trades by 1:
+            self.custom_info[pair]["num_trades"]-=1
+
+        # debugging stuff
+        if self.print_trades:
+            # use str.join() for speed
+            out = (current_time.strftime("%c"), " Sold: ", pair, ", rate: ", str(rate),", profit: ", str(trade.calc_profit_ratio(rate)), ", resell: ", str(self.custom_info[pair]["resell"]), ", trades: ", str(self.custom_info[pair]["num_trades"]))
+            print("".join(out))
+
+        return True
+
+    def check_open_trades(self, pair: str, rate: float, current_time: datetime):
+
+        # retrieve information about current open pairs
+        tr_info = self.get_trade_information(pair)
+
+        # update number of open trades for the pair
+        self.custom_info[pair]["num_trades"] = tr_info[1]
+        self.custom_num_open_pairs["num_open_pairs"] = len(tr_info[0])
+        # update value of the last open price
+        self.custom_info[pair]["latest_open_rate"] = tr_info[2]
+
+        # don't buy if we have enough trades for this pair
+        if self.custom_info[pair]["num_trades"] >= self.max_trades_per_pair:
+            # lock if we already have enough pairs open, will be unlocked after last item of a pair is sold
+            self.lock_pair(pair, until=datetime.now(timezone.utc) + timedelta(days=100), reason='Trades per pair limit')
+            self.custom_info[pair]["rebuy"] = 0
+            return False
+
+        # don't buy if we have enough pairs
+        if self.custom_num_open_pairs["num_open_pairs"] >= self.max_open_pairs:
+            if not pair in tr_info[0]:
+                # lock if this pair is not in our list, will be unlocked after the next sell
+                self.lock_pair(pair, until=datetime.now(timezone.utc) + timedelta(days=100), reason='Open pairs limit')
+                self.custom_info[pair]["rebuy"] = 0
+                return False
+
+        # don't buy at a higher price, try until time limit is exceeded; skips if it's the first trade'
+        if rate > self.custom_info[pair]["latest_open_rate"] and self.custom_info[pair]["latest_open_rate"] != 0.0:
+            # how long do we want to try buying cheaper before we look for other pairs?
+            if (current_time - self.custom_info[pair]['last_open_date']).seconds/3600 > self.rebuy_time_limit_hours:
+                self.custom_info[pair]["rebuy"] = 0
+                self.unlock_reason('Open pairs limit')
+            return False
+
+        # set rebuy flag if num_trades < limit-1
+        if self.custom_info[pair]["num_trades"] < self.max_trades_per_pair-1:
+            self.custom_info[pair]["rebuy"] = 1
+        else:
+            self.custom_info[pair]["rebuy"] = 0
+
+        # update rate
+        self.custom_info[pair]["latest_open_rate"] = rate
+
+        #update date open
+        self.custom_info[pair]["last_open_date"] = current_time
+
+        # increment trade count by 1
+        self.custom_info[pair]["num_trades"]+=1
+
+        return True
+
+    # custom function to help with the strategy
+    def get_trade_information(self, pair:str):
+
+        latest_open_rate, trade_count = 0, 0.0
+        # store all open pairs
+        open_pairs = []
+
+        ### start nested function
+        def compare_trade(trade: Trade):
+            nonlocal trade_count, latest_open_rate, pair
+            if trade.pair == pair:
+                # update latest_rate
+                latest_open_rate = trade.open_rate
+                trade_count+=1
+            return trade.pair
+        ### end nested function
+
+        # replaced for loop with map for speed
+        open_pairs = map(compare_trade, Trade.get_open_trades())
+        # remove duplicates
+        open_pairs = (list(dict.fromkeys(open_pairs)))
+
+        #print(*open_pairs, sep="\n")
+
+        # put this all together to reduce the amount of loops
+        return open_pairs, trade_count, latest_open_rate

freqtrade/configuration/configuration.py

@@ -137,6 +137,12 @@ class Configuration:
         setup_logging(config)
 
