perf(qav3): tune pivot labeling optimization

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 814b9030174ad2b8192d6181c7641c174ec5a01f..ce3f6297d793cf13e1294ca7b541f04943bd7bfd 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -1,6 +1,7 @@
 from enum import IntEnum
 import logging
 import json
+from statistics import median
 import time
 import numpy as np
 import pandas as pd
@@ -45,7 +46,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
     https://github.com/sponsors/robcaulk
     """
 
-    version = "3.7.75"
+    version = "3.7.76"
 
     @cached_property
     def _optuna_config(self) -> dict:
@@ -1074,8 +1075,8 @@ def zigzag(
     natr_period: int = 14,
     natr_ratio: float = 6.0,
 ) -> tuple[list[int], list[float], list[int]]:
-    min_confirmation_window: int = 2
-    max_confirmation_window: int = 6
+    min_confirmation_window: int = 3
+    max_confirmation_window: int = 5
     n = len(df)
     if df.empty or n < max(natr_period, 2 * max_confirmation_window + 1):
         return [], [], []
@@ -1128,7 +1129,7 @@ def zigzag(
     ) -> int:
         volatility_quantile = calculate_volatility_quantile(pos)
         if np.isnan(volatility_quantile):
-            return int(round(np.median([min_window, max_window])))
+            return int(round(median([min_window, max_window])))
 
         return np.clip(
             round(max_window - (max_window - min_window) * volatility_quantile),
@@ -1139,11 +1140,11 @@ def zigzag(
     def calculate_depth(
         pos: int,
         min_depth: int = 6,
-        max_depth: int = 24,
+        max_depth: int = 26,
     ) -> int:
         volatility_quantile = calculate_volatility_quantile(pos)
         if np.isnan(volatility_quantile):
-            return int(round(np.median([min_depth, max_depth])))
+            return int(round(median([min_depth, max_depth])))
 
         return np.clip(
             round(max_depth - (max_depth - min_depth) * volatility_quantile),
@@ -1155,12 +1156,15 @@ def zigzag(
         pos: int,
         min_strength: float = 0.5,
         max_strength: float = 1.5,
+        volatility_exponent: float = 1.5,
     ) -> float:
         volatility_quantile = calculate_volatility_quantile(pos)
         if np.isnan(volatility_quantile):
-            return np.median([min_strength, max_strength])
+            return median([min_strength, max_strength])
 
-        return min_strength + (max_strength - min_strength) * volatility_quantile
+        return min_strength + (max_strength - min_strength) * (
+            volatility_quantile**volatility_exponent
+        )
 
     def update_candidate_pivot(pos: int, value: float, direction: TrendDirection):
         nonlocal candidate_pivot_pos, candidate_pivot_value, candidate_pivot_direction
@@ -1378,7 +1382,7 @@ def label_objective(
         max_label_period_candles,
         step=candles_step,
     )
-    label_natr_ratio = trial.suggest_float("label_natr_ratio", 4.0, 16.0, step=0.01)
+    label_natr_ratio = trial.suggest_float("label_natr_ratio", 2.0, 10.0, step=0.01)
 
     df = df.iloc[
         -(

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index f72386291c7506d44c8725bbddf9c793091323d1..1c022f39009830ef898909aeec5c122f01e54608 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -60,7 +60,7 @@ class QuickAdapterV3(IStrategy):
     INTERFACE_VERSION = 3
 
     def version(self) -> str:
-        return "3.3.79"
+        return "3.3.80"
 
     timeframe = "5m"
 

diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py
index a64191431c9edad8c3a624482c14051da4a08399..0c2cd95aeb1ee92658116ae8574ff92afbdd58ce 100644 (file)
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -1,5 +1,6 @@
 from enum import IntEnum
 from functools import lru_cache
+from statistics import median
 import numpy as np
 import pandas as pd
 import scipy as sp
@@ -379,8 +380,8 @@ def zigzag(
     natr_period: int = 14,
     natr_ratio: float = 6.0,
 ) -> tuple[list[int], list[float], list[int]]:
-    min_confirmation_window: int = 2
-    max_confirmation_window: int = 6
+    min_confirmation_window: int = 3
+    max_confirmation_window: int = 5
     n = len(df)
     if df.empty or n < max(natr_period, 2 * max_confirmation_window + 1):
         return [], [], []
@@ -433,7 +434,7 @@ def zigzag(
     ) -> int:
         volatility_quantile = calculate_volatility_quantile(pos)
         if np.isnan(volatility_quantile):
-            return int(round(np.median([min_window, max_window])))
+            return int(round(median([min_window, max_window])))
 
         return np.clip(
             round(max_window - (max_window - min_window) * volatility_quantile),
@@ -444,11 +445,11 @@ def zigzag(
     def calculate_depth(
         pos: int,
         min_depth: int = 6,
-        max_depth: int = 24,
+        max_depth: int = 26,
     ) -> int:
         volatility_quantile = calculate_volatility_quantile(pos)
         if np.isnan(volatility_quantile):
-            return int(round(np.median([min_depth, max_depth])))
+            return int(round(median([min_depth, max_depth])))
 
         return np.clip(
             round(max_depth - (max_depth - min_depth) * volatility_quantile),
@@ -460,12 +461,15 @@ def zigzag(
         pos: int,
         min_strength: float = 0.5,
         max_strength: float = 1.5,
+        volatility_exponent: float = 1.5,
     ) -> float:
         volatility_quantile = calculate_volatility_quantile(pos)
         if np.isnan(volatility_quantile):
-            return np.median([min_strength, max_strength])
+            return median([min_strength, max_strength])
 
-        return min_strength + (max_strength - min_strength) * volatility_quantile
+        return min_strength + (max_strength - min_strength) * (
+            volatility_quantile**volatility_exponent
+        )
 
     def update_candidate_pivot(pos: int, value: float, direction: TrendDirection):
         nonlocal candidate_pivot_pos, candidate_pivot_value, candidate_pivot_direction