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The Thalesians

For quant/FX commentary check our newly relaunched blog at

Read our Thalesians paper on 4pm FX here, which was featured in the Wall Street Journal, Why FX Traders Trade: A Reminder, by Katie Martin (11 March 2014)

The Thalesians are a think tank of dedicated professionals with an interest in quantitative finance, economics, mathematics, physics and computer science, not necessarily in that order.

The group was founded in Sep 2008, by Paul Bilokon (then a quantitative analyst at Lehman Brothers specialising in foreign exchange, and a part-time researcher at Imperial College), and two of his friends and colleagues: Matthew Dixon (then a quantitative analyst at Deutsche Bank) and Saeed Amen (then a quantitative strategist at Lehman Brothers).

The Thalesians were originally based in London, UK. In Jan 2011, the organisation became truly global when Matthew Dixon brought it to the United States where he runs both the Thalesians NYC seminars with local organizer Harvey Stein and the Thalesians SF seminars.

Systematic trading publications - In late 2013, we started published ground breaking quant strategy notes. Our effort was lead by Saeed Amen, using nearly a decade of his experience both creating and later trading systematic trading models in FX at major investment banks. The Thalesians were also mentioned in the national press for the first time in the Independent in Sep 2013.

Systematic trading consulting - In Jan 2014, we started offering bespoke consulting services in FX markets, signing up our first client, a major US hedge fund. Our services includes the creation of bespoke systematic trading models and other quant analysis of financial markets, such as currency hedging and FX transaction cost analysis. For more information on our quant strategy consulting services and quant strategy notes, please contact and visit Quant Strategy.

Our Philosophy

We are named after Thales of Miletus (Θαλῆς ὁ Μιλήσιος), a pre-Socratic Greek philosopher who lived in ca. 624 BC-ca. 546 BC. Thales was a mathematician and is familiar to many secondary school students for one of his theorems in geometry.

But more relevantly to us, he was one of the first users of options:

"Thales, so the story goes, because of his poverty was taunted with the uselessness of philosophy; but from his knowledge of astronomy he had observed while it was still winter that there was going to be a large crop of olives, so he raised a small sum of money and paid round deposits for the whole of the olive-presses in Miletus and Chios, which he hired at a low rent as nobody was running him up; and when the season arrived, there was a sudden demand for a number of presses at the same time, and by letting them out on what terms he liked he realised a large sum of money, so proving that it is easy for philosophers to be rich if they choose, but this is not what they care about."Aristotle, Politics, 1259a.

The morale of this anecdote is that it is easy for philosophers to be rich if they choose; the famous Milesian went ahead and proved it.

We, the Thalesians, admire him for that. But we also share many of his values, for example his core belief that a happy man is defined as one "ὁ τὸ μὲν σῶμα ὑγιής, τὴν δὲ ψυχὴν εὔπορος, τὴν δὲ φύσιν εὐπαίδευτος" (who is healthy in body, resourceful in soul and of a readily teachable nature).

This wiki was created to serve as a source of information on quantitative finance, to collate references to various related resources, and to serve as a convergence point for the Thalesians, our colleagues and collaborators. It grew out of Paul Bilokon's finance wiki, which he started in February, 2007.

We believe that secrecy and fidelity are important in the world of finance. But we also acknowledge the power of information sharing in open societies. Let your business logic remain a closely guarded secret. But release everything else into the public domain. What goes around, comes around; this will ultimately spare you reinventing the wheel.

Forthcoming Events

Thalesians Seminar (New York) — Prof. Naumann — Adjoint Algorithmic Differentiation Software Tool Support for Computational Finance



July 15th, 2014:

5:30 PM Registration

5:45 PM Seminar Begins

6:45 PM Q/A


New York Public Library- Science, Industry, and Business 188 Madison Ave, New York, NY.

Enter and leave the building at 188 Madison Avenue. Signs will direct you to the lower level and conference room 014/015.


You can register for this event on Meetup here.


We discuss recent developments in Adjoint Algorithmic Differentiation (AAD) software tool support in the context of large-scale parameter calibration methods in Computational Finance. First-and second-order derivative-based approaches to solving the underlying numerical optimization problems are considered. Specific mathematical and structural properties of the underlying simulation are exploited. The superiority of AAD software tools over manual approaches to the implementation of adjoint financial models as well as over numerical approximation of the required sensitivities by finite differences (bumping) is illustrated. This talk is meant to show you how AAD can be applied in practice in a robust and sustained fashion.


