Equilibrium found for multi-agent trading with transaction costs.
problem Designing a trading equilibrium for multiple agents with transaction costs.
method Proving the existence of a continuous-time Radner equilibrium with incentives and transaction costs.
result Each agent optimally trades for a specific time interval before stopping, influenced by transaction costs.
TradingAgents uses LLM-powered multi-agent framework for financial trading.
problem Lack of collaborative dynamics in multi-agent financial trading systems.
method Inspired by real-world trading firms, TradingAgents features specialized LLM-powered agents and a risk management team.
result Framework outperforms baseline models in trading performance metrics.
Survey examines LLMs in financial trading.
problem Using LLMs to outperform professional traders in finance.
method Comprehensive review of current research on LLMs in financial trading.
result LLMs can potentially outperform professional traders in backtesting.
PredictionMarketBench benchmarks trading agents on prediction markets.
problem Evaluating trading agents on prediction markets with realistic conditions.
method Deterministic replay of historical data, execution-realistic simulator, agent interface.
result Fee-aware algorithmic strategies outperform naive agents in volatile episodes.
Study optimal trading strategies with differing views and market prices.
problem Maximizing portfolio value with subjective asset value vs market price.
method Mean-field game approach to analyze interactions among agents with differing signals.
result Cross-sectional distribution of agents' inventories and price distribution dependence on shared information.
ATLAS uses LLMs to adaptively trade by optimizing prompts and coordinating agents.
problem Adapting LLMs for real-time financial decision-making in noisy markets.
method ATLAS integrates structured market data, uses Adaptive-OPRO for prompt optimization, and employs multi-agent coordination.
result Adaptive-OPRO consistently outperforms fixed prompts in financial trading.
The study classifies and imitates trading agents in financial markets.
problem Classifying and imitating trading agents in continuous double auctions.
method Developed an agent-based model for trading, applied opponent modeling for classification, and used behavioral cloning for imitation.
result Techniques for classification and imitation were experimentally compared and evaluated.
Agentic LLMs improve trading by estimating market risk.
problem Lack of principled model-building step in agentic frameworks for finance.
method Developed an agentic system using LLMs to discover stochastic differential equations for financial time series.
result Model-informed trading strategies outperform standard LLM-based agents, improving Sharpe ratios.
Gray-box attack improves on white-box methods for trading agents.
problem Robustness of Deep RL trading agents against adversarial attacks.
method Hybrid Deep Neural Network policy for gray-box adversarial attack.
result Adversary can reduce trading agent's reward by 214.17%.
Enhanced financial trading system using multi-agent LLMs with layered memory.
problem Inefficient prioritization of tasks in LLMs due to their memory processing.
method Introducing a multi-agent framework with layered memories and inter-agent debate.
result Superior automated trading accuracy and decision robustness.
We prove the existence of an equilibrium in a model with transaction costs and price impact where two agents are incentivized to trade towards a target. The two types of frictions -- price impact and transaction costs -- lead the agents to two distinct changes in their optimal investment approach: price impact causes a…
A simple learning agent learns to trade in an agent-based market model.
problem Optimal execution of trades in an agent-based financial market model.
method Asynchronous trading through a matching engine, varying initial order sizes and state spaces, calibration of empirical stylized facts and price impact curves.
result Smaller state space agents converge faster in learning and can trade intuitively using spread and volume states.
Agent Trading Arena trains LLMs in real-time financial markets to improve numerical reasoning.
problem Limited real-world training for LLMs in financial markets.
method Virtual zero-sum stock market with competitive multi-agent trading.
result LLMs perform better with chart-based visualizations and a reflection module.
TraderTalk uses LLMs to simulate human trading interactions in financial markets.
problem Simulating realistic human trading interactions in financial markets.
method Hybrid ABM with LLM-generated behaviors for detailed conversations.
result Successfully replicates trade-to-order volume ratios in financial markets.
Securities markets are quintessential complex adaptive systems in which heterogeneous agents compete in an attempt to maximize returns. Species of trading agents are also subject to evolutionary pressure as entire classes of strategies become obsolete and new classes emerge. Using an agent-based model of interacting he…
Paper introduces a trading agent using LLMs for risk assessment and trading recommendations.
problem Developing a trading agent that can handle financial risks effectively.
method Extending CPPO algorithm with LLM-generated risk assessment and trading signals from financial news.
result Backtesting shows improved performance of the trading agent compared to benchmarks.
Reinforcement learning crypto agent achieves high returns on Bitcoin derivatives.
problem Maximizing returns on volatile cryptocurrency markets.
method Online transfer learning with an echo state network and recurrent reinforcement learning.
result Achieves a total return of 350%, net of transaction costs, over five years.
