Merge pull request #1306 from xmatthias/feat/functional_tests
Funcional tests / backtest stoploss alignment
This commit is contained in:
Matthias
GitHub
commit
e0489878d8
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@ -206,21 +206,37 @@ class Backtesting(object):
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buy_signal = sell_row.buy
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sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
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sell_row.sell)
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sell_row.sell, low=sell_row.low, high=sell_row.high)
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if sell.sell_flag:
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trade_dur = int((sell_row.date - buy_row.date).total_seconds() // 60)
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# Special handling if high or low hit STOP_LOSS or ROI
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if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
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# Set close_rate to stoploss
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closerate = trade.stop_loss
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elif sell.sell_type == (SellType.ROI):
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# get entry in min_roi >= to trade duration
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roi_entry = max(list(filter(lambda x: trade_dur >= x,
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self.strategy.minimal_roi.keys())))
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roi = self.strategy.minimal_roi[roi_entry]
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# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
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closerate = - (trade.open_rate * roi + trade.open_rate *
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(1 + trade.fee_open)) / (trade.fee_close - 1)
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else:
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closerate = sell_row.open
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return BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_percent(rate=sell_row.open),
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profit_abs=trade.calc_profit(rate=sell_row.open),
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profit_percent=trade.calc_profit_percent(rate=closerate),
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profit_abs=trade.calc_profit(rate=closerate),
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open_time=buy_row.date,
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close_time=sell_row.date,
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trade_duration=int((
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sell_row.date - buy_row.date).total_seconds() // 60),
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trade_duration=trade_dur,
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open_index=buy_row.Index,
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close_index=sell_row.Index,
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open_at_end=False,
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open_rate=buy_row.open,
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close_rate=sell_row.open,
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close_rate=closerate,
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sell_reason=sell.sell_type
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)
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if partial_ticker:
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@ -260,7 +276,7 @@ class Backtesting(object):
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position_stacking: do we allow position stacking? (default: False)
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:return: DataFrame
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"""
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headers = ['date', 'buy', 'open', 'close', 'sell']
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headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
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processed = args['processed']
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max_open_trades = args.get('max_open_trades', 0)
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position_stacking = args.get('position_stacking', False)
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@ -272,10 +272,10 @@ class Trade(_DECL_BASE):
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self,
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fee: Optional[float] = None) -> float:
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"""
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Calculate the open_rate in BTC
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Calculate the open_rate including fee.
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:param fee: fee to use on the open rate (optional).
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If rate is not set self.fee will be used
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:return: Price in BTC of the open trade
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:return: Price in of the open trade incl. Fees
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"""
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buy_trade = (Decimal(self.amount) * Decimal(self.open_rate))
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@ -287,7 +287,7 @@ class Trade(_DECL_BASE):
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rate: Optional[float] = None,
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fee: Optional[float] = None) -> float:
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"""
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Calculate the close_rate in BTC
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Calculate the close_rate including fee
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:param fee: fee to use on the close rate (optional).
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If rate is not set self.fee will be used
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:param rate: rate to compare with (optional).
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@ -307,12 +307,12 @@ class Trade(_DECL_BASE):
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rate: Optional[float] = None,
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fee: Optional[float] = None) -> float:
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"""
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Calculate the profit in BTC between Close and Open trade
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Calculate the absolute profit in stake currency between Close and Open trade
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:param fee: fee to use on the close rate (optional).
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If rate is not set self.fee will be used
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:param rate: close rate to compare with (optional).
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If rate is not set self.close_rate will be used
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:return: profit in BTC as float
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:return: profit in stake currency as float
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"""
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open_trade_price = self.calc_open_trade_price()
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close_trade_price = self.calc_close_trade_price(
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@ -203,18 +203,22 @@ class IStrategy(ABC):
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return buy, sell
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def should_sell(self, trade: Trade, rate: float, date: datetime, buy: bool,
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sell: bool) -> SellCheckTuple:
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sell: bool, low: float = None, high: float = None) -> SellCheckTuple:
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"""
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This function evaluate if on the condition required to trigger a sell has been reached
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if the threshold is reached and updates the trade record.
