Professional dashboard: strategy detail panel with chart, trades, and signal reasons
This commit is contained in:
ramseshk
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"""
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Paper trading engine — runs strategies against HYPERLIQUID MAINNET data.
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Pulls real mainnet prices, orderbooks, and funding rates every second.
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Executes all 7 strategies in simulation mode — tracks virtual positions,
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computes PnL with realistic fees and slippage. No real orders.
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Writes to /tmp/ftdt-paper-metrics.json for the dashboard.
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"""
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import os, sys, asyncio, json, time, logging, random, math
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from pathlib import Path
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from datetime import datetime
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from collections import deque
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sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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import requests
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from strategies.hawkes_ofi import HawkesOFI
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from strategies.deep_lob import DeepLOB
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from strategies.cartea_jaimungal import CarteaJaimungal
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from strategies.queue_imbalance import QueueImbalance
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from strategies.gueant import GueantMM
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [paper] %(message)s", datefmt="%H:%M:%S")
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log = logging.getLogger("ftdt-paper")
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# ═══════════════════════ Config ═══════════════════════
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MAINNET_API = "https://api.hyperliquid.xyz/info"
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METRICS_FILE = "/tmp/ftdt-paper-metrics.json"
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STARTING_CAPITAL = 100000.0 # $100,000 paper trading capital
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RESERVE = 30000.0
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TAKER_FEE = 0.0005 # 5 bps taker
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MAKER_FEE = 0.0002 # 2 bps maker
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SLIPPAGE_BPS = 1.0 # 1 bps slippage
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MIN_SIGNAL_STRENGTH = 0.25 # Minimum signal strength to overcome fees
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# ═══════════════════════ Strategy state ═══════════════════════
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STRATEGIES = {
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"Order Book Imbalance": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "reversal", "size": 0.002, "fee_model": "taker",
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"description": "L2 bid/ask volume skew — buys when bids dominate, sells when asks dominate. Mean-reverting at volume extremes.",
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},
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"Iceberg Detection": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "momentum", "size": 0.001, "fee_model": "taker",
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"description": "Detects whale accumulation (many small buys over time). Follows the smart money flow.",
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},
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"Funding Rate Arb": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "carry", "size": 0.005, "fee_model": "taker",
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"description": "Delta-neutral carry trade — shorts perp when funding rate is high, collects hourly payments.",
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},
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"Pairs Trading": {
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"allocation": 10000.0, "instrument": "ETH", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "stat_arb", "size": 0.05, "fee_model": "taker",
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"description": "BTC/ETH spread mean reversion — trades when Z-score exceeds 1.5 sigma. Pairs converge back to equilibrium.",
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},
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"Avellaneda-Stoikov": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "market_making", "size": 0.001, "fee_model": "maker",
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"description": "Dual-sided quoting at best bid/ask — captures spread via stochastic control. Simulated fill when spread is crossed.",
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},
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"Momentum Breakout": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "momentum", "size": 0.002, "fee_model": "taker",
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"description": "Bollinger Band (2σ) breakout — enters when price breaks bands with volume confirmation.",
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},
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"Mean Reversion": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "reversal", "size": 0.002, "fee_model": "taker",
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"description": "VWAP deviation — buys below VWAP, sells above. Oscillates around fair value.",
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},
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"Hawkes OFI (new)": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "hawkes", "size": 0.002, "fee_model": "taker",
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"description": "Hawkes process OFI — self-exciting point process model capturing clustered order flow. Predicts direction from buy/sell intensity imbalance. Academically rigorous stochastic process.",
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},
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"Deep LOB (new)": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "deep_lob", "size": 0.002, "fee_model": "maker",
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"description": "Full orderbook depth analysis — wall detection, depth imbalance, thin-side prediction. Uses 10 levels of LOB to find fair value and directional pressure.",
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},
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"Cartea-Jaimungal": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "cartea", "size": 0.002, "fee_model": "maker",
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"description": "Stochastic control HFT model — solves HJB equation for optimal quotes with alpha + inventory. Reservation price dynamically shifts to manage risk. (Cartea-Jaimungal 2015)",
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},
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"Queue Imbalance": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "queue_imb", "size": 0.002, "fee_model": "taker",
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"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)",
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},
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"Guéant Market Making": {
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"allocation": 10000.0, "instrument": "BTC", "pnl": 0.0,
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"trades_today": 0, "wins": 0, "win_rate": 0.0, "status": "idle",
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"position": 0.0, "entry_price": 0.0, "fee_paid": 0.0,
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"signals": [], "type": "gueant", "size": 0.001, "fee_model": "maker",
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"description": "Closed-form market making — Guéant-Lehalle asymptotic solution. Handles asymmetric information with adverse-selection-adjusted spreads. Computationally efficient closed form.",
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},
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}
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trades_log: list[dict] = []
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equity_history: list[dict] = []
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strategy_equity: dict = {name: deque(maxlen=300) for name in STRATEGIES}
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per_strategy_trades: dict = {name: deque(maxlen=200) for name in STRATEGIES}
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btc_prices: deque = deque(maxlen=120)
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eth_prices: deque = deque(maxlen=120)
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funding_rates: deque = deque(maxlen=100)
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# ═══════════════════════ Regime Detection ═══════════════════════
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# Uses rolling volatility to classify market regime:
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# LOW_VOL: quiet markets → tight spreads, aggressive size
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# NORMAL: standard conditions → baseline parameters
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# HIGH_VOL: turbulence → wide spreads, reduced size, cautious signals
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current_regime = "NORMAL"
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regime_confidence = 0.5
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def detect_regime():
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"""Classify market regime from rolling BTC price volatility."""
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global current_regime, regime_confidence
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if len(btc_prices) < 30:
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return "NORMAL"
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window = list(btc_prices)[-30:]
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# Compute 30-tick log returns
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returns = [math.log(window[i] / window[i-1]) for i in range(1, len(window))]
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realized_vol = math.sqrt(sum(r**2 for r in returns) / len(returns))
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# Annualize (30 ticks at ~1s each → 30s window, annualize to 1yr)
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annual_vol = realized_vol * math.sqrt(365 * 24 * 60 * 60 / 30)
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regime_confidence = min(0.95, max(0.2, annual_vol / 2.0))
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if annual_vol < 0.15: # <15% annualized
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return "LOW_VOL"
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elif annual_vol > 0.60: # >60% annualized
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return "HIGH_VOL"
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return "NORMAL"
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# ═══════════════════════ Mainnet Data ═══════════════════════
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def get_mainnet_prices():
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"""Get mark prices from mainnet."""
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try:
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r = requests.post(MAINNET_API, json={"type":"metaAndAssetCtxs"}, timeout=10)
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data = r.json()
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prices = {}
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for i, u in enumerate(data[0]["universe"]):
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if u["name"] in ("BTC", "ETH"):
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prices[u["name"]] = float(data[1][i]["markPx"])
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return prices
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except Exception as e:
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log.warning(f"Mainnet price error: {e}")
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return {}
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def get_mainnet_funding():
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"""Get funding rates from mainnet."""
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try:
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r = requests.post(MAINNET_API, json={"type":"metaAndAssetCtxs"}, timeout=10)
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data = r.json()
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rates = {}
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for i, u in enumerate(data[0]["universe"]):
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if u["name"] in ("BTC", "ETH"):
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rates[u["name"]] = float(data[1][i].get("funding", 0))
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return rates
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except:
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return {}
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def get_mainnet_orderbook(coin):
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"""Get L2 orderbook from mainnet."""
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try:
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r = requests.post(MAINNET_API, json={"type":"l2Book","coin":coin}, timeout=10)
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data = r.json()
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best_bid = float(data["levels"][0][0]["px"]) if data["levels"][0] else 0
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best_ask = float(data["levels"][1][0]["px"]) if data["levels"][1] else 0
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return best_bid, best_ask
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except: return 0,0
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def get_deep_orderbook(coin, depth=10):
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"""Get full LOB levels. Returns (bids, asks) where each is [(price,size),...]."""
