Expert developer course
of trading strategies
(for algorithmic traders)
If you are an algorithmic trader and want to hone your skills in developing trading algorithms, this is your course.
Who is it for?
To all those initiates who wish:
Enter correctly and successfully in the fascinating world of algorithmic trading.
To all algorithmic trader interested in:
The optimal application of the scientific method in financial markets for your own benefit.
To all the traders that want:
Successfully take advantage of automated trading in your daily operations.
Why this course?
One of the most widespread mistakes among beginning traders is to blindly trust a way of trading learned somewhere without first checking (even if only by eye) if that kind of trading had worked for him in particular in the past. After all, if he hadn’t, there’s no point in expecting him to in the future. However, since they do not perform such a check, most of them never succeed in trading.
Precisely because of such failure, more and more discretionary traders are becoming interested in algorithmic trading. At the end of the day, this is based on the application of the scientific method in financial markets, which are big words. But since they are unaware or poorly learned to apply this method correctly and successfully, they end up failing again without understanding the reason, even losing sight of the fact that others, on the other hand, succeed because they do something that they do not do or that they do not do correctly.
More than one makes the mistake of optimizing with all the history. Others are more knowledgeable, but mistakenly believe that just in-sample and out-of-sample optimization tests will suffice to find out what to expect from their trading strategies. And so we could continue mentioning more frequent errors resulting from erroneous preconceived ideas and/or poor training in the matter. Hence precisely the need for a course like this, which aims to successfully and correctly train algorithmic traders.
This is a journey that starts absolutely from scratch here. And that it will continue with you to optimally create and evaluate your own trading algorithms to then execute them automatically if and only if they have successfully passed all the scientific tests to which you submit them. And as long as they also adapt to your risk and investment profile.
It is finally in your hands to start such an exciting journey!
Module 1: General framework of algorithmic trading
- Lesson 1: Introduction. What is an algorithm, discretionary versus mechanical trading algorithms, advantages and disadvantages of automated trading, classification of algorithms, management with algorithms in the universe of investment strategies.
- Lesson 2: Conceptual framework. Unpredictability of the markets, inefficiencies, fractal structure of the market, leptokurtosis in the profitability curve of the markets, problem with chance, positive mathematical expectation, what is a winning trading algorithm.
- Lesson 3: Overview of the entire process. From the idea to the management, evaluation of statistical results, common mistake ignored when interpreting statistics.
Module 2: detailed process of creating, evaluating and optimizing trading algorithms
- Lesson 4: Bases for the creation of trading algorithms. learning from discretionary trading, trend algorithms, anti-trend algorithms, volatility expansion or explosion algorithms, different methods to get out of a position and adapt to volatility, other techniques for creating algorithms: candlestick patterns, gaps, seasonal patterns, . .., general advice.
- Lesson 5: The tests inside and outside the sample. The scientific method, learning from Physics how to put it into practice in financial markets, correct obtaining of the optimal parameters of our algorithms, validation of algorithms with fixed window and moving window optimizations, efficiency of the out-of-sample test, reoptimization frequency of algorithms, advantages and disadvantages of the different types of optimization.
- Lesson 6: Montecarlo method. What it is, different procedures for its application.
Module 3: Additional questions
- Lesson 7: Coarse versus fine optimization. The importance of risk management, distinction between an improvement and a variant, problems with the continuous history of futures and their solution, causes of overfitting, time to stop an algorithm, implicit risks in algorithmic management and how to combat them, optimization portfolio, limitations of the scientific method applied to markets.
- Lesson 8. Recommended bibliography.
Degree in Physics, D.E.A. in Physics (doctoral postgraduate in Physics) and Algorithmic or Quantitative Trader. Since 2004 he has been working in the Research and Development of mathematical trading algorithms, having made different external collaborations with Interdin S.B.V. and Renta 4 Manager. After these collaborations, he ended up working as an alternative management fund manager at Renta 4 Gestora, focusing most of his time on quantitative issues. Subsequently, he began a new purely quantitative stage at Quark Technologies S.L., a company of which he was vice-president, co-founder and main technical manager, continuing his quantitative work as Quant Developer at the English Prop Trading company Saxon Financials Ltd. later he ended up dedicating to quantitative CTA management with automated trading algorithms at GesTrading Managemente Ltd., a company he co-founded in Miami (Florida – USA), having also worked for the R&D and automated trading system rental company GesTrading Strategies. He is currently focused on his doctoral thesis while collaborating on the GesTrading spin-off project founded by his partner Enrique Valdenebro: PattInvestor.