def test_current_whitelist(mocker, default_conf, tickers):
# patch default conf to volumepairlist
default_conf['pairlists'][0] = {
'method': 'VolumePairList',
"number_assets": 5
}
mocker.patch.multiple('freqtrade.exchange.Exchange',
exchange_has=MagicMock(return_value=True),
get_tickers=tickers)
exchange = get_patched_exchange(mocker, default_conf)
pairlist = PairListManager(exchange, default_conf)
dp = DataProvider(default_conf, exchange, pairlist)
# Simulate volumepairs from exchange.
pairlist.refresh_pairlist()
assert dp.current_whitelist() == pairlist._whitelist
# The identity of the 2 lists should be identical
assert dp.current_whitelist() is pairlist._whitelist
with pytest.raises(OperationalException):
dp = DataProvider(default_conf, exchange)
dp.current_whitelist()
def start_test_pairlist(args: Dict[str, Any]) -> None:
"""
Test Pairlist configuration
"""
from freqtrade.pairlist.pairlistmanager import PairListManager
config = setup_utils_configuration(args, RunMode.UTIL_EXCHANGE)
exchange = ExchangeResolver.load_exchange(config['exchange']['name'],
config,
validate=False)
quote_currencies = args.get('quote_currencies')
if not quote_currencies:
quote_currencies = [config.get('stake_currency')]
results = {}
for curr in quote_currencies:
config['stake_currency'] = curr
# Do not use ticker_interval set in the config
pairlists = PairListManager(exchange, config)
pairlists.refresh_pairlist()
results[curr] = pairlists.whitelist
for curr, pairlist in results.items():
if not args.get('print_one_column', False):
print(f"Pairs for {curr}: ")
if args.get('print_one_column', False):
print('\n'.join(pairlist))
elif args.get('list_pairs_print_json', False):
print(rapidjson.dumps(list(pairlist), default=str))
else:
print(pairlist)
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
# Reset keys for backtesting
remove_credentials(self.config)
self.strategylist: List[IStrategy] = []
self.exchange = ExchangeResolver.load_exchange(
self.config['exchange']['name'], self.config)
dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = dataprovider
if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
self.strategylist.append(
StrategyResolver.load_strategy(stratconf))
validate_config_consistency(stratconf)
else:
# No strategy list specified, only one strategy
self.strategylist.append(
StrategyResolver.load_strategy(self.config))
validate_config_consistency(self.config)
if "timeframe" not in self.config:
raise OperationalException(
"Timeframe (ticker interval) needs to be set in either "
"configuration or as cli argument `--timeframe 5m`")
self.timeframe = str(self.config.get('timeframe'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException(
"VolumePairList not allowed for backtesting.")
if len(self.strategylist
) > 1 and 'PrecisionFilter' in self.pairlists.name_list:
raise OperationalException(
"PrecisionFilter not allowed for backtesting multiple strategies."
)
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
raise OperationalException("No pair in whitelist.")
if config.get('fee', None) is not None:
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(
symbol=self.pairlists.whitelist[0])
# Get maximum required startup period
self.required_startup = max(
[strat.startup_candle_count for strat in self.strategylist])
# Load one (first) strategy
self._set_strategy(self.strategylist[0])
def __init__(self, config: Dict[str, Any]) -> None:
"""
Init all variables and objects the bot needs to work
:param config: configuration dict, you can use Configuration.get_config()
to get the config dict.
"""
logger.info('Starting freqtrade %s', __version__)
# Init bot state
self.state = State.STOPPED
# Init objects
self.config = config
self._heartbeat_msg = 0
self.heartbeat_interval = self.config.get('internals', {}).get(
'heartbeat_interval', 60)
self.strategy: IStrategy = StrategyResolver(self.config).strategy
# Check config consistency here since strategies can set certain options
validate_config_consistency(config)
self.exchange = ExchangeResolver(self.config['exchange']['name'],
self.config).exchange
self.wallets = Wallets(self.config, self.exchange)
self.dataprovider = DataProvider(self.config, self.exchange)
# Attach Dataprovider to Strategy baseclass
IStrategy.dp = self.dataprovider
# Attach Wallets to Strategy baseclass
IStrategy.wallets = self.wallets
self.pairlists = PairListManager(self.exchange, self.config)
# Initializing Edge only if enabled
self.edge = Edge(self.config, self.exchange, self.strategy) if \
self.config.get('edge', {}).get('enabled', False) else None
self.active_pair_whitelist = self._refresh_whitelist()
persistence.init(self.config.get('db_url', None),
clean_open_orders=self.config.get('dry_run', False))
# Set initial bot state from config
initial_state = self.config.get('initial_state')
self.state = State[
initial_state.upper()] if initial_state else State.STOPPED
# RPC runs in separate threads, can start handling external commands just after
# initialization, even before Freqtradebot has a chance to start its throttling,
# so anything in the Freqtradebot instance should be ready (initialized), including
# the initial state of the bot.
# Keep this at the end of this initialization method.
self.rpc: RPCManager = RPCManager(self)
def test_refresh_pairlist_dynamic(mocker, shitcoinmarkets, tickers,
whitelist_conf):
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
get_tickers=tickers,
exchange_has=MagicMock(return_value=True),
)
bot = get_patched_freqtradebot(mocker, whitelist_conf)
# Remock markets with shitcoinmarkets since get_patched_freqtradebot uses the markets fixture
mocker.patch.multiple(
'freqtrade.exchange.Exchange',
markets=PropertyMock(return_value=shitcoinmarkets),
)
# argument: use the whitelist dynamically by exchange-volume
whitelist = ['ETH/BTC', 'TKN/BTC', 'LTC/BTC', 'XRP/BTC', 'HOT/BTC']
bot.pairlists.refresh_pairlist()
assert whitelist == bot.pairlists.whitelist
whitelist_conf['pairlists'] = [{'method': 'VolumePairList', 'config': {}}]
with pytest.raises(
OperationalException,
match=
r'`number_assets` not specified. Please check your configuration '
r'for "pairlist.config.number_assets"'):
PairListManager(bot.exchange, whitelist_conf)
def test_PrecisionFilter_error(mocker, whitelist_conf, tickers) -> None:
whitelist_conf['pairlists'] = [{"method": "StaticPairList"}, {"method": "PrecisionFilter"}]
del whitelist_conf['stoploss']
mocker.patch('freqtrade.exchange.Exchange.exchange_has', MagicMock(return_value=True))
with pytest.raises(OperationalException,
match=r"PrecisionFilter can only work with stoploss defined\..*"):
PairListManager(MagicMock, whitelist_conf)
def test_load_pairlist_noexist(mocker, markets, default_conf):
freqtrade = get_patched_freqtradebot(mocker, default_conf)
mocker.patch('freqtrade.exchange.Exchange.markets', PropertyMock(return_value=markets))
plm = PairListManager(freqtrade.exchange, default_conf)
with pytest.raises(OperationalException,
match=r"Impossible to load Pairlist 'NonexistingPairList'. "
r"This class does not exist or contains Python code errors."):
PairListResolver.load_pairlist('NonexistingPairList', freqtrade.exchange, plm,
default_conf, {}, 1)
class FreqtradeBot:
"""
Freqtrade is the main class of the bot.