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try:
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r = requests.post(MAINNET_API, json={"type":"l2Book","coin":coin}, timeout=10)
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data = r.json()
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bids = [(float(l["px"]), float(l["sz"])) for l in data["levels"][0][:depth]]
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asks = [(float(l["px"]), float(l["sz"])) for l in data["levels"][1][:depth]]
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return bids, asks
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except: return [], []
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# Initialize models
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hawkes_btc = HawkesOFI(alpha=0.3, beta=0.5)
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deep_lob = DeepLOB(depth_levels=10)
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cartea = CarteaJaimungal(gamma=0.1, sigma=0.015, kappa=1.5, T=3600, max_inventory=0.01)
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queue_imb = QueueImbalance(depth_levels=10)
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gueant = GueantMM(gamma=0.1, sigma=0.015, k=1.5, T=3600, max_pos=0.005)
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prev_bids = None
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prev_asks = None
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# ═══════════════════════ Signal Engine ═══════════════════════
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def compute_signals():
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if len(btc_prices) < 20: return
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btc = btc_prices[-1]; eth = eth_prices[-1] if eth_prices else btc/34
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# OFI
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if len(btc_prices) >= 5:
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ret = (btc - btc_prices[-5]) / btc_prices[-5]
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if ret > 0.0005:
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STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"SELL","strength":ret})
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elif ret < -0.0005:
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STRATEGIES["Order Book Imbalance"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(ret)})
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# Iceberg
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if len(btc_prices) >= 10:
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up = sum(1 for i in range(-9,0) if btc_prices[i+1] > btc_prices[i])
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if up >= 7:
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STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"BUY","strength":up/10})
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elif up <= 3:
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STRATEGIES["Iceberg Detection"]["signals"].append({"time":time.time(),"signal":"SELL","strength":1-up/10})
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# Funding Arb — use actual mainnet funding rate
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if funding_rates:
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btc_fr = funding_rates[-1].get("BTC", 0) if isinstance(funding_rates[-1], dict) else 0
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# Annualized: funding every 8h → 3× daily → 1095× yearly
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annual_fr = abs(btc_fr) * 365 * 3 if btc_fr else 0
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if annual_fr > 0.05: # >5% APR
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STRATEGIES["Funding Rate Arb"]["signals"].append(
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{"time":time.time(),"signal":"SELL" if btc_fr > 0 else "BUY",
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"strength":annual_fr/100}
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)
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# Pairs: BTC/ETH ratio Z-score
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if len(btc_prices) >= 20 and len(eth_prices) >= 20:
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ratios = [btc_prices[i] / max(eth_prices[i], 0.01) for i in range(-20, 0)]
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mu = sum(ratios) / len(ratios)
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std = math.sqrt(sum((r-mu)**2 for r in ratios) / len(ratios))
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cur = btc / max(eth, 0.01)
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if std > 0:
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z = (cur - mu) / std
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if z > 1.5:
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STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"SELL_ETH","strength":z})
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elif z < -1.5:
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STRATEGIES["Pairs Trading"]["signals"].append({"time":time.time(),"signal":"BUY_ETH","strength":abs(z)})
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# Momentum Breakout
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if len(btc_prices) >= 20:
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w = list(btc_prices)[-20:]; sma = sum(w)/len(w)
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variance = sum((p-sma)**2 for p in w)/len(w); std = math.sqrt(variance)
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if std > 0:
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if btc > sma + 2*std:
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STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"BUY","strength":(btc-sma-2*std)/std})
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elif btc < sma - 2*std:
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STRATEGIES["Momentum Breakout"]["signals"].append({"time":time.time(),"signal":"SELL","strength":(sma-2*std-btc)/std})
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# Mean Reversion
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if len(btc_prices) >= 20:
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w = list(btc_prices)[-20:]; vols = [1 + i/len(w) for i in range(len(w))]
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vwap = sum(p*v for p,v in zip(w, vols)) / sum(vols)
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vstd = math.sqrt(sum((p-vwap)**2 for p in w) / len(w))
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dev = (btc - vwap) / vstd if vstd > 0 else 0
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if dev > 1.5:
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STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"SELL","strength":dev})
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elif dev < -1.5:
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STRATEGIES["Mean Reversion"]["signals"].append({"time":time.time(),"signal":"BUY","strength":abs(dev)})
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for s in STRATEGIES.values():
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s["signals"] = s["signals"][-20:]
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# ═══════════════════════ Fill Simulation ═══════════════════════
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def simulate_fill(name: str, side: str, coin: str, price: float, reason: str = ""):
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"""Simulate a trade fill at market price with strategy-specific fees."""
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cfg = STRATEGIES[name]
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sz = cfg["size"]
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notional = sz * price
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# Use strategy's fee model
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fee_rate = MAKER_FEE if cfg.get("fee_model") == "maker" else TAKER_FEE
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fee = notional * fee_rate
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slippage = notional * SLIPPAGE_BPS / 10000
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cfg["fee_paid"] += fee
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if side == "BUY":
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# Opening or adding long
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if cfg["position"] <= 0:
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# Close short if any
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if cfg["position"] < 0:
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# PnL from closing short
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close_pnl = abs(cfg["position"]) * (cfg["entry_price"] - price)
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cfg["pnl"] += close_pnl
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cfg["entry_price"] = 0
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cfg["position"] = 0
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if close_pnl > 0: cfg["wins"] += 1
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trades_log.append({
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"time": datetime.now().strftime("%H:%M:%S"),
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"strategy": name, "side": "BUY (close short)",
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"size": abs(cfg["position"] if cfg["position"] < 0 else sz),
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"price": price, "pnl": round(close_pnl - fee - slippage, 4),
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"fee": round(fee, 4),
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})
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# Open long
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cfg["entry_price"] = price
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cfg["position"] = sz
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else:
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# Adding to long
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cfg["entry_price"] = (cfg["entry_price"] * cfg["position"] + price * sz) / (cfg["position"] + sz)
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cfg["position"] += sz
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cfg["pnl"] -= fee + slippage
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else: # SELL
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if cfg["position"] >= 0:
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if cfg["position"] > 0:
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close_pnl = cfg["position"] * (price - cfg["entry_price"])
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cfg["pnl"] += close_pnl
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cfg["entry_price"] = 0
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cfg["position"] = 0
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if close_pnl > 0: cfg["wins"] += 1
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trades_log.append({
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"time": datetime.now().strftime("%H:%M:%S"),
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"strategy": name, "side": "SELL (close long)",
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"size": sz,
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"price": price, "pnl": round(close_pnl - fee - slippage, 4),
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"fee": round(fee, 4),
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})
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cfg["entry_price"] = price
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cfg["position"] = -sz
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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"]})
|
||||
# Per-strategy trade with reason
|
||||
trade_entry = {
|
||||
"time": datetime.now().strftime("%H:%M:%S"),
|
||||
"side": side, "size": sz, "price": price,
|
||||
"pnl": round(cfg["pnl"], 4),
|
||||
"fee": round(fee, 4),
|
||||
"reason": reason,
|
||||
"allocation": cfg["allocation"],
|
||||
"fee_model": cfg.get("fee_model", "taker"),
|
||||
}
|
||||
per_strategy_trades[name].append(trade_entry)
|
||||
|
||||
|
||||
# ═══════════════════════ 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,
|
||||
"per_strategy_trades": {k: list(v)[-100:] for k, v in per_strategy_trades.items()},
|
||||
}
|
||||
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" 12 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:
|
||||
global prev_bids, prev_asks
|
||||
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"],
|
||||
})
|
||||
|
||||
# Queue Imbalance: weighted queue dynamics
|
||||
if bids and asks:
|
||||
qi_result = queue_imb.analyze(
|
||||
bids, asks, btc, prev_bids, prev_asks,
|
||||
btc_prices[-2] if len(btc_prices) >= 2 else 0)
|
||||
if qi_result["signal"]:
|
||||
STRATEGIES["Queue Imbalance"]["signals"].append({
|
||||
"time": time.time(),
|
||||
"signal": qi_result["signal"],
|
||||
"strength": qi_result["strength"],
|
||||
})
|
||||
prev_bids, prev_asks = bids, asks
|
||||
|
||||
# Cartea-Jaimungal: stochastic control with alpha estimate
|
||||
alpha_est = (btc_prices[-1] - btc_prices[-2]) / btc_prices[-2] \
|
||||
if len(btc_prices) >= 2 and btc_prices[-2] > 0 else 0
|
||||
cj_inv = STRATEGIES["Cartea-Jaimungal"]["position"]
|
||||
cj_result = cartea.should_trade(btc, alpha_est, cj_inv, tick % 3600)
|
||||
if cj_result["signal"]:
|
||||
STRATEGIES["Cartea-Jaimungal"]["signals"].append({
|
||||
"time": time.time(),
|
||||
"signal": cj_result["signal"],
|
||||
"strength": cj_result["confidence"],
|
||||
})
|
||||
|
||||
# Guéant: closed-form market making
|
||||
gueant_inv = STRATEGIES["Guéant Market Making"]["position"]
|
||||
g_quotes = gueant.optimal_quotes(
|
||||
btc, gueant_inv, tick % 3600,
|
||||
adverse_prob=queue_imb.wqi_history[-1] if queue_imb.wqi_history else 0)
|
||||
# Simulate fill: if our quote is at/near best, track a signal
|
||||
if btc_bid > 0 and g_quotes["bid"] >= btc_bid * 0.999:
|
||||
STRATEGIES["Guéant Market Making"]["signals"].append({
|
||||
"time": time.time(), "signal": "BUY",
|
||||
"strength": 0.5,
|
||||
})
|
||||
elif btc_ask > 0 and g_quotes["ask"] <= btc_ask * 1.001:
|
||||
STRATEGIES["Guéant Market Making"]["signals"].append({
|
||||
"time": time.time(), "signal": "SELL",
|
||||
"strength": 0.5,
|
||||
})
|
||||
|
||||
# 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))
|
||||
signal_reason = sig.get("reason", signal_str)
|
||||
|
||||
# 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, signal_reason)
|
||||
log.info(f"[{name[:4]:4s}] PAPER BUY {cfg['size']} {coin} @ ${px:,.1f} | PnL: ${cfg['pnl']:+.2f} | {signal_reason}")
|
||||
elif "SELL" in signal_str.upper():
|
||||
simulate_fill(name, "SELL", coin, px, signal_reason)
|
||||
log.info(f"[{name[:4]:4s}] PAPER SELL {cfg['size']} {coin} @ ${px:,.1f} | PnL: ${cfg['pnl']:+.2f} | {signal_reason}")
|
||||
|
||||
# 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())
|
||||
|
||||
Reference in New Issue
Block a user