ramseshk
3520056313
The metaAndAssetCtxs endpoint returns asset names in a parallel universe array — asset contexts don't have a "name" field. Fixed the API module to index by the universe array properly. Also added get_mark_price() helper. Tested against testnet: BTC funding 0.00125% per 8h, mark $62,873.
FTDT Quant Lab — Quantitative Trading Strategies
A collection of quantitative trading strategies running on Hyperliquid Testnet via Nautilus Trader. Built as part of my professional portfolio to demonstrate algorithmic trading, market microstructure, and risk management skills.
What's inside
Five strategies, from simple to advanced:
| # | Strategy | Concept |
|---|---|---|
| 1 | Order Book Imbalance | Trades on L2 bid/ask pressure |
| 2 | Iceberg / TWAP Detection | Follows whale accumulation patterns |
| 3 | Funding Rate Arbitrage | Delta-neutral carry trade |
| 4 | Pairs Trading (BTC/ETH) | Cointegration-based stat arb |
| 5 | Avellaneda-Stoikov Market Making | Stochastic optimal control |
All strategies share a common risk manager and portfolio tracker.
Quick start
# Install dependencies
pip install -r requirements.txt
# Set your Hyperliquid testnet key
export HYPERLIQUID_TESTNET_PK=0x...
# Run live (testnet only)
python live/node.py
Project layout
ftdt-quant-lab/
├── config/ # Per-strategy YAML configuration
├── strategies/ # Strategy implementations
├── common/ # Risk manager, portfolio tracker, metrics
├── backtests/ # Historical backtest runners
├── live/ # Live trading node (Hyperliquid Testnet)
├── docs/ # Documentation and strategy writeups
└── notebooks/ # Analysis notebooks
Strategy details
See docs/STRATEGIES.md for a walkthrough of each strategy.
Risk warning
This is testnet only. These strategies are educational — they are not financial advice and have no alpha guarantee. Never run them on mainnet without thorough backtesting and your own due diligence.
Built by Ramses Echikh · Part of my quant trading portfolio