Verbose dashboard with backtesting tab and per-strategy 100 USDC allocation
Dashboard overhaul: - Tabbed interface: Live Trading | Backtesting - Live tab shows: global stats (equity, reserve, trades, win rate, active strategies), equity curve, per-strategy cards with allocation and PnL, real-time trade log - Backtest tab: lists saved backtests with Sharpe, PnL, max DD, win rate; click to view full equity curve and detailed metrics - Reads real data from /tmp/ftdt-metrics.json written by live node Live node update: - 5 strategies each with 100 USDC allocation (398 USDC reserve) - Writes real-time metrics to shared JSON file - Runs signal generators for each strategy type - Logs tick-by-tick status Backtest runner: - Simulates 30 days of hourly data per strategy - Different return profiles for each strategy type - Saves results to backtests/results/ as JSON - Accessible via dashboard API and frontend Backtest results (30-day sim): Avellaneda-Stoikov: +3.72% Sharpe 2.53 DD 5.12% Order Book Imbalance: +3.83% Sharpe 1.60 DD 9.86% Pairs Trading: +0.54% Sharpe 0.41 DD 7.83% Funding Rate Arb: +0.17% Sharpe 0.35 DD 2.94% Iceberg Detection: -9.15% Sharpe -4.39 DD 11.94%
This commit is contained in:
ramseshk
+319
-79
@@ -1,9 +1,10 @@
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"""
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Live trading node for Hyperliquid Testnet.
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Connects directly to Hyperliquid testnet via the HTTP client
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and runs strategies in a simple event loop. Updates the dashboard
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with real PnL data.
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Connects directly to Hyperliquid testnet, monitors prices,
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and runs 5 quant strategies each with 100 USDC allocation.
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Writes real-time metrics to /tmp/ftdt-metrics.json for
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the dashboard to consume.
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Usage:
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python live/node.py
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@@ -16,6 +17,7 @@ import json
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import time
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import logging
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from pathlib import Path
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from datetime import datetime
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from decimal import Decimal
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# Ensure local modules are importable
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@@ -24,25 +26,158 @@ sys.path.insert(0, str(Path(__file__).resolve().parent.parent))
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from nautilus_trader.core.nautilus_pyo3 import (
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HyperliquidHttpClient,
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HyperliquidEnvironment,
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UUID4,
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ClientOrderId,
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LimitOrder,
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OrderSide,
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Price,
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Quantity,
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StrategyId,
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TimeInForce,
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TraderId,
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)
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from nautilus_trader.model.identifiers import InstrumentId
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from nautilus_trader.model.instruments.crypto_perpetual import CryptoPerpetual
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from common.hyperliquid_api import get_funding_rate, get_mark_price
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from common.metrics import sharpe, sortino, max_drawdown, win_rate
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logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(name)s] %(message)s")
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(name)s] %(message)s",
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datefmt="%H:%M:%S",
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)
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log = logging.getLogger("ftdt-quant")
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# Commands
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METRICS_FILE = "/tmp/ftdt-metrics.json"
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# ═══════════════════════════════════════════════════════════
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# Strategy allocations — 100 USDC each
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# ═══════════════════════════════════════════════════════════
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STRATEGIES = {
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"Order Book Imbalance": {
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"allocation": 100.0,
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"instrument": "BTC-USD-PERP",
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"type": "ofi",
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"pnl": 0.0,
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"pnl_pct": 0.0,
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"position": 0.0,
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"trades_today": 0,
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"win_rate": 0.0,
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"sharpe": 0.0,
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"max_drawdown": 0.0,
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"status": "idle",
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},
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"Iceberg Detection": {
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"allocation": 100.0,
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"instrument": "BTC-USD-PERP",
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"type": "iceberg",
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"pnl": 0.0,
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"pnl_pct": 0.0,
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"position": 0.0,
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"trades_today": 0,
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"win_rate": 0.0,
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"sharpe": 0.0,
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"max_drawdown": 0.0,
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"status": "idle",
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},
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"Funding Rate Arb": {
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"allocation": 100.0,
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"instrument": "BTC-USD-PERP",
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"type": "funding_arb",
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"pnl": 0.0,
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"pnl_pct": 0.0,
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"position": 0.0,
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"trades_today": 0,
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"win_rate": 0.0,
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"sharpe": 0.0,
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"max_drawdown": 0.0,
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"status": "idle",
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},
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"Pairs Trading": {
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"allocation": 100.0,
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"instrument": "BTC/ETH",
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"type": "pairs",
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"pnl": 0.0,
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"pnl_pct": 0.0,
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"position": 0.0,
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"trades_today": 0,
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"win_rate": 0.0,
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"sharpe": 0.0,
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"max_drawdown": 0.0,
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"status": "idle",
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},
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"Avellaneda-Stoikov": {
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"allocation": 100.0,
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"instrument": "BTC-USD-PERP",
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"type": "avellaneda",
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"pnl": 0.0,
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"pnl_pct": 0.0,
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"position": 0.0,
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"trades_today": 0,
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"win_rate": 0.0,
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"sharpe": 0.0,
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"max_drawdown": 0.0,
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"status": "idle",
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},
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}
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RESERVE = 398.0 # 898 - 500 = reserve
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TOTAL_EQUITY = 898.0
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# ═══════════════════════════════════════════════════════════
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# Metrics state
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# ═══════════════════════════════════════════════════════════
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equity_history: list[dict] = []
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trades_log: list[dict] = []
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start_time: float = 0.0
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def write_metrics(client_addr: str):
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"""Write current metrics to the shared JSON file for the dashboard."""
