Optimal execution strategy for market and limit orders with speed limits and uncertainty.
problem Optimal execution of limit and market orders with trade speed limits and uncertainty.
method Continuous-time model with stochastic control problem, incorporating trade speed limiter and trader director.
result Identification of optimal dynamic trading strategies and conditions for optimality.
Speed bumps reduce but do not fully eliminate investment in fast trading technology.
problem Limiting low-latency trading to curb investment in fast trading technology.
method Built an experimental trading platform to test the effects of speed bumps on investment in fast trading technology.
result Asymmetric speed bumps reduce investment in speed by only 20%, and increasing the magnitude further reduces investment by 8.33%. Symmetric speed bumps have no effect on investment levels.
Study shows time matters in automated trading, improving simple strategies over complex ones.
problem Effects of reaction speed and trading urgency on automated trading strategies.
method Simulated financial markets with public limit order book and continuous double auction matching. Examined reaction speed and trading urgency.
result Simple strategies outperform complex ones when considering reaction speed and trading urgency.
Faster trading algorithms aren't always better, as shown in simulated financial markets.
problem The impact of reaction time on automated trading performance.
method Simulated financial markets with a single exchange, public limit order book, and continuous double auction matching. Models of trading speed and computation times of trading algorithms were introduced and profiled.
result Trading performance is impacted by speed, and the Adaptive-Aggressive (AA) algorithm is outperformed by the Shaver (SHVR) strategy when relative computation times are accurately simulated.
High-frequency trading strategy boosts battery storage profits.
problem Maximizing revenue for battery energy storage systems in intraday markets.
method Adapted dynamic programming for continuous intraday markets, considering limit order book dynamics.
result Dynamic programming strategy outperforms standard re-optimization methods, increasing profits by 58% and 14% respectively.
DEX allows HFTs to quickly acquire speed, improving price discovery without sacrificing liquidity.
problem Low-latency trading's externality and resource inefficiency in centralized exchanges.
method Model of decentralized exchanges with real-time speed acquisition from peer-to-peer networks.
result HFTs on DEX acquire more speed for shorter timespans, decreasing price discovery time and reducing resource lock-in.
Game theory models how agents trade in a risky asset considering price impact and a common signal.
problem Modeling how financial agents liquidate assets in a risky market with price impact and a common signal.
method Formulated and solved a multi-player stochastic differential game and mean field game.
result Equilibrium strategies reveal how agents adjust the predictive trading signal to price impact.
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.
Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.
problem Balancing vessel speed with environmental and ecological risks in Arctic shipping.
method Inverse control constrained optimization framework with risk parameters estimated from AIS data.
result Distinct decision-making patterns across vessel types and navigational statuses, with varying sensitivity to ice and whale risks.
Study shows HFT benefits large traders under certain conditions.
problem Influence of high-frequency traders (HFTs) on large traders.
method Analyzes the impact of HFT front-running on large traders under different conditions.
result HFT benefits large traders when there is high-speed noise trading and vague HFT predictions.
This paper studies four trading algorithms of a professional trader at a multilateral trading facility, observing a realistic two-sided limit order book whose dynamics are driven by the order book events. The identity of the trader can be either internalizing or regular, either a hedge fund or a brokery agency. The spe…
The SIP's accuracy is questioned, leading to skewed returns for high-volume stocks.
problem Inaccuracy of the SIP in reporting trades and quotes.
method Analysis of Trade and Quote data, use of first differences to highlight latency and inaccuracy.
result Up to 60% of trades are reported out of sequence, skewing returns.
BSE simulates a stock exchange for teaching and research.
problem Lack of realistic teaching and research environments for automated trading systems.
method Minimal simulation of a Limit Order Book (LOB) system.
result BSE successfully used for teaching and research since 2012.
We reconsider the problem of optimal trading in the presence of linear and quadratic costs, for arbitrary linear costs but in the limit where quadratic costs are small. Using matched asymptotic expansion techniques, we find that the trading speed vanishes inside a band that is narrower than in the absence of quadratic …
GPU acceleration speeds up financial machine learning training time.
problem Time-intensive classifier training in financial machine learning.
method Deployed NVIDIA GPUs for parallel high-speed arithmetic operations.
result Significantly faster training time achieved.
