diff --git a/freqtrade/data/btanalysis.py b/freqtrade/data/btanalysis.py
index f6f7e5541..834d41263 100644
--- a/freqtrade/data/btanalysis.py
+++ b/freqtrade/data/btanalysis.py
@@ -3,6 +3,7 @@ Helpers when analyzing backtest data
 """
 import logging
 from pathlib import Path
+from typing import Dict
 
 import numpy as np
 import pandas as pd
@@ -125,7 +126,23 @@ def extract_trades_of_period(dataframe: pd.DataFrame, trades: pd.DataFrame) -> p
     return trades
 
 
-def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str):
+def combine_tickers_with_mean(tickers: Dict[str, pd.DataFrame], column: str = "close"):
+    """
+    Combine multiple dataframes "column"
+    :param tickers: Dict of Dataframes, dict key should be pair.
+    :param column: Column in the original dataframes to use
+    :return: DataFrame with the column renamed to the dict key, and a column
+        named mean, containing the mean of all pairs.
+    """
+    df_comb = pd.concat([tickers[pair].set_index('date').rename(
+        {column: pair}, axis=1)[pair] for pair in tickers], axis=1)
+
+    df_comb['mean'] = df_comb.mean(axis=1)
+
+    return df_comb
+
+
+def create_cum_profit(df: pd.DataFrame, trades: pd.DataFrame, col_name: str) -> pd.DataFrame:
     """
     Adds a column `col_name` with the cumulative profit for the given trades array.
     :param df: DataFrame with date index
diff --git a/freqtrade/tests/data/test_btanalysis.py b/freqtrade/tests/data/test_btanalysis.py
index 01e5dc90d..e8872f9a4 100644
--- a/freqtrade/tests/data/test_btanalysis.py
+++ b/freqtrade/tests/data/test_btanalysis.py
@@ -5,11 +5,14 @@ from arrow import Arrow
 from pandas import DataFrame, to_datetime
 
 from freqtrade.arguments import Arguments, TimeRange
-from freqtrade.data.btanalysis import (BT_DATA_COLUMNS, create_cum_profit,
+from freqtrade.data.btanalysis import (BT_DATA_COLUMNS,
+                                       combine_tickers_with_mean,
+                                       create_cum_profit,
                                        extract_trades_of_period,
                                        load_backtest_data, load_trades,
                                        load_trades_from_db)
-from freqtrade.data.history import load_pair_history, make_testdata_path
+from freqtrade.data.history import (load_data, load_pair_history,
+                                    make_testdata_path)
 from freqtrade.tests.test_persistence import create_mock_trades
 
 
@@ -97,6 +100,19 @@ def test_load_trades(default_conf, mocker):
     assert bt_mock.call_count == 1
 
 
+def test_combine_tickers_with_mean():
+    pairs = ["ETH/BTC", "XLM/BTC"]
+    tickers = load_data(datadir=None,
+                        pairs=pairs,
+                        ticker_interval='5m'
+                        )
+    df = combine_tickers_with_mean(tickers)
+    assert isinstance(df, DataFrame)
+    assert "ETH/BTC" in df.columns
+    assert "XLM/BTC" in df.columns
+    assert "mean" in df.columns
+
+
 def test_create_cum_profit():
     filename = make_testdata_path(None) / "backtest-result_test.json"
     bt_data = load_backtest_data(filename)
diff --git a/scripts/plot_profit.py b/scripts/plot_profit.py
index cd507100f..248eeb7b0 100755
--- a/scripts/plot_profit.py
+++ b/scripts/plot_profit.py
@@ -13,7 +13,7 @@ import plotly.graph_objs as go
 from plotly import tools
 
 from freqtrade.arguments import ARGS_PLOT_PROFIT, Arguments
-from freqtrade.data.btanalysis import create_cum_profit
+from freqtrade.data.btanalysis import create_cum_profit, combine_tickers_with_mean
 from freqtrade.optimize import setup_configuration
 from freqtrade.plot.plotting import FTPlots, store_plot_file
 from freqtrade.state import RunMode
@@ -36,9 +36,7 @@ def plot_profit(config: Dict[str, Any]) -> None:
     # this could be useful to gauge the overall market trend
 
     # Combine close-values for all pairs, rename columns to "pair"
-    df_comb = pd.concat([plot.tickers[pair].set_index('date').rename(
-        {'close': pair}, axis=1)[pair] for pair in plot.tickers], axis=1)
-    df_comb['mean'] = df_comb.mean(axis=1)
+    df_comb = combine_tickers_with_mean(plot.tickers, "close")
 
     # Add combined cumulative profit
     df_comb = create_cum_profit(df_comb, trades, 'cum_profit')