Back in March we ran a content survey and found that many of you were interested in a refresher course for the key mathematical topics needed to understand deep learning and quant finance in general. Since deep learning is going to be a big part of this year's content we thought it would be(...)

This is the third in a new advanced series of posts written by Imanol Pérez, a PhD researcher in Mathematics at Oxford University and an expert guest contributor to QuantStart. In this post Imanol applies the Theory of Rough Paths to the task of handwritten digit classification—a common task for(...)

This is the third in a series of posts written by Frank Smietana, an expert guest contributor to QuantStart. In this detailed post Frank examines the different algorithmic trading strategies carried out by quantitative hedge funds. Click for parts one and two. - Mike. Institutional asset managers(...)

his is the second in a new advanced series of posts written by Imanol Pérez, a PhD researcher in Mathematics at Oxford University, and a new expert guest contributor to QuantStart. In this post Imanol continues the theoretical discussion of Rough Paths and Signatures and begins applying them within(...)

This is the second in a series of posts written by Frank Smietana, an expert guest contributor to QuantStart. In this detailed post Frank takes a look at the different ways in which an algorithmic trading business can be established—and why you might want to consider it. - Mike. Setting up an(...)

To date QuantStart has generally written on topics that are applicable to the beginner or intermediate quant practitioner. However we have recently begun to receive requests from academics and advanced practitioners asking for more content on research-level topics. This is the first in a new series(...)

This is the first in an exciting series of posts written by Frank Smietana, a new expert guest contributor to QuantStart. In this insightful new article Frank looks at the different career roles that are available in the systematic trading space. - Mike. The changing role of humans in capital(...)

Almost a year ago QuantStart discussed deep learning and introduced the Theano library via a logistic regression example. Given the recent results of the QuantStart 2017 Content Survey it was decided that an up to date beginner-friendly article was needed to introduce deep learning from first(...)

In the previous article on Hidden Markov Models it was shown how their application to index returns data could be used as a mechanism for discovering latent "market regimes". The returns of the S&P500 were analysed using the R statistical programming environment. It was seen that(...)

In this article the issue of when to retire a trading strategy will be considered. It will present brief reasons why strategies eventually end up underperforming. It will discuss how this can be measured over time and then describe an implementation in QSTrader that provides this functionality. The(...)

The QuantStart team are very happy to announce that the full version of Advanced Algorithmic Trading has now been released. This brings the total number of pages to 517. To access the full version customers simply need to follow the download link received in the original pre-order purchase email.(...)

In addition to the "usual" tricks of statistical arbitrage, trend-following and fundamental analysis, many quant shops (and retail quants!) engage in natural language processing (NLP) techniques to build systematic strategies. Such techniques fall under the banner of Sentiment Analysis.(...)

In previous articles the concept of cointegration was considered. It was shown how cointegrated pairs of equities or ETFs could lead to profitable mean-reverting trading opportunities. Two specific tests were outlined–the Cointegrated Augmented Dickey-Fuller (CADF) test and the Johansen test–that(...)

In this article the concept of unsupervised clustering will be considered. In quantitative finance finding groups of similar assets, or regimes in asset price series is extremely useful. It can aid in the development of filters, or entry and exit rules. This helps improve profitability for certain(...)

In a previous article the decision tree (DT) was introduced as a supervised learning method. In the article it was mentioned that the real power of DTs lies in their ability to perform extremely well as predictors when utilised in a statistical ensemble. In this article it will be shown how(...)

A couple of weeks ago I flew out to Singapore to give a talk at the Quantopian Singapore QuantCon. The event was absolutely fantastic with an incredibly diverse and interesting set of talks. I gave a talk was on the topic of Hunting For Alpha In Alternative Data. Here is a brief summary of the(...)

In a previous article the monthly rebalance feature of the open-source backtesting library QSTrader was demonstrated on a simplistic equities/bonds ETF mix portfolio. In this article new streamlined code will be presented to allow straightforward modification of the portfolio weightings. In(...)

Many institutional global asset managers are constrained by the need to invest in long-only strategies with zero or minimal leverage. This means that their strategies are often highly correlated to "the market" (usually the S&P500 index). While it is difficult to minimise this(...)

Last Tuesday I flew out to New York City, USA to give a talk at the Quantopian NYC Meetup and moderate a panel on "Programming Wars" at the Trading Show New York 2016. Both events were extremely interesting and I met a lot of great people. I want to write a brief summary of the trip as it(...)

In the previous article in the series Hidden Markov Models were introduced. They were discussed in the context of the broader class of Markov Models. They were motivated by the need for quantitative traders to have the ability to detect market regimes in order to adjust how their quant strategies(...)

This is a short post to let QuantStart readers know that I'll be speaking at some events in New York and Singapore over the next couple of months: Wednesday, October 5th 2016, NYC - I'll be talking about "The Quest for Profitability", along with Seong Lee who will be talking(...)

Previously on QuantStart we have considered the mathematical underpinnings of State Space Models and Kalman Filters, as well as the application of the pykalman library to a pair of ETFs to dynamically adjust a hedge ratio as a basis for a mean reverting trading strategy. In this article we will(...)

The world of quantitative finance continues to evolve at a rapid pace. Even in the last four years of the existence of this site the market for quant jobs has shifted significantly. In this article we outline these shifts. The advice on what is likely to be in demand in the next few years will be(...)

A consistent challenge for quantitative traders is the frequent behaviour modification of financial markets, often abruptly, due to changing periods of government policy, regulatory environment and other macroeconomic effects. Such periods are known colloquially as "market regimes" and(...)

In the first and second articles in the series we looked at the courses that are taken in the first half of a four-year undergraduate mathematics degree - and how to learn these modules on your own. In the first year we discussed the basics - Linear Algebra, Ordinary Differential Equations, Real(...)

