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inverse backtest logic to loop over time - not pairs (more realistic)

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Matthias 2018-10-15 22:02:23 +02:00
parent cc3d05488b
commit e94da7ca41
1 changed files with 33 additions and 7 deletions
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40
freqtrade/optimize/backtesting.py
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@ -66,6 +66,7 @@ class Backtesting(object):
if self.config.get('strategy_list', None):
# Force one interval
self.ticker_interval = str(self.config.get('ticker_interval'))
self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[self.ticker_interval]
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
@ -86,6 +87,8 @@ class Backtesting(object):
"""
self.strategy = strategy
self.ticker_interval = self.config.get('ticker_interval')
self.ticker_interval_mins = constants.TICKER_INTERVAL_MINUTES[self.ticker_interval]
self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
self.advise_buy = strategy.advise_buy
self.advise_sell = strategy.advise_sell
@ -280,8 +283,13 @@ class Backtesting(object):
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
position_stacking = args.get('position_stacking', False)
start_date = args.get('start_date')
end_date = args.get('end_date')
trades = []
trade_count_lock: Dict = {}
ticker: Dict = {}
pairs = []
# Create ticker dict
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
@ -296,15 +304,29 @@ class Backtesting(object):
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker = [x for x in ticker_data.itertuples()]
ticker[pair] = [x for x in ticker_data.itertuples()]
pairs.append(pair)
lock_pair_until: Dict = {}
tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
index = 0
# Loop timerange and test per pair
while tmp < end_date:
# print(f"time: {tmp}")
for i, pair in enumerate(ticker):
try:
row = ticker[pair][index]
except IndexError:
# missing Data for one pair ...
# TODO:howto handle this
# logger.warning(f"i: {index} - {tmp} did not exist for {pair}")
continue
lock_pair_until = None
for index, row in enumerate(ticker):
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if not position_stacking:
if lock_pair_until is not None and row.date <= lock_pair_until:
if pair in lock_pair_until and row.date <= lock_pair_until[pair]:
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
@ -313,17 +335,19 @@ class Backtesting(object):
trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
trade_entry = self._get_sell_trade_entry(pair, row, ticker[pair][index + 1:],
trade_count_lock, args)
if trade_entry:
lock_pair_until = trade_entry.close_time
lock_pair_until[pair] = trade_entry.close_time
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
# This happens only if the buy-signal was with the last candle
lock_pair_until = ticker_data.iloc[-1].date
lock_pair_until[pair] = end_date
tmp += timedelta(minutes=self.ticker_interval_mins)
index += 1
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
@ -390,6 +414,8 @@ class Backtesting(object):
'processed': preprocessed,
'max_open_trades': max_open_trades,
'position_stacking': self.config.get('position_stacking', False),
'start_date': min_date,
'end_date': max_date,
}
)