conflict resolved => new backtest low and high params

freqtrade/optimize/backtesting.py

@@ -206,21 +206,37 @@ class Backtesting(object):
 
             buy_signal = sell_row.buy
             sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
-                                             sell_row.sell)
+                                             sell_row.sell, low=sell_row.low, high=sell_row.high)
             if sell.sell_flag:
 
+                trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60)
+                # Special handling if high or low hit STOP_LOSS or ROI
+                if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
+                    # Set close_rate to stoploss
+                    closerate = trade.stop_loss
+                elif sell.sell_type == (SellType.ROI):
+                    # get entry in min_roi >= to trade duration
+                    roi_entry = max(list(filter(lambda x: trade_dur >= x,
+                                                self.strategy.minimal_roi.keys())))
+                    roi = self.strategy.minimal_roi[roi_entry]
+
+                    # - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
+                    closerate = - (trade.open_rate * roi + trade.open_rate *
+                                   (1 + trade.fee_open)) / (trade.fee_close - 1)
+                else:
+                    closerate = sell_row.open
+
                 return BacktestResult(pair=pair,
-                                      profit_percent=trade.calc_profit_percent(rate=sell_row.open),
+                                      profit_percent=trade.calc_profit_percent(rate=closerate),
-                                      profit_abs=trade.calc_profit(rate=sell_row.open),
+                                      profit_abs=trade.calc_profit(rate=closerate),
                                       open_time=buy_row.date,
                                       close_time=sell_row.date,
-                                      trade_duration=int((
+                                      trade_duration=trade_dur,
-                                          sell_row.date - buy_row.date).total_seconds() // 60),
                                       open_index=buy_row.Index,
                                       close_index=sell_row.Index,
                                       open_at_end=False,
                                       open_rate=buy_row.open,
-                                      close_rate=sell_row.open,
+                                      close_rate=closerate,
                                       sell_reason=sell.sell_type
                                       )
         if partial_ticker:
@@ -260,7 +276,7 @@ class Backtesting(object):
             position_stacking: do we allow position stacking? (default: False)
         :return: DataFrame
         """
-        headers = ['date', 'buy', 'open', 'close', 'sell']
+        headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
         processed = args['processed']
         max_open_trades = args.get('max_open_trades', 0)
         position_stacking = args.get('position_stacking', False)

freqtrade/persistence.py

@@ -272,10 +272,10 @@ class Trade(_DECL_BASE):
             self,
             fee: Optional[float] = None) -> float:
         """
-        Calculate the open_rate in BTC
+        Calculate the open_rate including fee.
         :param fee: fee to use on the open rate (optional).
         If rate is not set self.fee will be used
-        :return: Price in BTC of the open trade
+        :return: Price in of the open trade incl. Fees
         """
 
         buy_trade = (Decimal(self.amount) * Decimal(self.open_rate))
@@ -287,7 +287,7 @@ class Trade(_DECL_BASE):
             rate: Optional[float] = None,
             fee: Optional[float] = None) -> float:
         """
-        Calculate the close_rate in BTC
+        Calculate the close_rate including fee
         :param fee: fee to use on the close rate (optional).
         If rate is not set self.fee will be used
         :param rate: rate to compare with (optional).
@@ -307,12 +307,12 @@ class Trade(_DECL_BASE):
             rate: Optional[float] = None,
             fee: Optional[float] = None) -> float:
         """
-        Calculate the profit in BTC between Close and Open trade
+        Calculate the absolute profit in stake currency between Close and Open trade
         :param fee: fee to use on the close rate (optional).
         If rate is not set self.fee will be used
         :param rate: close rate to compare with (optional).
         If rate is not set self.close_rate will be used
-        :return:  profit in BTC as float
+        :return:  profit in stake currency as float
         """
         open_trade_price = self.calc_open_trade_price()
         close_trade_price = self.calc_close_trade_price(

freqtrade/strategy/interface.py

@@ -203,23 +203,24 @@ class IStrategy(ABC):
         return buy, sell
 
     def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
-                    sell: bool, force_stoploss: float=0) -> SellCheckTuple:
+                    sell: bool, low: float = None, high: float = None, force_stoploss: float = 0) -> SellCheckTuple:
         """
         This function evaluate if on the condition required to trigger a sell has been reached
         if the threshold is reached and updates the trade record.
         :return: True if trade should be sold, False otherwise
         """
-        current_profit = trade.calc_profit_percent(rate)
-        stoplossflag = self.stop_loss_reached(
-            current_rate=rate,
-            trade=trade,
-            current_time=date,
-            current_profit=current_profit,
-            force_stoploss=force_stoploss)
 
