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_historic_ohlcv(mocker, default_conf, ohlcv_history):
historymock = MagicMock(return_value=ohlcv_history)
mocker.patch("freqtrade.data.dataprovider.load_pair_history", historymock)
dp = DataProvider(default_conf, None)
data = dp.historic_ohlcv("UNITTEST/BTC", "5m")
assert isinstance(data, DataFrame)
assert historymock.call_count == 1
assert historymock.call_args_list[0][1]["timeframe"] == "5m"
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.strategy: IStrategy = StrategyResolver(self.config).strategy
# Check config consistency here since strategies can set certain options
validate_config_consistency(config)
self.rpc: RPCManager = RPCManager(self)
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
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']
persistence.init(self.config.get('db_url', None),
clean_open_orders=self.config.get('dry_run', False))
# Stoploss on exchange does not make sense, therefore we need to disable that.
if (self.dataprovider.runmode == RunMode.DRY_RUN and
self.strategy.order_types.get('stoploss_on_exchange', False)):
logger.info("Disabling stoploss_on_exchange during dry-run.")
self.strategy.order_types['stoploss_on_exchange'] = False
config['order_types']['stoploss_on_exchange'] = 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
def test_historic_ohlcv(mocker, default_conf, ticker_history):
historymock = MagicMock(return_value=ticker_history)
mocker.patch("freqtrade.data.dataprovider.load_pair_history", historymock)
# exchange = get_patched_exchange(mocker, default_conf)
dp = DataProvider(default_conf, None)
data = dp.historic_ohlcv("UNITTEST/BTC", "5m")
assert isinstance(data, DataFrame)
assert historymock.call_count == 1
assert historymock.call_args_list[0][1]["datadir"] is None
assert historymock.call_args_list[0][1]["refresh_pairs"] is False
assert historymock.call_args_list[0][1]["ticker_interval"] == "5m"
def test_market(mocker, default_conf, markets):
api_mock = MagicMock()
api_mock.markets = markets
exchange = get_patched_exchange(mocker, default_conf, api_mock=api_mock)
dp = DataProvider(default_conf, exchange)
res = dp.market('ETH/BTC')
assert type(res) is dict
assert 'symbol' in res
assert res['symbol'] == 'ETH/BTC'
res = dp.market('UNITTEST/BTC')
assert res is None
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 not be identical, but a copy
assert dp.current_whitelist() is not pairlist._whitelist
with pytest.raises(OperationalException):
dp = DataProvider(default_conf, exchange)
dp.current_whitelist()
def test_orderbook(mocker, default_conf, order_book_l2):
api_mock = MagicMock()
api_mock.fetch_l2_order_book = order_book_l2
exchange = get_patched_exchange(mocker, default_conf, api_mock=api_mock)
dp = DataProvider(default_conf, exchange)
res = dp.orderbook('ETH/BTC', 5)
assert order_book_l2.call_count == 1
assert order_book_l2.call_args_list[0][0][0] == 'ETH/BTC'
assert order_book_l2.call_args_list[0][0][1] >= 5
assert type(res) is dict
assert 'bids' in res
assert 'asks' in res
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.strategy: IStrategy = StrategyResolver(self.config).strategy
self.rpc: RPCManager = RPCManager(self)
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
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']
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
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:
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] = []
self.exchange = ExchangeResolver(self.config['exchange']['name'],
self.config).exchange
self.fee = self.exchange.get_fee()
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(stratconf).strategy)
else:
# No strategy list specified, only one strategy
self.strategylist.append(StrategyResolver(self.config).strategy)
# Load one (first) strategy
self._set_strategy(self.strategylist[0])
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] = []
exchange_name = self.config.get('exchange', {}).get('name',
'bittrex').title()
self.exchange = ExchangeResolver(exchange_name, self.config).exchange
self.fee = self.exchange.get_fee()
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):
# Force one interval
self.ticker_interval = str(self.config.get('ticker_interval'))
self.ticker_interval_mins = timeframe_to_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])
def test_informative_decorator(mocker, default_conf):
test_data_5m = generate_test_data('5m', 40)
test_data_30m = generate_test_data('30m', 40)
test_data_1h = generate_test_data('1h', 40)
data = {
('XRP/USDT', '5m'): test_data_5m,
('XRP/USDT', '30m'): test_data_30m,
('XRP/USDT', '1h'): test_data_1h,
('LTC/USDT', '5m'): test_data_5m,
('LTC/USDT', '30m'): test_data_30m,
('LTC/USDT', '1h'): test_data_1h,
('BTC/USDT', '30m'): test_data_30m,
('BTC/USDT', '5m'): test_data_5m,
('BTC/USDT', '1h'): test_data_1h,
('ETH/USDT', '1h'): test_data_1h,
('ETH/USDT', '30m'): test_data_30m,
('ETH/BTC', '1h'): test_data_1h,
}
from .strats.informative_decorator_strategy import InformativeDecoratorTest
default_conf['stake_currency'] = 'USDT'
strategy = InformativeDecoratorTest(config=default_conf)
strategy.dp = DataProvider({}, None, None)
mocker.patch.object(strategy.dp,
'current_whitelist',
return_value=['XRP/USDT', 'LTC/USDT', 'BTC/USDT'])
assert len(
strategy._ft_informative) == 6 # Equal to number of decorators used
informative_pairs = [('XRP/USDT', '1h'), ('LTC/USDT', '1h'),
('XRP/USDT', '30m'), ('LTC/USDT', '30m'),
('BTC/USDT', '1h'), ('BTC/USDT', '30m'),
('BTC/USDT', '5m'), ('ETH/BTC', '1h'),
('ETH/USDT', '30m')]
for inf_pair in informative_pairs:
assert inf_pair in strategy.gather_informative_pairs()
def test_historic_ohlcv(pair, timeframe):
return data[(pair, timeframe or strategy.timeframe)].copy()
mocker.patch('freqtrade.data.dataprovider.DataProvider.historic_ohlcv',
side_effect=test_historic_ohlcv)
analyzed = strategy.advise_all_indicators(
{p: data[(p, strategy.timeframe)]
for p in ('XRP/USDT', 'LTC/USDT')})
expected_columns = [
'rsi_1h',
'rsi_30m', # Stacked informative decorators
'btc_usdt_rsi_1h', # BTC 1h informative
'rsi_BTC_USDT_btc_usdt_BTC/USDT_30m', # Column formatting
'rsi_from_callable', # Custom column formatter
'eth_btc_rsi_1h', # Quote currency not matching stake currency
'rsi',
'rsi_less', # Non-informative columns
'rsi_5m', # Manual informative dataframe
]
for _, dataframe in analyzed.items():
for col in expected_columns:
assert col in dataframe.columns
def test_available_pairs(mocker, default_conf, ohlcv_history):
exchange = get_patched_exchange(mocker, default_conf)
timeframe = default_conf["timeframe"]
exchange._klines[("XRP/BTC", timeframe)] = ohlcv_history
exchange._klines[("UNITTEST/BTC", timeframe)] = ohlcv_history
dp = DataProvider(default_conf, exchange)
assert len(dp.available_pairs) == 2
assert dp.available_pairs == [("XRP/BTC", timeframe), ("UNITTEST/BTC", timeframe), ]
def test_available_pairs(mocker, default_conf, ticker_history):
exchange = get_patched_exchange(mocker, default_conf)
ticker_interval = default_conf["ticker_interval"]
exchange._klines[("XRP/BTC", ticker_interval)] = ticker_history
exchange._klines[("UNITTEST/BTC", ticker_interval)] = ticker_history
dp = DataProvider(default_conf, exchange)
assert len(dp.available_pairs) == 2
assert dp.available_pairs == [
("XRP/BTC", ticker_interval),
("UNITTEST/BTC", ticker_interval),
]
def test_historic_ohlcv_dataformat(mocker, default_conf, ohlcv_history):
hdf5loadmock = MagicMock(return_value=ohlcv_history)
jsonloadmock = MagicMock(return_value=ohlcv_history)
mocker.patch(
"freqtrade.data.history.hdf5datahandler.HDF5DataHandler._ohlcv_load",
hdf5loadmock)
mocker.patch(
"freqtrade.data.history.jsondatahandler.JsonDataHandler._ohlcv_load",
jsonloadmock)
default_conf["runmode"] = RunMode.BACKTEST
exchange = get_patched_exchange(mocker, default_conf)
dp = DataProvider(default_conf, exchange)
data = dp.historic_ohlcv("UNITTEST/BTC", "5m")
assert isinstance(data, DataFrame)
hdf5loadmock.assert_not_called()
jsonloadmock.assert_called_once()
# Switching to dataformat hdf5
hdf5loadmock.reset_mock()
jsonloadmock.reset_mock()
default_conf["dataformat_ohlcv"] = "hdf5"
dp = DataProvider(default_conf, exchange)
data = dp.historic_ohlcv("UNITTEST/BTC", "5m")
assert isinstance(data, DataFrame)
hdf5loadmock.assert_called_once()
jsonloadmock.assert_not_called()
def test_refresh(mocker, default_conf, ohlcv_history):
refresh_mock = MagicMock()
mocker.patch("freqtrade.exchange.Exchange.refresh_latest_ohlcv",
refresh_mock)
exchange = get_patched_exchange(mocker, default_conf, id="binance")
timeframe = default_conf["timeframe"]
pairs = [("XRP/BTC", timeframe), ("UNITTEST/BTC", timeframe)]
pairs_non_trad = [("ETH/USDT", timeframe), ("BTC/TUSD", "1h")]
dp = DataProvider(default_conf, exchange)
dp.refresh(pairs)
assert refresh_mock.call_count == 1
assert len(refresh_mock.call_args[0]) == 1
assert len(refresh_mock.call_args[0][0]) == len(pairs)
assert refresh_mock.call_args[0][0] == pairs
refresh_mock.reset_mock()
dp.refresh(pairs, pairs_non_trad)
assert refresh_mock.call_count == 1
assert len(refresh_mock.call_args[0]) == 1
assert len(
refresh_mock.call_args[0][0]) == len(pairs) + len(pairs_non_trad)
assert refresh_mock.call_args[0][0] == pairs + pairs_non_trad
def load_and_plot_trades(config: Dict[str, Any]):
"""
From configuration provided
- Initializes plot-script
- Get candle (OHLCV) data
- Generate Dafaframes populated with indicators and signals based on configured strategy
- Load trades executed during the selected period
- Generate Plotly plot objects
- Generate plot files
:return: None
"""
strategy = StrategyResolver.load_strategy(config)
exchange = ExchangeResolver.load_exchange(config['exchange']['name'],
config)
IStrategy.dp = DataProvider(config, exchange)
plot_elements = init_plotscript(config, list(exchange.markets),
strategy.startup_candle_count)
timerange = plot_elements['timerange']
trades = plot_elements['trades']
pair_counter = 0
for pair, data in plot_elements["ohlcv"].items():
pair_counter += 1
logger.info("analyse pair %s", pair)
df_analyzed = strategy.analyze_ticker(data, {'pair': pair})
df_analyzed = trim_dataframe(df_analyzed, timerange)
if not trades.empty:
trades_pair = trades.loc[trades['pair'] == pair]
trades_pair = extract_trades_of_period(df_analyzed, trades_pair)
else:
trades_pair = trades
fig = generate_candlestick_graph(
pair=pair,
data=df_analyzed,
trades=trades_pair,
indicators1=config.get('indicators1', []),
indicators2=config.get('indicators2', []),
plot_config=strategy.plot_config if hasattr(
strategy, 'plot_config') else {})
store_plot_file(fig,
filename=generate_plot_filename(
pair, config['timeframe']),
directory=config['user_data_dir'] / 'plot')
logger.info('End of plotting process. %s plots generated', pair_counter)
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 config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee(
symbol=self.config['exchange']['pair_whitelist'][0])
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 "ticker_interval" not in self.config:
raise OperationalException(
"Ticker-interval needs to be set in either configuration "
"or as cli argument `--ticker-interval 5m`")
self.timeframe = str(self.config.get('ticker_interval'))
self.timeframe_min = timeframe_to_minutes(self.timeframe)
# 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 test_get_analyzed_dataframe(mocker, default_conf, ohlcv_history):
default_conf["runmode"] = RunMode.DRY_RUN
timeframe = default_conf["timeframe"]
exchange = get_patched_exchange(mocker, default_conf)
dp = DataProvider(default_conf, exchange)
dp._set_cached_df("XRP/BTC", timeframe, ohlcv_history)
dp._set_cached_df("UNITTEST/BTC", timeframe, ohlcv_history)
assert dp.runmode == RunMode.DRY_RUN
dataframe, time = dp.get_analyzed_dataframe("UNITTEST/BTC", timeframe)
assert ohlcv_history.equals(dataframe)
assert isinstance(time, datetime)
dataframe, time = dp.get_analyzed_dataframe("XRP/BTC", timeframe)
assert ohlcv_history.equals(dataframe)
assert isinstance(time, datetime)
dataframe, time = dp.get_analyzed_dataframe("NOTHING/BTC", timeframe)
assert dataframe.empty
assert isinstance(time, datetime)
assert time == datetime(1970, 1, 1, tzinfo=timezone.utc)
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
# Ensure using dry-run
self.config['dry_run'] = True
self.config['stake_amount'] = constants.UNLIMITED_STAKE_AMOUNT
self.exchange = ExchangeResolver.load_exchange(
self.config['exchange']['name'], self.config)
self.strategy = StrategyResolver.load_strategy(self.config)
self.strategy.dp = DataProvider(config, self.exchange)
validate_config_consistency(self.config)
self.edge = Edge(config, self.exchange, self.strategy)
# Set refresh_pairs to false for edge-cli (it must be true for edge)
self.edge._refresh_pairs = False
self.edge._timerange = TimeRange.parse_timerange(
None if self.config.get('timerange') is None else str(
self.config.get('timerange')))
def test__analyze_ticker_internal_skip_analyze(ohlcv_history, mocker,
caplog) -> None:
caplog.set_level(logging.DEBUG)
ind_mock = MagicMock(side_effect=lambda x, meta: x)
buy_mock = MagicMock(side_effect=lambda x, meta: x)
sell_mock = MagicMock(side_effect=lambda x, meta: x)
mocker.patch.multiple(
'freqtrade.strategy.interface.IStrategy',
advise_indicators=ind_mock,
advise_buy=buy_mock,
advise_sell=sell_mock,
)
strategy = DefaultStrategy({})
strategy.dp = DataProvider({}, None, None)
strategy.process_only_new_candles = True
ret = strategy._analyze_ticker_internal(ohlcv_history, {'pair': 'ETH/BTC'})
assert 'high' in ret.columns
assert 'low' in ret.columns
assert 'close' in ret.columns
assert isinstance(ret, DataFrame)
assert ind_mock.call_count == 1
assert buy_mock.call_count == 1
assert buy_mock.call_count == 1
assert log_has('TA Analysis Launched', caplog)
assert not log_has('Skipping TA Analysis for already analyzed candle',
caplog)
caplog.clear()
