def test_load_data_with_new_pair_1min(ticker_history, mocker, caplog,
default_conf) -> None:
"""
Test load_data() with 1 min ticker
"""
mocker.patch('freqtrade.exchange.Exchange.get_history',
return_value=ticker_history)
exchange = get_patched_exchange(mocker, default_conf)
file = os.path.join(os.path.dirname(__file__), '..', 'testdata',
'MEME_BTC-1m.json')
_backup_file(file)
# do not download a new pair if refresh_pairs isn't set
optimize.load_data(None,
ticker_interval='1m',
refresh_pairs=False,
pairs=['MEME/BTC'])
assert os.path.isfile(file) is False
assert log_has(
'No data for pair: "MEME/BTC", Interval: 1m. '
'Use --refresh-pairs-cached to download the data',
caplog.record_tuples)
# download a new pair if refresh_pairs is set
optimize.load_data(None,
ticker_interval='1m',
refresh_pairs=True,
exchange=exchange,
pairs=['MEME/BTC'])
assert os.path.isfile(file) is True
assert log_has('Download the pair: "MEME/BTC", Interval: 1m',
caplog.record_tuples)
_clean_test_file(file)
def test_load_data_1min_ticker(ticker_history, mocker, caplog) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_history',
return_value=ticker_history)
file = os.path.join(os.path.dirname(__file__), '..', 'testdata',
'UNITTEST_BTC-1m.json')
_backup_file(file, copy_file=True)
optimize.load_data(None, ticker_interval='1m', pairs=['UNITTEST/BTC'])
assert os.path.isfile(file) is True
assert not log_has('Download the pair: "UNITTEST/BTC", Interval: 1m',
caplog.record_tuples)
_clean_test_file(file)
def test_load_data_with_new_pair_1min(ticker_history, mocker, caplog) -> None:
"""
Test load_data() with 1 min ticker
"""
mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
file = 'freqtrade/tests/testdata/BTC_MEME-1.json'
_backup_file(file)
optimize.load_data(None, ticker_interval=1, pairs=['BTC_MEME'])
assert os.path.isfile(file) is True
assert log_has('Download the pair: "BTC_MEME", Interval: 1 min', caplog.record_tuples)
_clean_test_file(file)
def test_load_data_5min_ticker(ticker_history, mocker, caplog) -> None:
"""
Test load_data() with 5 min ticker
"""
mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
file = 'freqtrade/tests/testdata/BTC_ETH-5.json'
_backup_file(file, copy_file=True)
optimize.load_data(None, pairs=['BTC_ETH'], ticker_interval=5)
assert os.path.isfile(file) is True
assert not log_has('Download the pair: "BTC_ETH", Interval: 5 min', caplog.record_tuples)
_clean_test_file(file)
def test_load_data_with_new_pair_1min(default_conf, ticker_history, mocker, caplog):
mocker.patch('freqtrade.optimize.get_ticker_history', return_value=ticker_history)
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
file = 'freqtrade/tests/testdata/BTC_MEME-1.json'
_backup_file(file)
optimize.load_data(None, ticker_interval=1, pairs=['BTC_MEME'])
assert os.path.isfile(file) is True
assert ('freqtrade.optimize',
logging.INFO,
'Download the pair: "BTC_MEME", Interval: 1 min'
) in caplog.record_tuples
_clean_test_file(file)
def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None:
"""
Test Backtesting.backtest() method with 1 min ticker
"""
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
# Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(None,
ticker_interval='1m',
pairs=['UNITTEST/BTC'])
data = trim_dictlist(data, -200)
results = backtesting.backtest({
'stake_amount':
default_conf['stake_amount'],
'processed':
backtesting.tickerdata_to_dataframe(data),
'max_open_trades':
1,
'realistic':
True
})
assert not results.empty
assert len(results) == 1
def load_data_test(what):
timerange = TimeRange(None, 'line', 0, -101)
data = optimize.load_data(None,
ticker_interval='1m',
pairs=['UNITTEST/BTC'],
timerange=timerange)
pair = data['UNITTEST/BTC']
datalen = len(pair)
# Depending on the what parameter we now adjust the
# loaded data looks:
# pair :: [[ 1509836520000, unix timestamp in ms
# 0.00162008, open
# 0.00162008, high
# 0.00162008, low
# 0.00162008, close
# 108.14853839 base volume
# ]]
base = 0.001
if what == 'raise':
return {
'UNITTEST/BTC': [
[
pair[x][0], # Keep old dates
x * base, # But replace O,H,L,C
x * base + 0.0001,
x * base - 0.0001,
x * base,
pair[x][5], # Keep old volume
] for x in range(0, datalen)
]
}
if what == 'lower':
return {
'UNITTEST/BTC': [
[
pair[x][0], # Keep old dates
1 - x * base, # But replace O,H,L,C
1 - x * base + 0.0001,
1 - x * base - 0.0001,
1 - x * base,
pair[x][5] # Keep old volume
] for x in range(0, datalen)
]
}
if what == 'sine':
hz = 0.1 # frequency
return {
'UNITTEST/BTC': [
[
pair[x][0], # Keep old dates
math.sin(x * hz) / 1000 + base, # But replace O,H,L,C
math.sin(x * hz) / 1000 + base + 0.0001,
math.sin(x * hz) / 1000 + base - 0.0001,
math.sin(x * hz) / 1000 + base,
pair[x][5] # Keep old volume
] for x in range(0, datalen)
]
}
return data
def test_backtest(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf['stake_amount'], optimize.preprocess(data),
10, True)
assert not results.empty
def test_init(default_conf, mocker) -> None:
exchange = get_patched_exchange(mocker, default_conf)
assert {} == optimize.load_data(
'',
exchange=exchange,
pairs=[],
refresh_pairs=True,
ticker_interval=default_conf['ticker_interval'])
def test_backtest(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 10, True)
