fix(qav3): np.close() -> np.isclose()

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
index bcb72d9d60091fc917fa849271e40a042e9d8811..d640b52a98a558902a76ff4cdf532212419211eb 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -44,7 +44,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
     https://github.com/sponsors/robcaulk
     """
 
-    version = "3.7.6"
+    version = "3.7.7"
 
     @cached_property
     def _optuna_config(self) -> dict:
@@ -979,7 +979,7 @@ def smoothed_max(series: pd.Series, temperature=1.0) -> float:
         return np.nan
     if temperature < 0:
         raise ValueError("temperature must be non-negative.")
-    if np.close(temperature, 0):
+    if np.isclose(temperature, 0):
         return data_array.max()
     return sp.special.logsumexp(temperature * data_array) / temperature
 
@@ -990,7 +990,7 @@ def smoothed_min(series: pd.Series, temperature=1.0) -> float:
         return np.nan
     if temperature < 0:
         raise ValueError("temperature must be non-negative.")
-    if np.close(temperature, 0):
+    if np.isclose(temperature, 0):
         return data_array.min()
     return -sp.special.logsumexp(-temperature * data_array) / temperature
 

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index 6f1abb22714d08a0be6f50bf59bb67ca57d6287d..341d2e1b9f3da15856172d9f8eb783b82d50e171 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -58,7 +58,7 @@ class QuickAdapterV3(IStrategy):
     INTERFACE_VERSION = 3
 
     def version(self) -> str:
-        return "3.3.2"
+        return "3.3.3"
 
     timeframe = "5m"
 
@@ -555,7 +555,7 @@ class QuickAdapterV3(IStrategy):
         stoploss_distance = self.get_stoploss_distance(df, trade, current_rate)
         if isna(stoploss_distance):
             return None
-        if np.close(stoploss_distance, 0):
+        if np.isclose(stoploss_distance, 0):
             return None
         sign = 1 if trade.is_short else -1
         return stoploss_from_absolute(
@@ -603,7 +603,7 @@ class QuickAdapterV3(IStrategy):
         take_profit_distance = self.get_take_profit_distance(df, trade, current_rate)
         if isna(take_profit_distance):
             return None
-        if np.close(take_profit_distance, 0):
+        if np.isclose(take_profit_distance, 0):
             return None
         if trade.is_short:
             take_profit_price = trade.open_rate - take_profit_distance