ret = strategy._analyze_ticker_internal(ohlcv_history, {'pair': 'ETH/BTC'})
# No analysis happens as process_only_new_candles is true
assert ind_mock.call_count == 1
assert buy_mock.call_count == 1
assert buy_mock.call_count == 1
# only skipped analyze adds buy and sell columns, otherwise it's all mocked
assert 'buy' in ret.columns
assert 'sell' in ret.columns
assert ret['buy'].sum() == 0
assert ret['sell'].sum() == 0
assert not log_has('TA Analysis Launched', caplog)
assert log_has('Skipping TA Analysis for already analyzed candle', caplog)
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] = []
self.exchange = ExchangeResolver(self.config['exchange']['name'],
self.config).exchange
if config.get('fee'):
self.fee = config['fee']
else:
self.fee = self.exchange.get_fee()
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(stratconf).strategy)
else:
# No strategy list specified, only one strategy
self.strategylist.append(StrategyResolver(self.config).strategy)
if "ticker_interval" not in self.config:
raise OperationalException(
"Ticker-interval needs to be set in either configuration "
"or as cli argument `--ticker-interval 5m`")
self.ticker_interval = str(self.config.get('ticker_interval'))
self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
# Load one (first) strategy
self._set_strategy(self.strategylist[0])
def test_ticker(mocker, default_conf, tickers):
ticker_mock = MagicMock(return_value=tickers()['ETH/BTC'])
mocker.patch("freqtrade.exchange.Exchange.fetch_ticker", ticker_mock)
exchange = get_patched_exchange(mocker, default_conf)
dp = DataProvider(default_conf, exchange)
res = dp.ticker('ETH/BTC')
assert type(res) is dict
assert 'symbol' in res
assert res['symbol'] == 'ETH/BTC'
ticker_mock = MagicMock(side_effect=ExchangeError('Pair not found'))
mocker.patch("freqtrade.exchange.Exchange.fetch_ticker", ticker_mock)
exchange = get_patched_exchange(mocker, default_conf)
dp = DataProvider(default_conf, exchange)
res = dp.ticker('UNITTEST/BTC')
assert res == {}
def _rpc_analysed_history_full(config, pair: str, timeframe: str,
timerange: str) -> Dict[str, Any]:
timerange_parsed = TimeRange.parse_timerange(timerange)
_data = load_data(
datadir=config.get("datadir"),
pairs=[pair],
timeframe=timeframe,
timerange=timerange_parsed,
data_format=config.get('dataformat_ohlcv', 'json'),
)
if pair not in _data:
raise RPCException(f"No data for {pair}, {timeframe} in {timerange} found.")
from freqtrade.data.dataprovider import DataProvider
from freqtrade.resolvers.strategy_resolver import StrategyResolver
strategy = StrategyResolver.load_strategy(config)
strategy.dp = DataProvider(config, exchange=None, pairlists=None)
df_analyzed = strategy.analyze_ticker(_data[pair], {'pair': pair})
return RPC._convert_dataframe_to_dict(strategy.get_strategy_name(), pair, timeframe,
df_analyzed, arrow.Arrow.utcnow().datetime)
def __init__(self, config: Dict[str, Any]) -> None:
LoggingMixin.show_output = False
self.config = config
self.results: Dict[str, Any] = {}
config['dry_run'] = True
self.run_ids: Dict[str, str] = {}
self.strategylist: List[IStrategy] = []
self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(
self.config['exchange']['name'], self.config)
self.dataprovider = DataProvider(self.config, self.exchange)
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.init_backtest_detail()
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException(
"VolumePairList not allowed for backtesting. "
"Please use StaticPairlist instead.")
if 'PerformanceFilter' in self.pairlists.name_list:
raise OperationalException(
"PerformanceFilter 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.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])
self.timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
# Get maximum required startup period
self.required_startup = max(
[strat.startup_candle_count for strat in self.strategylist])
# Add maximum startup candle count to configuration for informative pairs support
self.config['startup_candle_count'] = self.required_startup
self.exchange.validate_required_startup_candles(
self.required_startup, self.timeframe)
self.init_backtest()
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:
LoggingMixin.show_output = False
self.config = config
self.results: Dict[str, Any] = {}
config['dry_run'] = True
self.run_ids: Dict[str, str] = {}
self.strategylist: List[IStrategy] = []
self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(
self.config['exchange']['name'], self.config)
self.dataprovider = DataProvider(self.config, self.exchange)
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.init_backtest_detail()
self.pairlists = PairListManager(self.exchange, self.config)
if 'VolumePairList' in self.pairlists.name_list:
raise OperationalException(
"VolumePairList not allowed for backtesting. "
"Please use StaticPairlist instead.")
if 'PerformanceFilter' in self.pairlists.name_list:
raise OperationalException(
"PerformanceFilter 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.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])
self.timerange = TimeRange.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
# Get maximum required startup period
self.required_startup = max(
[strat.startup_candle_count for strat in self.strategylist])
# Add maximum startup candle count to configuration for informative pairs support
self.config['startup_candle_count'] = self.required_startup
self.exchange.validate_required_startup_candles(
self.required_startup, self.timeframe)
self.init_backtest()
def __del__(self):
self.cleanup()
def cleanup(self):
LoggingMixin.show_output = True
PairLocks.use_db = True
Trade.use_db = True
def init_backtest_detail(self):
# Load detail timeframe if specified
self.timeframe_detail = str(self.config.get('timeframe_detail', ''))
if self.timeframe_detail:
self.timeframe_detail_min = timeframe_to_minutes(
self.timeframe_detail)
if self.timeframe_min <= self.timeframe_detail_min:
raise OperationalException(
"Detail timeframe must be smaller than strategy timeframe."
)
else:
self.timeframe_detail_min = 0
self.detail_data: Dict[str, DataFrame] = {}
def init_backtest(self):
self.prepare_backtest(False)
self.wallets = Wallets(self.config, self.exchange, log=False)
self.progress = BTProgress()
self.abort = False
def _set_strategy(self, strategy: IStrategy):
"""
Load strategy into backtesting
"""
self.strategy: IStrategy = strategy
strategy.dp = self.dataprovider
# Attach Wallets to Strategy baseclass
strategy.wallets = self.wallets
# 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_protections(self, strategy: IStrategy):
if self.config.get('enable_protections', False):
conf = self.config
if hasattr(strategy, 'protections'):
conf = deepcopy(conf)
conf['protections'] = strategy.protections
self.protections = ProtectionManager(self.config,
strategy.protections)
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
"""
Loads backtest data and returns the data combined with the timerange
as tuple.
