From bf7dc5dea6015ba51bd11ca6a3a39a60da164583 Mon Sep 17 00:00:00 2001
From: =?utf8?q?J=C3=A9r=C3=B4me=20Benoit?= <jerome.benoit@piment-noir.org>
Date: Wed, 11 Jun 2025 23:01:49 +0200
Subject: [PATCH] fix(qav3): add locking to pivot optimization ops
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Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>
---
 .../freqaimodels/QuickAdapterRegressorV3.py   | 59 +++++++++++--------
 .../user_data/strategies/QuickAdapterV3.py    |  2 +-
 2 files changed, 34 insertions(+), 27 deletions(-)

diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
index d0a953c..8599ea3 100644
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -4,6 +4,7 @@ import logging
 import json
 import random
 from statistics import median
+import threading
 import time
 import numpy as np
 import pandas as pd
@@ -91,6 +92,10 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
             and self._optuna_config.get("enabled")
             and self.data_split_parameters.get("test_size", TEST_SIZE) > 0
         )
+        self._optuna_locks = {
+            "label": threading.RLock(),
+            "throttle": threading.RLock(),
+        }
         self._optuna_hp_value: dict[str, float] = {}
         self._optuna_train_value: dict[str, float] = {}
         self._optuna_label_values: dict[str, list] = {}
@@ -203,17 +208,18 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
             raise RuntimeError("Failed to initialize optuna label candle pool")
 
     def set_optuna_label_candle(self, pair: str) -> None:
-        if len(self._optuna_label_candle_pool) == 0:
-            self.init_optuna_label_candle_pool()
-        self._optuna_label_candle[pair] = self._optuna_label_candle_pool.pop()
-        optuna_label_available_candles = (
-            set(self.get_optuna_label_all_candles())
-            - set(self._optuna_label_candle_pool)
-            - set(self._optuna_label_candle.values())
-        )
-        if len(optuna_label_available_candles) > 0:
-            self._optuna_label_candle_pool.extend(optuna_label_available_candles)
-            random.shuffle(self._optuna_label_candle_pool)
+        with self._optuna_locks.get("label"):
+            if len(self._optuna_label_candle_pool) == 0:
+                self.init_optuna_label_candle_pool()
+            self._optuna_label_candle[pair] = self._optuna_label_candle_pool.pop()
+            optuna_label_available_candles = (
+                set(self.get_optuna_label_all_candles())
+                - set(self._optuna_label_candle_pool)
+                - set(self._optuna_label_candle.values())
+            )
+            if len(optuna_label_available_candles) > 0:
+                self._optuna_label_candle_pool.extend(optuna_label_available_candles)
+                random.shuffle(self._optuna_label_candle_pool)
 
     def fit(self, data_dictionary: dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
         """
@@ -316,22 +322,23 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
     ) -> None:
         if namespace != "label":
             raise ValueError(f"Invalid namespace: {namespace}")
-        self._optuna_label_candles[pair] += 1
-        if self._optuna_label_candles[pair] >= self._optuna_label_candle[pair]:
-            try:
-                callback()
-            except Exception as e:
-                logger.error(
-                    f"Error executing optuna {pair} {namespace} callback: {str(e)}",
-                    exc_info=True,
+        with self._optuna_locks.get("throttle"):
+            self._optuna_label_candles[pair] += 1
+            if self._optuna_label_candles[pair] >= self._optuna_label_candle[pair]:
+                try:
+                    callback()
+                except Exception as e:
+                    logger.error(
+                        f"Error executing optuna {pair} {namespace} callback: {str(e)}",
+                        exc_info=True,
+                    )
+                finally:
+                    self._optuna_label_candles[pair] = 0
+                    self.set_optuna_label_candle(pair)
+            else:
+                logger.info(
+                    f"Optuna {pair} {namespace} callback throttled, still {self._optuna_label_candle[pair] - self._optuna_label_candles[pair]} candles to go"
                 )
-            finally:
-                self._optuna_label_candles[pair] = 0
-                self.set_optuna_label_candle(pair)
-        else:
-            logger.info(
-                f"Optuna {pair} {namespace} callback throttled, still {self._optuna_label_candle[pair] - self._optuna_label_candles[pair]} candles to go"
-            )
 
     def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
         warmed_up = True
diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index 2d77e83..0d54790 100644
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -61,7 +61,7 @@ class QuickAdapterV3(IStrategy):
     INTERFACE_VERSION = 3
 
     def version(self) -> str:
-        return "3.3.87"
+        return "3.3.88"
 
     timeframe = "5m"
 
-- 
2.43.0