FTDT Quant Lab

Production multi-strategy quant trading system running on Hyperliquid.
Live testnet node, paper trading simulator, historical backtesting, and real-time dashboard.

Live: https://ftdt.io/cv

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Stack

Layer	Technology
Runtime	Python 3.13 (async trading)
API Client	nautilus_trader (Hyperliquid SDK, Rust bindings)
Dashboard	Next.js 16 (static export) + shadcn/ui + Framer Motion
Design System	Hallmark Cobalt — Ubuntu font, hairline borders, cool paper palette
Reverse Proxy	Caddy → auto HTTPS
WebSocket	FastAPI (live/paper streaming)
Data	PostgreSQL 17 (ftdt_quant), JSON metrics files
Backtesting	Custom dollar-bar engine + numpy
Infra	OVH VPS (4 vCPU, 8GB RAM, Debian 13), 2GB swap

~5,300 lines of Python + TypeScript. 67 commits since July 2026.

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Repository Structure

ftdt-quant-lab/
├── live/
│   ├── node.py                  # Live trading node — testnet, 9 strategies
│   └── paper_trader.py          # Paper trading — mainnet data, 10 strategies
├── strategies/
│   ├── orderbook_imbalance.py   # L2 bid/ask volume skew (OBI)
│   ├── iceberg_detection.py     # Whale TWAP accumulation detection
│   ├── funding_arb.py           # Delta-neutral carry — spot/perp funding
│   ├── pairs_trading.py         # BTC/ETH ratio Z-score (1.5σ)
│   ├── avellaneda_stoikov.py    # Dual-sided stochastic control MM
│   ├── kalman_pairs/            # Kalman-filter adaptive hedge ratio
│   ├── hawkes_ofi.py            # Hawkes process order flow
│   ├── deep_lob.py              # Deep LOB CNN feature extraction
│   ├── queue_imbalance.py       # Weighted queue dynamics
│   ├── hurst_vpin.py            # Hurst exponent + VPIN directional
│   ├── hurst_vpin_live.py       # Lightweight Hurst/VPIN for live tick stream
│   └── quant_report.py          # QF-Lib style quant analytics
├── dashboard/
│   ├── server.py                # FastAPI backend — WS, REST, static files
│   └── next/
│       └── src/
│           ├── app/             # Main page + layout
│           ├── components/      # QuantReport, StrategyCard, L2Terminal
│           └── lib/             # Types, API client
├── backtests/
│   ├── run.py                   # Backtest runner
│   └── results/
│       └── historical/          # JSON backtest snapshots (32 entries)
├── common/                      # Shared utilities
│   ├── risk.py, risk_manager.py
│   ├── hyperliquid_api.py
│   └── portfolio.py, metrics.py
├── config/
│   └── fee_tiers.py             # Perp/spot fee schedules
└── infrastructure/
    ├── Caddyfile                # Reverse proxy config
    └── systemd/                 # Service units (pending)

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Strategies — Current State

Live Node (Hyperliquid Testnet — 9 strategies, $100 each)

#	Strategy	Type	Asset	Size	PnL	Trades	Win
1	Order Book Imbalance	reversal	BTC	0.000200	$0.00	0	—
2	Iceberg Detection	momentum	BTC	0.000210	$0.00	2	0%
3	Funding Rate Arb	carry	BTC	0.000220	$0.00	0	—
4	Pairs Trading	stat_arb	ETH	0.006000	+$0.74	9	67%
5	Avellaneda-Stoikov	market_making	BTC	0.000230	-$1.35	32	0%
6	Momentum Breakout	momentum	ETH	0.000500	$0.00	0	—
7	Mean Reversion	reversal	ETH	0.000500	$0.00	0	—
8	Kalman Pairs	stat_arb	ETH	0.005000	$0.00	0	—
9	Hurst VPIN	momentum	BTC	0.000240	$0.00	0	—

Execution: GTC POST-ONLY limit orders. Signals every 5 ticks (5s), dual-sided for A-S.
Fee model: Maker 0.02% (testnet).

Paper Trader (Hyperliquid Mainnet data — 10 strategies, $100 each)

Same set + Queue Imbalance. Real mainnet orderbook + funding data. Fee model: taker 0.05% / maker 0.02%. Trades simulated with 1bps slippage.

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Historical Backtests

32 backtest snapshots across 8 strategies × 4 coins (BTC, ETH, HYPE, VVV).
Hurst/VPIN BTC: 46 trades, 96% win rate, +1.10% on synthetic trending data.

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Priority Analysis

Strategies showing real signal

Strategy	Signal	Status
Pairs Trading	✅	+$0.74, 67% win rate — only profitable live strategy
Avellaneda-Stoikov	⚠️	32 trades but losing — spread capture not covering fees
Iceberg Detection	⚠️	2 trades — rare signals, needs threshold tuning
Hurst VPIN	🔬	96% win in backtest, 0 live trades — very selective
Mean Reversion	⏳	0 trades — VWAP deviation not crossing 1.0σ
Momentum	⏳	0 trades — Bollinger 1.2σ too tight for ETH

Recommendation: focus investment here

1. Pairs Trading — #1 priority. Only live winner. Extend to more pairs (SOL, ARB, OP). Add Kalman dynamic hedge ratio. This is the clearest path to sustained PnL.

2. Hurst/VPIN — #2 priority. Backtest shows strong edge (96% win). Needs real market data (not synthetic) and 3-day candle feed to trigger more signals. The selectivity IS the edge — don't dilute it.

3. Avellaneda-Stoikov — Needs inventory control. 32 trades losing because adverse selection. Add skew-aware quoting (update reserve price based on queue imbalance).

4. Iceberg Detection — Lower detection threshold. Currently requires 7/10 consecutive ticks same direction — too strict.

5. Funding Rate Arb — Real Hyperliquid funding data already plumbed. Test threshold from 3% → 1% APR. Prefunding detection (predict next rate before announcement).

6. Backtest engine — Replace synthetic data with real Hyperliquid candles. Add walk-forward optimization. The hurst_vpin.py infrastructure is ready.

Skip for now

• OBI / Mean Reversion / Momentum — 0 trades. Signal thresholds need fundamental redesign, not just tuning.
• Cartea-Jaimungal / Gueant MM — academic models, not adapted to crypto microstructure.
• DeepLOB / Hawkes OFI — dependency-heavy, no live integration.

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Next Steps

# Clone and deploy
git clone https://git.ftdt.io/rams/ftdt-quant-lab.git
cd ftdt-quant-lab
python3 -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt        # (pending — currently manual)

# Start services
python live/node.py &                  # Trading node
python live/paper_trader.py &          # Paper simulator
python dashboard/server.py --port 9175  # Dashboard backend

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Roadmap

• Docker Compose for reproducible deployment
• Walk-forward backtest on real Hyperliquid candle data
• Extend Pairs Trading to BTC/SOL, BTC/ARB
• Hurst/VPIN 3-day candle feed → real live signals
• Memory leak proofing — current guard at 512MB RSS
• systemd service unit files for auto-restart
• Grafana + Prometheus monitoring dashboard

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Built with Hermes Agent · Hallmark Cobalt · Ubuntu fonts