"""
self.progress.init_step(BacktestState.DATALOAD, 1)
data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe,
timerange=self.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
self.timerange.adjust_start_if_necessary(
timeframe_to_seconds(self.timeframe), self.required_startup,
min_date)
self.progress.set_new_value(1)
return data, self.timerange
def load_bt_data_detail(self) -> None:
"""
Loads backtest detail data (smaller timeframe) if necessary.
"""
if self.timeframe_detail:
self.detail_data = history.load_data(
datadir=self.config['datadir'],
pairs=self.pairlists.whitelist,
timeframe=self.timeframe_detail,
timerange=self.timerange,
startup_candles=0,
fail_without_data=True,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
else:
self.detail_data = {}
def prepare_backtest(self, enable_protections):
"""
Backtesting setup method - called once for every call to "backtest()".
"""
PairLocks.use_db = False
PairLocks.timeframe = self.config['timeframe']
Trade.use_db = False
PairLocks.reset_locks()
Trade.reset_trades()
self.rejected_trades = 0
self.dataprovider.clear_cache()
if enable_protections:
self._load_protections(self.strategy)
def check_abort(self):
"""
Check if abort was requested, raise DependencyException if that's the case
Only applies to Interactive backtest mode (webserver mode)
"""
if self.abort:
self.abort = False
raise DependencyException("Stop requested")
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.
:param processed: a processed dictionary with format {pair, data}, which gets cleared to
optimize memory usage!
"""
# 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', 'buy_tag',
'exit_tag'
]
data: Dict = {}
self.progress.init_step(BacktestState.CONVERT, len(processed))
# Create dict with data
for pair in processed.keys():
pair_data = processed[pair]
self.check_abort()
self.progress.increment()
if not pair_data.empty:
pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
pair_data.loc[:,
'buy_tag'] = None # cleanup if buy_tag is exist
pair_data.loc[:,
'exit_tag'] = None # cleanup if exit_tag is exist
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {
'pair': pair
}).copy()
# Trim startup period from analyzed dataframe
df_analyzed = processed[pair] = pair_data = trim_dataframe(
df_analyzed,
self.timerange,
startup_candles=self.required_startup)
# 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.loc[:, 'buy_tag'] = df_analyzed.loc[:,
'buy_tag'].shift(1)
df_analyzed.loc[:,
'exit_tag'] = df_analyzed.loc[:,
'exit_tag'].shift(1)
# Update dataprovider cache
self.dataprovider._set_cached_df(pair, self.timeframe, df_analyzed)
df_analyzed = df_analyzed.drop(df_analyzed.head(1).index)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
data[pair] = df_analyzed[headers].values.tolist()
return data
def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade,
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):
if trade.stop_loss > sell_row[HIGH_IDX]:
# our stoploss was already higher than candle high,
# possibly due to a cancelled trade exit.
# sell at open price.
return sell_row[OPEN_IDX]
# Special case: trailing triggers within same candle as trade opened. Assume most
# pessimistic price movement, which is moving just enough to arm stoploss and
# immediately going down to stop price.
if sell.sell_type == SellType.TRAILING_STOP_LOSS and trade_dur == 0:
if (not self.strategy.use_custom_stoploss
and self.strategy.trailing_stop
and self.strategy.trailing_only_offset_is_reached and
self.strategy.trailing_stop_positive_offset is not None
and self.strategy.trailing_stop_positive):
# Worst case: price reaches stop_positive_offset and dives down.
stop_rate = (
sell_row[OPEN_IDX] *
(1 + abs(self.strategy.trailing_stop_positive_offset) -
abs(self.strategy.trailing_stop_positive)))
else:
# Worst case: price ticks tiny bit above open and dives down.
stop_rate = sell_row[OPEN_IDX] * (1 -
abs(trade.stop_loss_pct))
assert stop_rate < sell_row[HIGH_IDX]
# Limit lower-end to candle low to avoid sells below the low.
# This still remains "worst case" - but "worst realistic case".
return max(sell_row[LOW_IDX], stop_rate)
# 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]
return close_rate
else:
# This should not be reached...
return sell_row[OPEN_IDX]
else:
return sell_row[OPEN_IDX]
def _get_adjust_trade_entry_for_candle(self, trade: LocalTrade,
row: Tuple) -> LocalTrade:
current_profit = trade.calc_profit_ratio(row[OPEN_IDX])
min_stake = self.exchange.get_min_pair_stake_amount(
trade.pair, row[OPEN_IDX], -0.1)
max_stake = self.wallets.get_available_stake_amount()
stake_amount = strategy_safe_wrapper(
self.strategy.adjust_trade_position,
default_retval=None)(trade=trade,
current_time=row[DATE_IDX].to_pydatetime(),
current_rate=row[OPEN_IDX],
current_profit=current_profit,
min_stake=min_stake,
max_stake=max_stake)
# Check if we should increase our position
if stake_amount is not None and stake_amount > 0.0:
pos_trade = self._enter_trade(trade.pair, row, stake_amount, trade)
if pos_trade is not None:
return pos_trade
return trade
def _get_sell_trade_entry_for_candle(
self, trade: LocalTrade, sell_row: Tuple) -> Optional[LocalTrade]:
# Check if we need to adjust our current positions
if self.strategy.position_adjustment_enable:
trade = self._get_adjust_trade_entry_for_candle(trade, sell_row)
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell = self.strategy.should_sell(
trade,
sell_row[OPEN_IDX], # type: ignore
sell_candle_time,
sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX],
high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade.close_date = sell_candle_time
trade_dur = int(
(trade.close_date_utc - trade.open_date_utc).total_seconds() //
60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
# call the custom exit price,with default value as previous closerate
current_profit = trade.calc_profit_ratio(closerate)
if sell.sell_type in (SellType.SELL_SIGNAL, SellType.CUSTOM_SELL):
# Custom exit pricing only for sell-signals
closerate = strategy_safe_wrapper(
self.strategy.custom_exit_price,
default_retval=closerate)(pair=trade.pair,
trade=trade,
current_time=sell_row[DATE_IDX],
proposed_rate=closerate,
current_profit=current_profit)
