Store pair datafrmes in dataprovider for backtesting.
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@ -173,3 +173,6 @@ class DataProvider:
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return self._pairlists.whitelist.copy()
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else:
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raise OperationalException("Dataprovider was not initialized with a pairlist provider.")
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def clear_cache(self):
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self.__cached_pairs = {}
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@ -64,8 +64,8 @@ class Backtesting:
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self.exchange = ExchangeResolver.load_exchange(self.config['exchange']['name'], self.config)
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dataprovider = DataProvider(self.config, self.exchange)
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IStrategy.dp = dataprovider
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self.dataprovider = DataProvider(self.config, self.exchange)
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IStrategy.dp = self.dataprovider
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if self.config.get('strategy_list', None):
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for strat in list(self.config['strategy_list']):
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@ -96,7 +96,7 @@ class Backtesting:
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"PrecisionFilter not allowed for backtesting multiple strategies."
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)
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dataprovider.add_pairlisthandler(self.pairlists)
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self.dataprovider.add_pairlisthandler(self.pairlists)
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self.pairlists.refresh_pairlist()
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if len(self.pairlists.whitelist) == 0:
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@ -176,6 +176,7 @@ class Backtesting:
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Trade.use_db = False
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PairLocks.reset_locks()
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Trade.reset_trades()
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self.dataprovider.clear_cache()
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def _get_ohlcv_as_lists(self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
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"""
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@ -266,7 +267,8 @@ class Backtesting:
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pair=trade.pair, trade=trade, order_type='limit', amount=trade.amount,
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rate=closerate,
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time_in_force=time_in_force,
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sell_reason=sell.sell_reason):
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sell_reason=sell.sell_reason,
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current_time=sell_row[DATE_IDX].to_pydatetime()):
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return None
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trade.close(closerate, show_msg=False)
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@ -286,7 +288,7 @@ class Backtesting:
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# Confirm trade entry:
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if not strategy_safe_wrapper(self.strategy.confirm_trade_entry, default_retval=True)(
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pair=pair, order_type=order_type, amount=stake_amount, rate=row[OPEN_IDX],
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time_in_force=time_in_force):
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time_in_force=time_in_force, current_time=row[DATE_IDX].to_pydatetime()):
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return None
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if stake_amount and (not min_stake_amount or stake_amount > min_stake_amount):
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@ -348,6 +350,10 @@ class Backtesting:
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trades: List[LocalTrade] = []
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self.prepare_backtest(enable_protections)
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# Update dataprovider cache
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for pair, dataframe in processed.items():
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self.dataprovider._set_cached_df(pair, self.timeframe, dataframe)
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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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@ -399,27 +399,27 @@ def test_custom_sell(default_conf, fee, caplog) -> None:
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)
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now = arrow.utcnow().datetime
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res = strategy.should_sell(None, trade, 1, now, False, False, None, None, 0)
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res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
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assert res.sell_flag is False
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assert res.sell_type == SellType.NONE
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strategy.custom_sell = MagicMock(return_value=True)
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res = strategy.should_sell(None, trade, 1, now, False, False, None, None, 0)
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res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
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assert res.sell_flag is True
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assert res.sell_type == SellType.CUSTOM_SELL
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assert res.sell_reason == 'custom_sell'
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strategy.custom_sell = MagicMock(return_value='hello world')
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res = strategy.should_sell(None, trade, 1, now, False, False, None, None, 0)
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res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
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assert res.sell_type == SellType.CUSTOM_SELL
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assert res.sell_flag is True
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assert res.sell_reason == 'hello world'
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caplog.clear()
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strategy.custom_sell = MagicMock(return_value='h' * 100)
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res = strategy.should_sell(None, trade, 1, now, False, False, None, None, 0)
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res = strategy.should_sell(trade, 1, now, False, False, None, None, 0)
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assert res.sell_type == SellType.CUSTOM_SELL
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assert res.sell_flag is True
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assert res.sell_reason == 'h' * 64
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