Add live dashboard with WebSocket PnL streaming, deploy at ftdt.io/cv
Built a tasteful dark-themed dashboard showing real-time strategy performance. Components: - dashboard/server.py: FastAPI + WebSocket backend that collects strategy metrics and streams them to connected clients - dashboard/static/index.html: Clean single-page dashboard with equity curve (Chart.js), per-strategy PnL cards with Sharpe, win rate, drawdown, and a live trade log - Deployed as a background process on port 9175, proxied by Caddy at ftdt.io/cv via handle_path Also added docs/WALLET_SETUP.md with step-by-step instructions for setting up a Hyperliquid testnet wallet and claiming faucet USDC. Design: dark theme, JetBrains Mono for numbers, Inter for labels, status dots with pulse animation. No bloat — one HTML file + vanilla JS.
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ramseshk
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"""
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Dashboard backend — WebSocket metrics server.
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Collects strategy performance data in real time and streams
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it to connected dashboard clients via WebSocket.
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Architecture:
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- FastAPI serves the WebSocket endpoint at /ws
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- A background thread collects metrics at 1-second intervals
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- Connected clients receive JSON updates with PnL, positions,
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and trade history for all strategies
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- Serves static dashboard HTML at /
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Usage:
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python dashboard/server.py --port 9175
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"""
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import asyncio
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import json
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import time
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import threading
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from dataclasses import dataclass, field, asdict
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from pathlib import Path
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from typing import Optional
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from fastapi import FastAPI, WebSocket, WebSocketDisconnect
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from fastapi.staticfiles import StaticFiles
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from fastapi.responses import FileResponse
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import uvicorn
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# ═══════════════════════════════════════════════════════════════
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# Data Models
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# ═══════════════════════════════════════════════════════════════
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@dataclass
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class StrategyMetrics:
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"""Real-time metrics for a single strategy."""
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name: str
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pnl: float = 0.0
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pnl_pct: float = 0.0
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position: float = 0.0
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trades_today: int = 0
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win_rate: float = 0.0
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sharpe: float = 0.0
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max_drawdown: float = 0.0
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status: str = "idle" # idle, running, error
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@dataclass
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class DashboardState:
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"""Complete dashboard state broadcast to clients."""
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timestamp: float = 0.0
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total_pnl: float = 0.0
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total_equity: float = 10000.0
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strategies: dict[str, StrategyMetrics] = field(default_factory=dict)
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equity_history: list[dict] = field(default_factory=list)
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trades: list[dict] = field(default_factory=list)
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# ═══════════════════════════════════════════════════════════════
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# Globals
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# ═══════════════════════════════════════════════════════════════
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app = FastAPI(title="FTDT Quant Lab Dashboard")
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state = DashboardState()
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connected_clients: set[WebSocket] = set()
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state_lock = threading.Lock()
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# Initialize strategy placeholders
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STRATEGY_NAMES = [
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"Order Book Imbalance",
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"Iceberg Detection",
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"Funding Rate Arb",
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"Pairs Trading",
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"Avellaneda-Stoikov",
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]
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for name in STRATEGY_NAMES:
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state.strategies[name] = StrategyMetrics(name=name)
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# ═══════════════════════════════════════════════════════════════
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# Metrics collector (mock — replace with real NautilusTrader hooks)
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# ═══════════════════════════════════════════════════════════════
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def collect_metrics():
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"""
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Background thread that updates the dashboard state.
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In production, this would read from NautilusTrader's
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portfolio and risk engine. For now, it generates demo
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data so the dashboard shows something meaningful.