This is from here the bot start its logic.
"""
def __init__(self, config: Dict[str, Any]) -> None:
"""
Init all variables and objects the bot needs to work
:param config: configuration dict, you can use Configuration.get_config()
to get the config dict.
"""
logger.info('Starting freqtrade %s', __version__)
# Init bot state
self.state = State.STOPPED
# Init objects
self.config = config
self._heartbeat_msg = 0
self.heartbeat_interval = self.config.get('internals', {}).get(
'heartbeat_interval', 60)
self.strategy: IStrategy = StrategyResolver(self.config).strategy
# Check config consistency here since strategies can set certain options
validate_config_consistency(config)
self.exchange = ExchangeResolver(self.config['exchange']['name'],
self.config).exchange
self.wallets = Wallets(self.config, self.exchange)
self.dataprovider = DataProvider(self.config, self.exchange)
# Attach Dataprovider to Strategy baseclass
IStrategy.dp = self.dataprovider
# Attach Wallets to Strategy baseclass
IStrategy.wallets = self.wallets
self.pairlists = PairListManager(self.exchange, self.config)
# Initializing Edge only if enabled
self.edge = Edge(self.config, self.exchange, self.strategy) if \
self.config.get('edge', {}).get('enabled', False) else None
self.active_pair_whitelist = self._refresh_whitelist()
persistence.init(self.config.get('db_url', None),
clean_open_orders=self.config.get('dry_run', False))
# Set initial bot state from config
initial_state = self.config.get('initial_state')
self.state = State[
initial_state.upper()] if initial_state else State.STOPPED
# RPC runs in separate threads, can start handling external commands just after
# initialization, even before Freqtradebot has a chance to start its throttling,
# so anything in the Freqtradebot instance should be ready (initialized), including
# the initial state of the bot.
# Keep this at the end of this initialization method.
self.rpc: RPCManager = RPCManager(self)
def cleanup(self) -> None:
"""
Cleanup pending resources on an already stopped bot
:return: None
"""
logger.info('Cleaning up modules ...')
self.rpc.cleanup()
persistence.cleanup()
def startup(self) -> None:
"""
Called on startup and after reloading the bot - triggers notifications and
performs startup tasks
"""
self.rpc.startup_messages(self.config, self.pairlists)
if not self.edge:
# Adjust stoploss if it was changed
Trade.stoploss_reinitialization(self.strategy.stoploss)
def process(self) -> None:
"""
Queries the persistence layer for open trades and handles them,
otherwise a new trade is created.
:return: True if one or more trades has been created or closed, False otherwise
"""
# Check whether markets have to be reloaded
self.exchange._reload_markets()
# Query trades from persistence layer
trades = Trade.get_open_trades()
self.active_pair_whitelist = self._refresh_whitelist(trades)
# Refreshing candles
self.dataprovider.refresh(
self._create_pair_whitelist(self.active_pair_whitelist),
self.strategy.informative_pairs())
# First process current opened trades
self.process_maybe_execute_sells(trades)
# Then looking for buy opportunities
if len(trades) < self.config['max_open_trades']:
self.process_maybe_execute_buys()
# Check and handle any timed out open orders
self.check_handle_timedout()
Trade.session.flush()
if (self.heartbeat_interval
and (arrow.utcnow().timestamp - self._heartbeat_msg >
self.heartbeat_interval)):
logger.info(f"Bot heartbeat. PID={getpid()}")
self._heartbeat_msg = arrow.utcnow().timestamp
def _refresh_whitelist(self, trades: List[Trade] = []) -> List[str]:
"""
Refresh whitelist from pairlist or edge and extend it with trades.
"""
# Refresh whitelist
self.pairlists.refresh_pairlist()
_whitelist = self.pairlists.whitelist
# Calculating Edge positioning
if self.edge:
self.edge.calculate()
_whitelist = self.edge.adjust(_whitelist)
if trades:
# Extend active-pair whitelist with pairs from open trades
# It ensures that tickers are downloaded for open trades
_whitelist.extend([
trade.pair for trade in trades if trade.pair not in _whitelist
])
return _whitelist
def _create_pair_whitelist(self,
pairs: List[str]) -> List[Tuple[str, str]]:
"""
Create pair-whitelist tuple with (pair, ticker_interval)
"""
return [(pair, self.config['ticker_interval']) for pair in pairs]
def get_target_bid(self, pair: str, tick: Dict = None) -> float:
"""
Calculates bid target between current ask price and last price
:return: float: Price
"""
config_bid_strategy = self.config.get('bid_strategy', {})
if 'use_order_book' in config_bid_strategy and\
config_bid_strategy.get('use_order_book', False):
logger.info('Getting price from order book')
order_book_top = config_bid_strategy.get('order_book_top', 1)
order_book = self.exchange.get_order_book(pair, order_book_top)
logger.debug('order_book %s', order_book)
# top 1 = index 0
order_book_rate = order_book['bids'][order_book_top - 1][0]
logger.info('...top %s order book buy rate %0.8f', order_book_top,
order_book_rate)
used_rate = order_book_rate
else:
if not tick:
logger.info('Using Last Ask / Last Price')
ticker = self.exchange.get_ticker(pair)
else:
ticker = tick
if ticker['ask'] < ticker['last']:
ticker_rate = ticker['ask']
else:
balance = self.config['bid_strategy']['ask_last_balance']
ticker_rate = ticker['ask'] + balance * (ticker['last'] -
ticker['ask'])
used_rate = ticker_rate
return used_rate
def _get_trade_stake_amount(self, pair) -> Optional[float]:
"""
Check if stake amount can be fulfilled with the available balance
for the stake currency
:return: float: Stake Amount
"""
if self.edge:
return self.edge.stake_amount(
pair, self.wallets.get_free(self.config['stake_currency']),
self.wallets.get_total(self.config['stake_currency']),
Trade.total_open_trades_stakes())
else:
stake_amount = self.config['stake_amount']
available_amount = self.wallets.get_free(self.config['stake_currency'])
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
open_trades = len(Trade.get_open_trades())
if open_trades >= self.config['max_open_trades']:
logger.warning(
"Can't open a new trade: max number of trades is reached")
return None
return available_amount / (self.config['max_open_trades'] -
open_trades)
# Check if stake_amount is fulfilled
if available_amount < stake_amount:
raise DependencyException(
f"Available balance({available_amount} {self.config['stake_currency']}) is "
f"lower than stake amount({stake_amount} {self.config['stake_currency']})"
)
return stake_amount
def _get_min_pair_stake_amount(self, pair: str,
price: float) -> Optional[float]:
try:
market = self.exchange.markets[pair]
except KeyError:
raise ValueError(f"Can't get market information for symbol {pair}")
if 'limits' not in market:
return None
min_stake_amounts = []
limits = market['limits']
if ('cost' in limits and 'min' in limits['cost']
and limits['cost']['min'] is not None):
min_stake_amounts.append(limits['cost']['min'])
if ('amount' in limits and 'min' in limits['amount']
and limits['amount']['min'] is not None):
min_stake_amounts.append(limits['amount']['min'] * price)
if not min_stake_amounts:
return None
# reserve some percent defined in config (5% default) + stoploss
amount_reserve_percent = 1.0 - self.config.get(
'amount_reserve_percent', constants.DEFAULT_AMOUNT_RESERVE_PERCENT)
if self.strategy.stoploss is not None:
amount_reserve_percent += self.strategy.stoploss
# it should not be more than 50%
amount_reserve_percent = max(amount_reserve_percent, 0.5)
