get rid of TICKER_INTERVAL_MINUTES dict, use ccxt's parse_timeframe() instead

freqtrade/data/converter.py

@@ -4,8 +4,8 @@ Functions to convert data from one format to another
 import logging
 import pandas as pd
 from pandas import DataFrame, to_datetime
+from freqtrade.misc import timeframe_to_minutes
 
-from freqtrade.constants import TICKER_INTERVAL_MINUTES
 
 logger = logging.getLogger(__name__)
 
@@ -65,9 +65,9 @@ def ohlcv_fill_up_missing_data(dataframe: DataFrame, ticker_interval: str) -> Da
         'close': 'last',
         'volume': 'sum'
     }
-    tick_mins = TICKER_INTERVAL_MINUTES[ticker_interval]
+    ticker_minutes = timeframe_to_minutes(ticker_interval)
     # Resample to create "NAN" values
-    df = dataframe.resample(f'{tick_mins}min', on='date').agg(ohlc_dict)
+    df = dataframe.resample(f'{ticker_minutes}min', on='date').agg(ohlc_dict)
 
     # Forwardfill close for missing columns
     df['close'] = df['close'].fillna(method='ffill')

freqtrade/data/history.py

@@ -12,10 +12,12 @@ from typing import Optional, List, Dict, Tuple, Any
 import arrow
 from pandas import DataFrame
 
-from freqtrade import misc, constants, OperationalException
+from freqtrade import misc, OperationalException
+from freqtrade.arguments import TimeRange
 from freqtrade.data.converter import parse_ticker_dataframe
 from freqtrade.exchange import Exchange
-from freqtrade.arguments import TimeRange
+from freqtrade.misc import timeframe_to_minutes
+
 
 logger = logging.getLogger(__name__)
 
@@ -163,7 +165,7 @@ def load_cached_data_for_updating(filename: Path, tick_interval: str,
         if timerange.starttype == 'date':
             since_ms = timerange.startts * 1000
         elif timerange.stoptype == 'line':
-            num_minutes = timerange.stopts * constants.TICKER_INTERVAL_MINUTES[tick_interval]
+            num_minutes = timerange.stopts * timeframe_to_minutes(tick_interval)
             since_ms = arrow.utcnow().shift(minutes=num_minutes).timestamp * 1000
 
     # read the cached file