# 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

---

## 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.

---

## 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)
```

---

## 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.

---

## 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.

---

## 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.

---

## Next Steps

```bash
# 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
```

---

## 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*