From f54fecaebaaa009dd4a5da2c83dcb85516be8a6a Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Wed, 2 Sep 2020 19:58:26 +0200
Subject: [PATCH 1/8] Expose helpermethods thorugh freqtrade.strategy

---
 freqtrade/strategy/__init__.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/freqtrade/strategy/__init__.py b/freqtrade/strategy/__init__.py
index 40a4a0bea..91ea0e075 100644
--- a/freqtrade/strategy/__init__.py
+++ b/freqtrade/strategy/__init__.py
@@ -1 +1,4 @@
-from freqtrade.strategy.interface import IStrategy  # noqa: F401
+# flake8: noqa: F401
+from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_prev_date,
+                                timeframe_to_seconds, timeframe_to_next_date, timeframe_to_msecs)
+from freqtrade.strategy.interface import IStrategy

From e268bd192e85868cb4da9e74dceb652d249ffbe6 Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Wed, 2 Sep 2020 19:59:04 +0200
Subject: [PATCH 2/8] Fix informative sample documentation

---
 docs/strategy-customization.md | 12 +++++++++++-
 1 file changed, 11 insertions(+), 1 deletion(-)

diff --git a/docs/strategy-customization.md b/docs/strategy-customization.md
index be08faa2d..4362c251f 100644
--- a/docs/strategy-customization.md
+++ b/docs/strategy-customization.md
@@ -483,6 +483,10 @@ if self.dp:
 ### Complete Data-provider sample
 
 ```python
+from freqtrade.strategy import IStrategy, timeframe_to_minutes
+from pandas import DataFrame
+import pandas as pd
+
 class SampleStrategy(IStrategy):
     # strategy init stuff...
 
@@ -518,9 +522,15 @@ class SampleStrategy(IStrategy):
         # Assuming inf_tf = '1d' - then the columns will now be:
         # date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
 
+        # Shift date by 1 Frequency unit
+        # 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)
+        informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')
+
         # Combine the 2 dataframes
         # all indicators on the informative sample MUST be calculated before this point
-        dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_{inf_tf}', how='left')
+        dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_merge_{inf_tf}', how='left')
         # FFill to have the 1d value available in every row throughout the day.
         # Without this, comparisons would only work once per day.
         dataframe = dataframe.ffill()

From 79ea8cf7719879e942d4c49dddd63cafd2d38cfe Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Wed, 2 Sep 2020 20:02:41 +0200
Subject: [PATCH 3/8] Improve wording

---
 docs/strategy-customization.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/strategy-customization.md b/docs/strategy-customization.md
index 4362c251f..e2548e510 100644
--- a/docs/strategy-customization.md
+++ b/docs/strategy-customization.md
@@ -522,7 +522,7 @@ class SampleStrategy(IStrategy):
         # Assuming inf_tf = '1d' - then the columns will now be:
         # date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
 
-        # Shift date by 1 Frequency unit
+        # Shift date by 1 candle
         # 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)

From bd4f3d838ab959c3c8ff9f0e39daaeb62c3bddce Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Fri, 4 Sep 2020 19:44:35 +0200
Subject: [PATCH 4/8] Implement merge_informative_pairs helper

---
 freqtrade/strategy/__init__.py          |  1 +
 freqtrade/strategy/strategy_helper.py   | 39 ++++++++++++++++
 tests/strategy/test_strategy_helpers.py | 61 +++++++++++++++++++++++++
 3 files changed, 101 insertions(+)
 create mode 100644 freqtrade/strategy/strategy_helper.py
 create mode 100644 tests/strategy/test_strategy_helpers.py

diff --git a/freqtrade/strategy/__init__.py b/freqtrade/strategy/__init__.py
index 91ea0e075..5758bbbcc 100644
--- a/freqtrade/strategy/__init__.py
+++ b/freqtrade/strategy/__init__.py
@@ -2,3 +2,4 @@
 from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_prev_date,
                                 timeframe_to_seconds, timeframe_to_next_date, timeframe_to_msecs)
 from freqtrade.strategy.interface import IStrategy
+from freqtrade.strategy.strategy_helper import merge_informative_pairs
diff --git a/freqtrade/strategy/strategy_helper.py b/freqtrade/strategy/strategy_helper.py
new file mode 100644
index 000000000..ce98cccba
--- /dev/null
+++ b/freqtrade/strategy/strategy_helper.py
