Stock Market Algo Developer / Quant Developer Intern

Stock Market Algo Developer (Quant Developer) Job Type: Full-Time Experience: 0–5 Years Location: Remote Department: Technology / Quantitative Research Industry: FinTech / Stock Market / Algorithmic Trading About the Role We are looking for a Stock Market Algo Developer / Quant Developer to design, develop, backtest, and deploy algorithmic trading strategies for financial markets. The ideal candidate should have strong programming skills, a good understanding of stock market concepts, quantitative analysis, trading strategies, APIs, and real-time market data. You will work closely with quantitative researchers and the technology team to convert trading ideas into reliable and scalable automated trading systems. Key Responsibilities Design and develop algorithmic trading strategies for equity, derivatives, and other financial markets. Convert trading strategies and quantitative models into production-ready code. Develop backtesting frameworks to evaluate trading strategies using historical market data. Analyze strategy performance using metrics such as: Profit & Loss (P&L) Sharpe Ratio Maximum Drawdown Win/Loss Ratio CAGR Risk-Reward Ratio Develop and maintain real-time trading systems and execution engines. Integrate with stock broker and exchange APIs for market data and order execution. Work with WebSocket/REST APIs for real-time market data and trading. Implement risk-management rules including position sizing, stop-loss, exposure limits, and daily loss limits. Optimize trading algorithms for performance, reliability, and low latency. Develop tools for market-data processing, signal generation, order management, and portfolio monitoring. Monitor live strategies and troubleshoot execution, connectivity, and data issues. Maintain clean, well-documented, and testable code. Collaborate with quantitative researchers, traders, and backend engineers. Required Skills Programming Strong proficiency in Python. Good knowledge of Pandas, NumPy, SciPy and related quantitative libraries. Experience with SQL and relational databases such as PostgreSQL/MySQL. Knowledge of Git and software development best practices. Experience with REST APIs and WebSockets. Quantitative & Trading Knowledge Strong understanding of Indian stock markets / NSE / BSE. Understanding of: Equity trading Futures & Options Options Greeks Technical indicators Statistical analysis Market microstructure Risk management Portfolio management Experience developing and backtesting quantitative trading strategies. Understanding of order types, execution logic, slippage, brokerage, and transaction costs. Broker / Market APIs Experience with one or more trading APIs such as: Zerodha Kite Connect Fyers API Upstox API Angel One SmartAPI Dhan API Alice Blue API 5paisa API Other broker/exchange APIs Good to Have Experience with high-frequency or low-latency trading systems. Knowledge of C++ or Java. Experience with Redis, Kafka, or other real-time data systems. Experience with Docker and Linux. Knowledge of cloud platforms such as AWS, Azure, or GCP. Experience working with tick-level or high-frequency market data. Knowledge of machine learning applied to financial markets. Experience with options strategy development. Experience building trading dashboards and monitoring systems. Understanding of exchange protocols and market-data feeds. Qualifications Bachelor's or Master's degree in Computer Science, Mathematics, Statistics, Finance, Engineering, Economics, or a related field. 2–5 years of relevant experience in algorithmic trading, quantitative development, fintech, or financial technology. Strong analytical and problem-solving skills. Strong interest in financial markets and algorithmic trading. What We Are Looking For We are looking for someone who can go beyond simply writing code and understand the complete trading lifecycle: Market Data → Strategy → Signal → Risk Management → Order Execution → Position Management → Monitoring → Performance Analysis The candidate should be comfortable taking a trading idea, converting it into an algorithm, back testing it, validating its performance, and deploying it into a live trading environment. Key Performance Expectations Reliable and scalable trading systems. Accurate market-data processing. Robust order execution. Minimal execution errors and downtime. Well-tested and documented strategies. Continuous improvement of strategy performance and risk management.