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on Market Microstructure |
| By: | Luca Lalor; Anatoliy Swishchuk |
| Abstract: | In this paper, we propose an event-driven Limit Order Book (LOB) model that captures twelve of the most observed LOB events in exchange-based financial markets. To model these events, we propose using the state-of-the-art Neural Hawkes process, a more robust alternative to traditional Hawkes process models. More specifically, this model captures the dynamic relationships between different event types, particularly their long- and short-term interactions, using a Long Short-Term Memory neural network. Using this framework, we construct a midprice process that captures the event-driven behavior of the LOB by simulating high-frequency dynamics like how they appear in real financial markets. The empirical results show that our model captures many of the broader characteristics of the price fluctuations, particularly in terms of their overall volatility. We apply this LOB simulation model within a Deep Reinforcement Learning Market-Making framework, where the trading agent can now complete trade order fills in a manner that closely resembles real-market trade execution. Here, we also compare the results of the simulated model with those from real data, highlighting how the overall performance and the distribution of trade order fills closely align with the same analysis on real data. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.17417 |
| By: | Leonardo Berti; Bardh Prenkaj; Paola Velardi |
| Abstract: | 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. |
| Date: | 2025–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.07071 |
| By: | Munipalle, Pravith |
| Abstract: | Bot trading, or algorithmic trading, has transformed modern financial markets by using advanced technologies like artificial intelligence and machine learning to execute trades with unparalleled speed and efficiency. This paper examines the mechanisms and types of trading bots, their impact on market liquidity, efficiency, and stability, and the ethical and regulatory challenges they pose. Key findings highlight the dual nature of bot trading—enhancing market performance while introducing systemic risks, such as those observed during the 2010 Flash Crash. Emerging technologies like blockchain and predictive analytics, along with advancements in AI, present opportunities for innovation but also underscore the need for robust regulations and ethical design. To provide deeper insights, we conducted an experiment analyzing the performance of different trading bot strategies in simulated market conditions, revealing the potential and pitfalls of these systems under varying scenarios. |
| Date: | 2024–12–22 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:p98zv_v1 |
| By: | CY Yan; Steve Keol; Xo Co; Nate Leung |
| Abstract: | Decentralized exchanges (DEXs) face persistent challenges in liquidity retention and user engagement due to inefficiencies in conventional automated market maker (AMM) designs. This work proposes a dual-mechanism framework to address these limitations: a ``Better Market Maker (BMM)'', which is a liquidity-optimized AMM based on a power-law invariant ($X^nY = K$, $n = 4$), and a dynamic rebate system (DRS) for redistributing transaction fees. The segment-specific BMM reduces impermanent loss by 36\% compared to traditional constant-product ($XY = K$) models, while retaining 3.98x more liquidity during price volatility. The DRS allocates fees ($\gamma V$, $\gamma \in \{0.003, 0.005, 0.01\}$) with a rebate ratio $\rho \in [0.3, 0.4]$ to incentivize trader participation and maintain continuous capital injection. Simulations under high-volatility conditions demonstrate impermanent loss reductions of 36.0\% and 40\% higher user engagement compared to static fee models. By segmenting markets into high-, mid-, and low-volatility regimes, the framework achieves liquidity depth comparable to centralized exchanges (CEXs) while maintaining decentralized governance and retaining value within the cryptocurrency ecosystem. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.20001 |
| By: | Jakob Albers; Mihai Cucuringu; Sam Howison; Alexander Y. Shestopaloff |
| Abstract: | Working at a very granular level, using data from a live trading experiment on the Binance linear Bitcoin perpetual-the most liquid crypto market worldwide-we examine the effects of (i) basic order book mechanics and (ii) the strong persistence of price changes from the immediate to the short timescale, revealing the interplay between returns, queue sizes, and orders' queue positions. For maker orders, we find a negative correlation between fill likelihood and subsequent short-term returns, posing a significant challenge for maker order-based strategies, while the main hurdle with taker orders is overcoming the taker fee. These dynamics render natural (and commonly-cited) trading strategies highly unprofitable. Finally, we use the understanding gained to identify situations (Reversals) in which a successful trading strategy can operate; we construct a signal for Reversals and demonstrate its efficacy. |
| Date: | 2025–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2502.18625 |