Merge pull request #2430 from freqtrade/startup_period_bt

Add Startup period for strategies

docs/backtesting.md

@@ -72,6 +72,8 @@ The exported trades can be used for [further analysis](#further-backtest-result-
 freqtrade backtesting --export trades --export-filename=backtest_samplestrategy.json
 ```
 
+Please also read about the [strategy startup period](strategy-customization.md#strategy-startup-period).
+
 #### Supplying custom fee value
 
 Sometimes your account has certain fee rebates (fee reductions starting with a certain account size or monthly volume), which are not visible to ccxt.

docs/strategy-customization.md

@@ -117,6 +117,37 @@ def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame
     Look into the [user_data/strategies/sample_strategy.py](https://github.com/freqtrade/freqtrade/blob/develop/user_data/strategies/sample_strategy.py).
     Then uncomment indicators you need.
 
+### Strategy startup period
+
+Most indicators have an instable startup period, in which they are either not available, or the calculation is incorrect. This can lead to inconsistencies, since Freqtrade does not know how long this instable period should be.
+To account for this, the strategy can be assigned the `startup_candle_count` attribute.
+This should be set to the maximum number of candles that the strategy requires to calculate stable indicators.
+
+In this example strategy, this should be set to 100 (`startup_candle_count = 100`), since the longest needed history is 100 candles.
+
+``` python
+    dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
+```
+
+By letting the bot know how much history is needed, backtest trades can start at the specified timerange during backtesting and hyperopt.
+
+!!! Warning
+    `startup_candle_count` should be below `ohlcv_candle_limit` (which is 500 for most exchanges) - since only this amount of candles will be available during Dry-Run/Live Trade operations.
+
+#### Example
+
+Let's try to backtest 1 month (January 2019) of 5m candles using the an example strategy with EMA100, as above.
+
+``` bash
+freqtrade backtesting --timerange 20190101-20190201 --ticker-interval 5m
+```
+
+Assuming `startup_candle_count` is set to 100, backtesting knows it needs 100 candles to generate valid buy signals. It will load data from `20190101 - (100 * 5m)` - which is ~2019-12-31 15:30:00.
+If this data is available, indicators will be calculated with this extended timerange. The instable startup period (up to 2019-01-01 00:00:00) will then be removed before starting backtesting.
+
+!!! Note
+    If data for the startup period is not available, then the timerange will be adjusted to account for this startup period - so Backtesting would start at 2019-01-01 08:30:00.
+
 ### Buy signal rules
 
 Edit the method `populate_buy_trend()` in your strategy file to update your buy strategy.

freqtrade/configuration/timerange.py

@@ -1,11 +1,14 @@
 """
 This module contains the argument manager class
 """
+import logging
 import re
 from typing import Optional
 
 import arrow
 
+logger = logging.getLogger(__name__)
+
 
 class TimeRange:
     """
@@ -27,6 +30,34 @@ class TimeRange:
         return (self.starttype == other.starttype and self.stoptype == other.stoptype
                 and self.startts == other.startts and self.stopts == other.stopts)
 
+    def subtract_start(self, seconds) -> None:
+        """
+        Subtracts <seconds> from startts if startts is set.
+        :param seconds: Seconds to subtract from starttime
+        :return: None (Modifies the object in place)
+        """
+        if self.startts:
+            self.startts = self.startts - seconds
+
+    def adjust_start_if_necessary(self, ticker_interval_secs: int, startup_candles: int,
+                                  min_date: arrow.Arrow) -> None:
+        """
+        Adjust startts by <startup_candles> candles.
+        Applies only if no startup-candles have been available.
