Add documentation for merge_informative_pair helper

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
+        # Use the helper function merge_informative_pair to safely merge the pair
-        informative.columns = [f"{col}_{inf_tf}" for col in informative.columns]
+        # Automatically renames the columns and merges a shorter timeframe dataframe and a longer timeframe informative pair
-        # 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()
+        # 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.

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

freqtrade/strategy/strategy_helper.py

@@ -2,7 +2,7 @@ import pandas as pd
 from freqtrade.exchange import timeframe_to_minutes
 
 
-def merge_informative_pairs(dataframe: pd.DataFrame, informative: 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.

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