- Fundamentals: Mathematics and Statistics for Quants
- Calculus: Focus on stochastic calculus as it is widely used in quantitative finance.
- Probability and Statistics: Emphasize on statistical inference, hypothesis testing, regression analysis, and time series analysis.
- Linear Algebra: This is crucial for understanding many machine learning algorithms.
- Financial Mathematics: Understand the time value of money, discounted cash flows, and pricing of financial derivatives.
- Fundamentals of Machine Learning
- Basic Algorithms: Start with linear regression, logistic regression, decision trees, and move towards more complex algorithms like SVM, random forests, and boosting methods.
- Use-cases in Quant: Understand how these algorithms can be applied to predict financial markets, such as predicting stock prices or identifying trading signals.
- Data Science in Quant Space
- Algorithms in ML/DS: Deepen your understanding of advanced algorithms, including neural networks, reinforcement learning, and natural language processing.
- Application in Quant-Finance: Learn how to apply these advanced algorithms to quant finance problems, such as algorithmic trading, portfolio optimization, risk management, etc.
- Time-Series Analysis and Python
- ML for Time-Series: Understand specific techniques for time-series data, such as ARIMA, state space models, and recurrent neural networks (RNN).
- Python: Master libraries like NumPy, Pandas, Scikit-Learn, TensorFlow, and PyTorch. Also, learn to use Jupyter notebooks for interactive data analysis.
- Cloud Architecture for Trading Systems
- Well-Architected Framework for Quant/Trading Industry: Learn about cloud solutions like AWS, Azure, or Google Cloud Understand how to design and implement a scalable and reliable cloud architecture for trading systems.
- Infrastructure as Code (IaC) with Terraform: Learn how to automate the setup of your cloud infrastructure.
- Continuous Integration/Continuous Deployment (CI/CD) with Jenkins: Understand how to automate the testing and deployment of your trading systems.
- Kubernetes for Cloud-Native Solutions: Learn how to use Kubernetes for deploying and managing containerized applications.
- MLOps Frameworks
- Feature Store: Understand the concept of a feature store and how it can help in managing features for your machine learning models.
- Continuous Deployment: Learn how to continuously deploy your machine learning models to ensure they are always up-to-date.
- Continuous Training: Understand how to continuously train your models with new data to ensure their performance does not degrade over time.
- Quant-Trading Strategy Execution
- Backtesting: Learn how to backtest your trading strategies to evaluate their performance before live trading.
- API Integration: Understand how to integrate with trading APIs to send orders to the market.
- Trade Execution: Learn about different order types and execution algorithms to minimize the market impact of your trades.
In addition to the above, you should also consider:
- Financial Markets and Products
- Understand the structure and operation of financial markets.
- Learn about different financial products like stocks, bonds, futures, options, etc., and their pricing models.
- Risk Management
- Learn about different types of risk (market risk, credit risk, operational risk) and how to measure and manage them.
- Regulatory Environment
- Understand the regulatory environment in which financial institutions operate.
- Soft Skills
- Improve your communication and presentation skills. These are crucial for explaining complex financial concepts to non-technical stakeholders.
- Networking and Industry Exposure
- Attend industry conferences, seminars, and meetups. Networking with industry professionals can provide valuable insights and opportunities.