+        :param ticker_interval_secs: Ticker interval in seconds e.g. `timeframe_to_seconds('5m')`
+        :param startup_candles: Number of candles to move start-date forward
+        :param min_date: Minimum data date loaded. Key kriterium to decide if start-time
+                         has to be moved
+        :return: None (Modifies the object in place)
+        """
+        if (not self.starttype or (startup_candles
+                                   and min_date.timestamp >= self.startts)):
+            # If no startts was defined, or backtest-data starts at the defined backtest-date
+            logger.warning("Moving start-date by %s candles to account for startup time.",
+                           startup_candles)
+            self.startts = (min_date.timestamp + ticker_interval_secs * startup_candles)
+            self.starttype = 'date'
+
     @staticmethod
     def parse_timerange(text: Optional[str]):
         """

freqtrade/data/history.py

@@ -8,17 +8,19 @@ Includes:
 
 import logging
 import operator
+from copy import deepcopy
 from datetime import datetime
 from pathlib import Path
 from typing import Any, Dict, List, Optional, Tuple
 
 import arrow
+import pytz
 from pandas import DataFrame
 
 from freqtrade import OperationalException, misc
 from freqtrade.configuration import TimeRange
 from freqtrade.data.converter import parse_ticker_dataframe, trades_to_ohlcv
-from freqtrade.exchange import Exchange, timeframe_to_minutes
+from freqtrade.exchange import Exchange, timeframe_to_minutes, timeframe_to_seconds
 
 logger = logging.getLogger(__name__)
 
@@ -49,6 +51,19 @@ def trim_tickerlist(tickerlist: List[Dict], timerange: TimeRange) -> List[Dict]:
     return tickerlist[start_index:stop_index]
 
 
+def trim_dataframe(df: DataFrame, timerange: TimeRange) -> DataFrame:
+    """
+    Trim dataframe based on given timerange
+    """
+    if timerange.starttype == 'date':
+        start = datetime.fromtimestamp(timerange.startts, tz=pytz.utc)
+        df = df.loc[df['date'] >= start, :]
+    if timerange.stoptype == 'date':
+        stop = datetime.fromtimestamp(timerange.stopts, tz=pytz.utc)
+        df = df.loc[df['date'] <= stop, :]
+    return df
+
+
 def load_tickerdata_file(datadir: Path, pair: str, ticker_interval: str,
                          timerange: Optional[TimeRange] = None) -> Optional[list]:
     """
@@ -113,7 +128,8 @@ def load_pair_history(pair: str,
                       refresh_pairs: bool = False,
                       exchange: Optional[Exchange] = None,
                       fill_up_missing: bool = True,
-                      drop_incomplete: bool = True
+                      drop_incomplete: bool = True,
+                      startup_candles: int = 0,
                       ) -> DataFrame:
     """
     Loads cached ticker history for the given pair.
@@ -126,9 +142,15 @@ def load_pair_history(pair: str,
     :param exchange: Exchange object (needed when using "refresh_pairs")
     :param fill_up_missing: Fill missing values with "No action"-candles
     :param drop_incomplete: Drop last candle assuming it may be incomplete.
+    :param startup_candles: Additional candles to load at the start of the period
     :return: DataFrame with ohlcv data
     """
 
+    timerange_startup = deepcopy(timerange)
+    if startup_candles > 0 and timerange_startup:
+        logger.info('Using indicator startup period: %s ...', startup_candles)
+        timerange_startup.subtract_start(timeframe_to_seconds(ticker_interval) * startup_candles)
+
     # The user forced the refresh of pairs
     if refresh_pairs:
         download_pair_history(datadir=datadir,
@@ -137,11 +159,11 @@ def load_pair_history(pair: str,
                               ticker_interval=ticker_interval,
                               timerange=timerange)
 
-    pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange)
+    pairdata = load_tickerdata_file(datadir, pair, ticker_interval, timerange=timerange_startup)
 
     if pairdata:
-        if timerange:
+        if timerange_startup:
-            _validate_pairdata(pair, pairdata, timerange)
+            _validate_pairdata(pair, pairdata, timerange_startup)
         return parse_ticker_dataframe(pairdata, ticker_interval, pair=pair,
                                       fill_missing=fill_up_missing,
                                       drop_incomplete=drop_incomplete)
@@ -160,10 +182,22 @@ def load_data(datadir: Path,
               exchange: Optional[Exchange] = None,
               timerange: Optional[TimeRange] = None,
               fill_up_missing: bool = True,
+              startup_candles: int = 0,
+              fail_without_data: bool = False
               ) -> Dict[str, DataFrame]:
     """
     Loads ticker history data for a list of pairs
-    :return: dict(<pair>:<tickerlist>)
+    :param datadir: Path to the data storage location.
