feat: proper Grid MM, Composite MM, Hurst/VPIN, Iceberg, A-S strategies

New strategies (strategies/nt/):
- GridMMNT: symmetric limit order grid around mid-price, captures spread from
  oscillation. Simulates fills from candle high/low. Rebuilds grid every 20 bars.
- CompositeMMNT: weighted ensemble of OBI (30%) + A-S inventory skew (40%) +
  Hurst/VPIN (30%). Votes: +1 long, -1 short, 0 neutral. Entry |score| > 0.5.
- IcebergNT: volume spike detection for whale accumulation. Dual-mode:
  candle proxy (volume > avg*2.5, >= 3 consecutive same-direction) and
  L2 wall detection (single level > avg*3). Exit on stop-loss/time/spike-fade.

Fixed strategies:
- Hurst/VPIN VBT: added proper VPIN proxy from candle volume (buy_vol when close
  > open, sell_vol when close < open). 50-bar rolling VPIN window. Signal:
  H>0.55 AND VPIN>0.25 AND |direction|>0.05. Exit: H<0.45 or direction flips.
- Hurst/VPIN paper trader: added HurstVPINLive integration (was missing entirely)
- A-S VBT: replaced placeholder spread filter with proper A-S simulation using
  reservation price formula (mid - q*gamma*sigma^2*tau), inventory tracking
- A-S NT formula: fixed to standard: mid - q*gamma*sigma^2*tau (was scaled by
  notional and gamma_scale improperly)
- Iceberg VBT: new volume spike detection replacing the old trend proxy

Registry: all 7 strategies now ✅ (pairs, hurst_vpin, as_mm, obi, grid_mm,
         composite_mm, iceberg)

VBT backtest results (500 BTC 1h bars):
  pairs:       -2.81%  13 trades   38% win
  hurst_vpin:  -0.77%   1 trade    (VPIN now active, very selective)
  as_mm:       -16.38%  73 trades  29% win
  obi:         -7.19%  15 trades    7% win
  grid_mm:     -4.79%  22 trades  33% win
  iceberg:     0 trades (threshold strict for 1h BTC data)

strategies/nt/as_mm_nt.py

@@ -112,10 +112,8 @@ class ASMarketMakingNT(BaseHlStrategy):
 
         # Reservation price from A-S formula
         q_notional = self._inventory * mid
-        gamma_eff = self._gamma * self._gamma_scale
-        tau_rem = max(self._tau - t, 0.01)
         sigma_sq = max(self._sigma ** 2, 0.000001)
-        reservation = mid - q_notional * gamma_eff * sigma_sq * tau_rem
+        reservation = mid - self._inventory * self._gamma * sigma_sq * self._tau
 
         # Quote sides based on reservation vs market
         quote_bid = reservation >= best_bid or abs(self._inventory) < self._max_inventory * 0.1