@@ -0,0 +1,39 @@
+import pandas as pd
+from freqtrade.exchange import timeframe_to_minutes
+
+
+def merge_informative_pairs(dataframe: pd.DataFrame, informative: pd.DataFrame,
+                            timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
+    """
+    Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
+
+    Since dates are candle open dates, merging a 15m candle that starts at 15:00, and a
+    1h candle that starts at 15:00 will result in all candles to know the close at 16:00
+    which they should not know.
+
+    Moves the date of the informative pair by 1 time interval forward.
+    This way, the 14:00 1h candle is merged to 15:00 15m candle, since the 14:00 1h candle is the
+    last candle that's closed at 15:00, 15:15, 15:30 or 15:45.
+
+    :param dataframe: Original dataframe
+    :param informative: Informative pair, most likely loaded via dp.get_pair_dataframe
+    :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')
+
+    informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
+
+    # Combine the 2 dataframes
+    # all indicators on the informative sample MUST be calculated before this point
+    dataframe = pd.merge(dataframe, informative, left_on='date',
+                         right_on=f'date_merge_{timeframe_inf}', how='left')
+    dataframe = dataframe.drop(f'date_merge_{timeframe_inf}', axis=1)
+
+    if ffill:
+        dataframe = dataframe.ffill()
+
+    return dataframe
diff --git a/tests/strategy/test_strategy_helpers.py b/tests/strategy/test_strategy_helpers.py
new file mode 100644
index 000000000..89bbba2c1
--- /dev/null
+++ b/tests/strategy/test_strategy_helpers.py
@@ -0,0 +1,61 @@
+import pandas as pd
+import numpy as np
+
+from freqtrade.strategy import merge_informative_pairs, timeframe_to_minutes
+
+
+def generate_test_data(timeframe: str, size: int):
+    np.random.seed(42)
+    tf_mins = timeframe_to_minutes(timeframe)
+
+    base = np.random.normal(20, 2, size=size)
+
+    date = pd.period_range('2020-07-05', periods=size, freq=f'{tf_mins}min').to_timestamp()
+    df = pd.DataFrame({
+        'date': date,
+        'open': base,
+        'high': base + np.random.normal(2, 1, size=size),
+        'low': base - np.random.normal(2, 1, size=size),
+        'close': base + np.random.normal(0, 1, size=size),
+        'volume': np.random.normal(200, size=size)
+    }
+    )
+    df = df.dropna()
+    return df
+
+
+def test_merge_informative_pairs():
+    data = generate_test_data('15m', 40)
+    informative = generate_test_data('1h', 40)
+
+    result = merge_informative_pairs(data, informative, '1h', 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_1h' in result.columns
+
+    assert 'open' in result.columns
+    assert 'open_1h' in result.columns
+    assert result['open'].equals(data['open'])
+
+    assert 'close' in result.columns
+    assert 'close_1h' in result.columns
+    assert result['close'].equals(data['close'])
+
+    assert 'volume' in result.columns
+    assert 'volume_1h' in result.columns
+    assert result['volume'].equals(data['volume'])
+
+    # First 4 rows are empty
+    assert result.iloc[0]['date_1h'] is pd.NaT
+    assert result.iloc[1]['date_1h'] is pd.NaT
+    assert result.iloc[2]['date_1h'] is pd.NaT
+    assert result.iloc[3]['date_1h'] is pd.NaT
+    # Next 4 rows contain the starting date (0:00)
+    assert result.iloc[4]['date_1h'] == result.iloc[0]['date']
+    assert result.iloc[5]['date_1h'] == result.iloc[0]['date']
+    assert result.iloc[6]['date_1h'] == result.iloc[0]['date']
+    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']

From 7bc89279148872e43d4f6f6233ffbc864bc1f436 Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Fri, 4 Sep 2020 20:02:31 +0200
Subject: [PATCH 5/8] Add documentation for merge_informative_pair helper

---
 docs/strategy-customization.md          | 79 +++++++++++++++++++------
 freqtrade/strategy/__init__.py          |  2 +-
 freqtrade/strategy/strategy_helper.py   |  7 ++-
 tests/strategy/test_strategy_helpers.py |  6 +-
 4 files changed, 71 insertions(+), 23 deletions(-)

diff --git a/docs/strategy-customization.md b/docs/strategy-customization.md
index e2548e510..c791be615 100644
--- a/docs/strategy-customization.md
+++ b/docs/strategy-customization.md
@@ -483,9 +483,8 @@ if self.dp:
 ### Complete Data-provider sample
 
 ```python
-from freqtrade.strategy import IStrategy, timeframe_to_minutes
+from freqtrade.strategy import IStrategy, merge_informative_pairs
 from pandas import DataFrame
-import pandas as pd
 
 class SampleStrategy(IStrategy):
     # strategy init stuff...
@@ -517,23 +516,12 @@ class SampleStrategy(IStrategy):
         # Get the 14 day rsi
         informative['rsi'] = ta.RSI(informative, timeperiod=14)
 
-        # Rename columns to be unique
-        informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
-        # Assuming inf_tf = '1d' - then the columns will now be:
-        # date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
-
-        # Shift date by 1 candle
-        # 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)
-        informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')
-
-        # Combine the 2 dataframes
-        # all indicators on the informative sample MUST be calculated before this point
-        dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_merge_{inf_tf}', how='left')
+        # 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.
-        dataframe = dataframe.ffill()
+        # Full documentation of this method, see below
+        dataframe = merge_informative_pair(dataframe, informative_pairs, inf_tf,   ffill=True)
 
         # Calculate rsi of the original dataframe (5m timeframe)
         dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
@@ -557,6 +545,63 @@ class SampleStrategy(IStrategy):
 
 ***
 
+## Helper functions
+
+### *merge_informative_pair()*
+
+This method helps you merge an informative pair to a regular dataframe without lookahead bias.
+It's there to help you merge the dataframe in a safe and consistent way.
+
+Options:
+
+- Rename the columns for you to create unique columns
+- Merge the dataframe without lookahead bias
+- Forward-fill (optional)
+
+All columns of the informative dataframe will be available on the returning dataframe in a renamed fashion:
+
+!!! Example "Column renaming"
+    Assuming `inf_tf = '1d'` the resulting columns will be:
+
+    ``` python
+    'date', 'open', 'high', 'low', 'close', 'rsi'                     # from the original dataframe
+    'date_1d', 'open_1d', 'high_1d', 'low_1d', 'close_1d', 'rsi_1d'   # from the informative dataframe
+    ```
+
+??? Example "Column renaming - 1h"
+    Assuming `inf_tf = '1h'` the resulting columns will be:
+
+    ``` python
+    'date', 'open', 'high', 'low', 'close', 'rsi'                     # from the original dataframe
+    'date_1h', 'open_1h', 'high_1h', 'low_1h', 'close_1h', 'rsi_1h'   # from the informative dataframe 
+    ```
+
+??? Example "Custom implementation"
+    A custom implementation for this is possible, and can be done as follows:
+
+    ``` python
+    # Rename columns to be unique
+    informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
+    # Assuming inf_tf = '1d' - then the columns will now be:
+    # date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
+
+    # Shift date by 1 candle
+    # 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)
+    informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes, 'm')
+
+    # Combine the 2 dataframes
+    # all indicators on the informative sample MUST be calculated before this point
+    dataframe = pd.merge(dataframe, informative, left_on='date', right_on=f'date_merge_{inf_tf}', how='left')
+    # FFill to have the 1d value available in every row throughout the day.
+    # Without this, comparisons would only work once per day.
+    dataframe = dataframe.ffill()
+
+    ```
+
+***
+
 ## Additional data (Wallets)
 