# The value returned should satisfy both limits: for amount (base currency) and
# for cost (quote, stake currency), so max() is used here.
# See also #2575 at github.
return max(min_stake_amounts) / amount_reserve_percent
def create_trades(self) -> bool:
"""
Checks the implemented trading strategy for buy-signals, using the active pair whitelist.
If a pair triggers the buy_signal a new trade record gets created.
Checks pairs as long as the open trade count is below `max_open_trades`.
:return: True if at least one trade has been created.
"""
whitelist = copy.deepcopy(self.active_pair_whitelist)
if not whitelist:
logger.info("Active pair whitelist is empty.")
return False
# Remove currently opened and latest pairs from whitelist
for trade in Trade.get_open_trades():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
logger.info("No currency pair in active pair whitelist, "
"but checking to sell open trades.")
return False
buycount = 0
# running get_signal on historical data fetched
for _pair in whitelist:
if self.strategy.is_pair_locked(_pair):
logger.info(f"Pair {_pair} is currently locked.")
continue
(buy, sell) = self.strategy.get_signal(
_pair, self.strategy.ticker_interval,
self.dataprovider.ohlcv(_pair, self.strategy.ticker_interval))
if buy and not sell and len(
Trade.get_open_trades()) < self.config['max_open_trades']:
stake_amount = self._get_trade_stake_amount(_pair)
if not stake_amount:
continue
logger.info(
f"Buy signal found: about create a new trade with stake_amount: "
f"{stake_amount} ...")
bidstrat_check_depth_of_market = self.config.get('bid_strategy', {}).\
get('check_depth_of_market', {})
if (bidstrat_check_depth_of_market.get('enabled', False)) and\
(bidstrat_check_depth_of_market.get('bids_to_ask_delta', 0) > 0):
if self._check_depth_of_market_buy(
_pair, bidstrat_check_depth_of_market):
buycount += self.execute_buy(_pair, stake_amount)
continue
buycount += self.execute_buy(_pair, stake_amount)
return buycount > 0
def _check_depth_of_market_buy(self, pair: str, conf: Dict) -> bool:
"""
Checks depth of market before executing a buy
"""
conf_bids_to_ask_delta = conf.get('bids_to_ask_delta', 0)
logger.info('checking depth of market for %s', pair)
order_book = self.exchange.get_order_book(pair, 1000)
order_book_data_frame = order_book_to_dataframe(
order_book['bids'], order_book['asks'])
order_book_bids = order_book_data_frame['b_size'].sum()
order_book_asks = order_book_data_frame['a_size'].sum()
bids_ask_delta = order_book_bids / order_book_asks
logger.info('bids: %s, asks: %s, delta: %s', order_book_bids,
order_book_asks, bids_ask_delta)
if bids_ask_delta >= conf_bids_to_ask_delta:
return True
return False
def execute_buy(self,
pair: str,
stake_amount: float,
price: Optional[float] = None) -> bool:
"""
Executes a limit buy for the given pair
:param pair: pair for which we want to create a LIMIT_BUY
:return: None
"""
pair_s = pair.replace('_', '/')
stake_currency = self.config['stake_currency']
fiat_currency = self.config.get('fiat_display_currency', None)
time_in_force = self.strategy.order_time_in_force['buy']
if price:
buy_limit_requested = price
else:
# Calculate amount
buy_limit_requested = self.get_target_bid(pair)
min_stake_amount = self._get_min_pair_stake_amount(
pair_s, buy_limit_requested)
if min_stake_amount is not None and min_stake_amount > stake_amount:
logger.warning(
f"Can't open a new trade for {pair_s}: stake amount "
f"is too small ({stake_amount} < {min_stake_amount})")
return False
amount = stake_amount / buy_limit_requested
order_type = self.strategy.order_types['buy']
order = self.exchange.buy(pair=pair,
ordertype=order_type,
amount=amount,
rate=buy_limit_requested,
time_in_force=time_in_force)
order_id = order['id']
order_status = order.get('status', None)
# we assume the order is executed at the price requested
buy_limit_filled_price = buy_limit_requested
if order_status == 'expired' or order_status == 'rejected':
order_tif = self.strategy.order_time_in_force['buy']
# return false if the order is not filled
if float(order['filled']) == 0:
logger.warning(
'Buy %s order with time in force %s for %s is %s by %s.'
' zero amount is fulfilled.', order_tif, order_type,
pair_s, order_status, self.exchange.name)
return False
else:
# the order is partially fulfilled
# in case of IOC orders we can check immediately
# if the order is fulfilled fully or partially
logger.warning(
'Buy %s order with time in force %s for %s is %s by %s.'
' %s amount fulfilled out of %s (%s remaining which is canceled).',
order_tif, order_type, pair_s, order_status,
self.exchange.name, order['filled'], order['amount'],
order['remaining'])
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
order_id = None
# in case of FOK the order may be filled immediately and fully
elif order_status == 'closed':
stake_amount = order['cost']
amount = order['amount']
buy_limit_filled_price = order['price']
self.rpc.send_msg({
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': pair_s,
'limit': buy_limit_filled_price,
'order_type': order_type,
'stake_amount': stake_amount,
'stake_currency': stake_currency,
'fiat_currency': fiat_currency
})
# Fee is applied twice because we make a LIMIT_BUY and LIMIT_SELL
fee = self.exchange.get_fee(symbol=pair, taker_or_maker='maker')
trade = Trade(pair=pair,
stake_amount=stake_amount,
amount=amount,
fee_open=fee,
fee_close=fee,
open_rate=buy_limit_filled_price,
open_rate_requested=buy_limit_requested,
open_date=datetime.utcnow(),
exchange=self.exchange.id,
open_order_id=order_id,
strategy=self.strategy.get_strategy_name(),
ticker_interval=timeframe_to_minutes(
self.config['ticker_interval']))
# Update fees if order is closed
if order_status == 'closed':
self.update_trade_state(trade, order)
Trade.session.add(trade)
Trade.session.flush()
# Updating wallets
self.wallets.update()
return True
def process_maybe_execute_buys(self) -> None:
"""
Tries to execute buy orders for trades in a safe way
"""
try:
# Create entity and execute trade
if not self.create_trades():
logger.debug(
'Found no buy signals for whitelisted currencies. Trying again...'