+    :param ticker_interval: Ticker-interval (e.g. "5m")
+    :param pairs: List of pairs to load
+    :param refresh_pairs: Refresh pairs from exchange.
+        (Note: Requires exchange to be passed as well.)
+    :param exchange: Exchange object (needed when using "refresh_pairs")
+    :param timerange: Limit data to be loaded to this timerange
+    :param fill_up_missing: Fill missing values with "No action"-candles
+    :param startup_candles: Additional candles to load at the start of the period
+    :param fail_without_data: Raise OperationalException if no data is found.
+    :return: dict(<pair>:<Dataframe>)
     TODO: refresh_pairs is still used by edge to keep the data uptodate.
         This should be replaced in the future. Instead, writing the current candles to disk
         from dataprovider should be implemented, as this would avoid loading ohlcv data twice.
@@ -176,9 +210,13 @@ def load_data(datadir: Path,
                                  datadir=datadir, timerange=timerange,
                                  refresh_pairs=refresh_pairs,
                                  exchange=exchange,
-                                 fill_up_missing=fill_up_missing)
+                                 fill_up_missing=fill_up_missing,
+                                 startup_candles=startup_candles)
         if hist is not None:
             result[pair] = hist
+
+    if fail_without_data and not result:
+        raise OperationalException("No data found. Terminating.")
     return result
 
 

freqtrade/edge/__init__.py

@@ -100,7 +100,8 @@ class Edge:
             ticker_interval=self.strategy.ticker_interval,
             refresh_pairs=self._refresh_pairs,
             exchange=self.exchange,
-            timerange=self._timerange
+            timerange=self._timerange,
+            startup_candles=self.strategy.startup_candle_count,
         )
 
         if not data:

freqtrade/exchange/exchange.py

@@ -228,6 +228,7 @@ class Exchange:
             self.validate_pairs(config['exchange']['pair_whitelist'])
             self.validate_ordertypes(config.get('order_types', {}))
             self.validate_order_time_in_force(config.get('order_time_in_force', {}))
+            self.validate_required_startup_candles(config.get('startup_candle_count', 0))
 
         # Converts the interval provided in minutes in config to seconds
         self.markets_refresh_interval: int = exchange_config.get(
@@ -443,6 +444,16 @@ class Exchange:
             raise OperationalException(
                 f'Time in force policies are not supported for {self.name} yet.')
 
+    def validate_required_startup_candles(self, startup_candles) -> None:
+        """
+        Checks if required startup_candles is more than ohlcv_candle_limit.
+        Requires a grace-period of 5 candles - so a startup-period up to 494 is allowed by default.
+        """
+        if startup_candles + 5 > self._ft_has['ohlcv_candle_limit']:
+            raise OperationalException(
+                f"This strategy requires {startup_candles} candles to start. "
+                f"{self.name} only provides {self._ft_has['ohlcv_candle_limit']}.")
+
     def exchange_has(self, endpoint: str) -> bool:
         """
         Checks if exchange implements a specific API endpoint.

