def get_signal(self, exchange: Exchange, pair: str,
interval: str) -> Tuple[bool, bool]:
"""
Calculates current signal based several technical analysis indicators
:param pair: pair in format ANT/BTC
:param interval: Interval to use (in min)
:return: (Buy, Sell) A bool-tuple indicating buy/sell signal
"""
ticker_hist = exchange.get_ticker_history(pair, interval)
if not ticker_hist:
logger.warning('Empty ticker history for pair %s', pair)
return False, False
try:
dataframe = self.analyze_ticker(ticker_hist)
except ValueError as error:
logger.warning('Unable to analyze ticker for pair %s: %s', pair,
str(error))
return False, False
except Exception as error:
logger.exception(
'Unexpected error when analyzing ticker for pair %s: %s', pair,
str(error))
return False, False
if dataframe.empty:
logger.warning('Empty dataframe for pair %s', pair)
return False, False
latest = dataframe.iloc[-1]
# Check if dataframe is out of date
signal_date = arrow.get(latest['date'])
interval_minutes = constants.TICKER_INTERVAL_MINUTES[interval]
if signal_date < (arrow.utcnow() -
timedelta(minutes=(interval_minutes + 5))):
logger.warning(
'Outdated history for pair %s. Last tick is %s minutes old',
pair, (arrow.utcnow() - signal_date).seconds // 60)
return False, False
(buy, sell) = latest[SignalType.BUY.value] == 1, latest[
SignalType.SELL.value] == 1
logger.debug('trigger: %s (pair=%s) buy=%s sell=%s', latest['date'],
pair, str(buy), str(sell))
return buy, sell
def download_backtesting_testdata(datadir: str,
exchange: Exchange,
pair: str,
tick_interval: str = '5m',
timerange: Optional[TimeRange] = None) -> None:
"""
Download the latest ticker intervals from the exchange for the pairs passed in parameters
The data is downloaded starting from the last correct ticker interval data that
esists in a cache. If timerange starts earlier than the data in the cache,
the full data will be redownloaded
Based on @Rybolov work: https://github.com/rybolov/freqtrade-data
:param pairs: list of pairs to download
:param tick_interval: ticker interval
:param timerange: range of time to download
:return: None
"""
path = make_testdata_path(datadir)
filepair = pair.replace("/", "_")
filename = os.path.join(path, f'{filepair}-{tick_interval}.json')
logger.info(
'Download the pair: "%s", Interval: %s',
pair,
tick_interval
)
data, since_ms = load_cached_data_for_updating(filename, tick_interval, timerange)
logger.debug("Current Start: %s", misc.format_ms_time(data[1][0]) if data else 'None')
logger.debug("Current End: %s", misc.format_ms_time(data[-1][0]) if data else 'None')
new_data = exchange.get_ticker_history(pair=pair, tick_interval=tick_interval,
since_ms=since_ms)
data.extend(new_data)
logger.debug("New Start: %s", misc.format_ms_time(data[0][0]))
logger.debug("New End: %s", misc.format_ms_time(data[-1][0]))
misc.file_dump_json(filename, data)
'exchange': {
'name': args.exchange,
'pair_whitelist': []
}
})
pairs_not_available = []
for pair in PAIRS:
if pair not in exchange._api.markets:
pairs_not_available.append(pair)
print(f"skipping pair {pair}")
continue
for tick_interval in timeframes:
print(f'downloading pair {pair}, interval {tick_interval}')
data = exchange.get_ticker_history(pair, tick_interval, since_ms=since_time)
if not data:
print('\tNo data was downloaded')
break
print('\tData was downloaded for period %s - %s' % (
arrow.get(data[0][0] / 1000).format(),
arrow.get(data[-1][0] / 1000).format()))
# save data
pair_print = pair.replace('/', '_')
filename = f'{pair_print}-{tick_interval}.json'
misc.file_dump_json(os.path.join(dl_path, filename), data)
if pairs_not_available:
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'))
class Backtesting(object):
"""
Backtesting class, this class contains all the logic to run a backtest
To run a backtest:
backtesting = Backtesting(config)
backtesting.start()
"""
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
self.analyze = Analyze(self.config)
self.ticker_interval = self.analyze.strategy.ticker_interval
self.tickerdata_to_dataframe = self.analyze.tickerdata_to_dataframe
self.populate_buy_trend = self.analyze.populate_buy_trend
self.populate_sell_trend = self.analyze.populate_sell_trend
