Extract backtest 1 candle from main function
This commit is contained in:
Matthias
parent
0e8cf366f5
commit
8534dfb0d4
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@ -1067,54 +1067,15 @@ class Backtesting:
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return None
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return row
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def backtest(self, processed: Dict, # noqa: max-complexity: 13
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start_date: datetime, end_date: datetime,
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max_open_trades: int = 0, position_stacking: bool = False,
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enable_protections: bool = False) -> Dict[str, Any]:
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def backtest_loop(
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self, row: Tuple, pair: str, current_time: datetime, end_date: datetime,
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max_open_trades: int, position_stacking: bool, enable_protections: bool,
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open_trade_count_start: int) -> int:
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"""
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Implement backtesting functionality
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NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
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Of course try to not have ugly code. By some accessor are sometime slower than functions.
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Avoid extensive logging in this method and functions it calls.
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:param processed: a processed dictionary with format {pair, data}, which gets cleared to
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optimize memory usage!
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:param start_date: backtesting timerange start datetime
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:param end_date: backtesting timerange end datetime
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:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
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:param position_stacking: do we allow position stacking?
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:param enable_protections: Should protections be enabled?
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:return: DataFrame with trades (results of backtesting)
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Backtesting processing for one candle.
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"""
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self.prepare_backtest(enable_protections)
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# Ensure wallets are uptodate (important for --strategy-list)
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self.wallets.update()
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# Use dict of lists with data for performance
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# (looping lists is a lot faster than pandas DataFrames)
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data: Dict = self._get_ohlcv_as_lists(processed)
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# Indexes per pair, so some pairs are allowed to have a missing start.
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indexes: Dict = defaultdict(int)
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current_time = start_date + timedelta(minutes=self.timeframe_min)
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self.progress.init_step(BacktestState.BACKTEST, int(
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(end_date - start_date) / timedelta(minutes=self.timeframe_min)))
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# Loop timerange and get candle for each pair at that point in time
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while current_time <= end_date:
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open_trade_count_start = LocalTrade.bt_open_open_trade_count
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self.check_abort()
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for i, pair in enumerate(data):
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row_index = indexes[pair]
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row = self.validate_row(data, pair, row_index, current_time)
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if not row:
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continue
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row_index += 1
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indexes[pair] = row_index
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self.dataprovider._set_dataframe_max_index(row_index)
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for t in list(LocalTrade.bt_trades_open_pp[pair]):
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# 1. Manage currently open orders of active trades
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if self.manage_open_orders(t, current_time, row):
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@ -1173,8 +1134,60 @@ class Backtesting:
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# logger.debug(f"{pair} - Backtesting exit {trade}")
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LocalTrade.close_bt_trade(trade)
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self.wallets.update()
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self.run_protections(
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enable_protections, pair, current_time, trade.trade_direction)
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self.run_protections(enable_protections, pair, current_time, trade.trade_direction)
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return open_trade_count_start
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def backtest(self, processed: Dict, # noqa: max-complexity: 13
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start_date: datetime, end_date: datetime,
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max_open_trades: int = 0, position_stacking: bool = False,
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enable_protections: bool = False) -> Dict[str, Any]:
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"""
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Implement backtesting functionality
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NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
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Of course try to not have ugly code. By some accessor are sometime slower than functions.
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Avoid extensive logging in this method and functions it calls.
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:param processed: a processed dictionary with format {pair, data}, which gets cleared to
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optimize memory usage!
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:param start_date: backtesting timerange start datetime
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:param end_date: backtesting timerange end datetime
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:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
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:param position_stacking: do we allow position stacking?
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:param enable_protections: Should protections be enabled?
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:return: DataFrame with trades (results of backtesting)
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"""
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self.prepare_backtest(enable_protections)
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# Ensure wallets are uptodate (important for --strategy-list)
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self.wallets.update()
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# Use dict of lists with data for performance
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# (looping lists is a lot faster than pandas DataFrames)
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data: Dict = self._get_ohlcv_as_lists(processed)
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# Indexes per pair, so some pairs are allowed to have a missing start.
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indexes: Dict = defaultdict(int)
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current_time = start_date + timedelta(minutes=self.timeframe_min)
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self.progress.init_step(BacktestState.BACKTEST, int(
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(end_date - start_date) / timedelta(minutes=self.timeframe_min)))
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# Loop timerange and get candle for each pair at that point in time
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while current_time <= end_date:
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open_trade_count_start = LocalTrade.bt_open_open_trade_count
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self.check_abort()
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for i, pair in enumerate(data):
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row_index = indexes[pair]
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row = self.validate_row(data, pair, row_index, current_time)
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if not row:
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continue
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row_index += 1
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indexes[pair] = row_index
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self.dataprovider._set_dataframe_max_index(row_index)
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open_trade_count_start = self.backtest_loop(
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row, pair, current_time, end_date, max_open_trades,
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position_stacking, enable_protections, open_trade_count_start)
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# Move time one configured time_interval ahead.
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self.progress.increment()
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