def test_backtest_multi_pair(default_conf, fee, mocker, tres, pair):
def _trend_alternate_hold(dataframe=None, metadata=None):
"""
Buy every xth candle - sell every other xth -2 (hold on to pairs a bit)
"""
if metadata['pair'] in ('ETH/BTC', 'LTC/BTC'):
multi = 20
else:
multi = 18
dataframe['buy'] = np.where(dataframe.index % multi == 0, 1, 0)
dataframe['sell'] = np.where(
(dataframe.index + multi - 2) % multi == 0, 1, 0)
return dataframe
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
pairs = ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC', 'NXT/BTC']
data = history.load_data(datadir=None, ticker_interval='5m', pairs=pairs)
# Only use 500 lines to increase performance
data = trim_dictlist(data, -500)
# Remove data for one pair from the beginning of the data
data[pair] = data[pair][tres:]
# We need to enable sell-signal - otherwise it sells on ROI!!
default_conf['experimental'] = {"use_sell_signal": True}
default_conf['ticker_interval'] = '5m'
backtesting = Backtesting(default_conf)
backtesting.advise_buy = _trend_alternate_hold # Override
backtesting.advise_sell = _trend_alternate_hold # Override
data_processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(data_processed)
backtest_conf = {
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 3,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
results = backtesting.backtest(backtest_conf)
# Make sure we have parallel trades
assert len(evaluate_result_multi(results, '5min', 2)) > 0
# make sure we don't have trades with more than configured max_open_trades
assert len(evaluate_result_multi(results, '5min', 3)) == 0
backtest_conf = {
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 1,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
results = backtesting.backtest(backtest_conf)
assert len(evaluate_result_multi(results, '5min', 1)) == 0
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'
timerange = TimeRange(None, 'line', 0, -201)
data = history.load_data(datadir=None,
ticker_interval='5m',
pairs=['UNITTEST/BTC'],
timerange=timerange)
data_processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(data_processed)
results = backtesting.backtest({
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 10,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
})
assert not results.empty
assert len(results) == 2
expected = pd.DataFrame({
'pair': [pair, pair],
'profit_percent': [0.0, 0.0],
'profit_abs': [0.0, 0.0],
'open_time':
pd.to_datetime([
Arrow(2018, 1, 29, 18, 40, 0).datetime,
Arrow(2018, 1, 30, 3, 30, 0).datetime
],
utc=True),
'close_time':
pd.to_datetime([
Arrow(2018, 1, 29, 22, 35, 0).datetime,
Arrow(2018, 1, 30, 4, 10, 0).datetime
],
utc=True),
'open_index': [78, 184],
'close_index': [125, 192],
'trade_duration': [235, 40],
'open_at_end': [False, False],
'open_rate': [0.104445, 0.10302485],
'close_rate': [0.104969, 0.103541],
'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 or is between high and low
ln = data_pair.loc[data_pair["date"] == t["close_time"]]
assert (round(ln.iloc[0]["open"], 6) == round(t["close_rate"], 6)
or round(ln.iloc[0]["low"], 6) < round(
t["close_rate"], 6) < round(ln.iloc[0]["high"], 6))
def generate_optimizer(self, _params: Dict) -> Dict:
params = self.get_args(_params)
if self.has_space('roi'):
self.strategy.minimal_roi = self.custom_hyperopt.generate_roi_table(
params)
if self.has_space('buy'):
self.advise_buy = self.custom_hyperopt.buy_strategy_generator(
params)
elif hasattr(self.custom_hyperopt, 'populate_buy_trend'):
self.advise_buy = self.custom_hyperopt.populate_buy_trend # type: ignore
if self.has_space('sell'):
self.advise_sell = self.custom_hyperopt.sell_strategy_generator(
params)
elif hasattr(self.custom_hyperopt, 'populate_sell_trend'):
self.advise_sell = self.custom_hyperopt.populate_sell_trend # type: ignore
if self.has_space('stoploss'):
self.strategy.stoploss = params['stoploss']
processed = load(TICKERDATA_PICKLE)
min_date, max_date = get_timeframe(processed)
self.min_date = min_date
self.max_date = max_date
results = self.backtest({
'stake_amount':
self.config['stake_amount'],
'processed':
processed,
'position_stacking':
self.config.get('position_stacking', True),
'start_date':
min_date,
'end_date':
max_date,
})
result_explanation = self.format_results(results)
#total_profit = results.profit_percent.sum()
total_profit = results.profit_percent
trade_count = len(results.index)
trade_duration = results.trade_duration.mean()
if trade_count == 0:
return {
'loss': MAX_LOSS,
'params': params,
'result': result_explanation,
}
loss = self.calculate_loss(total_profit, trade_count)
return {
'loss': loss,
'params': params,
'result': result_explanation,
}
def test_get_timeframe(default_conf, mocker) -> None:
patch_exchange(mocker)
strategy = DefaultStrategy(default_conf)
data = strategy.tickerdata_to_dataframe(
history.load_data(datadir=None,
ticker_interval='1m',
pairs=['UNITTEST/BTC']))