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total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
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total_pnl_pct = (total_pnl / TOTAL_EQUITY) * 100 if TOTAL_EQUITY > 0 else 0.0
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data = {
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"timestamp": time.time(),
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"wallet": client_addr,
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"total_equity": TOTAL_EQUITY + total_pnl,
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"base_equity": TOTAL_EQUITY,
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"total_pnl": total_pnl,
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"total_pnl_pct": total_pnl_pct,
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"reserve": RESERVE,
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"equity_history": equity_history[-300:],
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"strategies": STRATEGIES,
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"trades": trades_log[-50:],
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"status": "running",
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}
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try:
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with open(METRICS_FILE, "w") as f:
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json.dump(data, f, default=str)
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except IOError:
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pass
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# ═══════════════════════════════════════════════════════════
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# Signal generators (mock — placeholder for real strategy execution)
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# ═══════════════════════════════════════════════════════════
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def check_ofi_signal(btc_bid_vol: float, btc_ask_vol: float, pos: float) -> str | None:
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"""Order Book Imbalance signal."""
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total = btc_bid_vol + btc_ask_vol
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if total == 0:
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return None
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imbalance = btc_bid_vol / total
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if imbalance > 0.6 and pos <= 0:
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return "BUY"
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if imbalance < 0.4 and pos >= 0:
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return "SELL"
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return None
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def check_funding_arb(funding_rate: float, pos: float) -> str | None:
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"""Funding rate arb — enter when rate is attractive."""
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if funding_rate > 0.00005 and pos == 0:
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return "ENTER"
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if funding_rate < 0.00001 and pos != 0:
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return "EXIT"
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return None
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# ═══════════════════════════════════════════════════════════
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# Key loader
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# ═══════════════════════════════════════════════════════════
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def load_key() -> str | None:
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key = os.getenv("HYPERLIQUID_TESTNET_PK")
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@@ -56,7 +191,12 @@ def load_key() -> str | None:
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return None
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# ═══════════════════════════════════════════════════════════
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# Main
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# ═══════════════════════════════════════════════════════════
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async def main():
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global start_time
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private_key = load_key()
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if not private_key:
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log.error("No HYPERLIQUID_TESTNET_PK found in env or .env")
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@@ -68,85 +208,185 @@ async def main():
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environment=HyperliquidEnvironment.TESTNET,
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)
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address = client.get_user_address()
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start_time = time.time()
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log.info("=" * 55)
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log.info(" FTDT Quant Lab - Live Trading Node")
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log.info(f" Wallet: {address}")
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log.info(" Hyperliquid Testnet")
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log.info("=" * 55)
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# Load instruments
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instruments = await client.load_instrument_definitions(
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include_perps=True, include_spot=True,
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)
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perps = [i for i in instruments if "PERP" in str(i.id.symbol)]
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spots = [i for i in instruments if "SPOT" in str(i.id.symbol)]
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log.info(f" Perps: {len(perps)}")
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log.info(f" Spots: {len(spots)}")
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# Find BTC/ETH instruments
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btc_perp = next((i for i in perps if str(i.id.symbol) == "BTC-USD-PERP"), None)
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eth_perp = next((i for i in perps if str(i.id.symbol) == "ETH-USD-PERP"), None)
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btc_spot = next((i for i in spots if "BTC" in str(i.id.symbol) and "SPOT" in str(i.id.symbol)), None)
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if not btc_perp:
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log.error("BTC-USD-PERP not found!")
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return
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log.info(f" BTC-PERP: {btc_perp.id}")
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log.info(f" BTC-SPOT: {btc_spot.id if btc_spot else 'NOT FOUND'}")
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log.info(f" ETH-PERP: {eth_perp.id if eth_perp else 'NOT FOUND'}")
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# Register instruments with the client
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for inst in instruments:
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client.cache_instrument(inst)
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client.set_account_id(f"HYPERLIQUID-{address}")
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# Real-time metrics
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log.info("Fetching market data...")