 The strategy provides access to the `Wallets` object. This contains the current balances on the exchange.
diff --git a/freqtrade/strategy/__init__.py b/freqtrade/strategy/__init__.py
index 5758bbbcc..d1510489e 100644
--- a/freqtrade/strategy/__init__.py
+++ b/freqtrade/strategy/__init__.py
@@ -2,4 +2,4 @@
 from freqtrade.exchange import (timeframe_to_minutes, timeframe_to_prev_date,
                                 timeframe_to_seconds, timeframe_to_next_date, timeframe_to_msecs)
 from freqtrade.strategy.interface import IStrategy
-from freqtrade.strategy.strategy_helper import merge_informative_pairs
+from freqtrade.strategy.strategy_helper import merge_informative_pair
diff --git a/freqtrade/strategy/strategy_helper.py b/freqtrade/strategy/strategy_helper.py
index ce98cccba..2684e7b03 100644
--- a/freqtrade/strategy/strategy_helper.py
+++ b/freqtrade/strategy/strategy_helper.py
@@ -2,8 +2,8 @@ import pandas as pd
 from freqtrade.exchange import timeframe_to_minutes
 
 
-def merge_informative_pairs(dataframe: pd.DataFrame, informative: pd.DataFrame,
-                            timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
+def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
+                           timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
     """
     Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
 
@@ -15,6 +15,9 @@ def merge_informative_pairs(dataframe: pd.DataFrame, informative: pd.DataFrame,
     This way, the 14:00 1h candle is merged to 15:00 15m candle, since the 14:00 1h candle is the
     last candle that's closed at 15:00, 15:15, 15:30 or 15:45.
 
+    Assuming inf_tf = '1d' - then the resulting columns will be:
+    date_1d, open_1d, high_1d, low_1d, close_1d, rsi_1d
+
     :param dataframe: Original dataframe
     :param informative: Informative pair, most likely loaded via dp.get_pair_dataframe
     :param timeframe_inf: Timeframe of the informative pair sample.
diff --git a/tests/strategy/test_strategy_helpers.py b/tests/strategy/test_strategy_helpers.py
index 89bbba2c1..9201d91e1 100644
--- a/tests/strategy/test_strategy_helpers.py
+++ b/tests/strategy/test_strategy_helpers.py
@@ -1,7 +1,7 @@
 import pandas as pd
 import numpy as np
 
-from freqtrade.strategy import merge_informative_pairs, timeframe_to_minutes
+from freqtrade.strategy import merge_informative_pair, timeframe_to_minutes
 
 
 def generate_test_data(timeframe: str, size: int):
@@ -24,11 +24,11 @@ def generate_test_data(timeframe: str, size: int):
     return df
 
 
-def test_merge_informative_pairs():
+def test_merge_informative_pair():
     data = generate_test_data('15m', 40)
     informative = generate_test_data('1h', 40)
 
-    result = merge_informative_pairs(data, informative, '1h', ffill=True)
+    result = merge_informative_pair(data, informative, '1h', ffill=True)
     assert isinstance(result, pd.DataFrame)
     assert len(result) == len(data)
     assert 'date' in result.columns

From cc684c51415afd7e1b682242ecd8c76f594e8c1d Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Fri, 4 Sep 2020 20:09:02 +0200
Subject: [PATCH 6/8] Correctly handle identical timerame merges