+        # Set current rate to low for backtesting sell
+        current_rate = low or rate
+        current_profit = trade.calc_profit_percent(current_rate)
+        stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
+                                              current_time=date, current_profit=current_profit,
+                                              force_stoploss=force_stoploss)
         if stoplossflag.sell_flag:
             return stoplossflag
-
+        # Set current rate to low for backtesting sell
+        current_rate = high or rate
+        current_profit = trade.calc_profit_percent(current_rate)
         experimental = self.config.get('experimental', {})
 
         if buy and experimental.get('ignore_roi_if_buy_signal', False):

freqtrade/tests/optimize/__init__.py

@@ -0,0 +1,45 @@
+from typing import NamedTuple, List
+
+import arrow
+from pandas import DataFrame
+
+from freqtrade.strategy.interface import SellType
+
+ticker_start_time = arrow.get(2018, 10, 3)
+ticker_interval_in_minute = 60
+
+
+class BTrade(NamedTuple):
+    """
+    Minimalistic Trade result used for functional backtesting
+    """
+    sell_reason: SellType
+    open_tick: int
+    close_tick: int
+
+
+class BTContainer(NamedTuple):
+    """
+    Minimal BacktestContainer defining Backtest inputs and results.
+    """
+    data: List[float]
+    stop_loss: float
+    roi: float
+    trades: List[BTrade]
+    profit_perc: float
+
+
+def _get_frame_time_from_offset(offset):
+    return ticker_start_time.shift(
+        minutes=(offset * ticker_interval_in_minute)).datetime
+
+
+def _build_backtest_dataframe(ticker_with_signals):
+    columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell']
+
+    frame = DataFrame.from_records(ticker_with_signals, columns=columns)
+    frame['date'] = frame['date'].apply(_get_frame_time_from_offset)
+    # Ensure floats are in place
+    for column in ['open', 'high', 'low', 'close', 'volume']:
+        frame[column] = frame[column].astype('float64')
+    return frame