assert not results.empty
def load_dataframe_pair(pairs, strategy):
ld = load_data(None, ticker_interval='5m', pairs=pairs)
assert isinstance(ld, dict)
assert isinstance(pairs[0], str)
dataframe = ld[pairs[0]]
dataframe = strategy.analyze_ticker(dataframe, {'pair': pairs[0]})
return dataframe
def load_dataframe_pair(pairs):
ld = load_data(None, ticker_interval=5, pairs=pairs)
assert isinstance(ld, dict)
assert isinstance(pairs[0], str)
dataframe = ld[pairs[0]]
analyze = Analyze({'strategy': 'DefaultStrategy'})
dataframe = analyze.analyze_ticker(dataframe)
return dataframe
def test_init(default_conf, mocker) -> None:
conf = {'exchange': {'pair_whitelist': []}}
mocker.patch('freqtrade.optimize.hyperopt_optimize_conf', return_value=conf)
assert {} == optimize.load_data(
'',
pairs=[],
refresh_pairs=True,
ticker_interval=int(default_conf['ticker_interval'])
)
def test_get_timeframe(default_conf, mocker) -> None:
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
data = backtesting.tickerdata_to_dataframe(
optimize.load_data(None, ticker_interval='1m', pairs=['UNITTEST/BTC']))
min_date, max_date = backtesting.get_timeframe(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:58:00+00:00'
def test_backtest_1min_ticker_interval(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
# Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
results = backtest(default_conf['stake_amount'], optimize.preprocess(data),
1, True)
assert not results.empty
def test_backtest_1min_ticker_interval(default_conf, mocker):
mocker.patch.dict('freqtrade.main._CONF', default_conf)
exchange._API = Bittrex({'key': '', 'secret': ''})
# Run a backtesting for an exiting 5min ticker_interval
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
results = backtest(default_conf['stake_amount'],
optimize.preprocess(data), 1, True)
assert not results.empty
def load_data_test(what):
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
data = trim_dictlist(data, -100)
pair = data['BTC_UNITEST']
datalen = len(pair)
# Depending on the what parameter we now adjust the
# loaded data looks:
# pair :: [{'O': 0.123, 'H': 0.123, 'L': 0.123,
# 'C': 0.123, 'V': 123.123,
# 'T': '2017-11-04T23:02:00', 'BV': 0.123}]
base = 0.001
if what == 'raise':
return {
'BTC_UNITEST': [
{
'T': pair[x]['T'], # Keep old dates
'V': pair[x]['V'], # Keep old volume
'BV': pair[x]['BV'], # keep too
'O': x * base, # But replace O,H,L,C
'H': x * base + 0.0001,
'L': x * base - 0.0001,
'C': x * base
} for x in range(0, datalen)
]
}
if what == 'lower':
return {
'BTC_UNITEST': [
{
'T': pair[x]['T'], # Keep old dates
'V': pair[x]['V'], # Keep old volume
'BV': pair[x]['BV'], # keep too
'O': 1 - x * base, # But replace O,H,L,C
'H': 1 - x * base + 0.0001,
'L': 1 - x * base - 0.0001,
'C': 1 - x * base
} for x in range(0, datalen)
]
}
if what == 'sine':
hz = 0.1 # frequency
return {
'BTC_UNITEST': [
{
'T': pair[x]['T'], # Keep old dates
'V': pair[x]['V'], # Keep old volume
'BV': pair[x]['BV'], # keep too
# But replace O,H,L,C
'O': math.sin(x * hz) / 1000 + base,
'H': math.sin(x * hz) / 1000 + base + 0.0001,
'L': math.sin(x * hz) / 1000 + base - 0.0001,
'C': math.sin(x * hz) / 1000 + base
} for x in range(0, datalen)
]
}
return data
def start(args):
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
exchange._API = Bittrex({'key': '', 'secret': ''})
logger.info('Using config: %s ...', args.config)
config = misc.load_config(args.config)
logger.info('Using ticker_interval: %s ...', args.ticker_interval)
data = {}
pairs = config['exchange']['pair_whitelist']
if args.live:
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair, args.ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
data = optimize.load_data(args.datadir, pairs=pairs, ticker_interval=args.ticker_interval,
refresh_pairs=args.refresh_pairs)
logger.info('Using stake_currency: %s ...', config['stake_currency'])
logger.info('Using stake_amount: %s ...', config['stake_amount'])
max_open_trades = 0
if args.realistic_simulation:
logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
max_open_trades = config['max_open_trades']
# Monkey patch config
from freqtrade import main
main._CONF = config
preprocessed = preprocess(data)
# Print timeframe
min_date, max_date = get_timeframe(preprocessed)
logger.info('Measuring data from %s up to %s ...', min_date.isoformat(), max_date.isoformat())
# Execute backtest and print results
results = backtest(
stake_amount=config['stake_amount'],
processed=preprocessed,
max_open_trades=max_open_trades,
realistic=args.realistic_simulation,
sell_profit_only=config.get('experimental', {}).get('sell_profit_only', False),
stoploss=config.get('stoploss'),
use_sell_signal=config.get('experimental', {}).get('use_sell_signal', False)
)
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'], args.ticker_interval)
)
def start(self) -> None:
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = load_data(datadir=str(self.config.get('datadir')),
pairs=self.config['exchange']['pair_whitelist'],
ticker_interval=self.ticker_interval,
timerange=timerange)
if self.has_space('buy'):
self.analyze.populate_indicators = Hyperopt.populate_indicators # type: ignore
self.processed = self.tickerdata_to_dataframe(data)
logger.info('Preparing Trials..')
signal.signal(signal.SIGINT, self.signal_handler)
# read trials file if we have one
if os.path.exists(
self.trials_file) and os.path.getsize(self.trials_file) > 0:
self.trials = self.read_trials()
self.current_tries = len(self.trials.results)
self.total_tries += self.current_tries
logger.info('Continuing with trials. Current: %d, Total: %d',
self.current_tries, self.total_tries)
try:
best_parameters = fmin(fn=self.generate_optimizer,
space=self.hyperopt_space(),
algo=tpe.suggest,
max_evals=self.total_tries,
trials=self.trials)
results = sorted(self.trials.results, key=itemgetter('loss'))
best_result = results[0]['result']
except ValueError:
best_parameters = {}
best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
'try with more epochs (param: -e).'
# Improve best parameter logging display
if best_parameters:
best_parameters = space_eval(self.hyperopt_space(),
best_parameters)
logger.info('Best parameters:\n%s',
json.dumps(best_parameters, indent=4))
if 'roi_t1' in best_parameters:
logger.info('ROI table:\n%s',
self.generate_roi_table(best_parameters))
logger.info('Best Result:\n%s', best_result)
# Store trials result to file to resume next time
self.save_trials()
def _make_backtest_conf(mocker, conf=None, pair='UNITTEST/BTC', record=None):
data = optimize.load_data(None, ticker_interval='8m', pairs=[pair])
data = trim_dictlist(data, -201)
patch_exchange(mocker)
backtesting = Backtesting(conf)
return {
'stake_amount': conf['stake_amount'],
'processed': backtesting.tickerdata_to_dataframe(data),
'max_open_trades': 10,
'position_stacking': False,
'record': record
}
def test_backtest(default_conf, fee, mocker) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
pair = 'UNITTEST/BTC'
data = optimize.load_data(None,
ticker_interval='5m',
pairs=['UNITTEST/BTC'])
data = trim_dictlist(data, -200)
data_processed = backtesting.tickerdata_to_dataframe(data)
results = backtesting.backtest({
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 10,
'position_stacking': False
})
assert not results.empty
assert len(results) == 2
expected = pd.DataFrame({
'pair': [pair, pair],
'profit_percent': [0.00029975, 0.00056708],
'profit_abs': [1.49e-06, 7.6e-07],
'open_time': [
Arrow(2018, 1, 29, 18, 40, 0).datetime,
Arrow(2018, 1, 30, 3, 30, 0).datetime
],
'close_time': [
Arrow(2018, 1, 29, 22, 40, 0).datetime,
Arrow(2018, 1, 30, 4, 20, 0).datetime
],
'open_index': [77, 183],
'close_index': [125, 193],
'trade_duration': [240, 50],
'open_at_end': [False, False],
'open_rate': [0.104445, 0.10302485],
'close_rate': [0.105, 0.10359999],
'sell_reason': [SellType.ROI, SellType.ROI]
})
pd.testing.assert_frame_equal(results, expected)
data_pair = data_processed[pair]
for _, t in results.iterrows():
ln = data_pair.loc[data_pair["date"] == t["open_time"]]
# Check open trade rate alignes to open rate
assert ln is not None
assert round(ln.iloc[0]["open"], 6) == round(t["open_rate"], 6)
# check close trade rate alignes to close rate
ln = data_pair.loc[data_pair["date"] == t["close_time"]]
assert round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6)
def load_data_test(what):
data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
data = trim_dictlist(data, -100)
pair = data['BTC_UNITEST']
datalen = len(pair)
# Depending on the what parameter we now adjust the
# loaded data looks:
# pair :: [{'O': 0.123, 'H': 0.123, 'L': 0.123,
# 'C': 0.123, 'V': 123.123,
# 'T': '2017-11-04T23:02:00', 'BV': 0.123}]
base = 0.001
if what == 'raise':
return {'BTC_UNITEST':
[{'T': pair[x]['T'], # Keep old dates
'V': pair[x]['V'], # Keep old volume
'BV': pair[x]['BV'], # keep too
'O': x * base, # But replace O,H,L,C
'H': x * base + 0.0001,
'L': x * base - 0.0001,
'C': x * base} for x in range(0, datalen)]}
if what == 'lower':
return {'BTC_UNITEST':
[{'T': pair[x]['T'], # Keep old dates
'V': pair[x]['V'], # Keep old volume
'BV': pair[x]['BV'], # keep too
'O': 1 - x * base, # But replace O,H,L,C
'H': 1 - x * base + 0.0001,
'L': 1 - x * base - 0.0001,
'C': 1 - x * base} for x in range(0, datalen)]}
if what == 'sine':