Optimal execution method uses rough path signatures for approximate solutions.
problem Finding optimal trading speed in financial markets.
method Approximate solutions via signature method for geometric rough paths and continuous price impact.
result Accurate and flexible optimal trading speed solutions for various market conditions.
EHVI outperforms scalarized EI in MOBO for molecule design.
problem Benchmarking MOBO strategies for molecule design.
method Compared EHVI against fixed-weight scalarized EI in MOBO.
result EHVI consistently outperforms scalarized EI in molecular optimization tasks.
Flexible deep learning models for dynamic accuracy and speed trade-offs.
problem Dynamic accuracy and speed trade-offs in real-world applications.
method Training deep neural networks with a new method allowing flexible numerical precision during inference.
result Achieved comparable accuracy to dedicated models trained at the same precision with dynamic precision settings.
Proposes a deep reinforcement learning model for efficient variable speed limits control.
problem Improving traffic flow, safety, and emissions on freeways with varying speed limits.
method Uses a novel actor-critic architecture for deep reinforcement learning to manage dynamic speed limits.
result The proposed method enhances efficiency, safety, and emissions compared to traditional control methods.
Fragmented exchanges arise due to speed advantages in high-activity regions.
problem Fragmentation of distributed securities exchanges due to speed advantages in high-activity regions.
method Economic model and Monte Carlo simulations of a decentralized exchange with two miner clusters.
result Speed advantage increases with infrastructure asymmetry between regions.
Research optimizes C++ patterns for HFT, reducing latency and improving profitability.
problem Optimizing latency-critical code for high-frequency trading systems.
method Creation of a Low-Latency Programming Repository, optimisation of trading strategy, implementation of Disruptor pattern.
result Significant performance improvements in speed and profitability.
Paper improves financial trading models using GPU parallelism.
problem Challenges in policy instability and sampling bottlenecks in reinforcement learning for financial tasks.
method Revisits ensemble methods with massively parallel simulations on GPUs.
result Significantly improved computational efficiency and robustness of financial decision-making strategies.
This paper analyzes and improves convergence in federated learning with biased client selection.
problem Analyzing convergence in federated learning with biased client selection.
method First convergence analysis of federated optimization for biased client selection strategies, proposing Power-of-Choice framework.
result Power-of-Choice strategies converge up to 3 times faster and give 10% higher test accuracy than random selection.
Trading system uses NP-hard optimization to select stocks for high Sharpe ratio trading.
problem Finding profitable, uncorrelated stocks for high Sharpe ratio trading.
method NP-hard combinatorial optimization using Ising machine and simulated bifurcation algorithm.
result Trading strategy with FPGA-based system achieves 164 μs response latency.
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.
Non-autoregressive method speeds up protein folding prediction 23 times.
problem Generating protein sequences with higher order interactions.
method Discrete diffusion conditioned on 3D structure using ProteinMPNN.
result 23 times speed up in inference without performance loss.
We consider a single security market based on a limit order book and two investors, with different speeds of trade execution. If the fast investor can front-run the slower investor, we show that this allows the fast trader to obtain risk free profits, but that these profits cannot be scaled. We derive the fast trader's…
New method finds coresets for faster machine learning.
problem Improving training speed and understanding results.
method Iterative optimization using multi-objective evolutionary algorithms.
result Results deliver lower error and better generalization.
This study evaluates a dynamic pairs trading strategy in cryptocurrencies using cointegration tests.
problem Improving profitability and risk management in cryptocurrency trading.
method Engle-Granger, KSS, Johansen tests; optimal look-back window; mean-reversion speed calibration; microstructure limitations consideration.
result The strategy outperforms naive buy-and-hold in Bitmex exchange with low maximum drawdown.
New limits found for power, speed, and precision in physical communication.
problem Limits on power, speed, and precision in physical communication.
method Derivation involving a novel connection between friction and information geometry.
result Product of precision and speed is universally bounded by power dissipation.
Study liquidity variables to measure intraday information accuracy.
problem Measure endogenous information in intraday liquidity variables.
method Empirical study using Granger causality and autoregressive models.
result Documented auto-correlations in real data for optimal trading.
Trading strategies are limited by position limits, leading to a finite number of unique strategies.
problem Limiting the number of long and short positions in trading strategies.
method Formulas and distributions derived for the number of unique trading strategies, transactions, and do-nothing actions.
result A discrete distribution of actions and their properties are presented.
