One of the first strategies I ever developed in our currency trading community involved volatility breakout trading in the EUR/USD. This strategy – which I called Teyacanani – first saw the markets in 2009 and has been consistently profitable from then to present. Although the strategy did not survive for too long in its simplest initial form – as we improved it with time – the strategy has remained completely unmodified since 2013. This system remains exceptionally consistent and continues to be one of our most trusted strategies through the years. On this post I want to talk a little bit mo ..read more

Up until now I have mainly dedicated this blog to the development and discussion of algorithmic currency trading strategies. However I have always been interested in passive investing strategies in the stock/bond market but have never gone into that subject within this blog. Although I won’t do that – get into the depths of passive investing strategies here – I wanted to share with you some thoughts about a new book I have published about passive investing strategies. The book is called “Passive investing on steroids” and you can find it on this link. Within this post I will talk a little bit ..read more

With bitcoin recently reaching above the 11,000 USD per BTC mark and other cryptocurrencies such as ethereum gaining traction as well, it has now become common place for people to start developing trading systems for the cryptocurrency space. One issue with this however is that cryptocurrency data is by no means centralized – like regular Forex data – which makes it difficult for traders to get a good historical data source. Many of the data repositories that are available online are not free – charging even thousands of dollars for data – while others contain data that is simply not updated ..read more

In my previous post we discussed the use of return thresholds in the creation of a classifier in order to improve the out-of-sample (OS) performance of trading strategies. In essence instead of simply predicting whether a system’s future return was above or below zero we tried to predict whether the return was above or below a given threshold (Th). This showed to significantly increase relative performance within testing sets for a sample random forest (RF) machine learning algorithm for OS result classification.  However – after trying on a few other algorithms – it became clear that th ..read more

On last week’s post (here) we discussed a new random forest based model that I created for the prediction of out-of-sample (OS) returns in our price-action based trading system repository at Asirikuy. This is the last from a series of currently 7 different models used for OS predictions in our community. Thinking about how I could improve these models even further I decided to implement and idea – borrowed from my work in reinforcement learning – to change the way in which the models perform classification. Today I am going to talk about what this modification is all about and how it can dras ..read more

I have written several posts in the past about the building of RF models for the prediction of OS returns in our price-action based trading system repository at Asirikuy (you can read more here, here and here). So far we have built 7 different models to attempt to solve this problem, 2 of these models are meant to predict only the first sixth months of OS performance for systems that have just been mined (new systems without an OS) while the other 5 models aim to provide continuous predictions for the following weeks/months for our trading strategies, training with data that comes solely from ..read more

Last week we took a look into the Asirikuy PA system repository and how the correlation between in-sample (IS) and real out-of-sample (OS) Profit Factor (PF) values changed as a function of the group of systems we looked at. This week we are going to be doing the same exercise looking at the IS/OS Sharpe ratio (SR) values of the repository in order to evaluate how the Pearson correlation of these values change as a function of different group selections among systems above a given trade threshold. I would also like to point out that the Pearson correlation is probably not an ideal metric for ..read more

On last week’s post we talked about how the in-sample profit factor (PF) and sharpe ratio (SR) statistics in our PA system repository correlate with their out-of-sample values. It was clear from those findings that the correlation between in-sample and out-of-sample variables is increasing as a function of trade number, corroborating earlier evidence using pseudo out-of-sample periods that showed this same behavior. However it is also interesting to consider whether this correlation is fundamentally located across a certain portion of the in-sample variable values or whether it is evenly dist ..read more

The key to profitable trading under real out-of-sample (OS) conditions – meaning under data that didn’t exist when you created the strategy – is to be able to draw some type of predictions about which strategies will be better performing in the future. This means that you must have some idea about how the past characteristics of the system will relate with its future characteristics. I have discussed this several times in the past – read here and here – where it seems that in-sample (IS) return related statistical characteristics bear little or no relationship with future system characteristi ..read more

Reinforcement learning (RL) has been an important focus for me since I finished my machine learning nanodegree at Udacity. This is because reinforcement learning is substantially different from our other machine learning strategies – which use moving window supervised learning approaches – and therefore a potentially important source of diversification for our trading. However it is often very difficult to understand how reinforcement learning systems work and perhaps more importantly, to be able to predict what sorts of actions they might take given some market conditions. Today I want to of ..read more