diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py
index e3fb9b946..5a0770953 100644
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@@ -67,30 +67,29 @@ def generate_text_table(
     return tabulate(tabular_data, headers=headers, floatfmt=floatfmt)
 
 
-def get_sell_trade_entry(pair, row, buy_subset, ticker, trade_count_lock, args):
+def get_sell_trade_entry(pair, row, partial_ticker, trade_count_lock, args):
     stake_amount = args['stake_amount']
     max_open_trades = args.get('max_open_trades', 0)
     trade = Trade(open_rate=row.close,
-                  open_date=row.Index,
+                  open_date=row.date,
                   stake_amount=stake_amount,
                   amount=stake_amount / row.open,
                   fee=exchange.get_fee()
                   )
 
     # calculate win/lose forwards from buy point
-    sell_subset = ticker[ticker.index > row.Index][['close', 'sell', 'buy']]
-    for row2 in sell_subset.itertuples(index=True):
+    for row2 in partial_ticker:
         if max_open_trades > 0:
             # Increase trade_count_lock for every iteration
-            trade_count_lock[row2.Index] = trade_count_lock.get(row2.Index, 0) + 1
+            trade_count_lock[row2.date] = trade_count_lock.get(row2.date, 0) + 1
 
         buy_signal = row2.buy
-        if(should_sell(trade, row2.close, row2.Index, buy_signal, row2.sell)):
+        if should_sell(trade, row2.close, row2.date, buy_signal, row2.sell):
             return row2, (pair,
                           trade.calc_profit_percent(rate=row2.close),
                           trade.calc_profit(rate=row2.close),
-                          (row2.Index - row.Index).seconds // 60
-                          ), row2.Index
+                          (row2.date - row.date).seconds // 60
+                          ), row2.date
     return None
 
 
@@ -107,6 +106,7 @@ def backtest(args) -> DataFrame:
         stoploss: use stoploss
     :return: DataFrame
     """
+    headers = ['date', 'buy', 'open', 'close', 'sell']
     processed = args['processed']
     max_open_trades = args.get('max_open_trades', 0)
     realistic = args.get('realistic', True)
@@ -116,29 +116,29 @@ def backtest(args) -> DataFrame:
     trade_count_lock: dict = {}
     exchange._API = Bittrex({'key': '', 'secret': ''})
     for pair, pair_data in processed.items():
-        pair_data['buy'], pair_data['sell'] = 0, 0
-        ticker = populate_sell_trend(populate_buy_trend(pair_data))
-        if 'date' in ticker:
-            ticker.set_index('date', inplace=True)
-        # for each buy point
+        pair_data['buy'], pair_data['sell'] = 0, 0  # cleanup from previous run
+
+        ticker_data = populate_sell_trend(populate_buy_trend(pair_data))[headers]
+        ticker = [x for x in ticker_data.itertuples()]
+
         lock_pair_until = None
-        headers = ['buy', 'open', 'close', 'sell']
-        buy_subset = ticker[(ticker.buy == 1) & (ticker.sell == 0)][headers]
-        for row in buy_subset.itertuples(index=True):
+        for index, row in enumerate(ticker):
+            if row.buy == 0 or row.sell == 1:
+                continue  # skip rows where no buy signal or that would immediately sell off
+
             if realistic:
-                if lock_pair_until is not None and row.Index <= lock_pair_until:
+                if lock_pair_until is not None and row.date <= lock_pair_until:
                     continue
             if max_open_trades > 0:
                 # Check if max_open_trades has already been reached for the given date
-                if not trade_count_lock.get(row.Index, 0) < max_open_trades:
+                if not trade_count_lock.get(row.date, 0) < max_open_trades:
                     continue
 
             if max_open_trades > 0:
                 # Increase lock
-                trade_count_lock[row.Index] = trade_count_lock.get(row.Index, 0) + 1
+                trade_count_lock[row.date] = trade_count_lock.get(row.date, 0) + 1
 
-            ret = get_sell_trade_entry(pair, row, buy_subset, ticker,
-                                       trade_count_lock, args)
+            ret = get_sell_trade_entry(pair, row, ticker[index+1:], trade_count_lock, args)
             if ret:
                 row2, trade_entry, next_date = ret
                 lock_pair_until = next_date
@@ -148,9 +148,9 @@ def backtest(args) -> DataFrame:
                     # record a tuple of pair, current_profit_percent,
                     # entry-date, duration
                     records.append((pair, trade_entry[1],
-                                    row.Index.strftime('%s'),
-                                    row2.Index.strftime('%s'),
-                                    row.Index, trade_entry[3]))
+                                    row.date.strftime('%s'),
+                                    row2.date.strftime('%s'),
+                                    row.date, trade_entry[3]))
     # For now export inside backtest(), maybe change so that backtest()
     # returns a tuple like: (dataframe, records, logs, etc)
     if record and record.find('trades') >= 0: