High frequency trading

This article examines the role which the theories of corporate disclosure and public regulation play in ensuring effective corporate disclosure regulation. The article makes a case for public regulation, arguing that the public nature of material information lends support to the view that neglect or failure to publish material information should be enforced through the public machinery rather than through private orderings alone. The article further contends that effective corporate information disclosure and insider dealing regulation requires both robust public enforcement and complementary private enforcement mechanisms. Through an analysis of the Capital Cost Reduction Theory, the Liquidity Enhancement Theory, and the Hydraulic Theory of Disclosure, this article demonstrates that incorporating these theories into the legal framework is likely to result in more effective regulation of corporate information disclosure and insider dealing, thereby promoting market cleanliness, transparency, and investor confidence.

This paper examines the role of algorithmic trading in modern financial markets. Additionally, order types, characteristics, and special features of algorithmic trading are described under the lens provided by the large development of high frequency trading technology. Special order types are examined together with an intuitive description of the implied dynamics of the order book conditional to special orders (iceberg and hidden). The chapter provides an analysis of the transaction costs associated with trading activity and examines the most common trading strategy employed in the market. It also examines optimal execution strategy with the description of the Efficient Trading Frontier. These concepts represent the tools needed to understand the most recent innovations in financial markets and the most recent advances in microstructures research.

The DNenc Engine is a hyper-accelerated symmetric cryptographic engine that completely offloads data obfuscation pipelines to the GPU, designed to eliminate sequential CPU core limitations in ultra-low-latency environments such as High-Frequency Trading (HFT) and live telemetry routing.

Utilizing a proprietary 5-dimensional GF(P) spatial manifold derived from the Dynamic Needle Framework, the engine operates in a massively parallel Counter (CTR) mode within isolated VRAM. Empirical benchmarks demonstrate a compute-bound peak hardware throughput of 4.52 GB/s on a consumer-grade Nvidia RTX 3070, engineered to scale linearly across enterprise clusters via GPUDirect RDMA integration. The output keystream has been verified to generate uniform, incompressible entropy via the complete NIST SP 800-22 statistical test suite, establishing a robust 160-bit post-quantum security margin.

This repository contains the foundational architectural whitepaper detailing the mathematical derivations, coordinate projection mechanics, and hardware execution parameters.

This paper analyzes the structural mechanisms that produce and sustain the 74•89% client loss rate documented by European (ESMA/MiFID II), American (CFTC), and Australian (ASIC) regulators across the retail foreign exchange industry. We identify four layers of structural bias: (i) the B-book and hybrid dealer model, in which the broker's profit is inversely correlated with client performance; (ii) a quadruple opacity in execution infrastructure, comprising the MetaTrader Manager Terminal, the Bridge and LP rebate architecture, the voice execution channel reserved for institutional participants, and the cumulative friction calculation invisible to retail; (iii) a five-tier acquisition funnel • introducing brokers, digital affiliates, commercial educators, signal providers, and copytrading platforms • whose aggregate function is the continuous production of depositing accounts rather than profitable traders; and (iv) a pseudoscientific pedagogical corpus, centered on technical analysis, whose empirical status has been systematically rejected by five decades of academic literature.  Working paper adapted from: CrossVol Research (2026). FX Traders vs Brokers: Vanilla and Exotic Options, Forwards, and Other OTC Structures: What Retail Traders Never See. Amazon Kindle, ASIN: B0GX31WB5F. https://www.amazon.com/dp/B0GX31WB5F

The GBP/USD currency pair, commonly known as "Cable," remains one of the most traded instruments in global foreign exchange markets. Every day, billions of dollars flow through this currency pair as banks, hedge funds, corporations, proprietary trading firms, and retail traders participate in price discovery. While the pair remains attractive because of its liquidity and volatility, the environment in which it trades has changed dramatically over the past decade. Today's markets are faster, more data-driven, and increasingly dominated by automation. Economic releases are analyzed within milliseconds, algorithmic systems react instantly to price changes, and liquidity providers constantly update quotes in response to changing market conditions. As a result, many traders who rely solely on manual execution are finding it increasingly difficult to compete. In fast-moving conditions, success depends not only on predicting market direction but also on execution speed, consistency, and disciplined risk management. Manual traders often struggle with these requirements because human decision-making introduces delays and emotional biases. Automated trading systems, by contrast, can process information and execute trades at speeds impossible for humans to match. This article examines why manual GBP/USD trading is becoming less effective in modern markets, compares manual and automated approaches, and explains how advanced risk management frameworks in automated trading help improve long-term performance. The Changing Nature of GBP/USD Markets

