Develops a new trading strategy for statistical arbitrage with path-dependent signals.
problem Optimal execution in statistical arbitrage strategies with dynamic predictive signals.
method Signature-based framework modeling alpha and trading speed as linear functionals of truncated signature of market path.
result Fitted policy achieves higher return on turnover compared to a z-score benchmark.
Unified coagent network theory with execution paths.
problem Hierarchical reinforcement learning exploration-exploitation trade-off.
method Formalized coagent networks, introduced execution paths, revisited coagent network theory.
result Shorter proof of policy gradient theorem, generalizable to asynchronous coagents.
One-shot path planning for multiple agents using neural networks.
problem Efficiently generating optimal or near-optimal paths for multiple agents in robotics.
method Utilizes fully convolutional neural networks for one-shot multi-agent path planning.
result Demonstrates successful generation of optimal or near-optimal paths in over 85% of cases for multi-path planning.
We present a method for obtaining approximate solutions to the problem of optimal execution, based on a signature method. The framework is general, only requiring that the price process is a geometric rough path and the price impact function is a continuous function of the trading speed. Following an approximation of t…
ICON-OCnet solves optimal execution problems with neural networks and few examples.
problem Optimal order execution in markets with unknown price impact.
method Transformer-based neural network architecture (ICON-OCnet) that learns price impact from few examples and applies it to optimal execution strategies.
result ICON-OCnet accurately infers price impact models and retrieves optimal execution strategies for various propagator kernels.
A new VWAP execution method using transformer and signature features.
problem Asset-specific model training and complex temporal dependencies.
method Combining transformer-based design with path signatures for capturing geometric features.
result GFT-Sig model achieves superior performance in VWAP loss metrics.
Regularizes decision trees to reduce inference time by up to 4x with minimal accuracy loss.
problem Optimizing decision tree execution time on resource-constrained devices.
method Regularizes impurity computation during CART algorithm training to favor highly asymmetric distributions.
result Reduces inference time by up to 4x with minimal accuracy loss.
LEMs extend transformer-based architectures for complex execution problems.
problem Handling flexible time boundaries and multiple execution constraints in deep learning.
method Decouples market information processing from execution allocation decisions using TKANs, VSNs, and multi-head attention mechanisms.
result LEMs achieve superior execution performance compared to traditional benchmarks.
Neural execution solves complex graph problems like bipartite matching.
problem Solving complex graph algorithms like maximum bipartite matching.
method Reduces bipartite matching to a flow problem and uses Ford-Fulkerson for maximum flow.
result Neural network achieves optimal matching almost 100% of the time.
Lazy, perfectly informed investors trade infrequently due to costs.
problem The paradox of an omniscient yet lazy investor trading infrequently.
method Formalized the paradox using geometric and fractional Brownian motion models, derived closed-form profit functions, and proved existence and uniqueness of the optimal trading frequency.
result The optimal trading frequency can be interpreted through the fractal dimension of the price path.
Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for Deep Neural Networks (DNNs). More specifically, we formalise coverage criteria for DNNs that have been studied in the literature, and then d…
We define the concept of good trade execution and we construct explicit adapted good trade execution strategies in the framework of linear temporary market impact. Good trade execution strategies are dynamic, in the sense that they react to the actual realisation of the traded asset price path over the trading period; …
Exact simulation method for market impact estimation under various execution strategies.
problem Estimating market impact from observed price trajectories under different execution strategies.
method Conditional simulation of point processes under perturbed intensities.
result Exact, event-driven algorithm for reconstructing counterfactual paths.
New simulation approaches to evaluating path-dependent options without matrix inversion issues nor Euler bias are evaluated. They employ three main contributions: Stochastic approximation replaces regression in the LSM algorithm; Explicit weak solutions to stochastic differential equations are developed and applied to …
Agents learn to outperform in trading by using past and current prices.
problem Optimal trading performance beyond theoretical limits.
method Two-agent Almgren-Chriss liquidation game, schedule-learning, DDQN architectures.
result Agents with access to past and current prices achieve supra-competitive outcomes.
Generalist neural learner can execute multiple algorithms.
problem Building models that can execute multiple algorithms.
method Single graph neural network processor, incorporating knowledge from specialist models.
result Generalist learner can execute multiple algorithms with improved performance.
