Algo Trader

Intellectual Capital Hr Consulting

Mumbai

On-site

INR 2,000,000 - 4,000,000

Full time

14 days+

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Job summary

Algo Trader Options is seeking an experienced quant professional to join our team and develop medium-frequency options strategies using Python. You will design, backtest, optimise and deploy algorithmic trading rules and monitor live performance.

The successful candidate will bridge trading ideas with technology, analyze options pricing, volatility and market microstructure, and collaborate with traders and developers to push robust, scalable strategies.

Qualifications

Bachelor’s degree in Finance, Mathematics, Statistics, Computer Science, Engineering or related.
3+ years of experience in algorithmic trading, quantitative trading or similar.
Strong hands-on Python for trading, quantitative analysis or financial modelling.
Deep understanding of Options and Derivatives, including Greeks and IV.
Experience developing back-tested systematic/algorithmic trading strategies.
Knowledge of market microstructure, execution costs and risk management.

Responsibilities

Design, develop and implement algorithmic options trading strategies using Python.
Back-test strategies with historical data and assess robustness across markets.
Analyse option pricing, volatility surfaces, Greeks and implied volatilities.
Monitor live trading performance and manage risk and execution.
Collaborate with traders, researchers and technology teams to deploy strategies.

Skills

Python

Options and Derivatives

Medium-Frequency Trading

Backtesting

Risk Management

Education

Bachelor's degree in Finance, Mathematics, Statistics, Computer Science, Engineering or related

Tools

NumPy

Pandas

SciPy

Statsmodels

SQL

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

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