Cryptocurrency markets are much more dynamic than traditional markets due to high volatility, 24/7 trading, and sudden volume fluctuations, and high-frequency users need ultra-low latency order processing engines that require execution times in milliseconds. For these engines to work quickly and accurately, their order processing algorithms must be optimized and supported by balanced tree and hash-based data structures. Furthermore, the security and scalability of systems are critical; robust encryption protocols, anti-cyberattack measures, and horizontal/vertical scalability are imperative to ensure user trust and handle heavy transaction volumes. For these needs, an order processing engine platform that is fully compatible with centralized and decentralized exchanges, operates with low latency and can handle high transaction volumes has been developed in this study. The platform architecture has been designed with Command Query Responsibility Segregation (CQRS) and Event Driven Architecture (EDA) approaches. The Red-Black Tree data structure has been used while developing the order processing engine. The STRIDE methodology has been applied to ensure the security of the system and precautions against attacks have been developed in this context. The platform performance has been evaluated by comparing it to conventional matching engines and analyzing their latency and throughput measures. The results obtained revealed that the developed engine increases operating efficiency and provides horizontal scalability by keeping the average latency below 1 millisecond in a high volatility data load scenario. Additionally, the delay distribution and coherence (Jitter) of the platform in the high volatility load scenario have been examined and it has been confirmed that the platform has a predictable performance.
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Latency arbitrage-trading against a price quote that has not yet incorporated information already reflected in a faster reference market-is among the most frequently discussed and least formally documented phenomena in retail foreign exchange. While the academic literature on low-latency trading in equity markets is mature, the retail FX context differs in structurally important ways: prices are intermediated through a chain of liquidity providers, aggregators, and brokers; the reference market against which a quote may be judged stale is fragmented rather than consolidated; and the principal counterparty to a retail latency arbitrageur is frequently the broker itself rather than another trader. This paper surveys the mechanism of latency arbitrage in retail FX, organises the strategy space into a taxonomy, examines the infrastructure that determines feasibility, and analyses the detection methods and countermeasures that brokers deploy in response. We frame the broker-arbitrageur relationship as a coevolutionary dynamic and connect it to the established literature on toxic arbitrage and the economics of trading speed. The treatment is descriptive and integrative: it aims to render a phenomenon well understood by practitioners legible to a market-microstructure research audience, and to identify open questions amenable to empirical work.

An open-source methodology and Python reference implementation for measuring retail forex broker execution quality across five orthogonal dimensions: matching latency, slippage asymmetry, spread widening, last-look hold time, and requote rate. MIT licensed. Companion public dataset for community contributions.

The Stock Exchange of Thailand provides an ideal platform for comparing the trading characteristics of warrants and their underlying stocks since both of them trade in the same market under identical trading rules. If their patterns diverge significantly, it may be possible for an astute trader to devise profitable arbitrage strategies during the life of the warrants. We find that both their patterns are downward-sloping for spreads, U-shaped for flow toxicity, volatility, depth concentration, and trading volume; and upward-sloping for depth and market order flow ratio. This implies that trading under identical market structures leads to similar trading characteristics. We document that flow toxicity is negatively related to spread and positively related to depth, market order flow ratio, trade size, trading volume, and volatility.

The abstract describes HFT Quartet + POC as a high-frequency crypto trading framework that combines four statistical regime-detection tools—lag-1 autocorrelation, variance ratio, Reynolds-number turbulence modeling, and Higuchi fractal dimension—with volume profile Point of Control confirmation. Its goal is to detect whether the market is trending, mean-reverting, turbulent, or structurally stable, then adjust position sizing dynamically.

It claims strong empirical performance on 30-second Bitcoin data, including 70–80% win rates, Sharpe ratios above 2.5, max drawdown under 8%, and roughly 30% fewer false signals compared with traditional methods. The abstract positions the system as computationally efficient, automated, and adaptable across timeframes.