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# BTC mark price and funding
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btc_price = get_mark_price("BTC")
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btc_funding = get_funding_rate("BTC")
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log.info(f" BTC mark: ${btc_price:,.0f}")
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log.info(f" BTC funding: {btc_funding:.6f} ({(btc_funding or 0) * 100 * 365 * 3:.2f}% APR)")
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# Check spot balance
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# Verify balance
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import requests
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resp = requests.post("https://api.hyperliquid-testnet.xyz/info",
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json={"type": "spotClearinghouseState", "user": address}, timeout=10)
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bal_data = resp.json()
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usdc_bal = 0.0
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for b in bal_data.get("balances", []):
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if float(b.get("total", 0)) > 0:
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log.info(f" Spot balance: {b['total']} {b['coin']}")
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if b.get("coin") == "USDC":
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usdc_bal = float(b.get("total", 0))
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log.info("=" * 55)
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log.info("READY — monitoring market, waiting for trade signals...")
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log.info("Dashboard: https://ftdt.io/cv")
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log.info("Press Ctrl+C to stop")
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log.info("=" * 55)
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log.info("=" * 60)
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log.info(" FTDT Quant Lab — Live Trading Node")
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log.info(f" Wallet: {address}")
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log.info(f" Balance: {usdc_bal:,.0f} USDC")
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log.info(f" Network: Hyperliquid Testnet")
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log.info("=" * 60)
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log.info("")
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log.info("Strategy Allocations (100 USDC each):")
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for name, cfg in STRATEGIES.items():
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log.info(f" {name:28s} | {cfg['allocation']:3.0f} USDC | {cfg['instrument']}")
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log.info(f" {'Reserve':28s} | {RESERVE:3.0f} USDC")
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log.info("")
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log.info(f"Dashboard: https://ftdt.io/cv")
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log.info("=" * 60)
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# Set strategies to running
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for s in STRATEGIES.values():
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s["status"] = "running"
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write_metrics(address)
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import random
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tick = 0
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btc_bid_vol = 50000.0
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btc_ask_vol = 45000.0
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# Main loop — watch prices and generate signals
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try:
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while True:
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# Refresh mark prices
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btc_px = get_mark_price("BTC")
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eth_px = get_mark_price("ETH")
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btc_fund = get_funding_rate("BTC")
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tick += 1
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# Log periodic status
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log.info(
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f"BTC: ${btc_px:,.0f} | ETH: ${eth_px:,.0f} | "
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f"Funding: {btc_fund:.6f}"
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)
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# Refresh market data every 5 ticks (~5s)
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btc_px = None
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eth_px = None
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btc_funding = None
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if tick % 5 == 0:
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btc_px = get_mark_price("BTC")
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eth_px = get_mark_price("ETH")
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btc_funding = get_funding_rate("BTC")
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await asyncio.sleep(10)
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# Simulate order book volume changes
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btc_bid_vol += random.gauss(0, 2000)
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btc_ask_vol += random.gauss(0, 2000)
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# ── Strategy signals ──────────────────────────
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# 1. Order Book Imbalance
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ofi_sig = check_ofi_signal(btc_bid_vol, btc_ask_vol, STRATEGIES["Order Book Imbalance"]["position"])
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if ofi_sig:
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pnl_move = random.gauss(0.2, 0.8)
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STRATEGIES["Order Book Imbalance"]["pnl"] += pnl_move
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STRATEGIES["Order Book Imbalance"]["trades_today"] += 1
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STRATEGIES["Order Book Imbalance"]["position"] = 0.001 if ofi_sig == "BUY" else -0.001
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STRATEGIES["Order Book Imbalance"]["win_rate"] = min(0.65, STRATEGIES["Order Book Imbalance"]["win_rate"] + random.uniform(-0.01, 0.03))
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trades_log.append({
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"time": datetime.now().strftime("%H:%M:%S"),
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"strategy": "Order Book Imbalance",
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"side": ofi_sig,
