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%
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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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Reads live metrics from a shared JSON file (written by the live node)
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and serves backtest results from disk. Streams everything to
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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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- /ws — WebSocket for real-time streaming
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- /backtests — list available backtest results
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- /backtest/{name} — serve specific backtest result
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- / — static HTML dashboard
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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 os
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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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from fastapi.responses import FileResponse, JSONResponse
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import uvicorn
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# ═══════════════════════════════════════════════════════════
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# Constants
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# ═══════════════════════════════════════════════════════════
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# ═══════════════════════════════════════════════════════════════
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# Data Models
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# ═══════════════════════════════════════════════════════════════
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METRICS_FILE = "/tmp/ftdt-metrics.json"
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BACKTEST_DIR = "/home/debian/ftdt-quant-lab/backtests/results"
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STATIC_DIR = Path(__file__).parent / "static"
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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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# Ensure backtest dir exists
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os.makedirs(BACKTEST_DIR, exist_ok=True)
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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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# ═══════════════════════════════════════════════════════════
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# App
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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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loop: Optional[asyncio.AbstractEventLoop] = None
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# ═══════════════════════════════════════════════════════════════
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# Metrics collector (mock — replace with real NautilusTrader hooks)
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# ═══════════════════════════════════════════════════════════════
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# ═══════════════════════════════════════════════════════════
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# Metrics reader
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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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def read_metrics() -> dict:
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"""Read the shared metrics file written by the live node."""
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try:
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if os.path.exists(METRICS_FILE):
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with open(METRICS_FILE) as f:
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return json.load(f)
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except (json.JSONDecodeError, IOError):
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pass
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return _empty_metrics()
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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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def _empty_metrics() -> dict:
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return {
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"timestamp": time.time(),
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"wallet": "0x...",
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"total_equity": 898.0,
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"total_pnl": 0.0,
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"total_pnl_pct": 0.0,
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"equity_history": [],
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"strategies": {},
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"trades": [],
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"status": "starting",
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}
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# ═══════════════════════════════════════════════════════════
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# Background broadcaster
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# ═══════════════════════════════════════════════════════════
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async def broadcast_to_client(ws: WebSocket, payload: str):
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try:
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await ws.send_text(payload)
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except Exception:
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connected_clients.discard(ws)
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def broadcast_loop():
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"""Continuously read metrics and broadcast to all clients."""
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while True:
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time.sleep(1)
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t += 1
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data = read_metrics()
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payload = json.dumps(data, default=str)
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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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if loop:
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asyncio.run_coroutine_threadsafe(
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ws.send_text(payload), loop
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broadcast_to_client(ws, 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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# ═══════════════════════════════════════════════════════════
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# WebSocket
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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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# Send initial state immediately
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data = read_metrics()
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await websocket.send_text(json.dumps(data, default=str))
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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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# ═══════════════════════════════════════════════════════════
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# Backtest endpoints
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# ═══════════════════════════════════════════════════════════
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STATIC_DIR = Path(__file__).parent / "static"
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@app.get("/api/backtests")
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async def list_backtests():
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"""List all saved backtest results."""
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results = []
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if os.path.isdir(BACKTEST_DIR):
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for fname in sorted(os.listdir(BACKTEST_DIR), reverse=True):
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if fname.endswith(".json"):
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fpath = os.path.join(BACKTEST_DIR, fname)
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try:
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with open(fpath) as f:
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data = json.load(f)
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results.append({
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"name": fname.replace(".json", ""),
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"strategy": data.get("strategy", "unknown"),
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"start": data.get("start_time"),
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"end": data.get("end_time"),
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"sharpe": data.get("sharpe", 0),
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"pnl_pct": data.get("pnl_pct", 0),
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"max_dd": data.get("max_dd", 0),
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"win_rate": data.get("win_rate", 0),
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})
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except (json.JSONDecodeError, IOError):
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pass
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return JSONResponse(results)
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@app.get("/api/backtest/{name}")
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async def get_backtest(name: str):
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"""Get full backtest result data."""
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fpath = os.path.join(BACKTEST_DIR, f"{name}.json")
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if os.path.exists(fpath):
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with open(fpath) as f:
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return JSONResponse(json.load(f))
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return JSONResponse({"error": "not found"}, status_code=404)
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# ═══════════════════════════════════════════════════════════
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# Static
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# ═══════════════════════════════════════════════════════════
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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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app.mount("/static", StaticFiles(directory=str(STATIC_DIR)), name="static")
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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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# ═══════════════════════════════════════════════════════════
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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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parser.add_argument("--host", default="0.0.0.0")
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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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# Start metrics broadcaster
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broadcaster = threading.Thread(target=broadcast_loop, daemon=True)
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broadcaster.start()
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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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print(f" Backtests: /api/backtests")
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uvicorn.run(app, host=args.host, port=args.port, log_level="warning")
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Reference in New Issue
Block a user