fix(qav3): do not tighten stoploss prematurely

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

diff --git a/ReforceXY/user_data/freqaimodels/ReforceXY.py b/ReforceXY/user_data/freqaimodels/ReforceXY.py
index 5ca50939d2184e2659c3009b9ea72aef687f859f..54bb40506353f497d57c60c0929be5500c4e609a 100644 (file)
--- a/ReforceXY/user_data/freqaimodels/ReforceXY.py
+++ b/ReforceXY/user_data/freqaimodels/ReforceXY.py
@@ -4,7 +4,7 @@ import json
 import logging
 import time
 import warnings
-from enum import Enum
+from enum import IntEnum
 from functools import lru_cache
 from pathlib import Path
 from typing import Any, Callable, Dict, Optional, Tuple, Type
@@ -51,7 +51,7 @@ warnings.filterwarnings("ignore", category=ExperimentalWarning)
 logger = logging.getLogger(__name__)
 
 
-class ForceActions(Enum):
+class ForceActions(IntEnum):
     Take_profit = 1
     Stop_loss = 2
     Timeout = 3

diff --git a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
index dca9aef52eb585ef2dd4ffc4065a559d96b63499..4d4e4550dd867a40824d8122402dcdd9ca666230 100644 (file)
--- a/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
+++ b/quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
@@ -1341,7 +1341,7 @@ def label_objective(
     candles_step: int,
 ) -> tuple[float, int]:
     min_label_period_candles, max_label_period_candles, candles_step = (
-        get_min_max_label_period_candles(candles_step)
+        get_min_max_label_period_candles(fit_live_predictions_candles, candles_step)
     )
 
     label_period_candles = trial.suggest_int(

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index b06da77aa36c28e45f649c4f1398385d8d7f27eb..01ec99430eb2ee003f7946b3a4d68bd7d17c0a5b 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -765,8 +765,7 @@ class QuickAdapterV3(IStrategy):
             * (trade_natr / 100.0)
             * self.get_label_natr_ratio_percent(trade.pair, natr_ratio_percent)
             * QuickAdapterV3.get_stoploss_log_factor(
-                trade_duration_candles
-                + int(round(QuickAdapterV3.get_trade_exit_stage(trade) ** (1.5)))
+                trade_duration_candles + int(round(trade.nr_of_successful_exits**1.5))
             )
         )
 
@@ -1030,8 +1029,8 @@ class QuickAdapterV3(IStrategy):
         current_deviation = self._calculate_current_deviation(
             df,
             pair,
-            min_natr_ratio_percent=0.00985,
-            max_natr_ratio_percent=0.0995,
+            min_natr_ratio_percent=0.00995,
+            max_natr_ratio_percent=0.0999,
             interpolation_direction="direct",
         )
         if isna(current_deviation):

diff --git a/quickadapter/user_data/strategies/Utils.py b/quickadapter/user_data/strategies/Utils.py
index d7b6628f1ce52135df112ec143e348dbe878c8cc..e3ded5f98216a2c44b0dd862829d33652e6279ee 100644 (file)
--- a/quickadapter/user_data/strategies/Utils.py
+++ b/quickadapter/user_data/strategies/Utils.py
@@ -946,14 +946,42 @@ def soft_extremum(series: pd.Series, alpha: float) -> float:
 
 @lru_cache(maxsize=8)
 def get_min_max_label_period_candles(
+    fit_live_predictions_candles: int,
     candles_step: int,
     min_label_period_candles: int = 8,
     max_label_period_candles: int = 48,
+    max_time_candles: int = 48,
+    max_horizon_fraction: float = 1.0 / 3.0,
+    min_label_period_candles_fallback: int = 8,
+    max_label_period_candles_fallback: int = 28,
 ) -> tuple[int, int, int]:
     if min_label_period_candles > max_label_period_candles:
-        min_label_period_candles, max_label_period_candles = (
-            max_label_period_candles,
-            min_label_period_candles,
+        raise ValueError(
+            "min_label_period_candles must be less than or equal to max_label_period_candles"
+        )
+
+    capped_time_candles = max(1, floor_to_step(max_time_candles, candles_step))
+    capped_horizon_candles = max(
+        1,
+        floor_to_step(
+            max(1, int(fit_live_predictions_candles * max_horizon_fraction)),
+            candles_step,
+        ),
+    )
+    max_label_period_candles = min(
+        max_label_period_candles, capped_time_candles, capped_horizon_candles
+    )
+
+    if min_label_period_candles > max_label_period_candles:
+        fallback_high = min(
+            max_label_period_candles_fallback,
+            capped_time_candles,
+            capped_horizon_candles,
+        )
+        return (
+            min(min_label_period_candles_fallback, fallback_high),
+            fallback_high,
+            1,
         )
 
     if candles_step <= (max_label_period_candles - min_label_period_candles):