class Backtesting(object):
"""
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
self.config['exchange']['key'] = ''
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
self.strategylist: List[IStrategy] = []
if self.config.get('strategy_list', None):
# Force one interval
self.ticker_interval = str(self.config.get('ticker_interval'))
for strat in list(self.config['strategy_list']):
stratconf = deepcopy(self.config)
stratconf['strategy'] = strat
self.strategylist.append(StrategyResolver(stratconf).strategy)
else:
# only one strategy
strat = StrategyResolver(self.config).strategy
self.strategylist.append(StrategyResolver(self.config).strategy)
# Load one strategy
self._set_strategy(self.strategylist[0])
self.exchange = Exchange(self.config)
self.fee = self.exchange.get_fee()
def _set_strategy(self, strategy):
"""
Load strategy into backtesting
"""
self.strategy = strategy
self.ticker_interval = self.config.get('ticker_interval')
self.tickerdata_to_dataframe = strategy.tickerdata_to_dataframe
self.advise_buy = strategy.advise_buy
self.advise_sell = strategy.advise_sell
@staticmethod
def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
timeframe = [
(arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
for frame in data.values()
]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
stake_currency = str(self.config.get('stake_currency'))
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['pair', 'buy count', 'avg profit %', 'cum profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
for pair in data:
result = results[results.pair == pair]
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_percent.sum() * 100.0,
result.profit_abs.sum(),
str(timedelta(
minutes=round(result.trade_duration.mean()))) if not result.empty else '0:00',
len(result[result.profit_abs > 0]),
len(result[result.profit_abs < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
def _generate_text_table_sell_reason(self, data: Dict[str, Dict], results: DataFrame) -> str:
"""
Generate small table outlining Backtest results
"""
tabular_data = []
headers = ['Sell Reason', 'Count']
for reason, count in results['sell_reason'].value_counts().iteritems():
tabular_data.append([reason.value, count])
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
def _generate_text_table_strategy(self, all_results: dict) -> str:
"""
Generate summary table per strategy
"""
stake_currency = str(self.config.get('stake_currency'))
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', 'd', '.1f', '.1f')
tabular_data = []
headers = ['Strategy', 'buy count', 'avg profit %', 'cum profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss']
for strategy, results in all_results.items():
tabular_data.append([
strategy,
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
str(timedelta(
minutes=round(results.trade_duration.mean()))) if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
return tabulate(tabular_data, headers=headers, floatfmt=floatfmt, tablefmt="pipe")
def _store_backtest_result(self, recordfilename: str, results: DataFrame,
strategyname: Optional[str] = None) -> None:
records = [(t.pair, t.profit_percent, t.open_time.timestamp(),
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
for index, t in results.iterrows()]
if records:
if strategyname:
# Inject strategyname to filename
recname = Path(recordfilename)
recordfilename = str(Path.joinpath(
recname.parent, f'{recname.stem}-{strategyname}').with_suffix(recname.suffix))
logger.info('Dumping backtest results to %s', recordfilename)
file_dump_json(recordfilename, records)
def _get_sell_trade_entry(
self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict, args: Dict) -> Optional[BacktestResult]:
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(
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
)
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
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
buy_signal = sell_row.buy
sell = self.strategy.should_sell(trade, sell_row.open, sell_row.date, buy_signal,
sell_row.sell)
if sell.sell_flag:
return BacktestResult(pair=pair,
profit_percent=trade.calc_profit_percent(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=False,
open_rate=buy_row.open,
close_rate=sell_row.open,
sell_reason=sell.sell_type
)
if partial_ticker:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
btr = BacktestResult(pair=pair,
profit_percent=trade.calc_profit_percent(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('Force_selling still open trade %s with %s perc - %s', btr.pair,
btr.profit_percent, btr.profit_abs)
return btr
return None
def backtest(self, args: Dict) -> DataFrame:
"""
Implements 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, logging on this method
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
position_stacking: do we allow position stacking? (default: False)
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
position_stacking = args.get('position_stacking', False)
trades = []
trade_count_lock: Dict = {}
for pair, pair_data in processed.items():
pair_data['buy'], pair_data['sell'] = 0, 0 # cleanup from previous run
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker = [x for x in ticker_data.itertuples()]
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:
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
trade_entry = self._get_sell_trade_entry(pair, row, ticker[index + 1:],
trade_count_lock, args)
if trade_entry:
lock_pair_until = 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
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = self.exchange.get_candle_history(pair, self.ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = optimize.load_data(
self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get('refresh_pairs', False),
exchange=self.exchange,
timerange=timerange
)
if not data:
logger.critical("No data found. Terminating.")
return
# 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
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.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
logger.info(
'Measuring 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(
{
'stake_amount': self.config.get('stake_amount'),
'processed': preprocessed,
'max_open_trades': max_open_trades,
'position_stacking': self.config.get('position_stacking', False),
}
)
for strategy, results in all_results.items():
if self.config.get('export', False):
self._store_backtest_result(self.config['exportfilename'], results,
strategy if len(self.strategylist) > 1 else None)
print(f"Result for strategy {strategy}")
print(' BACKTESTING REPORT '.center(119, '='))
print(self._generate_text_table(data, results))
print(' SELL REASON STATS '.center(119, '='))
print(self._generate_text_table_sell_reason(data, results))
print(' LEFT OPEN TRADES REPORT '.center(119, '='))
print(self._generate_text_table(data, results.loc[results.open_at_end]))
print()
if len(all_results) > 1:
# Print Strategy summary table
print(' Strategy Summary '.center(119, '='))
print(self._generate_text_table_strategy(all_results))
print('\nFor more details, please look at the detail tables above')
class FreqtradeBot(object):
"""
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 object the bot need to work
:param config: configuration dict, you can use the Configuration.get_config()
method to get the config dict.