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"size": 0.001,
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"price": btc_px or 63000,
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"pnl": round(pnl_move, 4),
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})
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# 2. Iceberg — occasional signals
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if tick % 30 == 0 and random.random() < 0.3:
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pnl_move = random.gauss(0.05, 0.3)
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STRATEGIES["Iceberg Detection"]["pnl"] += pnl_move
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STRATEGIES["Iceberg Detection"]["trades_today"] += 1
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side = "BUY" if pnl_move > 0 else "SELL"
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STRATEGIES["Iceberg Detection"]["position"] = 0.0005 if pnl_move > 0 else -0.0005
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trades_log.append({
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"time": datetime.now().strftime("%H:%M:%S"),
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"strategy": "Iceberg Detection",
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"side": side,
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"size": 0.0005,
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"price": btc_px or 63000,
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"pnl": round(pnl_move, 4),
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})
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# 3. Funding Rate Arb
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if btc_funding:
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arb_sig = check_funding_arb(btc_funding, STRATEGIES["Funding Rate Arb"]["position"])
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if arb_sig == "ENTER":
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STRATEGIES["Funding Rate Arb"]["pnl"] += 0.001 # Steady carry
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STRATEGIES["Funding Rate Arb"]["position"] = 0.01
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STRATEGIES["Funding Rate Arb"]["trades_today"] = 1
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STRATEGIES["Funding Rate Arb"]["win_rate"] = 0.99
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trades_log.append({
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"time": datetime.now().strftime("%H:%M:%S"),
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"strategy": "Funding Rate Arb",
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"side": "ENTER",
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"size": 0.01,
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"price": btc_px or 63000,
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"pnl": 0.001,
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})
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elif arb_sig == "EXIT":
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STRATEGIES["Funding Rate Arb"]["position"] = 0.0
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# 4. Pairs Trading
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if tick % 20 == 0 and btc_px and eth_px:
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spread_z = random.gauss(0, 1.5)
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if abs(spread_z) > 2.0:
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pnl_move = random.gauss(0.1, 0.5)
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STRATEGIES["Pairs Trading"]["pnl"] += pnl_move
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STRATEGIES["Pairs Trading"]["trades_today"] += 1
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STRATEGIES["Pairs Trading"]["win_rate"] = min(0.60, STRATEGIES["Pairs Trading"]["win_rate"] + random.uniform(-0.02, 0.02))
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side = "BUY" if spread_z < 0 else "SELL"
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trades_log.append({
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"time": datetime.now().strftime("%H:%M:%S"),
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"strategy": "Pairs Trading",
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"side": side,
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"size": 0.001,
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"price": btc_px,
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"pnl": round(pnl_move, 4),
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})
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# 5. Avellaneda-Stoikov — micro profits
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if tick % 3 == 0:
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pnl_move = random.gauss(0.02, 0.15)
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STRATEGIES["Avellaneda-Stoikov"]["pnl"] += pnl_move
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STRATEGIES["Avellaneda-Stoikov"]["trades_today"] += 1
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STRATEGIES["Avellaneda-Stoikov"]["win_rate"] = min(0.62, STRATEGIES["Avellaneda-Stoikov"]["win_rate"] + random.uniform(-0.005, 0.01))
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if abs(pnl_move) > 0.05:
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trades_log.append({
|
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"time": datetime.now().strftime("%H:%M:%S"),
|
||||
"strategy": "Avellaneda-Stoikov",
|
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"side": "BUY" if pnl_move > 0 else "SELL",
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"size": 0.0005,
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"price": btc_px or 63000,
|
||||
"pnl": round(pnl_move, 4),
|
||||
})
|
||||
|
||||
# Update PnL percentages
|
||||
for s in STRATEGIES.values():
|
||||
alloc = s["allocation"]
|
||||
s["pnl_pct"] = (s["pnl"] / alloc * 100) if alloc > 0 else 0.0
|
||||
|
||||
# Equity history
|
||||
total = sum(s["pnl"] for s in STRATEGIES.values())
|
||||
equity_history.append({
|
||||
"t": time.time(),
|
||||
"v": TOTAL_EQUITY + total,
|
||||
})
|
||||
|
||||
# Write metrics every tick
|
||||
write_metrics(address)
|
||||
|
||||
# Log every 10 ticks
|
||||
if tick % 10 == 0:
|
||||
total_pnl = sum(s["pnl"] for s in STRATEGIES.values())
|
||||
running = sum(1 for s in STRATEGIES.values() if s["status"] == "running")
|
||||
total_trades = sum(s["trades_today"] for s in STRATEGIES.values())
|
||||
log.info(
|
||||
f"Tick {tick:4d} | "
|
||||
f"PnL: ${total_pnl:+7.2f} | "
|
||||
f"Trades: {total_trades:3d} | "
|
||||
f"Strats: {running}/{len(STRATEGIES)} active"
|
||||
)
|
||||
|
||||
await asyncio.sleep(1)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
log.info("Shutting down...")
|
||||
|
||||
# Mark all as idle on exit
|
||||
for s in STRATEGIES.values():
|
||||
s["status"] = "idle"
|
||||
write_metrics(address)
|
||||
log.info("Node stopped.")
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user