Background Developing economies offer compelling investment opportunities but are often overlooked due to perceived instability. This study challenges the bias toward developed markets by exploring whether deep learning, applied to denoised high-frequency data, can unlock superior intraday returns in developing economies. Methods We compared three developed markets (the USA, Japan, and Singapore) against three developing markets (India, Brazil, and Malaysia). A standardised pipeline retrieved minute-level data for ~20 stocks per market, selected via K-means clustering based on market capitalisation and beta. Final portfolio inclusion was validated based on model performance (adjusted R 2 scores and minimal overfitting). Data was denoised using wavelet transforms, autoencoders, and Kalman filters sequentially, with hyperparameters optimised via Optuna to filter microstructure volatility through noise. We trained multiple deep learning architectures, including LSTM, Bi-LSTM, GRU, TCN, Transformers, Conv1D-LSTM and CNN-LSTM, employing walkforward validation to forecast the next 150-minute prices. Results The models showed high predictive power, with adjusted R 2 values Open Peer Review Approval Status AWAITING PEER REVIEW Any reports and responses or comments on the article can be found at the end of the article.

We apply the path signature transform-a canonical lossless encoding of multidimensional time series rooted in Chen's theorem (1954) and Rough Path Theory (Lyons 1998)-to the joint trajectory of bid price, ask price, bid volume, and ask volume extracted from limit order book (LOB) data. Truncating the signature at order 4 yields a 341-dimensional feature vector that constitutes a sufficient statistic for predicting the next mid-price direction without any manual feature engineering. Evaluated on the FI-2010 benchmark dataset (NASDAQ, 10 stocks, 10 trading days, 10-level LOB) and on proprietary US equity tick data (2020-2024), the signature feature set outperforms standard hand-crafted LOB features (OBI, spread, VPIN, microprice) by 35-56% in terms of Information Coefficient (IC) at 100-500ms prediction horizons. We further demonstrate that the 5 most informative signature terms-cross-variation of bid price and bid volume, Lévy area between bid and ask price paths, and three third-order interaction terms-have direct microstructure interpretations, suggesting that the signature is not merely a black-box feature extractor but a structured decomposition of LOB information. GPUaccelerated computation via the Signatory library achieves full 341-feature extraction in under 0.8ms, making the approach compatible with live HFT deployment.

Many researchers have tried to optimize pairs trading as the numbers of opportunities for arbitrage profit have gradually decreased. Pairs trading is a market-neutral strategy; it profits if the given condition is satisfied within a given trading window, and if not, there is a risk of loss. In this study, we propose an optimized pairs-trading strategy using deep reinforcement learning—particularly with the deep Q-network—utilizing various trading and stop-loss boundaries. More specifically, if spreads hit trading thresholds and reverse to the mean, the agent receives a positive reward. However, if spreads hit stop-loss thresholds or fail to reverse to the mean after hitting the trading thresholds, the agent receives a negative reward. The agent is trained to select the optimum level of discretized trading and stop-loss boundaries given a spread to maximize the expected sum of discounted future profits. Pairs are selected from stocks on the S&P 500 Index using a cointegration test. W...

Economic news releases-including Non-Farm Payrolls, Federal Open Market Committee interest rate decisions, and Consumer Price Index announcements-produce the largest and most predictable short-term price displacements in the retail foreign exchange market. This paper examines the technical architecture of automated news trading systems capable of exploiting these displacements at the retail level. We analyze three critical components: machine-readable data feed infrastructure, pre-hedging execution mechanics, and slippage utilization algorithms. Empirical observations from live trading environments demonstrate that automated systems consistently execute within 5-15 milliseconds of official data release, compared to 500-800 milliseconds for manual execution-a structural advantage that cannot be overcome through skill or experience. We further examine broker selection criteria specific to news trading, indicator filtering methodologies for reducing low-probability setups, and the dual-account architecture that enables systematic performance comparison across execution environments. The analysis draws on 25 years of practical experience developing and operating retail arbitrage and news trading software.

High-frequency trading (HFT) in retail forex and cryptocurrency markets has undergone a structural transformation between 2020 and 2026. What was once the exclusive domain of institutional participants with proprietary co-location infrastructure has become accessible to professional retail traders through purpose-built execution platforms, lightweight FIX protocol implementations, and commercial colocation services. This paper examines the technical architecture required for competitive retail HFT in 2026, with particular focus on three interdependent components: (1) the EasyFIX protocol as a lightweight implementation of the Financial Information eXchange standard for simultaneous multi-broker connectivity, (2) VPS colocation strategy and its quantifiable impact on round-trip execution latency, and (3) the relationship between execution latency, slippage, and strategy profitability across latency arbitrage, lock arbitrage, and triangular arbitrage strategy types. The paper draws on operational data from the SharpTrader arbitrage platform, developed and maintained by BJF Trading Group since 2000, and used by professional traders across 50+ countries. Findings indicate that colocation-level latency reduction (from ~200ms to <5ms round-trip) is the single most significant determinant of latency arbitrage profitability, while lock and statistical arbitrage strategies exhibit substantially lower latency sensitivity. Implications for retail traders evaluating HFT infrastructure investment are discussed.

