diff --git a/freqtrade/tests/optimize/test_backtesting.py b/freqtrade/tests/optimize/test_backtesting.py
index 5f899a48a..4ef4b1606 100644
--- a/freqtrade/tests/optimize/test_backtesting.py
+++ b/freqtrade/tests/optimize/test_backtesting.py
@@ -11,6 +11,13 @@ from freqtrade.optimize.backtesting import backtest, generate_text_table, get_ti
 import freqtrade.optimize.backtesting as backtesting
 
 
+def trim_dictlist(dl, num):
+    new = {}
+    for pair, pair_data in dl.items():
+        new[pair] = pair_data[num:]
+    return new
+
+
 def test_generate_text_table():
     results = pd.DataFrame(
         {
@@ -43,6 +50,7 @@ def test_backtest(default_conf, mocker):
     exchange._API = Bittrex({'key': '', 'secret': ''})
 
     data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
+    data = trim_dictlist(data, -200)
     results = backtest(default_conf['stake_amount'],
                        optimize.preprocess(data), 10, True)
     assert not results.empty
@@ -54,18 +62,12 @@ def test_backtest_1min_ticker_interval(default_conf, mocker):
 
     # Run a backtesting for an exiting 5min ticker_interval
     data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
+    data = trim_dictlist(data, -200)
     results = backtest(default_conf['stake_amount'],
                        optimize.preprocess(data), 1, True)
     assert not results.empty
 
 
-def trim_dictlist(dl, num):
-    new = {}
-    for pair, pair_data in dl.items():
-        new[pair] = pair_data[num:]
-    return new
-
-
 def load_data_test(what):
     data = optimize.load_data(None, ticker_interval=1, pairs=['BTC_UNITEST'])
     data = trim_dictlist(data, -100)
@@ -125,6 +127,7 @@ def simple_backtest(config, contour, num_results):
 def test_backtest2(default_conf, mocker):
     mocker.patch.dict('freqtrade.main._CONF', default_conf)
     data = optimize.load_data(None, ticker_interval=5, pairs=['BTC_ETH'])
+    data = trim_dictlist(data, -200)
     results = backtest(default_conf['stake_amount'],
                        optimize.preprocess(data), 10, True)
     assert not results.empty