hz = 0.1 # frequency
return {'BTC_UNITEST':
[{'T': pair[x]['T'], # Keep old dates
'V': pair[x]['V'], # Keep old volume
'BV': pair[x]['BV'], # keep too
# But replace O,H,L,C
'O': math.sin(x * hz) / 1000 + base,
'H': math.sin(x * hz) / 1000 + base + 0.0001,
'L': math.sin(x * hz) / 1000 + base - 0.0001,
'C': math.sin(x * hz) / 1000 + base} for x in range(0, datalen)]}
return data
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...',
self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
timerange = Arguments.parse_timerange(self.config.get('timerange'))
data = optimize.load_data(self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=False,
timerange=timerange)
preprocessed = self.ML_tickerdata_to_dataframe(data)
out_coin = 'BTC_XMR'
test_ratio = 0.2
data = ml_utils.run_pipeline(preprocessed[out_coin], out_coin,
test_ratio)
initial_cash = 100 # in USD
transaction_cost = 0.01 # as ratio for fee
alpha = 0.05 # ratio of portfolio value that we trade each transaction
(trading_portfolio,
trading_stats) = self.run_buy_sell_strategy(data, initial_cash,
transaction_cost, alpha)
print(trading_portfolio)
#print(trading_portfolio.head(),"Head of trading series.")
print("Stats from trading: ", trading_stats)
# plots trading portfolio value in time next to out_coin price
trading_portfolio.plot(subplots=True, figsize=(6, 6))
plt.legend(loc='best')
plt.show()
def start(self) -> None:
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = load_data(datadir=str(self.config.get('datadir')),
pairs=self.config['exchange']['pair_whitelist'],
ticker_interval=self.ticker_interval,
timerange=timerange)
if self.has_space('buy'):
self.strategy.advise_indicators = Hyperopt.populate_indicators # type: ignore
dump(self.tickerdata_to_dataframe(data), TICKERDATA_PICKLE)
self.exchange = None # type: ignore
self.load_previous_results()
cpus = multiprocessing.cpu_count()
logger.info(f'Found {cpus} CPU cores. Let\'s make them scream!')
opt = self.get_optimizer(cpus)
EVALS = max(self.total_tries // cpus, 1)
try:
with Parallel(n_jobs=cpus) as parallel:
for i in range(EVALS):
asked = opt.ask(n_points=cpus)
f_val = self.run_optimizer_parallel(parallel, asked)
opt.tell(asked, [i['loss'] for i in f_val])
self.trials += f_val
for j in range(cpus):
self.log_results({
'loss': f_val[j]['loss'],
'current_tries': i * cpus + j,
'total_tries': self.total_tries,
'result': f_val[j]['result'],
})
except KeyboardInterrupt:
print('User interrupted..')
self.save_trials()
self.log_trials_result()
def plot_analyzed_dataframe(args: Namespace) -> None:
"""
Calls analyze() and plots the returned dataframe
:return: None
"""
global _CONF
# Load the configuration
_CONF.update(setup_configuration(args))
# Set the pair to audit
pair = args.pair
if pair is None:
logger.critical('Parameter --pair mandatory;. E.g --pair ETH/BTC')
exit()
if '/' not in pair:
logger.critical('--pair format must be XXX/YYY')
exit()
# Set timerange to use
timerange = Arguments.parse_timerange(args.timerange)
# Load the strategy
try:
analyze = Analyze(_CONF)
exchange = Exchange(_CONF)
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
args.strategy)
exit()
# Set the ticker to use
tick_interval = analyze.get_ticker_interval()
# Load pair tickers
tickers = {}
if args.live:
logger.info('Downloading pair.')
tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
else:
tickers = optimize.load_data(datadir=_CONF.get("datadir"),
pairs=[pair],
ticker_interval=tick_interval,
refresh_pairs=_CONF.get(
'refresh_pairs', False),
timerange=timerange)
# No ticker found, or impossible to download
if tickers == {}:
exit()
# Get trades already made from the DB
trades: List[Trade] = []
if args.db_url:
persistence.init(_CONF)
trades = Trade.query.filter(Trade.pair.is_(pair)).all()
dataframes = analyze.tickerdata_to_dataframe(tickers)
dataframe = dataframes[pair]
dataframe = analyze.populate_buy_trend(dataframe)
dataframe = analyze.populate_sell_trend(dataframe)
if len(dataframe.index) > 750:
logger.warning('Ticker contained more than 750 candles, clipping.')
