Deep Learning can Replicate Adaptive Traders in a Limit-Order-Book Financial Market

We report successful results from using deep learning neural networks (DLNNs) to learn, purely by observation, the behavior of profitable traders in an electronic market closely modelled on the limit-order-book (LOB) market mechanisms that are commonly found in the real-world global financial markets for equities (stocks & shares), currencies, bonds, commodities, and derivatives. Successful real human traders, and advanced automated algorithmic trading systems, learn from experience and adapt over time as market conditions change; our DLNN learns to copy this adaptive trading behavior. A novel aspect of our work is that we do not involve the conventional approach of attempting to predict time-series of prices of tradeable securities. Instead, we collect large volumes of training data by observing only the quotes issued by a successful sales-trader in the market, details of the orders that trader is executing, and the data available on the LOB (as would usually be provided by a centralized exchange) over the period that the trader is active. In this paper we demonstrate that suitably configured DLNNs can learn to replicate the trading behavior of a successful adaptive automated trader, an algorithmic system previously demonstrated to outperform human traders. We also demonstrate that DLNNs can learn to perform better (i.e., more profitably) than the trader that provided the training data. We believe that this is the first ever demonstration that DLNNs can successfully replicate a human-like, or super-human, adaptive trader operating in a realistic emulation of a real-world financial market. Our results can be considered as proof-of-concept that a DLNN could, in principle, observe the actions of a human trader in a real financial market and over time learn to trade equally as well as that human trader, and possibly better.Comment: 8 pages, 4 figures. To be presented at IEEE Symposium on Computational Intelligence in Financial Engineering (CIFEr), Bengaluru; Nov 18-21, 201

Kernel Belief Propagation

We propose a nonparametric generalization of belief propagation, Kernel Belief Propagation (KBP), for pairwise Markov random fields. Messages are represented as functions in a reproducing kernel Hilbert space (RKHS), and message updates are simple linear operations in the RKHS. KBP makes none of the assumptions commonly required in classical BP algorithms: the variables need not arise from a finite domain or a Gaussian distribution, nor must their relations take any particular parametric form. Rather, the relations between variables are represented implicitly, and are learned nonparametrically from training data. KBP has the advantage that it may be used on any domain where kernels are defined (Rd, strings, groups), even where explicit parametric models are not known, or closed form expressions for the BP updates do not exist. The computational cost of message updates in KBP is polynomial in the training data size. We also propose a constant time approximate message update procedure by representing messages using a small number of basis functions. In experiments, we apply KBP to image denoising, depth prediction from still images, and protein configuration prediction: KBP is faster than competing classical and nonparametric approaches (by orders of magnitude, in some cases), while providing significantly more accurate results

Kernel Bayes' rule

A nonparametric kernel-based method for realizing Bayes' rule is proposed, based on representations of probabilities in reproducing kernel Hilbert spaces. Probabilities are uniquely characterized by the mean of the canonical map to the RKHS. The prior and conditional probabilities are expressed in terms of RKHS functions of an empirical sample: no explicit parametric model is needed for these quantities. The posterior is likewise an RKHS mean of a weighted sample. The estimator for the expectation of a function of the posterior is derived, and rates of consistency are shown. Some representative applications of the kernel Bayes' rule are presented, including Baysian computation without likelihood and filtering with a nonparametric state-space model.Comment: 27 pages, 5 figure

Analyse Factorielle Discriminante Multi-voie

L'analyse factorielle discriminante est étendue aux données multi-voie, c'est-à-dire aux données pour lesquelles plusieurs modalités ont été observées pour chaque variable. Les données multi-voie sont ainsi structurées en tenseur. L'extension proposée repose sur une modélisation des axes discriminants. Cette modélisation prend en compte la structure tensorielle des données. Les gains attendus par rapport aux méthodes consistant à construire un classifieur à partir de la matrice obtenue par dépliement du tenseur, sont une meilleure interprétabilité et un meilleur comportement vis-à-vis du surapprentissage, phénomène d'autant plus présent dans le contexte multi-voie que le nombre de modalités est grand. Un algorithme de directions alternées permet d'obtenir les axes discriminants. Les performances obtenues sur données simulées permettent de confirmer ces gains

Auditory display of seismic data: On the use of experts' categorizations and verbal descriptions as heuristics for geoscience

