Add position_adjustment_enable config keyword to enable it.

freqtrade/configuration/configuration.py

@@ -173,6 +173,9 @@ class Configuration:
         if 'sd_notify' in self.args and self.args['sd_notify']:
             config['internals'].update({'sd_notify': True})
 
+        if config.get('position_adjustment_enable', False):
+            logger.warning('`position_adjustment` has been enabled for strategy.')
+
     def _process_datadir_options(self, config: Dict[str, Any]) -> None:
         """
         Extract information for sys.argv and load directory configurations

freqtrade/freqtradebot.py

@@ -179,6 +179,7 @@ class FreqtradeBot(LoggingMixin):
             self.exit_positions(trades)
 
         # Check if we need to adjust our current positions before attempting to buy new trades.
+        if self.config.get('position_adjustment_enable', False):
             self.process_open_trade_positions()
 
         # Then looking for buy opportunities

freqtrade/optimize/backtesting.py

@@ -404,6 +404,8 @@ class Backtesting:
     def _get_sell_trade_entry_for_candle(self, trade: LocalTrade,
                                          sell_row: Tuple) -> Optional[LocalTrade]:
         
+        # Check if we need to adjust our current positions
+        if self.config.get('position_adjustment_enable', False):
             trade = self._get_adjust_trade_entry_for_candle(trade, sell_row)
 
         sell_candle_time = sell_row[DATE_IDX].to_pydatetime()

tests/optimize/test_backtesting_adjust_position.py

@@ -0,0 +1,95 @@
+# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
+
+import random
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+from unittest.mock import MagicMock, PropertyMock
+import logging
+
+import numpy as np
+import pandas as pd
+import pytest
+from arrow import Arrow
+
+from freqtrade.commands.optimize_commands import setup_optimize_configuration, start_backtesting
+from freqtrade.configuration import TimeRange
+from freqtrade.data import history
+from freqtrade.data.btanalysis import BT_DATA_COLUMNS, evaluate_result_multi
+from freqtrade.data.converter import clean_ohlcv_dataframe
+from freqtrade.data.dataprovider import DataProvider
+from freqtrade.data.history import get_timerange
+from freqtrade.enums import RunMode, SellType
+from freqtrade.exceptions import DependencyException, OperationalException
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.persistence import LocalTrade
+from freqtrade.resolvers import StrategyResolver
+from tests.conftest import (get_args, log_has, log_has_re, patch_exchange,
+                            patched_configuration_load_config_file)
+
+def test_backtest_position_adjustment(default_conf, fee, mocker, testdatadir) -> None:
+    default_conf['use_sell_signal'] = False
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
+    mocker.patch("freqtrade.exchange.Exchange.get_min_pair_stake_amount", return_value=0.00001)
+    patch_exchange(mocker)
+    default_conf.update({
+        "position_adjustment_enable": True,
+        "stake_amount": 100.0,
+        "dry_run_wallet": 1000.0,
+        "strategy": "StrategyTestPositionAdjust"
+    })
+    backtesting = Backtesting(default_conf)
+    backtesting._set_strategy(backtesting.strategylist[0])
+    pair = 'UNITTEST/BTC'
+    timerange = TimeRange('date', None, 1517227800, 0)
+    data = history.load_data(datadir=testdatadir, timeframe='5m', pairs=['UNITTEST/BTC'],
+                             timerange=timerange)
+    processed = backtesting.strategy.advise_all_indicators(data)
+    min_date, max_date = get_timerange(processed)
+    result = backtesting.backtest(
+        processed=processed,
+        start_date=min_date,
+        end_date=max_date,
+        max_open_trades=10,
+        position_stacking=False,
+    )
+    results = result['results']
+    assert not results.empty
+    assert len(results) == 2
+
+    expected = pd.DataFrame(
+        {'pair': [pair, pair],
+         'stake_amount': [500.0, 100.0],
+         'amount': [4806.87657523, 970.63960782],
+         'open_date': pd.to_datetime([Arrow(2018, 1, 29, 18, 40, 0).datetime,
+                                      Arrow(2018, 1, 30, 3, 30, 0).datetime], utc=True
+                                     ),
+         'close_date': pd.to_datetime([Arrow(2018, 1, 29, 22, 00, 0).datetime,
+                                       Arrow(2018, 1, 30, 4, 10, 0).datetime], utc=True),
+         'open_rate': [0.10401764894444211, 0.10302485],
+         'close_rate': [0.10453904066847439, 0.103541],
+         'fee_open': [0.0025, 0.0025],
+         'fee_close': [0.0025, 0.0025],
+         'trade_duration': [200, 40],
+         'profit_ratio': [0.0, 0.0],
+         'profit_abs': [0.0, 0.0],
+         'sell_reason': [SellType.ROI.value, SellType.ROI.value],
+         'initial_stop_loss_abs': [0.0940005, 0.09272236],
+         'initial_stop_loss_ratio': [-0.1, -0.1],
+         'stop_loss_abs': [0.0940005, 0.09272236],
+         'stop_loss_ratio': [-0.1, -0.1],
+         'min_rate': [0.10370188, 0.10300000000000001],
+         'max_rate': [0.10481985, 0.1038888],
+         'is_open': [False, False],
+         'buy_tag': [None, None],
+         })
+    pd.testing.assert_frame_equal(results, expected)
+    data_pair = processed[pair]
+    for _, t in results.iterrows():
+        ln = data_pair.loc[data_pair["date"] == t["open_date"]]
+        # Check open trade rate alignes to open rate
+        assert ln is not None
+        # check close trade rate alignes to close rate or is between high and low
+        ln = data_pair.loc[data_pair["date"] == t["close_date"]]
+        assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6) or
+                round(ln.iloc[0]["low"], 6) < round(
+                t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))

