def test_load_data_with_new_pair_1min(ticker_history_list, mocker, caplog,
default_conf, testdatadir) -> None:
"""
Test load_pair_history() with 1 min ticker
"""
mocker.patch('freqtrade.exchange.Exchange.get_historic_ohlcv',
return_value=ticker_history_list)
exchange = get_patched_exchange(mocker, default_conf)
file = testdatadir / 'MEME_BTC-1m.json'
_backup_file(file)
# do not download a new pair if refresh_pairs isn't set
load_pair_history(datadir=testdatadir, timeframe='1m', pair='MEME/BTC')
assert not file.is_file()
assert log_has(
'No history data for pair: "MEME/BTC", timeframe: 1m. '
'Use `freqtrade download-data` to download the data', caplog)
# download a new pair if refresh_pairs is set
refresh_data(datadir=testdatadir,
timeframe='1m',
pairs=['MEME/BTC'],
exchange=exchange)
load_pair_history(datadir=testdatadir, timeframe='1m', pair='MEME/BTC')
assert file.is_file()
assert log_has_re(
'Download history data for pair: "MEME/BTC", timeframe: 1m '
'and store in .*', caplog)
_clean_test_file(file)
def test_init_with_refresh(default_conf, mocker) -> None:
exchange = get_patched_exchange(mocker, default_conf)
refresh_data(datadir='',
pairs=[],
timeframe=default_conf['ticker_interval'],
exchange=exchange)
assert {} == load_data(datadir='',
pairs=[],
timeframe=default_conf['ticker_interval'])
def calculate(self, pairs: List[str]) -> bool:
if self.fee is None and pairs:
self.fee = self.exchange.get_fee(pairs[0])
heartbeat = self.edge_config.get('process_throttle_secs')
if (self._last_updated > 0) and (
self._last_updated + heartbeat > arrow.utcnow().int_timestamp):
return False
data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using local backtesting data (using whitelist in given config) ...')
if self._refresh_pairs:
timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds(
self.strategy.timeframe) * self.strategy.startup_candle_count)
refresh_data(
datadir=self.config['datadir'],
pairs=pairs,
exchange=self.exchange,
timeframe=self.strategy.timeframe,
timerange=timerange_startup,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
# Download informative pairs too
res = defaultdict(list)
for p, t in self.strategy.gather_informative_pairs():
res[t].append(p)
for timeframe, inf_pairs in res.items():
timerange_startup = deepcopy(self._timerange)
timerange_startup.subtract_start(timeframe_to_seconds(
timeframe) * self.strategy.startup_candle_count)
refresh_data(
datadir=self.config['datadir'],
pairs=inf_pairs,
exchange=self.exchange,
timeframe=timeframe,
timerange=timerange_startup,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
data = load_data(
datadir=self.config['datadir'],
pairs=pairs,
timeframe=self.strategy.timeframe,
timerange=self._timerange,
startup_candles=self.strategy.startup_candle_count,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
if not data:
# Reinitializing cached pairs
self._cached_pairs = {}
logger.critical("No data found. Edge is stopped ...")
return False
# Fake run-mode to Edge
prior_rm = self.config['runmode']
self.config['runmode'] = RunMode.EDGE
preprocessed = self.strategy.advise_all_indicators(data)
self.config['runmode'] = prior_rm
# Print timeframe
min_date, max_date = get_timerange(preprocessed)
logger.info(f'Measuring data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '
f'up to {max_date.strftime(DATETIME_PRINT_FORMAT)} '
f'({(max_date - min_date).days} days)..')
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
trades: list = []
for pair, pair_data in preprocessed.items():
# Sorting dataframe by date and reset index
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
# If no trade found then exit
if len(trades) == 0:
logger.info("No trades found.")
return False
# Fill missing, calculable columns, profit, duration , abs etc.
trades_df = self._fill_calculable_fields(DataFrame(trades))
self._cached_pairs = self._process_expectancy(trades_df)
self._last_updated = arrow.utcnow().int_timestamp
return True
def calculate(self) -> bool:
pairs = self.config['exchange']['pair_whitelist']
heartbeat = self.edge_config.get('process_throttle_secs')
if (self._last_updated > 0) and (
self._last_updated + heartbeat > arrow.utcnow().timestamp):
return False
data: Dict[str, Any] = {}
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using local backtesting data (using whitelist in given config) ...')
if self._refresh_pairs:
refresh_data(
datadir=self.config['datadir'],
pairs=pairs,
exchange=self.exchange,
timeframe=self.strategy.ticker_interval,
timerange=self._timerange,
)
data = load_data(
datadir=self.config['datadir'],
pairs=pairs,
timeframe=self.strategy.ticker_interval,
timerange=self._timerange,
startup_candles=self.strategy.startup_candle_count,
data_format=self.config.get('dataformat_ohlcv', 'json'),
)
if not data:
# Reinitializing cached pairs
self._cached_pairs = {}
logger.critical("No data found. Edge is stopped ...")
return False
preprocessed = self.strategy.ohlcvdata_to_dataframe(data)
# Print timeframe
min_date, max_date = get_timerange(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days) ...',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
headers = ['date', 'buy', 'open', 'close', 'sell', 'high', 'low']
trades: list = []
for pair, pair_data in preprocessed.items():
# Sorting dataframe by date and reset index
pair_data = pair_data.sort_values(by=['date'])
pair_data = pair_data.reset_index(drop=True)
df_analyzed = self.strategy.advise_sell(
self.strategy.advise_buy(pair_data, {'pair': pair}), {'pair': pair})[headers].copy()
trades += self._find_trades_for_stoploss_range(df_analyzed, pair, self._stoploss_range)
# If no trade found then exit
if len(trades) == 0:
logger.info("No trades found.")
return False
# Fill missing, calculable columns, profit, duration , abs etc.
trades_df = self._fill_calculable_fields(DataFrame(trades))
self._cached_pairs = self._process_expectancy(trades_df)
self._last_updated = arrow.utcnow().timestamp
return True