freqtrade/optimize/backtesting.py

@@ -15,7 +15,7 @@ from freqtrade import OperationalException
 from freqtrade.configuration import TimeRange
 from freqtrade.data import history
 from freqtrade.data.dataprovider import DataProvider
-from freqtrade.exchange import timeframe_to_minutes
+from freqtrade.exchange import timeframe_to_minutes, timeframe_to_seconds
 from freqtrade.misc import file_dump_json
 from freqtrade.persistence import Trade
 from freqtrade.resolvers import ExchangeResolver, StrategyResolver
@@ -90,6 +90,8 @@ class Backtesting:
         self.ticker_interval = str(self.config.get('ticker_interval'))
         self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
 
+        # 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])
 
@@ -103,6 +105,31 @@ class Backtesting:
         # And the regular "stoploss" function would not apply to that case
         self.strategy.order_types['stoploss_on_exchange'] = False
 
+    def load_bt_data(self):
+        timerange = TimeRange.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']),
+            pairs=self.config['exchange']['pair_whitelist'],
+            ticker_interval=self.ticker_interval,
+            timerange=timerange,
+            startup_candles=self.required_startup,
+            fail_without_data=True,
+        )
+
+        min_date, max_date = history.get_timeframe(data)
+
+        logger.info(
+            'Loading data from %s up to %s (%s days)..',
+            min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
+        )
+        # Adjust startts forward if not enough data is available
+        timerange.adjust_start_if_necessary(timeframe_to_seconds(self.ticker_interval),
+                                            self.required_startup, min_date)
+
+        return data, timerange
+
     def _generate_text_table(self, data: Dict[str, Dict], results: DataFrame,
                              skip_nan: bool = False) -> str:
         """
@@ -412,39 +439,18 @@ class Backtesting:
         :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'])
-
-        timerange = TimeRange.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']),
-            pairs=pairs,
-            ticker_interval=self.ticker_interval,
-            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
+
+        data, timerange = self.load_bt_data()
+
         all_results = {}
-
-        min_date, max_date = history.get_timeframe(data)
-
-        logger.info(
-            'Backtesting with data from %s up to %s (%s days)..',
-            min_date.isoformat(),
-            max_date.isoformat(),
-            (max_date - min_date).days
-        )
-
         for strat in self.strategylist:
             logger.info("Running backtesting for Strategy %s", strat.get_strategy_name())
             self._set_strategy(strat)
@@ -452,6 +458,15 @@ class Backtesting:
             # need to reprocess data every time to populate signals
             preprocessed = self.strategy.tickerdata_to_dataframe(data)
 
+            # Trim startup period from analyzed dataframe
+            for pair, df in preprocessed.items():
+                preprocessed[pair] = history.trim_dataframe(df, timerange)
+            min_date, max_date = history.get_timeframe(preprocessed)
+
+            logger.info(
+                'Backtesting with 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(
                 {

freqtrade/optimize/hyperopt.py

@@ -22,8 +22,7 @@ from pandas import DataFrame
 from skopt import Optimizer
 from skopt.space import Dimension
 
-from freqtrade.configuration import TimeRange
+from freqtrade.data.history import get_timeframe, trim_dataframe
-from freqtrade.data.history import load_data, get_timeframe
 from freqtrade.misc import round_dict
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
@@ -379,30 +378,19 @@ class Hyperopt:
             )
 
     def start(self) -> None:
-        timerange = TimeRange.parse_timerange(None if self.config.get(
+        data, timerange = self.backtesting.load_bt_data()
-            'timerange') is None else str(self.config.get('timerange')))
-        data = load_data(
-            datadir=Path(self.config['datadir']),
-            pairs=self.config['exchange']['pair_whitelist'],
-            ticker_interval=self.backtesting.ticker_interval,
-            timerange=timerange
-        )
 
-        if not data:
+        preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
-            logger.critical("No data found. Terminating.")
-            return
 
+        # Trim startup period from analyzed dataframe
+        for pair, df in preprocessed.items():
+            preprocessed[pair] = trim_dataframe(df, timerange)
         min_date, max_date = get_timeframe(data)
 
         logger.info(
             'Hyperopting with data from %s up to %s (%s days)..',
-            min_date.isoformat(),
+            min_date.isoformat(), max_date.isoformat(), (max_date - min_date).days
-            max_date.isoformat(),
-            (max_date - min_date).days
         )
-
-        preprocessed = self.backtesting.strategy.tickerdata_to_dataframe(data)
-
         dump(preprocessed, self.tickerdata_pickle)
 
         # We don't need exchange instance anymore while running hyperopt

freqtrade/resolvers/strategy_resolver.py

@@ -57,6 +57,7 @@ class StrategyResolver(IResolver):
                       ("order_time_in_force",             None,        False),
                       ("stake_currency",                  None,        False),
                       ("stake_amount",                    None,        False),
+                      ("startup_candle_count",            None,        False),
                       ("use_sell_signal",                 True,        True),
                       ("sell_profit_only",                False,       True),
                       ("ignore_roi_if_buy_signal",        False,       True),

freqtrade/strategy/default_strategy.py

@@ -39,6 +39,9 @@ class DefaultStrategy(IStrategy):
         'stoploss_on_exchange': False
     }
 
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
     # Optional time in force for orders
     order_time_in_force = {
         'buy': 'gtc',
@@ -105,9 +108,6 @@ class DefaultStrategy(IStrategy):
         # EMA - Exponential Moving Average
         dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
 
-        # SMA - Simple Moving Average
-        dataframe['sma'] = ta.SMA(dataframe, timeperiod=40)
-
         return dataframe
 
     def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

freqtrade/strategy/interface.py

@@ -103,6 +103,9 @@ class IStrategy(ABC):
     # run "populate_indicators" only for new candle
     process_only_new_candles: bool = False
 
+    # Count of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 0
+
     # Class level variables (intentional) containing
     # the dataprovider (dp) (access to other candles, historic data, ...)
     # and wallets - access to the current balance.
@@ -421,6 +424,7 @@ class IStrategy(ABC):
     def tickerdata_to_dataframe(self, tickerdata: Dict[str, List]) -> Dict[str, DataFrame]:
         """
         Creates a dataframe and populates indicators for given ticker data
+        Used by optimize operations only, not during dry / live runs.
         """
         return {pair: self.advise_indicators(pair_data, {'pair': pair})
                 for pair, pair_data in tickerdata.items()}

tests/data/test_history.py

@@ -95,6 +95,23 @@ def test_load_data_1min_ticker(ticker_history, mocker, caplog, testdatadir) -> N
     _clean_test_file(file)
 
 
+def test_load_data_startup_candles(mocker, caplog, default_conf, testdatadir) -> None:
+    ltfmock = mocker.patch('freqtrade.data.history.load_tickerdata_file',
+                           MagicMock(return_value=None))
+    timerange = TimeRange('date', None, 1510639620, 0)
+    history.load_pair_history(pair='UNITTEST/BTC', ticker_interval='1m',
+                              datadir=testdatadir, timerange=timerange,
+                              startup_candles=20,
+                              )
+    assert log_has(
+        'Using indicator startup period: 20 ...', caplog
+    )
+    assert ltfmock.call_count == 1
+    assert ltfmock.call_args_list[0][1]['timerange'] != timerange
+    # startts is 20 minutes earlier
+    assert ltfmock.call_args_list[0][1]['timerange'].startts == timerange.startts - 20 * 60
+
+
 def test_load_data_with_new_pair_1min(ticker_history_list, mocker, caplog,
                                       default_conf, testdatadir) -> None:
     """
@@ -427,6 +444,46 @@ def test_trim_tickerlist(testdatadir) -> None:
     assert not ticker
 
 
+def test_trim_dataframe(testdatadir) -> None:
+    data = history.load_data(
+        datadir=testdatadir,
+        ticker_interval='1m',
+        pairs=['UNITTEST/BTC']
+    )['UNITTEST/BTC']
+    min_date = int(data.iloc[0]['date'].timestamp())
+    max_date = int(data.iloc[-1]['date'].timestamp())
+    data_modify = data.copy()
+
+    # Remove first 30 minutes (1800 s)
+    tr = TimeRange('date', None, min_date + 1800, 0)
+    data_modify = history.trim_dataframe(data_modify, tr)
+    assert not data_modify.equals(data)
+    assert len(data_modify) < len(data)
+    assert len(data_modify) == len(data) - 30
+    assert all(data_modify.iloc[-1] == data.iloc[-1])
+    assert all(data_modify.iloc[0] == data.iloc[30])
+
+    data_modify = data.copy()
+    # Remove last 30 minutes (1800 s)
+    tr = TimeRange(None, 'date', 0, max_date - 1800)
+    data_modify = history.trim_dataframe(data_modify, tr)
+    assert not data_modify.equals(data)
+    assert len(data_modify) < len(data)
+    assert len(data_modify) == len(data) - 30
+    assert all(data_modify.iloc[0] == data.iloc[0])
+    assert all(data_modify.iloc[-1] == data.iloc[-31])
+
+    data_modify = data.copy()
+    # Remove first 25 and last 30 minutes (1800 s)
+    tr = TimeRange('date', 'date', min_date + 1500, max_date - 1800)
+    data_modify = history.trim_dataframe(data_modify, tr)
+    assert not data_modify.equals(data)
+    assert len(data_modify) < len(data)
+    assert len(data_modify) == len(data) - 55
+    # first row matches 25th original row
+    assert all(data_modify.iloc[0] == data.iloc[25])
+
+
 def test_file_dump_json_tofile(testdatadir) -> None:
     file = testdatadir / 'test_{id}.json'.format(id=str(uuid.uuid4()))
     data = {'bar': 'foo'}

tests/edge/test_edge.py

@@ -256,7 +256,7 @@ def test_edge_heartbeat_calculate(mocker, edge_conf):
 
 
 def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=False,
-                     timerange=None, exchange=None):
+                     timerange=None, exchange=None, *args, **kwargs):
     hz = 0.1
     base = 0.001
 

tests/exchange/test_exchange.py

@@ -523,6 +523,24 @@ def test_validate_order_types_not_in_config(default_conf, mocker):
     Exchange(conf)
 
 
+def test_validate_required_startup_candles(default_conf, mocker, caplog):
+    api_mock = MagicMock()
+    mocker.patch('freqtrade.exchange.Exchange.name', PropertyMock(return_value='Binance'))
+
+    mocker.patch('freqtrade.exchange.Exchange._init_ccxt', api_mock)
+    mocker.patch('freqtrade.exchange.Exchange.validate_timeframes', MagicMock())
+    mocker.patch('freqtrade.exchange.Exchange._load_async_markets', MagicMock())
+    mocker.patch('freqtrade.exchange.Exchange.validate_pairs', MagicMock())
+
+    default_conf['startup_candle_count'] = 20
+    ex = Exchange(default_conf)
+    assert ex
+    default_conf['startup_candle_count'] = 600
+
+    with pytest.raises(OperationalException, match=r'This strategy requires 600.*'):
+        Exchange(default_conf)
+
+
 def test_exchange_has(default_conf, mocker):
     exchange = get_patched_exchange(mocker, default_conf)
     assert not exchange.exchange_has('ASDFASDF')

tests/optimize/test_backtesting.py

@@ -117,7 +117,7 @@ def simple_backtest(config, contour, num_results, mocker, testdatadir) -> None:
 
 
 def mocked_load_data(datadir, pairs=[], ticker_interval='0m', refresh_pairs=False,
-                     timerange=None, exchange=None, live=False):
+                     timerange=None, exchange=None, live=False, *args, **kwargs):
     tickerdata = history.load_tickerdata_file(datadir, 'UNITTEST/BTC', '1m', timerange=timerange)
     pairdata = {'UNITTEST/BTC': parse_ticker_dataframe(tickerdata, '1m', pair="UNITTEST/BTC",
                                                        fill_missing=True)}
@@ -494,7 +494,7 @@ def test_backtesting_start_no_data(default_conf, mocker, caplog, testdatadir) ->
     def get_timeframe(input1):
         return Arrow(2017, 11, 14, 21, 17), Arrow(2017, 11, 14, 22, 59)
 
-    mocker.patch('freqtrade.data.history.load_data', MagicMock(return_value={}))
+    mocker.patch('freqtrade.data.history.load_pair_history', MagicMock(return_value=None))
     mocker.patch('freqtrade.data.history.get_timeframe', get_timeframe)
     mocker.patch('freqtrade.exchange.Exchange.refresh_latest_ohlcv', MagicMock())
     patch_exchange(mocker)
@@ -511,10 +511,8 @@ def test_backtesting_start_no_data(default_conf, mocker, caplog, testdatadir) ->
     default_conf['timerange'] = '20180101-20180102'
 
     backtesting = Backtesting(default_conf)
+    with pytest.raises(OperationalException, match='No data found. Terminating.'):
         backtesting.start()
-    # check the logs, that will contain the backtest result
-
-    assert log_has('No data found. Terminating.', caplog)
 
 
 def test_backtest(default_conf, fee, mocker, testdatadir) -> None:
@@ -838,6 +836,8 @@ def test_backtest_start_timerange(default_conf, mocker, caplog, testdatadir):
         f'Using data directory: {testdatadir} ...',
         'Using stake_currency: BTC ...',
         'Using stake_amount: 0.001 ...',
+        'Loading data from 2017-11-14T20:57:00+00:00 '
+        'up to 2017-11-14T22:58:00+00:00 (0 days)..',
         'Backtesting with data from 2017-11-14T21:17:00+00:00 '
         'up to 2017-11-14T22:58:00+00:00 (0 days)..',
         'Parameter --enable-position-stacking detected ...'
@@ -892,6 +892,8 @@ def test_backtest_start_multi_strat(default_conf, mocker, caplog, testdatadir):
         f'Using data directory: {testdatadir} ...',
         'Using stake_currency: BTC ...',
         'Using stake_amount: 0.001 ...',
+        'Loading data from 2017-11-14T20:57:00+00:00 '
+        'up to 2017-11-14T22:58:00+00:00 (0 days)..',
         'Backtesting with data from 2017-11-14T21:17:00+00:00 '
         'up to 2017-11-14T22:58:00+00:00 (0 days)..',
         'Parameter --enable-position-stacking detected ...',

tests/optimize/test_hyperopt.py

@@ -228,7 +228,7 @@ def test_start(mocker, default_conf, caplog) -> None:
 
 def test_start_no_data(mocker, default_conf, caplog) -> None:
     patched_configuration_load_config_file(mocker, default_conf)
-    mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock(return_value={}))
+    mocker.patch('freqtrade.data.history.load_pair_history', MagicMock(return_value=None))
     mocker.patch(
         'freqtrade.optimize.hyperopt.get_timeframe',
         MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
@@ -242,10 +242,9 @@ def test_start_no_data(mocker, default_conf, caplog) -> None:
         '--epochs', '5'
     ]
     args = get_args(args)
+    with pytest.raises(OperationalException, match='No data found. Terminating.'):
         start_hyperopt(args)
 
-    assert log_has('No data found. Terminating.', caplog)
-
 
 def test_start_filelock(mocker, default_conf, caplog) -> None:
     start_mock = MagicMock(side_effect=Timeout(Hyperopt.get_lock_filename(default_conf)))
@@ -393,7 +392,8 @@ def test_roi_table_generation(hyperopt) -> None:
 
 def test_start_calls_optimizer(mocker, default_conf, caplog, capsys) -> None:
     dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
-    mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
+                 MagicMock(return_value=(MagicMock(), None)))
     mocker.patch(
         'freqtrade.optimize.hyperopt.get_timeframe',
         MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
@@ -608,7 +608,8 @@ def test_continue_hyperopt(mocker, default_conf, caplog):
 
 def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
     dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
-    mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
+                 MagicMock(return_value=(MagicMock(), None)))
     mocker.patch(
         'freqtrade.optimize.hyperopt.get_timeframe',
         MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
@@ -645,7 +646,8 @@ def test_print_json_spaces_all(mocker, default_conf, caplog, capsys) -> None:
 
 def test_print_json_spaces_roi_stoploss(mocker, default_conf, caplog, capsys) -> None:
     dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
-    mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
+                 MagicMock(return_value=(MagicMock(), None)))
     mocker.patch(
         'freqtrade.optimize.hyperopt.get_timeframe',
         MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
@@ -682,7 +684,8 @@ def test_print_json_spaces_roi_stoploss(mocker, default_conf, caplog, capsys) ->
 
 def test_simplified_interface_roi_stoploss(mocker, default_conf, caplog, capsys) -> None:
     dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
-    mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
+                 MagicMock(return_value=(MagicMock(), None)))
     mocker.patch(
         'freqtrade.optimize.hyperopt.get_timeframe',
         MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
@@ -728,7 +731,8 @@ def test_simplified_interface_roi_stoploss(mocker, default_conf, caplog, capsys)
 
 def test_simplified_interface_all_failed(mocker, default_conf, caplog, capsys) -> None:
     mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
-    mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
+                 MagicMock(return_value=(MagicMock(), None)))
     mocker.patch(