fig = generate_graph(pair=pair,
trades=trades,
data=dataframe.tail(750),
args=args)
plot(fig, filename=os.path.join('user_data', 'freqtrade-plot.html'))
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair, self.ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
timerange = Arguments.parse_timerange(self.config.get('timerange'))
data = optimize.load_data(
self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get('refresh_pairs', False),
timerange=timerange
)
# Ignore max_open_trades in backtesting, except realistic flag was passed
if self.config.get('realistic_simulation', False):
max_open_trades = self.config['max_open_trades']
else:
logger.info('Ignoring max_open_trades (realistic_simulation not set) ...')
max_open_trades = 0
preprocessed = self.tickerdata_to_dataframe(data)
print(preprocessed)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days)..',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
# Execute backtest and print results
sell_profit_only = self.config.get('experimental', {}).get('sell_profit_only', False)
use_sell_signal = self.config.get('experimental', {}).get('use_sell_signal', False)
results = self.backtest(
{
'stake_amount': self.config.get('stake_amount'),
'processed': preprocessed,
'max_open_trades': max_open_trades,
'realistic': self.config.get('realistic_simulation', False),
'sell_profit_only': sell_profit_only,
'use_sell_signal': use_sell_signal,
'record': self.config.get('export')
}
)
logger.info(
'\n==================================== '
'BACKTESTING REPORT'
' ====================================\n'
'%s',
self._generate_text_table(
data,
results
)
)
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...',
self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = self.exchange.get_ticker_history(
pair, self.ticker_interval)
else:
logger.info(
'Using local backtesting data (using whitelist in given config) ...'
)
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = optimize.load_data(self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get(
'refresh_pairs', False),
exchange=self.exchange,
timerange=timerange)
if not data:
logger.critical("No data found. Terminating.")
return
# Ignore max_open_trades in backtesting, except realistic flag was passed
if self.config.get('realistic_simulation', False):
max_open_trades = self.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (realistic_simulation not set) ...')
max_open_trades = 0
preprocessed = self.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
logger.info('Measuring data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(),
(max_date - min_date).days)
# Execute backtest and print results
results = self.backtest({
'stake_amount':
self.config.get('stake_amount'),
'processed':
preprocessed,
'max_open_trades':
max_open_trades,
'realistic':
self.config.get('realistic_simulation', False),
})
if self.config.get('export', False):
self._store_backtest_result(self.config.get('exportfilename'),
results)
logger.info(
'\n======================================== '
'BACKTESTING REPORT'
' =========================================\n'
'%s', self._generate_text_table(data, results))
logger.info(
'\n====================================== '
'LEFT OPEN TRADES REPORT'
' ======================================\n'
'%s',
self._generate_text_table(data, results.loc[results.open_at_end]))
def _load_pair_as_ticks(pair, tickfreq):
ticks = optimize.load_data(None, ticker_interval=tickfreq, pairs=[pair])
ticks = trim_dictlist(ticks, -201)
return ticks[pair]
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...', self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = self.exchange.get_candle_history(pair, self.ticker_interval)
else:
logger.info('Using local backtesting data (using whitelist in given config) ...')
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = optimize.load_data(
self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get('refresh_pairs', False),
exchange=self.exchange,
timerange=timerange
)
if not data:
logger.critical("No data found. Terminating.")
return
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
max_open_trades = self.config['max_open_trades']
else:
logger.info('Ignoring max_open_trades (--disable-max-market-positions was used) ...')
max_open_trades = 0
all_results = {}
for strat in self.strategylist:
logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
self._set_strategy(strat)
# need to reprocess data every time to populate signals
preprocessed = self.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
logger.info(
'Measuring data from %s up to %s (%s days)..',
min_date.isoformat(),
max_date.isoformat(),
(max_date - min_date).days
)
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest(
{
'stake_amount': self.config.get('stake_amount'),
'processed': preprocessed,
'max_open_trades': max_open_trades,
'position_stacking': self.config.get('position_stacking', False),
}
)
for strategy, results in all_results.items():
if self.config.get('export', False):
self._store_backtest_result(self.config['exportfilename'], results,
strategy if len(self.strategylist) > 1 else None)
print(f"Result for strategy {strategy}")
print(' BACKTESTING REPORT '.center(119, '='))
print(self._generate_text_table(data, results))
print(' SELL REASON STATS '.center(119, '='))
print(self._generate_text_table_sell_reason(data, results))
print(' LEFT OPEN TRADES REPORT '.center(119, '='))
print(self._generate_text_table(data, results.loc[results.open_at_end]))
print()
if len(all_results) > 1:
# Print Strategy summary table
print(' Strategy Summary '.center(119, '='))
print(self._generate_text_table_strategy(all_results))
print('\nFor more details, please look at the detail tables above')
def test_get_timeframe():
data = preprocess(
optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST']))
min_date, max_date = get_timeframe(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
def start(args):
global TOTAL_TRIES, PROCESSED, SPACE, TRIALS, _CURRENT_TRIES
TOTAL_TRIES = args.epochs
exchange._API = Bittrex({'key': '', 'secret': ''})
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='\n%(message)s',
)
logger.info('Using config: %s ...', args.config)
config = load_config(args.config)
pairs = config['exchange']['pair_whitelist']
PROCESSED = optimize.preprocess(
optimize.load_data(args.datadir,
pairs=pairs,
ticker_interval=args.ticker_interval))
if args.mongodb:
logger.info('Using mongodb ...')
logger.info(
'Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!'