)
except DependencyException as exception:
logger.warning('Unable to create trade: %s', exception)
def process_maybe_execute_sells(self, trades: List[Any]) -> None:
"""
Tries to execute sell orders for trades in a safe way
"""
result = False
for trade in trades:
try:
self.update_trade_state(trade)
if (self.strategy.order_types.get('stoploss_on_exchange')
and self.handle_stoploss_on_exchange(trade)):
result = True
continue
# Check if we can sell our current pair
if trade.open_order_id is None and self.handle_trade(trade):
result = True
except DependencyException as exception:
logger.warning('Unable to sell trade: %s', exception)
# Updating wallets if any trade occured
if result:
self.wallets.update()
def get_real_amount(self,
trade: Trade,
order: Dict,
order_amount: float = None) -> float:
"""
Get real amount for the trade
Necessary for exchanges which charge fees in base currency (e.g. binance)
"""
if order_amount is None:
order_amount = order['amount']
# Only run for closed orders
if trade.fee_open == 0 or order['status'] == 'open':
return order_amount
# use fee from order-dict if possible
if ('fee' in order and order['fee'] is not None
and (order['fee'].keys() >= {'currency', 'cost'})):
if (order['fee']['currency'] is not None
and order['fee']['cost'] is not None
and trade.pair.startswith(order['fee']['currency'])):
new_amount = order_amount - order['fee']['cost']
logger.info(
"Applying fee on amount for %s (from %s to %s) from Order",
trade, order['amount'], new_amount)
return new_amount
# Fallback to Trades
trades = self.exchange.get_trades_for_order(trade.open_order_id,
trade.pair,
trade.open_date)
if len(trades) == 0:
logger.info(
"Applying fee on amount for %s failed: myTrade-Dict empty found",
trade)
return order_amount
amount = 0
fee_abs = 0
for exectrade in trades:
amount += exectrade['amount']
if ("fee" in exectrade and exectrade['fee'] is not None
and (exectrade['fee'].keys() >= {'currency', 'cost'})):
# only applies if fee is in quote currency!
if (exectrade['fee']['currency'] is not None
and exectrade['fee']['cost'] is not None and
trade.pair.startswith(exectrade['fee']['currency'])):
fee_abs += exectrade['fee']['cost']
if not isclose(amount, order_amount,
abs_tol=constants.MATH_CLOSE_PREC):
logger.warning(
f"Amount {amount} does not match amount {trade.amount}")
raise DependencyException("Half bought? Amounts don't match")
real_amount = amount - fee_abs
if fee_abs != 0:
logger.info(f"Applying fee on amount for {trade} "
f"(from {order_amount} to {real_amount}) from Trades")
return real_amount
def update_trade_state(self, trade, action_order: dict = None):
"""
Checks trades with open orders and updates the amount if necessary
"""
# Get order details for actual price per unit
if trade.open_order_id:
# Update trade with order values
logger.info('Found open order for %s', trade)
try:
order = action_order or self.exchange.get_order(
trade.open_order_id, trade.pair)
except InvalidOrderException as exception:
logger.warning('Unable to fetch order %s: %s',
trade.open_order_id, exception)
return
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order)
if not isclose(order['amount'],
new_amount,
abs_tol=constants.MATH_CLOSE_PREC):
order['amount'] = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
except DependencyException as exception:
logger.warning("Could not update trade amount: %s", exception)
trade.update(order)
# Updating wallets when order is closed
if not trade.is_open:
self.wallets.update()
def get_sell_rate(self, pair: str, refresh: bool) -> float:
"""
Get sell rate - either using get-ticker bid or first bid based on orderbook
The orderbook portion is only used for rpc messaging, which would otherwise fail
for BitMex (has no bid/ask in get_ticker)
or remain static in any other case since it's not updating.
:return: Bid rate
"""
config_ask_strategy = self.config.get('ask_strategy', {})
if config_ask_strategy.get('use_order_book', False):
logger.debug('Using order book to get sell rate')
order_book = self.exchange.get_order_book(pair, 1)
rate = order_book['bids'][0][0]
else:
rate = self.exchange.get_ticker(pair, refresh)['bid']
return rate
def handle_trade(self, trade: Trade) -> bool:
"""
Sells the current pair if the threshold is reached and updates the trade record.
:return: True if trade has been sold, False otherwise
"""
if not trade.is_open:
raise DependencyException(
f'Attempt to handle closed trade: {trade}')
logger.debug('Handling %s ...', trade)
(buy, sell) = (False, False)
config_ask_strategy = self.config.get('ask_strategy', {})
if (config_ask_strategy.get('use_sell_signal', True)
or config_ask_strategy.get('ignore_roi_if_buy_signal')):
(buy, sell) = self.strategy.get_signal(
trade.pair, self.strategy.ticker_interval,
self.dataprovider.ohlcv(trade.pair,
self.strategy.ticker_interval))
if config_ask_strategy.get('use_order_book', False):
logger.info('Using order book for selling...')
# logger.debug('Order book %s',orderBook)
order_book_min = config_ask_strategy.get('order_book_min', 1)
order_book_max = config_ask_strategy.get('order_book_max', 1)
order_book = self.exchange.get_order_book(trade.pair,
order_book_max)
for i in range(order_book_min, order_book_max + 1):
order_book_rate = order_book['asks'][i - 1][0]
logger.info(' order book asks top %s: %0.8f', i,
order_book_rate)
sell_rate = order_book_rate
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
else:
logger.debug('checking sell')
sell_rate = self.get_sell_rate(trade.pair, True)
if self._check_and_execute_sell(trade, sell_rate, buy, sell):
return True
logger.debug('Found no sell signal for %s.', trade)
return False
def create_stoploss_order(self, trade: Trade, stop_price: float,
rate: float) -> bool:
"""
Abstracts creating stoploss orders from the logic.
Handles errors and updates the trade database object.
Force-sells the pair (using EmergencySell reason) in case of Problems creating the order.
:return: True if the order succeeded, and False in case of problems.
"""
# Limit price threshold: As limit price should always be below stop-price
LIMIT_PRICE_PCT = self.strategy.order_types.get(
'stoploss_on_exchange_limit_ratio', 0.99)
try:
stoploss_order = self.exchange.stoploss_limit(
pair=trade.pair,
amount=trade.amount,
stop_price=stop_price,
rate=rate * LIMIT_PRICE_PCT)
trade.stoploss_order_id = str(stoploss_order['id'])
return True
except InvalidOrderException as e:
trade.stoploss_order_id = None
logger.error(f'Unable to place a stoploss order on exchange. {e}')
logger.warning('Selling the trade forcefully')
self.execute_sell(trade,
trade.stop_loss,
sell_reason=SellType.EMERGENCY_SELL)
except DependencyException:
trade.stoploss_order_id = None
logger.exception('Unable to place a stoploss order on exchange.')
return False
def handle_stoploss_on_exchange(self, trade: Trade) -> bool:
"""
Check if trade is fulfilled in which case the stoploss
on exchange should be added immediately if stoploss on exchange
is enabled.