"""
logger.info(
'Starting freqtrade %s',
__version__,
)
# Init bot states
self.state = State.STOPPED
# Init objects
self.config = config
self.strategy: IStrategy = StrategyResolver(self.config).strategy
self.rpc: RPCManager = RPCManager(self)
self.persistence = None
self.exchange = Exchange(self.config)
self._init_modules()
def _init_modules(self) -> None:
"""
Initializes all modules and updates the config
:return: None
"""
# Initialize all modules
persistence.init(self.config)
# Set initial application state
initial_state = self.config.get('initial_state')
if initial_state:
self.state = State[initial_state.upper()]
else:
self.state = State.STOPPED
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 worker(self, old_state: State = None) -> State:
"""
Trading routine that must be run at each loop
:param old_state: the previous service state from the previous call
:return: current service state
"""
# Log state transition
state = self.state
if state != old_state:
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'{state.name.lower()}'
})
logger.info('Changing state to: %s', state.name)
if state == State.RUNNING:
self._startup_messages()
if state == State.STOPPED:
time.sleep(1)
elif state == State.RUNNING:
min_secs = self.config.get('internals',
{}).get('process_throttle_secs',
constants.PROCESS_THROTTLE_SECS)
nb_assets = self.config.get('dynamic_whitelist', None)
self._throttle(func=self._process,
min_secs=min_secs,
nb_assets=nb_assets)
return state
def _startup_messages(self) -> None:
if self.config.get('dry_run', False):
self.rpc.send_msg({
'type':
RPCMessageType.WARNING_NOTIFICATION,
'status':
'Dry run is enabled. All trades are simulated.'
})
stake_currency = self.config['stake_currency']
stake_amount = self.config['stake_amount']
minimal_roi = self.config['minimal_roi']
ticker_interval = self.config['ticker_interval']
exchange_name = self.config['exchange']['name']
strategy_name = self.config.get('strategy', '')
self.rpc.send_msg({
'type':
RPCMessageType.CUSTOM_NOTIFICATION,
'status':
f'*Exchange:* `{exchange_name}`\n'
f'*Stake per trade:* `{stake_amount} {stake_currency}`\n'
f'*Minimum ROI:* `{minimal_roi}`\n'
f'*Ticker Interval:* `{ticker_interval}`\n'
f'*Strategy:* `{strategy_name}`'
})
if self.config.get('dynamic_whitelist', False):
top_pairs = 'top ' + str(self.config.get('dynamic_whitelist', 20))
specific_pairs = ''
else:
top_pairs = 'whitelisted'
specific_pairs = '\n' + ', '.join(self.config['exchange'].get(
'pair_whitelist', ''))
self.rpc.send_msg({
'type':
RPCMessageType.STATUS_NOTIFICATION,
'status':
f'Searching for {top_pairs} {stake_currency} pairs to buy and sell...'
f'{specific_pairs}'
})
def _throttle(self, func: Callable[..., Any], min_secs: float, *args,
**kwargs) -> Any:
"""
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param min_secs: minimum execution time in seconds
:return: Any
"""
start = time.time()
result = func(*args, **kwargs)
end = time.time()
duration = max(min_secs - (end - start), 0.0)
logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
time.sleep(duration)
return result
def _process(self, nb_assets: Optional[int] = 0) -> bool:
"""
Queries the persistence layer for open trades and handles them,
otherwise a new trade is created.
:param: nb_assets: the maximum number of pairs to be traded at the same time
:return: True if one or more trades has been created or closed, False otherwise
"""
state_changed = False
try:
# Refresh whitelist based on wallet maintenance
sanitized_list = self._refresh_whitelist(
self._gen_pair_whitelist(self.config['stake_currency'])
if nb_assets else self.config['exchange']['pair_whitelist'])
# Keep only the subsets of pairs wanted (up to nb_assets)
final_list = sanitized_list[:nb_assets] if nb_assets else sanitized_list
self.config['exchange']['pair_whitelist'] = final_list
# Query trades from persistence layer
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
# First process current opened trades
for trade in trades:
state_changed |= self.process_maybe_execute_sell(trade)
# Then looking for buy opportunities
if len(trades) < self.config['max_open_trades']:
state_changed = self.process_maybe_execute_buy()
if 'unfilledtimeout' in self.config:
# Check and handle any timed out open orders
self.check_handle_timedout()
Trade.session.flush()
except TemporaryError as error:
logger.warning('%s, retrying in 30 seconds...', error)
time.sleep(constants.RETRY_TIMEOUT)
except OperationalException:
tb = traceback.format_exc()
hint = 'Issue `/start` if you think it is safe to restart.'
self.rpc.send_msg({
'type':
RPCMessageType.STATUS_NOTIFICATION,
'status':
f'OperationalException:\n```\n{tb}```{hint}'
})
logger.exception('OperationalException. Stopping trader ...')
self.state = State.STOPPED
return state_changed
@cached(TTLCache(maxsize=1, ttl=1800))
def _gen_pair_whitelist(self,
base_currency: str,
key: str = 'quoteVolume') -> List[str]:
"""
Updates the whitelist with with a dynamically generated list
:param base_currency: base currency as str
:param key: sort key (defaults to 'quoteVolume')
:return: List of pairs
"""
if not self.exchange.exchange_has('fetchTickers'):
raise OperationalException(
'Exchange does not support dynamic whitelist.'