)
db_name = 'freqtrade_hyperopt'
TRIALS = MongoTrials('mongo://127.0.0.1:1234/{}/jobs'.format(db_name),
exp_key='exp1')
else:
logger.info('Preparing Trials..')
signal.signal(signal.SIGINT, signal_handler)
# read trials file if we have one
if os.path.exists(TRIALS_FILE):
TRIALS = read_trials()
_CURRENT_TRIES = len(TRIALS.results)
TOTAL_TRIES = TOTAL_TRIES + _CURRENT_TRIES
logger.info(
'Continuing with trials. Current: {}, Total: {}'.format(
_CURRENT_TRIES, TOTAL_TRIES))
try:
best_parameters = fmin(fn=optimizer,
space=SPACE,
algo=tpe.suggest,
max_evals=TOTAL_TRIES,
trials=TRIALS)
results = sorted(TRIALS.results, key=itemgetter('loss'))
best_result = results[0]['result']
except ValueError:
best_parameters = {}
best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
'try with more epochs (param: -e).'
# Improve best parameter logging display
if best_parameters:
best_parameters = space_eval(SPACE, best_parameters)
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
logger.info('Best Result:\n%s', best_result)
# Store trials result to file to resume next time
save_trials(TRIALS)
def plot_profit(args: Namespace) -> None:
"""
Plots the total profit for all pairs.
Note, the profit calculation isn't realistic.
But should be somewhat proportional, and therefor useful
in helping out to find a good algorithm.
"""
# We need to use the same pairs, same tick_interval
# and same timeperiod as used in backtesting
# to match the tickerdata against the profits-results
timerange = Arguments.parse_timerange(args.timerange)
config = Configuration(args).get_config()
# Init strategy
try:
analyze = Analyze({'strategy': config.get('strategy')})
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
config.get('strategy'))
exit(1)
# Load the profits results
try:
filename = args.exportfilename
with open(filename) as file:
data = json.load(file)
except FileNotFoundError:
logger.critical(
'File "backtest-result.json" not found. This script require backtesting '
'results to run.\nPlease run a backtesting with the parameter --export.'
)
exit(1)
# Take pairs from the cli otherwise switch to the pair in the config file
if args.pair:
filter_pairs = args.pair
filter_pairs = filter_pairs.split(',')
else:
filter_pairs = config['exchange']['pair_whitelist']
tick_interval = analyze.strategy.ticker_interval
pairs = config['exchange']['pair_whitelist']
if filter_pairs:
pairs = list(set(pairs) & set(filter_pairs))
logger.info('Filter, keep pairs %s' % pairs)
tickers = optimize.load_data(datadir=config.get('datadir'),
pairs=pairs,
ticker_interval=tick_interval,
refresh_pairs=False,
timerange=timerange)
dataframes = analyze.tickerdata_to_dataframe(tickers)
# NOTE: the dataframes are of unequal length,
# 'dates' is an merged date array of them all.
dates = misc.common_datearray(dataframes)
min_date = int(min(dates).timestamp())
max_date = int(max(dates).timestamp())
num_iterations = define_index(min_date, max_date, tick_interval) + 1
# Make an average close price of all the pairs that was involved.
# this could be useful to gauge the overall market trend
# We are essentially saying:
# array <- sum dataframes[*]['close'] / num_items dataframes
# FIX: there should be some onliner numpy/panda for this
avgclose = np.zeros(num_iterations)
num = 0
for pair, pair_data in dataframes.items():
close = pair_data['close']
maxprice = max(close) # Normalize price to [0,1]
logger.info('Pair %s has length %s' % (pair, len(close)))
for x in range(0, len(close)):
avgclose[x] += close[x] / maxprice
# avgclose += close
num += 1
avgclose /= num
# make an profits-growth array
pg = make_profit_array(data, num_iterations, min_date, tick_interval,
filter_pairs)
#
# Plot the pairs average close prices, and total profit growth
#
avgclose = go.Scattergl(
x=dates,
y=avgclose,
name='Avg close price',
)
profit = go.Scattergl(
x=dates,
y=pg,
name='Profit',
)
fig = tools.make_subplots(rows=3,
cols=1,
shared_xaxes=True,
row_width=[1, 1, 1])
fig.append_trace(avgclose, 1, 1)
fig.append_trace(profit, 2, 1)
for pair in pairs:
pg = make_profit_array(data, num_iterations, min_date, tick_interval,
pair)
pair_profit = go.Scattergl(
x=dates,
y=pg,
name=pair,
)
fig.append_trace(pair_profit, 3, 1)
plot(fig, filename=os.path.join('user_data', 'freqtrade-profit-plot.html'))
def start(args):
global TOTAL_TRIES, PROCESSED, SPACE, TRIALS, _CURRENT_TRIES
TOTAL_TRIES = args.epochs
exchange._API = Bittrex({'key': '', 'secret': ''})
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='\n%(message)s',
)
logger.info('Using config: %s ...', args.config)
config = load_config(args.config)
pairs = config['exchange']['pair_whitelist']
PROCESSED = optimize.preprocess(optimize.load_data(
args.datadir, pairs=pairs, ticker_interval=args.ticker_interval))
if args.mongodb:
logger.info('Using mongodb ...')
