ramseshk
169 lines
7.1 KiB
Markdown
169 lines
7.1 KiB
Markdown
# FTDT Quant Lab
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Production multi-strategy quant trading system running on Hyperliquid.
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Live testnet node, paper trading simulator, historical backtesting, and real-time dashboard.
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**Live:** https://ftdt.io/cv
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---
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## Stack
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| Layer | Technology |
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|-------|-----------|
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| **Runtime** | Python 3.13 (async trading) |
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| **API Client** | nautilus_trader (Hyperliquid SDK, Rust bindings) |
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| **Dashboard** | Next.js 16 (static export) + shadcn/ui + Framer Motion |
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| **Design System** | Hallmark Cobalt — Ubuntu font, hairline borders, cool paper palette |
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| **Reverse Proxy** | Caddy → auto HTTPS |
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| **WebSocket** | FastAPI (live/paper streaming) |
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| **Data** | PostgreSQL 17 (`ftdt_quant`), JSON metrics files |
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| **Backtesting** | Custom dollar-bar engine + numpy |
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| **Infra** | OVH VPS (4 vCPU, 8GB RAM, Debian 13), 2GB swap |
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~5,300 lines of Python + TypeScript. 67 commits since July 2026.
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---
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## Repository Structure
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```
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ftdt-quant-lab/
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├── live/
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│ ├── node.py # Live trading node — testnet, 9 strategies
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│ └── paper_trader.py # Paper trading — mainnet data, 10 strategies
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├── strategies/
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│ ├── orderbook_imbalance.py # L2 bid/ask volume skew (OBI)
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│ ├── iceberg_detection.py # Whale TWAP accumulation detection
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│ ├── funding_arb.py # Delta-neutral carry — spot/perp funding
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│ ├── pairs_trading.py # BTC/ETH ratio Z-score (1.5σ)
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│ ├── avellaneda_stoikov.py # Dual-sided stochastic control MM
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│ ├── kalman_pairs/ # Kalman-filter adaptive hedge ratio
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│ ├── hawkes_ofi.py # Hawkes process order flow
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│ ├── deep_lob.py # Deep LOB CNN feature extraction
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│ ├── queue_imbalance.py # Weighted queue dynamics
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│ ├── hurst_vpin.py # Hurst exponent + VPIN directional
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│ ├── hurst_vpin_live.py # Lightweight Hurst/VPIN for live tick stream
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│ └── quant_report.py # QF-Lib style quant analytics
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├── dashboard/
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│ ├── server.py # FastAPI backend — WS, REST, static files
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│ └── next/
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│ └── src/
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│ ├── app/ # Main page + layout
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│ ├── components/ # QuantReport, StrategyCard, L2Terminal
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│ └── lib/ # Types, API client
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├── backtests/
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│ ├── run.py # Backtest runner
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│ └── results/
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│ └── historical/ # JSON backtest snapshots (32 entries)
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├── common/ # Shared utilities
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│ ├── risk.py, risk_manager.py
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│ ├── hyperliquid_api.py
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│ └── portfolio.py, metrics.py
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├── config/
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│ └── fee_tiers.py # Perp/spot fee schedules
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└── infrastructure/
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├── Caddyfile # Reverse proxy config
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└── systemd/ # Service units (pending)
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```
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---
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## Strategies — Current State
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### Live Node (Hyperliquid Testnet — 9 strategies, $100 each)
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| # | Strategy | Type | Asset | Size | PnL | Trades | Win |
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|---|----------|------|-------|------|-----|--------|-----|
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| 1 | Order Book Imbalance | reversal | BTC | 0.000200 | $0.00 | 0 | — |
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| 2 | Iceberg Detection | momentum | BTC | 0.000210 | $0.00 | 2 | 0% |
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| 3 | Funding Rate Arb | carry | BTC | 0.000220 | $0.00 | 0 | — |
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| 4 | Pairs Trading | stat_arb | ETH | 0.006000 | **+$0.74** | 9 | 67% |
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| 5 | Avellaneda-Stoikov | market_making | BTC | 0.000230 | -$1.35 | 32 | 0% |
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| 6 | Momentum Breakout | momentum | ETH | 0.000500 | $0.00 | 0 | — |
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| 7 | Mean Reversion | reversal | ETH | 0.000500 | $0.00 | 0 | — |
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| 8 | Kalman Pairs | stat_arb | ETH | 0.005000 | $0.00 | 0 | — |
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| 9 | Hurst VPIN | momentum | BTC | 0.000240 | $0.00 | 0 | — |
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**Execution:** GTC POST-ONLY limit orders. Signals every 5 ticks (5s), dual-sided for A-S.