'Please edit your config and restart the bot')
tickers = self.exchange.get_tickers()
# check length so that we make sure that '/' is actually in the string
tickers = [
v for k, v in tickers.items()
if len(k.split('/')) == 2 and k.split('/')[1] == base_currency
]
sorted_tickers = sorted(tickers, reverse=True, key=lambda t: t[key])
pairs = [s['symbol'] for s in sorted_tickers]
return pairs
def _refresh_whitelist(self, whitelist: List[str]) -> List[str]:
"""
Check available markets and remove pair from whitelist if necessary
:param whitelist: the sorted list (based on BaseVolume) of pairs the user might want to
trade
:return: the list of pairs the user wants to trade without the one unavailable or
black_listed
"""
sanitized_whitelist = whitelist
markets = self.exchange.get_markets()
markets = [
m for m in markets if m['quote'] == self.config['stake_currency']
]
known_pairs = set()
for market in markets:
pair = market['symbol']
# pair is not int the generated dynamic market, or in the blacklist ... ignore it
if pair not in whitelist or pair in self.config['exchange'].get(
'pair_blacklist', []):
continue
# else the pair is valid
known_pairs.add(pair)
# Market is not active
if not market['active']:
sanitized_whitelist.remove(pair)
logger.info(
'Ignoring %s from whitelist. Market is not active.', pair)
# We need to remove pairs that are unknown
final_list = [x for x in sanitized_whitelist if x in known_pairs]
return final_list
def get_target_bid(self, ticker: Dict[str, float]) -> float:
"""
Calculates bid target between current ask price and last price
:param ticker: Ticker to use for getting Ask and Last Price
:return: float: Price
"""
if ticker['ask'] < ticker['last']:
return ticker['ask']
balance = self.config['bid_strategy']['ask_last_balance']
return ticker['ask'] + balance * (ticker['last'] - ticker['ask'])
def _get_trade_stake_amount(self) -> Optional[float]:
"""
Check if stake amount can be fulfilled with the available balance
for the stake currency
:return: float: Stake Amount
"""
stake_amount = self.config['stake_amount']
avaliable_amount = self.exchange.get_balance(
self.config['stake_currency'])
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
open_trades = len(
Trade.query.filter(Trade.is_open.is_(True)).all())
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 avaliable_amount / (self.config['max_open_trades'] -
open_trades)
# Check if stake_amount is fulfilled
if avaliable_amount < stake_amount:
raise DependencyException(
'Available balance(%f %s) is lower than stake amount(%f %s)' %
(avaliable_amount, self.config['stake_currency'], stake_amount,
self.config['stake_currency']))
return stake_amount
def _get_min_pair_stake_amount(self, pair: str,
price: float) -> Optional[float]:
markets = self.exchange.get_markets()
markets = [m for m in markets if m['symbol'] == pair]
if not markets:
raise ValueError(
f'Can\'t get market information for symbol {pair}')
market = markets[0]
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
amount_reserve_percent = 1 - 0.05 # reserve 5% + stoploss
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)
return min(min_stake_amounts) / amount_reserve_percent
def create_trade(self) -> bool:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
:return: True if a trade object has been created and persisted, False otherwise
"""
interval = self.strategy.ticker_interval
stake_amount = self._get_trade_stake_amount()
if not stake_amount:
return False
logger.info(
'Checking buy signals to create a new trade with stake_amount: %f ...',
stake_amount)
whitelist = copy.deepcopy(self.config['exchange']['pair_whitelist'])
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
raise DependencyException('No currency pairs in whitelist')
# Pick pair based on buy signals
for _pair in whitelist:
logger.info('Checking buy signals: %s ', _pair)
#thistory = self.exchange.get_candle_history(_pair, interval)
#(buy, sell) = self.strategy.get_signal(_pair, interval, thistory)
(buy, sell) = (False, False)
(buy, sell) = self.exchange.get_indicators(_pair)
#print(buy, sell)
if buy and not sell:
return self.execute_buy(_pair, stake_amount)
return False
def execute_buy(self, pair: str, stake_amount: float) -> bool:
"""
Executes a limit buy for the given pair
:param pair: pair for which we want to create a LIMIT_BUY
:return: None
"""
order_id = 0
pair_s = pair.replace('_', '/')
pair_url = self.exchange.get_pair_detail_url(pair)
stake_currency = self.config['stake_currency']
fiat_currency = self.config.get('fiat_display_currency', None)
# Calculate amount
buy_limit = self.get_target_bid(self.exchange.get_ticker(pair))
min_stake_amount = self._get_min_pair_stake_amount(pair_s, buy_limit)
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
order_id = self.exchange.buy(pair, buy_limit, amount)['orderId']
if order_id != 0:
self.rpc.send_msg({
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': pair_s,
'market_url': pair_url,
'limit': buy_limit,
'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')
#!!!currency = pair[pair.find('/')+1:]
#balances = self.exchange.get_balances()
#amount = balances.get(currency)
trade = Trade(pair=pair,
stake_amount=stake_amount,
amount=amount,
fee_open=fee,
fee_close=fee,
open_rate=buy_limit,
open_rate_requested=buy_limit,
open_date=datetime.utcnow(),
exchange=self.exchange.id,
open_order_id=order_id,
strategy=self.strategy.get_strategy_name(),
ticker_interval=constants.TICKER_INTERVAL_MINUTES[
self.config['ticker_interval']])
Trade.session.add(trade)
Trade.session.flush()
return True
def process_maybe_execute_buy(self) -> bool:
"""
Tries to execute a buy trade in a safe way
:return: True if executed
"""
try:
# Create entity and execute trade
if self.create_trade():
return True
logger.info(
'Found no buy signals for whitelisted currencies. Trying again..'
)
return False
except DependencyException as exception:
logger.warning('Unable to create trade: %s', exception)
return False
def process_maybe_execute_sell(self, trade: Trade) -> bool:
"""
Tries to execute a sell trade
:return: True if executed
"""
try:
# 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)
order = self.exchange.get_order(trade.open_order_id,
trade.pair)
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order)
if order['amount'] != new_amount:
order['amount'] = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
except OperationalException as exception:
logger.warning("could not update trade amount: %s",
exception)
trade.update(order)
if trade.is_open and trade.open_order_id is None:
# Check if we can sell our current pair
return self.handle_trade(trade)
except DependencyException as exception:
logger.warning('Unable to sell trade: %s', exception)
return False
def get_real_amount(self, trade: Trade, order: Dict) -> float:
"""
Get real amount for the trade
Necessary for self.exchanges which charge fees in base currency (e.g. binance)
"""
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'] and (order['fee'].keys() >=
{'currency', 'cost'}):
if 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'].keys() >=
{'currency', 'cost'}):
