arXiv subjects

Emmanuel Sérié

Publications and source records attributed to Emmanuel Sérié.

7 recordsLinked to original sources

arXiv2026

OxyMake: A Content-Addressed Workflow Engine

A workflow engine's job is to decide which results survive a change to code, data, or machine. Almost every engine answers with information local to where the work happened - a file timestamp, or a provenance record written beside one copy of the project - so the answer is worthless anywhere else: the next machine, the next collaborator, the next checkout recomputes what is already correct. Engines that decide by content instead often buy the property with infrastructure: a background service, or a store outside the project. OxyMake is built on the position that the validity of a result should be a property of the work, not of the place it ran, and that this need not cost an operational system. A result's identity is a pure function of what its rule declares: command text, inputs, parameters, environment. On a given platform and version of the tool, the same declaration yields the same identity in every copy of a project whose inputs lie inside it, decided by a single Rust binary with nothing running behind it. Identity is not reuse - results must still travel - but it is what makes reuse decidable at all. We describe the design, measure repeated execution, and model-check three specifications of the state layer over a bounded space of states. The guarantees reach exactly as far as the workflow declares, and no further.

cs.SE↗

arXiv2021

WAX-ML: A Python library for machine learning and feedback loops on streaming data

Wax is what you put on a surfboard to avoid slipping. It is an essential tool to go surfing... We introduce WAX-ML a research-oriented Python library providing tools to design powerful machine learning algorithms and feedback loops working on streaming data. It strives to complement JAX with tools dedicated to time series. WAX-ML makes JAX-based programs easy to use for end-users working with pandas and xarray for data manipulation. It provides a simple mechanism for implementing feedback loops, allows the implementation of online learning and reinforcement learning algorithms with functions, and makes them easy to integrate by end-users working with the object-oriented reinforcement learning framework from the Gym library. It is released with an Apache open-source license on GitHub at https://github.com/eserie/wax-ml.

cs.LG↗

arXiv2020

Deep Deterministic Portfolio Optimization

Can deep reinforcement learning algorithms be exploited as solvers for optimal trading strategies? The aim of this work is to test reinforcement learning algorithms on conceptually simple, but mathematically non-trivial, trading environments. The environments are chosen such that an optimal or close-to-optimal trading strategy is known. We study the deep deterministic policy gradient algorithm and show that such a reinforcement learning agent can successfully recover the essential features of the optimal trading strategies and achieve close-to-optimal rewards.

q-fin.MF↗

arXiv2017

The "Size Premium" in Equity Markets: Where is the Risk?

We find that when measured in terms of dollar-turnover, and once $β$-neutralised and Low-Vol neutralised, the Size Effect is alive and well. With a long term t-stat of $5.1$, the "Cold-Minus-Hot" (CMH) anomaly is certainly not less significant than other well-known factors such as Value or Quality. As compared to market-cap based SMB, CMH portfolios are much less anti-correlated to the Low-Vol anomaly. In contrast with standard risk premia, size-based portfolios are found to be virtually unskewed. In fact, the extreme risk of these portfolios is dominated by the large cap leg; small caps actually have a positive (rather than negative) skewness. The only argument that favours a risk premium interpretation at the individual stock level is that the extreme drawdowns are more frequent for small cap/turnover stocks, even after accounting for volatility. This idiosyncratic risk is however clearly diversifiable.

q-fin.PM↗

arXiv2016

Agnostic Risk Parity: Taming Known and Unknown-Unknowns

Markowitz' celebrated optimal portfolio theory generally fails to deliver out-of-sample diversification. In this note, we propose a new portfolio construction strategy based on symmetry arguments only, leading to "Eigenrisk Parity" portfolios that achieve equal realized risk on all the principal components of the covariance matrix. This holds true for any other definition of uncorrelated factors. We then specialize our general formula to the most agnostic case where the indicators of future returns are assumed to be uncorrelated and of equal variance. This "Agnostic Risk Parity" (AGP) portfolio minimizes unknown-unknown risks generated by over-optimistic hedging of the different bets. AGP is shown to fare quite well when applied to standard technical strategies such as trend following.

q-fin.PM↗

arXiv2006

Cosmological model with Born-Infeld type scalar field

The non-abelian generalization of the Born-Infeld non-linear lagrangian is extended to the non-commutative geometry of matrices on a manifold. In this case not only the usual SU(n) gauge fields appear, but also a natural generalization of the multiplet of scalar Higgs fields, with the double-well potential as a first approximation. The matrix realization of non-commutative geometry provides a natural framework in which the notion of a determinant can be easily generalized and used as the lowest-order term in a gravitational lagrangian of a new kind. As a result, we obtain a Born-Infeld-like lagrangian as a root of sufficiently high order of a combination of metric, gauge potentials and the scalar field interactions. We then analyze the behavior of cosmological models based on this lagrangian. It leads to primordial inflation with varying speed, with possibility of early deceleration ruled by the relative strength of the Higgs field.

gr-qc↗

arXiv2006

Noncommutative gauge theories with Endomorphism algebras

We formulate a Yang-Mills action principle for noncommutative connections on an endomorphism algebra of a vector bundle. It is shown that there is an influence of the topology of the vector bundle onto the structure of the vacuums of the theory in a non common way. This model displays a new kind of symmetry breaking mechanism. Some mathematical tools are developed in relation with endomorphism algebras and a new approach of the usual Chern-Weil homomorphism in topology is given.

math-ph↗