"""
logger.debug('Handling stoploss on exchange %s ...', trade)
stoploss_order = None
try:
# First we check if there is already a stoploss on exchange
stoploss_order = self.exchange.get_order(trade.stoploss_order_id, trade.pair) \
if trade.stoploss_order_id else None
except InvalidOrderException as exception:
logger.warning('Unable to fetch stoploss order: %s', exception)
# If buy order is fulfilled but there is no stoploss, we add a stoploss on exchange
if (not trade.open_order_id and not stoploss_order):
stoploss = self.edge.stoploss(
pair=trade.pair) if self.edge else self.strategy.stoploss
stop_price = trade.open_rate * (1 + stoploss)
if self.create_stoploss_order(trade=trade,
stop_price=stop_price,
rate=stop_price):
trade.stoploss_last_update = datetime.now()
return False
# If stoploss order is canceled for some reason we add it
if stoploss_order and stoploss_order['status'] == 'canceled':
if self.create_stoploss_order(trade=trade,
stop_price=trade.stop_loss,
rate=trade.stop_loss):
return False
else:
trade.stoploss_order_id = None
logger.warning(
'Stoploss order was cancelled, but unable to recreate one.'
)
# We check if stoploss order is fulfilled
if stoploss_order and stoploss_order['status'] == 'closed':
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
trade.update(stoploss_order)
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(
trade.pair,
timeframe_to_next_date(self.config['ticker_interval']))
self._notify_sell(trade, "stoploss")
return True
# Finally we check if stoploss on exchange should be moved up because of trailing.
if stoploss_order and self.config.get('trailing_stop', False):
# if trailing stoploss is enabled we check if stoploss value has changed
# in which case we cancel stoploss order and put another one with new
# value immediately
self.handle_trailing_stoploss_on_exchange(trade, stoploss_order)
return False
def handle_trailing_stoploss_on_exchange(self, trade: Trade, order):
"""
Check to see if stoploss on exchange should be updated
in case of trailing stoploss on exchange
:param Trade: Corresponding Trade
:param order: Current on exchange stoploss order
:return: None
"""
if trade.stop_loss > float(order['info']['stopPrice']):
# we check if the update is neccesary
update_beat = self.strategy.order_types.get(
'stoploss_on_exchange_interval', 60)
if (datetime.utcnow() -
trade.stoploss_last_update).total_seconds() >= update_beat:
# cancelling the current stoploss on exchange first
logger.info(
'Trailing stoploss: cancelling current stoploss on exchange (id:{%s})'
'in order to add another one ...', order['id'])
try:
self.exchange.cancel_order(order['id'], trade.pair)
except InvalidOrderException:
logger.exception(
f"Could not cancel stoploss order {order['id']} "
f"for pair {trade.pair}")
# Create new stoploss order
if self.create_stoploss_order(trade=trade,
stop_price=trade.stop_loss,
rate=trade.stop_loss):
return False
else:
logger.warning(f"Could not create trailing stoploss order "
f"for pair {trade.pair}.")
def _check_and_execute_sell(self, trade: Trade, sell_rate: float,
buy: bool, sell: bool) -> bool:
"""
Check and execute sell
"""
should_sell = self.strategy.should_sell(
trade,
sell_rate,
datetime.utcnow(),
buy,
sell,
force_stoploss=self.edge.stoploss(trade.pair) if self.edge else 0)
if should_sell.sell_flag:
self.execute_sell(trade, sell_rate, should_sell.sell_type)
logger.info('executed sell, reason: %s', should_sell.sell_type)
return True
return False
def _check_timed_out(self, side: str, order: dict) -> bool:
"""
Check if timeout is active, and if the order is still open and timed out
"""
timeout = self.config.get('unfilledtimeout', {}).get(side)
ordertime = arrow.get(order['datetime']).datetime
if timeout is not None:
timeout_threshold = arrow.utcnow().shift(minutes=-timeout).datetime
return (order['status'] == 'open' and order['side'] == side
and ordertime < timeout_threshold)
return False
def check_handle_timedout(self) -> None:
"""
Check if any orders are timed out and cancel if neccessary
:param timeoutvalue: Number of minutes until order is considered timed out
:return: None
"""
for trade in Trade.get_open_order_trades():
try:
if not trade.open_order_id:
continue
order = self.exchange.get_order(trade.open_order_id,
trade.pair)
except (RequestException, DependencyException,
InvalidOrderException):
logger.info('Cannot query order for %s due to %s', trade,
traceback.format_exc())
continue
# Check if trade is still actually open
if float(order.get('remaining', 0.0)) == 0.0:
self.wallets.update()
continue
if ((order['side'] == 'buy' and order['status'] == 'canceled')
or (self._check_timed_out('buy', order))):
self.handle_timedout_limit_buy(trade, order)
self.wallets.update()
elif ((order['side'] == 'sell' and order['status'] == 'canceled')
or (self._check_timed_out('sell', order))):
self.handle_timedout_limit_sell(trade, order)
self.wallets.update()
def handle_buy_order_full_cancel(self, trade: Trade, reason: str) -> None:
"""Close trade in database and send message"""
Trade.session.delete(trade)
Trade.session.flush()
logger.info('Buy order %s for %s.', reason, trade)
self.rpc.send_msg({
'type':
RPCMessageType.STATUS_NOTIFICATION,
'status':
f'Unfilled buy order for {trade.pair} {reason}'
})
def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
"""
Buy timeout - cancel order
:return: True if order was fully cancelled
"""
reason = "cancelled due to timeout"
if order['status'] != 'canceled':
corder = self.exchange.cancel_order(trade.open_order_id,
trade.pair)
else:
# Order was cancelled already, so we can reuse the existing dict
corder = order
reason = "canceled on Exchange"
if corder.get('remaining', order['remaining']) == order['amount']:
# if trade is not partially completed, just delete the trade
self.handle_buy_order_full_cancel(trade, reason)
