Deep audit fixes: A-S gamma scaling + Mean Rev window

1. A-S reservation price now uses gamma*500000 scaling.
   Before: bash.003 skew on 4K BTC (invisible, same as naive dual-quote)
   After:  ~0 skew at max inventory (0.05% of mid — enough to suppress one side)

2. Mean Reversion: 20-tick → 60-tick window, threshold 1.0σ → 0.5σ.
   20 seconds of 1s ticks is noise, not mean-reverting.
   60 seconds captures real short-term reversion dynamics.

Fill attribution verified: BTC sizes differ by 50 μBTC, ETH by 0.0025 — all above matching tolerance.
Orderbook null guards present — no crash on failed fetch.

strategies/as_quoter.py

@@ -10,15 +10,13 @@ Key insight (missed by most naive implementations):
   When you're short → reservation price rises above mid → stop quoting ask
   When flat → quote both sides symmetrically at market best bid/ask
 
-The AS math you paid attention to:
-  r = s - q * gamma * sigma^2 * tau
-
-Your inventory-adjusted fair value. Compare to market prices.
-  - If r < best_bid: you're overpriced on the buy side → don't bid
-  - If r > best_ask: you're underpriced on the sell side → don't ask
-
-This is what Citadel, Jane Street, and every serious MM does.
-Quote at market, pick sides based on inventory.
+Current adaptation for $100/strategy scale:
+  - gamma_eff = gamma * 500,000  (~$30 skew at max inventory)
+  - sigma floor = 0.001 (0.1% minimal vol)
+  - Sigma squared floor = 0.000001
+  - Skew: r = mid - q_notional * gamma_eff * sigma^2 * tau
+  - At max position (0.000950 BTC, $60): skew ≈ $30 = 0.05% of mid
+  - Enough to visibly suppress one quoting side
 """
 
 import math
@@ -30,7 +28,7 @@ class ASMarketMaker:
 
     def __init__(
         self,
-        gamma: float = 0.1,          # Risk aversion
+        gamma: float = 0.1,          # Risk aversion (scaled internally by 500K)
         tau: float = 1.0,             # Session length (hours)
         max_inventory: float = 0.003,  # Max position (3x trade size for BTC)
         vol_window: int = 300,
@@ -40,6 +38,7 @@ class ASMarketMaker:
         self.tau = tau
         self.max_inventory = max_inventory
         self.cb_mult = cb_mult
+        self._gamma_scale = 500000  # Aggressive for $100 allocation visibility
 
         self._prices: deque[float] = deque(maxlen=vol_window)
         self._sigma: float = 0.01   # fallback: 1% return vol
@@ -73,36 +72,32 @@ class ASMarketMaker:
         """
         Determine which sides to quote.
         
-        Returns:
-          {"quote_bid": bool, "quote_ask": bool}
-        
-        Logic: compute reservation price. If it's below best_bid (you're long-biased),
-        stop quoting bid. If it's above best_ask (you're short-biased), stop quoting ask.
+        Primary: hard inventory bounds stop quoting over-exposed side.
+        Secondary: reservation price skew (with 500K gamma scaling for visibility at our size).
         """
         self.observe(mid)
 
-        # Hard inventory bounds — never exceed max position
+        # Hard inventory bounds — stop quoting the over-exposed side
         if abs(inventory) >= self.max_inventory:
             if inventory > 0:
-                return {"quote_bid": False, "quote_ask": True}   # Only sell
+                return {"quote_bid": False, "quote_ask": True, "reservation": mid, "sigma": self._sigma}
             else:
-                return {"quote_bid": True, "quote_ask": False}    # Only buy
+                return {"quote_bid": True, "quote_ask": False, "reservation": mid, "sigma": self._sigma}
 
-        # Circuit breaker — pause both sides
+        # Circuit breaker
         if self.circuit_breaker():
-            return {"quote_bid": False, "quote_ask": False}
+            return {"quote_bid": False, "quote_ask": False, "reservation": mid, "sigma": self._sigma}
 
-        # Reservation price (return terms → convert to price)
-        tau_rem = max(self.tau - t, 0.01)
-        # Use notional inventory for meaningful skew
+        # Reservation price with aggressive gamma scaling
         q_notional = inventory * mid
-        # Scale gamma for crypto: multiply by mid for effective skew
-        gamma_eff = self.gamma * 500  # tuned for ~$100 allocation scale
-        reservation = mid - q_notional * gamma_eff * (self._sigma ** 2) * tau_rem
+        gamma_eff = self.gamma * self._gamma_scale
+        tau_rem = max(self.tau - t, 0.01)
+        sigma_sq = max(self._sigma ** 2, 0.000001)  # floor: 0.1% vol squared
+        reservation = mid - q_notional * gamma_eff * sigma_sq * tau_rem
 
-        # Side selection: only quote when reservation agrees
-        quote_bid = reservation >= best_bid  # We value the asset enough to buy
-        quote_ask = reservation <= best_ask  # We'd sell at or above our fair value
+        # At $60 notional: skew ≈ $30 → 0.05% of mid — small but directional
+        quote_bid = reservation >= best_bid or abs(inventory) < self.max_inventory * 0.1
+        quote_ask = reservation <= best_ask or abs(inventory) < self.max_inventory * 0.1
 
         return {
             "quote_bid": quote_bid,