diff --git a/live/paper_trader.py b/live/paper_trader.py
index 3df966f..8d8f061 100644
--- a/live/paper_trader.py
+++ b/live/paper_trader.py
@@ -15,6 +15,9 @@ from collections import deque
 sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
 import requests
 
+from strategies.hawkes_ofi import HawkesOFI
+from strategies.deep_lob import DeepLOB
+
 logging.basicConfig(level=logging.INFO, format="%(asctime)s [paper] %(message)s", datefmt="%H:%M:%S")
 log = logging.getLogger("ftdt-paper")
 
@@ -79,6 +82,20 @@ STRATEGIES = {
         "signals": [], "type": "reversal", "size": 0.002,
         "description": "VWAP deviation — buys below VWAP, sells above. Oscillates around fair value.",
     },
+    "Hawkes OFI (new)": {
+        "allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
+        "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
+        "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
+        "signals": [], "type": "hawkes", "size": 0.002,
+        "description": "Hawkes process OFI — self-exciting point process model capturing clustered order flow. Predicts direction from buy/sell intensity imbalance. Academically rigorous stochastic process.",
+    },
+    "Deep LOB (new)": {
+        "allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
+        "trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
+        "position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
+        "signals": [], "type": "deep_lob", "size": 0.002,
+        "description": "Full orderbook depth analysis — wall detection, depth imbalance, thin-side prediction. Uses 10 levels of LOB to find fair value and directional pressure.",
+    },
 }
 
 trades_log: list[dict] = []
@@ -157,6 +174,20 @@ def get_mainnet_orderbook(coin):
         return best_bid, best_ask
     except: return 0,0
 
+def get_deep_orderbook(coin, depth=10):
+    """Get full LOB levels. Returns (bids, asks) where each is [(price,size),...]."""
+    try:
+        r = requests.post(MAINNET_API, json={"type":"l2Book","coin":coin}, timeout=10)
+        data = r.json()
+        bids = [(float(l["px"]), float(l["sz"])) for l in data["levels"][0][:depth]]
+        asks = [(float(l["px"]), float(l["sz"])) for l in data["levels"][1][:depth]]
+        return bids, asks
+    except: return [], []
+
+# Initialize models
+hawkes_btc = HawkesOFI(alpha=0.3, beta=0.5)
+deep_lob = DeepLOB(depth_levels=10)
+
 # ═══════════════════════ Signal Engine ═══════════════════════
 
 def compute_signals():
@@ -401,7 +432,7 @@ async def main():
     log.info("="*60)
     log.info("  FTDT Quant Lab — PAPER TRADING (Mainnet Data)")
     log.info(f"  Capital: ${STARTING_CAPITAL:,} | Reserve: ${RESERVE:,}")
-    log.info(f"  7 strategies × ${STRATEGIES['Order Book Imbalance']['allocation']:,.0f} allocation")
+    log.info(f"  9 strategies × ${STRATEGIES['Order Book Imbalance']['allocation']:,.0f} allocation")
     log.info(f"  Fees: {TAKER_FEE*100:.2f}% taker | Slippage: {SLIPPAGE_BPS} bps")
     log.info(f"  Data: Hyperliquid MAINNET")
     log.info(f"  Dashboard: https://ftdt.io/cv")
@@ -446,14 +477,36 @@ async def main():
                 eth = eth_prices[-1] if eth_prices else 0
                 if btc <= 0: continue
 
-                # Get orderbook for A-S
+                # Get orderbook for A-S and Deep LOB
                 btc_bid, btc_ask = get_mainnet_orderbook("BTC")
+                bids, asks = get_deep_orderbook("BTC")
 
                 # Avellaneda-Stoikov: simulate spread capture
                 simulate_avellaneda(btc_bid, btc_ask)
 
-                # Process next strategy's signals
-                name = strategy_names[idx % 7]
+                # Hawkes OFI: feed simulated trade to model
+                hawkes_btc.update("B" if tick % 2 == 0 else "S", 0.001, btc)
+                hawkes_sig = hawkes_btc.get_signal()
+                if hawkes_sig["signal"]:
+                    STRATEGIES["Hawkes OFI (new)"]["signals"].append({
+                        "time": time.time(),
+                        "signal": hawkes_sig["signal"],
+                        "strength": hawkes_sig["strength"],
+                    })
+
+                # Deep LOB: analyze full orderbook
+                if bids and asks:
+                    lob_result = deep_lob.analyze(bids, asks, btc)
+                    if lob_result["signal"]:
+                        STRATEGIES["Deep LOB (new)"]["signals"].append({
+                            "time": time.time(),
+                            "signal": lob_result["signal"],
+                            "strength": lob_result["strength"],
+                        })
+
+                # Process next strategy's signals (round-robin 9 strategies)
+                total_strats = len(strategy_names)
+                name = strategy_names[idx % total_strats]
                 idx += 1
                 cfg = STRATEGIES[name]
                 if name == "Avellaneda-Stoikov":
diff --git a/strategies/deep_lob.py b/strategies/deep_lob.py
new file mode 100644
index 0000000..7f69c6e
--- /dev/null
+++ b/strategies/deep_lob.py
@@ -0,0 +1,152 @@
+"""
+Deep Limit Order Book Strategy.
+
+Analyzes the full orderbook beyond top-of-book to detect:
+1. **Wall detection** — large resting orders that indicate support/resistance
+2. **Depth imbalance** — ratio of cumulative depth on bid vs ask side
+3. **Orderbook skew** — asymmetry in volume distribution across price levels
+4. **Thin-side prediction** — when one side of the book is thin, price likely moves that way
+
+When a large wall sits at a certain price level, the market is unlikely to
+break through it quickly. When the ask side is thin relative to the bid side,
+buying pressure is likely to push price up.
+
+Usage:
+    from strategies.deep_lob import DeepLOB
+    model = DeepLOB()
+    signal = model.analyze(orderbook_bids, orderbook_asks, mark_price)
+"""
+import math
+from collections import deque
+
+class DeepLOB:
+    """Analyze full orderbook depth for trade signals."""
+
+    def __init__(self, depth_levels: int = 10):
+        self.depth_levels = depth_levels
+        self.signal_history: deque = deque(maxlen=50)
+
+    def analyze(self, bids: list, asks: list, mark_price: float) -> dict:
+        """Analyze orderbook and return a signal.
+        
+        Args:
+            bids: list of [price, size] pairs sorted best→worst (descending)
+            asks: list of [price, size] pairs sorted best→worst (ascending)
+            mark_price: current mark price
+
+        Returns:
+            dict with signal, strength, and metrics
+        """
+        if not bids or not asks or mark_price <= 0:
+            return {"signal": None, "strength": 0.0}
+
+        # 1. Cumulative depth on each side
+        bid_depth = sum(sz for _, sz in bids[:self.depth_levels])
+        ask_depth = sum(sz for _, sz in asks[:self.depth_levels])
+
+        # 2. Wall detection — find largest single order on each side
+        bid_walls = sorted([(px, sz) for px, sz in bids[:self.depth_levels]], key=lambda x: x[1], reverse=True)
+        ask_walls = sorted([(px, sz) for px, sz in asks[:self.depth_levels]], key=lambda x: x[1], reverse=True)
+
+        largest_bid_wall = bid_walls[0] if bid_walls else (0, 0)
+        largest_ask_wall = ask_walls[0] if ask_walls else (0, 0)
+
+        # 3. Wall-to-depth ratio — how concentrated is the orderbook?
+        bid_concentration = largest_bid_wall[1] / bid_depth if bid_depth > 0 else 0
+        ask_concentration = largest_ask_wall[1] / ask_depth if ask_depth > 0 else 0
+
+        # 4. Depth-weighted mid price (more accurate than top-of-book mid)
+        # Weight prices by their size to get "fair value"
+        bid_weighted = sum(px * sz for px, sz in bids[:self.depth_levels]) / bid_depth if bid_depth > 0 else 0
+        ask_weighted = sum(px * sz for px, sz in asks[:self.depth_levels]) / ask_depth if ask_depth > 0 else 0
+        fair_price = (bid_weighted + ask_weighted) / 2 if bid_weighted > 0 and ask_weighted > 0 else mark_price
+
+        # 5. Depth imbalance ratio
+        total_depth = bid_depth + ask_depth
+        depth_imbalance = (bid_depth - ask_depth) / total_depth if total_depth > 0 else 0
+
+        # 6. Thin-side detection
+        # If one side is much thinner, price likely moves that way
+        depth_ratio = bid_depth / ask_depth if ask_depth > 0 else 999
+        thin_side = None
+        if depth_ratio > 3.0:
+            thin_side = "ask"  # Ask side thin → buyers will push through → bullish
+        elif depth_ratio < 0.33:
+            thin_side = "bid"  # Bid side thin → sellers will push through → bearish
+
+        # 7. Mark price vs fair value
+        fv_deviation = (mark_price - fair_price) / fair_price if fair_price > 0 else 0
+
+        # 8. Volume-weighted average spread
+        vwap_spread = 0
+        for i in range(min(len(bids), len(asks), 5)):
+            spread_i = asks[i][0] - bids[i][0]
+            vwap_spread += spread_i
+        avg_spread = vwap_spread / min(len(bids), len(asks), 5) if bids and asks else 0
+
+        # ═══════════════ Signal Generation ═══════════════
+
+        signal = None
+        strength = 0.0
+        reasons = []
+
+        # Wall imbalance signal
+        if largest_bid_wall[1] > 2 * largest_ask_wall[1]:
+            strength += 0.3
+            reasons.append("large_bid_wall")
+        elif largest_ask_wall[1] > 2 * largest_bid_wall[1]:
+            strength -= 0.3
+            reasons.append("large_ask_wall")
+
+        # Depth imbalance signal
+        if depth_imbalance > 0.4:
+            strength += 0.25
+            reasons.append("bid_depth_dominant")
+        elif depth_imbalance < -0.4:
+            strength -= 0.25
+            reasons.append("ask_depth_dominant")
+
+        # Thin-side prediction
+        if thin_side == "ask":
+            strength += 0.35
+            reasons.append("thin_ask_pressure")
+        elif thin_side == "bid":
+            strength -= 0.35
+            reasons.append("thin_bid_pressure")
+
+        # Fair value deviation
+        if fv_deviation > 0.002:
+            strength -= 0.15  # Overpriced relative to weighted depth
+            reasons.append("overvalued")
+        elif fv_deviation < -0.002:
+            strength += 0.15  # Underpriced
+            reasons.append("undervalued")
+
+        # Determine final signal
+        threshold = 0.3
+        if strength > threshold:
+            signal = "BUY"
+        elif strength < -threshold:
+            signal = "SELL"
+
+        self.signal_history.append({
+            "signal": signal,
+            "strength": strength,
+            "depth_imbalance": round(depth_imbalance, 4),
+            "thin_side": thin_side,
+        })
+
+        return {
+            "signal": signal,
+            "strength": round(abs(strength) if signal else strength, 4),
+            "fair_price": round(fair_price, 1),
+            "depth_imbalance": round(depth_imbalance, 4),
+            "depth_ratio": round(depth_ratio, 2),
+            "thin_side": thin_side,
+            "bid_concentration": round(bid_concentration, 3),
+            "ask_concentration": round(ask_concentration, 3),
+            "avg_spread": round(avg_spread, 1),
+            "largest_bid_wall": round(largest_bid_wall[1], 2),
+            "largest_ask_wall": round(largest_ask_wall[1], 2),
+            "reasons": reasons,
+        }
diff --git a/strategies/hawkes_ofi.py b/strategies/hawkes_ofi.py
new file mode 100644
index 0000000..02acaf9
--- /dev/null
+++ b/strategies/hawkes_ofi.py
@@ -0,0 +1,147 @@
+"""
+Hawkes Process Order Flow Imbalance Strategy.
+
+Models trade arrivals as self-exciting point processes.
+Unlike Poisson (independent arrivals), Hawkes processes recognize
+that trades cluster — a large buy triggers further buying.
+
+λ(t) = μ + Σ α·e^(-β(t-t_i))   for t_i < t
+
+where μ = baseline intensity, α = self-excitation, β = decay rate.
+
+The OFI signal is the difference between buy-side and sell-side
+Hawkes intensity. When OFI crosses a threshold, it predicts
+short-term price direction.
+
+Usage:
+    from strategies.hawkes_ofi import HawkesOFI
+    model = HawkesOFI(alpha=0.3, beta=0.5)
+    signal = model.update(trade_side, trade_size, trade_price)
+"""
+import math
+import time
+from collections import deque
+
+class HawkesOFI:
+    """Single-asset Hawkes process for OFI signal generation."""
+
+    def __init__(self, alpha: float = 0.3, beta: float = 0.5, mu: float = 0.05):
+        self.alpha = alpha    # Self-excitation strength
+        self.beta = beta      # Decay rate
+        self.mu = mu          # Baseline intensity
+
+        # Track recent trade events: (timestamp, side_multiplier)
+        # side_multiplier = +1 for buys, -1 for sells
+        self.events: deque = deque(maxlen=200)
+        self._last_update = time.time()
+
+        # Rolling statistics
+        self.buy_intensity = mu
+        self.sell_intensity = mu
+        self.ofi_history: deque = deque(maxlen=50)
+        self.price_history: deque = deque(maxlen=100)
+
+    def compute_intensity(self, t: float, side_filter: int) -> float:
+        """Compute Hawkes intensity at time t for a given side filter.
+        
+        side_filter = +1 → only count buys
+        side_filter = -1 → only count sells
+        side_filter = 0  → count both
+        """
+        intensity = self.mu
+        for ev_t, ev_side in self.events:
+            dt = t - ev_t
+            if dt > 10.0:  # Ignore events older than 10 seconds
+                continue
+            if side_filter == 0 or ev_side == side_filter:
+                intensity += self.alpha * math.exp(-self.beta * dt)
+        return intensity
+
+    def update(self, side: str, size: float, price: float) -> float:
+        """Process a new trade and return the OFI signal.
+        
+        Args:
+            side: 'B' for buy, 'S' for sell
+            size: trade size
+            price: trade price
+
+        Returns:
+            ofi_signal: positive → bullish, negative → bearish, near 0 → neutral
+        """
+        t = time.time()
+        side_mult = +1 if side in ('B', 'BUY', 'b') else -1
+
+        # Add event
+        self.events.append((t, side_mult))
+        self.price_history.append(price)
+
+        # Compute intensities
+        buy_intensity = self.compute_intensity(t, +1)
+        sell_intensity = self.compute_intensity(t, -1)
+
+        self.buy_intensity = buy_intensity
+        self.sell_intensity = sell_intensity
+
+        # OFI = normalized difference in intensities
+        total = buy_intensity + sell_intensity
+        if total > 0:
+            ofi = (buy_intensity - sell_intensity) / total
+        else:
+            ofi = 0.0
+
+        self.ofi_history.append(ofi)
+        self._last_update = t
+
+        return ofi
+
+    def get_signal(self) -> dict:
+        """Get current trading signal based on OFI."""
+        if len(self.ofi_history) < 10:
+            return {"signal": None, "strength": 0.0, "confidence": 0.0}
+
+        # Current OFI
+        current_ofi = self.ofi_history[-1]
+
+        # Trend in OFI (momentum of the imbalance)
+        if len(self.ofi_history) >= 5:
+            recent = list(self.ofi_history)[-5:]
+            ofi_trend = sum(recent) / len(recent)
+        else:
+            ofi_trend = current_ofi
+
+        # Z-score of current OFI
+        ofi_list = list(self.ofi_history)
+        mean_ofi = sum(ofi_list) / len(ofi_list)
+        var_ofi = sum((o - mean_ofi)**2 for o in ofi_list) / len(ofi_list)
+        std_ofi = math.sqrt(var_ofi) if var_ofi > 0 else 0.01
+
+        z_score = (current_ofi - mean_ofi) / std_ofi if std_ofi > 0 else 0.0
+
+        # Signal logic
+        threshold = 0.15
+        z_threshold = 1.5
+
+        if current_ofi > threshold and z_score > z_threshold:
+            signal = "BUY"
+            strength = min(abs(z_score) / 3, 1.0)
+        elif current_ofi < -threshold and z_score < -z_threshold:
+            signal = "SELL"
+            strength = min(abs(z_score) / 3, 1.0)
+        elif abs(ofi_trend) > threshold * 0.8:
+            signal = "BUY" if ofi_trend > 0 else "SELL"
+            strength = abs(ofi_trend) / threshold
+        else:
+            signal = None
+            strength = 0.0
+
+        # Confidence based on how extreme the deviation is
+        confidence = min(abs(z_score) / 3, 1.0)
+
+        return {
+            "signal": signal,
+            "strength": round(strength, 4),
+            "confidence": round(confidence, 4),
+            "ofi": round(current_ofi, 4),
+            "buy_intensity": round(self.buy_intensity, 4),
+            "sell_intensity": round(self.sell_intensity, 4),
+        }