Quantitative Developer (C++)

Company Description

Trade W is a leading multi-asset trading platform with over seven years of industry experience, providing global users with secure, convenient, and efficient access to the financial markets.

Role Description

This is a full-time on-site role for a Quantitative Developer , located in Dubai.

Responsibilities

Market-Making Optimization & Execution

• Implement quoting logic, inventory targets, dynamic spreads, passive/active switching, and hedge intensity.
• Link toxicity/mark-out (e.g., VPIN/Kyle λ) to spreads and hedging.
• Build multi-LP / cross-exchange routing and cost-aware execution

Strategy Research & Signal Development

• Formulate testable hypotheses for MM/stat-arb/funding-basis/order-book/flow-toxicity signals.
• Engineer features (L1/L2, imbalance, mark-out, volatility, liquidity/impact) and produce evidence-based research notes.
• Translate findings into parameterized rules and production-ready configs.

Backtesting & Performance Evaluation

• Build trustworthy back tests with cost/slippage/latency/partial-fill modeling and venue-specific constraints.
• Run walk-forward/rolling validation, capacity & turnover analysis, and P&L attribution (spread, fees, carry/basis, slippage).
• Perform stress/scenario tests; track Sharpe/IR, max DD, VaR/ES; run ablations to verify signal credibility.

Trading Data Analysis & Live Iteration

• Analyze orders/trades/positions for mark-out, adverse selection, TCA, and market-quality KPIs.
• Segment users (retail/pro/arb/HFT) to inform MM parameters and routing.
• Maintain daily dashboards & alerts; monitor backtest↔live drift and close gaps quickly.
• Run parameter/signal A/B experiments; publish concise weekly updates and quarterly roadmaps.
• Keep runbooks for events/new listings (risk bands, spreads, leverage) and coordinate rollouts with Dev/Risk.

Framework & Systems

• Design an end-to-end framework where research = backtest = simulation = production (unified data, cost/latency models, parameterized configs, experiment tracking, auto-reports).
• Real-time risk monitoring: inventory/exposure/leverage/hedge deviation/latency/failure-rate with anomaly detection, circuit-breakers, grade-down, and auto-recovery.

Must-have

• Languages: C++
• Financial modeling & statistics (probability/time-series, microstructure awareness); comfort with perps/futures, funding & basis.
• Data analysis: Python (pandas/numpy/numba/asyncio/statsmodels/scikit-learn) and strong SQL; careful EDA and reproducible notebooks.
• Backtesting discipline: cost/impact modeling, leakage controls, validation hygiene.
• Engineering mindset: clean code, testing, ability to convert research into robust production rules/services.
• Clear communication: crisp writing, explicit assumptions, defensible conclusions for business/risk stakeholders.
• 2-8 years of experience

Tech Stack (reference)

• Languages: C++, Java, Python, SQL
• Data: PostgreSQL, ClickHouse, kdb+
• Analysis/Modeling: NumPy, pandas, statsmodels, scikit-learn, PyTorch/TensorFlow, cvxpy, QuantLib, arch
• Backtesting: vectorbt, backtrader, zipline
• Visualization: Matplotlib, Seaborn, Plotly, Tableau/PowerBI
• Workflow: Jupyter, Airflow/Dagste, Git
• Crypto(Optional): web3.py/ethers/solana-py, Dune/Flipside, Safe/Fireblocks

Nice-to-have

• Order-book microstructure, MM/inventory control; toxicity metrics (mark-out/VPIN/Kyle λ).
• ClickHouse/PostgreSQL, materialized views, high-throughput writes/queries; dashboards (Metabase/Superset/Plotly); experiment/version tracking.
• Streaming & orchestration: Kafka/Redpanda, Flink or Spark Streaming, Airflow/Dagster; data quality (e.g., Great Expectations).
• Systems & deployment: Linux, Docker/K8s, Prometheus/Grafana, GitHub Actions, observability/traceability.
• Production MM or cross-exchange arb track record; FIX/REST/WebSocket integrations (Binance/OKX/Bybit, etc.).
• Low-latency optimization (zero-copy, lock-free queues, batch I/O, kernel/network tuning).
• DEX/on-chain analytics (Dune/The Graph; web3.py/ethers/solana-py)