Correctly handle identical timerame merges

docs/strategy-customization.md

@@ -518,10 +518,10 @@ class SampleStrategy(IStrategy):
 
         # Use the helper function merge_informative_pair to safely merge the pair
         # Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
-        # FFill to have the 1d value available in every row throughout the day.
-        # Without this, comparisons would only work once per day.
+        # use ffill to have the 1d value available in every row throughout the day.
+        # Without this, comparisons between columns of the original and the informative pair would only work once per day.
         # Full documentation of this method, see below
-        dataframe = merge_informative_pair(dataframe, informative_pairs, inf_tf,   ffill=True)
+        dataframe = merge_informative_pair(dataframe, informative_pairs, self.timeframe, inf_tf, ffill=True)
 
         # Calculate rsi of the original dataframe (5m timeframe)
         dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
@@ -589,6 +589,7 @@ All columns of the informative dataframe will be available on the returning data
     # This is necessary since the data is always the "open date"
     # and a 15m candle starting at 12:15 should not know the close of the 1h candle from 12:00 to 13:00
     minutes = timeframe_to_minutes(inf_tf)
+    # Only do this if the timeframes are different:
     informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')
 
     # Combine the 2 dataframes

freqtrade/strategy/strategy_helper.py

@@ -3,7 +3,7 @@ from freqtrade.exchange import timeframe_to_minutes
 
 
 def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
-                           timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
+                           timeframe: str, timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
     """
     Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
 
@@ -20,13 +20,18 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
 
     :param dataframe: Original dataframe
     :param informative: Informative pair, most likely loaded via dp.get_pair_dataframe
+    :param timeframe: Timeframe of the original pair sample.
     :param timeframe_inf: Timeframe of the informative pair sample.
     :param ffill: Forwardfill missing values - optional but usually required
     """
     # Rename columns to be unique
 
-    minutes = timeframe_to_minutes(timeframe_inf)
-    informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')
+    minutes_inf = timeframe_to_minutes(timeframe_inf)
+    if timeframe == timeframe_inf:
+        # No need to forwardshift if the timeframes are identical
+        informative['date_merge'] = informative["date"]
+    else:
+        informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes_inf, 'm')
 
     informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
 

tests/strategy/test_strategy_helpers.py

@@ -28,7 +28,7 @@ def test_merge_informative_pair():
     data = generate_test_data('15m', 40)
     informative = generate_test_data('1h', 40)
 
-    result = merge_informative_pair(data, informative, '1h', ffill=True)
+    result = merge_informative_pair(data, informative, '15m', '1h', ffill=True)
     assert isinstance(result, pd.DataFrame)
     assert len(result) == len(data)
     assert 'date' in result.columns
@@ -59,3 +59,30 @@ def test_merge_informative_pair():
     assert result.iloc[7]['date_1h'] == result.iloc[0]['date']
     # Next 4 rows contain the next Hourly date original date row 4
     assert result.iloc[8]['date_1h'] == result.iloc[4]['date']
+
+
+def test_merge_informative_pair_same():
+    data = generate_test_data('15m', 40)
+    informative = generate_test_data('15m', 40)
+
+    result = merge_informative_pair(data, informative, '15m', '15m', ffill=True)
+    assert isinstance(result, pd.DataFrame)
+    assert len(result) == len(data)
+    assert 'date' in result.columns
+    assert result['date'].equals(data['date'])
+    assert 'date_15m' in result.columns
+
+    assert 'open' in result.columns
+    assert 'open_15m' in result.columns
+    assert result['open'].equals(data['open'])
+
+    assert 'close' in result.columns
+    assert 'close_15m' in result.columns
+    assert result['close'].equals(data['close'])
+
+    assert 'volume' in result.columns
+    assert 'volume_15m' in result.columns
+    assert result['volume'].equals(data['volume'])
+
+    # Dates match 1:1
+    assert result['date_15m'].equals(result['date'])