diff --git a/templates/xauusd-atr-breakout/README.md b/templates/xauusd-atr-breakout/README.md
index ebb2108..89e6846 100644
--- a/templates/xauusd-atr-breakout/README.md
+++ b/templates/xauusd-atr-breakout/README.md
@@ -8,5 +8,6 @@ The default gate is intentionally responsive so PPE produces more events than a
 
 - `breakout_window`: `12`
 - `atr_mult`: `0.05`
+- no runtime package dependencies
 
 If it overtrades, raise `atr_mult` first. If it is still too quiet, shorten `breakout_window`.
diff --git a/templates/xauusd-atr-breakout/quant.config.json b/templates/xauusd-atr-breakout/quant.config.json
index f612ef0..d185a13 100644
--- a/templates/xauusd-atr-breakout/quant.config.json
+++ b/templates/xauusd-atr-breakout/quant.config.json
@@ -10,14 +10,8 @@
     "breakout_window": 12,
     "atr_mult": 0.05
   },
-  "training_requirements": [
-    "numpy",
-    "pandas"
-  ],
-  "inference_requirements": [
-    "numpy",
-    "pandas"
-  ],
+  "training_requirements": [],
+  "inference_requirements": [],
   "symbol": "XAUUSD",
   "description": "XAUUSD ATR breakout rule baseline",
   "disclaimer": "Educational template only. Not financial advice."
diff --git a/templates/xauusd-atr-breakout/strategy.py b/templates/xauusd-atr-breakout/strategy.py
index 7d322b9..b274434 100644
--- a/templates/xauusd-atr-breakout/strategy.py
+++ b/templates/xauusd-atr-breakout/strategy.py
@@ -1,23 +1,47 @@
-import numpy as np
-import pandas as pd
+def _clean_candles(candles):
+    cleaned = []
+    for candle in candles:
+        try:
+            cleaned.append({
+                "open": float(candle[0]),
+                "high": float(candle[1]),
+                "low": float(candle[2]),
+                "close": float(candle[3]),
+                "volume": float(candle[4]) if len(candle) > 4 else 1.0,
+            })
+        except Exception:
+            continue
+    return cleaned
 
 
-def _df(candles):
-    df = pd.DataFrame(candles, columns=["open", "high", "low", "close", "volume"])
-    for col in df.columns:
-        df[col] = pd.to_numeric(df[col], errors="coerce")
-    return df.dropna().reset_index(drop=True)
-
-
-def _atr(df, n):
-    prev = df["close"].shift()
-    tr = pd.concat([(df["high"] - df["low"]), (df["high"] - prev).abs(), (df["low"] - prev).abs()], axis=1).max(axis=1)
-    return tr.ewm(span=n, adjust=False).mean()
+def _atr(candles, n):
+    if len(candles) < 2:
+        return 0.0
+    alpha = 2.0 / (n + 1.0)
+    value = None
+    prev_close = candles[0]["close"]
+    for candle in candles[1:]:
+        tr = max(
+            candle["high"] - candle["low"],
+            abs(candle["high"] - prev_close),
+            abs(candle["low"] - prev_close),
+        )
+        value = tr if value is None else value + alpha * (tr - value)
+        prev_close = candle["close"]
+    return float(value or 0.0)
 
 
 def train(data, config):
     params = config.get("parameters", {})
-    return {"params": params, "name": "xauusd_atr_breakout"}, {"training_bars": int(len(data)), "model": "rule_baseline"}
+    return {
+        "params": {
+            "lookback": int(params.get("lookback", 64)),
+            "atr_window": int(params.get("atr_window", 14)),
+            "breakout_window": int(params.get("breakout_window", 12)),
+            "atr_mult": float(params.get("atr_mult", 0.05)),
+        },
+        "name": "xauusd_atr_breakout",
+    }, {"training_bars": int(len(data)), "model": "edge_rule_baseline"}
 
 
 def predict(model, market_data, config):
@@ -26,16 +50,44 @@ def predict(model, market_data, config):
     atr_window = int(params.get("atr_window", 14))
     breakout_window = int(params.get("breakout_window", 12))
     atr_mult = float(params.get("atr_mult", 0.05))
-    candles = market_data.get("candles", [])
+    candles = _clean_candles(market_data.get("candles", []))
+
     if len(candles) < lookback:
-        return {"signal": "HOLD", "confidence": 0.0, "metadata": {"reason": "not_enough_candles"}}
-    df = _df(candles[-lookback:])
-    atr = float(_atr(df, atr_window).iloc[-1])
-    close = float(df["close"].iloc[-1])
-    high = float(df["high"].iloc[-breakout_window:-1].max())
-    low = float(df["low"].iloc[-breakout_window:-1].min())
-    if close > high + atr * atr_mult:
-        return {"signal": "UP", "confidence": 0.64, "metadata": {"breakout": "high", "atr": atr}}
-    if close < low - atr * atr_mult:
-        return {"signal": "DOWN", "confidence": 0.64, "metadata": {"breakout": "low", "atr": atr}}
-    return {"signal": "HOLD", "confidence": 0.2, "metadata": {"high": high, "low": low, "atr": atr}}
+        return {
+            "signal": "HOLD",
+            "confidence": 0.0,
+            "metadata": {"reason": "not_enough_candles", "got": len(candles), "need": lookback},
+        }
+
+    window = candles[-lookback:]
+    previous = window[-breakout_window - 1:-1]
+    if len(previous) < breakout_window:
+        return {"signal": "HOLD", "confidence": 0.0, "metadata": {"reason": "not_enough_breakout_window"}}
+
+    atr = _atr(window, atr_window)
+    close = window[-1]["close"]
+    high = max(candle["high"] for candle in previous)
+    low = min(candle["low"] for candle in previous)
+    upper = high + atr * atr_mult
+    lower = low - atr * atr_mult
+
+    if close > upper:
+        edge = (close - upper) / max(atr, 1e-9)
+        return {
+            "signal": "UP",
+            "confidence": round(min(0.55 + edge, 0.92), 4),
+            "metadata": {"breakout": "high", "atr": round(atr, 6), "upper": round(upper, 6), "close": round(close, 6)},
+        }
+    if close < lower:
+        edge = (lower - close) / max(atr, 1e-9)
+        return {
+            "signal": "DOWN",
+            "confidence": round(min(0.55 + edge, 0.92), 4),
+            "metadata": {"breakout": "low", "atr": round(atr, 6), "lower": round(lower, 6), "close": round(close, 6)},
+        }
+
+    return {
+        "signal": "HOLD",
+        "confidence": 0.2,
+        "metadata": {"high": round(high, 6), "low": round(low, 6), "atr": round(atr, 6), "upper": round(upper, 6), "lower": round(lower, 6)},
+    }