min_date, max_date = optimize.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 _make_backtest_conf(mocker, conf=None, pair='UNITTEST/BTC', record=None):
data = history.load_data(datadir=None, ticker_interval='1m', pairs=[pair])
data = trim_dictlist(data, -201)
patch_exchange(mocker)
backtesting = Backtesting(conf)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(processed)
return {
'stake_amount': conf['stake_amount'],
'processed': processed,
'max_open_trades': 10,
'position_stacking': False,
'record': record,
'start_date': min_date,
'end_date': max_date,
}
def test_validate_backtest_data(default_conf, mocker, caplog) -> None:
patch_exchange(mocker)
strategy = DefaultStrategy(default_conf)
timerange = TimeRange('index', 'index', 200, 250)
data = strategy.tickerdata_to_dataframe(
history.load_data(datadir=None,
ticker_interval='5m',
pairs=['UNITTEST/BTC'],
timerange=timerange))
min_date, max_date = optimize.get_timeframe(data)
caplog.clear()
assert not optimize.validate_backtest_data(
data, min_date, max_date, constants.TICKER_INTERVAL_MINUTES["5m"])
assert len(caplog.record_tuples) == 0
def test_validate_backtest_data_warn(default_conf, mocker, caplog) -> None:
patch_exchange(mocker)
strategy = DefaultStrategy(default_conf)
data = strategy.tickerdata_to_dataframe(
history.load_data(datadir=None,
ticker_interval='1m',
pairs=['UNITTEST/BTC']))
min_date, max_date = optimize.get_timeframe(data)
caplog.clear()
assert optimize.validate_backtest_data(
data, min_date, max_date, constants.TICKER_INTERVAL_MINUTES["1m"])
assert len(caplog.record_tuples) == 1
assert log_has(
"UNITTEST/BTC has missing frames: expected 14396, got 13680, that's 716 missing values",
caplog.record_tuples)
def simple_backtest(config, contour, num_results, mocker) -> None:
patch_exchange(mocker)
config['ticker_interval'] = '1m'
backtesting = Backtesting(config)
data = load_data_test(contour)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(processed)
assert isinstance(processed, dict)
results = backtesting.backtest({
'stake_amount': config['stake_amount'],
'processed': processed,
'max_open_trades': 1,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
})
# results :: <class 'pandas.core.frame.DataFrame'>
assert len(results) == num_results
def test_ohlcv_fill_up_missing_data(caplog):
data = load_pair_history(datadir=None,
ticker_interval='1m',
refresh_pairs=False,
pair='UNITTEST/BTC',
fill_up_missing=False)
caplog.set_level(logging.DEBUG)
data2 = ohlcv_fill_up_missing_data(data, '1m')
assert len(data2) > len(data)
# Column names should not change
assert (data.columns == data2.columns).all()
assert log_has(
f"Missing data fillup: before: {len(data)} - after: {len(data2)}",
caplog.record_tuples)
# Test fillup actually fixes invalid backtest data
min_date, max_date = get_timeframe({'UNITTEST/BTC': data})
assert validate_backtest_data({'UNITTEST/BTC': data}, min_date, max_date,
1)
assert not validate_backtest_data({'UNITTEST/BTC': data2}, min_date,
max_date, 1)
def test_backtest_results(default_conf, fee, mocker, caplog, data) -> None:
"""
run functional tests
"""
default_conf["stoploss"] = data.stop_loss
default_conf["minimal_roi"] = {"0": data.roi}
default_conf['ticker_interval'] = tests_ticker_interval
mocker.patch('freqtrade.exchange.Exchange.get_fee',
MagicMock(return_value=0.0))
patch_exchange(mocker)
frame = _build_backtest_dataframe(data.data)
backtesting = Backtesting(default_conf)
backtesting.advise_buy = lambda a, m: frame
backtesting.advise_sell = lambda a, m: frame
caplog.set_level(logging.DEBUG)
pair = 'UNITTEST/BTC'
# Dummy data as we mock the analyze functions
data_processed = {pair: DataFrame()}
min_date, max_date = get_timeframe({pair: frame})
results = backtesting.backtest({
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 10,
'start_date': min_date,
'end_date': max_date,
})
print(results.T)
assert len(results) == len(data.trades)
assert round(results["profit_percent"].sum(),
3) == round(data.profit_perc, 3)
for c, trade in enumerate(data.trades):
res = results.iloc[c]
assert res.sell_reason == trade.sell_reason
assert res.open_time == _get_frame_time_from_offset(trade.open_tick)
assert res.close_time == _get_frame_time_from_offset(trade.close_tick)
def test_backtest_1min_ticker_interval(default_conf, fee, mocker) -> None:
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
backtesting = Backtesting(default_conf)
# Run a backtesting for an exiting 1min ticker_interval
timerange = TimeRange(None, 'line', 0, -200)
data = history.load_data(datadir=None,
ticker_interval='1m',
pairs=['UNITTEST/BTC'],
timerange=timerange)
processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(processed)
results = backtesting.backtest({
'stake_amount': default_conf['stake_amount'],
'processed': processed,
'max_open_trades': 1,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
})
assert not results.empty
assert len(results) == 1
def test_backtest_multi_pair(default_conf, fee, mocker):
