Rachit Jain

Associate Consultant

Mumbai, Maharashtra, India0 mo experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in developing quantitative finance models.
Proficient in machine learning applications in finance.
Strong background in data engineering and analytics.

Stackforce AI infers this person is a Fintech professional specializing in quantitative analysis and machine learning.

Contact

rachitbitw@gmail.com LinkedIn

Skills

Core Skills

Quantitative FinanceData EngineeringMachine Learning

Other Skills

AWS EC2Advanced MathematicsAlpha GenerationAmazon QuickSightAnalytical SkillsArtificial Intelligence (AI)BacktestingBacktesting ModelBlack-LittermanC (Programming Language)C++ClusteringComputer ScienceComputer VisionCustom Indicators

Experience

Algo one ai

Quantitative Analyst

Apr 2024 – Present · 1 yr 11 mos

Designed and developed a comprehensive backtesting model for 1,000+ tickers with 10 years of stock and options data.
Designed and implemented a data pipeline and warehouse and the backtesting model on AWS EC2, for large-scale financial datasets, optimizing storage and retrieval.
Developed and optimized 150+ custom indicators to provide insights and create impactful trading stratergies.
Implemented Value at Risk (VaR), stress testing, option rolling and Monte Carlo simulations for enhanced backtesting and risk analysis.

AWS EC2Data PipelineBacktesting ModelCustom IndicatorsValue at RiskMonte Carlo Simulation+2

Worldquant

Research Consultant

Oct 2023 – Apr 2024 · 6 mos · Mumbai, Maharashtra, India · Remote

Learning and creating new and efficient alphas.

Meytrix

Machine Learning & Finance Intern

Jun 2023 – Jul 2023 · 1 mo · Mumbai, Maharashtra, India · On-site

Developed 7 advanced portfolio optimization tools for stocks on Indian market listed at NSE & BSE.
Significantly expanded the company’s capabilities by conducting rigorous testing and backtesting of these tools, extending allocation coverage from 15 to 700 stocks.
Engineered customizable solutions, empowering users to tailor portfolios based on their preferences and risk profiles, utilizing historical data, financial ratios, and scores for optimal stock selection. Used models such as Markowitz Theory, Black-Litterman, Relative Rotation Graphs, Clustering and more.

Portfolio Optimization ToolsBacktestingMarkowitz TheoryBlack-LittermanClusteringQuantitative Finance+1