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:return: True if trade should be sold, False otherwise
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"""
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current_profit = trade.calc_profit_percent(rate)
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stoplossflag = self.stop_loss_reached(current_rate=rate, trade=trade, current_time=date,
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current_profit=current_profit)
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# Set current rate to low for backtesting sell
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current_rate = low or rate
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current_profit = trade.calc_profit_percent(current_rate)
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stoplossflag = self.stop_loss_reached(current_rate=current_rate, trade=trade,
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current_time=date, current_profit=current_profit)
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if stoplossflag.sell_flag:
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return stoplossflag
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# Set current rate to low for backtesting sell
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current_rate = high or rate
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current_profit = trade.calc_profit_percent(current_rate)
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experimental = self.config.get('experimental', {})
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if buy and experimental.get('ignore_roi_if_buy_signal', False):
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@ -0,0 +1,45 @@
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from typing import NamedTuple, List
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import arrow
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from pandas import DataFrame
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from freqtrade.strategy.interface import SellType
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ticker_start_time = arrow.get(2018, 10, 3)
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ticker_interval_in_minute = 60
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class BTrade(NamedTuple):
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"""
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Minimalistic Trade result used for functional backtesting
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"""
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sell_reason: SellType
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open_tick: int
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close_tick: int
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class BTContainer(NamedTuple):
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"""
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Minimal BacktestContainer defining Backtest inputs and results.
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"""
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data: List[float]
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stop_loss: float
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roi: float
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trades: List[BTrade]
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profit_perc: float
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def _get_frame_time_from_offset(offset):
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return ticker_start_time.shift(
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minutes=(offset * ticker_interval_in_minute)).datetime
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def _build_backtest_dataframe(ticker_with_signals):
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columns = ['date', 'open', 'high', 'low', 'close', 'volume', 'buy', 'sell']
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frame = DataFrame.from_records(ticker_with_signals, columns=columns)
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frame['date'] = frame['date'].apply(_get_frame_time_from_offset)
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# Ensure floats are in place
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for column in ['open', 'high', 'low', 'close', 'volume']:
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frame[column] = frame[column].astype('float64')
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return frame
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@ -0,0 +1,188 @@
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# pragma pylint: disable=missing-docstring, W0212, line-too-long, C0103, unused-argument
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import logging
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from unittest.mock import MagicMock
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from pandas import DataFrame
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import pytest
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from freqtrade.optimize.backtesting import Backtesting
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from freqtrade.strategy.interface import SellType
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from freqtrade.tests.optimize import (BTrade, BTContainer, _build_backtest_dataframe,
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_get_frame_time_from_offset)
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from freqtrade.tests.conftest import patch_exchange
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# Test 0 Minus 8% Close
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# Test with Stop-loss at 1%
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# TC1: Stop-Loss Triggered 1% loss
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tc0 = BTContainer(data=[
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# D O H L C V B S
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[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
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[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
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[2, 4987, 5012, 4600, 4600, 6172, 0, 0], # exit with stoploss hit
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[3, 4975, 5000, 4980, 4977, 6172, 0, 0],
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[4, 4977, 4987, 4977, 4995, 6172, 0, 0],
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[5, 4995, 4995, 4995, 4950, 6172, 0, 0]],
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stop_loss=-0.01, roi=1, profit_perc=-0.01,
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trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
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)
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# Test 1 Minus 4% Low, minus 1% close
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# Test with Stop-Loss at 3%
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# TC2: Stop-Loss Triggered 3% Loss
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tc1 = BTContainer(data=[
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# D O H L C V B S
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[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
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[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
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[2, 4987, 5012, 4962, 4975, 6172, 0, 0],
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[3, 4975, 5000, 4800, 4962, 6172, 0, 0], # exit with stoploss hit
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[4, 4962, 4987, 4937, 4950, 6172, 0, 0],
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[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
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stop_loss=-0.03, roi=1, profit_perc=-0.03,
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trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=3)]
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)
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# Test 3 Candle drops 4%, Recovers 1%.
