Algo Trader

Intellectual Capital HR Consulting Pvt. Ltd.; Ahmedabad District; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Intellectual Capital HR Consulting Pvt. Ltd. seeks an experienced Algo Trader to join a quantitative trading team focused on Medium-Frequency Strategies in options markets. The role blends options expertise with Python-based development for back-testing, deployment and live monitoring of strategies.

The ideal candidate will bridge trading ideas and technology, analyse market data and implement robust risk controls while scaling strategies for live execution.

Qualifications

• Bachelor's degree or higher in a relevant field.
• 3+ years in algorithmic/quantitative trading or a similar role.
• Strong Python programming for trading and modelling.
• Solid understanding of options, derivatives, Greeks, and volatility.
• Experience in developing and back-testing systematic trading strategies.
• Knowledge of market microstructure, execution and costs.

Responsibilities

• Design, develop and implement algorithmic options trading strategies using Python.
• Back-test strategies with historical data and assess robustness across markets.
• Optimize strategies for risk-adjusted returns and execution efficiency.
• Monitor live trading, track P&L, exposure and market conditions.
• Develop risk-management controls and hedging techniques.
• Collaborate with traders, researchers and developers to improve infrastructure.

Skills

Python

Options trading

Quantitative analysis

Risk management

Trading strategies

Backtesting

P&L analysis

Education

Bachelor's degree in Finance, Mathematics, Statistics, Computer Science

Tools

NumPy

Pandas

SciPy

SQL

APIs

Job description

Algo Trader Options | Python | MFT Strategies

Job Summary

We are looking for an experienced Algo Trader to join our quantitative trading team and work on Medium-Frequency Trading (MFT) strategies focused on options.

The role combines options trading expertise, quantitative analysis and Python programming to develop, back-test, optimise and deploy algorithmic trading strategies. The successful candidate will be expected to understand options market behaviour, identify trading opportunities, translate strategies into systematic rules and continuously monitor and improve live strategies.

The ideal candidate is someone who can bridge the gap between trading ideas and technology - with a strong understanding of options, market data, execution and risk management, along with hands-on Python skills.

Key Responsibilities

1. Algorithmic Strategy Development

• Design, develop and implement algorithmic options trading strategies using Python.
• Convert trading ideas and market observations into systematic, rule-based strategies.
• Develop strategy logic for entry, exit, position sizing, hedging and execution.
• Back-test strategies using historical market data and evaluate their robustness across different market conditions.
• Optimise strategies for risk-adjusted returns, execution efficiency and scalability.
• Participate in the research, development and deployment of new MFT strategies.

2. Options Strategy & Market Research

• Conduct quantitative and fundamental research across options and underlying markets.
• Analyse option pricing, implied volatility, volatility surfaces, Greeks, spreads, skew and term structure.
• Identify market inefficiencies, pricing anomalies and potential systematic trading opportunities.
• Track market trends, corporate events, macroeconomic indicators and other factors that may influence volatility and options pricing.
• Continuously evaluate existing strategies and identify opportunities for improvement.

3. Backtesting & Performance Analysis

• Build and maintain reliable back-testing and strategy-analysis frameworks in Python.
• Analyse historical performance using metrics such as P&L, Sharpe ratio, drawdown, win rate, turnover, hit ratio and risk-adjusted returns.
• Conduct sensitivity and stress testing across different market regimes.
• Identify sources of strategy alpha, slippage, transaction costs and performance deterioration.
• Validate strategies before deployment into live trading environments.

4. Live Trading & Strategy Monitoring

• Monitor live algorithmic strategies and trading performance in real time.
• Track executions, positions, exposure, P&L, liquidity and market conditions.
• Identify execution issues, abnormal strategy behaviour or deviations from expected performance.
• Work closely with technology and trading teams to troubleshoot and resolve live trading issues.
• Continuously evaluate live performance against back-tested expectations.

5. Risk Management

• Develop and implement systematic risk-management controls using Python.
• Monitor real-time position, exposure, volatility and portfolio-level risk.
• Implement appropriate controls for position limits, loss limits, hedging and exposure management.
• Analyse strategy behaviour during extreme volatility and adverse market conditions.
• Ensure strategies operate within predefined risk parameters.

6. Collaboration & Continuous Improvement

• Work closely with traders, quantitative researchers, developers and technology teams.
• Clearly communicate trading logic, research findings, strategy performance and risk observations.
• Document strategy assumptions, methodology, testing results and production changes.
• Contribute ideas to improve the team's research process, trading infrastructure and strategy development framework.

Required Qualifications & Skills

• Bachelor's degree in Finance, Mathematics, Statistics, Computer Science, Engineering or a related discipline.
• 3+ years of relevant experience in algorithmic trading, quantitative trading, options trading or a similar role.
• Strong hands-on experience with Python for trading, quantitative analysis or financial modelling.
• Strong understanding of Options and Derivatives, including Greeks, implied volatility, option pricing and volatility behaviour.
• Experience developing and back-testing systematic/algorithmic trading strategies.
• Understanding of market microstructure, execution, liquidity and transaction costs.
• Strong analytical and problem-solving skills.
• Ability to work with large financial datasets and derive actionable trading insights.
• Strong understanding of risk management and portfolio exposure.

Good to Have

• Experience with Medium-Frequency Trading (MFT) strategies.
• Experience trading index or single-stock options.
• Knowledge of quantitative libraries such as NumPy, Pandas, SciPy and Statsmodels.
• Experience with APIs, market-data feeds and automated execution systems.
• Knowledge of options volatility strategies, statistical arbitrage or market-neutral strategies.
• Experience working with tick-level, intraday or high-frequency market data.
• Understanding of databases such as SQL.
• Experience taking strategies from research backtesting paper trading production.
• Exposure to C++ or other programming languages is an advantage.

Ideal Candidate

We are looking for someone who is:

• A trader first, but technically strong enough to code.
• Comfortable converting an intuition or trading hypothesis into a systematic strategy.
• Highly analytical and data-driven.
• Curious about market behaviour and continuously looking for inefficiencies.
• Disciplined about risk and statistical validation.
• Comfortable working in a fast-paced quantitative trading environment.
• Able to distinguish between a strategy that looks good in backtesting and one that can actually survive live markets.

Key Skills

Algo Trading | Options Trading | Medium-Frequency Trading | Python | Quantitative Trading | Strategy Development | Backtesting | Options Pricing | Implied Volatility | Greeks | Volatility Trading | Statistical Analysis | Risk Management | Execution | Market Microstructure | Algorithmic Trading