Merge pull request #6276 from clover-es/feat/short

Add support for shorts in strategy.stoploss_from_absolute()

docs/strategy-callbacks.md

@@ -284,11 +284,11 @@ class AwesomeStrategy(IStrategy):
 
         # evaluate highest to lowest, so that highest possible stop is used
         if current_profit > 0.40:
-            return stoploss_from_open(0.25, current_profit)
+            return stoploss_from_open(0.25, current_profit, is_short=trade.is_short)
         elif current_profit > 0.25:
-            return stoploss_from_open(0.15, current_profit)
+            return stoploss_from_open(0.15, current_profit, is_short=trade.is_short)
         elif current_profit > 0.20:
-            return stoploss_from_open(0.07, current_profit)
+            return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
 
         # return maximum stoploss value, keeping current stoploss price unchanged
         return 1

docs/strategy-customization.md

@@ -791,7 +791,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 
     Say the open price was $100, and `current_price` is $121 (`current_profit` will be `0.21`).  
 
-    If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit)` which will return `0.1157024793`.  11.57% below $121 is $107, which is the same as 7% above $100.
+    If we want a stop price at 7% above the open price we can call `stoploss_from_open(0.07, current_profit, False)` which will return `0.1157024793`.  11.57% below $121 is $107, which is the same as 7% above $100.
 
 
     ``` python
@@ -811,7 +811,7 @@ Stoploss values returned from `custom_stoploss` must specify a percentage relati
 
             # once the profit has risen above 10%, keep the stoploss at 7% above the open price
             if current_profit > 0.10:
-                return stoploss_from_open(0.07, current_profit)
+                return stoploss_from_open(0.07, current_profit, is_short=trade.is_short)
 
             return 1
 
@@ -832,7 +832,7 @@ In some situations it may be confusing to deal with stops relative to current ra
 
 ??? Example "Returning a stoploss using absolute price from the custom stoploss function"
 
-    If we want to trail a stop price at 2xATR below current proce we can call `stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)`.
+    If we want to trail a stop price at 2xATR below current proce we can call `stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate, is_short=trade.is_short)`.
 
     ``` python
 
@@ -852,7 +852,7 @@ In some situations it may be confusing to deal with stops relative to current ra
                             current_rate: float, current_profit: float, **kwargs) -> float:
             dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
             candle = dataframe.iloc[-1].squeeze()
-            return stoploss_from_absolute(current_rate - (candle['atr'] * 2), current_rate)
+            return stoploss_from_absolute(current_rate - (candle['atr'] * 2, is_short=trade.is_short), current_rate)
 
     ```
 

freqtrade/strategy/strategy_helper.py

@@ -69,7 +69,7 @@ def merge_informative_pair(dataframe: pd.DataFrame, informative: pd.DataFrame,
 def stoploss_from_open(
     open_relative_stop: float,
     current_profit: float,
-    for_short: bool = False
+    is_short: bool = False
 ) -> float:
     """
 
@@ -84,15 +84,15 @@ def stoploss_from_open(
 
     :param open_relative_stop: Desired stop loss percentage relative to open price
     :param current_profit: The current profit percentage
-    :param for_short: When true, perform the calculation for short instead of long
+    :param is_short: When true, perform the calculation for short instead of long
     :return: Stop loss value relative to current price
     """
 
     # formula is undefined for current_profit -1 (longs) or 1 (shorts), return maximum value
-    if (current_profit == -1 and not for_short) or (for_short and current_profit == 1):
+    if (current_profit == -1 and not is_short) or (is_short and current_profit == 1):
         return 1
 
-    if for_short is True:
+    if is_short is True:
         stoploss = -1+((1-open_relative_stop)/(1-current_profit))
     else:
         stoploss = 1-((1+open_relative_stop)/(1+current_profit))
@@ -102,9 +102,8 @@ def stoploss_from_open(
     return max(stoploss, 0.0)
 
 
-def stoploss_from_absolute(stop_rate: float, current_rate: float) -> float:
+def stoploss_from_absolute(stop_rate: float, current_rate: float, is_short: bool = False) -> float:
     """
-    TODO-lev: Update this method with "is_short" formula
     Given current price and desired stop price, return a stop loss value that is relative to current
     price.
 
@@ -115,6 +114,7 @@ def stoploss_from_absolute(stop_rate: float, current_rate: float) -> float:
 
     :param stop_rate: Stop loss price.
     :param current_rate: Current asset price.
+    :param is_short: When true, perform the calculation for short instead of long
     :return: Positive stop loss value relative to current price
     """
 
@@ -123,6 +123,10 @@ def stoploss_from_absolute(stop_rate: float, current_rate: float) -> float:
         return 1
 
     stoploss = 1 - (stop_rate / current_rate)
+    if is_short:
+        stoploss = -stoploss
 
-    # negative stoploss values indicate the requested stop price is higher than the current price
-    return max(stoploss, 0.0)
+    # negative stoploss values indicate the requested stop price is higher/lower
+    # (long/short) than the current price
+    # shorts can yield stoploss values higher than 1, so limit that as well
+    return max(min(stoploss, 1.0), 0.0)

tests/strategy/test_strategy_helpers.py

@@ -153,11 +153,22 @@ def test_stoploss_from_open():
 
 
 def test_stoploss_from_absolute():
-    assert stoploss_from_absolute(90, 100) == 1 - (90 / 100)
-    assert stoploss_from_absolute(100, 100) == 0
-    assert stoploss_from_absolute(110, 100) == 0
-    assert stoploss_from_absolute(100, 0) == 1
-    assert stoploss_from_absolute(0, 100) == 1
+    assert pytest.approx(stoploss_from_absolute(90, 100)) == 1 - (90 / 100)
+    assert pytest.approx(stoploss_from_absolute(90, 100)) == 0.1
+    assert pytest.approx(stoploss_from_absolute(95, 100)) == 0.05
+    assert pytest.approx(stoploss_from_absolute(100, 100)) == 0
+    assert pytest.approx(stoploss_from_absolute(110, 100)) == 0
+    assert pytest.approx(stoploss_from_absolute(100, 0)) == 1
+    assert pytest.approx(stoploss_from_absolute(0, 100)) == 1
+
+    assert pytest.approx(stoploss_from_absolute(90, 100, True)) == 0
+    assert pytest.approx(stoploss_from_absolute(100, 100, True)) == 0
+    assert pytest.approx(stoploss_from_absolute(110, 100, True)) == -(1 - (110/100))
+    assert pytest.approx(stoploss_from_absolute(110, 100, True)) == 0.1
+    assert pytest.approx(stoploss_from_absolute(105, 100, True)) == 0.05
+    assert pytest.approx(stoploss_from_absolute(100, 0, True)) == 1
+    assert pytest.approx(stoploss_from_absolute(0, 100, True)) == 0
+    assert pytest.approx(stoploss_from_absolute(100, 1, True)) == 1
 
 
 # TODO-lev: @pytest.mark.parametrize('candle_type', ['mark', ''])