def evaluate_result_multi(results, freq, max_open_trades):
# Find overlapping trades by expanding each trade once per period
# and then counting overlaps
dates = [
pd.Series(
pd.date_range(row[1].open_time, row[1].close_time, freq=freq))
for row in results[['open_time', 'close_time']].iterrows()
]
deltas = [len(x) for x in dates]
dates = pd.Series(pd.concat(dates).values, name='date')
df2 = pd.DataFrame(np.repeat(results.values, deltas, axis=0),
columns=results.columns)
df2 = df2.astype(dtype={
"open_time": "datetime64",
"close_time": "datetime64"
})
df2 = pd.concat([dates, df2], axis=1)
df2 = df2.set_index('date')
df_final = df2.resample(freq)[['pair']].count()
return df_final[df_final['pair'] > max_open_trades]
def _trend_alternate_hold(dataframe=None, metadata=None):
"""
Buy every 8th candle - sell every other 8th -2 (hold on to pairs a bit)
"""
multi = 8
dataframe['buy'] = np.where(dataframe.index % multi == 0, 1, 0)
dataframe['sell'] = np.where(
(dataframe.index + multi - 2) % multi == 0, 1, 0)
if metadata['pair'] in ('ETH/BTC', 'LTC/BTC'):
dataframe['buy'] = dataframe['buy'].shift(-4)
dataframe['sell'] = dataframe['sell'].shift(-4)
return dataframe
mocker.patch('freqtrade.exchange.Exchange.get_fee', fee)
patch_exchange(mocker)
pairs = ['ADA/BTC', 'DASH/BTC', 'ETH/BTC', 'LTC/BTC', 'NXT/BTC']
data = history.load_data(datadir=None, ticker_interval='5m', pairs=pairs)
data = trim_dictlist(data, -500)
# We need to enable sell-signal - otherwise it sells on ROI!!
default_conf['experimental'] = {"use_sell_signal": True}
default_conf['ticker_interval'] = '5m'
backtesting = Backtesting(default_conf)
backtesting.advise_buy = _trend_alternate_hold # Override
backtesting.advise_sell = _trend_alternate_hold # Override
data_processed = backtesting.strategy.tickerdata_to_dataframe(data)
min_date, max_date = get_timeframe(data_processed)
backtest_conf = {
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 3,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
results = backtesting.backtest(backtest_conf)
# Make sure we have parallel trades
assert len(evaluate_result_multi(results, '5min', 2)) > 0
# make sure we don't have trades with more than configured max_open_trades
assert len(evaluate_result_multi(results, '5min', 3)) == 0
backtest_conf = {
'stake_amount': default_conf['stake_amount'],
'processed': data_processed,
'max_open_trades': 1,
'position_stacking': False,
'start_date': min_date,
'end_date': max_date,
}
results = backtesting.backtest(backtest_conf)
assert len(evaluate_result_multi(results, '5min', 1)) == 0
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data: Dict[str, Any] = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...',
self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
self.exchange.refresh_tickers(pairs, self.ticker_interval)
data = self.exchange._klines
else:
logger.info(
'Using local backtesting data (using whitelist in given config) ...'
)
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = history.load_data(datadir=Path(self.config['datadir'])
if self.config.get('datadir') else None,
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get(
'refresh_pairs', False),
exchange=self.exchange,
timerange=timerange)
if not data:
logger.critical("No data found. Terminating.")
return
# Use max_open_trades in backtesting, except --disable-max-market-positions is set
if self.config.get('use_max_market_positions', True):
max_open_trades = self.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (--disable-max-market-positions was used) ...'
)
max_open_trades = 0
all_results = {}
for strat in self.strategylist:
logger.info("Running backtesting for Strategy %s",
strat.get_strategy_name())
self._set_strategy(strat)
min_date, max_date = optimize.get_timeframe(data)
# Validate dataframe for missing values (mainly at start and end, as fillup is called)
optimize.validate_backtest_data(
data, min_date, max_date,
constants.TICKER_INTERVAL_MINUTES[self.ticker_interval])
logger.info('Measuring data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(),
(max_date - min_date).days)
# need to reprocess data every time to populate signals
preprocessed = self.strategy.tickerdata_to_dataframe(data)
# Execute backtest and print results
all_results[self.strategy.get_strategy_name()] = self.backtest({
'stake_amount':
self.config.get('stake_amount'),
'processed':
preprocessed,
'max_open_trades':
max_open_trades,
'position_stacking':
self.config.get('position_stacking', False),
'start_date':
min_date,
'end_date':
max_date,
})
for strategy, results in all_results.items():
if self.config.get('export', False):
self._store_backtest_result(
self.config['exportfilename'], results,
strategy if len(self.strategylist) > 1 else None)
print(f"Result for strategy {strategy}")
print(' BACKTESTING REPORT '.center(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],
True))
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')
