Merge branch 'develop' into hyperopt-trailing-space

freqtrade/optimize/__init__.py

@@ -78,7 +78,7 @@ def start_hyperopt(args: Dict[str, Any]) -> None:
     except Timeout:
         logger.info("Another running instance of freqtrade Hyperopt detected.")
         logger.info("Simultaneous execution of multiple Hyperopt commands is not supported. "
-                    "Hyperopt module is resource hungry. Please run your Hyperopts sequentially "
+                    "Hyperopt module is resource hungry. Please run your Hyperopt sequentially "
                     "or on separate machines.")
         logger.info("Quitting now.")
         # TODO: return False here in order to help freqtrade to exit

freqtrade/optimize/backtesting.py

@@ -83,8 +83,8 @@ class Backtesting:
         if "ticker_interval" not in self.config:
             raise OperationalException("Ticker-interval needs to be set in either configuration "
                                        "or as cli argument `--ticker-interval 5m`")
-        self.ticker_interval = str(self.config.get('ticker_interval'))
-        self.ticker_interval_mins = timeframe_to_minutes(self.ticker_interval)
+        self.timeframe = str(self.config.get('ticker_interval'))
+        self.timeframe_mins = timeframe_to_minutes(self.timeframe)
 
         # Get maximum required startup period
         self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
@@ -108,7 +108,7 @@ class Backtesting:
         data = history.load_data(
             datadir=Path(self.config['datadir']),
             pairs=self.config['exchange']['pair_whitelist'],
-            ticker_interval=self.ticker_interval,
+            timeframe=self.timeframe,
             timerange=timerange,
             startup_candles=self.required_startup,
             fail_without_data=True,
@@ -121,7 +121,7 @@ class Backtesting:
             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),
+        timerange.adjust_start_if_necessary(timeframe_to_seconds(self.timeframe),
                                             self.required_startup, min_date)
 
         return data, timerange
@@ -375,7 +375,7 @@ class Backtesting:
         lock_pair_until: Dict = {}
         # Indexes per pair, so some pairs are allowed to have a missing start.
         indexes: Dict = {}
-        tmp = start_date + timedelta(minutes=self.ticker_interval_mins)
+        tmp = start_date + timedelta(minutes=self.timeframe_mins)
 
         # Loop timerange and get candle for each pair at that point in time
         while tmp < end_date:
@@ -427,7 +427,7 @@ class Backtesting:
                     lock_pair_until[pair] = end_date.datetime
 
             # Move time one configured time_interval ahead.
-            tmp += timedelta(minutes=self.ticker_interval_mins)
+            tmp += timedelta(minutes=self.timeframe_mins)
         return DataFrame.from_records(trades, columns=BacktestResult._fields)
 
     def start(self) -> None:

freqtrade/optimize/hyperopt_interface.py

@@ -1,6 +1,6 @@
 """
 IHyperOpt interface
-This module defines the interface to apply for hyperopts
+This module defines the interface to apply for hyperopt
 """
 import logging
 import math
@@ -27,8 +27,8 @@ def _format_exception_message(method: str, space: str) -> str:
 
 class IHyperOpt(ABC):
     """
-    Interface for freqtrade hyperopts
-    Defines the mandatory structure must follow any custom hyperopts
+    Interface for freqtrade hyperopt
+    Defines the mandatory structure must follow any custom hyperopt
 
     Class attributes you can use:
         ticker_interval -> int: value of the ticker interval to use for the strategy
@@ -106,10 +106,10 @@ class IHyperOpt(ABC):
         roi_t_alpha = 1.0
         roi_p_alpha = 1.0
 
-        ticker_interval_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
+        timeframe_mins = timeframe_to_minutes(IHyperOpt.ticker_interval)
 
         # We define here limits for the ROI space parameters automagically adapted to the
-        # ticker_interval used by the bot:
+        # timeframe used by the bot:
         #
         # * 'roi_t' (limits for the time intervals in the ROI tables) components
         #   are scaled linearly.
@@ -117,8 +117,8 @@ class IHyperOpt(ABC):
         #
         # The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
         # method for the 5m ticker interval.
-        roi_t_scale = ticker_interval_mins / 5
-        roi_p_scale = math.log1p(ticker_interval_mins) / math.log1p(5)
+        roi_t_scale = timeframe_mins / 5
+        roi_p_scale = math.log1p(timeframe_mins) / math.log1p(5)
         roi_limits = {
             'roi_t1_min': int(10 * roi_t_scale * roi_t_alpha),
             'roi_t1_max': int(120 * roi_t_scale * roi_t_alpha),

freqtrade/optimize/hyperopt_loss_interface.py

@@ -1,6 +1,6 @@
 """
 IHyperOptLoss interface
-This module defines the interface for the loss-function for hyperopts
+This module defines the interface for the loss-function for hyperopt
 """
 
 from abc import ABC, abstractmethod
@@ -11,7 +11,7 @@ from pandas import DataFrame
 
 class IHyperOptLoss(ABC):
     """
-    Interface for freqtrade hyperopts Loss functions.
+    Interface for freqtrade hyperopt Loss functions.
     Defines the custom loss function (`hyperopt_loss_function()` which is evaluated every epoch.)
     """
     ticker_interval: str