Hawkes OFI + Deep LOB: two new strategies from advanced microstructure research

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":