NAMAN SINGH RANA

Product Manager

Alwar, India2 yrs experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in machine learning and quantitative finance.
Experience in AI startups and established firms.
Strong background in deep learning applications.

Stackforce AI infers this person is a Quantitative Analyst with expertise in AI and machine learning for financial applications.

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Skills

Core Skills

Machine LearningArtificial IntelligenceQuantitative AnalysisFinancial Modeling

Other Skills

ReBeL algorithmPublic Belief Statesself-play RL pipelineyield curve fittingStochastic Differential Equationsregularization techniquespolynomial splinesdynamic grid search algorithmC++Python (Programming Language)GitManagementHTMLJavaBash

About

IIT Bombay Computer Science graduate with a Minor in Management. My background involves applying machine learning and quantitative techniques across diverse domains, including AI for gaming, recommendation systems, and quantitative finance. I have gained experience working within both AI startups like Tangli.AI and Proshort.AI and established quantitative firms such as Quadeye Securities. These opportunities provided exposure to different challenging work environments and technical problems. Currently working as a Quantitative Strategist at Quadeye Securities, where I focus on applying computational and quantitative methods to research and develop systematic approaches to trading. My core interests lie in Deep Learning and applying computational techniques to solve challenging problems. Always exploring new developments in technology. Outside of work, I enjoy reading history and playing tennis. Open to connecting with professionals to discuss innovative ideas in technology, AI, and data-driven approaches.

Experience

2 yrs

Total Experience

2 yrs

Average Tenure

2 yrs

Current Experience

Quadeye

Quant

Jun 2024 – Present · 2 yrs · On-site

Tangli

AI Intern

Oct 2023 – Nov 2023 · 1 mo · Remote

Poker Bot
1. Implemented ReBeL algorithm (DRL+CFR Search) for AI decision-making in imperfect-information games.
2. Applied Public Belief States (PBS) to effectively model and manage uncertainty from hidden information.
3. Engineered a self-play RL pipeline enabling autonomous agent learning towards a Nash Equilibrium.

ReBeL algorithmPublic Belief Statesself-play RL pipelineMachine LearningArtificial Intelligence

Quadeye

Quantitative Analyst

Jun 2023 – Jul 2023 · 1 mo · Gurugram, Haryana, India · On-site

Explored and evaluated various yield curve fitting models, assessing their performance using error and sensitivity metrics.
Implemented advanced term structure models, utilizing Stochastic Differential Equations (SDEs) to capture the dynamics of yield curve pricing.
Developed a customized model surpassing the baseline Nelson-Siegel model for yield curve fitting, employing sophisticated regularization techniques to mitigate overfitting.
Investigated polynomial splines as a flexible approach for accurately fitting yield curves data.
Designed and implemented a sub-optimal 2D dynamic grid search algorithm, optimizing the selection of spline parameters for enhanced computational efficiency in linear time.
Evaluated trading strategies based on risk and volatility of underlying instruments, optimizing investment decisions.

yield curve fittingStochastic Differential Equationsregularization techniquespolynomial splinesdynamic grid search algorithmQuantitative Analysis+1