Financial fraud detection must work extremely fast. Even a small delay of a few milliseconds can affect company revenue and frustrate users. This paper explains how we built a very fast fraud detection system that can handle millions of transactions every second and still make decisions in under 100 milliseconds. The system uses real-time data streaming instead of slow batch processing. It combines event streaming, real-time processing, fast in-memory data storage, and optimized machine-learning models to detect fraud instantly. Compared to traditional batch-based systems, this approach reduces processing time by 73% while still achieving very high accuracy (99.97%). We tested the system on real payment platforms such as digital wallets and peer-to-peer payment systems. According to the test result, the system can process about 2.4 billion transactions per month, with the majority of the decisions completed in under 80 milliseconds, even during peak load. Overall, this work provides both practical design guidance and performance insights for building fast, reliable, real-time fraud detection systems used in critical financial applications.

This article presents a powerful and innovative approach to using spiking neural networks for financial time series prediction. The novelty of our approach, which we call Unsupervised Spike Learning, is that it predicts spikes in the price time series instead of price movement and direction. We adopt the unsupervised Spike-Timing-Dependent-Plasticity (STDP) learning method, such that teaching signals are no longer required. A new model topology for spiking neural networks is adopted in our approach and several spike definitions are presented. Based on this new approach, three rewarding high-frequency trading strategies are developed and backtested. Using intra-day high-frequency tick-data of crude oil futures prices and gold futures prices, the results of various experiments using Unsupervised Spike Learning are reported. The experimental results show that this approach performs well, achieving high accuracy in predicting price spikes. Moreover, our experiments demonstrate that using the STDP learning rule, a spiking neural network can recognize patterns hidden in historical price time series and can also be used to predict price spikes. We also report the results of applying novel intra-day high-frequency trading strategies that combine our Unsupervised Spike Learning approach with traditional trading strategies. These new trading strategies are back-tested in Crude Oil futures market. The Sharpe Ratio of each strategy is more than 20, showing that the new strategies are continuously profitable.

This article presents a powerful and innovative approach to using spiking neural networks for financial time series prediction. The novelty of our approach, which we call Unsupervised Spike Learning, is that it predicts spikes in the price time series instead of price movement and direction. We adopt the unsupervised Spike-Timing-Dependent-Plasticity (STDP) learning method, such that teaching signals are no longer required. A new model topology for spiking neural networks is adopted in our approach and several spike definitions are presented. Based on this new approach, three rewarding high-frequency trading strategies are developed and backtested. Using intra-day high-frequency tick-data of crude oil futures prices and gold futures prices, the results of various experiments using Unsupervised Spike Learning are reported. The experimental results show that this approach performs well, achieving high accuracy in predicting price spikes. Moreover, our experiments demonstrate that using the STDP learning rule, a spiking neural network can recognize patterns hidden in historical price time series and can also be used to predict price spikes. We also report the results of applying novel intra-day high-frequency trading strategies that combine our Unsupervised Spike Learning approach with traditional trading strategies. These new trading strategies are back-tested in Crude Oil futures market. The Sharpe Ratio of each strategy is more than 20, showing that the new strategies are continuously profitable.

his article presents a powerful and innovative approach to using spiking neural networks for financial time-series prediction. The novelty of our approach, which we call "unsupervised spike learning, " is that it predicts spikes in the price time series instead of price movement and direction. We adopt the unsupervised spike-timing-dependent-plasticity (STDP) learning method, such that teaching signals are no longer required. A new model topology for spiking neural networks is adopted in our approach and several spike definitions are presented. Based on this new approach, three rewarding high-frequency trading strategies are developed and backtested. Using intra-day high-frequency tick data for crude oil futures and gold futures prices, the results of various experiments using unsupervised spike learning are reported. The experimental results show that this approach performs well, achieving high accuracy in predicting price spikes. Moreover, our experiments demonstrate that using the STDP learning rule, a spiking neural network can recognize patterns hidden in historical price time series and can also be used to predict price spikes. We also report the results of applying novel intra-day high-frequency trading strategies that combine our unsupervised spike learning approach with traditional trading strategies. These new trading strategies are backtested in the crude oil futures market. The Sharpe ratio of each strategy is more than 20, showing that the new strategies are continuously profitable.

This paper describes simulations and analysis of flash crash scenarios in an agent-based modelling framework. We design, implement, and assess a novel high-frequency agent-based financial market simulator that generates realistic millisecond-level financial price time series for the E-Mini S&P 500 futures market. Specifically, a microstructure model of a single security traded on a central limit order book is provided, where different types of traders follow different behavioural rules. The model is calibrated using the machine learning surrogate modelling approach. Statistical test and moment coverage ratio results show that the model has excellent capability of reproducing realistic stylised facts in financial markets. By introducing an institutional trader that mimics the real-world Sell Algorithm 1 on May 6th, 2010, the proposed high-frequency agent-based financial market simulator is used to simulate the Flash Crash that took place that day. We scrutinise the market dynamics during the simulated flash crash and show that the simulated dynamics are consistent with what happened in historical flash crash scenarios. With the help of Monte Carlo simulations, we discover functional relationships between the amplitude of the simulated 2010 Flash Crash and three conditions: the percentage of volume of the Sell Algorithm, the market maker inventory limit, and the trading frequency of fundamental traders. Similar analyses are carried out for mini flash crash events. An innovative "Spiking Trader" is introduced to the model, aiming at precipitating mini flash crash events. We analyse the market dynamics during the course of a typical simulated mini flash crash event and study the conditions affecting its characteristics. The proposed model can be used for testing resiliency and robustness of trading algorithms and providing advice for policymakers.

New regulatory data reveal extensive discriminatory pricing in the foreign exchange derivatives market, in which dealer-banks and their non-financial clients trade over-the-counter. After controlling for contract characteristics, dealer fixed effects, and market conditions, we find that the client at the 75th percentile of the spread distribution pays an average of 30 pips over the market mid-price, compared to competitive spreads of less than 2.5 pips paid by the bottom 25% of clients. Higher spreads are paid by less sophisticated clients. However, trades on multi-dealer request-for-quote platforms exhibit competitive spreads regardless of client sophistication, thereby eliminating discriminatory pricing.

New regulatory data reveal extensive discriminatory pricing in the foreign exchange derivatives market, in which dealer-banks and their non-financial clients trade over-the-counter. After controlling for contract characteristics, dealer fixed effects, and market conditions, we find that the client at the 75th percentile of the spread distribution pays an average of 30 pips over the market mid-price, compared to competitive spreads of less than 2.5 pips paid by the bottom 25% of clients. Higher spreads are paid by less sophisticated clients. However, trades on multi-dealer request-for-quote platforms exhibit competitive spreads regardless of client sophistication, thereby eliminating discriminatory pricing.

New regulatory data reveal extensive discriminatory pricing in the foreign exchange derivatives market, in which dealer-banks and their non-financial clients trade over-the-counter. After controlling for contract characteristics, dealer fixed effects, and market conditions, we find that the client at the 75th percentile of the spread distribution pays an average of 30 pips over the market mid-price, compared to competitive spreads of less than 2.5 pips paid by the bottom 25% of clients. Higher spreads are paid by less sophisticated clients. However, trades on multi-dealer request-for-quote platforms exhibit competitive spreads regardless of client sophistication, thereby eliminating discriminatory pricing.

New regulatory data reveal extensive price discrimination against non-financial clients in the FX derivatives market. The client at the 90th percentile pays an effective spread of 0.5%, while the bottom quarter incur transaction costs of less than 0.02%. Consistent with models of search frictions in over-the-counter markets, dealers charge higher spreads to less sophisticated clients. However, price discrimination is eliminated when clients trade through multi-dealer request-for-quote platforms. We also document that dealers extract rents from captive clients and market opacity, but only for contracts negotiated bilaterally with unsophisticated clients.

The Midnight Opening Gap (MNOG) refers to the price displacement between the Previous Day's Close (23:59 New York time) and the Midnight Open (00:00 New York time). This gap represents an overnight imbalance formed during the Asian session and is proposed as a daily balancing mechanism influencing intraday price behavior, including retracements, liquidity sweeps, and directional bias. Positioned within the framework of the Inner Circle Trader (ICT) methodology developed by Michael J. Huddleston, MNOG extends concepts such as the New Week Opening Gap into a daily time-based model. While the acronym "MNOG" may be novel, the underlying principle aligns with established research on opening price gaps, mean reversion, and non-trading period effects. Empirical observations from practitioner backtesting suggest retracement probabilities in the range of 60-65%, with improved outcomes when combined with higher time frame bias and liquidity-based confluence. However, MNOG should not be considered a standalone trading strategy; its effectiveness depends on structured integration within a broader execution model.