         'freqtrade.optimize.hyperopt.get_timeframe',
         MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
@@ -757,7 +761,8 @@ def test_simplified_interface_all_failed(mocker, default_conf, caplog, capsys) -
 
 def test_simplified_interface_buy(mocker, default_conf, caplog, capsys) -> None:
     dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
-    mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
+                 MagicMock(return_value=(MagicMock(), None)))
     mocker.patch(
         'freqtrade.optimize.hyperopt.get_timeframe',
         MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
@@ -802,7 +807,8 @@ def test_simplified_interface_buy(mocker, default_conf, caplog, capsys) -> None:
 
 def test_simplified_interface_sell(mocker, default_conf, caplog, capsys) -> None:
     dumper = mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
-    mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
+                 MagicMock(return_value=(MagicMock(), None)))
     mocker.patch(
         'freqtrade.optimize.hyperopt.get_timeframe',
         MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))
@@ -853,7 +859,8 @@ def test_simplified_interface_sell(mocker, default_conf, caplog, capsys) -> None
 ])
 def test_simplified_interface_failed(mocker, default_conf, caplog, capsys, method, space) -> None:
     mocker.patch('freqtrade.optimize.hyperopt.dump', MagicMock())
-    mocker.patch('freqtrade.optimize.hyperopt.load_data', MagicMock())
+    mocker.patch('freqtrade.optimize.backtesting.Backtesting.load_bt_data',
+                 MagicMock(return_value=(MagicMock(), None)))
     mocker.patch(
         'freqtrade.optimize.hyperopt.get_timeframe',
         MagicMock(return_value=(datetime(2017, 12, 10), datetime(2017, 12, 13)))

tests/test_timerange.py

@@ -1,10 +1,11 @@
 # pragma pylint: disable=missing-docstring, C0103
+import arrow
 import pytest
 
 from freqtrade.configuration import TimeRange
 
 
-def test_parse_timerange_incorrect() -> None:
+def test_parse_timerange_incorrect():
 
     assert TimeRange('date', None, 1274486400, 0) == TimeRange.parse_timerange('20100522-')
     assert TimeRange(None, 'date', 0, 1274486400) == TimeRange.parse_timerange('-20100522')
@@ -28,3 +29,37 @@ def test_parse_timerange_incorrect() -> None:
 
     with pytest.raises(Exception, match=r'Incorrect syntax.*'):
         TimeRange.parse_timerange('-')
+
+
+def test_subtract_start():
+    x = TimeRange('date', 'date', 1274486400, 1438214400)
+    x.subtract_start(300)
+    assert x.startts == 1274486400 - 300
+
+    # Do nothing if no startdate exists
+    x = TimeRange(None, 'date', 0, 1438214400)
+    x.subtract_start(300)
+    assert not x.startts
+
+    x = TimeRange('date', None, 1274486400, 0)
+    x.subtract_start(300)
+    assert x.startts == 1274486400 - 300
+
+
+def test_adjust_start_if_necessary():
+    min_date = arrow.Arrow(2017, 11, 14, 21, 15, 00)
+
+    x = TimeRange('date', 'date', 1510694100, 1510780500)
+    # Adjust by 20 candles - min_date == startts
+    x.adjust_start_if_necessary(300, 20, min_date)
+    assert x.startts == 1510694100 + (20 * 300)
+
+    x = TimeRange('date', 'date', 1510700100, 1510780500)
+    # Do nothing, startup is set and different min_date
+    x.adjust_start_if_necessary(300, 20, min_date)
+    assert x.startts == 1510694100 + (20 * 300)
+
+    x = TimeRange(None, 'date', 0, 1510780500)
+    # Adjust by 20 candles = 20 * 5m
+    x.adjust_start_if_necessary(300, 20, min_date)
+    assert x.startts == 1510694100 + (20 * 300)

user_data/strategies/sample_strategy.py

@@ -59,6 +59,9 @@ class SampleStrategy(IStrategy):
     sell_profit_only = False
     ignore_roi_if_buy_signal = False
 
+    # Number of candles the strategy requires before producing valid signals
+    startup_candle_count: int = 20
+
     # Optional order type mapping.
     order_types = {
         'buy': 'limit',