inverse backtest logic to loop over time - not pairs (more realistic)
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@ -66,6 +66,7 @@ class Backtesting(object):
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if self.config.get('strategy_list', None):
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# Force one interval
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self.ticker_interval = str(self.config.get('ticker_interval'))
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self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[self.ticker_interval]
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for strat in list(self.config['strategy_list']):
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stratconf = deepcopy(self.config)
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stratconf['strategy'] = strat
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@ -86,6 +87,8 @@ class Backtesting(object):
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"""
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self.strategy = strategy
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self.ticker_interval = self.config.get('ticker_interval')
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self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[self.ticker_interval]
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self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
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self.advise_buy = strategy.advise_buy
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self.advise_sell = strategy.advise_sell
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@ -280,8 +283,13 @@ class Backtesting(object):
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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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start_date = args.get('start_date')
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end_date = args.get('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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@ -296,15 +304,29 @@ class Backtesting(object):
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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 = [x for x in ticker_data.itertuples()]
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ticker[pair] = [x for x in ticker_data.itertuples()]
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pairs.append(pair)
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lock_pair_until: 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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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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try:
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row = ticker[pair][index]
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except IndexError:
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# missing Data for one pair ...
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# TODO:howto handle this
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# logger.warning(f"i: {index} - {tmp} did not exist for {pair}")
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continue
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lock_pair_until = None
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for index, row in enumerate(ticker):
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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 lock_pair_until is not None and row.date <= lock_pair_until:
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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 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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@ -313,17 +335,19 @@ class Backtesting(object):
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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[index + 1:],
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trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][index + 1:],
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trade_count_lock, args)
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if trade_entry:
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lock_pair_until = trade_entry.close_time
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lock_pair_until[pair] = trade_entry.close_time
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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 = ticker_data.iloc[-1].date
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lock_pair_until[pair] = end_date
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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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@ -390,6 +414,8 @@ class Backtesting(object):
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'processed': preprocessed,
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'max_open_trades': max_open_trades,
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'position_stacking': self.config.get('position_stacking', False),
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'start_date': min_date,
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'end_date': max_date,
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}
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)
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