fix(qav3): fix various features computation

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

diff --git a/quickadapter/user_data/strategies/QuickAdapterV3.py b/quickadapter/user_data/strategies/QuickAdapterV3.py
index cc4baaaf9f763a3878674727173e4c2a7e4d4be7..cea1349e805597c010df2eb68ee84fa68a586be7 100644 (file)
--- a/quickadapter/user_data/strategies/QuickAdapterV3.py
+++ b/quickadapter/user_data/strategies/QuickAdapterV3.py
@@ -167,9 +167,10 @@ class QuickAdapterV3(IStrategy):
         dataframe["%-bb_width"] = (
             dataframe["bb_upperband"] - dataframe["bb_lowerband"]
         ) / dataframe["bb_middleband"]
-        dataframe["%-ibs"] = (dataframe["close"] - dataframe["low"]) / (
-            dataframe["high"] - dataframe["low"]
-        )
+        dataframe["%-ibs"] = (
+            (dataframe["close"] - dataframe["low"])
+            / (dataframe["high"] - dataframe["low"])
+        ).fillna(0.0)
         # dataframe["ema_50"] = ta.EMA(dataframe, timeperiod=50)
         # dataframe["ema_12"] = ta.EMA(dataframe, timeperiod=12)
         # dataframe["ema_26"] = ta.EMA(dataframe, timeperiod=26)
@@ -216,8 +217,12 @@ class QuickAdapterV3(IStrategy):
             dataframe["close"], dataframe["vwap_lowerband"]
         )
         dataframe["%-body"] = dataframe["close"] - dataframe["open"]
-        dataframe["%-tail"] = (dataframe["close"] - dataframe["low"]).abs()
-        dataframe["%-wick"] = (dataframe["high"] - dataframe["close"]).abs()
+        dataframe["%-tail"] = (
+            np.minimum(dataframe["open"], dataframe["close"]) - dataframe["low"]
+        ).clip(lower=0)
+        dataframe["%-wick"] = (
+            dataframe["high"] - np.maximum(dataframe["open"], dataframe["close"])
+        ).clip(lower=0)
         pp = pivots_points(dataframe)
         dataframe["pivot"] = pp["pivot"]
         dataframe["r1"] = pp["r1"]
@@ -423,21 +428,22 @@ class QuickAdapterV3(IStrategy):
         }[extrema_smoothing]
 
 
-def top_percent_change(dataframe: DataFrame, length: int) -> float:
+def top_percent_change(dataframe: DataFrame, length: int) -> Series:
     """
-    Percentage change of the current close from the range maximum Open price
+    Percentage change of the current close relative from the range maximum open price
     :param dataframe: DataFrame The original OHLC dataframe
-    :param length: int The length to look back
+    :param length: int The period length to look back
     """
     if length == 0:
-        return (dataframe["open"] - dataframe["close"]) / dataframe["close"]
+        return ((dataframe["close"] - dataframe["open"]) / dataframe["open"]).fillna(
+            0.0
+        )
     else:
-        return (
-            dataframe["open"].rolling(length).max() - dataframe["close"]
-        ) / dataframe["close"]
+        open_max = dataframe["open"].rolling(length).max()
+        return ((dataframe["close"] - open_max) / open_max).fillna(0.0)
 
 
-def chaikin_mf(df: DataFrame, periods=20):
+def chaikin_mf(df: DataFrame, periods=20) -> Series:
     close = df["close"]
     low = df["low"]
     high = df["high"]
@@ -445,12 +451,12 @@ def chaikin_mf(df: DataFrame, periods=20):
     mfv = ((close - low) - (high - close)) / (high - low)
     mfv = mfv.fillna(0.0)
     mfv *= volume
-    cmf = mfv.rolling(periods).sum() / volume.rolling(periods).sum()
+    cmf = mfv.rolling(window=periods).sum() / volume.rolling(window=periods).sum()
     return Series(cmf, name="cmf")
 
 
 # VWAP bands
-def VWAPB(dataframe: DataFrame, window=20, num_of_std=1):
+def VWAPB(dataframe: DataFrame, window=20, num_of_std=1) -> tuple:
     vwap = qtpylib.rolling_vwap(dataframe, window=window)
     rolling_std = vwap.rolling(window=window).std()
     vwap_low = vwap - (rolling_std * num_of_std)
@@ -458,14 +464,20 @@ def VWAPB(dataframe: DataFrame, window=20, num_of_std=1):
     return vwap_low, vwap, vwap_high
 
 
-def EWO(dataframe: DataFrame, sma1_length=5, sma2_length=35):
-    sma1 = ta.EMA(dataframe, timeperiod=sma1_length)
-    sma2 = ta.EMA(dataframe, timeperiod=sma2_length)
-    smadif = (sma1 - sma2) / dataframe["close"] * 100
+def EWO(
+    dataframe: DataFrame, sma1_length=5, sma2_length=34, normalize=False
+) -> DataFrame:
+    sma1 = ta.SMA(dataframe, timeperiod=sma1_length)
+    sma2 = ta.SMA(dataframe, timeperiod=sma2_length)
+    smadif = sma1 - sma2
+    if normalize:
+        smadif = ((smadif / dataframe["close"]) * 100).fillna(
+            0.0
+        )  # Optional normalization
     return smadif
 
 
-def zlewma(series: Series, length: int):
+def zlewma(series: Series, length: int) -> Series:
     """
     Calculate the ZLEWMA (Zero Lag Exponential Weighted Moving Average) of a series.
     """
@@ -474,5 +486,5 @@ def zlewma(series: Series, length: int):
     return series.ewm(span=length).mean()
 
 
-def get_distance(p1: float, p2: float):
+def get_distance(p1: Series | float, p2: Series | float) -> Series | float:
     return abs((p1) - (p2))