---
 docs/strategy-customization.md          |  7 +++---
 freqtrade/strategy/strategy_helper.py   | 11 +++++++---
 tests/strategy/test_strategy_helpers.py | 29 ++++++++++++++++++++++++-
 3 files changed, 40 insertions(+), 7 deletions(-)

diff --git a/docs/strategy-customization.md b/docs/strategy-customization.md
index c791be615..7396f2a89 100644
--- a/docs/strategy-customization.md
+++ b/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
diff --git a/freqtrade/strategy/strategy_helper.py b/freqtrade/strategy/strategy_helper.py
index 2684e7b03..0fa7f4258 100644
--- a/freqtrade/strategy/strategy_helper.py
+++ b/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]
 
diff --git a/tests/strategy/test_strategy_helpers.py b/tests/strategy/test_strategy_helpers.py
index 9201d91e1..4b29bf304 100644
--- a/tests/strategy/test_strategy_helpers.py
+++ b/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'])

From 71af64af94ba07a19181618e58f0dbfa8480709f Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Fri, 4 Sep 2020 20:10:43 +0200
Subject: [PATCH 7/8] Move comment to the right place

---
 freqtrade/strategy/strategy_helper.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/freqtrade/strategy/strategy_helper.py b/freqtrade/strategy/strategy_helper.py
index 0fa7f4258..1fbf618bd 100644
--- a/freqtrade/strategy/strategy_helper.py
+++ b/freqtrade/strategy/strategy_helper.py
@@ -24,7 +24,6 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
     :param timeframe_inf: Timeframe of the informative pair sample.
     :param ffill: Forwardfill missing values - optional but usually required
     """
-    # Rename columns to be unique
 
     minutes_inf = timeframe_to_minutes(timeframe_inf)
     if timeframe == timeframe_inf:
@@ -33,6 +32,7 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
     else:
         informative['date_merge'] = informative["date"] + pd.to_timedelta(minutes_inf, 'm')
 
+    # Rename columns to be unique
     informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
 
     # Combine the 2 dataframes

From 7852feab0566ecdda19e705cd88c7c7bd61cd52b Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Mon, 7 Sep 2020 09:06:43 +0200
Subject: [PATCH 8/8] support smaller timeframes

---
 docs/strategy-customization.md        | 6 +++++-
 freqtrade/strategy/strategy_helper.py | 3 ++-
 2 files changed, 7 insertions(+), 2 deletions(-)

diff --git a/docs/strategy-customization.md b/docs/strategy-customization.md
index 7396f2a89..a1de1044c 100644
--- a/docs/strategy-customization.md
+++ b/docs/strategy-customization.md
@@ -521,7 +521,7 @@ class SampleStrategy(IStrategy):
         # 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, self.timeframe, inf_tf, ffill=True)
+        dataframe = merge_informative_pair(dataframe, informative, self.timeframe, inf_tf, ffill=True)
 
         # Calculate rsi of the original dataframe (5m timeframe)
         dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
@@ -601,6 +601,10 @@ All columns of the informative dataframe will be available on the returning data
 
     ```
 
+!!! Warning "Informative timeframe < timeframe"
+    Using informative timeframes smaller than the dataframe timeframe is not recommended with this method, as it will not use any of the additional information this would provide.
+    To use the more detailed information properly, more advanced methods should be applied (which are out of scope for freqtrade documentation, as it'll depend on the respective need).
+
 ***
 
 ## Additional data (Wallets)
diff --git a/freqtrade/strategy/strategy_helper.py b/freqtrade/strategy/strategy_helper.py
index 1fbf618bd..1a5b2d0f8 100644
--- a/freqtrade/strategy/strategy_helper.py
+++ b/freqtrade/strategy/strategy_helper.py
@@ -26,7 +26,8 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
     """
 
     minutes_inf = timeframe_to_minutes(timeframe_inf)
-    if timeframe == timeframe_inf:
+    minutes = timeframe_to_minutes(timeframe)
+    if minutes >= minutes_inf:
         # No need to forwardshift if the timeframes are identical
         informative['date_merge'] = informative["date"]
     else: