diff --git a/freqtrade/data/dataprovider.py b/freqtrade/data/dataprovider.py
index c6519d2b8..0206ed0a9 100644
--- a/freqtrade/data/dataprovider.py
+++ b/freqtrade/data/dataprovider.py
@@ -90,8 +90,12 @@ class DataProvider:
         if saved_pair not in self.__cached_pairs_backtesting:
             timerange = TimeRange.parse_timerange(None if self._config.get(
                 'timerange') is None else str(self._config.get('timerange')))
+
+            # In backtesting, the training data was already add
+            add_train_candles = self.runmode in (RunMode.DRY_RUN, RunMode.LIVE)
+            startup_candles = self.get_required_startup(str(timeframe), add_train_candles)
+
             # Move informative start time respecting startup_candle_count
-            startup_candles = self.get_required_startup(str(timeframe))
             tf_seconds = timeframe_to_seconds(str(timeframe))
             timerange.subtract_start(tf_seconds * startup_candles)
             self.__cached_pairs_backtesting[saved_pair] = load_pair_history(
@@ -105,7 +109,7 @@ class DataProvider:
             )
         return self.__cached_pairs_backtesting[saved_pair].copy()
 
-    def get_required_startup(self, timeframe: str) -> int:
+    def get_required_startup(self, timeframe: str, add_train_candles: bool = True) -> int:
         freqai_config = self._config.get('freqai', {})
         if not freqai_config.get('enabled', False):
             return self._config.get('startup_candle_count', 0)
@@ -115,7 +119,9 @@ class DataProvider:
             # make sure the startupcandles is at least the set maximum indicator periods
             self._config['startup_candle_count'] = max(startup_candles, max(indicator_periods))
             tf_seconds = timeframe_to_seconds(timeframe)
-            train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
+            train_candles = 0
+            if add_train_candles:
+                train_candles = freqai_config['train_period_days'] * 86400 / tf_seconds
             total_candles = int(self._config['startup_candle_count'] + train_candles)
             logger.info(f'Increasing startup_candle_count for freqai to {total_candles}')
             return total_candles
diff --git a/freqtrade/optimize/backtesting.py b/freqtrade/optimize/backtesting.py
index f54d59b5a..6220caab6 100644
--- a/freqtrade/optimize/backtesting.py
+++ b/freqtrade/optimize/backtesting.py
@@ -143,10 +143,14 @@ class Backtesting:
 
         # Get maximum required startup period
         self.required_startup = max([strat.startup_candle_count for strat in self.strategylist])
-        # Add maximum startup candle count to configuration for informative pairs support
-        self.config['startup_candle_count'] = self.required_startup
         self.exchange.validate_required_startup_candles(self.required_startup, self.timeframe)
 
+        if self.config.get('freqai', {}).get('enabled', False):
+            # For FreqAI, increase the required_startup to includes the training data
+            self.required_startup = self.dataprovider.get_required_startup(self.timeframe)
+
+        # Add maximum startup candle count to configuration for informative pairs support
+        self.config['startup_candle_count'] = self.required_startup
         self.trading_mode: TradingMode = config.get('trading_mode', TradingMode.SPOT)
         # strategies which define "can_short=True" will fail to load in Spot mode.
         self._can_short = self.trading_mode != TradingMode.SPOT
@@ -221,7 +225,7 @@ class Backtesting:
             pairs=self.pairlists.whitelist,
             timeframe=self.timeframe,
             timerange=self.timerange,
-            startup_candles=self.dataprovider.get_required_startup(self.timeframe),
+            startup_candles=self.config['startup_candle_count'],
             fail_without_data=True,
             data_format=self.config.get('dataformat_ohlcv', 'json'),
             candle_type=self.config.get('candle_type_def', CandleType.SPOT)