Options Quant Researcher at BHFT

London, England, United Kingdom -

Full Time

Start Date

Immediate

Expiry Date

28 Sep, 26

Salary

0.0

Posted On

30 Jun, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, NumPy, Pandas, Matplotlib, SciPy, Options Pricing, Quantitative Research, Relative Value Strategies, Statistical Arbitrage, Volatility Modeling, Backtesting, Machine Learning, QuantLib, Risk Management, Derivatives Trading, Financial Engineering

Industry

Financial Services

Description

Company Description BHFT is a proprietary algorithmic trading firm. Our team manages the full trading cycle, from software development to creating and coding strategies and algorithms. Our trading operations cover key exchanges. The firm trades across a broad range of asset classes, including equities, equity derivatives, options, commodity futures, rates futures, etc. We employ a diverse and growing array of algorithmic trading strategies, utilizing both High-Frequency Trading (HFT) and Medium-Frequency Trading (MFT) approaches. Looking ahead, we are expanding into new markets and products. As a dynamic company, we continuously experiment with new markets, tools, and technologies. We’ve got a team of 200+ professionals, with a strong emphasis on technology - 70% are technical specialists in development, infrastructure, testing, and analytics spheres. The remaining part of the team supports our business operations, such as Risks, Compliance, Legal, Operations, and more. Our employees are located all around the world, from the United States to Hong Kong. Although we maintain office spaces, we currently operate as a 100% remote organization. At BHFT, clarity and transparency are at the core of our culture: we value open communication, ensuring that our processes are straightforward. Job Description We're looking for a Mid-Senior Quant Researcher specializing in options, with hands-on experience turning original strategy ideas into fully automated, production strategies in TradFi markets – working close to trading throughout. The mandate is to help build a single, unified options quoting/pricing engine that prices across all strikes, expiries, and underlyings, driven by a relative-value view on implied volatility across instruments in a unified delta order book (spot / futures / option legs). The alpha stack spans: Index vol arbitrage (IV differences between instruments) + single-stock IV ranking Calendar / term-structure spreads Skew (smile) arbitrage Implied Volatility vs. Realized Volatility Correlation via dispersion trading We expect the candidate to do some subset of these things: Own end-to-end options strategy research: hypothesis → data → modeling → backtesting → production → live monitoring and iteration Work on Relative Value, Statistical Arbitrage, and Spread Trading strategies specific to the options universe (the stack above) Build and own the volatility fitter the signals sit on – calibrating arbitrage-free, temporally stable surfaces (SVI/SSVI or a proposed alternative) on realistic data (wide bid/ask, missing strikes, gaps, latency), with attention to residual noise near expiry / illiquid strikes / events Translate strategy output into execution – routing a target delta-order across option legs to minimize Greek risk, with inventory-aware quoting that shifts price/size against live vega/gamma/skew, and awareness of options microstructure (spreads, queue, adverse selection, latency) Build and maintain mid-frequency (MFT), fully automated strategies with a strong live-performance focus Track record of deploying fully automated strategies with Sharpe > 2 (or demonstrable equivalent risk-adjusted performance) Design robust signal research pipelines (feature engineering, labeling, validation, regime analysis) Develop realistic backtests and live-simulation frameworks accounting for slippage, spreads, latency, partial fills, and market impact Work in tight feedback loops with trading and execution to improve PnL, robustness, and risk-adjusted performance Debug and tune research outputs under live conditions: data issues, execution artifacts, microstructure noise, and changing market regimes Qualifications Python (mandatory), strong use of NumPy, pandas, matplotlib, SciPy, and optimization/ML libraries Strong research engineering: clean code, reproducible experiments, versioning, and production readiness Hands-on experience developing Relative Value strategies Experience building systematic strategies in equities / futures / options / other listed derivatives (any strong TradFi systematic experience is relevant) Good knowledge of option maths and strong options intuition Familiarity with common quant tooling (e.g., QuantLib and/or in-house libraries) Nice to Have Experience with execution-aware modeling and/or close collaboration with execution / low-latency teams (HFT exposure a plus) Position-driven surface shaping – adapting surface/spread to current portfolio Greeks Practical experience applying ML/DL (PyTorch, TensorFlow, LightGBM) in trading, with careful validation and overfitting controls Experience trading exchange-margined derivatives where capital efficiency is a first-order constraint (NSE options, CME, Eurex) – comfortable optimizing return-on-margin under SPAN-style portfolio margining Direct experience in cross-instrument arbitrage (spot / futures / options) Additional Information What we offer: Experience a modern international technology company without the burden of bureaucracy. Enjoy excellent opportunities for professional growth and self-realization. Work remotely from anywhere in the world with a flexible schedule. Receive compensation for health insurance, sports activities, and non-professional training.

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Responsibilities

Develop and maintain a unified options quoting and pricing engine focusing on relative-value views of implied volatility. Own the end-to-end research cycle from hypothesis and modeling to production and live monitoring of automated strategies.