perf(qav3): use vectorized ops in more places

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 809242899ab56ae1c6d057d9d9870fadf0298a55..d4b971d42d17af6a6a31575b458335f68e0362b4 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -125,7 +125,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
             )
         if (
             not isinstance(self.freqai_info.get("identifier"), str)
-            or self.freqai_info.get("identifier").strip() == ""
+            or not self.freqai_info.get("identifier").strip()
         ):
             raise ValueError(
                 "FreqAI model requires 'identifier' defined in the freqai section configuration"
@@ -1322,6 +1322,10 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
         direction: Optional[optuna.study.StudyDirection] = None,
         directions: Optional[list[optuna.study.StudyDirection]] = None,
     ) -> Optional[optuna.study.Study]:
+        if direction is not None and directions is not None:
+            raise ValueError(
+                "Cannot specify both 'direction' and 'directions'. Use one or the other"
+            )
         is_study_single_objective = direction is not None and directions is None
         if (
             not is_study_single_objective
@@ -1458,6 +1462,10 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
         direction: Optional[optuna.study.StudyDirection] = None,
         directions: Optional[list[optuna.study.StudyDirection]] = None,
     ) -> Optional[optuna.study.Study]:
+        if direction is not None and directions is not None:
+            raise ValueError(
+                "Cannot specify both 'direction' and 'directions'. Use one or the other"
+            )
         identifier = self.freqai_info.get("identifier")
         study_name = f"{identifier}-{pair}-{namespace}"
         try:

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index cf9097f8a3622e21c90c4b42720bcc5b9b9267a5..12fff9deac81432a2e122497ea211ca5995dad3b 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -213,7 +213,7 @@ class QuickAdapterV3(IStrategy):
             )
         if (
             not isinstance(self.freqai_info.get("identifier"), str)
-            or self.freqai_info.get("identifier").strip() == ""
+            or not self.freqai_info.get("identifier").strip()
         ):
             raise ValueError(
                 "FreqAI strategy requires 'identifier' defined in the freqai section configuration"
@@ -487,8 +487,7 @@ class QuickAdapterV3(IStrategy):
                 f"{pair}: no extrema to label (label_period={QuickAdapterV3.td_format(label_period)} / {label_period_candles=} / {label_natr_ratio=:.2f})"
             )
         else:
-            for pivot_idx, pivot_dir in zip(pivots_indices, pivots_directions):
-                dataframe.at[pivot_idx, EXTREMA_COLUMN] = pivot_dir
+            dataframe.loc[pivots_indices, EXTREMA_COLUMN] = pivots_directions
             dataframe["minima"] = np.where(
                 dataframe[EXTREMA_COLUMN] == TrendDirection.DOWN, -1, 0
             )
@@ -759,6 +758,10 @@ class QuickAdapterV3(IStrategy):
         current_rate: float,
         natr_ratio_percent: float,
     ) -> Optional[float]:
+        if not (0.0 <= natr_ratio_percent <= 1.0):
+            raise ValueError(
+                f"natr_ratio_percent must be in [0, 1], got {natr_ratio_percent}"
+            )
         trade_duration_candles = self.get_trade_duration_candles(df, trade)
         if not QuickAdapterV3.is_trade_duration_valid(trade_duration_candles):
             return None
@@ -807,7 +810,7 @@ class QuickAdapterV3(IStrategy):
         candle_duration_secs = max(1, int(self._candle_duration_secs))
         candle_start_secs = (timestamp // candle_duration_secs) * candle_duration_secs
         callback_hash = get_callable_sha256(callback)
-        key = hashlib.sha256(f"{pair}|{callback_hash}".encode()).hexdigest()
+        key = hashlib.sha256(f"{pair}\x00{callback_hash}".encode()).hexdigest()
         if candle_start_secs != self.last_candle_start_secs.get(key):
             self.last_candle_start_secs[key] = candle_start_secs
             try:
@@ -817,6 +820,15 @@ class QuickAdapterV3(IStrategy):
                     f"Error executing callback for {pair}: {repr(e)}", exc_info=True
                 )
 
+            threshold_secs = 10 * candle_duration_secs
+            keys_to_remove = [
+                key
+                for key, ts in self.last_candle_start_secs.items()
+                if ts is not None and timestamp - ts > threshold_secs
+            ]
+            for key in keys_to_remove:
+                del self.last_candle_start_secs[key]
+
     def custom_stoploss(
         self,
         pair: str,

diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py
index 2a00157d2336673b6301b9cbe795d5fa8b4e4412..9b22ec343408695815c7c162cdd89d893e7a296d 100644 (file)
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -383,11 +383,15 @@ def smma(series: pd.Series, period: int, zero_lag=False, offset=0) -> pd.Series:
 
     if zero_lag:
         series = calculate_zero_lag(series, period=period)
-    smma = pd.Series(np.nan, index=series.index)
-    smma.iloc[period - 1] = series.iloc[:period].mean()
 
+    values = series.to_numpy()
+
+    smma_values = np.full(n, np.nan)
+    smma_values[period - 1] = np.mean(values[:period])
     for i in range(period, n):
-        smma.iloc[i] = (smma.iloc[i - 1] * (period - 1) + series.iloc[i]) / period
+        smma_values[i] = (smma_values[i - 1] * (period - 1) + values[i]) / period
+
+    smma = pd.Series(smma_values, index=series.index)
 
     if offset != 0:
         smma = smma.shift(offset)
@@ -831,6 +835,12 @@ def get_optuna_study_model_parameters(
         raise ValueError(
             f"Unsupported regressor model: {regressor} (supported: {', '.join(regressors)})"
         )
+    if not isinstance(expansion_ratio, (int, float)) or not (
+        0.0 <= expansion_ratio <= 1.0
+    ):
+        raise ValueError(
+            f"expansion_ratio must be a float between 0 and 1, got {expansion_ratio}"
+        )
     default_ranges = {
         "n_estimators": (100, 2000),
         "learning_rate": (1e-3, 0.5),