This article synthesizes foundational theories in economics and modern quantitative finance, with particular emphasis on how stochastic analysis, market microstructure, and machine learning jointly shape contemporary research and practice. We review classic frameworks for portfolio choice and asset pricing, the martingale approach to no-arbitrage valuation, and liquidity-sensitive models of execution and market making. We then integrate recent econometric advances-especially double/debiased machine learning for causal and structural estimation-and discuss emerging uses of large language models for measurement and prediction tasks under explicit validity conditions. The methodology section proposes a unified research-to-deployment pipeline that links theory-driven modeling to disciplined empirical evaluation. The analysis highlights implications for market efficiency, risk management, and financial stability, and identifies open problems at the intersection of equilibrium modeling, strategic interaction, and data-driven inference. We also highlight recent market-structure developments-such as the growth of zero-days-to-expiry index options, the transition to T+1 settlement in U.S. securities, and the expansion of central clearing in U.S. Treasuries-and discuss their quantitative implications for liquidity and intraday risk.

Les marchés financiers présentent des fluctuations aléatoires qui ne peuvent être décrites par des modèles déterministes classiques. Les équations différentielles stochastiques constituent aujourd'hui un outil central pour modéliser ces phénomènes. Le lemme d'Itô joue un rôle fondamental dans l'analyse des fonctions de processus aléatoires. Cet article explique comment passer des équations différentielles stochastiques au modèle de Black and Scholes utilisé pour l'évaluation des options financières. Plusieurs exemples pratiques illustrent les applications de ces résultats en finance quantitative et en actuariat.

Les marchés nanciers présentent des uctuations aléatoires qui ne peuvent être décrites par des modèles déterministes classiques. Les équations diérentielles stochastiques constituent aujourd'hui un outil central pour modéliser ces phénomènes. Le lemme d'Itô joue un rôle fondamental dans l'analyse des fonctions de processus aléatoires. Cet article explique comment passer des équations diérentielles stochastiques au modèle de BlackScholes utilisé pour l'évaluation des options nancières. Plusieurs exemples pratiques illustrent les applications de ces résultats en nance quantitative et en actuariat.

Este documento presenta un análisis exhaustivo del indicador técnico Parabolic SAR (Stop and Reverse), originalmente desarrollado por el analista J. Welles Wilder Jr.. El artículo aborda los fundamentos teóricos y la mecánica matemática del sistema, detallando el comportamiento del Factor de Aceleración (AF) y las reglas de reversión (Flip). Además de la teoría, el trabajo destaca por su enfoque práctico mediante una implementación algorítmica completa en Python.

Le lemme d'Itô est un résultat central du calcul stochastique, permettant de calculer la dynamique des fonctions de processus aléatoires. Dans les modèles multidimensionnels, la formulation tensorielle de ce lemme met en évidence la structure des dépendances et des covariances. Cet article présente la théorie du lemme d'Itô classique et tensoriel, suivi de plusieurs exemples pratiques appliqués à la nance et à l'actuariat, incluant le calcul de la dynamique de portefeuilles multi-actifs, le logarithme de prix d'actifs, et les produits de processus corrélés.

Este documento expone los fundamentos teóricos y la aplicación algorítmica del Oscilador Estocástico, una herramienta matemática de momentum diseñada originalmente por George Lane para medir la velocidad y la inercia del precio.

A través de un enfoque cuantitativo riguroso, la investigación demuestra cómo esta métrica permite anticipar giros del mercado (reversiones a la media) antes de que el precio cambie de dirección de manera evidente.

El texto trasciende la teoría gráfica tradicional para adentrarse en la codificación de sistemas institucionales de toma de decisiones automatizada.

