[REQ] Algorithmic Trading courses by Nick Firoozye

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Looking for these great algorithmic trading courses by Nick Firoozye

Algorithmic Trading Strategies:

This algorithmic trading course covers the fundamental principles of algorithmic trading, including trend-following strategies, carry trading, value trading, return to average and relative value strategies, as well as other more complex strategies such as short-gamma. We will discuss the rationale behind strategies, the designs of standard strategies, the pros and cons of different strategy design options and the expected benefits of diversification in portfolio strategies. Finally, since algotrading is prone to overfitting and, as a result, shows poor performance, we will discuss p-hacking ('financial charlatanism') and various strategies to avoid it. The course is based on mathematical and statistical justification and evidence, and also contains a description and properties of each strategy. After this course, you will be able to analyze,

develop and confirm quantitative trading strategies: - Learn to understand the mechanics of standard implementations of trading strategies based on risks and premiums for one asset and portfolio.


- Learn to identify the for and against different approaches to strategy development and common mistakes faced by algorithmic traders.
- You can develop new and improve algorithmic strategies.
- Learn to understand when strategies are often used work and when they don't.
- Learn to determine the statistical properties of strategies and distinguish mathematically proven strategies from empirical ones.
- The course provides insight into methods to prevent retraining.
- See a wide area of algorithmic trading strategies.
- Master the fundamental theory and mechanics of the most common strategies.
- Get an understanding of the principles and context required for new research on a large number of open questions in this area.
Course in
English.
Running time: 62 lectures, 6 hours.

Advanced Algorithmic Trading

This is a 12-hour self-study course (22 lectures) covering the entire cycle of advanced algorithmic trading - strategies that include both risk premiums and advanced strategies, including research and development methodology, as well as detailed information, including data sources, databases, class structure from signals to strategies, to portfolios, to optimizers and back-testers.
This course will look at examples of strategies for futures, stocks, fixed profits and
currencies. After this course, you will understand the entire process of

algorithmic trading, from data sources to trading solutions: - Learn to understand the sources of risk premia strategies, such as CTA yield and quantitative investment banking strategy.


- Collect the necessary components into a quantitative investment system from data/API sources, databases, back-testers, portfolio optimizers, and risk management tools.
- Learn to understand more general quantitative strategies, risk sources and core trading strategies in each investment class.
- Build a suitable class structure for algorithmic trading and portfolio optimization.
- Understand the place for machine learning techniques and models, how to use them and what steps to take to avoid retraining.
- Learn how to conduct research in the field of algotrading - steps that need to be taken to constantly research and develop new strategies and alpha sources.
- Learn how to structure your code, how to create appropriate classes, subclasses/meta classes, etc. to reduce the likelihood of spaghetti code and help you organize both the code base and your thoughts about it.
Course in
English.
Duration: 22 lectures, 12 hours.

http://www.exerfy.com/training/courses

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