     def _process_trading_options(self, config: Dict[str, Any]) -> None:
+
+        # Allow_position_stacking defaults to False
+        if not config.get('allow_position_stacking'):
+            config['allow_position_stacking'] = False
+        logger.info('Allow_position_stacking is set to ' + str(config['allow_position_stacking']))
+
         if config['runmode'] not in TRADING_MODES:
             return
 

freqtrade/freqtradebot.py

@@ -4,7 +4,7 @@ Freqtrade is the main module of this bot. It contains the class Freqtrade()
 import copy
 import logging
 import traceback
-from datetime import datetime, timezone
+from datetime import datetime, timedelta, timezone
 from math import isclose
 from threading import Lock
 from typing import Any, Dict, List, Optional
@@ -359,10 +359,12 @@ class FreqtradeBot(LoggingMixin):
             logger.info("Active pair whitelist is empty.")
             return trades_created
         # Remove pairs for currently opened trades from the whitelist
-        for trade in Trade.get_open_trades():
+        # Allow rebuying of the same pair if allow_position_stacking is set to True
-            if trade.pair in whitelist:
+        if not self.config['allow_position_stacking']:
-                whitelist.remove(trade.pair)
+            for trade in Trade.get_open_trades():
-                logger.debug('Ignoring %s in pair whitelist', trade.pair)
+                if trade.pair in whitelist:
+                    whitelist.remove(trade.pair)
+                    logger.debug('Ignoring %s in pair whitelist', trade.pair)
 
         if not whitelist:
             logger.info("No currency pair in active pair whitelist, "
@@ -592,6 +594,11 @@ class FreqtradeBot(LoggingMixin):
 
         self._notify_enter(trade, order_type)
 
+        # Lock pair for 1 timeframe duration to prevent immediate rebuys
+        if self.config['allow_position_stacking']:
+            self.strategy.lock_pair(trade.pair, datetime.now(timezone.utc) + timedelta(minutes=timeframe_to_minutes(self.config['timeframe'])),
+                                        reason='Prevent immediate rebuys')
+
         return True
 
     def _notify_enter(self, trade: Trade, order_type: str) -> None:

freqtrade/persistence/pairlock_middleware.py

@@ -103,6 +103,24 @@ class PairLocks():
         if PairLocks.use_db:
             PairLock.query.session.commit()
 
+    @staticmethod
+    def unlock_reason(reason: str, now: Optional[datetime] = None) -> None:
+        """
+        Release all locks for this reason.
+        :param reason: Which reason to unlock
+        :param now: Datetime object (generated via datetime.now(timezone.utc)).
+            defaults to datetime.now(timezone.utc)
+        """
+        if not now:
+            now = datetime.now(timezone.utc)
+        logger.info(f"Releasing all locks with reason \'{reason}\'.")
+        locks = PairLocks.get_all_locks()
+        for lock in locks:
+            if lock.reason == reason:
+                lock.active = False
+        if PairLocks.use_db:
+            PairLock.query.session.commit()
+
     @staticmethod
     def is_global_lock(now: Optional[datetime] = None) -> bool:
         """

freqtrade/strategy/interface.py

@@ -443,6 +443,15 @@ class IStrategy(ABC, HyperStrategyMixin):
         """
         PairLocks.unlock_pair(pair, datetime.now(timezone.utc))
 
+    def unlock_reason(self, reason: str) -> None:
+        """
+        Unlocks all pairs previously locked using lock_pair with specified reason.
+        Not used by freqtrade itself, but intended to be used if users lock pairs
+        manually from within the strategy, to allow an easy way to unlock pairs.
+        :param reason: Unlock pairs to allow trading again
+        """
+        PairLocks.unlock_reason(reason, datetime.now(timezone.utc))
+
     def is_pair_locked(self, pair: str, candle_date: datetime = None) -> bool:
         """
         Checks if a pair is currently locked