# Use the maximum between close_rate and low as we cannot sell outside of a candle.
closerate = min(max(closerate, sell_row[LOW_IDX]),
sell_row[HIGH_IDX])
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['sell']
if not strategy_safe_wrapper(self.strategy.confirm_trade_exit,
default_retval=True)(
pair=trade.pair,
trade=trade,
order_type='limit',
amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason,
current_time=sell_candle_time):
return None
trade.sell_reason = sell.sell_reason
# Checks and adds an exit tag, after checking that the length of the
# sell_row has the length for an exit tag column
if (len(sell_row) > EXIT_TAG_IDX
and sell_row[EXIT_TAG_IDX] is not None
and len(sell_row[EXIT_TAG_IDX]) > 0):
trade.sell_reason = sell_row[EXIT_TAG_IDX]
trade.close(closerate, show_msg=False)
return trade
return None
def _get_sell_trade_entry(self, trade: LocalTrade,
sell_row: Tuple) -> Optional[LocalTrade]:
if self.timeframe_detail and trade.pair in self.detail_data:
sell_candle_time = sell_row[DATE_IDX].to_pydatetime()
sell_candle_end = sell_candle_time + timedelta(
minutes=self.timeframe_min)
detail_data = self.detail_data[trade.pair]
detail_data = detail_data.loc[
(detail_data['date'] >= sell_candle_time)
& (detail_data['date'] < sell_candle_end)].copy()
if len(detail_data) == 0:
# Fall back to "regular" data if no detail data was found for this candle
return self._get_sell_trade_entry_for_candle(trade, sell_row)
detail_data.loc[:, 'buy'] = sell_row[BUY_IDX]
detail_data.loc[:, 'sell'] = sell_row[SELL_IDX]
detail_data.loc[:, 'buy_tag'] = sell_row[BUY_TAG_IDX]
detail_data.loc[:, 'exit_tag'] = sell_row[EXIT_TAG_IDX]
headers = [
'date', 'buy', 'open', 'close', 'sell', 'low', 'high',
'buy_tag', 'exit_tag'
]
for det_row in detail_data[headers].values.tolist():
res = self._get_sell_trade_entry_for_candle(trade, det_row)
if res:
return res
return None
else:
return self._get_sell_trade_entry_for_candle(trade, sell_row)
def _enter_trade(
self,
pair: str,
row: Tuple,
stake_amount: Optional[float] = None,
trade: Optional[LocalTrade] = None) -> Optional[LocalTrade]:
# let's call the custom entry price, using the open price as default price
propose_rate = strategy_safe_wrapper(
self.strategy.custom_entry_price, default_retval=row[OPEN_IDX])(
pair=pair,
current_time=row[DATE_IDX].to_pydatetime(),
proposed_rate=row[OPEN_IDX]) # default value is the open rate
# Move rate to within the candle's low/high rate
propose_rate = min(max(propose_rate, row[LOW_IDX]), row[HIGH_IDX])
min_stake_amount = self.exchange.get_min_pair_stake_amount(
pair, propose_rate, -0.05) or 0
max_stake_amount = self.wallets.get_available_stake_amount()
pos_adjust = trade is not None
if not pos_adjust:
try:
stake_amount = self.wallets.get_trade_stake_amount(pair, None)
except DependencyException:
return trade
stake_amount = strategy_safe_wrapper(
self.strategy.custom_stake_amount,
default_retval=stake_amount)(
pair=pair,
current_time=row[DATE_IDX].to_pydatetime(),
current_rate=propose_rate,
proposed_stake=stake_amount,
min_stake=min_stake_amount,
max_stake=max_stake_amount)
stake_amount = self.wallets.validate_stake_amount(
pair, stake_amount, min_stake_amount)
if not stake_amount:
# In case of pos adjust, still return the original trade
# If not pos adjust, trade is None
return trade
order_type = self.strategy.order_types['buy']
time_in_force = self.strategy.order_time_in_force['sell']
# Confirm trade entry:
if not pos_adjust:
if not strategy_safe_wrapper(
self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair,
order_type=order_type,
amount=stake_amount,
rate=propose_rate,
time_in_force=time_in_force,
current_time=row[DATE_IDX].to_pydatetime()):
return None
if stake_amount and (not min_stake_amount
or stake_amount > min_stake_amount):
amount = round(stake_amount / propose_rate, 8)
if trade is None:
# Enter trade
has_buy_tag = len(row) >= BUY_TAG_IDX + 1
trade = LocalTrade(
pair=pair,
open_rate=propose_rate,
open_date=row[DATE_IDX].to_pydatetime(),
stake_amount=stake_amount,
amount=amount,
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
buy_tag=row[BUY_TAG_IDX] if has_buy_tag else None,
exchange='backtesting',
orders=[])
order = Order(ft_is_open=False,
ft_pair=trade.pair,
symbol=trade.pair,
ft_order_side="buy",
side="buy",
order_type="market",
status="closed",
price=propose_rate,
average=propose_rate,
amount=amount,
filled=amount,
cost=stake_amount + trade.fee_open)
trade.orders.append(order)
if pos_adjust:
trade.recalc_trade_from_orders()
return trade
def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
"""
Handling of left open trades at the end of backtesting
"""
trades = []
for pair in open_trades.keys():
if len(open_trades[pair]) > 0:
for trade in open_trades[pair]:
sell_row = data[pair][-1]
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = SellType.FORCE_SELL.value
trade.close(sell_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
# Deepcopy object to have wallets update correctly
trade1 = deepcopy(trade)
trade1.is_open = True
trades.append(trade1)
return trades
def trade_slot_available(self, max_open_trades: int,
open_trade_count: int) -> bool:
# Always allow trades when max_open_trades is enabled.
if max_open_trades <= 0 or open_trade_count < max_open_trades:
return True
# Rejected trade
self.rejected_trades += 1
return False
def backtest(self,
processed: Dict,
start_date: datetime,
end_date: datetime,
max_open_trades: int = 0,
position_stacking: bool = False,
enable_protections: bool = False) -> Dict[str, Any]:
"""
Implement backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}, which gets cleared to
optimize memory usage!
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
:param position_stacking: do we allow position stacking?
:param enable_protections: Should protections be enabled?
:return: DataFrame with trades (results of backtesting)
"""
trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections)
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = defaultdict(int)
tmp = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
open_trade_count = 0
self.progress.init_step(
BacktestState.BACKTEST,
int((end_date - start_date) /
timedelta(minutes=self.timeframe_min)))
# Loop timerange and get candle for each pair at that point in time
while tmp <= end_date:
open_trade_count_start = open_trade_count
self.check_abort()
for i, pair in enumerate(data):
row_index = indexes[pair]
try:
# Row is treated as "current incomplete candle".
# Buy / sell signals are shifted by 1 to compensate for this.
row = data[pair][row_index]
except IndexError:
# missing Data for one pair at the end.
# Warnings for this are shown during data loading
continue
# Waits until the time-counter reaches the start of the data for this pair.
if row[DATE_IDX] > tmp:
continue
row_index += 1
indexes[pair] = row_index
self.dataprovider._set_dataframe_max_index(row_index)
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
if ((position_stacking or len(open_trades[pair]) == 0)
and self.trade_slot_available(max_open_trades,
open_trade_count_start)
and tmp != end_date and row[BUY_IDX] == 1
and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
trade = self._enter_trade(pair, row)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behaviour - not sure if this is correct
# Prevents buying if the trade-slot was freed in this candle
open_trade_count_start += 1
open_trade_count += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
open_trades[pair].append(trade)
LocalTrade.add_bt_trade(trade)
for trade in list(open_trades[pair]):
# also check the buying candle for sell conditions.
trade_entry = self._get_sell_trade_entry(trade, row)
# Sell occurred
if trade_entry:
# logger.debug(f"{pair} - Backtesting sell {trade}")
open_trade_count -= 1
open_trades[pair].remove(trade)
LocalTrade.close_bt_trade(trade)
trades.append(trade_entry)
if enable_protections:
self.protections.stop_per_pair(pair, row[DATE_IDX])
self.protections.global_stop(tmp)
# Move time one configured time_interval ahead.
self.progress.increment()
tmp += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
self.wallets.update()
results = trade_list_to_dataframe(trades)
return {
'results':
results,
'config':
self.strategy.config,
'locks':
PairLocks.get_all_locks(),
'rejected_signals':
self.rejected_trades,
'final_balance':
self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str,
DataFrame],
timerange: TimeRange):
self.progress.init_step(BacktestState.ANALYZE, 0)
logger.info("Running backtesting for Strategy %s",
strat.get_strategy_name())
backtest_start_time = datetime.now(timezone.utc)
self._set_strategy(strat)
strategy_safe_wrapper(self.strategy.bot_loop_start,
supress_error=True)()
# 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.advise_all_indicators(data)
# Trim startup period from analyzed dataframe
preprocessed_tmp = trim_dataframes(preprocessed, timerange,
self.required_startup)
if not preprocessed_tmp:
raise OperationalException(
"No data left after adjusting for startup candles.")
