Do not use ticker where it's not a ticker
This commit is contained in:
hroff-1902
@@ -88,8 +88,8 @@ class Backtesting:
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validate_config_consistency(self.config)
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if "ticker_interval" not in self.config:
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raise OperationalException("Ticker-interval needs to be set in either configuration "
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"or as cli argument `--ticker-interval 5m`")
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raise OperationalException("Timeframe (ticker interval) needs to be set in either "
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"configuration or as cli argument `--ticker-interval 5m`")
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self.timeframe = str(self.config.get('ticker_interval'))
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self.timeframe_min = timeframe_to_minutes(self.timeframe)
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@@ -151,32 +151,33 @@ class Backtesting:
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logger.info(f'Dumping backtest results to {recordfilename}')
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file_dump_json(recordfilename, records)
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def _get_ticker_list(self, processed: Dict) -> Dict[str, DataFrame]:
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def _get_ohlcv_as_lists(self, processed: Dict) -> 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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Helper function to convert a processed dataframes into lists 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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data: Dict = {}
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# Create dict with data
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for pair, pair_data in processed.items():
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pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
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pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
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ticker_data = self.strategy.advise_sell(
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dataframe = self.strategy.advise_sell(
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self.strategy.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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# To avoid using data from future, we use buy/sell signals shifted
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# from the previous candle
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dataframe.loc[:, 'buy'] = dataframe['buy'].shift(1)
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dataframe.loc[:, 'sell'] = dataframe['sell'].shift(1)
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ticker_data.drop(ticker_data.head(1).index, inplace=True)
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dataframe.drop(dataframe.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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data[pair] = [x for x in dataframe.itertuples()]
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return data
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def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,
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trade_dur: int) -> float:
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@@ -220,7 +221,7 @@ class Backtesting:
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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,
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partial_ohlcv: List, trade_count_lock: Dict,
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stake_amount: float, max_open_trades: int) -> Optional[BacktestResult]:
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trade = Trade(
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@@ -235,7 +236,7 @@ class Backtesting:
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)
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logger.debug(f"{pair} - Backtesting emulates creation of new trade: {trade}.")
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# calculate win/lose forwards from buy point
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for sell_row in partial_ticker:
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for sell_row in partial_ohlcv:
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if max_open_trades > 0:
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# Increase trade_count_lock for every iteration
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trade_count_lock[sell_row.date] = trade_count_lock.get(sell_row.date, 0) + 1
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@@ -259,9 +260,9 @@ class Backtesting:
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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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if partial_ohlcv:
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# no sell condition found - trade stil open at end of backtest period
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sell_row = partial_ticker[-1]
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sell_row = partial_ohlcv[-1]
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bt_res = BacktestResult(pair=pair,
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profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
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profit_abs=trade.calc_profit(rate=sell_row.open),
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@@ -308,8 +309,9 @@ class Backtesting:
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trades = []
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trade_count_lock: Dict = {}
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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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# 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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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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@@ -319,12 +321,12 @@ class Backtesting:
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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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for i, pair in enumerate(ticker):
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for i, pair in enumerate(data):
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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][indexes[pair]]
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row = data[pair][indexes[pair]]
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except IndexError:
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# missing Data for one pair at the end.
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# Warnings for this are shown during data loading
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@@ -352,7 +354,7 @@ class Backtesting:
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# since indexes has been incremented before, we need to go one step back to
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# also check the buying candle for sell conditions.
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trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][indexes[pair]-1:],
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trade_entry = self._get_sell_trade_entry(pair, row, data[pair][indexes[pair]-1:],
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trade_count_lock, stake_amount,
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max_open_trades)
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@@ -395,7 +397,7 @@ class Backtesting:
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self._set_strategy(strat)
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# need to reprocess data every time to populate signals
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preprocessed = self.strategy.tickerdata_to_dataframe(data)
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preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
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# Trim startup period from analyzed dataframe
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for pair, df in preprocessed.items():
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@@ -75,8 +75,8 @@ class Hyperopt:
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self.trials_file = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_results.pickle')
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self.tickerdata_pickle = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_tickerdata.pkl')
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self.data_pickle_file = (self.config['user_data_dir'] /
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'hyperopt_results' / 'hyperopt_data.pkl')
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self.total_epochs = config.get('epochs', 0)
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self.current_best_loss = 100
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@@ -130,7 +130,7 @@ class Hyperopt:
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"""
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Remove hyperopt pickle files to restart hyperopt.
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"""
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for f in [self.tickerdata_pickle, self.trials_file]:
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for f in [self.data_pickle_file, self.trials_file]:
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p = Path(f)
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if p.is_file():
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logger.info(f"Removing `{p}`.")
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@@ -454,7 +454,7 @@ class Hyperopt:
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self.backtesting.strategy.trailing_only_offset_is_reached = \
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d['trailing_only_offset_is_reached']
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processed = load(self.tickerdata_pickle)
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processed = load(self.data_pickle_file)
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min_date, max_date = get_timerange(processed)
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@@ -570,7 +570,7 @@ class Hyperopt:
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self.hyperopt_table_header = -1
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data, timerange = self.backtesting.load_bt_data()
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preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
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preprocessed = self.backtesting.strategy.ohlcvdata_to_dataframe(data)
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# Trim startup period from analyzed dataframe
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for pair, df in preprocessed.items():
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@@ -581,7 +581,7 @@ class Hyperopt:
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'Hyperopting with data from %s up to %s (%s days)..',
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min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
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)
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dump(preprocessed, self.tickerdata_pickle)
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dump(preprocessed, self.data_pickle_file)
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# We don't need exchange instance anymore while running hyperopt
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self.backtesting.exchange = None # type: ignore
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