# only applies if fee is in quote currency!
if trade.pair.startswith(exectrade['fee']['currency']):
fee_abs += exectrade['fee']['cost']
if amount != order_amount:
logger.warning(
f"amount {amount} does not match amount {trade.amount}")
raise OperationalException("Half bought? Amounts don't match")
real_amount = amount - fee_abs
if fee_abs != 0:
logger.info(f"""Applying fee on amount for {trade} \
(from {order_amount} to {real_amount}) from Trades""")
return real_amount
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
"""
getcontext().prec = 8
if not trade.is_open:
raise ValueError(f'attempt to handle closed trade: {trade}')
logger.debug('Handling %s ...', trade)
cur_rate_dec = self.exchange.get_ticker(trade.pair)['bid']
current_rate = cur_rate_dec
(buy, sell) = (False, False)
experimental = self.config.get('experimental', {})
if experimental.get('use_sell_signal') or experimental.get(
'ignore_roi_if_buy_signal'):
#ticker = self.exchange.get_candle_history(trade.pair, self.strategy.ticker_interval)
#(buy, sell) = self.strategy.get_signal(trade.pair, self.strategy.ticker_interval, ticker)
(buy, sell) = (False, False)
(buy, sell) = self.exchange.get_indicators(trade.pair)
should_sell = self.strategy.should_sell(trade, current_rate,
datetime.utcnow(), buy, sell)
if should_sell.sell_flag:
self.execute_sell(trade, current_rate, should_sell.sell_type)
print(trade, current_rate, should_sell.sell_type)
return True
logger.info(
'%s Found no sell signals for whitelisted currencies. Trying again..',
trade.pair)
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
"""
buy_timeout = self.config['unfilledtimeout']['buy']
sell_timeout = self.config['unfilledtimeout']['sell']
buy_timeoutthreashold = arrow.utcnow().shift(
minutes=-buy_timeout).datetime
sell_timeoutthreashold = arrow.utcnow().shift(
minutes=-sell_timeout).datetime
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
try:
# FIXME: Somehow the query above returns results
# where the open_order_id is in fact None.
# This is probably because the record got
# updated via /forcesell in a different thread.
if not trade.open_order_id:
continue
order = self.exchange.get_order(trade.open_order_id,
trade.pair)
except requests.exceptions.RequestException:
logger.info('Cannot query order for %s due to %s', trade,
traceback.format_exc())
continue
ordertime = arrow.get(order['datetime']).datetime
# Check if trade is still actually open
if int(order['remaining']) == 0:
continue
# Check if trade is still actually open
if order['status'] == 'open':
if order['side'] == 'buy' and ordertime < buy_timeoutthreashold:
self.handle_timedout_limit_buy(trade, order)
elif order[
'side'] == 'sell' and ordertime < sell_timeoutthreashold:
self.handle_timedout_limit_sell(trade, order)
# FIX: 20180110, why is cancel.order unconditionally here, whereas
# it is conditionally called in the
# handle_timedout_limit_sell()?
def handle_timedout_limit_buy(self, trade: Trade, order: Dict) -> bool:
"""Buy timeout - cancel order
:return: True if order was fully cancelled
"""
pair_s = trade.pair.replace('_', '/')
self.exchange.cancel_order(trade.open_order_id, trade.pair)
if order['remaining'] == order['amount']:
# if trade is not partially completed, just delete the trade
Trade.session.delete(trade)
Trade.session.flush()
logger.info('Buy order timeout for %s.', trade)
self.rpc.send_msg({
'type':
RPCMessageType.STATUS_NOTIFICATION,
'status':
f'Unfilled buy order for {pair_s} cancelled due to timeout'
})
return True
# if trade is partially complete, edit the stake details for the trade
# and close the order
trade.amount = order['amount'] - order['remaining']
trade.stake_amount = trade.amount * trade.open_rate
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 {pair_s} cancelled due to timeout'
})
return False
# FIX: 20180110, should cancel_order() be cond. or unconditionally called?
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
"""
pair_s = trade.pair.replace('_', '/')
if order['remaining'] == order['amount']:
# if trade is not partially completed, just cancel the trade
self.exchange.cancel_order(trade.open_order_id, trade.pair)
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 {pair_s} cancelled due to timeout'
})
logger.info('Sell order timeout for %s.', trade)
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
"""
# Execute sell and update trade record
order_id = 0
amount, quaselltrade = self.exchange.symbol_amount_prec_str(
trade.pair, trade.amount)
rate = self.exchange.symbol_price_prec_str(trade.pair, limit)
if amount > quaselltrade:
amount = quaselltrade
if amount > 0:
order_id = self.exchange.sell(str(trade.pair), limit,
amount)['orderId']
if order_id != 0:
print(order_id)
trade.open_order_id = order_id
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
profit_trade = trade.calc_profit(rate=limit)
current_rate = self.exchange.get_ticker(trade.pair)['bid']
profit_percent = trade.calc_profit_percent(limit)
pair_url = self.exchange.get_pair_detail_url(trade.pair)
gain = "profit" if profit_percent > 0 else "loss"
print('execute_sell ')
print(trade.pair, limit, current_rate, trade.sell_reason,
profit_trade)
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
'market_url': pair_url,
'limit': limit,
'amount': trade.amount,
'open_rate': trade.open_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
}
# 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)
Trade.session.flush()
class Backtesting(object):
"""
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
self.config['exchange']['key'] = ''
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
self.strategylist: List[IStrategy] = []
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
self.strategylist.append(StrategyResolver(stratconf).strategy)
else:
# only one strategy
self.strategylist.append(StrategyResolver(self.config).strategy)
# Load one strategy
self._set_strategy(self.strategylist[0])
self.exchange = Exchange(self.config)
self.fee = self.exchange.get_fee()
def _set_strategy(self, strategy):
"""
Load strategy into backtesting
"""
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
def _generate_text_table(self,
data: Dict[str, Dict],
results: DataFrame,
skip_nan: bool = False) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
stake_currency = str(self.config.get('stake_currency'))
max_open_trades = self.config.get('max_open_trades')
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = [
'pair', 'buy count', 'avg profit %', 'cum profit %',
'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
'profit', 'loss'
]
for pair in data:
result = results[results.pair == pair]
if skip_nan and result.profit_abs.isnull().all():
continue
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_percent.sum() * 100.0,
result.profit_abs.sum(),
result.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(minutes=round(result.trade_duration.mean())))
if not result.empty else '0:00',
len(result[result.profit_abs > 0]),
len(result[result.profit_abs < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(minutes=round(results.trade_duration.mean())))
if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(
tabular_data,
headers=headers, # type: ignore
floatfmt=floatfmt,
tablefmt="pipe")
def _generate_text_table_sell_reason(self, data: Dict[str, Dict],
results: DataFrame) -> str:
"""
Generate small table outlining Backtest results
"""
tabular_data = []
headers = ['Sell Reason', 'Count']
for reason, count in results['sell_reason'].value_counts().iteritems():
tabular_data.append([reason.value, count])
return tabulate(tabular_data, headers=headers, tablefmt="pipe")
def _generate_text_table_strategy(self, all_results: dict) -> str:
"""
Generate summary table per strategy
"""
stake_currency = str(self.config.get('stake_currency'))
max_open_trades = self.config.get('max_open_trades')
floatfmt = ('s', 'd', '.2f', '.2f', '.8f', '.2f', 'd', '.1f', '.1f')
tabular_data = []
headers = [
'Strategy', 'buy count', 'avg profit %', 'cum profit %',
'tot profit ' + stake_currency, 'tot profit %', 'avg duration',
'profit', 'loss'
]
for strategy, results in all_results.items():
tabular_data.append([
strategy,
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_percent.sum() * 100.0,
results.profit_abs.sum(),
results.profit_percent.sum() * 100.0 / max_open_trades,