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**Fee model:** Maker 0.02% (testnet).
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### Paper Trader (Hyperliquid Mainnet data — 10 strategies, $100 each)
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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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---
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## Historical Backtests
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32 backtest snapshots across 8 strategies × 4 coins (BTC, ETH, HYPE, VVV).
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Hurst/VPIN BTC: **46 trades, 96% win rate, +1.10%** on synthetic trending data.
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---
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## Priority Analysis
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### Strategies showing real signal
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| Strategy | Signal | Status |
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|----------|--------|--------|
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| **Pairs Trading** | ✅ | +$0.74, 67% win rate — only profitable live strategy |
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| **Avellaneda-Stoikov** | ⚠️ | 32 trades but losing — spread capture not covering fees |
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| **Iceberg Detection** | ⚠️ | 2 trades — rare signals, needs threshold tuning |
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| **Hurst VPIN** | 🔬 | 96% win in backtest, 0 live trades — very selective |
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| **Mean Reversion** | ⏳ | 0 trades — VWAP deviation not crossing 1.0σ |
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| **Momentum** | ⏳ | 0 trades — Bollinger 1.2σ too tight for ETH |
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### Recommendation: focus investment here
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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.
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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.
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3. **Avellaneda-Stoikov** — Needs inventory control. 32 trades losing because adverse selection. Add skew-aware quoting (update reserve price based on queue imbalance).
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4. **Iceberg Detection** — Lower detection threshold. Currently requires 7/10 consecutive ticks same direction — too strict.
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5. **Funding Rate Arb** — Real Hyperliquid funding data already plumbed. Test threshold from 3% → 1% APR. Prefunding detection (predict next rate before announcement).
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6. **Backtest engine** — Replace synthetic data with real Hyperliquid candles. Add walk-forward optimization. The `hurst_vpin.py` infrastructure is ready.
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### Skip for now
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- OBI / Mean Reversion / Momentum — 0 trades. Signal thresholds need fundamental redesign, not just tuning.
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- Cartea-Jaimungal / Gueant MM — academic models, not adapted to crypto microstructure.
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- DeepLOB / Hawkes OFI — dependency-heavy, no live integration.
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---
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## Next Steps
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```bash
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# Clone and deploy
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git clone https://git.ftdt.io/rams/ftdt-quant-lab.git
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cd ftdt-quant-lab
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python3 -m venv .venv && source .venv/bin/activate
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pip install -r requirements.txt # (pending — currently manual)
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# Start services
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python live/node.py & # Trading node
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python live/paper_trader.py & # Paper simulator
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python dashboard/server.py --port 9175 # Dashboard backend
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```
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---
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## Roadmap
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- [ ] Docker Compose for reproducible deployment
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- [ ] Walk-forward backtest on real Hyperliquid candle data
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- [ ] Extend Pairs Trading to BTC/SOL, BTC/ARB
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- [ ] Hurst/VPIN 3-day candle feed → real live signals
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- [ ] Memory leak proofing — current guard at 512MB RSS
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- [ ] systemd service unit files for auto-restart
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- [ ] Grafana + Prometheus monitoring dashboard
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---
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*Built with Hermes Agent · Hallmark Cobalt · Ubuntu fonts*
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