logger.info('Start scripts/start-mongodb.sh and start-hyperopt-worker.sh manually!')
db_name = 'freqtrade_hyperopt'
TRIALS = MongoTrials('mongo://127.0.0.1:1234/{}/jobs'.format(db_name), exp_key='exp1')
else:
logger.info('Preparing Trials..')
signal.signal(signal.SIGINT, signal_handler)
# read trials file if we have one
if os.path.exists(TRIALS_FILE):
TRIALS = read_trials()
_CURRENT_TRIES = len(TRIALS.results)
TOTAL_TRIES = TOTAL_TRIES + _CURRENT_TRIES
logger.info(
'Continuing with trials. Current: {}, Total: {}'
.format(_CURRENT_TRIES, TOTAL_TRIES))
try:
best_parameters = fmin(
fn=optimizer,
space=SPACE,
algo=tpe.suggest,
max_evals=TOTAL_TRIES,
trials=TRIALS
)
results = sorted(TRIALS.results, key=itemgetter('loss'))
best_result = results[0]['result']
except ValueError:
best_parameters = {}
best_result = 'Sorry, Hyperopt was not able to find good parameters. Please ' \
'try with more epochs (param: -e).'
# Improve best parameter logging display
if best_parameters:
best_parameters = space_eval(SPACE, best_parameters)
logger.info('Best parameters:\n%s', json.dumps(best_parameters, indent=4))
logger.info('Best Result:\n%s', best_result)
# Store trials result to file to resume next time
save_trials(TRIALS)
def start(args):
# Initialize logger
logging.basicConfig(
level=args.loglevel,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
)
exchange._API = Bittrex({'key': '', 'secret': ''})
logger.info('Using config: %s ...', args.config)
config = misc.load_config(args.config)
logger.info('Using ticker_interval: %s ...', args.ticker_interval)
data = {}
pairs = config['exchange']['pair_whitelist']
if args.live:
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = exchange.get_ticker_history(pair,
args.ticker_interval)
else:
logger.info(
'Using local backtesting data (using whitelist in given config) ...'
)
data = optimize.load_data(args.datadir,
pairs=pairs,
ticker_interval=args.ticker_interval,
refresh_pairs=args.refresh_pairs)
logger.info('Using stake_currency: %s ...', config['stake_currency'])
logger.info('Using stake_amount: %s ...', config['stake_amount'])
max_open_trades = 0
if args.realistic_simulation:
logger.info('Using max_open_trades: %s ...', config['max_open_trades'])
max_open_trades = config['max_open_trades']
# Monkey patch config
from freqtrade import main
main._CONF = config
preprocessed = preprocess(data)
# Print timeframe
min_date, max_date = get_timeframe(preprocessed)
logger.info('Measuring data from %s up to %s ...', min_date.isoformat(),
max_date.isoformat())
# Execute backtest and print results
results = backtest(
stake_amount=config['stake_amount'],
processed=preprocessed,
max_open_trades=max_open_trades,
realistic=args.realistic_simulation,
sell_profit_only=config.get('experimental',
{}).get('sell_profit_only', False),
stoploss=config.get('stoploss'),
use_sell_signal=config.get('experimental',
{}).get('use_sell_signal', False))
logger.info(
'\n==================================== BACKTESTING REPORT ====================================\n%s', # noqa
generate_text_table(data, results, config['stake_currency'],
args.ticker_interval))
def test_get_timeframe():
data = preprocess(optimize.load_data(
None, ticker_interval=1, pairs=['BTC_UNITEST']))
min_date, max_date = get_timeframe(data)
assert min_date.isoformat() == '2017-11-04T23:02:00+00:00'
assert max_date.isoformat() == '2017-11-14T22:59:00+00:00'
def plot_analyzed_dataframe(args: Namespace) -> None:
"""
Calls analyze() and plots the returned dataframe
:return: None
"""
global _CONF
# Load the configuration
_CONF.update(setup_configuration(args))
print(_CONF)
# Set the pair to audit
pair = args.pair
if pair is None:
logger.critical('Parameter --pair mandatory;. E.g --pair ETH/BTC')
exit()
if '/' not in pair:
logger.critical('--pair format must be XXX/YYY')
exit()
# Set timerange to use
timerange = Arguments.parse_timerange(args.timerange)
# Load the strategy
try:
strategy = StrategyResolver(_CONF).strategy
exchange = Exchange(_CONF)
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
args.strategy
)
exit()
# Set the ticker to use
tick_interval = strategy.ticker_interval
# Load pair tickers
tickers = {}
if args.live:
logger.info('Downloading pair.')
