Ayush Patel

Product Engineer

Indore, Madhya Pradesh, India2 yrs 4 mos experience

Key Highlights

Expert in building scalable quantitative trading systems.
Proficient in Python and AWS for financial applications.
Experienced in mentoring and conducting technical interviews.

Stackforce AI infers this person is a Fintech professional specializing in quantitative development and algorithmic trading.

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Skills

Core Skills

Quant DevelopmentAlgorithmic TradingBacktestingQuant ResearchBack-end Web Development

Other Skills

AWSAmazon DynamoDBAmazon S3ArcticDBCSSData AnalysisData PipelineData ProcessingData ScienceFeature DevelopmentFlaskGitHubHTMLJavaScriptMarket Analysis

About

"Code, Markets, and Everything In Between" From backend development to quantitative research, my journey has been about building systems that merge logic, data, and market intuition. I began my career in Python backend development - building scalable APIs and data systems, before moving into quantitative finance, where I found the perfect blend of analytics, mathematics, and programming. Over the last two years, I’ve worked across the complete quant pipeline - from strategy research and backtesting to optimization, live algo deployment, and infrastructure management. At my recent role in a quant startup: • Engineered modular, event-driven backtesting systems (75K+ combinations). • Developed and deployed 25+ live execution models - including directional, non-directional, and trend-based systems. • Built scalable AWS infrastructure integrated with ArcticDB & QuestDB. • Optimized performance using multiprocessing, async execution, and Redis caching. • Conducted 100+ technical interviews and mentored junior researchers. Before that, as a freelance quant researcher, I explored and implemented concepts like Beta, CAPM, Co-integration, Mean Reversion, Stationarity, and Trend Following models — linking statistical principles with trading logic. Currently deepening my learning in C++, and system design to enhance execution efficiency. And also, gaining knowledge aroung monte-carlo simulations, ARIMA, ARCH, GARCH, and option pricing models. Always open to meaningful discussions, collaborations, and full-time opportunities in Quantitative Development, Systematic Trading, and Strategy Research.

Experience

Tech options - mumbai

Quant Developer and Researcher

Apr 2025 – Sep 2025 · 5 mos · Mumbai, Maharashtra, India · On-site

Worked across the complete quant pipeline — from idea generation and model research to backtesting, optimization, and live deployment. The role evolved to combine quant research, quant development, and cross-team collaboration.
1. Designed and tested multiple systematic non-directional & directional options strategies - including straddles, strangles, credit/debit spreads, volatility/theta-based setups, and trend/mean-reversion models.
2. Engineered a modular, reusable backtesting framework (custom Python package) for scalable, high-speed testing; executed large-scale event-driven backtests (50K–75K+ combinations) to identify statistically robust edges.
3. Developed dynamic risk management systems - fixed/trailed SLs, premium-based entries, pyramiding and implemented a fully decoupled workflow for parameter generation, trade logging, metrics computation, and visualization.
4. Integrated ArcticDB for efficient time-series storage and fast retrieval of large backtesting datasets.
5. Transitioned research ideas into production by developing and deploying 25–30 live execution models across index options; architected a clean OOP-based algo engine ensuring modularity, scalability, and fault tolerance.
6. Built and maintained systematic intraday systems, covering setups like straddles, strangles, credit/debit spreads, volatility filters, and statistical rule-based executions.
7. Integrated multi-API systems for real-time order placement, reconciliation, and monitoring with low latency and robust error handling.
8. Leveraged Redis & QuestDB for caching and high-speed tick-level data storage, enabling real-time analytics, trade logging, and post-trade evaluation.
9. Set up and managed 8 AWS EC2 servers - 3 Linux execution VMs, 3 Ubuntu backtesting VMs, and 2 DB servers (ArcticDB, QuestDB); handled user access, resource allocation, and system monitoring.
10. Conducted 100+ technical interviews, mentored interns, and collaborated with teams to improve workflow and processes.

Quant DevelopmentBacktestingAlgorithmic TradingData AnalysisPython DevelopmentAWS+3

Freelance

Quant Research & Analytics

May 2023 – Apr 2025 · 1 yr 11 mos · Remote · Remote

1). Data Collection: Developed pipelines to fetch data from multiple sources, including Zerodha, NSE, and third-party vendors, covering spot, commodities, and derivatives markets.
2). Data Processing & Cleaning: Automated data enhancement processes by creating columns for contract expiry, moneyness, days to expiry, and other critical features.
3). Feature Development: Designed efficient logic to generate features such as option Greeks, premium coverage, rolling straddles, technical indicators (ATR, MACD, RSI), price action comparisons, and ranking systems.
4). Strategy Backtesting: Built extensive systems for testing intraday and positional algorithmic trading strategies, including Straddles, Strangles, Strangles-to-Straddle transitions, Credit & Debit Spreads, Momentum-based pyramiding models, Monthly multi-leg models, Indicator based setups, dynamic hedging adjustments, and more, enhancing profitability and refining performance metrics.
5). Report Analysis: Integrated statistical and performance evaluation models to assess strategy effectiveness across historical data, enabling in-depth analysis of metrics such as Sharpe ratio, drawdown, Calmar ratio, profitability, and additional performance indicators.
6). Feature Analysis: Developed a systematic pipeline to evaluate the impact of different features on strategy outcomes, identifying actionable market insights.
7). Implemented Multiprocessing, and Multithreading to optimize backtesting simulations, significantly reducing processing time by over 33% and maximizing resource utilization, enabling quicker evaluation of strategy combinations.

Quant ResearchData ProcessingBacktestingFeature DevelopmentStatistical Analysis

Bmos technologies pvt. ltd.

Python Developer

Jul 2022 – Aug 2022 · 1 mo · Remote

1). Created a real-time doctor’s appointment booking platform with features for scheduling, patient registration, and availability tracking using Python and Flask, ensuring efficient user flow.
2). Designed secure database management system using SQL to handle sensitive patient records and appointment details, ensuring data integrity and compliance with privacy standards.
3). Developed intuitive frontend features using HTML, CSS, and basic JavaScript, streamlining the booking process for patients and enhancing overall platform usability.

Python DevelopmentFlaskSQLHTMLCSSBack-End Web Development

Paprclip

Python Backend Developer

Oct 2020 – Jul 2021 · 9 mos · Remote

1). Designed and implemented backend architecture for a real-time financial analysis and global market tracking mobile app, ensuring seamless integration with front-end systems.
2). Developed RESTful APIs using Flask to provide real-time data retrieval and support key app features, including stock analysis, market sentiment tracking, and news aggregation.
3). Built an efficient and scalable data pipeline for continuous collection, cleaning, and storage, reducing manual intervention and improving data processing speed by 30%, enabling real-time updates for users.
4). Leveraged Python’s ThreadPoolExecutor to run synchronized tasks, ensuring up-to-date data retrieval and storage, and reduced server overhead by 20%.

Python DevelopmentFlaskRESTful APIsData PipelineBack-End Web Development