Prepare source-only public release for develop.

Add cluster audit pipeline, united EA updates, brochure generators, and publication hygiene (gitignore, MT5 path desensitization, pre-upload scan). Remove tracked reports, models, and binary artifacts from the repo.

Co-authored-by: Cursor <cursoragent@cursor.com>

backtesting/MT5/indicator_utils.py

@@ -10,13 +10,14 @@ import pandas as pd
 
 
 def calculate_rsi(prices: pd.Series, period: int = 14) -> pd.Series:
-    """Calculate RSI indicator."""
+    """Calculate RSI with Wilder smoothing (matches MT5 iRSI)."""
     delta = prices.diff()
-    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
-    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
-    rs = gain / loss
-    rsi = 100 - (100 / (1 + rs))
-    return rsi
+    gain = delta.clip(lower=0)
+    loss = (-delta).clip(lower=0)
+    avg_gain = gain.ewm(alpha=1 / period, adjust=False).mean()
+    avg_loss = loss.ewm(alpha=1 / period, adjust=False).mean()
+    rs = avg_gain / avg_loss.replace(0, np.nan)
+    return 100 - (100 / (1 + rs))
 
 
 def calculate_ema(prices: pd.Series, period: int = 50) -> pd.Series:
@@ -30,13 +31,35 @@ def calculate_sma(prices: pd.Series, period: int = 50) -> pd.Series:
 
 
 def calculate_atr(df: pd.DataFrame, period: int = 14) -> pd.Series:
-    """Calculate ATR indicator."""
+    """Calculate ATR with Wilder smoothing (matches MT5 iATR)."""
     high_low = df['high'] - df['low']
     high_close = np.abs(df['high'] - df['close'].shift())
     low_close = np.abs(df['low'] - df['close'].shift())
     tr = pd.concat([high_low, high_close, low_close], axis=1).max(axis=1)
-    atr = tr.rolling(window=period).mean()
-    return atr
+    return tr.ewm(alpha=1 / period, adjust=False).mean()
+
+
+def calculate_adx(df: pd.DataFrame, period: int = 14) -> pd.Series:
+    """Calculate ADX indicator (Wilder smoothing)."""
+    return calculate_dmi(df, period)["adx"]
+
+
+def calculate_dmi(df: pd.DataFrame, period: int = 14) -> pd.DataFrame:
+    """Calculate +DI, -DI, and ADX."""
+    high = df["high"]
+    low = df["low"]
+    close = df["close"]
+    up = high.diff()
+    down = -low.diff()
+    plus_dm = up.where((up > down) & (up > 0), 0.0)
+    minus_dm = down.where((down > up) & (down > 0), 0.0)
+    tr = pd.concat([high - low, (high - close.shift()).abs(), (low - close.shift()).abs()], axis=1).max(axis=1)
+    atr = tr.ewm(alpha=1 / period, adjust=False).mean()
+    plus_di = 100 * (plus_dm.ewm(alpha=1 / period, adjust=False).mean() / atr.replace(0, np.nan))
+    minus_di = 100 * (minus_dm.ewm(alpha=1 / period, adjust=False).mean() / atr.replace(0, np.nan))
+    dx = 100 * (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, np.nan)
+    adx = dx.ewm(alpha=1 / period, adjust=False).mean()
+    return pd.DataFrame({"plus_di": plus_di, "minus_di": minus_di, "adx": adx})
 
 
 def calculate_macd(prices: pd.Series, fast: int = 12, slow: int = 26, signal: int = 9) -> pd.DataFrame: