Merged feat/short into lev-strat

freqtrade/strategy/strategy_helper.py

@@ -5,7 +5,9 @@ from freqtrade.exchange import timeframe_to_minutes
 
 
 def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
-                           timeframe: str, timeframe_inf: str, ffill: bool = True) -> pd.DataFrame:
+                           timeframe: str, timeframe_inf: str, ffill: bool = True,
+                           append_timeframe: bool = True,
+                           date_column: str = 'date') -> pd.DataFrame:
     """
     Correctly merge informative samples to the original dataframe, avoiding lookahead bias.
 
@@ -25,6 +27,8 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.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
+    :param append_timeframe: Rename columns by appending timeframe.
+    :param date_column: A custom date column name.
     :return: Merged dataframe
     :raise: ValueError if the secondary timeframe is shorter than the dataframe timeframe
     """
@@ -33,25 +37,29 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
     minutes = timeframe_to_minutes(timeframe)
     if minutes == minutes_inf:
         # No need to forwardshift if the timeframes are identical
-        informative['date_merge'] = informative["date"]
+        informative['date_merge'] = informative[date_column]
     elif minutes < minutes_inf:
         # Subtract "small" timeframe so merging is not delayed by 1 small candle
         # Detailed explanation in https://github.com/freqtrade/freqtrade/issues/4073
         informative['date_merge'] = (
-            informative["date"] + pd.to_timedelta(minutes_inf, 'm') - pd.to_timedelta(minutes, 'm')
+            informative[date_column] + pd.to_timedelta(minutes_inf, 'm') -
+            pd.to_timedelta(minutes, 'm')
         )
     else:
         raise ValueError("Tried to merge a faster timeframe to a slower timeframe."
                          "This would create new rows, and can throw off your regular indicators.")
 
     # Rename columns to be unique
-    informative.columns = [f"{col}_{timeframe_inf}" for col in informative.columns]
+    date_merge = 'date_merge'
+    if append_timeframe:
+        date_merge = f'date_merge_{timeframe_inf}'
+        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)
+                         right_on=date_merge, how='left')
+    dataframe = dataframe.drop(date_merge, axis=1)
 
     if ffill:
         dataframe = dataframe.ffill()
@@ -97,3 +105,28 @@ def stoploss_from_open(
         return min(stoploss, 0.0)
     else:
         return max(stoploss, 0.0)
+
+
+def stoploss_from_absolute(stop_rate: float, current_rate: float) -> float:
+    """
+    Given current price and desired stop price, return a stop loss value that is relative to current
+    price.
+
+    The requested stop can be positive for a stop above the open price, or negative for
+    a stop below the open price. The return value is always >= 0.
+
+    Returns 0 if the resulting stop price would be above the current price.
+
+    :param stop_rate: Stop loss price.
+    :param current_rate: Current asset price.
+    :return: Positive stop loss value relative to current price
+    """
+
+    # formula is undefined for current_rate 0, return maximum value
+    if current_rate == 0:
+        return 1
+
+    stoploss = 1 - (stop_rate / current_rate)
+
+    # negative stoploss values indicate the requested stop price is higher than the current price
+    return max(stoploss, 0.0)