Merge branch 'develop' into pr/rokups/4596

freqtrade/optimize/backtesting.py

@@ -443,7 +443,8 @@ class Backtesting:
 
         # Trim startup period from analyzed dataframe
         for pair, df in preprocessed.items():
-            preprocessed[pair] = trim_dataframe(df, timerange)
+            preprocessed[pair] = trim_dataframe(df, timerange,
+                                                startup_candles=self.required_startup)
         min_date, max_date = history.get_timerange(preprocessed)
 
         logger.info(f'Backtesting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '

freqtrade/optimize/edge_cli.py

@@ -44,7 +44,7 @@ class EdgeCli:
             'timerange') is None else str(self.config.get('timerange')))
 
     def start(self) -> None:
-        result = self.edge.calculate()
+        result = self.edge.calculate(self.config['exchange']['pair_whitelist'])
         if result:
             print('')  # blank line for readability
             print(generate_edge_table(self.edge._cached_pairs))

freqtrade/optimize/hyperopt.py

@@ -384,7 +384,8 @@ class Hyperopt:
 
         # Trim startup period from analyzed dataframe
         for pair, df in preprocessed.items():
-            preprocessed[pair] = trim_dataframe(df, timerange)
+            preprocessed[pair] = trim_dataframe(df, timerange,
+                                                startup_candles=self.backtesting.required_startup)
         min_date, max_date = get_timerange(preprocessed)
 
         logger.info(f'Hyperopting with data from {min_date.strftime(DATETIME_PRINT_FORMAT)} '

freqtrade/optimize/hyperopt_interface.py

@@ -31,7 +31,7 @@ class IHyperOpt(ABC):
     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
+        timeframe -> int: value of the timeframe to use for the strategy
     """
     ticker_interval: str  # DEPRECATED
     timeframe: str
@@ -91,7 +91,7 @@ class IHyperOpt(ABC):
 
         This method implements adaptive roi hyperspace with varied
         ranges for parameters which automatically adapts to the
-        ticker interval used.
+        timeframe used.
 
         It's used by Freqtrade by default, if no custom roi_space method is defined.
         """
@@ -113,7 +113,7 @@ class IHyperOpt(ABC):
         # * 'roi_p' (limits for the ROI value steps) components are scaled logarithmically.
         #
         # The scaling is designed so that it maps exactly to the legacy Freqtrade roi_space()
-        # method for the 5m ticker interval.
+        # method for the 5m timeframe.
         roi_t_scale = timeframe_min / 5
         roi_p_scale = math.log1p(timeframe_min) / math.log1p(5)
         roi_limits = {