The rapid integration of artificial intelligence into financial markets is transform
ing the architecture of investment decision-making. As algorithmic trading systems
operate at increasing speed, scale, and autonomy, systemic risk may emerge not
from irrational human behavior but from synchronized algorithmic rationality. This
paper asks a critical question: Can AI trigger the next financial crisis, and if so,
who bears responsibility?
The study introduces the concept of a structural responsibility gap in AI-mediated
investment environments. By distinguishing computational output from normative
judgment, it argues that investment decisions inherently involve risk endorsement,
value commitment, and accountability beyond probabilistic calculation. When
judgment is delegated to autonomous systems, responsibility becomes layered and
fragmented across developers, institutions, investors, and regulators.
Through analysis of algorithmic feedback loops, synchronization dynamics, and
crisis amplification mechanisms—including flash crash events—the paper demon
strates how automated coordination may intensify systemic instability. To address
this gap, it proposes a Hierarchical Joint Responsibility Model designed to govern
delegated judgment without attributing moral agency to AI systems. The stability
of algorithmic finance ultimately depends not solely on technological control, but
on the structural redesign of accountability.

Intelligence risk models provide the foundations for decision-making systems capable of predicting, sensing, and managing risk under uncertainty. The past twenty-five years have seen the rapid integration of machine learning into business processes; however, many of these applications are built in silos and independent of risk modeling across the enterprise. For organizations to fully leverage the vast troves of data at their disposal, the  systems  that  govern  intelligence  risk  must  incorporate  machine  learning  methods. This  chapter  introduces  intelligent  risk  modeling  and  decision  frameworks.  Its  central theme  is  that  intelligent  risk  models  must  be  constructed  in  a  manner  that  aligns  with broader decision processes and the organization’s overall risk appetite. The framework subsumes the field of decision-making under uncertainty via probabilistic reasoning and Bayesian decision theory.

3.1. Introduction Real-time data pipelines can deliver information with very low latency to end users or applications, empowering real-time decision making, detection of urgent situations, and time-sensitive actions. Sectors such as transportation and logistics, telecommunications, social media, video games, and financial services require real-time data processing, and the  real-time  data  pipeline  architecture  can  be  implemented  in  these  domains.  The streaming  finance  data  pipeline  architecture  can  be  applied  in  the  finance  domain  for regulatory reporting, risk analytics, fraud detection, anomaly adaptation, and algorithmic trading in compliance with rules and regulations.Notably,  the  streaming  finance  data  pipeline  architecture  also  offers  a  finer  data  flow control model that complements the logic to satisfy user-defined security, privacy, and compliance  requirements.  Nevertheless,  besides  minimizing  end-to-end  latency,  the architecture focuses on the quality, provenance, support for observability, and security and  privacy  aspects.  Quality  techniques  are  meant  to  ensure  that  any  data  ingested conforms  to  application  requirements,  while  lineage  techniques  maintain  a  chain  ofcustody,  thereby  helping  in  auditability.  The  monitoring  of  the  pipeline  is  vital  for detecting violations, failures, and performance degradation.

The purpose of this study was to determine how the impact of changes the price fraction to the stock trading indicator that is volume, value, and frequency of trading transactions. Data were analyzed using the Mann-Whitney U test. The results show that the volume of stock trading is not affected significantly by the implementation of the tick size, whereas for the value of trade and frequency of trade significantly affected.

The awareness of the historical evolution of the negotiation over the centuries gives us a conflict resolution tool capable of adapting to the economic, cultural and scientific context and which draws its innovative strenght from the capability to improve its effectiveness by borrowing, within the strategies and techniques that characterize the methodological plexus, the empirical results of the other disciplines. As already happened with elaborations coming from intercultural communication, from the neurosciences and the cognitive psychology, the negotiation methodology is today faced with a new challenge: understandig the potential and the limits of the enormous development that in the technological field have been made in terms of Artificial Intelligence. The contribution in object wants to analyze Negotiation and Artificial Intelligence highlighting the potential relationship between this two apparently distant realities. We will proceed by describing the elements and application of these latter, focusing in particular on the Negotiation Team and on he strategic phase of the negotiation and how the Artificial Intelligence can become a performing element about it. By combining the Cognitive Technology of the Artificial Intelligence algorithm that allows on a semantic basis, the understanding, the reasoning, the answering with some specific properties of the Human Mind, such as passion, empathy, feeling (and creativity?), we highlight how, specially in the strategic phase of the negotiation, the negotiation methology could be significantly strenghtened in the effectiveness thanks to the intervantion of Artificial Intelligence tools, about it will be investigate the ipothetic role in relation to the Human component, making some hypotheses on how the Negotiation Teams of the future can be composed.