str(timedelta(minutes=round(results.trade_duration.mean())))
if not results.empty else '0:00',
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
# Ignore type as floatfmt does allow tuples but mypy does not know that
return tabulate(
tabular_data,
headers=headers, # type: ignore
floatfmt=floatfmt,
tablefmt="pipe")
def _store_backtest_result(self,
recordfilename: str,
results: DataFrame,
strategyname: Optional[str] = None) -> None:
records = [
(t.pair, t.profit_percent, t.open_time.timestamp(),
t.close_time.timestamp(), t.open_index - 1, t.trade_duration,
t.open_rate, t.close_rate, t.open_at_end, t.sell_reason.value)
for index, t in results.iterrows()
]
if records:
if strategyname:
# Inject strategyname to filename
recname = Path(recordfilename)
recordfilename = str(
Path.joinpath(
recname.parent,
f'{recname.stem}-{strategyname}').with_suffix(
recname.suffix))
logger.info('Dumping backtest results to %s', recordfilename)
file_dump_json(recordfilename, records)
def _get_sell_trade_entry(self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict,
args: Dict) -> Optional[BacktestResult]:
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(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)
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
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
buy_signal = sell_row.buy
sell = self.strategy.should_sell(trade,
sell_row.open,
sell_row.date,
buy_signal,
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)
# 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
closerate = trade.stop_loss
elif sell.sell_type == (SellType.ROI):
# get next entry in min_roi > to trade duration
# Interface.py skips on trade_duration <= duration
roi_entry = max(
list(
filter(lambda x: trade_dur >= x,
self.strategy.minimal_roi.keys())))
roi = self.strategy.minimal_roi[roi_entry]
# - (Expected abs profit + open_rate + open_fee) / (fee_close -1)
closerate = -(trade.open_rate * roi + trade.open_rate *
(1 + trade.fee_open)) / (trade.fee_close - 1)
else:
closerate = sell_row.open
return BacktestResult(
pair=pair,
profit_percent=trade.calc_profit_percent(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_ticker:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
btr = BacktestResult(
pair=pair,
profit_percent=trade.calc_profit_percent(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('Force_selling still open trade %s with %s perc - %s',
btr.pair, btr.profit_percent, btr.profit_abs)
return btr
return None
def backtest(self, args: Dict) -> DataFrame:
"""
Implements 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, logging on this method
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
position_stacking: do we allow position stacking? (default: False)
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell', 'low', 'high']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
position_stacking = args.get('position_stacking', False)
start_date = args['start_date']
end_date = args['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
ticker_data = self.advise_sell(
self.advise_buy(pair_data, {'pair': pair}),
{'pair': pair})[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
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 ...
# Warnings for this are shown by `validate_backtest_data`
continue
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 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
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
trade_entry = self._get_sell_trade_entry(
pair, row, ticker[pair][index + 1:], trade_count_lock,
args)
if trade_entry:
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[pair] = end_date
tmp += timedelta(minutes=self.ticker_interval_mins)
index += 1
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data: Dict[str, Any] = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...',
self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
self.exchange.refresh_latest_ohlcv([(pair, self.ticker_interval)
for pair in pairs])
data = {
key[0]: value
for key, value in self.exchange._klines.items()
}
else:
logger.info(
'Using local backtesting data (using whitelist in given config) ...'
)
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(datadir=Path(self.config['datadir'])
if self.config.get('datadir') else None,
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get(
'refresh_pairs', False),
exchange=self.exchange,
timerange=timerange)
if not data:
logger.critical("No data found. Terminating.")
return
# 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
all_results = {}
for strat in self.strategylist:
logger.info("Running backtesting for Strategy %s",
strat.get_strategy_name())
self._set_strategy(strat)
min_date, max_date = optimize.get_timeframe(data)
# Validate dataframe for missing values (mainly at start and end, as fillup is called)
optimize.validate_backtest_data(
data, min_date, max_date,
constants.TICKER_INTERVAL_MINUTES[self.ticker_interval])
logger.info('Measuring data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(),
(max_date - min_date).days)
# need to reprocess data every time to populate signals
preprocessed = self.strategy.tickerdata_to_dataframe(data)
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest({
'stake_amount':
self.config.get('stake_amount'),
'processed':
preprocessed,
'max_open_trades':
max_open_trades,
'position_stacking':
self.config.get('position_stacking', False),
'start_date':
min_date,
'end_date':
max_date,
})
for strategy, results in all_results.items():
if self.config.get('export', False):
self._store_backtest_result(
self.config['exportfilename'], results,
strategy if len(self.strategylist) > 1 else None)
print(f"Result for strategy {strategy}")
print(' BACKTESTING REPORT '.center(133, '='))
print(self._generate_text_table(data, results))
print(' SELL REASON STATS '.center(133, '='))
print(self._generate_text_table_sell_reason(data, results))
print(' LEFT OPEN TRADES REPORT '.center(133, '='))
print(
self._generate_text_table(data,
results.loc[results.open_at_end],
True))
print()
if len(all_results) > 1:
# Print Strategy summary table
print(' Strategy Summary '.center(133, '='))
print(self._generate_text_table_strategy(all_results))
print('\nFor more details, please look at the detail tables above')
class FreqtradeBot(object):
"""
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 states
self.state = State.STOPPED
# Init objects
self.config = config
self.strategy: IStrategy = StrategyResolver(self.config).strategy
self.rpc: RPCManager = RPCManager(self)
self.exchange = Exchange(self.config)
self.wallets = Wallets(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
pairlistname = self.config.get('pairlist',
{}).get('method', 'StaticPairList')
self.pairlists = PairListResolver(pairlistname, self,
self.config).pairlist
# 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: List[str] = self.config['exchange'][
'pair_whitelist']
self._init_modules()
def _init_modules(self) -> None:
"""
Initializes all modules and updates the config
:return: None
"""
# Initialize all modules
persistence.init(self.config)
# Set initial application state
initial_state = self.config.get('initial_state')
if initial_state:
self.state = State[initial_state.upper()]
else:
self.state = State.STOPPED
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 worker(self, old_state: State = None) -> State:
"""
Trading routine that must be run at each loop
:param old_state: the previous service state from the previous call
:return: current service state
"""
# Log state transition
state = self.state
if state != old_state:
self.rpc.send_msg({
'type': RPCMessageType.STATUS_NOTIFICATION,
'status': f'{state.name.lower()}'
})
logger.info('Changing state to: %s', state.name)
if state == State.RUNNING:
self.rpc.startup_messages(self.config, self.pairlists)
if state == State.STOPPED:
time.sleep(1)
elif state == State.RUNNING:
min_secs = self.config.get('internals',
{}).get('process_throttle_secs',
constants.PROCESS_THROTTLE_SECS)
self._throttle(func=self._process, min_secs=min_secs)
return state
def _throttle(self, func: Callable[..., Any], min_secs: float, *args,
**kwargs) -> Any:
"""
Throttles the given callable that it
takes at least `min_secs` to finish execution.