# Use preprocessed_tmp for date generation (the trimmed dataframe).
# Backtesting will re-trim the dataframes after buy/sell signal generation.
min_date, max_date = history.get_timerange(preprocessed_tmp)
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 store results
results = self.backtest(
processed=preprocessed,
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
results.update({
'run_id':
self.run_ids.get(strat.get_strategy_name(), ''),
'backtest_start_time':
int(backtest_start_time.timestamp()),
'backtest_end_time':
int(backtest_end_time.timestamp()),
})
self.all_results[self.strategy.get_strategy_name()] = results
return min_date, max_date
def _get_min_cached_backtest_date(self):
min_backtest_date = None
backtest_cache_age = self.config.get('backtest_cache',
constants.BACKTEST_CACHE_DEFAULT)
if self.timerange.stopts == 0 or datetime.fromtimestamp(
self.timerange.stopts,
tz=timezone.utc) > datetime.now(tz=timezone.utc):
logger.warning(
'Backtest result caching disabled due to use of open-ended timerange.'
)
elif backtest_cache_age == 'day':
min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(
days=1)
elif backtest_cache_age == 'week':
min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(
weeks=1)
elif backtest_cache_age == 'month':
min_backtest_date = datetime.now(tz=timezone.utc) - timedelta(
weeks=4)
return min_backtest_date
def load_prior_backtest(self):
self.run_ids = {
strategy.get_strategy_name(): get_strategy_run_id(strategy)
for strategy in self.strategylist
}
# Load previous result that will be updated incrementally.
# This can be circumvented in certain instances in combination with downloading more data
min_backtest_date = self._get_min_cached_backtest_date()
if min_backtest_date is not None:
self.results = find_existing_backtest_stats(
self.config['user_data_dir'] / 'backtest_results',
self.run_ids, min_backtest_date)
def start(self) -> None:
"""
Run backtesting end-to-end
:return: None
"""
data: Dict[str, Any] = {}
data, timerange = self.load_bt_data()
self.load_bt_data_detail()
logger.info("Dataload complete. Calculating indicators")
self.load_prior_backtest()
for strat in self.strategylist:
if self.results and strat.get_strategy_name(
) in self.results['strategy']:
# When previous result hash matches - reuse that result and skip backtesting.
logger.info(
f'Reusing result of previous backtest for {strat.get_strategy_name()}'
)
continue
min_date, max_date = self.backtest_one_strategy(
strat, data, timerange)
# Update old results with new ones.
if len(self.all_results) > 0:
results = generate_backtest_stats(data,
self.all_results,
min_date=min_date,
max_date=max_date)
if self.results:
self.results['metadata'].update(results['metadata'])
self.results['strategy'].update(results['strategy'])
self.results['strategy_comparison'].extend(
results['strategy_comparison'])
else:
self.results = results
if self.config.get('export', 'none') == 'trades':
store_backtest_stats(self.config['exportfilename'],
self.results)
# Results may be mixed up now. Sort them so they follow --strategy-list order.
if 'strategy_list' in self.config and len(self.results) > 0:
self.results['strategy_comparison'] = sorted(
self.results['strategy_comparison'],
key=lambda c: self.config['strategy_list'].index(c['key']))
self.results['strategy'] = dict(
sorted(
self.results['strategy'].items(),
key=lambda kv: self.config['strategy_list'].index(kv[0])))
if len(self.strategylist) > 0:
# Show backtest results
show_backtest_results(self.config, self.results)
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:
LoggingMixin.show_output = False
self.config = config
# Reset keys for backtesting
remove_credentials(self.config)
self.strategylist: List[IStrategy] = []
self.all_results: Dict[str, Dict] = {}
self.exchange = ExchangeResolver.load_exchange(
self.config['exchange']['name'], self.config)
self.dataprovider = DataProvider(self.config, None)
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 'PerformanceFilter' in self.pairlists.name_list:
raise OperationalException(
"PerformanceFilter 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.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])
Trade.use_db = False
Trade.reset_trades()
PairLocks.timeframe = self.config['timeframe']
PairLocks.use_db = False
PairLocks.reset_locks()
self.wallets = Wallets(self.config, self.exchange, log=False)
# Get maximum required startup period
self.required_startup = max(
[strat.startup_candle_count for strat in self.strategylist])
def __del__(self):
LoggingMixin.show_output = True
PairLocks.use_db = True
Trade.use_db = True
def _set_strategy(self, strategy: IStrategy):
"""
Load strategy into backtesting
"""
self.strategy: IStrategy = strategy
strategy.dp = self.dataprovider
# 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
if self.config.get('enable_protections', False):
conf = self.config
if hasattr(strategy, 'protections'):
conf = deepcopy(conf)
conf['protections'] = strategy.protections
self.protections = ProtectionManager(self.config,
strategy.protections)
def load_bt_data(self) -> Tuple[Dict[str, DataFrame], TimeRange]:
"""
Loads backtest data and returns the data combined with the timerange
as tuple.
"""
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 prepare_backtest(self, enable_protections):
"""
Backtesting setup method - called once for every call to "backtest()".
"""
PairLocks.use_db = False
PairLocks.timeframe = self.config['timeframe']
Trade.use_db = False
PairLocks.reset_locks()
Trade.reset_trades()
self.rejected_trades = 0
self.dataprovider.clear_cache()
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():
if not pair_data.empty:
pair_data.loc[:, 'buy'] = 0 # cleanup if buy_signal is exist
pair_data.loc[:, 'sell'] = 0 # cleanup if sell_signal is exist
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] = df_analyzed.values.tolist()
return data
def _get_close_rate(self, sell_row: Tuple, trade: LocalTrade,
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):
if trade.stop_loss > sell_row[HIGH_IDX]:
# our stoploss was already higher than candle high,
# possibly due to a cancelled trade exit.
# sell at open price.
return sell_row[OPEN_IDX]
# Special case: trailing triggers within same candle as trade opened. Assume most
# pessimistic price movement, which is moving just enough to arm stoploss and
# immediately going down to stop price.
if (sell.sell_type == SellType.TRAILING_STOP_LOSS
and trade_dur == 0
and self.strategy.trailing_stop_positive):
if self.strategy.trailing_only_offset_is_reached:
# Worst case: price reaches stop_positive_offset and dives down.
stop_rate = (
sell_row[OPEN_IDX] *
(1 + abs(self.strategy.trailing_stop_positive_offset) -
abs(self.strategy.trailing_stop_positive)))
else:
# Worst case: price ticks tiny bit above open and dives down.
stop_rate = sell_row[OPEN_IDX] * (
1 - abs(self.strategy.trailing_stop_positive))
assert stop_rate < sell_row[HIGH_IDX]
return stop_rate
# 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 min(max(close_rate, sell_row[LOW_IDX]),
sell_row[HIGH_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: LocalTrade,
sell_row: Tuple) -> Optional[LocalTrade]:
sell = self.strategy.should_sell(
trade,
sell_row[OPEN_IDX], # type: ignore
sell_row[DATE_IDX].to_pydatetime(),
sell_row[BUY_IDX],
sell_row[SELL_IDX],
low=sell_row[LOW_IDX],
high=sell_row[HIGH_IDX])
if sell.sell_flag:
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = sell.sell_reason
trade_dur = int(
(trade.close_date_utc - trade.open_date_utc).total_seconds() //
60)
closerate = self._get_close_rate(sell_row, trade, sell, trade_dur)
# Confirm trade exit:
time_in_force = self.strategy.order_time_in_force['sell']
if not strategy_safe_wrapper(
self.strategy.confirm_trade_exit, default_retval=True)(
pair=trade.pair,
trade=trade,
order_type='limit',
amount=trade.amount,
rate=closerate,
time_in_force=time_in_force,
sell_reason=sell.sell_reason,
current_time=sell_row[DATE_IDX].to_pydatetime()):
return None
trade.close(closerate, show_msg=False)
return trade
return None
def _enter_trade(self, pair: str, row: List) -> Optional[LocalTrade]:
try:
stake_amount = self.wallets.get_trade_stake_amount(pair, None)
except DependencyException:
return None
min_stake_amount = self.exchange.get_min_pair_stake_amount(
pair, row[OPEN_IDX], -0.05)
order_type = self.strategy.order_types['buy']
time_in_force = self.strategy.order_time_in_force['sell']
# Confirm trade entry:
if not strategy_safe_wrapper(
self.strategy.confirm_trade_entry, default_retval=True)(
pair=pair,
order_type=order_type,
amount=stake_amount,
rate=row[OPEN_IDX],
time_in_force=time_in_force,
current_time=row[DATE_IDX].to_pydatetime()):
return None
if stake_amount and (not min_stake_amount
or stake_amount > min_stake_amount):
# Enter trade
trade = LocalTrade(
pair=pair,
open_rate=row[OPEN_IDX],
open_date=row[DATE_IDX].to_pydatetime(),
stake_amount=stake_amount,
amount=round(stake_amount / row[OPEN_IDX], 8),
fee_open=self.fee,
fee_close=self.fee,
is_open=True,
exchange='backtesting',
)
return trade
return None
def handle_left_open(self, open_trades: Dict[str, List[LocalTrade]],
data: Dict[str, List[Tuple]]) -> List[LocalTrade]:
"""
Handling of left open trades at the end of backtesting
"""
trades = []
for pair in open_trades.keys():
if len(open_trades[pair]) > 0:
for trade in open_trades[pair]:
sell_row = data[pair][-1]
trade.close_date = sell_row[DATE_IDX].to_pydatetime()
trade.sell_reason = SellType.FORCE_SELL.value
trade.close(sell_row[OPEN_IDX], show_msg=False)
LocalTrade.close_bt_trade(trade)
# Deepcopy object to have wallets update correctly
trade1 = deepcopy(trade)
trade1.is_open = True
trades.append(trade1)
return trades
def trade_slot_available(self, max_open_trades: int,
open_trade_count: int) -> bool:
# Always allow trades when max_open_trades is enabled.
if max_open_trades <= 0 or open_trade_count < max_open_trades:
return True
# Rejected trade
self.rejected_trades += 1
return False
def backtest(self,
processed: Dict,
start_date: datetime,
end_date: datetime,
max_open_trades: int = 0,
position_stacking: bool = False,
enable_protections: bool = False) -> Dict[str, Any]:
"""
Implement backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid extensive logging in this method and functions it calls.
:param processed: a processed dictionary with format {pair, data}
:param start_date: backtesting timerange start datetime
:param end_date: backtesting timerange end datetime
:param max_open_trades: maximum number of concurrent trades, <= 0 means unlimited
:param position_stacking: do we allow position stacking?
:param enable_protections: Should protections be enabled?
:return: DataFrame with trades (results of backtesting)
"""
trades: List[LocalTrade] = []
self.prepare_backtest(enable_protections)
# Update dataprovider cache
for pair, dataframe in processed.items():
self.dataprovider._set_cached_df(pair, self.timeframe, dataframe)
# Use dict of lists with data for performance
# (looping lists is a lot faster than pandas DataFrames)
data: Dict = self._get_ohlcv_as_lists(processed)
# Indexes per pair, so some pairs are allowed to have a missing start.
indexes: Dict = defaultdict(int)
tmp = start_date + timedelta(minutes=self.timeframe_min)
open_trades: Dict[str, List[LocalTrade]] = defaultdict(list)
open_trade_count = 0
# 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):
row_index = indexes[pair]
try:
row = data[pair][row_index]
except IndexError:
# missing Data for one pair at the end.
# Warnings for this are shown during data loading
continue
# Waits until the time-counter reaches the start of the data for this pair.
if row[DATE_IDX] > tmp:
continue
row_index += 1
self.dataprovider._set_dataframe_max_index(row_index)
indexes[pair] = row_index
# without positionstacking, we can only have one open trade per pair.
# max_open_trades must be respected
# don't open on the last row
if ((position_stacking or len(open_trades[pair]) == 0)
and self.trade_slot_available(max_open_trades,
open_trade_count_start)
and tmp != end_date and row[BUY_IDX] == 1
and row[SELL_IDX] != 1
and not PairLocks.is_pair_locked(pair, row[DATE_IDX])):
trade = self._enter_trade(pair, row)
if trade:
# TODO: hacky workaround to avoid opening > max_open_trades
# This emulates previous behaviour - not sure if this is correct
# Prevents buying if the trade-slot was freed in this candle
open_trade_count_start += 1
open_trade_count += 1
# logger.debug(f"{pair} - Emulate creation of new trade: {trade}.")
open_trades[pair].append(trade)
LocalTrade.add_bt_trade(trade)
for trade in open_trades[pair]:
# 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)
LocalTrade.close_bt_trade(trade)
trades.append(trade_entry)
if enable_protections:
self.protections.stop_per_pair(pair, row[DATE_IDX])
self.protections.global_stop(tmp)
# Move time one configured time_interval ahead.
tmp += timedelta(minutes=self.timeframe_min)
trades += self.handle_left_open(open_trades, data=data)
self.wallets.update()
results = trade_list_to_dataframe(trades)
return {
'results':
results,
'config':
self.strategy.config,
'locks':
PairLocks.get_all_locks(),
'rejected_signals':
self.rejected_trades,
'final_balance':
self.wallets.get_total(self.strategy.config['stake_currency']),
}
def backtest_one_strategy(self, strat: IStrategy, data: Dict[str, Any],
timerange: TimeRange):
logger.info("Running backtesting for Strategy %s",
strat.get_strategy_name())
backtest_start_time = datetime.now(timezone.utc)
self._set_strategy(strat)
strategy_safe_wrapper(self.strategy.bot_loop_start,
supress_error=True)()
# 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
preprocessed = trim_dataframes(preprocessed, timerange,
self.required_startup)
if not preprocessed:
raise OperationalException(
"No data left after adjusting for startup candles.")
