Options Quant Researcher

BHFT

2d•Remote

About The Position

BHFT is seeking a Mid-Senior Quant Researcher specializing in options to join their proprietary algorithmic trading firm. The role involves turning original strategy ideas into fully automated, production strategies in TradFi markets, working closely with trading operations. The primary objective is to contribute to building a unified options quoting/pricing engine that prices across all strikes, expiries, and underlyings. This engine will be driven by a relative-value view on implied volatility across instruments within a unified delta order book, encompassing spot, futures, and option legs. The alpha stack includes index vol arbitrage, single-stock IV ranking, calendar/term-structure spreads, skew arbitrage, and implied vs. realized volatility correlation via dispersion trading.

Requirements

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 Haves

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)

Responsibilities

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
Build and own the volatility fitter – calibrating arbitrage-free, temporally stable surfaces (SVI/SSVI or a proposed alternative) on realistic data
Translate strategy output into execution – routing a target delta-order across option legs to minimize Greek risk, with inventory-aware quoting
Build and maintain mid-frequency (MFT), fully automated strategies with a strong live-performance focus
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