diff --git a/freqtrade/commands/data_commands.py b/freqtrade/commands/data_commands.py
index 7a896e495..bccc6ea9a 100644
--- a/freqtrade/commands/data_commands.py
+++ b/freqtrade/commands/data_commands.py
@@ -5,8 +5,9 @@ from typing import Any, Dict
 
 from freqtrade.configuration import TimeRange, setup_utils_configuration
 from freqtrade.constants import DATETIME_PRINT_FORMAT, DL_DATA_TIMEFRAMES, Config
-from freqtrade.data.converter import convert_ohlcv_format, convert_trades_format
-from freqtrade.data.history import convert_trades_to_ohlcv, download_data_main
+from freqtrade.data.converter import (convert_ohlcv_format, convert_trades_format,
+                                      convert_trades_to_ohlcv)
+from freqtrade.data.history import download_data_main
 from freqtrade.enums import RunMode, TradingMode
 from freqtrade.exceptions import OperationalException
 from freqtrade.exchange import timeframe_to_minutes
diff --git a/freqtrade/data/converter.py b/freqtrade/data/converter.py
index 7d9014e87..5a503faad 100644
--- a/freqtrade/data/converter.py
+++ b/freqtrade/data/converter.py
@@ -2,12 +2,14 @@
 Functions to convert data from one format to another
 """
 import logging
+from pathlib import Path
 from typing import Dict, List
 
 import numpy as np
 import pandas as pd
 from pandas import DataFrame, to_datetime
 
+from freqtrade.configuration import TimeRange
 from freqtrade.constants import (DEFAULT_DATAFRAME_COLUMNS, DEFAULT_TRADES_COLUMNS, TRADES_DTYPES,
                                  Config, TradeList)
 from freqtrade.enums import CandleType, TradingMode
@@ -260,6 +262,42 @@ def trades_to_ohlcv(trades: DataFrame, timeframe: str) -> DataFrame:
     return df_new.loc[:, DEFAULT_DATAFRAME_COLUMNS]
 
 
+def convert_trades_to_ohlcv(
+    pairs: List[str],
+    timeframes: List[str],
+    datadir: Path,
+    timerange: TimeRange,
+    erase: bool = False,
+    data_format_ohlcv: str = 'feather',
+    data_format_trades: str = 'feather',
+    candle_type: CandleType = CandleType.SPOT
+) -> None:
+    """
+    Convert stored trades data to ohlcv data
+    """
+    from freqtrade.data.history.idatahandler import get_datahandler
+    data_handler_trades = get_datahandler(datadir, data_format=data_format_trades)
+    data_handler_ohlcv = get_datahandler(datadir, data_format=data_format_ohlcv)
+    if not pairs:
+        pairs = data_handler_trades.trades_get_pairs(datadir)
+
+    logger.info(f"About to convert pairs: '{', '.join(pairs)}', "
+                f"intervals: '{', '.join(timeframes)}' to {datadir}")
+
+    for pair in pairs:
+        trades = data_handler_trades.trades_load(pair)
+        for timeframe in timeframes:
+            if erase:
+                if data_handler_ohlcv.ohlcv_purge(pair, timeframe, candle_type=candle_type):
+                    logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
+            try:
+                ohlcv = trades_to_ohlcv(trades, timeframe)
+                # Store ohlcv
+                data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv, candle_type=candle_type)
+            except ValueError:
+                logger.exception(f'Could not convert {pair} to OHLCV.')
+
+
 def convert_trades_format(config: Config, convert_from: str, convert_to: str, erase: bool):
     """
     Convert trades from one format to another format.
diff --git a/freqtrade/data/history/history_utils.py b/freqtrade/data/history/history_utils.py
index b95b48b90..1ad1060a4 100644
--- a/freqtrade/data/history/history_utils.py
+++ b/freqtrade/data/history/history_utils.py
@@ -9,9 +9,9 @@ from pandas import DataFrame, concat
 from freqtrade.configuration import TimeRange
 from freqtrade.constants import (DATETIME_PRINT_FORMAT, DEFAULT_DATAFRAME_COLUMNS,
                                  DL_DATA_TIMEFRAMES, Config)
-from freqtrade.data.converter import (clean_ohlcv_dataframe, ohlcv_to_dataframe,
-                                      trades_df_remove_duplicates, trades_list_to_df,
-                                      trades_to_ohlcv)
+from freqtrade.data.converter import (clean_ohlcv_dataframe, convert_trades_to_ohlcv,
+                                      ohlcv_to_dataframe, trades_df_remove_duplicates,
+                                      trades_list_to_df)
 from freqtrade.data.history.idatahandler import IDataHandler, get_datahandler
 from freqtrade.enums import CandleType
 from freqtrade.exceptions import OperationalException
@@ -429,41 +429,6 @@ def refresh_backtest_trades_data(exchange: Exchange, pairs: List[str], datadir:
     return pairs_not_available
 
 
-def convert_trades_to_ohlcv(
-    pairs: List[str],
-    timeframes: List[str],
-    datadir: Path,
-    timerange: TimeRange,
-    erase: bool = False,
-    data_format_ohlcv: str = 'feather',
-    data_format_trades: str = 'feather',
-    candle_type: CandleType = CandleType.SPOT
-) -> None:
-    """
-    Convert stored trades data to ohlcv data
-    """
-    data_handler_trades = get_datahandler(datadir, data_format=data_format_trades)
-    data_handler_ohlcv = get_datahandler(datadir, data_format=data_format_ohlcv)
-    if not pairs:
-        pairs = data_handler_trades.trades_get_pairs(datadir)
-
-    logger.info(f"About to convert pairs: '{', '.join(pairs)}', "
-                f"intervals: '{', '.join(timeframes)}' to {datadir}")
-
-    for pair in pairs:
-        trades = data_handler_trades.trades_load(pair)
-        for timeframe in timeframes:
-            if erase:
-                if data_handler_ohlcv.ohlcv_purge(pair, timeframe, candle_type=candle_type):
-                    logger.info(f'Deleting existing data for pair {pair}, interval {timeframe}.')
-            try:
-                ohlcv = trades_to_ohlcv(trades, timeframe)
-                # Store ohlcv
-                data_handler_ohlcv.ohlcv_store(pair, timeframe, data=ohlcv, candle_type=candle_type)
-            except ValueError:
-                logger.exception(f'Could not convert {pair} to OHLCV.')
-
-
 def get_timerange(data: Dict[str, DataFrame]) -> Tuple[datetime, datetime]:
     """
     Get the maximum common timerange for the given backtest data.