A common quant trading technique involves taking two assets that form a cointegrating relationship and utilising a mean-reverting approach to construct a trading strategy. This can be carried out by performing a linear regression between the two assets (such as a pair of ETFs) and using this to(...)

In this article we are going to consider a stastical machine learning method known as a Decision Tree. Decision Trees (DTs) are a supervised learning technique that predict values of responses by learning decision rules derived from features. They can be used in both a regression and a(...)

This post relates to a talk I gave in April at QuantCon 2016 in New York City. QuantCon was hosted by Quantopian and I was invited to talk about some of the topics discussed on QuantStart. I decided to talk about whether it is worth building your own backtesting system. This post goes into more(...)

The purpose of this article series is to introduce a very familiar technique, Linear Regression, in a more rigourous mathematical setting under a probabilistic, supervised learning interpretation. This will allow us to understand the probability framework that will subsequently be used for more(...)

I was emailed recently with a career-related question about jumping from one quant role to another. The question posed was "How can I make the jump from being a quant/software developer to a quant trader/researcher in a fund or investment bank?". This is certainly possible and does happen(...)

The majority of machine learning posts to date on QuantStart have all been about supervised learning. In this post we are going to take a look at unsupervised learning, which is a far more challenging area of machine learning. Supervised learning involves taking a number of data observations, each(...)

I was emailed yesterday with an interesting career question about working in High Frequency Trading (HFT). The question posed was "Is it possible to get a HFT-related job in a big company without a formal degree?". The short answer is that yes, it is possible. The longer answer is that it(...)

This is a quick update post to let readers know that the pre-order release of Advanced Algorithmic Trading has had a new update, adding over 50 pages of material. This brings the current release up to 250 pages. To access the new content, customers simply need to follow the download link received(...)

In the previous article on the Cointegrated Augmented Dickey Fuller (CADF) test we noted that one of the biggest drawbacks of the test was that it was only capable of being applied to two separate time series. However, we can clearly imagine a set of three or more financial assets that might share(...)

In the previous article on cointegration in R we simulated two non-stationary time series that formed a cointegrated pair under a specific linear combination. We made use of the statistical Augmented Dickey-Fuller, Phillips-Perron and Phillips-Ouliaris tests for the presence of unit roots and(...)

A while back we considered a trading model based on the application of the ARIMA and GARCH time series models to daily S&P500 data. We mentioned in that article as well as other previous time series analysis articles that we would eventually be considering mean reverting trading strategies and(...)

Over the last ten years the subject of deep learning has been one of the most discussed fields in machine learning and artificial intelligence. It has produced state-of-the-art results in areas as diverse as computer vision, image recognition, natural language processing and speech recognition.(...)

In the last article in the series we looked at the foundational courses that are often taken in a four-year undergraduate mathematics course. We saw that the major courses were Linear Algebra, Ordinary Differential Equations, Real Analysis and Probability. In the "second year" of our(...)

This is a quick update to let the QuantStart community know what has been happening in the last few months as it has been an exciting time "behind the scenes" of the site. Firstly, I spoke at the Quantopian QuantCon conference in New York last week. The conference was absolutely(...)

To date on QuantStart we have introduced Bayesian statistics, inferred a binomial proportion analytically with conjugate priors and have described the basics of Markov Chain Monte Carlo via the Metropolis algorithm. In this article we are going to introduce regression modelling in the Bayesian(...)

In previous discussions of Bayesian Inference we introduced Bayesian Statistics and considered how to infer a binomial proportion using the concept of conjugate priors. We discussed the fact that not all models can make use of conjugate priors and thus calculation of the posterior distribution(...)

"I'm actually really optimistic about the future of quants. The industry is more technical than ever, and there is as much need to understand the risks in the system as ever." - Robert C. Merton, quoted in "Risk," August 2012 In 1997, when Robert Merton won the Nobel Prize(...)

I am often asked in emails how to go about learning the necessary mathematics for getting a job in quantitative finance or data science if it isn't possible to head to university. This article is a response to such emails. I want to discuss how you can become a mathematical autodidact using(...)

In the current series on Advanced Trading Infrastructure we have described both the Position Class and the Portfolio Class - two essential components of a robust backtesting and live trading system. In this article we are going to extend our discussion to the Portfolio Handler Class, which will(...)

In the previous article in the Advanced Trading Infrastructure series I discussed and presented both the code and initial unit tests for the Position class that stores positional information about a trade. In this article we will consider the Portfolio class, used to store a list of Position(...)

At the end of last year I announced that I would be working on a series of articles regarding the development of an Advanced Trading Infrastructure. Since the initial announcement I haven't mentioned the project to any great extent. However, in this article I want to discuss the progress(...)

To date in our time series analysis posts we have considered linear time series models including ARMA, ARIMA as well as the GARCH model for conditional heteroskedasticity. In this article we are going to consider the theoretical basis of state space models, the primary benefit of which is that(...)

To date on QuantStart we have considered two major quantitative backtesting and live trading engines. The first arised from the Event-Drive Backtesting series I wrote back in March 2014. The second is QSForex, an open-source backtest and live trading engine that hooks into the OANDA Forex Broker(...)

Today's post is a guest post from Jacques Joubert, who runs QuantsPortal. Jacques emailed me recently and asked if I'd be willing to contribute to a post about how to get started in quant blogging. I was more than happy to do so, and Jacques wondered if it would make a good guest post for(...)

This is a short post to let you know that I'll be speaking at QuantCon on the April 9th 2016 in New York City. What is QuantCon? Over the past five years the software landscape for retail quant traders, and researchers at quant funds, has changed dramatically. The uptake of open source(...)