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# Entry Criteria Met
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# Candle drops 20%
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# Candle Data for test 3
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# Test with Stop-Loss at 2%
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# TC3: Trade-A: Stop-Loss Triggered 2% Loss
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# Trade-B: Stop-Loss Triggered 2% Loss
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tc2 = BTContainer(data=[
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# D O H L C V B S
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[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
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[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
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[2, 4987, 5012, 4800, 4975, 6172, 0, 0], # exit with stoploss hit
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[3, 4975, 5000, 4950, 4962, 6172, 1, 0],
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[4, 4975, 5000, 4950, 4962, 6172, 0, 0], # enter trade 2 (signal on last candle)
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[5, 4962, 4987, 4000, 4000, 6172, 0, 0], # exit with stoploss hit
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[6, 4950, 4975, 4975, 4950, 6172, 0, 0]],
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stop_loss=-0.02, roi=1, profit_perc=-0.04,
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trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2),
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BTrade(sell_reason=SellType.STOP_LOSS, open_tick=4, close_tick=5)]
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)
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# Test 4 Minus 3% / recovery +15%
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# Candle Data for test 3 – Candle drops 3% Closed 15% up
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# Test with Stop-loss at 2% ROI 6%
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# TC4: Stop-Loss Triggered 2% Loss
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tc3 = BTContainer(data=[
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# D O H L C V B S
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[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
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[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
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[2, 4987, 5750, 4850, 5750, 6172, 0, 0], # Exit with stoploss hit
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[3, 4975, 5000, 4950, 4962, 6172, 0, 0],
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[4, 4962, 4987, 4937, 4950, 6172, 0, 0],
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[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
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stop_loss=-0.02, roi=0.06, profit_perc=-0.02,
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trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
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)
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# Test 4 / Drops 0.5% Closes +20%
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# Set stop-loss at 1% ROI 3%
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# TC5: ROI triggers 3% Gain
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tc4 = BTContainer(data=[
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# D O H L C V B S
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[0, 5000, 5025, 4980, 4987, 6172, 1, 0],
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[1, 5000, 5025, 4980, 4987, 6172, 0, 0], # enter trade (signal on last candle)
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[2, 4987, 5025, 4975, 4987, 6172, 0, 0],
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[3, 4975, 6000, 4975, 6000, 6172, 0, 0], # ROI
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[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
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[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
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stop_loss=-0.01, roi=0.03, profit_perc=0.03,
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trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=3)]
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)
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# Test 6 / Drops 3% / Recovers 6% Positive / Closes 1% positve
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# Candle Data for test 6
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# Set stop-loss at 2% ROI at 5%
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# TC6: Stop-Loss triggers 2% Loss
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tc5 = BTContainer(data=[
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# D O H L C V B S
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[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
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[1, 5000, 5025, 4975, 4987, 6172, 0, 0], # enter trade (signal on last candle)
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[2, 4987, 5300, 4850, 5050, 6172, 0, 0], # Exit with stoploss
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[3, 4975, 5000, 4950, 4962, 6172, 0, 0],
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[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
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[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
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stop_loss=-0.02, roi=0.05, profit_perc=-0.02,
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trades=[BTrade(sell_reason=SellType.STOP_LOSS, open_tick=1, close_tick=2)]
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)
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# Test 7 - 6% Positive / 1% Negative / Close 1% Positve
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# Candle Data for test 7
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# Set stop-loss at 2% ROI at 3%
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# TC7: ROI Triggers 3% Gain
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tc6 = BTContainer(data=[
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# D O H L C V B S
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[0, 5000, 5025, 4975, 4987, 6172, 1, 0],
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[1, 5000, 5025, 4975, 4987, 6172, 0, 0],
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[2, 4987, 5300, 4950, 5050, 6172, 0, 0],
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[3, 4975, 5000, 4950, 4962, 6172, 0, 0],
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[4, 4962, 4987, 4972, 4950, 6172, 0, 0],
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[5, 4950, 4975, 4925, 4950, 6172, 0, 0]],
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stop_loss=-0.02, roi=0.03, profit_perc=0.03,
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trades=[BTrade(sell_reason=SellType.ROI, open_tick=1, close_tick=2)]