:param func: Any callable
:param min_secs: minimum execution time in seconds
:return: Any
"""
start = time.time()
result = func(*args, **kwargs)
end = time.time()
duration = max(min_secs - (end - start), 0.0)
logger.debug('Throttling %s for %.2f seconds', func.__name__, duration)
time.sleep(duration)
return result
def _process(self) -> bool:
"""
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
"""
state_changed = False
try:
# Refresh whitelist
self.pairlists.refresh_pairlist()
self.active_pair_whitelist = self.pairlists.whitelist
# Calculating Edge positiong
if self.edge:
self.edge.calculate()
self.active_pair_whitelist = self.edge.adjust(
self.active_pair_whitelist)
# Query trades from persistence layer
trades = Trade.query.filter(Trade.is_open.is_(True)).all()
# Extend active-pair whitelist with pairs from open trades
# ensures that tickers are downloaded for open trades
self.active_pair_whitelist.extend([
trade.pair for trade in trades
if trade.pair not in self.active_pair_whitelist
])
# Create pair-whitelist tuple with (pair, ticker_interval)
pair_whitelist_tuple = [(pair, self.config['ticker_interval'])
for pair in self.active_pair_whitelist]
# Refreshing candles
self.dataprovider.refresh(pair_whitelist_tuple,
self.strategy.informative_pairs())
# First process current opened trades
for trade in trades:
state_changed |= self.process_maybe_execute_sell(trade)
# Then looking for buy opportunities
if len(trades) < self.config['max_open_trades']:
state_changed = self.process_maybe_execute_buy()
if 'unfilledtimeout' in self.config:
# Check and handle any timed out open orders
self.check_handle_timedout()
Trade.session.flush()
except TemporaryError as error:
logger.warning(
f"Error: {error}, retrying in {constants.RETRY_TIMEOUT} seconds..."
)
time.sleep(constants.RETRY_TIMEOUT)
except OperationalException:
tb = traceback.format_exc()
hint = 'Issue `/start` if you think it is safe to restart.'
self.rpc.send_msg({
'type':
RPCMessageType.STATUS_NOTIFICATION,
'status':
f'OperationalException:\n```\n{tb}```{hint}'
})
logger.exception('OperationalException. Stopping trader ...')
self.state = State.STOPPED
return state_changed
def get_target_bid(self, pair: str) -> 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:
logger.info('Using Last Ask / Last Price')
ticker = self.exchange.get_ticker(pair)
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']
avaliable_amount = self.wallets.get_free(self.config['stake_currency'])
if stake_amount == constants.UNLIMITED_STAKE_AMOUNT:
open_trades = len(
Trade.query.filter(Trade.is_open.is_(True)).all())
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 avaliable_amount / (self.config['max_open_trades'] -
open_trades)
# Check if stake_amount is fulfilled
if avaliable_amount < stake_amount:
raise DependencyException(
f"Available balance({avaliable_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]:
markets = self.exchange.get_markets()
markets = [m for m in markets if m['symbol'] == pair]
if not markets:
raise ValueError(
f'Can\'t get market information for symbol {pair}')
market = markets[0]
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)
return min(min_stake_amounts) / amount_reserve_percent
def create_trade(self) -> bool:
"""
Checks the implemented trading indicator(s) for a randomly picked pair,
if one pair triggers the buy_signal a new trade record gets created
:return: True if a trade object has been created and persisted, False otherwise
"""
interval = self.strategy.ticker_interval
whitelist = copy.deepcopy(self.active_pair_whitelist)
# Remove currently opened and latest pairs from whitelist
for trade in Trade.query.filter(Trade.is_open.is_(True)).all():
if trade.pair in whitelist:
whitelist.remove(trade.pair)
logger.debug('Ignoring %s in pair whitelist', trade.pair)
if not whitelist:
raise DependencyException('No currency pairs in whitelist')
# running get_signal on historical data fetched
for _pair in whitelist:
(buy, sell) = self.strategy.get_signal(
_pair, interval,
self.dataprovider.ohlcv(_pair, self.strategy.ticker_interval))
if buy and not sell:
stake_amount = self._get_trade_stake_amount(_pair)
if not stake_amount:
return False
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):
return self.execute_buy(_pair, stake_amount)
else:
return False
return self.execute_buy(_pair, stake_amount)
return False
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('_', '/')
pair_url = self.exchange.get_pair_detail_url(pair)
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 = self.exchange.buy(pair=pair,
ordertype=self.strategy.order_types['buy'],
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_type = self.strategy.order_types['buy']
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']
order_id = None
self.rpc.send_msg({
'type': RPCMessageType.BUY_NOTIFICATION,
'exchange': self.exchange.name.capitalize(),
'pair': pair_s,
'market_url': pair_url,
'limit': buy_limit_filled_price,
'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=constants.TICKER_INTERVAL_MINUTES[
self.config['ticker_interval']])
Trade.session.add(trade)
Trade.session.flush()
# Updating wallets
self.wallets.update()
return True
def process_maybe_execute_buy(self) -> bool:
"""
Tries to execute a buy trade in a safe way
:return: True if executed
"""
try:
# Create entity and execute trade
if self.create_trade():
return True
logger.info(
'Found no buy signals for whitelisted currencies. Trying again..'
