For those of you that don’t know, the idea of creating an open-source package based on Advances in Financial Machine Learning stemmed from Ashu and my master’s project at WorldQuant University. The initial goal was to build out a package that we could leverage to help us extend the literature

In the summer of 2018 we attended a conference organized by Quantopian in which we heard Dr. Marcos Lopez de Prado outlined the challenges of building successful quantitative investment platforms. His book, Advances in Financial Machine Learning provides solutions to many of the problems faced by

In this article we delve into the challenge of making an asset price series stationary (for reasons discussed below) and preserving as much memory/signal from the original series. We take inspiration from Chapter 5 of the Advances in Financial Machine Learning (AFML) by Dr. Marcos Lopez de Prado

Welcome to the concept of Meta-Labeling. This blog post investigates the idea and tries to help build an intuition for what is taking place. The idea of meta-labeling is first mentioned in the textbook Advances in Financial Machine Learning by Marcos Lopez de Prado and promises to improve model and

mlfinlab is a “living and breathing” project in the sense that it is continually enhanced with new code from the chapters in the Advanced Financial Machine Learning book. We have built this on lean principles with the goal of providing the greatest value to the quantitative community. Currently

This weeks research was consumed by the concept of Meta-Labeling, how it works, and does it work out-of-sample? We have published a research report as well as an accompanying slide show. There was quite a bit of discussion this on the topic, the following is a link to a Github issue where a few

First of all we want to thank everyone who has reached out to us with ideas and contributions to our package. Without all of your help, none of this would be possible. We have done a lot of work this week and hope that this update provides you with more insight into both the package for Advances in

Dear Hedge Fund Enthusiasts, It’s been long since we sent out a newsletter but we would like to report that the Open Source Hedge Fund Project is alive and kicking again! My Msc in Financial Engineering has provided me with the unique opportunity to build an open source python package, like

Risk is not a simple entity, it comes in many flavours, and requires respect and consideration even when you least expect. If one was to try ‘totally avoid’ risk in their market endeavours they would most likely just be holding cash – where are the returns in that? The truth is that in almost

In the first section, this article describes a Dual Momentum study over an iShares country etfs basket with a new attempt to improve this well-known investing style. I chose iShares because it is the world largest family of Exchange Traded Funds (ETFs) from BlackRock. Although different stock

In this article I show that very basic quantitative trading strategies that generate returns from different market behaviours, when combined, can provide a more desirable and stable returns stream, as reflected in a Sharpe ratio higher than any individual strategy. We show how absolute returns can

For the last 6 months I have been focused on the process of building the full technology stack of an automated trading system. I have come across many challenges and learnt a great deal about the two different methods of backtesting (Vectorised and Event driven). In my journey to building an event

In a situation where you wish to determine the returns on an investment, one may have all the expertise to do this but without certain information (missing pieces) it would not be possible to derive to a conclusive figure. In practical terms “assume you have the value of all returns of all assets

Large institutions deal with immense amounts of currencies which enter and leave their accounts on a daily bases. Furthermore they have their own funds that it has to efficiently allocate so as to maximize their return on investment but also wish to hedge against adverse events. With a certain

Financial Applications Within the Monte Carlo realm a vast number of applications exist. In this final part I bring together all the previous work as well as put into practice the theory we have gathered so far. Applying the Metropolis-Hastings Algorithm From the previous section we deduced a way in

Markov Chains, Central Limit Theorem and the Metropolis-Hastings In the previous article I gave a generic overview of Monte Carlo as well as introduced importance sampling. We now dive deeper by giving strict definitions of some of the widely used and yet misunderstood or rather commonly neglected

The Monte Carlo, filled with a lot of mystery is defined by Anderson et al (1999) as the art of approximating an expectation by the sample mean of a function of simulated variables. Used as a code word between Stan Ulam and John von Neumann for the stochastic simulations they applied to building

In the previous 3 articles I discussed backtesting a trading strategy in Excel using the vectorised methodology. This article will cover the same strategy but in R. This article is more of a supplement to the already published article by Joshua Ulrich on FOSS Trading and is for readers looking for

Pinto, Henry, Robinson and Stowe (2010) define momentum indicators as valuation indicators that are based on the relationship between price or another fundamental, earnings for example, to a time series of its historical performance or to the fundamental’s expected future performance values. When

The relationship between risk and return has long been a topic for discussion and research. Investors and investment managers seek financial models that quantify risk and translate that risk into estimates of expected return on equity (Mullins, 1982). This post will look at and discuss the

In my previous article I went over how to add a position sizing rule and in this one I will complete homework exercise 2: adding a stop loss and trailing stop loss. Adding a stop loss in R is way easier than building it into Excel, I had to think for some time as to how I was going to break it down

Seminal work by Jegadeesh and Titman (1993) found that past winners outperform past losers over a horizon of 3-12 months. Investors thus take a long position on winner stocks and a short position on loser stocks in order to realise anomalous profits. This strategy is widely adopted and appears to be

To try and maximise return the correct recipe of ingredients must be brought together. Not only do we have to look at the quality of stock selection, but the weights and quantity of stocks required for maximising returns and minimising risk. Momentum investing looks to invest in top performing

Everyone has an opinion about what the state of the market will be in the short term or long term, never mind that stock prices follow a random walk or the possible clash between that comes between the invisible hand of the market and the regulatory rules made by policy makers. Returns in the market

Momentum is a market anomaly which many people have tried to explain but have not succeeded to a satisfactory extent. As to the source of momentum profits, others have tried to rationalize their origins whereas an opposing school of thought has searched for their origins in behavioural finance. In

In my previous article I started with an example of a vectorised backtest. In this one I will build on homework exercise 2 by adding position sizing. Please download this Excel document to follow the example, Click Here. Now there are many different ways in which to add position sizing and this

In my previous article I introduced the two main methods of backtesting, vectorised and event driven. In this article I will cover an example of a vectorised backtest in Excel. One of the benefits of using Excel is that you are able to see a visual representation of the data as the model progresses.

Foreword by Joshua Ulrich: Jacques reached out to me to discuss the Backtesting in Excel and R series on my blog, FOSS Trading. Inspired by that series, Jacques wanted to create a more detailed explanation of how to backtest a strategy in Excel and R, and then to extend the examples to an

The idea is to build a quantitative hedge fund strategy based on momentum investing. With the obvious interest that I see on Quantocracy, I felt that this would be a topic that many of us are interested in. I didn’t want to run the risk of previous employers saying I used their IP, so I hired 4

As to be expected, there are more than a few reasons behind quantitative hedge funds sustained losses. According to The Economist (2012), two main reasons for poor performances can be in the form of unexpected announcements from politicians and central banks which then cause unexpected market

Connect with Jacques on LinkedIn During my senior year, at a university that promised to teach me things, I found myself staring at financial statements wondering to myself "what is it the Quants see that I don't?" and so began my journey into quantitative finance. Throughout my