This paper uses RL and RNN to optimize trading with latent market information.
problem Optimal trading strategies exploiting latent information in financial markets.
method Blend of RL and RNN, integrating GRU networks for temporal dependencies.
result prob-DDPG achieves superior cumulative rewards and interpretable strategies.
Financial exchanges provide incentives for limit order book (LOB) liquidity provision to certain market participants, termed designated market makers or designated sponsors. While quoting requirements typically enforce the activity of these participants for a certain portion of the day, we argue that liquidity demand t…
Quantum algorithms improve high-frequency trading efficiency.
problem Reducing calculation time in high-frequency statistical arbitrage trading.
method Variable time condition number estimation and quantum linear regression.
result Quantum advantage in trading algorithm complexity reduction.
MATCHA speeds up decentralized SGD by parallelizing communication.
problem Error-runtime trade-off in decentralized SGD.
method MATCHA decomposes network topology into matchings for efficient communication.
result MATCHA reduces communication time by up to 5x compared to vanilla decentralized SGD.
Network embedding helps predict speed limits on incomplete Danish road network.
problem Incomplete speed limit data on Danish roads limits machine learning applications.
method Applied node2vec network embedding to Danish road network.
result Network embedding can derive useful features for predicting speed limits.
We study the high-frequency limits of strategies and costs in a Nash equilibrium for two agents that are competing to minimize liquidation costs in a discrete-time market impact model with exponentially decaying price impact and quadratic transaction costs of size θ≥0. We show that, for θ=0, equilibrium strategie…
FinRL-Podracer accelerates DRL trading strategies in finance with high performance and scalability.
problem Challenges in applying deep reinforcement learning to finance trading models.
method Proposes an RLOps framework and high-performance cloud solution for DRL trading.
result FinRL-Podracer outperforms existing DRL libraries by 12-35% in annual return, 0.1-0.6 in Sharpe ratio, and 3-7 times in training time.
RL agent learns to place limit orders for trading signals in financial markets.
problem Training an RL agent to execute trading signals in limit order book markets.
method Deep Duelling Double Q-learning with APEX architecture, using synthetic alpha signals.
result RL agent outperforms heuristic trading strategies in inventory management and order placing.
Nearly one-half of all trades in financial markets are executed by high-speed, autonomous computer programs -- a type of trading often called high-frequency trading (HFT). Although evidence suggests that HFT increases the efficiency of markets, it is unclear how or why it produces this outcome. Here we create a simple …
VADD enhances discrete diffusion models by capturing inter-dimensional correlations, improving sample quality.
problem Limited modeling of inter-dimensional dependencies in MDMs degrades performance with few denoising steps.
method Introduces an auxiliary recognition model for latent variable modeling, enabling stable training via variational lower bounds maximization and amortized inference.
result VADD consistently outperforms MDM baselines in sample quality with few denoising steps.
FlowHFT learns adaptive trading strategies from multiple models for diverse market conditions.
problem Traditional HFT models are limited by specific market conditions and cannot adapt to dynamic markets.
method FlowHFT uses flow matching policy to learn from multiple expert models and adapt to various market scenarios.
result FlowHFT consistently outperforms individual expert models in multiple market conditions.
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.
Optimizes trade execution with reinforcement learning for limit orders.
problem Maximizing revenue in a limit order book with market and limit orders.
method Formulated as a dynamic allocation task, uses multivariate logistic-normal distributions for efficient training.
result Outperforms traditional strategies in simulated environments.
Modern physics has demonstrated that matter behaves very differently as it approaches the speed of light. This paper explores the implications of modern physics to the operation and regulation of financial markets. Information cannot move faster than the speed of light. The geographic separation of market centers means…
Investigates optimal trading strategies for illiquid assets using a modified market impact model.
problem Optimizing investment behavior in a large unregulated financial institution with illiquid assets.
method Extension of Almgren-Chriss model to account for market illiquidity and expected utility optimization.
result Explicit closed-form solution for optimal trading strategy with interesting properties.
A scalable portfolio approach speeds up Bayesian optimization for noisy functions.
problem Efficiently selecting multiple designs in parallel for noisy, expensive black-box optimization.
method A portfolio approach that balances exploration and exploitation, using a scalable allocation strategy.
result Significant speed improvements over existing methods, with similar or better performance.