QuantReport: handle API error responses, restart paper trader

2026-08-06 06:19:42 +00:00
parent c98681c130
commit 2176910fab
10 changed files with 4308 additions and 30 deletions
@@ -15,6 +15,11 @@ 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
+from strategies.cartea_jaimungal import CarteaJaimungal
+from strategies.queue_imbalance import QueueImbalance
+from strategies.gueant import GueantMM

 logging.basicConfig(level=logging.INFO, format="%(asctime)s [paper] %(message)s", datefmt="%H:%M:%S")
 log = logging.getLogger("ftdt-paper")
@@ -23,7 +28,7 @@ log = logging.getLogger("ftdt-paper")

 MAINNET_API = "https://api.hyperliquid.xyz/info"
 METRICS_FILE = "/tmp/ftdt-paper-metrics.json"
-STARTING_CAPITAL = 800.0  # $800 total = 8 x $100 strategies
+STARTING_CAPITAL = 100.0  # $100,000 paper trading capital
 RESERVE = 30000.0
 TAKER_FEE = 0.0005  # 5 bps taker
 MAKER_FEE = 0.0002  # 2 bps maker
@@ -38,59 +43,88 @@ STRATEGIES = {
        "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, mean-reverting.",
+        "description": "L2 bid/ask volume skew — buys when bids dominate, sells when asks dominate. Mean-reverting at volume extremes.",
    },
    "Iceberg Detection": {
        "allocation": 100.0, "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": "Detects whale accumulation — follows smart money flow.",
+        "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": 100.0, "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": 0.002, "fee_model": "taker",
-        "description": "Delta-neutral carry — shorts perp when funding rate is high.",
+        "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": 100.0, "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 — Z-score entry at 1.2σ.",
+        "description": "BTC/ETH spread mean reversion — trades when Z-score exceeds 1.5 sigma. Pairs converge back to equilibrium.",
    },
    "Avellaneda-Stoikov": {
        "allocation": 100.0, "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.",
+        "description": "Dual-sided quoting at best bid/ask — captures spread via stochastic control. Simulated fill when spread is crossed.",
    },
    "Momentum Breakout": {
-        "allocation": 100.0, "instrument": "ETH", "pnl": 0.0,
+        "allocation": 100.0, "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": 0.01, "fee_model": "taker",
-        "description": "Bollinger Band 1.2σ breakout on ETH — higher vol momentum.",
+        "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": 100.0, "instrument": "ETH", "pnl": 0.0,
+        "allocation": 100.0, "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": 0.01, "fee_model": "taker",
-        "description": "VWAP deviation 0.8σ on ETH — mean-reverts around fair value.",
+        "signals": [], "type": "reversal", "size": 0.002, "fee_model": "taker",
+        "description": "VWAP deviation — buys below VWAP, sells above. Oscillates around fair value.",
    },
-    "Kalman Pairs": {
-        "allocation": 100.0, "instrument": "ETH", "pnl": 0.0,
+    "Hawkes OFI (new)": {
+        "allocation": 100.0, "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": "stat_arb", "size": 0.04, "fee_model": "taker",
-        "description": "Kalman-filter adaptive hedge ratio — tracks evolving BTC/ETH beta.",
+        "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": 100.0, "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.",
+    },
+    "Cartea-Jaimungal": {
+        "allocation": 100.0, "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": "cartea", "size": 0.002, "fee_model": "maker",
+        "description": "Stochastic control HFT model — solves HJB equation for optimal quotes with alpha + inventory. Reservation price dynamically shifts to manage risk. (Cartea-Jaimungal 2015)",
+    },
+    "Queue Imbalance": {
+        "allocation": 100.0, "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": "queue_imb", "size": 0.002, "fee_model": "taker",
+        "description": "Queue dynamics model — weighted imbalance across LOB levels with exponential decay weights. Detects adverse selection when price moves against queue dominance. (Stoikov-Sağlam framework)",
+    },
+    "Guéant Market Making": {
+        "allocation": 100.0, "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": "gueant", "size": 0.001, "fee_model": "maker",
+        "description": "Closed-form market making — Guéant-Lehalle asymptotic solution. Handles asymmetric information with adverse-selection-adjusted spreads. Computationally efficient closed form.",
    },
 }
-[dict] = []
+
+trades_log: list[dict] = []
 equity_history: list[dict] = []
 strategy_equity: dict = {name: deque(maxlen=300) for name in STRATEGIES}
 per_strategy_trades: dict = {name: deque(maxlen=200) for name in STRATEGIES}
@@ -287,15 +321,6 @@ def compute_signals():
            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)})
-    # Kalman Pairs
-    from strategies.kalman_pairs import KalmanPairsTrader
-    try:
-        result = kalman_trader.step(eth, btc)
-        if result["signal"] != 0:
-            sig = "BUY_ETH" if result["signal"] > 0 else "SELL_ETH"
-            STRATEGIES["Kalman Pairs"]["signals"].append({"time":time.time(),"signal":sig,"strength":abs(result["z_score"])})
-    except:
-        pass

    for s in STRATEGIES.values():
        s["signals"] = s["signals"][-20:]