Dr. Naumann has been professor of computer science with focus on numerical methods and software tools for Computational Science, Engineering, and Finance at RWTH Aachen University, Aachen, Germany, since 2004. He is head of the Steinbeis Consultancy Center for Simulation Sofware Analysis, Transformation, and Optimization, Aachen, Germany, and member and technical consultant of the Numerical Algorithms Group Ltd., Oxford, UK.

Dr. Naumann has published more than 100 scientific papers in peer-reviewed journals and in proceedings of international conferences. He is the author of “The Art of Differentiating Computer Programs. An Introduction to Algorithmic Differentiation” published by the Society for Industrial and Applied Mathematics in 2012. The AAD software developed by his group is actively used within a large number of numerical software projects and, in particular, by several tier-1 banks.


This is not an instructional program of the New York Public Library.

Thalesians Séance (Budapest) — George Cooper — Fixing the broken science of economics — a new paradigm to help rebuild economic theory

George Cooper

A very special thanks to Attila Agod for organising this talk! Our goal is to create a social convergence point for the quantitative financial professionals in Hungary with quarterly events. This our third event in Budapest!

Date and Time

7:00 p.m. on Friday 25th July, 2014.

7:00 p.m. Welcome drinks, 8:00 p.m. George Cooper's talk 9.00 p.m. Drink & Discuss, 12.00 a.m. Next pub


Palack Borbár, Szent Gellért sqr 3, Budapest, Hungary

You can register for this event on


The heterodox schools of economics refers to those areas of economics thought considered outside "mainstream economics". There are many different "heterodox theories" in existence. These include socialist, Marxian, Austrian and post-Keynesian amongst others. George's talk will be on the subject of unifying the heterodox schools of economics and fixing the broken science of economics. He will draw upon some themes from his new book "Money, Blood and Revolution: How Darwin and the doctor of King Charles I could turn economics into a science".


Dr George Cooper is the author of "Money, Blood and Revolution: How Darwin and the Doctor of King Charles I could turn economics into a science" and "The Origin of Financial Crises: Central banks, credit bubbles and the efficient market fallacy".

George has worked for Goldman Sachs, Deutsche Bank, J.P. Morgan and BlueCrest Capital Management in a variety of fund management and financial market strategy roles. Prior to joining the City, George worked as a research scientist at Durham University and as an engineer for Fujitsu microelectronics.


To be published here


To be published here


  • N/A

Recent Events

Thalesian Seminar (London) — Saeed Amen — Systematic FX gamma trading

Saeed Amen

Date and Time

7:30 p.m. on Wednesday, 2 July, 2014.


Ginger Room, Marriott Hotel, Canary Wharf, London, UK.

You can register for this event and pay online on


In this presentation we shall examine the properties of implied and realised volatility. Later, we shall develop vol trading strategies and look at how delta hedging impacts the P&L of these strategies. Finally, we shall ascertain the sensitivity of our vol trading model with respect to other factors like transaction costs and scheduled events.


Saeed Amen is a Managing Director and a Co-founder at Thalesians Ltd.

Saeed is currently publishing ground-breaking quant strategy notes at Thalesians Ltd., drawing upon nearly a decade of experience both creating and running systematic trading models successfully with real cash. Independently, he is also a systematic FX trader, running a proprietary trading book trading liquid G10 FX. He is currently also writing a book on trading which is due to be published by Palgrave Macmillan (preliminary title: Trading Thalesians - What the ancient world can teach us about trading today)

Saeed started his career at Lehman Brothers. He worked on the FX desk developing systematic trading models for both G10 and EM and was part of the team who developed the MarQCuS suite of models. He was also responsible for a systematic FX prop trading book and conducted research around high frequency FX including economic events. Later he was at Nomura as an Executive Director in Quantitative Strategy, also in FX, developing their model infrastructure and also running systematic FX prop risk. He graduated from Imperial College with a first class honours master's degree in Mathematics and Computer Science.


To be published here


To be published here


  • N/A

For older events, please see The Thalesians Quantitative Finance Seminars.


Masses and Buckets

You have M masses,  m_1, m_2, \ldots, m_M which you want to distribute across N buckets "as uniformly as possible". By this I mean that you are trying to minimise  \sum_{i=1}^N \sum_{j=i}^N (b_i - b_j)^2 , where bk is the sum of the masses in the k-th bucket. How would you achieve this?

To make this a little bit more concrete, suppose that I give you 20 masses, e.g. 23, 43, 12, 54, 7, 3, 5, 10, 54, 55, 26, 9, 9, 43, 54, 1, 8, 6, 38, 33. There are 4 buckets. How would you distribute the masses?

Please send your answers to paul, who happens to be at

[ Solution ]

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