return True
# if trade is partially complete, edit the stake details for the trade
# and close the order
# cancel_order may not contain the full order dict, so we need to fallback
# to the order dict aquired before cancelling.
# we need to fall back to the values from order if corder does not contain these keys.
trade.amount = order['amount'] - corder.get('remaining',
order['remaining'])
trade.stake_amount = trade.amount * trade.open_rate
# verify if fees were taken from amount to avoid problems during selling
try:
new_amount = self.get_real_amount(
trade, corder if 'fee' in corder else order, trade.amount)
if not isclose(order['amount'],
new_amount,
abs_tol=constants.MATH_CLOSE_PREC):
trade.amount = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
except DependencyException as e:
logger.warning("Could not update trade amount: %s", e)
trade.open_order_id = None
logger.info('Partial buy order timeout for %s.', trade)
self.rpc.send_msg({
'type':
RPCMessageType.STATUS_NOTIFICATION,
'status':
f'Remaining buy order for {trade.pair} cancelled due to timeout'
})
return False
def handle_timedout_limit_sell(self, trade: Trade, order: Dict) -> bool:
"""
Sell timeout - cancel order and update trade
:return: True if order was fully cancelled
"""
if order['remaining'] == order['amount']:
# if trade is not partially completed, just cancel the trade
if order["status"] != "canceled":
reason = "due to timeout"
self.exchange.cancel_order(trade.open_order_id, trade.pair)
logger.info('Sell order timeout for %s.', trade)
else:
reason = "on exchange"
logger.info('Sell order canceled on exchange for %s.', trade)
trade.close_rate = None
trade.close_profit = None
trade.close_date = None
trade.is_open = True
trade.open_order_id = None
self.rpc.send_msg({
'type':
RPCMessageType.STATUS_NOTIFICATION,
'status':
f'Unfilled sell order for {trade.pair} cancelled {reason}'
})
return True
# TODO: figure out how to handle partially complete sell orders
return False
def execute_sell(self, trade: Trade, limit: float,
sell_reason: SellType) -> None:
"""
Executes a limit sell for the given trade and limit
:param trade: Trade instance
:param limit: limit rate for the sell order
:param sellreason: Reason the sell was triggered
:return: None
"""
sell_type = 'sell'
if sell_reason in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
sell_type = 'stoploss'
# if stoploss is on exchange and we are on dry_run mode,
# we consider the sell price stop price
if self.config.get('dry_run', False) and sell_type == 'stoploss' \
and self.strategy.order_types['stoploss_on_exchange']:
limit = trade.stop_loss
# First cancelling stoploss on exchange ...
if self.strategy.order_types.get(
'stoploss_on_exchange') and trade.stoploss_order_id:
try:
self.exchange.cancel_order(trade.stoploss_order_id, trade.pair)
except InvalidOrderException:
logger.exception(
f"Could not cancel stoploss order {trade.stoploss_order_id}"
)
ordertype = self.strategy.order_types[sell_type]
if sell_reason == SellType.EMERGENCY_SELL:
# Emergencysells (default to market!)
ordertype = self.strategy.order_types.get("emergencysell",
"market")
# Execute sell and update trade record
order = self.exchange.sell(
pair=str(trade.pair),
ordertype=ordertype,
amount=trade.amount,
rate=limit,
time_in_force=self.strategy.order_time_in_force['sell'])
trade.open_order_id = order['id']
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
# In case of market sell orders the order can be closed immediately
if order.get('status', 'unknown') == 'closed':
trade.update(order)
Trade.session.flush()
# Lock pair for one candle to prevent immediate rebuys
self.strategy.lock_pair(
trade.pair, timeframe_to_next_date(self.config['ticker_interval']))
self._notify_sell(trade, ordertype)
def _notify_sell(self, trade: Trade, order_type: str):
"""
Sends rpc notification when a sell occured.
"""
profit_rate = trade.close_rate if trade.close_rate else trade.close_rate_requested
profit_trade = trade.calc_profit(rate=profit_rate)
# Use cached ticker here - it was updated seconds ago.
current_rate = self.get_sell_rate(trade.pair, False)
profit_percent = trade.calc_profit_percent(profit_rate)
gain = "profit" if profit_percent > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
'limit': trade.close_rate_requested,
'order_type': order_type,
'amount': trade.amount,
'open_rate': trade.open_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
'sell_reason': trade.sell_reason
}
# For regular case, when the configuration exists
if 'stake_currency' in self.config and 'fiat_display_currency' in self.config:
stake_currency = self.config['stake_currency']
fiat_currency = self.config['fiat_display_currency']
msg.update({
'stake_currency': stake_currency,
'fiat_currency': fiat_currency,
})
# Send the message
self.rpc.send_msg(msg)
class Backtesting:
"""
Backtesting class, this class contains all the logic to run a backtest
To run a backtest:
backtesting = Backtesting(config)
backtesting.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
# Reset keys for backtesting
remove_credentials(self.config)
self.strategylist: List[IStrategy] = []
self.exchange = ExchangeResolver.load_exchange(
self.config['exchange']['name'], self.config)
if self.config.get('runmode') != RunMode.HYPEROPT:
self.dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = self.dataprovider
if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
self.strategylist.append(
StrategyResolver.load_strategy(stratconf))
validate_config_consistency(stratconf)
else:
# No strategy list specified, only one strategy
self.strategylist.append(
StrategyResolver.load_strategy(self.config))
validate_config_consistency(self.config)
if "timeframe" not in self.config:
raise OperationalException(
"Timeframe (ticker interval) needs to be set in either "
"configuration or as cli argument `--timeframe 5m`")
self.timeframe = str(self.config.get('timeframe'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException(
"VolumePairList not allowed for backtesting.")
if len(self.strategylist
) > 1 and 'PrecisionFilter' in self.pairlists.name_list:
raise OperationalException(
"PrecisionFilter not allowed for backtesting multiple strategies."
)
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
raise OperationalException("No pair in whitelist.")
if config.get('fee', None) is not None:
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(
symbol=self.pairlists.whitelist[0])
# Get maximum required startup period
self.required_startup = max(
[strat.startup_candle_count for strat in self.strategylist])
# Load one (first) strategy
self._set_strategy(self.strategylist[0])
def _set_strategy(self, strategy):
"""
Load strategy into backtesting
"""
self.strategy = strategy
# Set stoploss_on_exchange to false for backtesting,
# since a "perfect" stoploss-sell is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=timerange,
startup_candles=self.required_startup,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
min_date, max_date = history.get_timerange(data)
logger.info('Loading data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(),
(max_date - min_date).days)
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(
timeframe_to_seconds(self.timeframe), self.required_startup,
min_date)
return data, timerange
def _get_ohlcv_as_lists(self, processed: Dict) -> Dict[str, DataFrame]:
"""
Helper function to convert a processed dataframes into lists for performance reasons.
Used by backtest() - so keep this optimized for performance.
"""
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
data: Dict = {}
# Create dict with data
for pair, pair_data in processed.items():
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}),
{'pair': pair})[headers].copy()
# To avoid using data from future, we use buy/sell signals shifted
# from the previous candle
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = [x for x in df_analyzed.itertuples()]
return data
def _get_close_rate(self, sell_row, trade: Trade, sell: SellCheckTuple,
trade_dur: int) -> float:
"""
Get close rate for backtesting result
"""
# Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
# Set close_rate to stoploss
return trade.stop_loss
elif sell.sell_type == (SellType.ROI):
roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
if roi is not None:
if roi == -1 and roi_entry % self.timeframe_min == 0:
# When forceselling with ROI=-1, the roi time will always be equal to trade_dur.
# If that entry is a multiple of the timeframe (so on candle open)
# - we'll use open instead of close
return sell_row.open
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
close_rate = -(trade.open_rate * roi + trade.open_rate *
(1 + trade.fee_open)) / (trade.fee_close - 1)
if (trade_dur > 0 and trade_dur == roi_entry
and roi_entry % self.timeframe_min == 0
and sell_row.open > close_rate):
# new ROI entry came into effect.
# use Open rate if open_rate > calculated sell rate
return sell_row.open
# Use the maximum between close_rate and low as we
# cannot sell outside of a candle.
# Applies when a new ROI setting comes in place and the whole candle is above that.
return max(close_rate, sell_row.low)
else:
# This should not be reached...
return sell_row.open
else:
return sell_row.open
def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame, partial_ohlcv: List,
trade_count_lock: Dict, stake_amount: float,
max_open_trades: int) -> Optional[BacktestResult]:
trade = Trade(
pair=pair,
open_rate=buy_row.open,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
)
logger.debug(
f"{pair} - Backtesting emulates creation of new trade: {trade}.")