Il presente lavoro si propone l'obiettivo di analizzare le potenziali sfide e criticità che l'utilizzo dell'Intelligenza Artificiale potrà determinare all'interno del sistema giuridico. La prima parte dell'articolo si propone di sottoporre al lettore elementi di valutazione che possano rappresentare l'inizio di un futuro percorso di indagine e ragionamento per i professionisti del settore. Nella seconda parte si analizzano i potenziali rapporti e i limiti dell'utilizzo dell'Intelligenza Artificiale all'interno di uno strumento di risoluzione dei conflitti come il negoziato.

The research at hand aims to define effectiveness of algorithmic trading, comparing with different benchmarks represented by several types of indexes. How big returns can be gotten by algorithmic trading, taking into account the costs of informational and trading infrastructure needed for robot trading implementation? To get the result, it's necessary to compare two opposite trading strategies: 1) Algorithmic trading (implemented by high-frequency trading robot (based on statistic arbitrage strategy) and trend-following trading robot (based on the indicator Exponential Moving Average with the Variable Factor of Smoothing)) 2) Index investing strategy (classical index strategies "buy and hold", implemented by four different types of indexes: Capitalization weight index, Fundamental indexing, Equal-weighted indexing, Risk-based indexation/minimal variance). According to the results, it was found that at the current phase of markets' development, it is theoretically possible for algorithmic trading (and especially high-frequency strategies) to exceed the returns of index strategy, but we should note two important factors: 1) Taking into account all of the costs of organization of high-frequency trading (brokerage and stock exchanges commissions, trade-related infrastructure maintenance, etc.), the difference in returns (with superiority of high-frequency strategy) will be much less (see more in section 4.3 Organization of trading infrastructure. Development of trading robots). 2) Given the fact that "markets' efficiency" is growing every year (see more about it further in thesis), and the returns of high-frequency strategies tends to decrease with time (see more about it further in thesis), it is quite logical to assume that it will be necessary to invest more and more in trading infrastructure to "fix" the returns of high-frequency trading strategies on a higher level, than the results of index investing strategies.

The rapid adoption of artificial intelligence (AI) in high-frequency trading (HFT) has transformed modern financial markets by enabling ultra-fast decision-making, pattern recognition, and automated execution of trades. While AI-driven HFT improves liquidity provision and market efficiency, concerns have emerged regarding its effects on market volatility, financial stability, and systemic risk. This study examines how AI-powered high-frequency trading influences shortterm price fluctuations, market resilience, and the propagation of financial shocks. Using empirical market data and econometric modeling, the research evaluates volatility dynamics, liquidity conditions, and risk transmission during periods of normal trading and market stress. The findings indicate that AI-driven HFT can reduce bid-ask spreads and enhance liquidity under stable conditions; however, during periods of uncertainty, algorithmic feedback loops may amplify volatility and contribute to abrupt price movements. Additionally, the speed and interconnectedness of AI-based trading systems increase the potential for systemic risk, particularly when multiple algorithms respond simultaneously to similar market signals. This study contributes to the growing literature on algorithmic finance by providing a balanced assessment of the benefits and risks associated with AI-driven HFT and offers insights for regulators seeking to ensure market stability in increasingly automated financial systems.

Financial markets have long relied on traditional quantitative models to support trading and risk management decisions. However, the increasing availability of large-scale financial data and advances in artificial intelligence (AI) have transformed the landscape of algorithmic trading. This study presents a comparative evaluation of traditional quantitative trading models and AI-based trading strategies in equity and derivatives markets. Traditional approaches, including statistical arbitrage, mean reversion, and factor-based models, are assessed alongside AI-driven methods such as machine learning classifiers, deep neural networks, and reinforcement learning frameworks. The comparison focuses on predictive accuracy, risk-adjusted returns, adaptability to market regimes, and robustness under volatile conditions. Empirical analysis is conducted using historical market data from equity indices and derivatives instruments, with performance evaluated through metrics such as Sharpe ratio, maximum drawdown, and hit rate. Results indicate that while traditional quantitative models offer interpretability and stability, AI-based strategies demonstrate superior adaptability and performance in complex, non-linear market environments, particularly in high-frequency and derivatives trading contexts. The findings highlight the growing relevance of AI in modern trading systems while emphasizing the continued importance of traditional models for transparency and risk control. This study provides valuable insights for traders, portfolio managers, and financial institutions seeking to integrate AI-driven strategies with established quantitative frameworks.