Senior Quantitative Developer

Role Overview 

We are seeking a Senior Quantitative Developer / Quant Trader to design, implement, and operate low-latency, systematic trading strategies across centralized and decentralized crypto markets. This role sits at the intersection of quantitative research, execution engineering, and risk-aware portfolio construction, working closely with the Fund Principal and investment leadership.

Key Responsibilities 

STRATEGY & QUANTITIVE RESEARCH

1- Design, implement, and operate market-neutral quantitative strategies, including:

• Perp–spot and cross-exchange basis

• Funding rate arbitrage

• Volatility carry and dispersion

• Statistical arbitrage (pairs, baskets, cointegration)

• Hedged DeFi yield strategies

2- Develop alpha signals using:

• Price action and market microstructure

• Order book imbalance and liquidity metrics

• Funding rates, open interest, and positioning data

• Regime detection and volatility filters

3- Analyze alpha decay, turnover, capacity, and scalability.

EXECUTION & TRADING SYSTEMS

1- Build and optimize low-latency execution systems in Go for live trading.

2- Design and maintain:

• Order Management Systems (OMS)

• Smart Order Routing (SOR)

• Exchange-specific execution logic

3- Optimize execution quality through:

• Slippage and market impact reduction

• Queue positioning and order placement logic

• Fee tier optimization and rebate capture

4- Implement robust risk-aware execution controls, including kill-switches and throttles.

CEFI & DEFI TRADING

1- Trade and integrate across:

• Spot, perpetuals, futures, and options

• Major centralized exchanges and prime brokers

2- Design and manage DeFi strategies involving:

• AMMs (Uniswap v3, Curve)

• Lending protocols (Aave, Compound)

• Perpetual DEXs

3- Incorporate on-chain execution costs, MEV awareness, and protocol-specific risks.

BACKTESTING & SIMULATION

1- Build and maintain tick-level backtesting frameworks in Python.

Accurately model:

• Fees, funding, and rebates

• Partial fills and order book dynamics

• Latency, slippage, and exchange-specific rules

2- Perform Monte Carlo simulations, stress testing, and regime analysis.

RISK & PORTFOLIO MANAGEMENT

1- Implement strategy-level and portfolio-level risk controls, including:

• Exposure and leverage limits

• Drawdown and tail-risk monitoring

• Correlation and concentration analysis

2- Support volatility targeting, capital allocation, and portfolio optimization decisions.

Required Skills

1- CODING & SYSTEMS ENGINEERING

Go (Primary – Production & Execution)

• Low-latency, concurrent system design

• OMS, execution engines, and market data pipelines

• Efficient memory management and profiling

• Network programming (WebSocket / streaming APIs)

• Lock-free or low-contention data structures

Python (Primary – Research & Simulation)

• NumPy, pandas / polars for vectorized analysis

• Backtesting and simulation framework development

• Data cleaning, feature engineering, and research pipelines

2- QUANTITIVE RESEARCH & MODELING

Alpha signal development and validation:

• Mean reversion, momentum, funding and basis signals

• Volatility and regime-based strategies

Time-series analysis:

• Stationarity testing

• Cointegration

• Signal decay and turnover analysis

Portfolio construction with transaction costs and constraints

Strong statistical validation and overfitting controls

3- MARKET MICROSTRUCTURE & EXECUTION

• Order book dynamics and queue mechanics

• Passive vs aggressive execution trade-offs

• Slippage and impact modeling

• Exchange-specific behaviors, fee structures, and liquidity profiles

4- MATHEMATICS & STATISTICS

• Probability theory and linear algebra

• Optimization techniques

• Basic stochastic processes

• Risk metrics (volatility, drawdown, CVaR)

5- INFRASTRUCTURE & TOOLING

• Linux

• Docker and CI/CD workflows

• Cloud basics (AWS / GCP)

• Monitoring, logging, and alerting systems

Experience Requirements

• 3–8+ years in systematic trading, HFT, or quantitative development.

• Proven experience running live trading strategies in production.

• Strong understanding of PnL attribution, drawdowns, and failure modes.

• Crypto markets experience (CeFi and/or DeFi) strongly preferred.