International audienceAuditory display can complement visual representations in order to better interpret scientific data. A previous article showed that the free categorization of “audified seismic signals” operated by listeners can be explained by various geophysical parameters. The present article confirms this result and shows that cognitive representations of listeners can be used as heuristics for the characterization of seismic signals. Free sorting tests are conducted with audified seismic signals, with the earthquake/seismometer relative location, playback audification speed, and earthquake magnitude as controlled variables. The analysis is built on partitions (categories) and verbal comments (categorization criteria). Participants from different backgrounds (acousticians or geoscientists) are contrasted in order to investigate the role of the participants' expertise. Sounds resulting from different earthquake/station distances or azimuths, crustal structure and topography along the path of the seismic wave, earthquake magnitude, are found to (a) be sorted into different categories, (b) elicit different verbal descriptions mainly focused on the perceived number of events, frequency content, and background noise level. Building on these perceptual results, acoustic descriptors are computed and geophysical interpretations are proposed in order to match the verbal descriptions. Another result is the robustness of the categories with respect to the audification speed factor

Tackling issues of coexistence between protected areas and communal lands: from a role playing game to an agent based model

Coexistence between actors living in a common environment is a recurrent issue throughout the world. In southern Africa, issues at the interface between agriculture and conservation are inescapable. Livestock herding for instance is a particularly relevant phenomenon to consider if one wants to study coexistence between protected areas and farming households leaving on their edges. Role playing games and agent based model can be used both to elicit local knowledge and strategies, and also to simulate the possible evolution of a given system. In this presentation we propose to describe a work conducted with farmers and livestock herders living in what we define as the Hwange National Park-Sikumi Forest SES (HNP-SF-SES), Zimbabwe. In our study area, cattle are driven within one of the protected areas (SF) throughout the year, resulting in (i) cattle predation by wild predators, and (ii) concerns about the capacity of the SF to effectively conserve wild herbivores. In order to better understand herders' strategies, we co-designed a role playing game with 10 members of this community. Such game is a tool that allows us to elicit herding practices, and to test different scenarios (e.g. climatic variations, alternative governance rules). We assume that a co-designed game will better represent players' reality, thus enhancing appropriation and finally allowing us to collect relevant data. The design process is already a direct first step towards an agent Based model as we co-formalized the local environment with the design team. Results of the playing sessions will be presented, so will the process of translating them into an autonomous agent based model used to simulate possible trajectories of our studied system

Price Variations in a Stock Market With Many Agents

Large variations in stock prices happen with sufficient frequency to raise doubts about existing models, which all fail to account for non-Gaussian statistics. We construct simple models of a stock market, and argue that the large variations may be due to a crowd effect, where agents imitate each other's behavior. The variations over different time scales can be related to each other in a systematic way, similar to the Levy stable distribution proposed by Mandelbrot to describe real market indices. In the simplest, least realistic case, exact results for the statistics of the variations are derived by mapping onto a model of diffusing and annihilating particles, which has been solved by quantum field theory methods. When the agents imitate each other and respond to recent market volatility, different scaling behavior is obtained. In this case the statistics of price variations is consistent with empirical observations. The interplay between ``rational'' traders whose behavior is derived from fundamental analysis of the stock, including dividends, and ``noise traders'', whose behavior is governed solely by studying the market dynamics, is investigated. When the relative number of rational traders is small, ``bubbles'' often occur, where the market price moves outside the range justified by fundamental market analysis. When the number of rational traders is larger, the market price is generally locked within the price range they define.Comment: 39 pages (Latex) + 20 Figures and missing Figure 1 (sorry), submitted to J. Math. Eco

Variable Selection in Partial Least Squares Methods: overview and recent developments

Recent developments in technology enable collecting a large amount of data from various sources. Moreover, many real world applications require studying relations among several groups of variables. The analysis of landscape matrices, i.e. matrices having more columns (variables, p) than rows (observations, n), is a challenging task in several domains. Two different kinds of problems arise when dealing with high dimensional data sets characterized by landscape matrices. The first refers to computational and numerical problems. The second deals with the difficulty in assessing and understanding the results. Dimension reduction seems to be a solution to solve both problems. We should distinguish between feature selection and feature extraction. The first refers to variable selection, while feature extraction aims to transform the data from high-dimensional space to low-dimensional space. Partial Least Squares (PLS) methods are classical feature extraction tools that work in the case of high-dimensional data sets. Since PLS methods do not require matrices inversion or diagonalization, they allow us to solve computational problems. However, results interpretation is still a hard problem when facing with very high-dimensional data sets. Moreover, recently Chun & Keles (2010) showed that asymptotic consistency of PLS regression estimator for the univariate case does not hold with the very large p and small n paradigm. Nowadays interest is increasing in developing new PLS methods able to be, at the same time, a feature extraction tool and a feature selection method. The first attempt to perform variable selection in univariate PLS Regression framework was presented by Bastien et al. in 2005. More recently Le Cao et al. (2008) and Chun & Keles (2010) proposed two different approaches to include variable selection in PLS Regression, based on L1 penalization (Tibshirani, 1996). In our work, we will investigate all these approaches and discuss the pros and cons. Moreover, a new version of PLS Path Modeling algorithm including variable selection will be presented