Akshay Zine

Business Development Executive

London, England, United Kingdom1 yr 6 mos experience

Key Highlights

Led development of systematic trading strategies.
Achieved high directional accuracy in options trading.
Expertise in high-frequency trading and risk management.

Stackforce AI infers this person is a Fintech professional specializing in quantitative trading and financial modeling.

Contact

Skills

Core Skills

Quantitative TradingOptions PricingMarket MakingTrading SystemsTrading Strategies

Other Skills

Adaptive signal calibrationAlgorithmic TradingAlpha GenerationAnalytical SkillsArtificial Neural NetworksAutomationBacktestingBashC++C++ ultra-low latency executionCalculusCommunicationConvolutional Neural Networks (CNN)Critical ThinkingData Analysis

About

As an MSc candidate in Mathematical Finance at the University of Warwick, I bring a strong background in quantitative trading and financial modelling. At Dolat Capital, I led the development of systematic trading strategies and built predictive options pricing models to manage consistent performance at the live trading desk. My expertise spans high-frequency trading, market making, and options pricing, where I’ve implemented robust risk management and execution strategies. Proficient in Python and C++, with skills in machine learning, statistical analysis, and time-series forecasting, I am eager to apply these technical skills in financial markets. I am currently seeking full-time opportunities.

Experience

1 yr 6 mos

Total Experience

1 yr 6 mos

Average Tenure

Current Experience

Dolat capital

Quantitative Trader

Jan 2023 – Jul 2024 · 1 yr 6 mos · Mumbai, Maharashtra, India · On-site

Designed and deployed predictive options trading models using tick-level market microstructure data; achieved 55–58% directional accuracy on short-term expiry options (NSE)
Integrated multi-timeframe signals into market-making execution logic with parameter tuning and backtesting against historical data
Constructed an ATM-centric systematic market-making strategy for NIFTY & MIDCPNIFTY options, focusing on quoting logic and adaptive signal calibration for stable PnL
Collaborated with developers to build and maintain a C++ ultra-low latency execution path (~2μs book built → order-send) deployed live in production
Implemented real-time risk adjustment using options greeks and position-based sizing, resulting in a 12% reduction in drawdowns
Analysed post-trade logs to iteratively improve signal quality and execution under live market conditions
Managed systematic and semi-automated strategies in live markets; maintained risk discipline and execution quality across volatile NSE expiry weeks.

Predictive options trading modelsMarket-making execution logicSystematic market-making strategyC++ ultra-low latency executionReal-time risk adjustmentPost-trade analysis+2