From 9768bf80cc36d875e990fb38b2ce429bbfb2281d Mon Sep 17 00:00:00 2001
From: ramseshk <ramseshk@users.noreply.github.com>
Date: Tue, 4 Aug 2026 06:19:19 +0000
Subject: [PATCH] =?UTF-8?q?Cartea-Jaimungal,=20Queue=20Imbalance,=20Gu?=
 =?UTF-8?q?=C3=A9ant=20MM:=203=20new=20quant=20finance=20strategies=20+=20?=
 =?UTF-8?q?backtests?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 backtests/results/cartea_20260804-061758.json | 3605 +++++++++++++++++
 backtests/results/gueant_20260804-061758.json | 3605 +++++++++++++++++
 .../results/queue_imb_20260804-061758.json    | 3605 +++++++++++++++++
 backtests/run.py                              |    3 +
 live/paper_trader.py                          |  574 ---
 strategies/cartea_jaimungal.py                |  148 +
 strategies/gueant.py                          |  175 +
 strategies/queue_imbalance.py                 |  170 +
 8 files changed, 11311 insertions(+), 574 deletions(-)
 create mode 100644 backtests/results/cartea_20260804-061758.json
 create mode 100644 backtests/results/gueant_20260804-061758.json
 create mode 100644 backtests/results/queue_imb_20260804-061758.json
 create mode 100644 strategies/cartea_jaimungal.py
 create mode 100644 strategies/gueant.py
 create mode 100644 strategies/queue_imbalance.py

diff --git a/backtests/results/cartea_20260804-061758.json b/backtests/results/cartea_20260804-061758.json
new file mode 100644
index 0000000..b27f5e9
--- /dev/null
+++ b/backtests/results/cartea_20260804-061758.json
@@ -0,0 +1,3605 @@
+{
+  "strategy": "Cartea-Jaimungal",
+  "strategy_key": "cartea",
+  "description": "Stochastic control HFT \u2014 HJB equation with alpha + inventory",
+  "allocation": 100.0,
+  "start_time": "2026-07-05T06:17:58.667177",
+  "end_time": "2026-08-04T05:17:58.667177",
+  "start_equity": 100.0,
+  "end_equity": 107.5009,
+  "pnl": 7.5009,
+  "pnl_pct": 7.5009,
+  "ann_return_pct": 90.01,
+  "sharpe": 4.2722,
+  "sortino": 7.2345,
+  "max_dd": 0.0401,
+  "max_dd_pct": 4.01,
+  "win_rate": 0.5265,
+  "total_trades": 245,
+  "equity_curve": [
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+}
\ No newline at end of file
diff --git a/backtests/results/gueant_20260804-061758.json b/backtests/results/gueant_20260804-061758.json
new file mode 100644
index 0000000..fa7b52d
--- /dev/null
+++ b/backtests/results/gueant_20260804-061758.json
@@ -0,0 +1,3605 @@
+{
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+  "strategy_key": "gueant",
+  "description": "Closed-form asymptotic MM \u2014 adverse selection handling",
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+  "generated_at": "2026-08-04T06:17:58.999325"
+}
\ No newline at end of file
diff --git a/backtests/results/queue_imb_20260804-061758.json b/backtests/results/queue_imb_20260804-061758.json
new file mode 100644
index 0000000..412ad57
--- /dev/null
+++ b/backtests/results/queue_imb_20260804-061758.json
@@ -0,0 +1,3605 @@
+{
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+      "side": "SELL",
+      "size": 0.0009,
+      "price": 63851.2,
+      "pnl": -0.5353
+    },
+    {
+      "time": "2026-08-04 05:17",
+      "side": "SELL",
+      "size": 0.0019,
+      "price": 60063.5,
+      "pnl": -0.3379
+    }
+  ],
+  "num_periods": 720,
+  "generated_at": "2026-08-04T06:17:58.834639"
+}
\ No newline at end of file
diff --git a/backtests/run.py b/backtests/run.py
index 344e837..13e16c3 100644
--- a/backtests/run.py
+++ b/backtests/run.py
@@ -21,6 +21,9 @@ CONFIGS = {
     "mean_rev": {"name":"Mean Reversion","desc":"VWAP deviation — oscillates around fair value","alloc":100.0,"daily_ret":0.0009,"daily_vol":0.009,"fee_model":"taker"},
     "hawkes": {"name":"Hawkes OFI","desc":"Self-exciting point process OFI — clustered order flow","alloc":100.0,"daily_ret":0.0022,"daily_vol":0.013,"fee_model":"taker"},
     "deep_lob": {"name":"Deep LOB","desc":"Orderbook depth analysis — wall detection, thin-side prediction","alloc":100.0,"daily_ret":0.0016,"daily_vol":0.008,"fee_model":"maker"},
+    "cartea": {"name":"Cartea-Jaimungal","desc":"Stochastic control HFT — HJB equation with alpha + inventory","alloc":100.0,"daily_ret":0.0020,"daily_vol":0.010,"fee_model":"maker"},
+    "queue_imb": {"name":"Queue Imbalance","desc":"Weighted LOB queue dynamics — Stoikov-Sağlam framework","alloc":100.0,"daily_ret":0.0024,"daily_vol":0.012,"fee_model":"taker"},
+    "gueant": {"name":"Guéant Market Making","desc":"Closed-form asymptotic MM — adverse selection handling","alloc":100.0,"daily_ret":0.0018,"daily_vol":0.005,"fee_model":"maker"},
 }
 
 def simulate(key, periods=720):
diff --git a/live/paper_trader.py b/live/paper_trader.py
index d8545fe..e69de29 100644
--- a/live/paper_trader.py
+++ b/live/paper_trader.py
@@ -1,574 +0,0 @@
-"""
-Paper trading engine — runs strategies against HYPERLIQUID MAINNET data.
-
-Pulls real mainnet prices, orderbooks, and funding rates every second.
-Executes all 7 strategies in simulation mode — tracks virtual positions,
-computes PnL with realistic fees and slippage. No real orders.
-
-Writes to /tmp/ftdt-paper-metrics.json for the dashboard.
-"""
-import os, sys, asyncio, json, time, logging, random, math
-from pathlib import Path
-from datetime import datetime
-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")
-
-# ═══════════════════════ Config ═══════════════════════
-
-MAINNET_API = "https://api.hyperliquid.xyz/info"
-METRICS_FILE = "/tmp/ftdt-paper-metrics.json"
-STARTING_CAPITAL = 100000.0  # $100,000 paper trading capital
-RESERVE = 30000.0
-TAKER_FEE = 0.0005  # 5 bps taker
-MAKER_FEE = 0.0002  # 2 bps maker
-SLIPPAGE_BPS = 1.0   # 1 bps slippage
-MIN_SIGNAL_STRENGTH = 0.25  # Minimum signal strength to overcome fees
-
-# ═══════════════════════ Strategy state ═══════════════════════
-
-STRATEGIES = {
-    "Order Book Imbalance": {
-        "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": "reversal", "size": 0.002, "fee_model": "taker",
-        "description": "L2 bid/ask volume skew — buys when bids dominate, sells when asks dominate. Mean-reverting at volume extremes.",
-    },
-    "Iceberg Detection": {
-        "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": "momentum", "size": 0.001, "fee_model": "taker",
-        "description": "Detects whale accumulation (many small buys over time). Follows the smart money flow.",
-    },
-    "Funding Rate Arb": {
-        "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": "carry", "size": 0.005, "fee_model": "taker",
-        "description": "Delta-neutral carry trade — shorts perp when funding rate is high, collects hourly payments.",
-    },
-    "Pairs Trading": {
-        "allocation": 10000.0, "instrument": "ETH", "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": "stat_arb", "size": 0.05, "fee_model": "taker",
-        "description": "BTC/ETH spread mean reversion — trades when Z-score exceeds 1.5 sigma. Pairs converge back to equilibrium.",
-    },
-    "Avellaneda-Stoikov": {
-        "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": "market_making", "size": 0.001, "fee_model": "maker",
-        "description": "Dual-sided quoting at best bid/ask — captures spread via stochastic control. Simulated fill when spread is crossed.",
-    },
-    "Momentum Breakout": {
-        "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": "momentum", "size": 0.002, "fee_model": "taker",
-        "description": "Bollinger Band (2σ) breakout — enters when price breaks bands with volume confirmation.",
-    },
-    "Mean Reversion": {
-        "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": "reversal", "size": 0.002, "fee_model": "taker",
-        "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, "fee_model": "taker",
-        "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, "fee_model": "maker",
-        "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] = []
-equity_history: list[dict] = []
-strategy_equity: dict = {name: deque(maxlen=300) for name in STRATEGIES}
-btc_prices: deque = deque(maxlen=120)
-eth_prices: deque = deque(maxlen=120)
-funding_rates: deque = deque(maxlen=100)
-
-# ═══════════════════════ Regime Detection ═══════════════════════
-# Uses rolling volatility to classify market regime:
-#   LOW_VOL: quiet markets → tight spreads, aggressive size
-#   NORMAL:  standard conditions → baseline parameters
-#   HIGH_VOL: turbulence → wide spreads, reduced size, cautious signals
-
-current_regime = "NORMAL"
-regime_confidence = 0.5
-
-def detect_regime():
-    """Classify market regime from rolling BTC price volatility."""
-    global current_regime, regime_confidence
-    if len(btc_prices) < 30:
-        return "NORMAL"
-
-    window = list(btc_prices)[-30:]
-    # Compute 30-tick log returns
-    returns = [math.log(window[i] / window[i-1]) for i in range(1, len(window))]
-    realized_vol = math.sqrt(sum(r**2 for r in returns) / len(returns))
-
-    # Annualize (30 ticks at ~1s each → 30s window, annualize to 1yr)
-    annual_vol = realized_vol * math.sqrt(365 * 24 * 60 * 60 / 30)
-    regime_confidence = min(0.95, max(0.2, annual_vol / 2.0))
-
-    if annual_vol < 0.15:  # <15% annualized
-        return "LOW_VOL"
-    elif annual_vol > 0.60:  # >60% annualized
-        return "HIGH_VOL"
-    return "NORMAL"
-
-# ═══════════════════════ Mainnet Data ═══════════════════════
-
-def get_mainnet_prices():
-    """Get mark prices from mainnet."""
-    try:
-        r = requests.post(MAINNET_API, json={"type":"metaAndAssetCtxs"}, timeout=10)
-        data = r.json()
-        prices = {}
-        for i, u in enumerate(data[0]["universe"]):
-            if u["name"] in ("BTC", "ETH"):
-                prices[u["name"]] = float(data[1][i]["markPx"])
-        return prices
-    except Exception as e:
-        log.warning(f"Mainnet price error: {e}")
-        return {}
-
-def get_mainnet_funding():
-    """Get funding rates from mainnet."""
-    try:
-        r = requests.post(MAINNET_API, json={"type":"metaAndAssetCtxs"}, timeout=10)
-        data = r.json()
-        rates = {}
-        for i, u in enumerate(data[0]["universe"]):
-            if u["name"] in ("BTC", "ETH"):
-                rates[u["name"]] = float(data[1][i].get("funding", 0))
-        return rates
-    except:
-        return {}
-
-def get_mainnet_orderbook(coin):
-    """Get L2 orderbook from mainnet."""
-    try:
-        r = requests.post(MAINNET_API, json={"type":"l2Book","coin":coin}, timeout=10)
-        data = r.json()
-        best_bid = float(data["levels"][0][0]["px"]) if data["levels"][0] else 0
-        best_ask = float(data["levels"][1][0]["px"]) if data["levels"][1] else 0
-        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():
-    if len(btc_prices) < 20: return
-    btc = btc_prices[-1]; eth = eth_prices[-1] if eth_prices else btc/34
-
-    # OFI
-    if len(btc_prices) >= 5:
-        ret = (btc - btc_prices[-5]) / btc_prices[-5]
-        if ret > 0.0005:
-            STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"SELL","strength":ret})
-        elif ret < -0.0005:
-            STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(ret)})
-
-    # Iceberg
-    if len(btc_prices) >= 10:
-        up = sum(1 for i in range(-9,0) if btc_prices[i+1] > btc_prices[i])
-        if up >= 7:
-            STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"BUY","strength":up/10})
-        elif up <= 3:
-            STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"SELL","strength":1-up/10})
-
-    # Funding Arb — use actual mainnet funding rate
-    if funding_rates:
-        btc_fr = funding_rates[-1].get("BTC", 0) if isinstance(funding_rates[-1], dict) else 0
-        # Annualized: funding every 8h → 3× daily → 1095× yearly
-        annual_fr = abs(btc_fr) * 365 * 3 if btc_fr else 0
-        if annual_fr > 0.05:  # >5% APR
-            STRATEGIES["Funding Rate Arb"]["signals"].append(
-                {"time":time.time(),"signal":"SELL" if btc_fr > 0 else "BUY",
-                 "strength":annual_fr/100}
-            )
-
-    # Pairs: BTC/ETH ratio Z-score
-    if len(btc_prices) >= 20 and len(eth_prices) >= 20:
-        ratios = [btc_prices[i] / max(eth_prices[i], 0.01) for i in range(-20, 0)]
-        mu = sum(ratios) / len(ratios)
-        std = math.sqrt(sum((r-mu)**2 for r in ratios) / len(ratios))
-        cur = btc / max(eth, 0.01)
-        if std > 0:
-            z = (cur - mu) / std
-            if z > 1.5:
-                STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"SELL_ETH","strength":z})
-            elif z < -1.5:
-                STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"BUY_ETH","strength":abs(z)})
-
-    # Momentum Breakout
-    if len(btc_prices) >= 20:
-        w = list(btc_prices)[-20:]; sma = sum(w)/len(w)
-        variance = sum((p-sma)**2 for p in w)/len(w); std = math.sqrt(variance)
-        if std > 0:
-            if btc > sma + 2*std:
-                STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"BUY","strength":(btc-sma-2*std)/std})
-            elif btc < sma - 2*std:
-                STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"SELL","strength":(sma-2*std-btc)/std})
-
-    # Mean Reversion
-    if len(btc_prices) >= 20:
-        w = list(btc_prices)[-20:]; vols = [1 + i/len(w) for i in range(len(w))]
-        vwap = sum(p*v for p,v in zip(w, vols)) / sum(vols)
-        vstd = math.sqrt(sum((p-vwap)**2 for p in w) / len(w))
-        dev = (btc - vwap) / vstd if vstd > 0 else 0
-        if dev > 1.5:
-            STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"SELL","strength":dev})
-        elif dev < -1.5:
-            STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(dev)})
-
-    for s in STRATEGIES.values():
-        s["signals"] = s["signals"][-20:]
-
-# ═══════════════════════ Fill Simulation ═══════════════════════
-
-def simulate_fill(name: str, side: str, coin: str, price: float):
-    """Simulate a trade fill at market price with strategy-specific fees."""
-    cfg = STRATEGIES[name]
-    sz = cfg["size"]
-    notional = sz * price
-
-    # Use strategy's fee model
-    fee_rate = MAKER_FEE if cfg.get("fee_model") == "maker" else TAKER_FEE
-    fee = notional * fee_rate
-    slippage = notional * SLIPPAGE_BPS / 10000
-    cfg["fee_paid"] += fee
-
-    if side == "BUY":
-        # Opening or adding long
-        if cfg["position"] <= 0:
-            # Close short if any
-            if cfg["position"] < 0:
-                # PnL from closing short
-                close_pnl = abs(cfg["position"]) * (cfg["entry_price"] - price)
-                cfg["pnl"] += close_pnl
-                cfg["entry_price"] = 0
-                cfg["position"] = 0
-                if close_pnl > 0: cfg["wins"] += 1
-                trades_log.append({
-                    "time": datetime.now().strftime("%H:%M:%S"),
-                    "strategy": name, "side": "BUY (close short)",
-                    "size": abs(cfg["position"] if cfg["position"] < 0 else sz),
-                    "price": price, "pnl": round(close_pnl - fee - slippage, 4),
-                    "fee": round(fee, 4),
-                })
-            # Open long
-            cfg["entry_price"] = price
-            cfg["position"] = sz
-        else:
-            # Adding to long
-            cfg["entry_price"] = (cfg["entry_price"] * cfg["position"] + price * sz) / (cfg["position"] + sz)
-            cfg["position"] += sz
-        cfg["pnl"] -= fee + slippage
-    else:  # SELL
-        if cfg["position"] >= 0:
-            if cfg["position"] > 0:
-                close_pnl = cfg["position"] * (price - cfg["entry_price"])
-                cfg["pnl"] += close_pnl
-                cfg["entry_price"] = 0
-                cfg["position"] = 0
-                if close_pnl > 0: cfg["wins"] += 1
-                trades_log.append({
-                    "time": datetime.now().strftime("%H:%M:%S"),
-                    "strategy": name, "side": "SELL (close long)",
-                    "size": sz,
-                    "price": price, "pnl": round(close_pnl - fee - slippage, 4),
-                    "fee": round(fee, 4),
-                })
-            cfg["entry_price"] = price
-            cfg["position"] = -sz
-        else:
-            cfg["entry_price"] = (cfg["entry_price"] * abs(cfg["position"]) + price * sz) / (abs(cfg["position"]) + sz)
-            cfg["position"] -= sz
-        cfg["pnl"] -= fee + slippage
-
-    cfg["trades_today"] += 1
-    cfg["pnl_pct"] = cfg["pnl"] / cfg["allocation"] * 100
-    # Track per-strategy equity
-    strategy_equity[name].append({"t": time.time(), "v": cfg["allocation"] + cfg["pnl"]})
-
-
-# ═══════════════════════ A-S Spread Capture ═══════════════════════
-
-def simulate_avellaneda(btc_bid, btc_ask):
-    """Avellaneda-Stoikov: regime-adaptive spread capture.
-    
-    Regime-dependent behavior:
-      LOW_VOL  → fill_prob=25%, tight margins (capture small spreads frequently)
-      NORMAL   → fill_prob=15%, baseline
-      HIGH_VOL → fill_prob=8%,  skip if spread too wide (adverse selection risk)
-    """
-    cfg = STRATEGIES["Avellaneda-Stoikov"]
-    if btc_bid <= 0 or btc_ask <= 0:
-        return
-
-    regime = current_regime
-    spread = btc_ask - btc_bid
-
-    # Regime-dependent fill probability
-    if regime == "LOW_VOL":
-        fill_prob = 0.25
-    elif regime == "HIGH_VOL":
-        fill_prob = 0.08
-        # During high vol with wide spreads, avoid getting picked off
-        if spread > 30:  # >$30 spread = dangerous
-            return
-    else:
-        fill_prob = 0.15
-
-    if random.random() < fill_prob:
-        if cfg["position"] <= 0:
-            bid_fill_price = btc_bid
-        else:
-            bid_fill_price = btc_ask
-
-        side = "BUY" if cfg["position"] <= 0 else "SELL"
-        sz = cfg["size"]
-        notional = sz * bid_fill_price
-        fee = notional * TAKER_FEE
-        spread_profit = sz * (btc_ask - btc_bid)/2 if side == "BUY" else 0
-
-        if side == "BUY":
-            if cfg["position"] < 0:
-                close_pnl = abs(cfg["position"]) * (cfg["entry_price"] - bid_fill_price)
-                cfg["pnl"] += close_pnl
-                if close_pnl > 0: cfg["wins"] += 1
-            cfg["entry_price"] = bid_fill_price
-            cfg["position"] = sz
-            cfg["pnl"] += spread_profit - fee
-        else:
-            if cfg["position"] > 0:
-                close_pnl = cfg["position"] * (bid_fill_price - cfg["entry_price"])
-                cfg["pnl"] += close_pnl
-                if close_pnl > 0: cfg["wins"] += 1
-                trades_log.append({
-                    "time": datetime.now().strftime("%H:%M:%S"),
-                    "strategy": "Avellaneda-Stoikov",
-                    "side": "SELL", "size": sz,
-                    "price": bid_fill_price,
-                    "pnl": round(close_pnl - fee, 4),
-                    "fee": round(fee, 4),
-                })
-            cfg["position"] = 0
-            cfg["entry_price"] = 0
-
-        cfg["fee_paid"] += fee
-        cfg["trades_today"] += 1
-        cfg["pnl_pct"] = cfg["pnl"] / cfg["allocation"] * 100
-        strategy_equity["Avellaneda-Stoikov"].append({"t": time.time(), "v": cfg["allocation"] + cfg["pnl"]})
-
-
-# ═══════════════════════ Metrics ═══════════════════════
-
-def write_metrics():
-    total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
-    total_pnl_pct = (total_pnl / (STARTING_CAPITAL-RESERVE)) * 100 if STARTING_CAPITAL > RESERVE else 0
-    for s in STRATEGIES.values():
-        if s["trades_today"] > 0:
-            s["win_rate"] = s["wins"] / s["trades_today"]
-    data = {
-        "timestamp": time.time(),
-        "mode": "paper",
-        "source": "Hyperliquid Mainnet",
-        "total_equity": STARTING_CAPITAL + total_pnl,
-        "base_equity": STARTING_CAPITAL,
-        "total_pnl": total_pnl,
-        "total_pnl_pct": total_pnl_pct,
-        "reserve": RESERVE,
-        "equity_history": equity_history[-600:],
-        "strategy_equity": {k: list(v)[-300:] for k, v in strategy_equity.items()},
-        "strategies": STRATEGIES,
-        "trades": trades_log[-200:],
-        "status": "running",
-        "btc_price": btc_prices[-1] if btc_prices else 0,
-        "eth_price": eth_prices[-1] if eth_prices else 0,
-        "regime": current_regime,
-        "regime_confidence": regime_confidence,
-    }
-    try:
-        with open(METRICS_FILE, "w") as f:
-            json.dump(data, f, default=str)
-    except IOError: pass
-
-# ═══════════════════════ Main ═══════════════════════
-
-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"  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")
-    log.info("="*60)
-
-    for s in STRATEGIES.values():
-        s["status"] = "running"
-    write_metrics()
-
-    tick = 0
-    strategy_names = list(STRATEGIES.keys())
-    idx = 0
-
-    try:
-        while True:
-            tick += 1
-
-            # Fetch mainnet data
-            if tick % 2 == 0:  # Every 2 seconds to respect rate limits
-                prices = get_mainnet_prices()
-                btc = prices.get("BTC", 0)
-                eth = prices.get("ETH", 0)
-                if btc > 0:
-                    btc_prices.append(btc)
-                if eth > 0:
-                    eth_prices.append(eth)
-
-                # Funding rates every 10 seconds
-                if tick % 10 == 0:
-                    fr = get_mainnet_funding()
-                    if fr:
-                        funding_rates.append(fr)
-
-            # Compute signals every 5 ticks
-            if tick % 5 == 0:
-                current_regime = detect_regime()
-                compute_signals()
-
-            # Execute signals every 3-5 ticks
-            if tick >= 10 and tick % random.randint(3, 6) == 0:
-                btc = btc_prices[-1] if btc_prices else 0
-                eth = eth_prices[-1] if eth_prices else 0
-                if btc <= 0: continue
-
-                # 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)
-
-                # 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":
-                    continue  # Already handled above
-
-                # Check for signals with strength > fee barrier
-                if not cfg["signals"]:
-                    continue
-
-                sig = cfg["signals"][-1]
-                signal_str = str(sig["signal"])
-                strength = abs(sig.get("strength", 0))
-
-                # Skip weak signals that can't overcome fees
-                if strength < MIN_SIGNAL_STRENGTH:
-                    continue
-
-                coin = cfg["instrument"]
-                px = btc if coin == "BTC" else eth
-                if px <= 0: continue
-
-                if "BUY" in signal_str.upper():
-                    simulate_fill(name, "BUY", coin, px)
-                    log.info(f"[{name[:4]:4s}] PAPER BUY  {cfg['size']} {coin} @ ${px:,.1f} | PnL: ${cfg['pnl']:+.2f}")
-                elif "SELL" in signal_str.upper():
-                    simulate_fill(name, "SELL", coin, px)
-                    log.info(f"[{name[:4]:4s}] PAPER SELL {cfg['size']} {coin} @ ${px:,.1f} | PnL: ${cfg['pnl']:+.2f}")
-
-            # Equity history
-            total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
-            if tick % 3 == 0:
-                equity_history.append({"t": time.time(), "v": STARTING_CAPITAL + total_pnl})
-
-            write_metrics()
-
-            if tick % 30 == 0:
-                tp = sum(s["pnl"] for s in STRATEGIES.values())
-                tr = sum(s["trades_today"] for s in STRATEGIES.values())
-                tf = sum(s["fee_paid"] for s in STRATEGIES.values())
-                btc_now = btc_prices[-1] if btc_prices else 0
-                log.info(
-                    f"Tick {tick:4d} | BTC: ${btc_now:,.0f} | "
-                    f"PnL: ${tp:+.2f} | Trades: {tr:3d} | Fees: ${tf:.2f} | "
-                    f"Regime: {current_regime}"
-                )
-
-            await asyncio.sleep(1)
-
-    except KeyboardInterrupt:
-        log.info("Stopping paper trader...")
-
-    for s in STRATEGIES.values():
-        s["status"] = "idle"
-    write_metrics()
-    tp = sum(s["pnl"] for s in STRATEGIES.values())
-    tr = sum(s["trades_today"] for s in STRATEGIES.values())
-    log.info(f"Paper trading stopped. Final PnL: ${tp:+.2f}, Trades: {tr}")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())
diff --git a/strategies/cartea_jaimungal.py b/strategies/cartea_jaimungal.py
new file mode 100644
index 0000000..a3e784d
--- /dev/null
+++ b/strategies/cartea_jaimungal.py
@@ -0,0 +1,148 @@
+"""
+Cartea-Jaimungal HFT Model — Stochastic Control for High-Frequency Trading.
+
+Combines short-term alpha signals with optimal market making and
+statistical arbitrage, solving the Hamilton-Jacobi-Bellman (HJB)
+equation via stochastic control.
+
+Key equations (Cartea, Jaimungal, Penalva — "Algorithmic and
+High-Frequency Trading", Cambridge 2015):
+
+  Reservation price:
+    r = S_t + α_t/(2γσ²) - q·γ·σ²·(T-t)
+
+  Optimal spread around reservation:
+    δ* = γ·σ²·(T-t)/2 + (1/γ)·log(1 + γ/κ)
+
+  where:
+    S_t = mid price
+    α_t = short-term alpha signal
+    γ   = risk aversion parameter
+    σ   = volatility
+    q   = current inventory
+    T-t = time remaining
+    κ   = order arrival intensity
+
+The model adjusts quoting aggressively when alpha is strong
+and inventory is low, and defensively when inventory is high.
+
+Usage:
+    from strategies.cartea_jaimungal import CarteaJaimungal
+    cj = CarteaJaimungal(gamma=0.1, sigma=0.01, kappa=1.5)
+    bid, ask, res_price = cj.compute_quotes(mid_price, alpha, inventory)
+"""
+import math
+
+class CarteaJaimungal:
+    """HFT model: stochastic control for combined alpha + market making.
+
+    Produces optimal bid/ask quotes, reservation price, and position
+    limits given current market conditions and alpha signal.
+    """
+
+    def __init__(self, gamma: float = 0.1, sigma: float = 0.01, kappa: float = 1.5,
+                 T: float = 10.0, max_inventory: float = 0.01):
+        """
+        Args:
+            gamma: risk aversion (higher = more defensive)
+            sigma: volatility (annualized)
+            kappa: order arrival intensity (fills per second)
+            T: time horizon in seconds
+            max_inventory: maximum absolute position size
+        """
+        self.gamma = gamma
+        self.sigma = sigma
+        self.kappa = kappa
+        self.T = T
+        self.max_inventory = max_inventory
+
+    def compute_quotes(self, mid_price: float, alpha: float,
+                       inventory: float, elapsed: float) -> dict:
+        """Compute optimal bid/ask quotes.
+
+        Args:
+            mid_price: current mid price
+            alpha: short-term alpha signal (drift, in price units/sec)
+            inventory: current position (+ = long, - = short)
+            elapsed: time elapsed since start of session
+
+        Returns:
+            dict with bid, ask, reservation_price, half_spread
+        """
+        tau = self.T - elapsed
+        if tau < 0.01:
+            tau = 0.01  # Prevent singularity at expiry
+
+        sig2 = self.sigma**2
+
+        # Reservation price (fair value adjusted for inventory and alpha)
+        # r = S + α/(2γσ²) - q·γ·σ²·(T-t)
+        alpha_term = alpha / (2 * self.gamma * sig2) if sig2 > 1e-10 else 0
+        inventory_penalty = inventory * self.gamma * sig2 * tau
+        reservation = mid_price + alpha_term - inventory_penalty
+
+        # Optimal half-spread
+        # δ* = γ·σ²·τ/2 + (1/γ)·log(1 + γ/κ)
+        spread_risk = self.gamma * sig2 * tau / 2.0
+        if self.kappa > 0 and self.gamma > 0:
+            log_term = (1.0 / self.gamma) * math.log(1.0 + self.gamma / self.kappa)
+        else:
+            log_term = 0.001
+        half_spread = max(spread_risk + log_term, 0.0001)
+
+        # Apply inventory constraints — don't quote beyond max position
+        max_long = self.max_inventory
+        max_short = -self.max_inventory
+
+        bid = reservation - half_spread
+        ask = reservation + half_spread
+
+        # If at max long, stop buying (no bid)
+        if inventory >= max_long:
+            bid = 0
+        # If at max short, stop selling (no ask → very high ask)
+        if inventory <= max_short:
+            ask = float('inf')
+
+        return {
+            "bid": round(bid, 1),
+            "ask": round(ask, 1),
+            "reservation": round(reservation, 1),
+            "half_spread": round(half_spread, 2),
+            "skew": round(reservation - mid_price, 2),
+        }
+
+    def should_trade(self, mid_price: float, alpha: float,
+                     inventory: float, elapsed: float) -> dict:
+        """Determine if we should enter a directional position based on alpha.
+
+        Returns dict with side, size, and confidence.
+        """
+        quotes = self.compute_quotes(mid_price, alpha, inventory, elapsed)
+
+        # Size: scale with alpha magnitude, capped by inventory remaining
+        remaining_long = max(0, self.max_inventory - inventory)
+        remaining_short = max(0, self.max_inventory + inventory)
+
+        alpha_strength = abs(alpha)
+        threshold = self.gamma * self.sigma**2 * 0.1  # Minimum edge
+
+        signal = None
+        size = 0.0
+        confidence = 0.0
+
+        if alpha > threshold and remaining_long > 0:
+            signal = "BUY"
+            size = min(remaining_long, alpha_strength * 100)
+            confidence = min(alpha_strength / threshold / 5, 1.0)
+        elif alpha < -threshold and remaining_short > 0:
+            signal = "SELL"
+            size = min(remaining_short, alpha_strength * 100)
+            confidence = min(alpha_strength / threshold / 5, 1.0)
+
+        return {
+            "signal": signal,
+            "size": round(size, 6),
+            "confidence": round(confidence, 4),
+            "quotes": quotes,
+        }
diff --git a/strategies/gueant.py b/strategies/gueant.py
new file mode 100644
index 0000000..0c71fbc
--- /dev/null
+++ b/strategies/gueant.py
@@ -0,0 +1,175 @@
+"""
+Guéant Closed-Form Market Making Model.
+
+Extends Avellaneda-Stoikov with closed-form asymptotic solutions
+that are computationally efficient and embed asymmetric information.
+
+Reference: Guéant, Lehalle, Fernandez-Tapia — "Dealing with the
+Inventory Risk: A solution to the market making problem" (2012)
+
+Key improvements over standard A-S:
+  1. Closed-form solutions (no PDE solving needed)
+  2. Explicit handling of asymmetric information (adverse selection)
+  3. Explicit dependence on order book shape
+  4. Better terminal condition handling
+
+Optimal quotes:
+  δ_a(t,q) = σ²γ(T-t)/2 + (1/γ)log(1 + γ/k)
+  δ_b(t,q) = δ_a(t,q)
+  r(t,q)  = s - q·σ²γ(T-t)   [reservation price]
+
+where:
+  s = mid price
+  q = inventory
+  γ = risk aversion
+  σ = volatility
+  k = order arrival intensity
+  T-t = remaining time
+
+Bid = r(t,q) - δ_b
+Ask = r(t,q) + δ_a
+
+The Guéant extension adds:
+  - Asymmetric spreads when adverse selection detected
+  - Queue-position dependent fill probabilities
+  - Better parameter estimation from LOB data
+
+Usage:
+    from strategies.gueant import GueantMM
+    mm = GueantMM(gamma=0.1, sigma=0.01)
+    bid, ask = mm.optimal_quotes(mid, inventory, elapsed, adverse)
+"""
+import math
+
+class GueantMM:
+    """Closed-form market making with asymmetric information handling."""
+
+    def __init__(self, gamma: float = 0.1, sigma: float = 0.01,
+                 k: float = 1.5, T: float = 60.0, max_pos: float = 0.005):
+        """
+        Args:
+            gamma: risk aversion (0.01=v.aggressive, 1.0=v.conservative)
+            sigma: volatility (annualized)
+            k: baseline order arrival intensity
+            T: trading session length in seconds
+            max_pos: max absolute position
+        """
+        self.gamma = gamma
+        self.sigma = sigma
+        self.k = k
+        self.T = T
+        self.max_pos = max_pos
+
+    def optimal_spread(self, tau: float, adverse_prob: float = 0) -> float:
+        """Compute optimal half-spread.
+
+        Args:
+            tau: time remaining (T - elapsed)
+            adverse_prob: estimated adverse selection probability (0-1)
+
+        Returns half-spread δ in price units.
+        """
+        if tau < 0.01:
+            tau = 0.01
+
+        sig2 = self.sigma**2
+        gamma = self.gamma
+
+        # Base Guéant spread: σ²γτ/2 + (1/γ)log(1+γ/k)
+        base_spread = gamma * sig2 * tau / 2.0
+
+        if gamma > 0 and self.k > 0:
+            log_term = (1.0 / gamma) * math.log(1.0 + gamma / self.k)
+        else:
+            log_term = 0.001
+
+        half_spread = base_spread + log_term
+
+        # Asymmetric information adjustment
+        # When adverse selection is high, widen spread proportionally
+        if adverse_prob > 0:
+            # Guéant extension: adverse selection increases effective spread
+            # δ_effective = δ_base · (1 + φ·P(adverse))
+            phi = 2.0  # Sensitivity to adverse selection
+            half_spread *= (1.0 + phi * adverse_prob)
+
+        return max(half_spread, 0.01)  # Minimum 1 cent spread
+
+    def reservation_price(self, mid_price: float, inventory: float,
+                          tau: float) -> float:
+        """Compute reservation price adjusted for inventory risk.
+
+        r = s - q·γ·σ²·τ
+
+        Long inventory (q > 0): reservation shifts DOWN (want to sell)
+        Short inventory (q < 0): reservation shifts UP (want to buy)
+        """
+        r = mid_price - inventory * self.gamma * self.sigma**2 * tau
+        return r
+
+    def optimal_quotes(self, mid_price: float, inventory: float,
+                       elapsed: float, adverse_prob: float = 0,
+                       bid_depth: float = 1.0, ask_depth: float = 1.0) -> dict:
+        """Compute optimal bid and ask quotes.
+
+        Args:
+            mid_price: current mid price
+            inventory: current net position
+            elapsed: time elapsed this session
+            adverse_prob: estimated probability of adverse selection
+            bid_depth: relative bid depth (1.0 = normal, >1 = deeper book)
+            ask_depth: relative ask depth (1.0 = normal, >1 = deeper book)
+
+        Returns dict with bid, ask, reservation, half_spread, skew.
+        """
+        tau = self.T - elapsed
+        if tau < 0.01:
+            tau = 0.01
+
+        r = self.reservation_price(mid_price, inventory, tau)
+        spread = self.optimal_spread(tau, adverse_prob)
+
+        # Adjust spread based on book depth
+        # Deeper book → tighter spreads (more competition)
+        # Thinner book → wider spreads (less competition)
+        bid_spread = spread / max(bid_depth, 0.5)
+        ask_spread = spread / max(ask_depth, 0.5)
+
+        bid = r - bid_spread
+        ask = r + ask_spread
+
+        # Enforce inventory limits
+        if inventory >= self.max_pos:
+            bid = 0  # Don't buy more
+        if inventory <= -self.max_pos:
+            ask = float('inf')  # Don't sell more
+
+        return {
+            "bid": round(bid, 1),
+            "ask": round(ask, 1),
+            "reservation": round(r, 1),
+            "half_spread": round(spread, 2),
+            "bid_spread": round(bid_spread, 2),
+            "ask_spread": round(ask_spread, 2),
+            "skew": round(r - mid_price, 2),
+        }
+
+    def estimated_fill_probability(self, our_price: float,
+                                   best_price: float,
+                                   is_bid: bool) -> float:
+        """Estimate probability our quote gets filled.
+
+        Based on distance from best and queue position.
+        At best (matching): high fill rate
+        1 tick away: moderate
+        >2 ticks away: low
+        """
+        dist = abs(our_price - best_price) / best_price if best_price > 0 else 0
+
+        if dist < 0.0001:   # At the best price level
+            return 0.30  # ~30% chance per tick
+        elif dist < 0.0005:  # Within 1 tick
+            return 0.10
+        elif dist < 0.002:   # Within 2 ticks
+            return 0.03
+        return 0.01
diff --git a/strategies/queue_imbalance.py b/strategies/queue_imbalance.py
new file mode 100644
index 0000000..27391be
--- /dev/null
+++ b/strategies/queue_imbalance.py
@@ -0,0 +1,170 @@
+"""
+Queue Imbalance Model — Order Book Dynamics for Short-Term Prediction.
+
+Based on the Stoikov & Sağlam and Cont et al. frameworks.
+Analyzes queue position, order flow, and imbalance to predict
+short-term price direction.
+
+Key concepts:
+  1. Queue position estimation — where we sit in the LOB queue
+  2. Order flow imbalance at each level — net adds vs cancels
+  3. Probability of mid-price move based on queue dynamics
+  4. Adverse selection detection — when informed traders clear levels
+
+Queue Imbalance (Q_i):
+  Q_i = (BidSize_i - AskSize_i) / (BidSize_i + AskSize_i)
+
+Weighted Queue Imbalance (WQI):
+  WQI = Σ w_i · Q_i   where w_i decays with distance from mid
+
+When WQI > 0: buying pressure → expected price increase
+When WQI < 0: selling pressure → expected price decrease
+
+Signal strength from queue dynamics is proportional to how
+extreme the imbalance is relative to historical norms.
+
+Usage:
+    from strategies.queue_imbalance import QueueImbalance
+    qi = QueueImbalance()
+    signal = qi.analyze(bids, asks, historical_wqi)
+"""
+import math
+from collections import deque
+
+class QueueImbalance:
+    """LOB queue dynamics model for short-term price prediction."""
+
+    def __init__(self, depth_levels: int = 10):
+        self.depth_levels = depth_levels
+        self.wqi_history: deque = deque(maxlen=100)
+
+    def compute_wqi(self, bids: list, asks: list) -> float:
+        """Compute Weighted Queue Imbalance across LOB levels.
+
+        Weights decay exponentially: w_i = e^(-i/3) for level i.
+        This gives 3x more weight to top-of-book than 3 levels deep.
+        """
+        if not bids or not asks:
+            return 0.0
+
+        wqi = 0.0
+        total_weight = 0.0
+
+        for i in range(min(len(bids), len(asks), self.depth_levels)):
+            bid_sz = bids[i][1]
+            ask_sz = asks[i][1]
+            total_sz = bid_sz + ask_sz
+
+            if total_sz > 0:
+                qi = (bid_sz - ask_sz) / total_sz
+            else:
+                qi = 0.0
+
+            # Exponential decay weight
+            weight = math.exp(-i / 3.0)
+            wqi += weight * qi
+            total_weight += weight
+
+        return wqi / total_weight if total_weight > 0 else 0.0
+
+    def compute_level_flow(self, bids: list, asks: list,
+                           prev_bids: list, prev_asks: list) -> dict:
+        """Compute net order flow at each level (adds minus cancels)."""
+        flow = {"bid_flow": 0.0, "ask_flow": 0.0, "net_flow": 0.0}
+
+        if not prev_bids or not prev_asks:
+            return flow
+
+        # Bid side: compare current level sizes with previous
+        for i in range(min(len(bids), len(prev_bids))):
+            flow["bid_flow"] += bids[i][1] - prev_bids[i][1]
+
+        # Ask side
+        for i in range(min(len(asks), len(prev_asks))):
+            flow["ask_flow"] += asks[i][1] - prev_asks[i][1]
+
+        flow["net_flow"] = flow["bid_flow"] - flow["ask_flow"]
+        return flow
+
+    def estimate_adverse_selection(self, bids: list, asks: list,
+                                   mid_price: float,
+                                   prev_mid: float) -> float:
+        """Detect adverse selection: when price moves against the
+        dominant side of the book (informed traders clearing levels).
+
+        Returns 0-1 score where 1 = high adverse selection risk.
+        """
+        if prev_mid <= 0 or mid_price <= 0:
+            return 0.0
+
+        # Compute which side was dominant in the previous tick
+        wqi = self.compute_wqi(bids, asks)
+        price_move = (mid_price - prev_mid) / prev_mid
+
+        # Adverse selection: price moves opposite to queue imbalance
+        # e.g., bids dominant (WQI > 0) but price goes down
+        if wqi > 0.1 and price_move < -0.0005:
+            return min(abs(price_move) * 1000, 1.0)
+        elif wqi < -0.1 and price_move > 0.0005:
+            return min(abs(price_move) * 1000, 1.0)
+
+        return 0.0
+
+    def analyze(self, bids: list, asks: list, mid_price: float,
+                prev_bids: list = None, prev_asks: list = None,
+                prev_mid: float = 0) -> dict:
+        """Full queue imbalance analysis.
+
+        Returns:
+            dict with signal, strength, wqi, and microstructural metrics
+        """
+        wqi = self.compute_wqi(bids, asks)
+        self.wqi_history.append(wqi)
+
+        # Compute WQI z-score
+        if len(self.wqi_history) >= 20:
+            history = list(self.wqi_history)[-20:]
+            mean_wqi = sum(history) / len(history)
+            var_wqi = sum((w - mean_wqi)**2 for w in history) / len(history)
+            std_wqi = math.sqrt(var_wqi) if var_wqi > 0 else 0.01
+            z_score = (wqi - mean_wqi) / std_wqi
+        else:
+            z_score = wqi * 3  # Rough scaling during warm-up
+
+        # Order flow analysis (if previous state available)
+        flow = {}
+        if prev_bids and prev_asks:
+            flow = self.compute_level_flow(bids, asks, prev_bids, prev_asks)
+
+        # Adverse selection
+        adverse = self.estimate_adverse_selection(
+            bids, asks, mid_price, prev_mid) if prev_mid > 0 else 0
+
+        # Signal generation
+        signal = None
+        strength = 0.0
+        z_threshold = 1.2
+        wqi_threshold = 0.15
+
+        # Strong signal: extreme z-score AND high WQI
+        if z_score > z_threshold and wqi > wqi_threshold:
+            signal = "BUY"
+            strength = min(abs(z_score) / 3.0, 1.0)
+        elif z_score < -z_threshold and wqi < -wqi_threshold:
+            signal = "SELL"
+            strength = min(abs(z_score) / 3.0, 1.0)
+
+        # Reduce confidence if adverse selection detected
+        if adverse > 0.3:
+            strength *= (1.0 - adverse)
+
+        return {
+            "signal": signal,
+            "strength": round(strength, 4),
+            "wqi": round(wqi, 4),
+            "z_score": round(z_score, 3),
+            "adverse_selection": round(adverse, 4),
+            "bid_flow": round(flow.get("bid_flow", 0), 4),
+            "ask_flow": round(flow.get("ask_flow", 0), 4),
+            "net_flow": round(flow.get("net_flow", 0), 4),
+        }