)
return False
except DependencyException as exception:
logger.warning('Unable to create trade: %s', exception)
return False
def process_maybe_execute_sell(self, trade: Trade) -> bool:
"""
Tries to execute a sell trade
:return: True if executed
"""
try:
# 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)
order = self.exchange.get_order(trade.open_order_id,
trade.pair)
# Try update amount (binance-fix)
try:
new_amount = self.get_real_amount(trade, order)
if order['amount'] != new_amount:
order['amount'] = new_amount
# Fee was applied, so set to 0
trade.fee_open = 0
except OperationalException as exception:
logger.warning("Could not update trade amount: %s",
exception)
trade.update(order)
if self.strategy.order_types.get(
'stoploss_on_exchange') and trade.is_open:
result = self.handle_stoploss_on_exchange(trade)
if result:
self.wallets.update()
return result
if trade.is_open and trade.open_order_id is None:
# Check if we can sell our current pair
result = self.handle_trade(trade)
# Updating wallets if any trade occured
if result:
self.wallets.update()
return result
except DependencyException as exception:
logger.warning('Unable to sell trade: %s', exception)
return False
def get_real_amount(self, trade: Trade, order: Dict) -> float:
"""
Get real amount for the trade
Necessary for self.exchanges which charge fees in base currency (e.g. binance)
"""
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'] and (order['fee'].keys() >=
{'currency', 'cost'}):
if 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'].keys() >=
{'currency', 'cost'}):
# only applies if fee is in quote currency!
if trade.pair.startswith(exectrade['fee']['currency']):
fee_abs += exectrade['fee']['cost']
if amount != order_amount:
logger.warning(
f"Amount {amount} does not match amount {trade.amount}")
raise OperationalException("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 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 ValueError(f'Attempt to handle closed trade: {trade}')
logger.debug('Handling %s ...', trade)
(buy, sell) = (False, False)
experimental = self.config.get('experimental', {})
if experimental.get('use_sell_signal') or experimental.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))
config_ask_strategy = self.config.get('ask_strategy', {})
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_sell(trade, sell_rate, buy, sell):
return True
else:
logger.debug('checking sell')
sell_rate = self.exchange.get_ticker(trade.pair)['bid']
if self.check_sell(trade, sell_rate, buy, sell):
return True
logger.debug('Found no sell signal for %s.', trade)
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.
"""
result = False
# If trade is open and the buy order is fulfilled but there is no stoploss,
# then we add a stoploss on exchange
if not trade.open_order_id and not trade.stoploss_order_id:
if self.edge:
stoploss = self.edge.stoploss(pair=trade.pair)
else:
stoploss = self.strategy.stoploss
stop_price = trade.open_rate * (1 + stoploss)
# limit price should be less than stop price.
# 0.99 is arbitrary here.
limit_price = stop_price * 0.99
stoploss_order_id = self.exchange.stoploss_limit(
pair=trade.pair,
amount=trade.amount,
stop_price=stop_price,
rate=limit_price)['id']
trade.stoploss_order_id = str(stoploss_order_id)
trade.stoploss_last_update = datetime.now()
# Or the trade open and there is already a stoploss on exchange.
# so we check if it is hit ...
elif trade.stoploss_order_id:
logger.debug('Handling stoploss on exchange %s ...', trade)
order = self.exchange.get_order(trade.stoploss_order_id,
trade.pair)
if order['status'] == 'closed':
trade.sell_reason = SellType.STOPLOSS_ON_EXCHANGE.value
trade.update(order)
result = True
elif 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, order)
return result
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 '
'in order to add another one ...')
if self.exchange.cancel_order(order['id'], trade.pair):
# creating the new one
stoploss_order_id = self.exchange.stoploss_limit(
pair=trade.pair,
amount=trade.amount,
stop_price=trade.stop_loss,
rate=trade.stop_loss * 0.99)['id']
trade.stoploss_order_id = str(stoploss_order_id)
def check_sell(self, trade: Trade, sell_rate: float, buy: bool,
sell: bool) -> bool:
if self.edge:
stoploss = self.edge.stoploss(trade.pair)
should_sell = self.strategy.should_sell(trade,
sell_rate,
datetime.utcnow(),
buy,
sell,
force_stoploss=stoploss)
else:
should_sell = self.strategy.should_sell(trade, sell_rate,
datetime.utcnow(), buy,
sell)
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_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
"""
buy_timeout = self.config['unfilledtimeout']['buy']
sell_timeout = self.config['unfilledtimeout']['sell']
buy_timeoutthreashold = arrow.utcnow().shift(
minutes=-buy_timeout).datetime
sell_timeoutthreashold = arrow.utcnow().shift(
minutes=-sell_timeout).datetime
for trade in Trade.query.filter(Trade.open_order_id.isnot(None)).all():
try:
# FIXME: Somehow the query above returns results
# where the open_order_id is in fact None.
# This is probably because the record got
# updated via /forcesell in a different thread.
if not trade.open_order_id:
continue
order = self.exchange.get_order(trade.open_order_id,
trade.pair)
except (RequestException, DependencyException):
logger.info('Cannot query order for %s due to %s', trade,
traceback.format_exc())
continue
ordertime = arrow.get(order['datetime']).datetime
# Check if trade is still actually open
if float(order['remaining']) == 0.0:
self.wallets.update()
continue
# Handle cancelled on exchange
if order['status'] == 'canceled':
if order['side'] == 'buy':
self.handle_buy_order_full_cancel(trade,
"canceled on Exchange")
elif order['side'] == 'sell':
self.handle_timedout_limit_sell(trade, order)
self.wallets.update()
# Check if order is still actually open
elif order['status'] == 'open':
if order['side'] == 'buy' and ordertime < buy_timeoutthreashold:
self.handle_timedout_limit_buy(trade, order)
self.wallets.update()
elif order[
'side'] == 'sell' and ordertime < sell_timeoutthreashold:
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
"""
self.exchange.cancel_order(trade.open_order_id, trade.pair)
if order['remaining'] == order['amount']:
# if trade is not partially completed, just delete the trade
self.handle_buy_order_full_cancel(trade,
"cancelled due to timeout")
return True
# if trade is partially complete, edit the stake details for the trade
# and close the order
trade.amount = order['amount'] - order['remaining']
trade.stake_amount = trade.amount * trade.open_rate
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:
self.exchange.cancel_order(trade.stoploss_order_id, trade.pair)
# Execute sell and update trade record
order_id = self.exchange.sell(
pair=str(trade.pair),
ordertype=self.strategy.order_types[sell_type],
amount=trade.amount,
rate=limit,
time_in_force=self.strategy.order_time_in_force['sell'])['id']
trade.open_order_id = order_id
trade.close_rate_requested = limit
trade.sell_reason = sell_reason.value
profit_trade = trade.calc_profit(rate=limit)
current_rate = self.exchange.get_ticker(trade.pair)['bid']
profit_percent = trade.calc_profit_percent(limit)
pair_url = self.exchange.get_pair_detail_url(trade.pair)
gain = "profit" if profit_percent > 0 else "loss"
msg = {
'type': RPCMessageType.SELL_NOTIFICATION,
'exchange': trade.exchange.capitalize(),
'pair': trade.pair,
'gain': gain,
'market_url': pair_url,
'limit': limit,
'amount': trade.amount,
'open_rate': trade.open_rate,
'current_rate': current_rate,
'profit_amount': profit_trade,
'profit_percent': profit_percent,
'sell_reason': sell_reason.value
}
# 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)
Trade.session.flush()
class Backtesting(object):
"""
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
self.analyze = Analyze(self.config)
self.ticker_interval = self.analyze.strategy.ticker_interval
self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
self.populate_buy_trend = self.analyze.populate_buy_trend
self.populate_sell_trend = self.analyze.populate_sell_trend
# Reset keys for backtesting
self.config['exchange']['key'] = ''
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
self.exchange = Exchange(self.config)
self.fee = self.exchange.get_fee()
@staticmethod
def get_timeframe(
data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
timeframe = [(arrow.get(min(frame.date)), arrow.get(max(frame.date)))
for frame in data.values()]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
def _generate_text_table(self, data: Dict[str, Dict],
results: DataFrame) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
stake_currency = str(self.config.get('stake_currency'))
floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
tabular_data = []
headers = [
'pair', 'buy count', 'avg profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss'
]
for pair in data:
result = results[results.pair == pair]
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_abs.sum(),
result.trade_duration.mean(),
len(result[result.profit_abs > 0]),
len(result[result.profit_abs < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_abs.sum(),
results.trade_duration.mean(),
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
return tabulate(tabular_data,
headers=headers,
floatfmt=floatfmt,
tablefmt="pipe")
def _store_backtest_result(self, recordfilename: Optional[str],
results: DataFrame) -> None:
records = [(trade_entry.pair, trade_entry.profit_percent,
trade_entry.open_time.timestamp(),
trade_entry.close_time.timestamp(),
trade_entry.open_index - 1, trade_entry.trade_duration)
for index, trade_entry in results.iterrows()]
if records:
logger.info('Dumping backtest results to %s', recordfilename)
file_dump_json(recordfilename, records)
def _get_sell_trade_entry(self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict,
args: Dict) -> Optional[BacktestResult]:
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(open_rate=buy_row.close,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
fee_open=self.fee,
fee_close=self.fee)
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
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
buy_signal = sell_row.buy
if self.analyze.should_sell(trade, sell_row.close, sell_row.date,
buy_signal, sell_row.sell):
return BacktestResult(
pair=pair,
profit_percent=trade.calc_profit_percent(
rate=sell_row.close),
profit_abs=trade.calc_profit(rate=sell_row.close),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=(sell_row.date - buy_row.date).seconds //
60,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False)
if partial_ticker:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
btr = BacktestResult(
pair=pair,
profit_percent=trade.calc_profit_percent(rate=sell_row.close),
profit_abs=trade.calc_profit(rate=sell_row.close),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=(sell_row.date - buy_row.date).seconds // 60,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=True)
logger.debug('Force_selling still open trade %s with %s perc - %s',
btr.pair, btr.profit_percent, btr.profit_abs)
return btr
return None
def backtest(self, args: Dict) -> DataFrame:
"""
Implements 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, logging on this method
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
realistic: do we try to simulate realistic trades? (default: True)
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', False)
trades = []
trade_count_lock: Dict = {}
for pair, pair_data in processed.items():
pair_data['buy'], pair_data[
'sell'] = 0, 0 # cleanup from previous run
ticker_data = self.populate_sell_trend(
self.populate_buy_trend(pair_data))[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker = [x for x in ticker_data.itertuples()]
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 realistic:
if lock_pair_until is not None and row.date <= lock_pair_until:
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
trade_entry = self._get_sell_trade_entry(
pair, row, ticker[index + 1:], trade_count_lock, args)
if trade_entry:
lock_pair_until = 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
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...',
self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = self.exchange.get_ticker_history(
pair, self.ticker_interval)
else:
logger.info(
'Using local backtesting data (using whitelist in given config) ...'
)
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = optimize.load_data(self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get(
'refresh_pairs', False),
exchange=self.exchange,
timerange=timerange)
if not data:
logger.critical("No data found. Terminating.")
return
# Ignore max_open_trades in backtesting, except realistic flag was passed
if self.config.get('realistic_simulation', False):
max_open_trades = self.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (realistic_simulation not set) ...')
max_open_trades = 0
preprocessed = self.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
logger.info('Measuring data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(),
(max_date - min_date).days)
# Execute backtest and print results
results = self.backtest({
'stake_amount':
self.config.get('stake_amount'),
'processed':
preprocessed,
'max_open_trades':
max_open_trades,
'realistic':
self.config.get('realistic_simulation', False),
})
if self.config.get('export', False):
self._store_backtest_result(self.config.get('exportfilename'),
results)
logger.info(
'\n======================================== '
'BACKTESTING REPORT'
' =========================================\n'
'%s', self._generate_text_table(data, results))
logger.info(
'\n====================================== '
'LEFT OPEN TRADES REPORT'
' ======================================\n'
'%s',
self._generate_text_table(data, results.loc[results.open_at_end]))
