Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually…
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,695 papers · 148 categories
Trend · papers per month
48 results for “strategy automation”
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.
No fair and strategy-proof automated market maker exists for more than two assets.
problem Designing a fair and strategy-proof automated market maker for multiple assets.
method Analyzing the weighted-product family of aggregation rules and their properties.
result No aggregation rule is both fair and strategy-proof for more than two assets.
Framework uses LLMs to automate strategy finding in quantitative finance.
problem Brittleness of traditional deep learning models in financial applications.
method Three-stage framework with prompt-engineered LLMs, multimodal agent-based evaluation, and dynamic weight optimization.
result Robust performance in Chinese & US markets, superior risk-adjusted performance.
Study analyzes impact of concentrated liquidity on trading fees and provider returns.
problem Impact of concentrated liquidity on trading fees and provider returns.
method Comparison of average liquidity provider returns before and after concentrated liquidity introduction; quantification of fundamental strategies performance.
result Concentrated liquidity strategies outperform in certain trading pairs and market conditions.
Automated framework forecasts correlated time series in minutes.
problem Forecasting correlated time series with high accuracy and efficiency.
method Data-driven iterative pruning, zero-shot search, fast parameter adaptation.
result Framework achieves state-of-the-art accuracy and is faster than existing methods.
We explore the competitive effects of reaction time of automated trading strategies in simulated financial markets containing a single exchange with public limit order book and continuous double auction matching. A large body of research conducted over several decades has been devoted to trading agent design and simula…
Study examines AutoML adaptation to evolving data.
problem Understanding and improving AutoML performance with concept drift.
method 6 concept drift adaptation strategies evaluated on various AutoML approaches.
result Robust AutoML techniques can be developed to handle concept drift.
While artificial intelligence (AI) and other automation technologies might lead to enormous progress in healthcare, they may also have undesired consequences for people working in the field. In this interdisciplinary study, we capture empirical evidence of not only what healthcare work could be automated, but also what…
This work analyzes impermanent loss in decentralized markets and provides a hedging strategy.
problem Impermanent loss in automated market makers (AMMs).
method Analytical derivation of a static replication formula using European options, and numerical example with real data.
result Guaranteed hedging coverage for all final prices within a predefined interval.
Study analyzes factors affecting profits in crypto liquidity provision.
problem Liquidity providers lack guidance for developing profitable strategies.
method Developed a measurement model based on impermanent loss to analyze key parameters.
result Uncovered influences of key parameters on LPs' profits.
Informer model with GMADL loss outperforms benchmarks in high frequency Bitcoin trading.
problem Developing automated trading strategies for high frequency Bitcoin data.
method Informer architecture with RMSE, GMADL, and Quantile loss functions.
result Informer model with GMADL loss function outperforms benchmarks in trading outcomes.
Paper uses AI to optimize crypto portfolios, showing better risk-adjusted returns.
problem Managing volatile crypto markets with high volatility.
method Multi-agent system designed to autonomously construct and evaluate crypto-asset allocations.
result Dynamic optimization strategy outperforms static equal weighting strategy in terms of risk-adjusted returns.
This study optimizes trading and arbitrage in decentralized finance's CPMs, revealing convexity costs and developing efficient strategies.
problem Optimizing trading and arbitrage in decentralized finance's constant product markets (CPMs).
method Developed models for CPMs in competing centralised exchanges, CPMs, and both venues. Derived computationally efficient strategies.
result Accurately estimated convexity costs in CPMs, which are linear in trade size and nonlinear in liquidity depth and exchange rate.
Deep RL ensemble strategy outperforms individual algorithms in stock trading.
problem Designing profitable stock trading strategies in a complex market.
method Ensemble of three deep reinforcement learning algorithms (PPO, A2C, DDPG) for stock trading.
result Deep ensemble strategy outperforms individual algorithms and traditional min-variance portfolio.
Study coevolutionary trading-agent dynamics in continuous strategies.
problem Understanding adaptive trading-agent interactions in complex markets.
method Experimental study of adaptive automated trading agents in a continuous strategy space.
result High-dimensional coevolutionary dynamics pose challenges in market analysis.
Optimizes liquidity withdrawal timing for AMM LPs to balance fees and impermanent loss.
problem Balancing fees and impermanent loss in automated market makers.
method Stochastic control problem with endogenous stopping time, numerical solutions via Euler scheme and Longstaff-Schwartz method.
result Optimal exit strategy depends on volatility, fees, and market dynamics.
Automated trading system with preprocessing and reinforcement learning.
problem Efficient portfolio trading for individual investors.
method Feature preprocessing, recurrent reinforcement learning, automated trading algorithm.
result System outperforms other strategies in profit and drawdown.
Study compares quantum and classical ML in crypto trading, finding hybrid models outperform.
problem Comparing quantum and classical machine learning in crypto trading strategies.
method Backtesting 10 models across multiple crypto assets using classical ML, quantum ML, hybrid models, and transformer models.
result Hybrid quantum models achieve superior performance with 13.99% return and 1.76 Sharpe ratio.
Paper uses LLMs for sector allocation, showing better returns.
problem Automated trading sector allocation inefficiencies.
method Systematic analysis of macroeconomic data and sentiment.
result LLM-based sector allocation outperforms traditional strategies.
Parallelizes MCTS for continuous domains using leaf and root parallelization.
problem Solving challenging tasks in continuous domains using MCTS.
method Extends existing parallelization strategies to continuous domains, focusing on leaf and root parallelization.
result Proposes two final selection strategies for continuous states in root parallelization.
Novel method reconstructs liquidity data for CLMMs, optimizing dynamic liquidity strategies.
problem Challenges in evaluating and optimizing CLMMs due to lack of historical liquidity data.
method Reconstructs historical liquidity states from swap transaction data using machine learning.
result Identifies outperformance of dynamic liquidity strategies over uniform allocation benchmarks.
This thesis studies CPMMs with CL, developing strategies for LTs and LPs.
problem Trading mechanisms and strategies for CPMMs with CL.
method Formalizes CPMMs with CL, develops strategies using market data and models.
result Derives optimal strategies for LTs and LPs in CPMMs with CL.
AutoFS combines trainers to improve feature selection efficiency and effectiveness.
problem Balancing feature selection efficiency and effectiveness.
method Interactive Reinforced Feature Selection (IRFS) framework with diverse trainers.
result Improved feature selection efficiency and effectiveness compared to existing methods.
Does Redundancy in AI Perception Systems Help to Test for Super-Human Automated Driving Performance?cs.LG
Redundancy in AI perception systems doesn't guarantee independent error occurrences.
problem Lack of direct statistical evidence of super-human automated driving performance.
method Investigated the effectiveness of redundancy in neural networks for independent error occurrences.
result Errors in neural networks for computer vision tasks are correlated, not independent.
Optimizes bidding strategies for LinkedIn ads across multiple platforms.
problem Optimizing automated bidding agents for dynamic online marketplaces.
method Developed a general optimization framework for buyer's interest, agnostic to auction mechanisms.
result Automatically guarantees the optimality of budget allocation across ad units and platforms.
A new DRL system using LSTM improves stock trading performance.
problem Adapting DRL to financial data with low signal-to-noise ratios.
method Cascaded LSTM networks for feature extraction and reinforcement learning.
result Our model outperforms previous models in cumulative returns and Sharp ratio.
PyODDS automates outlier detection for new data sources.
problem Manual outlier detection is inefficient and domain-specific.
method Automated end-to-end system with Apache Spark and database support.
result PyODDS optimizes outlier detection pipelines automatically.
Black box discrete optimization (BBDO) appears in wide range of engineering tasks. Evolutionary or other BBDO approaches have been applied, aiming at automating necessary tuning of system parameters, such as hyper parameter tuning of machine learning based systems when being installed for a specific task. However, auto…
We introduce a means of automating machine learning (ML) for big data tasks, by performing scalable stochastic Bayesian optimisation of ML algorithm parameters and hyper-parameters. More often than not, the critical tuning of ML algorithm parameters has relied on domain expertise from experts, along with laborious hand…
Decentralised fund framework allocates capital via tokenised vaults.
problem Traditional asset management's inefficiencies and centralisation.
method Permissionless, multi-strategy capital allocation through on-chain vaults.
result Self-regulating, cooperative optimisation across financial domains.
Tick-by-tick liquidity provision aims to maximize fees and reserves.
problem Maximizing fees and reserves in concentrated liquidity.
method Convex optimization for tick-level liquidity provision.
result Concentrating liquidity around current price is not always best.
Optimal design of automated market makers for decentralized exchanges.
problem Maximizing utility for liquidity providers in decentralized exchanges.
method Modeling a risk-averse liquidity provider's optimal strategy and the optimal design of automated market makers.
result The optimal unit trading fee increases with asset volatility.
We propose a strategy for automated trading, outline theoretical justification of the profitability of this strategy and overview the hypothetical results in application to currency pairs trading. The proposed methodology relies on the assumption that processes reflecting the dynamics of currency exchange rates are in …
Machine learning (ML) methods have been developing rapidly, but configuring and selecting proper methods to achieve a desired performance is increasingly difficult and tedious. To address this challenge, automated machine learning (AutoML) has emerged, which aims to generate satisfactory ML configurations for given tas…
Deriving conditional and marginal distributions using conjugacy relationships can be time consuming and error prone. In this paper, we propose a strategy for automating such derivations. Unlike previous systems which focus on relationships between pairs of random variables, our system (which we call Autoconj) operates …
Proposes deep mixture models for probabilistic price movement forecasting in high-frequency trading.
problem Probabilistic forecasting of price movements in high-frequency trading.
method Deep recurrent neural networks with probabilistic mixture models.
result Outperforms benchmark models in both metric-based and simulated trading scenarios.
Paper uses Bayesian optimization to find best Supertrend indicator settings.
problem Finding optimal trading parameters for the Supertrend indicator.
method Bayesian optimization to automate parameter selection.
result BO-optimized Supertrend strategy yields higher profits in backtesting.
Survey of AI in quant finance, from deep learning to LLMs.
problem Improving predictive modeling and automation in asset management.
method Exploring AI contributions to quant investment pipeline, from human-crafted features to LLMs.
result AI has enabled scalable modeling and autonomous agents in quant finance.
Deep RL applied for Indian stock trading strategies.
problem Designing profitable trading strategies for Indian stock markets.
method Applied deep reinforcement learning to ten Indian stock datasets.
result Models' performance compared and evaluated.
Paper proposes a new method for efficient hyperparameter optimization.
problem Challenging task of optimizing hyperparameters in machine learning.
method Sequential Uniform Design (SeqUD) strategy for adaptive and efficient exploration of hyperparameter space.
result The proposed SeqUD strategy outperforms existing methods in hyperparameter optimization.
In this paper, we confront the problem of deep learning's big labeled data requirements, offer a rule based strategy for extreme augmentation of small data sets and apply that strategy with the image to image translation model by Isola et al. (2016) to automate cel style cartoon coloring with very limited training data…
RandomNet uses random search to design neural architectures without much human intervention.
problem Designing neural architectures without excessive human intervention.
method Random search strategy for multimodal neural architecture design.
result RandomNet performs close to state-of-the-art on AV-MNIST with minimal human supervision.
This paper examines the uniform properties of AMMs in cryptocurrency markets.
problem Theoretical uniformity of AMMs despite diverse strategies.
method Derives a universal formula for liquidity provisioning and compares models.
result Constant function and token swap models are equivalent under uniform liquidity.
BOOST automates kernel and acquisition function selection in Bayesian optimization.
problem Inappropriate kernel and acquisition function combinations lead to poor performance in Bayesian optimization.
method BOOST uses offline evaluation to predict and select the best kernel-acquisition function pair.
result BOOST consistently improves over fixed-hyperparameter BO and is competitive with state-of-the-art adaptive methods.
This paper addresses practical challenges in portfolio optimisation for automated trading.
problem Implementing optimal portfolio weights into real trades with transaction costs and lot sizes.
method Two-stage framework: optimises portfolio weights first, then generates realistic trades.
result The two-stage approach effectively converts optimal portfolios into actionable trades, mitigating practical difficulties.
Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect for this progress are novel neural architectures. Currently employed architectures have mostly been developed manually by human experts, whi…
Mango automates hyperparameter tuning for large-scale ML training.
problem Manual hyperparameter tuning is tedious and inefficient for large-scale machine learning.
method Parallel hyperparameter tuning with intelligent search strategies and flexible abstractions.
result Mango achieves comparable performance to Hyperopt while supporting distributed computing.