Use closed candles for trend signals

crypto_spot_bot/market_data.py

@@ -76,6 +76,7 @@ class MarketData:
                     interval=self.settings.base_interval,
                     limit=self.settings.kline_limit,
                 )
+                candles = _closed_candles(candles, self.settings.base_interval)
                 add_indicators(candles)
                 self.candles[symbol] = candles
                 trend_candles = self.client.klines(
@@ -83,6 +84,7 @@ class MarketData:
                     interval=self.settings.trend_interval,
                     limit=self.settings.trend_kline_limit,
                 )
+                trend_candles = _closed_candles(trend_candles, self.settings.trend_interval)
                 add_indicators(trend_candles)
                 self.trend_candles[symbol] = trend_candles
                 bid, ask = self.client.orderbook_top(symbol)
@@ -173,6 +175,8 @@ class MarketData:
             start = int(row.get("start", 0))
             if start <= 0:
                 continue
+            if not _is_closed_kline_row(row, self.settings.base_interval):
+                continue
             by_timestamp[start] = Candle(
                 timestamp=start,
                 open=_float(row.get("open")),
@@ -231,3 +235,33 @@ class MarketData:
                 for symbol in self.symbols
             ],
         }
+
+
+def _closed_candles(candles: list[Candle], interval: str, now_ms: int | None = None) -> list[Candle]:
+    interval_ms = _interval_ms(interval)
+    if interval_ms <= 0:
+        return candles
+    now_ms = now_ms if now_ms is not None else int(utc_now().timestamp() * 1000)
+    return [candle for candle in candles if candle.timestamp + interval_ms <= now_ms]
+
+
+def _is_closed_kline_row(row: dict[str, Any], interval: str) -> bool:
+    confirm = row.get("confirm")
+    if isinstance(confirm, bool):
+        return confirm
+    start = int(row.get("start", 0) or 0)
+    interval_ms = _interval_ms(interval)
+    if start <= 0 or interval_ms <= 0:
+        return True
+    return start + interval_ms <= int(utc_now().timestamp() * 1000)
+
+
+def _interval_ms(interval: str) -> int:
+    normalized = str(interval).strip().upper()
+    if normalized == "D":
+        return 24 * 60 * 60 * 1000
+    if normalized == "W":
+        return 7 * 24 * 60 * 60 * 1000
+    if normalized.isdigit():
+        return int(normalized) * 60 * 1000
+    return 0