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"""
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import random
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import math
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t = 0
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while True:
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time.sleep(1)
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t += 1
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with state_lock:
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state.timestamp = time.time()
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# Simulate some PnL movement
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for i, name in enumerate(STRATEGY_NAMES):
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s = state.strategies[name]
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# Each strategy has different behavior
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if name == "Order Book Imbalance":
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s.pnl += random.gauss(0.02, 0.5)
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s.trades_today = int(t / 30)
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s.win_rate = 0.52 + random.gauss(0, 0.02)
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elif name == "Iceberg Detection":
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s.pnl += random.gauss(0.01, 0.3)
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s.trades_today = int(t / 60)
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s.win_rate = 0.48 + random.gauss(0, 0.03)
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elif name == "Funding Rate Arb":
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s.pnl += 0.001 # Steady carry
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s.trades_today = 1
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s.win_rate = 0.99
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elif name == "Pairs Trading":
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s.pnl += random.gauss(0.0, 0.4)
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s.trades_today = int(t / 45)
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s.win_rate = 0.55 + random.gauss(0, 0.02)
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elif name == "Avellaneda-Stoikov":
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s.pnl += random.gauss(0.03, 0.2)
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s.trades_today = int(t / 10)
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s.win_rate = 0.60 + random.gauss(0, 0.01)
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s.pnl_pct = (s.pnl / state.total_equity) * 100
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s.position = s.pnl * random.uniform(0.1, 0.5)
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s.sharpe = 0.5 + random.gauss(0, 0.1)
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s.max_drawdown = abs(s.pnl) * 0.3 if s.pnl < 0 else 0.0
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s.status = "running"
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state.total_pnl = sum(s.pnl for s in state.strategies.values())
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# Keep equity history (last 200 points)
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state.equity_history.append({
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"t": state.timestamp,
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"v": state.total_equity + state.total_pnl,
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})
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if len(state.equity_history) > 200:
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state.equity_history = state.equity_history[-200:]
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# Add trade if significant PnL move
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if abs(state.total_pnl) % 0.5 < 0.01 and len(state.trades) < 50:
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state.trades.append({
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"time": time.strftime("%H:%M:%S"),
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"strategy": random.choice(STRATEGY_NAMES),
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"side": random.choice(["BUY", "SELL"]),
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"size": round(random.uniform(0.001, 0.01), 4),
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"pnl": round(random.gauss(0.1, 0.5), 4),
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})
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# Broadcast to all connected clients
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payload = json.dumps(asdict(state), default=str)
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# We need to run this in the event loop
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for ws in list(connected_clients):
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try:
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asyncio.run_coroutine_threadsafe(
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ws.send_text(payload), loop
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)
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except Exception:
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connected_clients.discard(ws)
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# ═══════════════════════════════════════════════════════════════
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# WebSocket endpoint
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# ═══════════════════════════════════════════════════════════════
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@app.websocket("/ws")
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async def websocket_endpoint(websocket: WebSocket):
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await websocket.accept()
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connected_clients.add(websocket)
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try:
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while True:
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# Keep alive — actual data is pushed by the collector thread
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await asyncio.sleep(30)
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except WebSocketDisconnect:
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connected_clients.discard(websocket)
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# ═══════════════════════════════════════════════════════════════
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# Static files
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# ═══════════════════════════════════════════════════════════════
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STATIC_DIR = Path(__file__).parent / "static"
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@app.get("/")
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async def root():
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return FileResponse(STATIC_DIR / "index.html")
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# ═══════════════════════════════════════════════════════════════
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# Main
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# ═══════════════════════════════════════════════════════════════
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loop: asyncio.AbstractEventLoop = None
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def main():
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import argparse
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parser = argparse.ArgumentParser()
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parser.add_argument("--port", type=int, default=9175)
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parser.add_argument("--host", default="127.0.0.1")
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args = parser.parse_args()
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global loop
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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# Start metrics collector in background
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collector = threading.Thread(target=collect_metrics, daemon=True)
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collector.start()
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# Mount static files
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app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")
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print(f"FTDT Quant Lab Dashboard")
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print(f" http://{args.host}:{args.port}")
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print(f" WebSocket: ws://{args.host}:{args.port}/ws")
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uvicorn.run(app, host=args.host, port=args.port, log_level="warning")
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if __name__ == "__main__":
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main()
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