# Reset keys for backtesting
self.config['exchange']['key'] = ''
self.config['exchange']['secret'] = ''
self.config['exchange']['password'] = ''
self.config['exchange']['uid'] = ''
self.config['dry_run'] = True
self.exchange = Exchange(self.config)
self.fee = self.exchange.get_fee()
@staticmethod
def get_timeframe(
data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
"""
Get the maximum timeframe for the given backtest data
:param data: dictionary with preprocessed backtesting data
:return: tuple containing min_date, max_date
"""
timeframe = [(arrow.get(min(frame.date)), arrow.get(max(frame.date)))
for frame in data.values()]
return min(timeframe, key=operator.itemgetter(0))[0], \
max(timeframe, key=operator.itemgetter(1))[1]
def _generate_text_table(self, data: Dict[str, Dict],
results: DataFrame) -> str:
"""
Generates and returns a text table for the given backtest data and the results dataframe
:return: pretty printed table with tabulate as str
"""
stake_currency = str(self.config.get('stake_currency'))
floatfmt = ('s', 'd', '.2f', '.8f', '.1f')
tabular_data = []
headers = [
'pair', 'buy count', 'avg profit %',
'total profit ' + stake_currency, 'avg duration', 'profit', 'loss'
]
for pair in data:
result = results[results.pair == pair]
tabular_data.append([
pair,
len(result.index),
result.profit_percent.mean() * 100.0,
result.profit_abs.sum(),
result.trade_duration.mean(),
len(result[result.profit_abs > 0]),
len(result[result.profit_abs < 0])
])
# Append Total
tabular_data.append([
'TOTAL',
len(results.index),
results.profit_percent.mean() * 100.0,
results.profit_abs.sum(),
results.trade_duration.mean(),
len(results[results.profit_abs > 0]),
len(results[results.profit_abs < 0])
])
return tabulate(tabular_data,
headers=headers,
floatfmt=floatfmt,
tablefmt="pipe")
def _store_backtest_result(self, recordfilename: Optional[str],
results: DataFrame) -> None:
records = [(trade_entry.pair, trade_entry.profit_percent,
trade_entry.open_time.timestamp(),
trade_entry.close_time.timestamp(),
trade_entry.open_index - 1, trade_entry.trade_duration)
for index, trade_entry in results.iterrows()]
if records:
logger.info('Dumping backtest results to %s', recordfilename)
file_dump_json(recordfilename, records)
def _get_sell_trade_entry(self, pair: str, buy_row: DataFrame,
partial_ticker: List, trade_count_lock: Dict,
args: Dict) -> Optional[BacktestResult]:
stake_amount = args['stake_amount']
max_open_trades = args.get('max_open_trades', 0)
trade = Trade(open_rate=buy_row.close,
open_date=buy_row.date,
stake_amount=stake_amount,
amount=stake_amount / buy_row.open,
fee_open=self.fee,
fee_close=self.fee)
# calculate win/lose forwards from buy point
for sell_row in partial_ticker:
if max_open_trades > 0:
# Increase trade_count_lock for every iteration
trade_count_lock[sell_row.date] = trade_count_lock.get(
sell_row.date, 0) + 1
buy_signal = sell_row.buy
if self.analyze.should_sell(trade, sell_row.close, sell_row.date,
buy_signal, sell_row.sell):
return BacktestResult(
pair=pair,
profit_percent=trade.calc_profit_percent(
rate=sell_row.close),
profit_abs=trade.calc_profit(rate=sell_row.close),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=(sell_row.date - buy_row.date).seconds //
60,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=False)
if partial_ticker:
# no sell condition found - trade stil open at end of backtest period
sell_row = partial_ticker[-1]
btr = BacktestResult(
pair=pair,
profit_percent=trade.calc_profit_percent(rate=sell_row.close),
profit_abs=trade.calc_profit(rate=sell_row.close),
open_time=buy_row.date,
close_time=sell_row.date,
trade_duration=(sell_row.date - buy_row.date).seconds // 60,
open_index=buy_row.Index,
close_index=sell_row.Index,
open_at_end=True)
logger.debug('Force_selling still open trade %s with %s perc - %s',
btr.pair, btr.profit_percent, btr.profit_abs)
return btr
return None
def backtest(self, args: Dict) -> DataFrame:
"""
Implements backtesting functionality
NOTE: This method is used by Hyperopt at each iteration. Please keep it optimized.
Of course try to not have ugly code. By some accessor are sometime slower than functions.