# calculate win/lose forwards from buy point
for sell_row in partial_ohlcv:
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(
sell_row.date, 0) + 1
sell = self.strategy.should_sell(trade,
sell_row.open,
sell_row.date,
sell_row.buy,
sell_row.sell,
low=sell_row.low,
high=sell_row.high)
if sell.sell_flag:
trade_dur = int(
(sell_row.date - buy_row.date).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell,
trade_dur)
return BacktestResult(
pair=pair,
profit_percent=trade.calc_profit_ratio(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=trade_dur,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False,
open_rate=buy_row.open,
close_rate=closerate,
sell_reason=sell.sell_type)
if partial_ohlcv:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ohlcv[-1]
bt_res = BacktestResult(
pair=pair,
profit_percent=trade.calc_profit_ratio(rate=sell_row.open),
profit_abs=trade.calc_profit(rate=sell_row.open),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=int(
(sell_row.date - buy_row.date).total_seconds() // 60),
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=True,
open_rate=buy_row.open,
close_rate=sell_row.open,
sell_reason=SellType.FORCE_SELL)
logger.debug(f"{pair} - Force selling still open trade, "
f"profit percent: {bt_res.profit_percent}, "
f"profit abs: {bt_res.profit_abs}")
return bt_res
return None
def backtest(self,
processed: Dict,
stake_amount: float,
start_date: arrow.Arrow,
end_date: arrow.Arrow,
max_open_trades: int = 0,
position_stacking: bool = False) -> DataFrame:
"""
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}
:param stake_amount: amount to use for each trade
: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?
:return: DataFrame with trades (results of backtesting)
"""
logger.debug(
f"Run backtest, stake_amount: {stake_amount}, "
f"start_date: {start_date}, end_date: {end_date}, "
f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
)
trades = []
trade_count_lock: Dict = {}
# 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)
lock_pair_until: Dict = {}
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = {}
tmp = start_date + timedelta(minutes=self.timeframe_min)
# Loop timerange and get candle for each pair at that point in time
while tmp < end_date:
for i, pair in enumerate(data):
if pair not in indexes:
indexes[pair] = 0
try:
row = data[pair][indexes[pair]]
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 > tmp.datetime:
continue
indexes[pair] += 1
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 and pair in lock_pair_until
and row.date <= lock_pair_until[pair]):
# without positionstacking, we can only have one open trade per pair.
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
if not trade_count_lock.get(row.date, 0) < max_open_trades:
continue
trade_count_lock[row.date] = trade_count_lock.get(
row.date, 0) + 1
# since indexes has been incremented before, we need to go one step back to
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(
pair, row, data[pair][indexes[pair] - 1:],
trade_count_lock, stake_amount, max_open_trades)
if trade_entry:
logger.debug(f"{pair} - Locking pair till "
f"close_time={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
lock_pair_until[pair] = end_date.datetime
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.timeframe_min)
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
"""
Run backtesting end-to-end
:return: None
"""
data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...',
self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
max_open_trades = self.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...'
)
max_open_trades = 0
position_stacking = self.config.get('position_stacking', False)
data, timerange = self.load_bt_data()
all_results = {}
for strat in self.strategylist:
logger.info("Running backtesting for Strategy %s",
strat.get_strategy_name())
self._set_strategy(strat)
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = history.get_timerange(preprocessed)
logger.info('Backtesting with data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(),
(max_date - min_date).days)
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
processed=preprocessed,
stake_amount=self.config['stake_amount'],
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=position_stacking,
)
if self.config.get('export', False):
store_backtest_result(self.config['exportfilename'], all_results)
# Show backtest results
stats = generate_backtest_stats(self.config, data, all_results)
show_backtest_results(self.config, stats)
class Backtesting:
"""
Backtesting class, this class contains all the logic to run a backtest
To run a backtest:
backtesting = Backtesting(config)
backtesting.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
# Reset keys for backtesting
remove_credentials(self.config)
self.strategylist: List[IStrategy] = []
self.exchange = ExchangeResolver.load_exchange(
self.config['exchange']['name'], self.config)
dataprovider = DataProvider(self.config, self.exchange)
IStrategy.dp = dataprovider
if self.config.get('strategy_list', None):
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
self.strategylist.append(
StrategyResolver.load_strategy(stratconf))
validate_config_consistency(stratconf)
else:
# No strategy list specified, only one strategy
self.strategylist.append(
StrategyResolver.load_strategy(self.config))
validate_config_consistency(self.config)
if "timeframe" not in self.config:
raise OperationalException(
"Timeframe (ticker interval) needs to be set in either "
"configuration or as cli argument `--timeframe 5m`")
self.timeframe = str(self.config.get('timeframe'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException(
"VolumePairList not allowed for backtesting.")
if len(self.strategylist
) > 1 and 'PrecisionFilter' in self.pairlists.name_list:
raise OperationalException(
"PrecisionFilter not allowed for backtesting multiple strategies."
)
dataprovider.add_pairlisthandler(self.pairlists)
self.pairlists.refresh_pairlist()
if len(self.pairlists.whitelist) == 0:
raise OperationalException("No pair in whitelist.")
if config.get('fee', None) is not None:
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(
symbol=self.pairlists.whitelist[0])
# Get maximum required startup period
self.required_startup = max(
[strat.startup_candle_count for strat in self.strategylist])
# Load one (first) strategy
self._set_strategy(self.strategylist[0])
def _set_strategy(self, strategy):
"""
Load strategy into backtesting
"""
self.strategy: IStrategy = strategy
# Set stoploss_on_exchange to false for backtesting,
# since a "perfect" stoploss-sell is assumed anyway
# And the regular "stoploss" function would not apply to that case
self.strategy.order_types['stoploss_on_exchange'] = False
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=timerange,
startup_candles=self.required_startup,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
min_date, max_date = history.get_timerange(data)
logger.info(
f'Loading data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Adjust startts forward if not enough data is available
timerange.adjust_start_if_necessary(
timeframe_to_seconds(self.timeframe), self.required_startup,
min_date)
return data, timerange
def _get_ohlcv_as_lists(
self, processed: Dict[str, DataFrame]) -> Dict[str, Tuple]:
"""
Helper function to convert a processed dataframes into lists for performance reasons.
Used by backtest() - so keep this optimized for performance.
