Quantitative Trading Engineer / Quantitative Researcher
Working day: Monday to Friday, 9am to 6.30pm
Working location: Raffles Place
Salary: Discuss during interview
Responsibilities
Strategy Development:
- Use AI technologies such as machine learning and deep learning to intelligently optimize investment strategies and improve the performance of strategies in actual transactions.
- Extract predictive signals from financial data through both traditional statistical analysis methods and cutting-edge machine learning techniques.
- Formulate and apply mathematical modeling techniques to enhance existing trading strategies and perform innovative new research with the goal of identifying and capturing trading opportunities.
Data Analysis:
- Collect and analyze large financial datasets (market data, news, social media, etc.).
- Work with large data sets to predict and test statistical market patterns, combining knowledge of systems, mathematical techniques, and trading to identify the best places to improve the trading system.
- Build and optimize feature engineering pipelines to improve model prediction accuracy.
Financial Modeling:
- Construct, back-test, maintain, and improve financial models to support trading activities.
- Conceptualize valuation strategies, develop, and continuously improve upon mathematical models and help translate algorithms into code
- Conduct research and statistical analysis to build and refine monetization systems for trading signals
- Optimize the order execution and risk management of our trading system
- Create robust solutions to problems presented in the trading environment
- Build regression, classification, reinforcement learning and other models for market forecasting and trading decisions.
- Tuning the model's hyperparameters to improve model performance and stability.
Strategy backtesting and real-time testing:
- Backtest historical data to evaluate the strategy's yield, volatility, drawdown and other indicators.
- Deploy the strategy to the real-time environment, track performance and continuously optimize.
Requirements
- Master's degree or above in Statistics, Mathematics, Applied Mathematics, Computer Science, Financial Engineering or equivalent.
- At least 2 years of experience in quantitative trading and machine learning modeling.
- Proficient in programming languages such as Python, C++, Java, etc.
- Familiar with common machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn.
- Familiar with quantitative trading systems, data flows and algorithms.
- Deep understand the structure of financial markets and be familiar with markets such as virtual currencies, stocks, futures, and foreign exchange.
- Experience in large-scale data processing, proficient in using SQL, NoSQL, Hadoop and other tools.
Bonus Skills
- Experience in developing high-frequency trading strategies will be an advantage.
- Participated in AI competitions (such as Kaggle) or published papers in top conferences (such as NeurIPS, ICML) will be an advantage.
- Familiar with cutting-edge AI technologies such as reinforcement learning, and generative adversarial networks (GANs) will be an advantage.