Bayesian Algorithm Execution uses mutual information to infer properties of black-box functions efficiently.
problem Estimating computable properties of expensive black-box functions with limited evaluations.
method Sequentially choosing queries that maximize mutual information with respect to the algorithm's output.
result InfoBAX reduces query counts by up to 500 times compared to the original algorithm.
Existing methods for reducing the computational burden of neural networks at run-time, such as parameter pruning or dynamic computational path selection, focus solely on improving computational efficiency during inference. On the other hand, in this work, we propose a novel method which reduces the memory footprint and…
A new model predicts price concavity and reversion after metaorder execution.
problem Modeling market response to exogenous trades on limit order books.
method Developed a Non-Markovian Zero Intelligence model with a time-weighted mid-price return function.
result The model predicts concave price paths and price reversion after metaorder execution.
Algorithm minimizes regret and converges to equilibria in Markov games.
problem Regret minimization and convergence to equilibria in general-sum Markov games under adversarial opponents.
method Decentralized algorithm that uses policy optimization and controls path length to achieve sublinear regret.
result Sublinear regret guarantees for convergence to correlated equilibrium in Markov games.
Method predicts hardware resource usage by control software with guaranteed linear convergence.
problem Predicting time-varying hardware resource availability in control software.
method Path structured multimarginal Schrödinger bridge (MSBP) for learning stochastic resource usage.
result Guaranteed linear convergence to accurate prediction of hardware resource utilization.
KineticSim: A lightweight, high-performance execution engine for real-time market simulators
problem Simulating financial markets at scale with multi-agent models
method Reusable parallel design pattern: persistent, state-carrying clearing for iterative multi-agent reductions
result Reduces per-step critical-path depth from Theta(L+A) to Theta(log L + ceil(A/L))
Novel hybrid method for Bayesian network structure learning reduces computational time without sacrificing accuracy.
problem Bayesian network structure learning efficiency and accuracy trade-off.
method Partitioned PC (pPC), p-value adjacency thresholding (PATH), hybrid greedy initialization (HGI). result pHGS achieves significant computational reductions compared to the PC algorithm without sacrificing structure learning accuracy.
Paper develops models for better HFT and algorithmic trading.
problem Inaccurate LOB dynamics in financial markets.
method Semi-Markov and Hawkes jump-diffusion models for LOB dynamics.
result Improved trading strategies through precise model application.
Proposes a method to allocate time budgets in mixed criticality systems.
problem Managing execution time variability in mixed criticality systems.
method Quantifies execution time variability using statistical dispersion parameters and proposes a heuristic to allocate time budgets.
result The proposed heuristic reduces the probability of exceeding allocated budgets.
Optimal crypto order execution using cross-exchange signals.
problem Maximizing order execution efficiency in cryptocurrency markets.
method Reinforcement learning applied to cross-exchange data.
result Cross-exchange signals improve optimal execution outcomes.
The paper uses machine learning to detect malicious executable files.
problem Detecting malicious executable files using static analysis.
method Pre-processing, cleaning, encoding, feature selection, and ensemble training of classifiers.
result An ensemble of classifiers effectively detects malicious executable files.
We demonstrate an application of risk-sensitive reinforcement learning to optimizing execution in limit order book markets. We represent taking order execution decisions based on limit order book knowledge by a Markov Decision Process; and train a trading agent in a market simulator, which emulates multi-agent interact…
One challenge impeding the analysis of terabyte scale x-ray scattering data from the Linac Coherent Light Source LCLS, is determining the number of clusters required for the execution of traditional clustering algorithms. Here we demonstrate that previous work using bi-cross validation (BCV) to determine the number of …
The paper analyzes trade execution strategies for large traders in a stochastic market environment.
problem Analyzing trade execution strategies in a stochastic market with price impact.
method Formulated a Markov game model and used backward induction method of dynamic programming.
result Explicit closed-form execution strategy at Markov perfect equilibrium.
Optimized execution model using interbank and internal liquidity.
problem Minimizing market impact in trading.
method Integrates interbank limit and market orders with internal market-making liquidity.
result Reduces market impact and improves execution efficiency.
This work reduces DIM computation costs by training neural networks on single MC paths.
problem Training neural networks for Dynamic Initial Margin (DIM) computation in counterparty credit risk.
method Constructing a training dataset with noisy but unbiased DIM samples from single MC paths, employing a multi-output neural network structure.
result The approach reduces dataset generation cost to a single MC execution and validates its general applicability and efficiency.
