def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
"""
Handling of left open trades at the end of backtesting
"""
trades = []
for pair in open_trades.keys():
if len(open_trades[pair]) > 0:
for trade in open_trades[pair]:
sell_row = data[pair][-1]
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = SellType.FORCE_SELL.value
trade.close(sell_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
# Deepcopy object to have wallets update correctly
trade1 = deepcopy(trade)
trade1.is_open = True
trades.append(trade1)
return trades
def backtest(self,
processed: Dict,
start_date: datetime,
end_date: datetime,
max_open_trades: int = 0,
position_stacking: bool = False,
enable_protections: bool = False) -> Dict[str, Any]:
"""
Implement backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}, which gets cleared to
optimize memory usage!
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
:param position_stacking: do we allow position stacking?
:param enable_protections: Should protections be enabled?
:return: DataFrame with trades (results of backtesting)
"""
trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections)
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = defaultdict(int)
tmp = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
open_trade_count = 0
self.progress.init_step(
BacktestState.BACKTEST,
int((end_date - start_date) /
timedelta(minutes=self.timeframe_min)))
# Loop timerange and get candle for each pair at that point in time
while tmp <= end_date:
open_trade_count_start = open_trade_count
self.check_abort()
for i, pair in enumerate(data):
row_index = indexes[pair]
try:
# Row is treated as "current incomplete candle".
# Buy / sell signals are shifted by 1 to compensate for this.
row = data[pair][row_index]
except IndexError:
# missing Data for one pair at the end.
# Warnings for this are shown during data loading
continue
# Waits until the time-counter reaches the start of the data for this pair.
if row[DATE_IDX] > tmp:
continue
row_index += 1
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
if ((position_stacking or len(open_trades[pair]) == 0)
and self.trade_slot_available(max_open_trades,
open_trade_count_start)
and tmp != end_date and row[BUY_IDX] == 1
and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
trade = self._enter_trade(pair, row)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behaviour - not sure if this is correct
# Prevents buying if the trade-slot was freed in this candle
open_trade_count_start += 1
open_trade_count += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
open_trades[pair].append(trade)
LocalTrade.add_bt_trade(trade)
for trade in list(open_trades[pair]):
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occurred
if trade_entry:
# logger.debug(f"{pair} - Backtesting sell {trade}")
open_trade_count -= 1
open_trades[pair].remove(trade)
LocalTrade.close_bt_trade(trade)
trades.append(trade_entry)
if enable_protections:
self.protections.stop_per_pair(pair, row[DATE_IDX])
self.protections.global_stop(tmp)
# Move time one configured time_interval ahead.
self.progress.increment()
tmp += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
self.wallets.update()
results = trade_list_to_dataframe(trades)
return {
'results':
results,
'config':
self.strategy.config,
'locks':
PairLocks.get_all_locks(),
'rejected_signals':
self.rejected_trades,
'final_balance':
self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest(self,
processed: Dict,
start_date: datetime,
end_date: datetime,
max_open_trades: int = 0,
position_stacking: bool = False,
enable_protections: bool = False) -> Dict[str, Any]:
"""
Implement backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}, which gets cleared to
optimize memory usage!
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
:param position_stacking: do we allow position stacking?
:param enable_protections: Should protections be enabled?
:return: DataFrame with trades (results of backtesting)
"""
trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections)
# Ensure wallets are uptodate (important for --strategy-list)
self.wallets.update()
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = defaultdict(int)
current_time = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
open_trade_count = 0
self.progress.init_step(
BacktestState.BACKTEST,
int((end_date - start_date) /
timedelta(minutes=self.timeframe_min)))
# Loop timerange and get candle for each pair at that point in time
while current_time <= end_date:
open_trade_count_start = open_trade_count
self.check_abort()
for i, pair in enumerate(data):
row_index = indexes[pair]
row = self.validate_row(data, pair, row_index, current_time)
if not row:
continue
row_index += 1
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
# 1. Process buys.
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
if ((position_stacking or len(open_trades[pair]) == 0)
and self.trade_slot_available(max_open_trades,
open_trade_count_start)
and current_time != end_date and row[BUY_IDX] == 1
and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
trade = self._enter_trade(pair, row)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behavior - not sure if this is correct
# Prevents buying if the trade-slot was freed in this candle
open_trade_count_start += 1
open_trade_count += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
open_trades[pair].append(trade)
for trade in list(open_trades[pair]):
# 2. Process buy orders.
order = trade.select_order('buy', is_open=True)
if order and self._get_order_filled(order.price, row):
order.close_bt_order(current_time)
trade.open_order_id = None
LocalTrade.add_bt_trade(trade)
self.wallets.update()
# 3. Create sell orders (if any)
if not trade.open_order_id:
self._get_sell_trade_entry(
trade, row) # Place sell order if necessary
# 4. Process sell orders.
order = trade.select_order('sell', is_open=True)
if order and self._get_order_filled(order.price, row):
trade.open_order_id = None
trade.close_date = current_time
trade.close(order.price, show_msg=False)
# logger.debug(f"{pair} - Backtesting sell {trade}")
open_trade_count -= 1
open_trades[pair].remove(trade)
LocalTrade.close_bt_trade(trade)
trades.append(trade)
self.wallets.update()
self.run_protections(enable_protections, pair,
current_time)
# 5. Cancel expired buy/sell orders.
if self.check_order_cancel(trade, current_time):
# Close trade due to buy timeout expiration.
open_trade_count -= 1
open_trades[pair].remove(trade)
self.wallets.update()
# Move time one configured time_interval ahead.
self.progress.increment()
current_time += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
self.wallets.update()
results = trade_list_to_dataframe(trades)
return {
'results':
results,
'config':
self.strategy.config,
'locks':
PairLocks.get_all_locks(),
'rejected_signals':
self.rejected_trades,
'timedout_entry_orders':
self.timedout_entry_orders,
'timedout_exit_orders':
self.timedout_exit_orders,
'final_balance':
self.wallets.get_total(self.strategy.config['stake_currency']),
}