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 store results
results = self.backtest(
processed=preprocessed,
start_date=min_date,
end_date=max_date,
max_open_trades=max_open_trades,
position_stacking=self.config.get('position_stacking', False),
enable_protections=self.config.get('enable_protections', False),
)
backtest_end_time = datetime.now(timezone.utc)
results.update({
'backtest_start_time':
int(backtest_start_time.timestamp()),
'backtest_end_time':
int(backtest_end_time.timestamp()),
})
self.all_results[self.strategy.get_strategy_name()] = results
return min_date, max_date
def start(self) -> None:
"""
Run backtesting end-to-end
:return: None
"""
data: Dict[str, Any] = {}
data, timerange = self.load_bt_data()
logger.info("Dataload complete. Calculating indicators")
for strat in self.strategylist:
min_date, max_date = self.backtest_one_strategy(
strat, data, timerange)
if len(self.strategylist) > 0:
stats = generate_backtest_stats(data,
self.all_results,
min_date=min_date,
max_date=max_date)
if self.config.get('export', 'none') == 'trades':
store_backtest_stats(self.config['exportfilename'], stats)
# Show backtest results
show_backtest_results(self.config, stats)
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 state
self.state = State.STOPPED
# Init objects
self.config = config
self.strategy: IStrategy = StrategyResolver(self.config).strategy
self.rpc: RPCManager = RPCManager(self)
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
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']
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
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) -> 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
# Check whether markets have to be reloaded
self.exchange._reload_markets()
# Refresh whitelist
self.pairlists.refresh_pairlist()
self.active_pair_whitelist = self.pairlists.whitelist
# Calculating Edge positioning
if self.edge:
self.edge.calculate()
self.active_pair_whitelist = self.edge.adjust(self.active_pair_whitelist)
# Query trades from persistence layer
trades = Trade.get_open_trades()
# Extend active-pair whitelist with pairs from open trades
# It ensures that tickers are downloaded for open trades
self._extend_whitelist_with_trades(self.active_pair_whitelist, trades)
# Refreshing candles
self.dataprovider.refresh(self._create_pair_whitelist(self.active_pair_whitelist),
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()
return state_changed
def _extend_whitelist_with_trades(self, whitelist: List[str], trades: List[Any]):
"""
Extend whitelist with pairs from open trades
"""
whitelist.extend([trade.pair for trade in trades if trade.pair not in 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)
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)
if not whitelist:
logger.warning("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 whitelist, but checking to sell open trades.")
return False
# 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('_', '/')
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_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:
self.update_trade_state(trade)
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 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 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)
order = action_order or 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)
# 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 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.get_sell_rate(trade.pair, True)
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.
"""
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 trade open order id does not exist: buy order is fulfilled
buy_order_fulfilled = not trade.open_order_id
# Limit price threshold: As limit price should always be below price
limit_price_pct = 0.99
# If buy order is fulfilled but there is no stoploss, we add a stoploss on exchange
if (buy_order_fulfilled and not stoploss_order):
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.
limit_price = stop_price * limit_price_pct
try:
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()
return False
except DependencyException as exception:
logger.warning('Unable to place a stoploss order on exchange: %s', exception)
# If stoploss order is canceled for some reason we add it
if stoploss_order and stoploss_order['status'] == 'canceled':
try:
stoploss_order_id = self.exchange.stoploss_limit(
pair=trade.pair, amount=trade.amount,
stop_price=trade.stop_loss, rate=trade.stop_loss * limit_price_pct
)['id']
trade.stoploss_order_id = str(stoploss_order_id)
return False
except DependencyException as exception:
logger.warning('Stoploss order was cancelled, '
'but unable to recreate one: %s', exception)
# 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)
self.notify_sell(trade)
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}")
try:
# 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)
except DependencyException:
logger.exception(f"Could create trailing stoploss order "
f"for pair {trade.pair}.")
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:
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}")
# 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
Trade.session.flush()
self.notify_sell(trade)
def notify_sell(self, trade: Trade):
"""
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': self.strategy.order_types['sell'],
'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)
def test_get_pair_dataframe(mocker, default_conf, ohlcv_history):
default_conf["runmode"] = RunMode.DRY_RUN
timeframe = default_conf["timeframe"]
exchange = get_patched_exchange(mocker, default_conf)
exchange._klines[("XRP/BTC", timeframe)] = ohlcv_history
exchange._klines[("UNITTEST/BTC", timeframe)] = ohlcv_history
dp = DataProvider(default_conf, exchange)
assert dp.runmode == RunMode.DRY_RUN
assert ohlcv_history.equals(
dp.get_pair_dataframe("UNITTEST/BTC", timeframe))
assert isinstance(dp.get_pair_dataframe("UNITTEST/BTC", timeframe),
DataFrame)
assert dp.get_pair_dataframe("UNITTEST/BTC",
timeframe) is not ohlcv_history
assert not dp.get_pair_dataframe("UNITTEST/BTC", timeframe).empty
assert dp.get_pair_dataframe("NONESENSE/AAA", timeframe).empty
# Test with and without parameter
assert dp.get_pair_dataframe("UNITTEST/BTC", timeframe)\
.equals(dp.get_pair_dataframe("UNITTEST/BTC"))
default_conf["runmode"] = RunMode.LIVE
dp = DataProvider(default_conf, exchange)
assert dp.runmode == RunMode.LIVE
assert isinstance(dp.get_pair_dataframe("UNITTEST/BTC", timeframe),
DataFrame)
assert dp.get_pair_dataframe("NONESENSE/AAA", timeframe).empty
historymock = MagicMock(return_value=ohlcv_history)
mocker.patch("freqtrade.data.dataprovider.load_pair_history", historymock)
default_conf["runmode"] = RunMode.BACKTEST
dp = DataProvider(default_conf, exchange)
assert dp.runmode == RunMode.BACKTEST
assert isinstance(dp.get_pair_dataframe("UNITTEST/BTC", timeframe),
DataFrame)
from freqtrade.data.history import load_data
from freqtrade.exceptions import OperationalException, StrategyError
from freqtrade.persistence import PairLocks, Trade
from freqtrade.resolvers import StrategyResolver
from freqtrade.strategy.hyper import (BaseParameter, CategoricalParameter, DecimalParameter,
IntParameter, RealParameter)
from freqtrade.strategy.interface import SellCheckTuple, SellType
from freqtrade.strategy.strategy_wrapper import strategy_safe_wrapper
from tests.conftest import log_has, log_has_re
from .strats.default_strategy import DefaultStrategy
# Avoid to reinit the same object again and again
_STRATEGY = DefaultStrategy(config={})
_STRATEGY.dp = DataProvider({}, None, None)
def test_returns_latest_signal(mocker, default_conf, ohlcv_history):
ohlcv_history.loc[1, 'date'] = arrow.utcnow()
# Take a copy to correctly modify the call
mocked_history = ohlcv_history.copy()
mocked_history['sell'] = 0
mocked_history['buy'] = 0
mocked_history.loc[1, 'sell'] = 1
assert _STRATEGY.get_signal('ETH/BTC', '5m', mocked_history) == (False, True)
mocked_history.loc[1, 'sell'] = 0
mocked_history.loc[1, 'buy'] = 1
assert _STRATEGY.get_signal('ETH/BTC', '5m', mocked_history) == (True, False)