Paper uses DDPG to learn optimal execution strategies in dynamic markets.
problem Learning non-Markovian optimal execution strategies in dynamic financial markets.
method Introduces a novel actor-critic algorithm based on DDPG for transient price impact modeling.
result Successfully approximates optimal execution strategy through numerical experiments.
Develops a new model to optimize trading in markets.
problem Optimal execution of market securities with transaction costs.
method Introduces a utility function balancing market impact and transaction costs, incorporating existing optimal trading strategies.
result Demonstrates a new approach to balancing market impact and transaction costs.
Neural networks struggle with long sequences, but a new method improves their performance.
problem Neural networks struggle to generalize to longer sequences and unseen data.
method Proposed a learned conditional masking mechanism and binary encoding for numbers.
result Models can now generalize far outside their training range with near-perfect accuracy.
Paper proposes a novel policy distillation method for better order execution in noisy markets.
problem Effective order execution in noisy and imperfect market conditions.
method Policy distillation method to guide reinforcement learning towards optimal trading strategies.
result Significant improvements over various baselines in order execution.
Unified theory for optimal execution through signal-adaptive quotes in limit order books.
problem Optimal execution in limit order books with signal-dependent factors.
method Develops a unified solution theory for four execution criteria, incorporating signal-dependent drift, price impact, inventory risk, and execution risk.
result Explicit formulas reveal optimal quoting strategies and show signal-dependent drift can significantly affect execution.
Dynamic VWAP execution improves by 10-15% in liquid markets.
problem Improving VWAP execution in dynamic markets.
method Recurrent Neural Networks (RNNs) for capturing temporal market dynamics, dynamic adjustment mechanism.
result Significant performance gains in liquid markets (10-15%) over traditional methods.
Paper uses DRL to optimize trade execution, outperforming VWAP and TWAP.
problem Optimizing returns while minimizing risk in order execution.
method Deep Reinforcement Learning (DRL) for holistic optimization.
result DRL-based approach outperforms VWAP and TWAP in ROI and risk management.
Optimal trading strategies in fluctuating financial markets are analyzed using complex mathematical models.
problem Optimal execution of trades in markets with fluctuating liquidity and order book depth.
method Continuous-time limit order book model with càdlàg semimartingale strategies, quadratic BSDEs.
result Characterization of minimal execution costs and existence of optimal strategies.
Many learning agents impact a financial market model, showing complex dynamics.
problem Understanding the dynamics of financial markets with multiple learning agents.
method Agent-based model of financial market with multiple reinforcement learning agents interacting.
result Inclusion of learning agents changes market dynamics to match empirical data.
The volume weighted average price (VWAP) execution strategy is well known and widely used in practice. In this study, we explicitly introduce a trading volume process into the Almgren-Chriss model, which is a standard model for optimal execution. We then show that the VWAP strategy is the optimal execution strategy for…
RL agents optimize order execution in a realistic market simulation.
problem Optimal order execution challenges in a complex market.
method Multi-agent RL in a historical order book simulation.
result RL agents converge to TWAP strategies in some scenarios.
Sunshine trading theory predicts lower execution costs and liquidity provision through explicit preannouncements, but evidence is scarce in traditional markets.
problem Adverse selection on liquidity provision
method Reconstructing metaorders and comparing them with visible TWAP executions
result Visible TWAPs face lower execution costs and leave a smaller permanent price impact compared to hidden metaorders.
Optimal trade execution in a fluctuating market with stochastic liquidity.
problem Minimizing costs in a market with unpredictable liquidity.
method Developed a recursion to find the least costly trade execution strategy.
result Explicit recursion characterizes the least costly trade execution.
Paper optimizes broker performance by estimating execution costs.
problem Minimizing execution costs for large trades.
method Intraday modeling of execution cost components (linear and quadratic).
result Substantial improvements in estimating execution costs.
LLM-based trading systems vary in execution realism and reproducibility.
problem LLM-based trading systems vary in execution realism and reproducibility.
method Reproducibility audit of 30 trade-relevant primary studies.
result LLM-based trading systems vary in execution realism and reproducibility.
This paper develops a method to select a reference contract for multi-contract quoting to minimize execution risk.
problem Minimizing execution risk in multi-contract quoting sequences.
method Develops a diagnostic framework using order-flow Hawkes forecasts and CLF to select a stable reference contract.
result Event-history and LOB-state signals offer complementary views for reference-contract selection.