Avoid, logging on this method
:param args: a dict containing:
stake_amount: btc amount to use for each trade
processed: a processed dictionary with format {pair, data}
max_open_trades: maximum number of concurrent trades (default: 0, disabled)
realistic: do we try to simulate realistic trades? (default: True)
:return: DataFrame
"""
headers = ['date', 'buy', 'open', 'close', 'sell']
processed = args['processed']
max_open_trades = args.get('max_open_trades', 0)
realistic = args.get('realistic', False)
trades = []
trade_count_lock: Dict = {}
for pair, pair_data in processed.items():
pair_data['buy'], pair_data[
'sell'] = 0, 0 # cleanup from previous run
ticker_data = self.populate_sell_trend(
self.populate_buy_trend(pair_data))[headers].copy()
# to avoid using data from future, we buy/sell with signal from previous candle
ticker_data.loc[:, 'buy'] = ticker_data['buy'].shift(1)
ticker_data.loc[:, 'sell'] = ticker_data['sell'].shift(1)
ticker_data.drop(ticker_data.head(1).index, inplace=True)
# Convert from Pandas to list for performance reasons
# (Looping Pandas is slow.)
ticker = [x for x in ticker_data.itertuples()]
lock_pair_until = None
for index, row in enumerate(ticker):
if row.buy == 0 or row.sell == 1:
continue # skip rows where no buy signal or that would immediately sell off
if realistic:
if lock_pair_until is not None and row.date <= lock_pair_until:
continue
if max_open_trades > 0:
# Check if max_open_trades has already been reached for the given date
if not trade_count_lock.get(row.date, 0) < max_open_trades:
continue
trade_count_lock[row.date] = trade_count_lock.get(
row.date, 0) + 1
trade_entry = self._get_sell_trade_entry(
pair, row, ticker[index + 1:], trade_count_lock, args)
if trade_entry:
lock_pair_until = trade_entry.close_time
trades.append(trade_entry)
else:
# Set lock_pair_until to end of testing period if trade could not be closed
# This happens only if the buy-signal was with the last candle
lock_pair_until = ticker_data.iloc[-1].date
return DataFrame.from_records(trades, columns=BacktestResult._fields)
def start(self) -> None:
"""
Run a backtesting end-to-end
:return: None
"""
data = {}
pairs = self.config['exchange']['pair_whitelist']
logger.info('Using stake_currency: %s ...',
self.config['stake_currency'])
logger.info('Using stake_amount: %s ...', self.config['stake_amount'])
if self.config.get('live'):
logger.info('Downloading data for all pairs in whitelist ...')
for pair in pairs:
data[pair] = self.exchange.get_ticker_history(
pair, self.ticker_interval)
else:
logger.info(
'Using local backtesting data (using whitelist in given config) ...'
)
timerange = Arguments.parse_timerange(None if self.config.get(
'timerange') is None else str(self.config.get('timerange')))
data = optimize.load_data(self.config['datadir'],
pairs=pairs,
ticker_interval=self.ticker_interval,
refresh_pairs=self.config.get(
'refresh_pairs', False),
exchange=self.exchange,
timerange=timerange)
if not data:
logger.critical("No data found. Terminating.")
return
# Ignore max_open_trades in backtesting, except realistic flag was passed
if self.config.get('realistic_simulation', False):
max_open_trades = self.config['max_open_trades']
else:
logger.info(
'Ignoring max_open_trades (realistic_simulation not set) ...')
max_open_trades = 0
preprocessed = self.tickerdata_to_dataframe(data)
# Print timeframe
min_date, max_date = self.get_timeframe(preprocessed)
logger.info('Measuring data from %s up to %s (%s days)..',
min_date.isoformat(), max_date.isoformat(),
(max_date - min_date).days)
# Execute backtest and print results
results = self.backtest({
'stake_amount':
self.config.get('stake_amount'),
'processed':
preprocessed,
'max_open_trades':
max_open_trades,
'realistic':
self.config.get('realistic_simulation', False),
})
if self.config.get('export', False):
self._store_backtest_result(self.config.get('exportfilename'),
results)
logger.info(
'\n======================================== '
'BACKTESTING REPORT'
' =========================================\n'
'%s', self._generate_text_table(data, results))
logger.info(
'\n====================================== '
'LEFT OPEN TRADES REPORT'
' ======================================\n'
'%s',
self._generate_text_table(data, results.loc[results.open_at_end]))
