Svitlana Vyetrenko | DeepAI

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∙ 09/04/2023

ATMS: Algorithmic Trading-Guided Market Simulation

The effective construction of an Algorithmic Trading (AT) strategy often...

0 Song Wei, et al. ∙

∙ 07/04/2023

On the Constrained Time-Series Generation Problem

Synthetic time series are often used in practical applications to augmen...

0 Andrea Coletta, et al. ∙

∙ 07/03/2023

MADS: Modulated Auto-Decoding SIREN for time series imputation

Time series imputation remains a significant challenge across many field...

0 Tom Bamford, et al. ∙

∙ 06/22/2023

Conditional Generators for Limit Order Book Environments: Explainability, Challenges, and Robustness

Limit order books are a fundamental and widespread market mechanism. Thi...

0 Andrea Coletta, et al. ∙

∙ 06/12/2023

Deep Gaussian Mixture Ensembles

This work introduces a novel probabilistic deep learning technique calle...

0 Yousef El-Laham, et al. ∙

∙ 02/23/2023

K-SHAP: Policy Clustering Algorithm for Anonymous State-Action Pairs

Learning agent behaviors from observational data has shown to improve ou...

0 Andrea Coletta, et al. ∙

∙ 11/17/2022

DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift

In electronic trading markets, limit order books (LOBs) provide informat...

0 Defu Cao, et al. ∙

∙ 09/26/2022

Learning to simulate realistic limit order book markets from data as a World Agent

Multi-agent market simulators usually require careful calibration to emu...

0 Andrea Coletta, et al. ∙

∙ 09/22/2022

StyleTime: Style Transfer for Synthetic Time Series Generation

Neural style transfer is a powerful computer vision technique that can i...

0 Yousef El-Laham, et al. ∙

∙ 08/11/2022

HyperTime: Implicit Neural Representation for Time Series

Implicit neural representations (INRs) have recently emerged as a powerf...

51 Elizabeth Fons, et al. ∙

∙ 12/03/2021

Efficient Calibration of Multi-Agent Market Simulators from Time Series with Bayesian Optimization

Multi-agent market simulation is commonly used to create an environment ...

0 Yuanlu Bai, et al. ∙

∙ 10/27/2021

ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial Markets

Model-free Reinforcement Learning (RL) requires the ability to sample tr...

9 Selim Amrouni, et al. ∙

∙ 10/25/2021

Towards Realistic Market Simulations: a Generative Adversarial Networks Approach

Simulated environments are increasingly used by trading firms and invest...

0 Andrea Coletta, et al. ∙

∙ 08/02/2021

Learning who is in the market from time series: market participant discovery through adversarial calibration of multi-agent simulators

In electronic trading markets often only the price or volume time series...

18 Victor Storchan, et al. ∙

∙ 05/27/2021

Calibrating Over-Parametrized Simulation Models: A Framework via Eligibility Set

Stochastic simulation aims to compute output performance for complex mod...

0 Yuanlu Bai, et al. ∙

∙ 12/10/2019

Get Real: Realism Metrics for Robust Limit Order Book Market Simulations

Machine learning (especially reinforcement learning) methods for trading...

0 Svitlana Vyetrenko, et al. ∙

∙ 06/05/2019

Risk-Sensitive Compact Decision Trees for Autonomous Execution in Presence of Simulated Market Response

We demonstrate an application of risk-sensitive reinforcement learning t...

0 Svitlana Vyetrenko, et al. ∙

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