12-hour self-paced course covering the entire pipeline of advanced algorithmic trading - strategies including both risk premia and advanced strategies, including research and development methodology, and the gritty details including data sources, databases, class structure from signals to strategies to portfolios to optimizers to back-testers. Numerous examples of strategies in futures will be gone through in detail, while strategies in equities, fixed income, and FX will be described and referenced.
What am I going to get from this course?
- Understand sources of risk premia strategies, i.e., CTA and bank Quant investment strategy returns
- Understand more general quant strategies, the sources of risk, and basic trading strategies in each investment class.
- Assemble the necessary components from the data sources/APIs, to databases to back-testers to portfolio optimizers and risk management tools into a quant investment system
- Assemble appropriate class structure for algorithmic trading optimization and portfolio construction.
- Understand the place for ML methods and models, how to use them and steps to take to avoid over-fitting.
- Know how to do research into algo trading - the steps to take to continually research and develop new strategies and alpha sources.
- Know how to structure your code, create appropriate classes, sub/meta-classes, etc, to reduce the chance of spaghetti-code, help you organize the both the code-base and your thoughts on it.