diff --git a/freqtrade/constants.py b/freqtrade/constants.py
index 5fdd45916..f34232bb1 100644
--- a/freqtrade/constants.py
+++ b/freqtrade/constants.py
@@ -24,7 +24,7 @@ AVAILABLE_PAIRLISTS = ['StaticPairList', 'VolumePairList']
 DRY_RUN_WALLET = 999.9
 MATH_CLOSE_PREC = 1e-14  # Precision used for float comparisons
 
-TICKER_INTERVALS = [
+TIMEFRAMES = [
     '1m', '3m', '5m', '15m', '30m',
     '1h', '2h', '4h', '6h', '8h', '12h',
     '1d', '3d', '1w',
@@ -57,7 +57,7 @@ CONF_SCHEMA = {
     'type': 'object',
     'properties': {
         'max_open_trades': {'type': 'integer', 'minimum': -1},
-        'ticker_interval': {'type': 'string', 'enum': TICKER_INTERVALS},
+        'ticker_interval': {'type': 'string', 'enum': TIMEFRAMES},
         'stake_currency': {'type': 'string', 'enum': ['BTC', 'XBT', 'ETH', 'USDT', 'EUR', 'USD']},
         'stake_amount': {
             "type": ["number", "string"],
diff --git a/freqtrade/data/btanalysis.py b/freqtrade/data/btanalysis.py
index 2f7a234ce..379c80060 100644
--- a/freqtrade/data/btanalysis.py
+++ b/freqtrade/data/btanalysis.py
@@ -178,9 +178,9 @@ def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str,
     :return: Returns df with one additional column, col_name, containing the cumulative profit.
     """
     from freqtrade.exchange import timeframe_to_minutes
-    ticker_minutes = timeframe_to_minutes(timeframe)
-    # Resample to ticker_interval to make sure trades match candles
-    _trades_sum = trades.resample(f'{ticker_minutes}min', on='close_time')[['profitperc']].sum()
+    timeframe_minutes = timeframe_to_minutes(timeframe)
+    # Resample to timeframe to make sure trades match candles
+    _trades_sum = trades.resample(f'{timeframe_minutes}min', on='close_time')[['profitperc']].sum()
     df.loc[:, col_name] = _trades_sum.cumsum()
     # Set first value to 0
     df.loc[df.iloc[0].name, col_name] = 0
diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py
index 79478076b..2c2d116a4 100644
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@@ -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