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)
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TESTS = [
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tc0,
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tc1,
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tc2,
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tc3,
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tc4,
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tc5,
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tc6,
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]
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@pytest.mark.parametrize("data", TESTS)
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def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
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"""
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run functional tests
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"""
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default_conf["stoploss"] = data.stop_loss
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default_conf["minimal_roi"] = {"0": data.roi}
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mocker.patch('freqtrade.exchange.Exchange.get_fee', MagicMock(return_value=0.0))
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patch_exchange(mocker)
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frame = _build_backtest_dataframe(data.data)
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backtesting = Backtesting(default_conf)
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backtesting.advise_buy = lambda a, m: frame
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backtesting.advise_sell = lambda a, m: frame
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caplog.set_level(logging.DEBUG)
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pair = 'UNITTEST/BTC'
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# Dummy data as we mock the analyze functions
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data_processed = {pair: DataFrame()}
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results = backtesting.backtest(
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{
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'stake_amount': default_conf['stake_amount'],
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'processed': data_processed,
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'max_open_trades': 10,
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}
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)
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print(results.T)
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assert len(results) == len(data.trades)
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assert round(results["profit_percent"].sum(), 3) == round(data.profit_perc, 3)
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# if data.sell_r == SellType.STOP_LOSS:
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# assert log_has("Stop loss hit.", caplog.record_tuples)
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# else:
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# assert not log_has("Stop loss hit.", caplog.record_tuples)
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# log_test = (f'Force_selling still open trade UNITTEST/BTC with '
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# f'{results.iloc[-1].profit_percent} perc - {results.iloc[-1].profit_abs}')
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# if data.sell_r == SellType.FORCE_SELL:
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# assert log_has(log_test,
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# caplog.record_tuples)
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# else:
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# assert not log_has(log_test,
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# caplog.record_tuples)
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for c, trade in enumerate(data.trades):
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res = results.iloc[c]
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assert res.sell_reason == trade.sell_reason
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assert res.open_time == _get_frame_time_from_offset(trade.open_tick)
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assert res.close_time == _get_frame_time_from_offset(trade.close_tick)
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@ -518,18 +518,18 @@ def test_backtest(default_conf, fee, mocker) -> None:
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expected = pd.DataFrame(
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{'pair': [pair, pair],
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'profit_percent': [0.00029977, 0.00056716],
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'profit_abs': [1.49e-06, 7.6e-07],
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'profit_percent': [0.0, 0.0],
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'profit_abs': [0.0, 0.0],
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'open_time': [Arrow(2018, 1, 29, 18, 40, 0).datetime,
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Arrow(2018, 1, 30, 3, 30, 0).datetime],
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'close_time': [Arrow(2018, 1, 29, 22, 40, 0).datetime,
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Arrow(2018, 1, 30, 4, 20, 0).datetime],
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'close_time': [Arrow(2018, 1, 29, 22, 35, 0).datetime,
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Arrow(2018, 1, 30, 4, 15, 0).datetime],
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'open_index': [77, 183],
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'close_index': [125, 193],
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'trade_duration': [240, 50],
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'close_index': [124, 192],
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'trade_duration': [235, 45],
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'open_at_end': [False, False],
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'open_rate': [0.104445, 0.10302485],
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'close_rate': [0.105, 0.10359999],
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'close_rate': [0.104969, 0.103541],
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'sell_reason': [SellType.ROI, SellType.ROI]
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})
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pd.testing.assert_frame_equal(results, expected)
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@ -539,9 +539,11 @@ def test_backtest(default_conf, fee, mocker) -> None:
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# Check open trade rate alignes to open rate
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assert ln is not None
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||||
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)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue