In this article I use the random forest algorithm to forecast mid price dynamic over short time horizon i.e. a few seconds ahead. This is of particular interest to market makers to skew their bid/ask spread in the direction of the most favorable outcome. Most if not all the literature on the topic

I know this topic is addressed on a very regular basis on the web but I’m pretty sure sharing my experience will help some finance people. I’m currently working on Limit Order Book modeling. This means dealing with fairly big data sets. I have around 1 million observations per stock and per day.

It has been more than a year since my last post, I’ve been super busy with consulting assignments working on algorithmic/electronic trading. The workload is still heavy but I managed to find a few hours to write this post as I came across a new great tool: LOBSTER (and before anyone asks I’ve no

Nothing is more frustrating than a long piece of code with no standard way of naming elements, presenting code or organizing files. It’s not only unreadable but more importantly not reusable. Unfortunately, unlike other programming languages, R has no widely accepted coding best practices. Instead

I came across this excellent article lately “Machine learning at central banks” which I decided to use as a basis for a new cheat sheet called Machine Learning Modelling in R. The cheat sheet can be downloaded from RStudio cheat sheets repository. As the R ecosystem is now far too rich to

A foreword for the regular reader: this article has nothing to do with R With the increase of market “electronification”, algorithmic trading is becoming more and more popular. As a result, the regulator has paid a particular attention to this activity in the MIFID II regulation, designing a

I’m very pleased to announce my DataCamp course on Visualizing Time Series Data in R. This course is also part of the Time Series with R skills track. Feel free to have a look, the first chapter is free! Course Description As the saying goes, “A chart is worth a thousand words”. This is why

IQFeed provides streaming data services and trading solutions that cover the Agricultural, Energy and Financial marketplace. It is a well known and recognized data feed provider geared toward retail users and small institutions. The subscription price starts at around $80/month. Stanislav Kovalevsky

A few months ago a reader point me out this new way of connecting R and Excel. I don’t know for how long this has been around, but I never came across it and I’ve never seen any blog post or article about it. So I decided to write a post as the tool is really worth it and before anyone asks,

When testing trading strategies a common approach is to divide the initial data set into in sample data: the part of the data designed to calibrate the model and out of sample data: the part of the data used to validate the calibration and ensure that the performance created in sample will be

fidlr is an RSutio addin designed to simplify the financial data downloading process from various providers. This initial version is a wrapper around the getSymbols function in the quantmod package and only Yahoo, Google, FRED and Oanda are supported. I will probably add functionalities over time.

Doing quantitative research implies a lot of data crunching and one needs clean and reliable data to achieve this. What is really needed is clean data that is easily accessible (even without an internet connection). The most efficient way to do this for me has been to maintain a set of csv files.

There are many R time series tutorials floating around on the web this post is not designed to be one of them. Instead I want to introduce a list of the most useful tricks I came across when dealing with financial time series in R. Some of the functions presented here are incredibly powerful but

When it comes to managing a portfolio of stocks versus a benchmark the problem is very different from defining an absolute return strategy. In the former one has to hold more stocks than in the later where no stocks at all can be held if there is not good enough opportunity. The reason for that is