Multi-Asset Regime-Switching ScalperDynamic Parameter Selection via Empirical Walk-Forward Analysis

1def aggregate_signals(self, symbol: str, df: pd.DataFrame):
2    ff_signal     = self.elite_ff.generate_signal(df)      # 70%
3    static_signal = self.static_pivot.generate_signal(df)  # 30%
4
5    if ff_signal and static_signal:
6        if ff_signal.direction != static_signal.direction:
7            self._log_conflict(symbol, ff_signal, static_signal)
8            return self._build_order(ff_signal, allocation=0.70)
9        return self._blend_signals(ff_signal, static_signal)
10
11    if ff_signal:
12        return self._build_order(ff_signal, allocation=0.70)
13    if static_signal:
14        return self._build_order(static_signal, allocation=0.30)
15    return None

1class KlineBuffer:
2    def __init__(self, symbols: List[str], window: int = 1100):
3        self._buffers = {s: deque(maxlen=window) for s in symbols}
4        self._locks   = {s: asyncio.Lock() for s in symbols}
5        self._seeded  = {s: False for s in symbols}
6
7    async def seed_from_rest(self, symbol: str, exchange) -> None:
8        """One-time REST fetch. Blocks loop until complete."""
9        bars = await exchange.fetch_ohlcv(symbol, "1m", limit=1100)
10        async with self._locks[symbol]:
11            self._buffers[symbol].extend(bars)
12            self._seeded[symbol] = True
13
14    async def stream(self) -> None:
15        """Long-running WebSocket. Exponential backoff on disconnect."""
16        backoff = 1
17        while True:
18            try:
19                async with websockets.connect(self._ws_url()) as ws:
20                    backoff = 1
21                    async for msg in ws:
22                        await self._handle_kline(json.loads(msg))
23            except Exception:
24                await asyncio.sleep(backoff)
25                backoff = min(backoff * 2, 60)
26
27    async def get(self, symbol: str) -> pd.DataFrame:
28        """O(1) read under async lock."""
29        async with self._locks[symbol]:
30            return pd.DataFrame(list(self._buffers[symbol]),
31                                columns=["ts","open","high","low","close","vol"])