Merge pull request #1683 from gianlup/fix_bt_partial_data
Fix backtest problem with partial data
This commit is contained in:
Misagh
GitHub
commit
9dc2a30793
@ -210,6 +210,32 @@ class Backtesting(object):
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logger.info('Dumping backtest results to %s', recordfilename)
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file_dump_json(recordfilename, records)
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def _get_ticker_list(self, processed) -> Dict[str, DataFrame]:
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"""
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Helper function to convert a processed tickerlist into a list for performance reasons.
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Used by backtest() - so keep this optimized for performance.
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"""
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headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
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ticker: Dict = {}
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# Create ticker dict
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for pair, pair_data in processed.items():
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pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
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ticker_data = self.advise_sell(
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self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
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# to avoid using data from future, we buy/sell with signal from previous candle
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ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
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ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
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ticker_data.drop(ticker_data.head(1).index, inplace=True)
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# Convert from Pandas to list for performance reasons
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# (Looping Pandas is slow.)
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ticker[pair] = [x for x in ticker_data.itertuples()]
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return ticker
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def _get_sell_trade_entry(
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self, pair: str, buy_row: DataFrame,
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partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]:
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@ -304,7 +330,6 @@ 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', '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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@ -312,54 +337,50 @@ class Backtesting(object):
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end_date = args['end_date']
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trades = []
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trade_count_lock: Dict = {}
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ticker: Dict = {}
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pairs = []
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# Create ticker dict
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for pair, pair_data in processed.items():
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pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
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ticker_data = self.advise_sell(
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self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
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# to avoid using data from future, we buy/sell with signal from previous candle
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ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
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ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
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ticker_data.drop(ticker_data.head(1).index, inplace=True)
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# Convert from Pandas to list for performance reasons
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# (Looping Pandas is slow.)
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ticker[pair] = [x for x in ticker_data.itertuples()]
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pairs.append(pair)
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# Dict of ticker-lists for performance (looping lists is a lot faster than dataframes)
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ticker: Dict = self._get_ticker_list(processed)
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lock_pair_until: Dict = {}
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# Indexes per pair, so some pairs are allowed to have a missing start.
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indexes: Dict = {}
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tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
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index = 0
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# Loop timerange and test per pair
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# Loop timerange and get candle for each pair at that point in time
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while tmp < end_date:
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# print(f"time: {tmp}")
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for i, pair in enumerate(ticker):
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if pair not in indexes:
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indexes[pair] = 0
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try:
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row = ticker[pair][index]
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row = ticker[pair][indexes[pair]]
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except IndexError:
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# missing Data for one pair ...
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# missing Data for one pair at the end.
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# Warnings for this are shown by `validate_backtest_data`
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continue
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# Waits until the time-counter reaches the start of the data for this pair.
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if row.date > tmp.datetime:
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continue
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indexes[pair] += 1
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if row.buy == 0 or row.sell == 1:
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continue # skip rows where no buy signal or that would immediately sell off
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if not position_stacking:
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if pair in lock_pair_until and row.date <= lock_pair_until[pair]:
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continue
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if (not position_stacking and pair in lock_pair_until
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and row.date <= lock_pair_until[pair]):
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# without positionstacking, we can only have one open trade per pair.
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continue
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if max_open_trades > 0:
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# Check if max_open_trades has already been reached for the given date
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if not trade_count_lock.get(row.date, 0) < max_open_trades:
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continue
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trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
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trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][index + 1:],
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trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]:],
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trade_count_lock, args)
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if trade_entry:
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@ -367,11 +388,10 @@ class Backtesting(object):
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trades.append(trade_entry)
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else:
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# Set lock_pair_until to end of testing period if trade could not be closed
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# This happens only if the buy-signal was with the last candle
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lock_pair_until[pair] = end_date
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lock_pair_until[pair] = end_date.datetime
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# Move time one configured time_interval ahead.
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tmp += timedelta(minutes=self.ticker_interval_mins)
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index += 1
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return DataFrame.from_records(trades, columns=BacktestResult._fields)
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def start(self) -> None:
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@ -122,8 +122,8 @@ def test_edge_results(edge_conf, mocker, caplog, data) -> None:
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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.exit_type == 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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assert arrow.get(res.open_time) == _get_frame_time_from_offset(trade.open_tick)
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assert arrow.get(res.close_time) == _get_frame_time_from_offset(trade.close_tick)
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def test_adjust(mocker, edge_conf):
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@ -33,7 +33,7 @@ class BTContainer(NamedTuple):
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def _get_frame_time_from_offset(offset):
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return ticker_start_time.shift(minutes=(offset * TICKER_INTERVAL_MINUTES[tests_ticker_interval])
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).datetime.replace(tzinfo=None)
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).datetime
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def _build_backtest_dataframe(ticker_with_signals):
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@ -685,25 +685,32 @@ def test_backtest_alternate_buy_sell(default_conf, fee, mocker):
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assert len(results.loc[results.open_at_end]) == 0
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def test_backtest_multi_pair(default_conf, fee, mocker):
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@pytest.mark.parametrize("pair", ['ADA/BTC', 'LTC/BTC'])
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@pytest.mark.parametrize("tres", [0, 20, 30])
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def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair):
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def _trend_alternate_hold(dataframe=None, metadata=None):
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"""
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Buy every 8th candle - sell every other 8th -2 (hold on to pairs a bit)
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Buy every xth candle - sell every other xth -2 (hold on to pairs a bit)
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"""
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multi = 8
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if metadata['pair'] in('ETH/BTC', 'LTC/BTC'):
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multi = 20
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else:
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multi = 18
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dataframe['buy'] = np.where(dataframe.index % multi == 0, 1, 0)
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dataframe['sell'] = np.where((dataframe.index + multi - 2) % multi == 0, 1, 0)
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if metadata['pair'] in('ETH/BTC', 'LTC/BTC'):
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dataframe['buy'] = dataframe['buy'].shift(-4)
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dataframe['sell'] = dataframe['sell'].shift(-4)
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return dataframe
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mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
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patch_exchange(mocker)
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pairs = ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC', 'NXT/BTC']
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data = history.load_data(datadir=None, ticker_interval='5m', pairs=pairs)
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# Only use 500 lines to increase performance
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data = trim_dictlist(data, -500)
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# Remove data for one pair from the beginning of the data
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data[pair] = data[pair][tres:]
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# We need to enable sell-signal - otherwise it sells on ROI!!
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default_conf['experimental'] = {"use_sell_signal": True}
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default_conf['ticker_interval'] = '5m'
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