]> Piment Noir Git Repositories - freqai-strategies.git/commitdiff
fix(qav3): fix namespace collision
authorJérôme Benoit <jerome.benoit@piment-noir.org>
Sat, 24 May 2025 13:20:45 +0000 (15:20 +0200)
committerJérôme Benoit <jerome.benoit@piment-noir.org>
Sat, 24 May 2025 13:20:45 +0000 (15:20 +0200)
Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>
quickadapter/user_data/freqaimodels/QuickAdapterRegressorV3.py
quickadapter/user_data/strategies/QuickAdapterV3.py
quickadapter/user_data/strategies/Utils.py

index 1aea149411d215301b768ab91716b6fcc4354bbd..46fabc3f86cfd300e5e45e4411f87aa1e88c2375 100644 (file)
@@ -45,7 +45,7 @@ class QuickAdapterRegressorV3(BaseRegressionModel):
     https://github.com/sponsors/robcaulk
     """
 
-    version = "3.7.59"
+    version = "3.7.60"
 
     @cached_property
     def _optuna_config(self) -> dict:
index 0c50b4396df9af39c51281911b16bccbd85e6de3..289406a08a8d365d5559899fca538c6ce0367ed2 100644 (file)
@@ -18,8 +18,8 @@ import pandas_ta as pta
 from Utils import (
     alligator,
     bottom_change_percent,
+    calculate_zero_lag,
     get_ma_fn,
-    zero_lag,
     zigzag,
     ewo,
     non_zero_diff,
@@ -60,7 +60,7 @@ class QuickAdapterV3(IStrategy):
     INTERFACE_VERSION = 3
 
     def version(self) -> str:
-        return "3.3.61"
+        return "3.3.62"
 
     timeframe = "5m"
 
@@ -478,7 +478,7 @@ class QuickAdapterV3(IStrategy):
     def get_trade_natr(df: DataFrame, trade_duration_candles: int) -> Optional[float]:
         if not QuickAdapterV3.is_trade_duration_valid(trade_duration_candles):
             return None
-        trade_zl_natr = zero_lag(
+        trade_zl_natr = calculate_zero_lag(
             df["natr_label_period_candles"], period=trade_duration_candles
         )
         if trade_zl_natr.empty:
index 481ed9a03d3abbe62e89d68f95b93d372dc4eec3..5608969d7822da155cc2813bc5830e2110c10ffd 100644 (file)
@@ -127,7 +127,7 @@ def vwapb(dataframe: pd.DataFrame, window=20, num_of_std=1) -> tuple:
     return vwap_low, vwap, vwap_high
 
 
-def zero_lag(series: pd.Series, period: int) -> pd.Series:
+def calculate_zero_lag(series: pd.Series, period: int) -> pd.Series:
     """Applies a zero lag filter to reduce MA lag."""
     lag = max(int(0.5 * (period - 1)), 0)
     if lag == 0:
@@ -190,9 +190,9 @@ def frama(df: pd.DataFrame, period: int = 16, zero_lag=False) -> pd.Series:
     closes = df["close"]
 
     if zero_lag:
-        highs = zero_lag(highs, period=period)
-        lows = zero_lag(lows, period=period)
-        closes = zero_lag(closes, period=period)
+        highs = calculate_zero_lag(highs, period=period)
+        lows = calculate_zero_lag(lows, period=period)
+        closes = calculate_zero_lag(closes, period=period)
 
     fd = pd.Series(np.nan, index=closes.index)
     for i in range(period, n):
@@ -227,7 +227,7 @@ def smma(series: pd.Series, period: int, zero_lag=False, offset=0) -> pd.Series:
         return pd.Series(index=series.index, dtype=float)
 
     if zero_lag:
-        series = zero_lag(series, period=period)
+        series = calculate_zero_lag(series, period=period)
     smma = pd.Series(np.nan, index=series.index)
     smma.iloc[period - 1] = series.iloc[:period].mean()
 
@@ -266,8 +266,8 @@ def ewo(
     prices = get_price_fn(pricemode)(dataframe)
 
     if zero_lag:
-        prices_ma1 = zero_lag(prices, period=ma1_length)
-        prices_ma2 = zero_lag(prices, period=ma2_length)
+        prices_ma1 = calculate_zero_lag(prices, period=ma1_length)
+        prices_ma2 = calculate_zero_lag(prices, period=ma2_length)
     else:
         prices_ma1 = prices
         prices_ma2 = prices