Minor improvements in data.history

freqtrade/data/history.py

@@ -68,7 +68,7 @@ def trim_dataframe(df: DataFrame, timerange: TimeRange, df_date_col: str = 'date
 
 
 def load_tickerdata_file(datadir: Path, pair: str, timeframe: str,
-                         timerange: Optional[TimeRange] = None) -> Optional[list]:
+                         timerange: Optional[TimeRange] = None) -> List[Dict]:
     """
     Load a pair from file, either .json.gz or .json
     :return: tickerlist or None if unsuccessful
@@ -276,7 +276,7 @@ def _load_cached_data_for_updating(datadir: Path, pair: str, timeframe: str,
 
 
 def _download_pair_history(datadir: Path,
-                           exchange: Optional[Exchange],
+                           exchange: Exchange,
                            pair: str,
                            timeframe: str = '5m',
                            timerange: Optional[TimeRange] = None) -> bool:
@@ -293,11 +293,6 @@ def _download_pair_history(datadir: Path,
     :param timerange: range of time to download
     :return: bool with success state
     """
-    if not exchange:
-        raise OperationalException(
-            "Exchange needs to be initialized when downloading pair history data"
-        )
-
     try:
         logger.info(
             f'Download history data for pair: "{pair}", timeframe: {timeframe} '
@@ -447,18 +442,19 @@ def convert_trades_to_ohlcv(pairs: List[str], timeframes: List[str],
             store_tickerdata_file(datadir, pair, timeframe, data=ohlcv)
 
 
-def get_timeframe(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
+def get_timerange(data: Dict[str, DataFrame]) -> Tuple[arrow.Arrow, arrow.Arrow]:
     """
-    Get the maximum timeframe for the given backtest data
+    Get the maximum common timerange for the given backtest data.
+
     :param data: dictionary with preprocessed backtesting data
     :return: tuple containing min_date, max_date
     """
-    timeframe = [
+    timeranges = [
         (arrow.get(frame['date'].min()), arrow.get(frame['date'].max()))
         for frame in data.values()
     ]
-    return min(timeframe, key=operator.itemgetter(0))[0], \
-        max(timeframe, key=operator.itemgetter(1))[1]
+    return (min(timeranges, key=operator.itemgetter(0))[0],
+            max(timeranges, key=operator.itemgetter(1))[1])
 
 
 def validate_backtest_data(data: DataFrame, pair: str, min_date: datetime,

freqtrade/edge/__init__.py

@@ -120,7 +120,7 @@ class Edge:
         preprocessed = self.strategy.tickerdata_to_dataframe(data)
 
         # Print timeframe
-        min_date, max_date = history.get_timeframe(preprocessed)
+        min_date, max_date = history.get_timerange(preprocessed)
         logger.info(
             'Measuring data from %s up to %s (%s days) ...',
             min_date.isoformat(),

freqtrade/optimize/backtesting.py

@@ -117,7 +117,7 @@ class Backtesting:
             fail_without_data=True,
         )
 
-        min_date, max_date = history.get_timeframe(data)
+        min_date, max_date = history.get_timerange(data)
 
         logger.info(
             'Loading data from %s up to %s (%s days)..',
@@ -481,7 +481,7 @@ class Backtesting:
             # 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)
+            min_date, max_date = history.get_timerange(preprocessed)
 
             logger.info(
                 'Backtesting with data from %s up to %s (%s days)..',

freqtrade/optimize/hyperopt.py

@@ -23,7 +23,7 @@ from joblib import (Parallel, cpu_count, delayed, dump, load,
 from pandas import DataFrame
 
 from freqtrade import OperationalException
-from freqtrade.data.history import get_timeframe, trim_dataframe
+from freqtrade.data.history import get_timerange, trim_dataframe
 from freqtrade.misc import plural, round_dict
 from freqtrade.optimize.backtesting import Backtesting
 # Import IHyperOpt and IHyperOptLoss to allow unpickling classes from these modules
@@ -369,7 +369,7 @@ class Hyperopt:
 
         processed = load(self.tickerdata_pickle)
 
-        min_date, max_date = get_timeframe(processed)
+        min_date, max_date = get_timerange(processed)
 
         backtesting_results = self.backtesting.backtest(
             {
@@ -490,7 +490,7 @@ class Hyperopt:
         # 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)
+        min_date, max_date = get_timerange(data)
 
         logger.info(
             'Hyperopting with data from %s up to %s (%s days)..',