freqtrade/tests/optimize/test_backtest_detail.py

@@ -0,0 +1,188 @@
+# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
+import logging
+from unittest.mock import MagicMock
+
+from pandas import DataFrame
+import pytest
+
+
+from freqtrade.optimize.backtesting import Backtesting
+from freqtrade.strategy.interface import SellType
+from freqtrade.tests.optimize import (BTrade, BTContainer, _build_backtest_dataframe,
+                                      _get_frame_time_from_offset)
+from freqtrade.tests.conftest import patch_exchange
+
+
+# Test 0 Minus 8% Close
+# Test with Stop-loss at 1%
+# TC1: Stop-Loss Triggered 1% loss
+tc0 = BTContainer(data=[
+    # D  O     H     L     C     V    B  S
+    [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+    [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+    [2, 4987, 5012, 4600, 4600, 6172, 0, 0],  # exit with stoploss hit
+    [3, 4975, 5000, 4980, 4977, 6172, 0, 0],
+    [4, 4977, 4987, 4977, 4995, 6172, 0, 0],
+    [5, 4995, 4995, 4995, 4950, 6172, 0, 0]],
+    stop_loss=-0.01, roi=1, profit_perc=-0.01,
+    trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
+)
+
+
+# Test 1 Minus 4% Low, minus 1% close
+# Test with Stop-Loss at 3%
+# TC2: Stop-Loss Triggered 3% Loss
+tc1 = BTContainer(data=[
+    # D  O     H     L     C     V    B  S
+    [0, 5000, 5025, 4975, 4987, 6172,    1, 0],
+    [1, 5000, 5025, 4975, 4987, 6172,    0, 0],  # enter trade (signal on last candle)
+    [2, 4987, 5012, 4962, 4975, 6172,    0, 0],
+    [3, 4975, 5000, 4800, 4962, 6172,    0, 0],  # exit with stoploss hit
+    [4, 4962, 4987, 4937, 4950, 6172,    0, 0],
+    [5, 4950, 4975, 4925, 4950, 6172,    0, 0]],
+    stop_loss=-0.03, roi=1, profit_perc=-0.03,
+    trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=3)]
+    )
+
+
+# Test 3 Candle drops 4%, Recovers 1%.
+#               Entry Criteria Met
+# 	            Candle drops 20%
+# Candle Data for test 3
+# Test with Stop-Loss at 2%
+# TC3: Trade-A: Stop-Loss Triggered 2% Loss
+#          Trade-B: Stop-Loss Triggered 2% Loss
+tc2 = BTContainer(data=[
+    # D  O     H     L     C     V    B  S
+    [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+    [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+    [2, 4987, 5012, 4800, 4975, 6172, 0, 0],  # exit with stoploss hit
+    [3, 4975, 5000, 4950, 4962, 6172, 1, 0],
+    [4, 4975, 5000, 4950, 4962, 6172, 0, 0],  # enter trade 2 (signal on last candle)
+    [5, 4962, 4987, 4000, 4000, 6172, 0, 0],  # exit with stoploss hit
+    [6, 4950, 4975, 4975, 4950, 6172, 0, 0]],
+    stop_loss=-0.02, roi=1, profit_perc=-0.04,
+    trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2),
+            BTrade(sell_reason=SellType.STOP_LOSS, open_tick=4, close_tick=5)]
+)
+
+# Test 4 Minus 3% / recovery +15%
+# Candle Data for test 3 – Candle drops 3% Closed 15% up
+# Test with Stop-loss at 2% ROI 6%
+# TC4: Stop-Loss Triggered 2% Loss
+tc3 = BTContainer(data=[
+    # D  O     H     L     C     V    B  S
+    [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+    [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+    [2, 4987, 5750, 4850, 5750, 6172, 0, 0],  # Exit with stoploss hit
+    [3, 4975, 5000, 4950, 4962, 6172, 0, 0],
+    [4, 4962, 4987, 4937, 4950, 6172, 0, 0],
+    [5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
+    stop_loss=-0.02, roi=0.06, profit_perc=-0.02,
+    trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
+)
+
+# Test 4 / Drops 0.5% Closes +20%
+# Set stop-loss at 1% ROI 3%
+# TC5: ROI triggers 3% Gain
+tc4 = BTContainer(data=[
+    # D  O     H     L     C     V    B  S
+    [0, 5000, 5025, 4980, 4987, 6172, 1, 0],
+    [1, 5000, 5025, 4980, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+    [2, 4987, 5025, 4975, 4987, 6172, 0, 0],
+    [3, 4975, 6000, 4975, 6000, 6172, 0, 0],  # ROI
+    [4, 4962, 4987, 4972, 4950, 6172, 0, 0],
+    [5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
+    stop_loss=-0.01, roi=0.03, profit_perc=0.03,
+    trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
+)
+
+# Test 6 / Drops 3% / Recovers 6% Positive / Closes 1% positve
+# Candle Data for test 6
+# Set stop-loss at 2% ROI at 5%
+# TC6: Stop-Loss triggers 2% Loss
+tc5 = BTContainer(data=[
+    # D  O     H     L     C     V    B  S
+    [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+    [1, 5000, 5025, 4975, 4987, 6172, 0, 0],  # enter trade (signal on last candle)
+    [2, 4987, 5300, 4850, 5050, 6172, 0, 0],  # Exit with stoploss
+    [3, 4975, 5000, 4950, 4962, 6172, 0, 0],
+    [4, 4962, 4987, 4972, 4950, 6172, 0, 0],
+    [5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
+    stop_loss=-0.02, roi=0.05, profit_perc=-0.02,
+    trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
+)
+
+# Test 7 - 6% Positive / 1% Negative / Close 1% Positve
+# Candle Data for test 7
+# Set stop-loss at 2% ROI at 3%
+# TC7: ROI Triggers 3% Gain
+tc6 = BTContainer(data=[
+    # D  O     H     L     C     V    B  S
+    [0, 5000, 5025, 4975, 4987, 6172, 1, 0],
+    [1, 5000, 5025, 4975, 4987, 6172, 0, 0],
+    [2, 4987, 5300, 4950, 5050, 6172, 0, 0],
+    [3, 4975, 5000, 4950, 4962, 6172, 0, 0],
+    [4, 4962, 4987, 4972, 4950, 6172, 0, 0],
+    [5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
+    stop_loss=-0.02, roi=0.03, profit_perc=0.03,
+    trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=2)]
+    )
+
+TESTS = [
+    tc0,
+    tc1,
+    tc2,
+    tc3,
+    tc4,
+    tc5,
+    tc6,
+]
+
+
+@pytest.mark.parametrize("data", TESTS)
+def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
+    """
+    run functional tests
+    """
+    default_conf["stoploss"] = data.stop_loss
+    default_conf["minimal_roi"] = {"0": data.roi}
+    mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.0))
+    patch_exchange(mocker)
+    frame = _build_backtest_dataframe(data.data)
+    backtesting = Backtesting(default_conf)
+    backtesting.advise_buy = lambda a, m: frame
+    backtesting.advise_sell = lambda a, m: frame
+    caplog.set_level(logging.DEBUG)
+
+    pair = 'UNITTEST/BTC'
+    # Dummy data as we mock the analyze functions
+    data_processed = {pair: DataFrame()}
+    results = backtesting.backtest(
+        {
+            'stake_amount': default_conf['stake_amount'],
+            'processed': data_processed,
+            'max_open_trades': 10,
+        }
+    )
+    print(results.T)
+
+    assert len(results) == len(data.trades)
+    assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3)
+    # if data.sell_r == SellType.STOP_LOSS:
+    #     assert log_has("Stop loss hit.", caplog.record_tuples)
+    # else:
+    #     assert not log_has("Stop loss hit.", caplog.record_tuples)
+    # log_test = (f'Force_selling still open trade UNITTEST/BTC with '
+    #             f'{results.iloc[-1].profit_percent} perc - {results.iloc[-1].profit_abs}')
+    # if data.sell_r == SellType.FORCE_SELL:
+    #     assert log_has(log_test,
+    #                    caplog.record_tuples)
+    # else:
+    #     assert not log_has(log_test,
+    #                        caplog.record_tuples)
+    for c, trade in enumerate(data.trades):
+        res = results.iloc[c]
+        assert res.sell_reason == trade.sell_reason
+        assert res.open_time == _get_frame_time_from_offset(trade.open_tick)
+        assert res.close_time == _get_frame_time_from_offset(trade.close_tick)

