feat: AI agent, signal engine, surge detector, portfolio simulator

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

engine/signal.py

@@ -0,0 +1,45 @@
+from config import DEFAULT_WEIGHTS
+
+class SignalEngine:
+    def __init__(self):
+        self.weights = dict(DEFAULT_WEIGHTS)
+
+    def set_weights(self, weights: dict):
+        total = sum(weights.values())
+        if abs(total - 1.0) > 0.01:
+            raise ValueError(f"Weights must sum to 1.0, got {total}")
+        self.weights = weights
+
+    def compute_score(self, technical: float, news: float, social: float, ai: float) -> float:
+        score = (
+            technical * self.weights["technical"]
+            + news * self.weights["news"]
+            + social * self.weights["social"]
+            + ai * self.weights["ai"]
+        )
+        return round(score, 1)
+
+    def classify(self, score: float) -> str:
+        if score >= 70:
+            return "BUY"
+        elif score >= 40:
+            return "HOLD"
+        return "SELL"
+
+    def rank_coins(self, coins: dict[str, dict]) -> list[dict]:
+        results = []
+        for symbol, scores in coins.items():
+            composite = self.compute_score(
+                scores["technical"], scores["news"], scores["social"], scores["ai"]
+            )
+            results.append({
+                "symbol": symbol,
+                "technical": scores["technical"],
+                "news": scores["news"],
+                "social": scores["social"],
+                "ai": scores["ai"],
+                "composite": composite,
+                "signal": self.classify(composite),
+            })
+        results.sort(key=lambda x: x["composite"], reverse=True)
+        return results