feat: paper trader su USDC (ETH_USDC-PERPETUAL), pronto per operatività reale

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/live/cerbero_client.py

@@ -51,8 +51,8 @@ class CerberoClient:
 
     # --- Account ---
 
-    def get_account_summary(self) -> dict:
-        return self._post("/mcp-deribit/tools/get_account_summary")
+    def get_account_summary(self, currency: str = "USDC") -> dict:
+        return self._post("/mcp-deribit/tools/get_account_summary", {"currency": currency})
 
     def get_positions(self, currency: str = "ETH") -> list[dict]:
         return self._post("/mcp-deribit/tools/get_positions", {"currency": currency})

src/live/paper_trader.py

@@ -12,7 +12,9 @@ from src.live.cerbero_client import CerberoClient
 from src.live.signal_engine import SignalEngine
 
 LOG_DIR = Path(__file__).resolve().parents[2] / "data" / "paper_trades"
-INSTRUMENT = "ETH-PERPETUAL"
+INSTRUMENT = "ETH_USDC-PERPETUAL"
+TRAIN_INSTRUMENT = "ETH-PERPETUAL"  # storico più lungo per training
+CURRENCY = "USDC"
 RESOLUTION = "15"
 LEVERAGE = 3
 POSITION_PCT = 0.15
@@ -60,11 +62,11 @@ class PaperTrader:
         with open(self.status_path, "w") as f:
             json.dump(status, f, indent=2)
 
-    def fetch_candles(self, days: int = LOOKBACK_DAYS) -> pd.DataFrame:
+    def fetch_candles(self, days: int = LOOKBACK_DAYS, instrument: str | None = None) -> pd.DataFrame:
         end = datetime.now(timezone.utc)
         start = end - timedelta(days=days)
         candles = self.client.get_historical(
-            INSTRUMENT,
+            instrument or TRAIN_INSTRUMENT,
             start.strftime("%Y-%m-%d"),
             end.strftime("%Y-%m-%d"),
             RESOLUTION,
@@ -77,8 +79,8 @@ class PaperTrader:
         return df
 
     def train_model(self):
-        self.log("TRAINING", {"lookback_days": TRAIN_LOOKBACK_DAYS})
-        df = self.fetch_candles(TRAIN_LOOKBACK_DAYS)
+        self.log("TRAINING", {"lookback_days": TRAIN_LOOKBACK_DAYS, "instrument": TRAIN_INSTRUMENT})
+        df = self.fetch_candles(TRAIN_LOOKBACK_DAYS, TRAIN_INSTRUMENT)
         if df.empty:
             self.log("TRAINING_FAILED", {"reason": "no data"})
             return False
@@ -93,8 +95,8 @@ class PaperTrader:
         equity = account["equity"]
 
         notional = equity * POSITION_PCT
-        amount = round(notional / price, 1)
-        amount = max(amount, 1.0)
+        amount = round(notional / price, 3)
+        amount = max(amount, 0.001)
 
         side = "buy" if direction == "buy" else "sell"
 
@@ -180,7 +182,7 @@ class PaperTrader:
 
     def run_once(self) -> str:
         """Esegui un singolo ciclo. Ritorna lo stato."""
-        df = self.fetch_candles(LOOKBACK_DAYS)
+        df = self.fetch_candles(LOOKBACK_DAYS, TRAIN_INSTRUMENT)
         if df.empty:
             return "no_data"
 
@@ -204,7 +206,8 @@ class PaperTrader:
     def run(self, retrain_hours: int = 24):
         """Loop principale."""
         print("=" * 60)
-        print(f"  PAPER TRADER — {INSTRUMENT} {RESOLUTION}m")
+        print(f"  PAPER TRADER — {INSTRUMENT} (margine {CURRENCY})")
+        print(f"  Segnali da: {TRAIN_INSTRUMENT} {RESOLUTION}m")
         print(f"  Leva: {LEVERAGE}x, Position: {POSITION_PCT*100:.0f}%, Hold: {HOLD_BARS} barre")
         print(f"  Poll: ogni {POLL_SECONDS}s")
         print(f"  Log: {self.log_path}")