NEW Cryptos / Cryptocurrencies trading course 2024!
- Descrição
- Currículo
- FAQ
- Revisões
This course will give you exposure to how some of the best hedge funds on the planet trade different instruments and asset classes.
I have spent the vast majority of my career in front office roles at some of the most prestige funds on the planet.
My goal is to leverage my experience at Bluecrest Capital and Brevan Howard Asset Management and teach retail traders how institutional traders think about and trade financial markets. There is a clear disparity between the success of retail traders and hedge fund traders, most of this can be attributed to the information asymmetry between the two. Retail traders tend to be very technical analysis driven, whereas, whilst institutional traders incorporate technical analysis it is a small part of their trading process. They focus heavily on mathematical and statistical techniques to identify mis-pricing in markets and aim to take advantage of these asymmetric opportunities.
This course will give you exposure to one of the best strategies (in my opinion) for trading cryptocurrencies from a market neutral perspective.
The course will teach students how the strategy works, the core components of the strategy, how to implement it in python and how to generate trade ideas using the strategy. We will then focus our attention to backtesting as well as exploring additional ways the strategy can be further developed.
By the end of the course, students should be able to take advantage of statistically significant Relative Value trading opportunities when presented by the market.
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1
Introduction: My Professional Background, Course Outline and GoalsThis video explores my background academically as well as professionally.
I speak of my Bachelors degree in Economics as well as my Masters degree in Risk Management and Financial Engineering covering key modules and skills developed throughout which have helped me attain front office roles at the most sought after hedge funds.
I provide insight as to what my job consisted of at Brevan Howard Asset Management and BlueCrest Capital, the assets classes and instruments I analysed and traded as well as the framework I built out.
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2
Course outline, Motivation and Goals
In this video I present my motivations for building the course.
As you will see this is heavily driven by the fact that I thoroughly enjoy teaching, research and learning myself.
I am also eager to help those wanting to trade the financial market for a living, giving them exposure to institutional grade strategies some of the best hedge funds on the planet utilise.
I provide an overview of the course at a high level such that students are aware of the trading strategy and understand the steps needed in order to build it.
Lastly, I set my self goals I wish to accomplish by developing the course as well as goals I hope the student is able to achieve from taking part in the course.
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Key Terms
This video will give students exposure to the terminology commonly used at banks and hedge funds when discussing financial markets.
It will prepare them well for the remainder of the course and place everyone on an equal level playing field.
For those looking to advance their trading careers it is important that you understand this language to make dialogue with market makers, colleagues and economist easily understood.
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4
Strategy Overview
This video dives into the strategy in relative depth from an explanation perspective. My intention is to explain the strategy in Lehman's terms making it easily understandable. Students should know how the strategy works, the steps involved in building it and the reason behind conducting different statistics tests, prior to building the strategy in subsequent videos.
It explains what a cointegrated pairs RV strategy is at a high level before adopting a segmented approach in how one goes about building the strategy.
We begin by discussing the data selection and cleaning process for a list of cryptocurrencies in our universe, listing various methods of retrieving data and commonly used techniques for cleaning the data.
We proceed to speak of the importance of correlation and cointegration testing and how it serves a purpose in our strategy development process. Once we have completed correlation and cointegration analysis we leverage econometric regression modelling in order to devise a hedge ratio which is then in turn used to compute the spread of the corr/coint pairs.
Once the spread time series has been computed, we analyse the mean reversion tendencies of the spread before calling upon Z-Scores and other techniques for identifying outliers/dislocations in the spread of two cryptocurrencies.
Lastly, we generate trading signals based on pre-defined criteria and back test our strategy.
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6
Hedge Ratio Construction and Spread Analysis
The focus of this lecture is to understand why we need a hedge ratio when constructing a pairs trading strategy, what benefits it has for the strategy and how to compute a hedge ratio in python, leveraging econometric regression analysis.
Once we have our hedge ratio we are able to construct the spread between two cryptocurrency pairs.
The strategy is dubbed a RV trading strategy as it focuses on trading the spread between two cryptocurrencies ie the value of one cryptocurrency relative to another. Our intention is to therefore identify areas/levels of the spread which present trade opportunities we can capitalise on.
In order to trade the spread we test whether the spread has certain properties. Mean reversion tendencies or the tendency to mean revert is at the core of our focus. If the spread demonstrates mean reversion tendencies then we can successfully trade the normalisation of extremities of the spread to a high degree of confidence that it will revert to 'normal' levels.
We can assess the mean reversion tendencies of the spread using a range of different methodologies. In this course we focus on stationarity tests using the ADF framework.
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7
Generating Trading Signals using Z-Scores
This video focuses our attention on deriving Trading signals.
Once we have identified correlated (utilising 2nd order data/ 1st difference data) and cointegrated (utilising 1st order non-stationary data) pairs, devised a hedge ratio alleviating market risk, hence coining the trading strategy a market or beta neutral strategy, computed the spread, and deduced that the spread has mean reversion tendencies to a highly statistically significant level we can proceed to create signals which identify extremities in the spread and offer good risk:reward ratios.
We will construct z-scores using python in this video and build a system that allows us to graphically represent trading signals on the time series chart of the spread, making them easily identifiable.