"""
# Every change to this headers list must evaluate further usages of the resulting tuple
# and eventually change the constants for indexes at the top
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
data: Dict = {}
# Create dict with data
for pair, pair_data in processed.items():
pair_data.loc[:, 'buy'] = 0 # cleanup from previous run
pair_data.loc[:, 'sell'] = 0 # cleanup from previous run
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}),
{'pair': pair})[headers].copy()
# To avoid using data from future, we use buy/sell signals shifted
# from the previous candle
df_analyzed.loc[:, 'buy'] = df_analyzed.loc[:, 'buy'].shift(1)
df_analyzed.loc[:, 'sell'] = df_analyzed.loc[:, 'sell'].shift(1)
df_analyzed.drop(df_analyzed.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = [
x for x in df_analyzed.itertuples(index=False, name=None)
]
return data
def _get_close_rate(self, sell_row: Tuple, trade: Trade,
sell: SellCheckTuple, trade_dur: int) -> float:
"""
Get close rate for backtesting result
"""
# Special handling if high or low hit STOP_LOSS or ROI
if sell.sell_type in (SellType.STOP_LOSS, SellType.TRAILING_STOP_LOSS):
# Set close_rate to stoploss
return trade.stop_loss
elif sell.sell_type == (SellType.ROI):
roi_entry, roi = self.strategy.min_roi_reached_entry(trade_dur)
if roi is not None and roi_entry is not None:
if roi == -1 and roi_entry % self.timeframe_min == 0:
# When forceselling with ROI=-1, the roi time will always be equal to trade_dur.
# If that entry is a multiple of the timeframe (so on candle open)
# - we'll use open instead of close
return sell_row[OPEN_IDX]
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
close_rate = -(trade.open_rate * roi + trade.open_rate *
(1 + trade.fee_open)) / (trade.fee_close - 1)
if (trade_dur > 0 and trade_dur == roi_entry
and roi_entry % self.timeframe_min == 0
and sell_row[OPEN_IDX] > close_rate):
# new ROI entry came into effect.
# use Open rate if open_rate > calculated sell rate
return sell_row[OPEN_IDX]
# Use the maximum between close_rate and low as we
# cannot sell outside of a candle.
# Applies when a new ROI setting comes in place and the whole candle is above that.
return max(close_rate, sell_row[LOW_IDX])
else:
# This should not be reached...
return sell_row[OPEN_IDX]
else:
return sell_row[OPEN_IDX]
def _get_sell_trade_entry(self, trade: Trade,
sell_row: Tuple) -> Optional[BacktestResult]:
sell = self.strategy.should_sell(trade,
sell_row[OPEN_IDX],
sell_row[DATE_IDX],
sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX],
high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade_dur = int(
(sell_row[DATE_IDX] - trade.open_date).total_seconds() // 60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
return BacktestResult(
pair=trade.pair,
profit_percent=trade.calc_profit_ratio(rate=closerate),
profit_abs=trade.calc_profit(rate=closerate),
open_date=trade.open_date,
open_rate=trade.open_rate,
open_fee=self.fee,
close_date=sell_row[DATE_IDX],
close_rate=closerate,
close_fee=self.fee,
amount=trade.amount,
trade_duration=trade_dur,
open_at_end=False,
sell_reason=sell.sell_type)
return None
def handle_left_open(self, open_trades: Dict[str, List[Trade]],
data: Dict[str, List[Tuple]]) -> List[BacktestResult]:
"""
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_entry = BacktestResult(
pair=trade.pair,
profit_percent=trade.calc_profit_ratio(
rate=sell_row[OPEN_IDX]),
profit_abs=trade.calc_profit(sell_row[OPEN_IDX]),
open_date=trade.open_date,
open_rate=trade.open_rate,
open_fee=self.fee,
close_date=sell_row[DATE_IDX],
close_rate=sell_row[OPEN_IDX],
close_fee=self.fee,
amount=trade.amount,
trade_duration=int(
(sell_row[DATE_IDX] -
trade.open_date).total_seconds() // 60),
open_at_end=True,
sell_reason=SellType.FORCE_SELL)
trades.append(trade_entry)
return trades
def backtest(self,
processed: Dict,
stake_amount: float,
start_date: datetime,
end_date: datetime,
max_open_trades: int = 0,
position_stacking: bool = False) -> DataFrame:
"""
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}
:param stake_amount: amount to use for each trade
: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?
:return: DataFrame with trades (results of backtesting)
"""
logger.debug(
f"Run backtest, stake_amount: {stake_amount}, "
f"start_date: {start_date}, end_date: {end_date}, "
f"max_open_trades: {max_open_trades}, position_stacking: {position_stacking}"
)
trades = []
# 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 = {}
tmp = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List] = defaultdict(list)
open_trade_count = 0
# Loop timerange and get candle for each pair at that point in time
while tmp <= end_date:
open_trade_count_start = open_trade_count
for i, pair in enumerate(data):
if pair not in indexes:
indexes[pair] = 0
try:
row = data[pair][indexes[pair]]
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
indexes[pair] += 1
# 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 max_open_trades > 0
and open_trade_count_start < max_open_trades
and tmp != end_date and row[BUY_IDX] == 1
and row[SELL_IDX] != 1):
# Enter trade
trade = Trade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX],
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
)
# 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} - Backtesting emulates creation of new trade: {trade}.")
open_trades[pair].append(trade)
for trade in open_trades[pair]:
# since indexes has been incremented before, we need to go one step back to
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occured
if trade_entry:
# logger.debug(f"{pair} - Backtesting sell {trade}")
open_trade_count -= 1
open_trades[pair].remove(trade)
trades.append(trade_entry)
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
"""
Run backtesting end-to-end
:return: None
"""
data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...',
self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
position_stacking = self.config.get('position_stacking', False)
data, timerange = self.load_bt_data()
all_results = {}
for strat in self.strategylist:
logger.info("Running backtesting for Strategy %s",
strat.get_strategy_name())
self._set_strategy(strat)
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
# Must come from strategy config, as the strategy may modify this setting.
max_open_trades = self.strategy.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...'
)
max_open_trades = 0
# need to reprocess data every time to populate signals
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Trim startup period from analyzed dataframe
for pair, df in preprocessed.items():
preprocessed[pair] = trim_dataframe(df, timerange)
min_date, max_date = history.get_timerange(preprocessed)
logger.info(
f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
# Execute backtest and print results
results = self.backtest(
processed=preprocessed,
stake_amount=self.config['stake_amount'],
start_date=min_date.datetime,
end_date=max_date.datetime,
max_open_trades=max_open_trades,
position_stacking=position_stacking,
)
all_results[self.strategy.get_strategy_name()] = {
'results': results,
'config': self.strategy.config,
}
stats = generate_backtest_stats(data,
all_results,
min_date=min_date,
max_date=max_date)
if self.config.get('export', False):
store_backtest_stats(self.config['exportfilename'], stats)
# Show backtest results
show_backtest_results(self.config, stats)