freqtrade/tests/optimize/test_backtesting.py

@@ -518,18 +518,18 @@ def test_backtest(default_conf, fee, mocker) -> None:
 
     expected = pd.DataFrame(
         {'pair': [pair, pair],
-         'profit_percent': [0.00029977, 0.00056716],
+         'profit_percent': [0.0, 0.0],
-         'profit_abs': [1.49e-06, 7.6e-07],
+         'profit_abs': [0.0, 0.0],
          'open_time': [Arrow(2018, 1, 29, 18, 40, 0).datetime,
                        Arrow(2018, 1, 30, 3, 30, 0).datetime],
-         'close_time': [Arrow(2018, 1, 29, 22, 40, 0).datetime,
+         'close_time': [Arrow(2018, 1, 29, 22, 35, 0).datetime,
-                        Arrow(2018, 1, 30, 4, 20, 0).datetime],
+                        Arrow(2018, 1, 30, 4, 15, 0).datetime],
          'open_index': [77, 183],
-         'close_index': [125, 193],
+         'close_index': [124, 192],
-         'trade_duration': [240, 50],
+         'trade_duration': [235, 45],
          'open_at_end': [False, False],
          'open_rate': [0.104445, 0.10302485],
-         'close_rate': [0.105, 0.10359999],
+         'close_rate': [0.104969, 0.103541],
          'sell_reason': [SellType.ROI, SellType.ROI]
          })
     pd.testing.assert_frame_equal(results, expected)
@@ -539,9 +539,11 @@ def test_backtest(default_conf, fee, mocker) -> None:
         # Check open trade rate alignes to open rate
         assert ln is not None
         assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
-        # check close trade rate alignes to close rate
+        # check close trade rate alignes to close rate or is between high and low
         ln = data_pair.loc[data_pair["date"] == t["close_time"]]
-        assert round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6)
+        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))
 
 
 def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None:
@@ -580,7 +582,7 @@ def test_processed(default_conf, mocker) -> None:
 
 def test_backtest_pricecontours(default_conf, fee, mocker) -> None:
     mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
-    tests = [['raise', 18], ['lower', 0], ['sine', 16]]
+    tests = [['raise', 18], ['lower', 0], ['sine', 19]]
     for [contour, numres] in tests:
         simple_backtest(default_conf, contour, numres, mocker)
 

requirements.txt

@@ -1,8 +1,8 @@
-ccxt==1.17.464
+ccxt==1.17.480
 SQLAlchemy==1.2.13
 python-telegram-bot==11.1.0
 arrow==0.12.1
-cachetools==2.1.0
+cachetools==3.0.0
 requests==2.20.0
 urllib3==1.24.1
 wrapt==1.10.11
@@ -10,9 +10,9 @@ pandas==0.23.4
 scikit-learn==0.20.0
 scipy==1.1.0
 jsonschema==2.6.0
-numpy==1.15.3
+numpy==1.15.4
 TA-Lib==0.4.17
-pytest==3.9.3
+pytest==3.10.0
 pytest-mock==1.10.0
 pytest-asyncio==0.9.0
 pytest-cov==2.6.0