AstroQ AI Private Limited is a financial technology and artificial intelligence company developing advanced platforms for market research, financial analysis, trading intelligence, and data-driven forecasting.
Our technology ecosystem combines quantitative finance, artificial intelligence, technical analysis, market data, and alternative research models to help traders, investors, and financial professionals make more informed decisions across global markets.
We are looking for a skilled and analytical Quantitative Analyst who can develop, test, and improve financial models, trading strategies, market indicators, and risk-management systems.
The Quantitative Analyst will work with financial market data to identify patterns, develop predictive models, backtest trading strategies, and support the development of data-driven investment and trading products.
The ideal candidate should have a strong foundation in mathematics, statistics, finance, and programming. The candidate must be comfortable working with large datasets, evaluating strategy performance, and converting research ideas into practical quantitative models.
Quantitative Research
- Conduct quantitative research across equities, stock indices, commodities, currencies, bonds, ETFs, and other financial instruments.
- Study historical price, volume, volatility, fundamental, macroeconomic, and alternative datasets.
- Identify statistical relationships, market anomalies, momentum patterns, mean-reversion opportunities, and risk factors.
- Develop research frameworks for short-term, swing, positional, and long-term market strategies.
- Analyse market behaviour across different economic cycles and volatility conditions.
Trading Strategy Development
- Design systematic trading strategies using technical, statistical, fundamental, and machine-learning methods.
- Convert market research concepts into clearly defined entry, exit, stop-loss, and position-sizing rules.
- Develop strategies based on momentum, trend following, breakout, mean reversion, volatility, correlation, and relative strength.
- Create multi-asset and portfolio-level strategies.
- Improve existing strategies by reducing noise, drawdowns, overtrading, and execution risk.
- Work with the development team to integrate validated strategies into financial platforms and applications.
Backtesting and Validation
- Build reliable backtesting frameworks using historical financial data.
- Evaluate strategies using metrics such as:
- Annualised return
- Sharpe ratio
- Sortino ratio
- Maximum drawdown
- Win rate
- Profit factor
- Volatility
- Alpha and beta
- Value at Risk
- Expected shortfall
- Risk-adjusted return
- Conduct in-sample and out-of-sample testing.
- Perform walk-forward testing, sensitivity analysis, stress testing, and Monte Carlo simulations.
- Identify look-ahead bias, survivorship bias, data leakage, curve fitting, and over-optimisation.
- Maintain detailed records of research assumptions, model versions, results, and limitations.
Data Analysis and Modelling
- Collect, clean, structure, and validate large financial datasets.
- Build statistical and machine-learning models for market forecasting, signal generation, risk estimation, and portfolio allocation.
- Apply methods such as regression, time-series analysis, clustering, classification, probability modelling, and optimisation.
- Develop features from price, volume, volatility, technical indicators, financial statements, and macroeconomic data.
- Monitor data quality and investigate missing, delayed, or inconsistent information.
- Create dashboards, reports, and visualisations for communicating research findings.
Risk Management
- Develop quantitative risk-management frameworks for trading strategies and investment portfolios.
- Define position-sizing, exposure, leverage, stop-loss, and portfolio-allocation rules.
- Measure correlation, concentration risk, liquidity risk, volatility risk, and downside exposure.
- Analyse strategy behaviour during market crashes, geopolitical events, high-volatility periods, and changing interest-rate environments.
- Recommend controls that protect capital while maintaining the strategy’s return potential.
Model Monitoring and Improvement
- Monitor live and simulated strategy performance.
- Compare actual results with backtested expectations.
- Identify model decay, structural market changes, execution gaps, and unusual behaviour.
- Regularly recalibrate and improve models based on new data.
- Prepare performance reviews explaining what worked, what failed, and what should be changed.
- Maintain clear model documentation and version-control processes.
Collaboration
- Work closely with software developers, data engineers, financial analysts, traders, and product managers.
- Translate mathematical models into clear technical requirements.
- Support API development, trading-signal systems, dashboards, alerts, and financial analytics tools.
- Review model implementation to ensure that production logic matches the original research.
- Present quantitative findings in a clear and practical manner to technical and non-technical stakeholders.
Required Qualifications
- Bachelor’s or Master’s degree in Quantitative Finance, Financial Engineering, Mathematics, Statistics, Economics, Computer Science, Data Science, Engineering, or a related discipline.
- Strong understanding of financial markets and trading concepts.
- Good knowledge of probability, statistics, time-series analysis, and optimisation.
- Practical experience with Python for data analysis and quantitative modelling.
- Experience working with libraries such as Pandas, NumPy, SciPy, Statsmodels, Scikit-learn, Matplotlib, or similar tools.
- Experience in backtesting financial strategies.
- Strong analytical, logical, and problem-solving skills.
- Ability to communicate research findings clearly.
- High attention to detail and data accuracy.
Pay: ₹50,000.00 - ₹100,000.00 per month
Work Location: In person
