Financial markets are complex systems characterized by high statistical noise, nonlinearity, and constant evolution. Thus, modeling them is extremely hard. We address the task of generating realistic and responsive Limit Order Book (LOB) market simulations, which are fundamental for calibrating and testing trading strategies, performing market impact experiments, and generating synthetic market data. Previous works lack realism, usefulness, and responsiveness of the generated simulations. To bridge this gap, we propose a novel TRAnsformer-based Denoising Diffusion Probabilistic Engine for LOB Simulations (TRADES). TRADES generates realistic order flows conditioned on the state of the market, leveraging a transformer-based architecture that captures the temporal and spatial characteristics of high-frequency market data. There is a notable absence of quantitative metrics for evaluating generative market simulation models in the literature. To tackle this problem, we adapt the predictive score, a metric measured as an MAE, by training a stock price predictive model on synthetic data and testing it on real data. We compare TRADES with previous works on two stocks, reporting an x3.27 and x3.47 improvement over SoTA according to the predictive score, demonstrating that we generate useful synthetic market data for financial downstream tasks. We assess TRADES's market simulation realism and responsiveness, showing that it effectively learns the conditional data distribution and successfully reacts to an experimental agent, giving sprout to possible calibrations and evaluations of trading strategies and market impact experiments. We developed DeepMarket, the first open-source Python framework for market simulation with deep learning. Our repository includes a synthetic LOB dataset composed of TRADES's generates simulations. We release the code at github.com/LeonardoBerti00/DeepMarket.

Programming a Deep Neural Network from Scratch using MQL Language MetaTrader 5 -- Examples 20 October 2021, 10:33 21 622 Anddy Cabrera Introduction Since machine learning has recently gained popularity, many have heard about Deep Learning and desire to know how to apply it in the MQL language. I have seen simple implementations of artificial neurons with activation functions, but nothing that implements a real Deep Neural Network. In this article, I will introduce to you a Deep Neural Network implemented in the MQL language with its different activation functions, such as the hyperbolic tangent function for the hidden layers and the Softmax function for the output layer. We will move from the first step through the end to completely form the Deep Neural Network. 1. Making an Artificial Neuron It begins with the basic unit of a neural network: a single neuron. In this article, I will concentrate on the different parts of the type of neuron that we are going to use in our Deep Neural Network, although the biggest difference between types of the neurons is usually the activation function.

Recent research in robust optimization has shown an overfitting-like phenomenon in which models trained against adversarial attacks exhibit higher robustness on the training set compared to the test set. Although previous work provided theoretical explanations for this phenomenon using a robust PAC-Bayesian bound over the adversarial test error, related algorithmic derivations are at best only loosely connected to this bound, which implies that there is still a gap between their empirical success and our understanding of adversarial robustness theory. To close this gap, in this paper we consider a different form of the robust PAC-Bayesian bound and directly minimize it with respect to the model posterior. The derivation of the optimal solution connects PAC-Bayesian learning to the geometry of the robust loss surface through a Trace of Hessian (TrH) regularizer that measures the surface flatness. In practice, we restrict the TrH regularizer to the top layer only, which results in an analytical solution to the bound whose computational cost does not depend on the network depth. Finally, we evaluate our TrH regularization approach over CIFAR-10/100 and ImageNet using Vision Transformers (ViT) and compare against baseline adversarial robustness algorithms. Experimental results show that TrH regularization leads to improved ViT robustness that either matches or surpasses previous state-of-the-art approaches while at the same time requires less memory and computational cost.

Less than 24 hours into its fundraising, the Dash 2 Trade crypto trading intelligence platform has already raised more than $300,000 in the first stage of its presale. Positioning itself as the'Bloomberg Terminal for crypto' – but minus the $2,000 a month subscription, Dash 2 Trade is bringing the functionality and feature-set of a fully professional analytics and intelligence suite to the ordinary crypto trader. At the core of the Dash 2 Trade platform is its powerful and innovative dashboard. It's been in development for several months and is already at the minimum viable product stage, and the polished version will be ready to launch at the end of the presale. The Dash 2 Trade dashboard brings together all the tools, metrics, signals and indicators to turbo-charge trading performance.

We propose TRADE for robust tracking and 3D localization of a moving target in cluttered environments, from UAVs equipped with a single camera. Ultimately TRADE enables 3d-aware target following. Tracking-by-detection approaches are vulnerable to target switching, especially between similar objects. Thus, TRADE predicts and incorporates the target 3D trajectory to select the right target from the tracker's response map. Unlike static environments, depth estimation of a moving target from a single camera is a ill-posed problem. Therefore we propose a novel 3D localization method for ground targets on complex terrain. It reasons about scene geometry by combining ground plane segmentation, depth-from-motion and single-image depth estimation. The benefits of using TRADE are demonstrated as tracking robustness and depth accuracy on several dynamic scenes simulated in this work. Additionally, we demonstrate autonomous target following using a thermal camera by running TRADE on a quadcopter's board computer.

In adversarial machine learning, the popular $\ell_\infty$ threat model has been the focus of much previous work. While this mathematical definition of imperceptibility successfully captures an infinite set of additive image transformations that a model should be robust to, this is only a subset of all transformations which leave the semantic label of an image unchanged. Indeed, previous work also considered robustness to spatial attacks as well as other semantic transformations; however, designing defense methods against the composition of spatial and $\ell_{\infty}$ perturbations remains relatively underexplored. In the following, we improve the understanding of this seldom investigated compositional setting. We prove theoretically that no linear classifier can achieve more than trivial accuracy against a composite adversary in a simple statistical setting, illustrating its difficulty. We then investigate how state-of-the-art $\ell_{\infty}$ defenses can be adapted to this novel threat model and study their performance against compositional attacks. We find that our newly proposed TRADES$_{\text{All}}$ strategy performs the strongest of all. Analyzing its logit's Lipschitz constant for RT transformations of different sizes, we find that TRADES$_{\text{All}}$ remains stable over a wide range of RT transformations with and without $\ell_\infty$ perturbations.

ServiceTitan, which makes operating software for electricians, plumbers and the like, is stepping up its game by developing artificial intelligence of the type normally used by more sophisticated companies to streamline repetitive tasks and bring data to decision-making. ServiceTitan, the software developer for tradespeople such as electricians and plumbers, has moved into artificial intelligence. The Glendale company unveiled Titan Intelligence, or TI, at its Pantheon 2022 conference for its customers, including business owners, managers, IT and finance team members. The event was held at the Los Angeles Memorial Coliseum on April 20-22. Anmol Bhasin, chief technology officer for ServiceTitan, said he previously worked with AI at Salesforce.com Inc. and Groupon Inc., and his goal is to bring the same types of services that those companies offer to the trades.

Can you predict the market? Yes, you can, and if you cannot do not trade. We will discuss our Trading Manifesto in more detail in a following post. But here is a preview on what we consider a sound basis for trading. Real efficient and scientifically based algotrading should be based on the ability to predict market behavior.