tests/strategy/strats/strategy_test_position_adjust.py

@@ -0,0 +1,165 @@
+# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
+
+import talib.abstract as ta
+from pandas import DataFrame
+
+import freqtrade.vendor.qtpylib.indicators as qtpylib
+from freqtrade.strategy.interface import IStrategy
+from freqtrade.persistence import Trade
+from datetime import datetime
+
+class StrategyTestPositionAdjust(IStrategy):
+    """
+    Strategy used by tests freqtrade bot.
+    Please do not modify this strategy, it's  intended for internal use only.
+    Please look at the SampleStrategy in the user_data/strategy directory
+    or strategy repository https://github.com/freqtrade/freqtrade-strategies
+    for samples and inspiration.
+    """
+    INTERFACE_VERSION = 2
+
+    # Minimal ROI designed for the strategy
+    minimal_roi = {
+        "40": 0.0,
+        "30": 0.01,
+        "20": 0.02,
+        "0": 0.04
+    }
+
+    # Optimal stoploss designed for the strategy
+    stoploss = -0.10
+
+    # Optimal timeframe for the strategy
+    timeframe = '5m'
+
+    # Optional order type mapping
+    order_types = {
+        'buy': 'limit',
+        'sell': 'limit',
+        'stoploss': 'limit',
+        'stoploss_on_exchange': False
+    }
+
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
+    # Optional time in force for orders
+    order_time_in_force = {
+        'buy': 'gtc',
+        'sell': 'gtc',
+    }
+
+    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 Indicator
+        # ------------------------------------
+
+        # ADX
+        dataframe['adx'] = ta.ADX(dataframe)
+
+        # MACD
+        macd = ta.MACD(dataframe)
+        dataframe['macd'] = macd['macd']
+        dataframe['macdsignal'] = macd['macdsignal']
+        dataframe['macdhist'] = macd['macdhist']
+
+        # Minus Directional Indicator / Movement
+        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
+
+        # Plus Directional Indicator / Movement
+        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
+
+        # RSI
+        dataframe['rsi'] = ta.RSI(dataframe)
+
+        # Stoch fast
+        stoch_fast = ta.STOCHF(dataframe)
+        dataframe['fastd'] = stoch_fast['fastd']
+        dataframe['fastk'] = stoch_fast['fastk']
+
+        # Bollinger bands
+        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
+        dataframe['bb_lowerband'] = bollinger['lower']
+        dataframe['bb_middleband'] = bollinger['mid']
+        dataframe['bb_upperband'] = bollinger['upper']
+
+        # EMA - Exponential Moving Average
+        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
+
+        return dataframe
+
+    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> 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
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (dataframe['rsi'] < 35) &
+                (dataframe['fastd'] < 35) &
+                (dataframe['adx'] > 30) &
+                (dataframe['plus_di'] > 0.5)
+            ) |
+            (
+                (dataframe['adx'] > 65) &
+                (dataframe['plus_di'] > 0.5)
+            ),
+            '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
+        :param metadata: Additional information, like the currently traded pair
+        :return: DataFrame with buy column
+        """
+        dataframe.loc[
+            (
+                (
+                    (qtpylib.crossed_above(dataframe['rsi'], 70)) |
+                    (qtpylib.crossed_above(dataframe['fastd'], 70))
+                ) &
+                (dataframe['adx'] > 10) &
+                (dataframe['minus_di'] > 0)
+            ) |
+            (
+                (dataframe['adx'] > 70) &
+                (dataframe['minus_di'] > 0.5)
+            ),
+            'sell'] = 1
+        return dataframe
+
+    def adjust_trade_position(self, pair: str, trade: Trade, current_time: datetime,
+                              current_rate: float, current_profit: float, **kwargs):
+
+        if current_profit < -0.0075:
+            return self.wallets.get_trade_stake_amount(pair, None)
+
+        return None