tickers[pair] = exchange.get_candle_history(pair, tick_interval)
else:
tickers = optimize.load_data(
datadir=_CONF.get("datadir"),
pairs=[pair],
ticker_interval=tick_interval,
refresh_pairs=_CONF.get('refresh_pairs', False),
timerange=timerange,
exchange=Exchange(_CONF)
)
# No ticker found, or impossible to download
if tickers == {}:
exit()
# Get trades already made from the DB
trades = load_trades(args, pair, timerange)
dataframes = strategy.tickerdata_to_dataframe(tickers)
dataframe = dataframes[pair]
dataframe = strategy.advise_buy(dataframe, {'pair': pair})
dataframe = strategy.advise_sell(dataframe, {'pair': pair})
if len(dataframe.index) > args.plot_limit:
logger.warning('Ticker contained more than %s candles as defined '
'with --plot-limit, clipping.', args.plot_limit)
dataframe = dataframe.tail(args.plot_limit)
trades = trades.loc[trades['opents'] >= dataframe.iloc[0]['date']]
fig = generate_graph(
pair=pair,
trades=trades,
data=dataframe,
args=args
)
plot(fig, filename=str(Path('user_data').joinpath('freqtrade-plot.html')))
def plot_analyzed_dataframe(args: Namespace) -> None:
"""
Calls analyze() and plots the returned dataframe
:return: None
"""
pair = args.pair.replace('-', '_')
timerange = Arguments.parse_timerange(args.timerange)
# Init strategy
try:
analyze = Analyze({'strategy': args.strategy})
except AttributeError:
logger.critical(
'Impossible to load the strategy. Please check the file "user_data/strategies/%s.py"',
args.strategy)
exit()
tick_interval = analyze.strategy.ticker_interval
tickers = {}
if args.live:
logger.info('Downloading pair.')
# Init Bittrex to use public API
exchange._API = exchange.Bittrex({'key': '', 'secret': ''})
tickers[pair] = exchange.get_ticker_history(pair, tick_interval)
else:
tickers = optimize.load_data(datadir=args.datadir,
pairs=[pair],
ticker_interval=tick_interval,
refresh_pairs=False,
timerange=timerange)
dataframes = analyze.tickerdata_to_dataframe(tickers)
dataframe = dataframes[pair]
dataframe = analyze.populate_buy_trend(dataframe)
dataframe = analyze.populate_sell_trend(dataframe)
if len(dataframe.index) > 750:
logger.warning('Ticker contained more than 750 candles, clipping.')
data = dataframe.tail(750)
candles = go.Candlestick(x=data.date,
open=data.open,
high=data.high,
low=data.low,
close=data.close,
name='Price')
df_buy = data[data['buy'] == 1]
buys = go.Scattergl(x=df_buy.date,
y=df_buy.close,
mode='markers',
name='buy',
marker=dict(
symbol='triangle-up-dot',
size=9,
line=dict(width=1),
color='green',
))
df_sell = data[data['sell'] == 1]
sells = go.Scattergl(x=df_sell.date,
y=df_sell.close,
mode='markers',
name='sell',
marker=dict(
symbol='triangle-down-dot',
size=9,
line=dict(width=1),
color='red',
))
bb_lower = go.Scatter(
x=data.date,
y=data.bb_lowerband,
name='BB lower',
line={'color': "transparent"},
)
bb_upper = go.Scatter(
x=data.date,
y=data.bb_upperband,
name='BB upper',
fill="tonexty",
fillcolor="rgba(0,176,246,0.2)",
line={'color': "transparent"},
)
macd = go.Scattergl(x=data['date'], y=data['macd'], name='MACD')
macdsignal = go.Scattergl(x=data['date'],
y=data['macdsignal'],
name='MACD signal')
volume = go.Bar(x=data['date'], y=data['volume'], name='Volume')
fig = tools.make_subplots(
rows=3,
cols=1,
shared_xaxes=True,
row_width=[1, 1, 4],
vertical_spacing=0.0001,
)
fig.append_trace(candles, 1, 1)
fig.append_trace(bb_lower, 1, 1)
fig.append_trace(bb_upper, 1, 1)
fig.append_trace(buys, 1, 1)
fig.append_trace(sells, 1, 1)
fig.append_trace(volume, 2, 1)
fig.append_trace(macd, 3, 1)
fig.append_trace(macdsignal, 3, 1)
fig['layout'].update(title=args.pair)
fig['layout']['yaxis1'].update(title='Price')
fig['layout']['yaxis2'].update(title='Volume')
fig['layout']['yaxis3'].update(title='MACD')
plot(fig, filename='freqtrade-plot.html')