FinMem enhances LLM trading agents with layered memory and character design.
problem Developing purpose-driven LLM agents for financial decision-making.
method Integrates layered memory and character design modules into an LLM framework.
result Significantly enhanced trading performance in financial markets.
Study models opaque financial markets using multi-agent simulation.
problem Challenges in financial markets with obscured data availability.
method Multi-agent simulation with small-scale meta-heuristic methods.
result Captures bilateral market dynamics of OTC trading.
Dynamic sentiment analysis improves stock trading strategies.
problem Improving stock trading performance through dynamic agent switching.
method Combining sentiment analysis with deep reinforcement learning and dynamic agent switching.
result Dynamic sentiment-based trading strategy outperforms traditional methods.
StockAgent uses AI to simulate real-world stock trading, analyzing external factors and profitability.
problem Investors need to understand how external factors affect stock trading.
method Developed StockAgent, a multi-agent system driven by large language models.
result Identified how external factors impact trading behavior and profitability.
ContestTrade uses competitive teams to improve LLM trading performance.
problem High sensitivity to market noise in LLM-based trading systems.
method Internal competitive mechanism, data and research teams, real-time evaluation.
result Significantly outperforms other systems across various metrics.
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.
The autonomous trading agent is one of the most actively studied areas of artificial intelligence to solve the capital market portfolio management problem. The two primary goals of the portfolio management problem are maximizing profit and restrainting risk. However, most approaches to this problem solely take account …
Agent learns to trade currency pairs with improved risk management.
problem Improving systematic FX trading performance with online transfer learning.
method Online inductive transfer learning using feature representation from Gaussian mixture model to a reinforcement learning agent.
result Annualized portfolio information ratio of 0.52, compound return of 9.3%.
Short proof shows wealth condensation in trading model.
problem Proving wealth condensation in trading model.
method Short proof extending to modified model with wealth advantage.
result Wealth condensation occurs almost surely in modified model.
Liquidation is the process of selling a large number of shares of one stock sequentially within a given time frame, taking into consideration the costs arising from market impact and a trader's risk aversion. The main challenge in optimizing liquidation is to find an appropriate modeling system that can incorporate the…
Model shows how traders' interactions can create market patterns.
problem Explaining stylized facts in high-frequency trading markets.
method Agent-based model of limit order book trading with zero-intelligence agents.
result Scale-free connectivity between traders reproduces market patterns, while no interaction does not.
A microeconomic approach is proposed to derive the fluctuations of risky asset price, where the market participants are modeled as prospect trading agents. As asset price is generated by the temporary equilibrium between demand and supply, the agents' trading behaviors can affect the price process in turn, which is cal…
JaxMARL-HFT accelerates MARL for HFT with 240x speedup.
problem Heavy computational cost in MARL for HFT.
method GPU-accelerated JAX framework for multi-agent RL.
result Agents learn to outperform benchmarks in HFT.
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.
AGENTICAITA uses AI agents to autonomously trade markets without human intervention.
problem Inability of traditional trading systems to adapt to market complexity.
method Introduces an agentic AI framework with specialized LLM agents reasoning, negotiating, and acting.
result Demonstrated operational correctness and non-trivial inter-agent negotiation in live market conditions.
We present a simple dynamical model for describing trading interactions between agents in a social network by considering only two dynamical variables, namely money and goods or services, that are assumed conserved over the whole time span of the agents' trading transactions. A key feature of the model is that agent-to…
Deep neural net solves multi-agent optimal trading problem.
problem Optimal trade execution for multiple agents and assets.
method Residual U-net with self-attention for viscosity solution approximation.
result Neural network approach outperforms finite difference methods.
Study examines if LLMs' trading styles match real market behavior.
problem Lack of behavioral consistency in LLMs' trading strategies.
method Year-long simulations with LLMs, operationalizing behavioral finance drivers, and comparing with financial theory.
result LLMs' strategy switching is only partially consistent with behavioral finance theories.
Generative tools mimic stock market traders using synthetic data.
problem Imitating trading behavior of stock market participants.
method Modified state-space model applied to limit order book data, trained on synthetic data generated from a heterogeneous agent-based model.
result Model's predicted distribution matches ground truths from the agent-based model.
Benchmark evaluates LLM trading agents by masking identifiers to prevent memory leaks.
problem Evaluate LLM trading agents without relying on market memory or noise.
method Data-side masking protocol, Barra-style performance attribution framework.
result LLM agents' returns are largely explained by market and style exposure, not stock selection.
AI agents in experimental markets exhibit behavioral patterns that aggregate into market dynamics.
problem Understanding AI trading behavior and its impact on market dynamics.
method Experimental asset markets populated by AI agents trained on Large Language Models (LLMs).
result AI agents' behavior leads to market dynamics similar to human traders, including bubbles.
AI traders learn to exploit meta-orders from slower traders, increasing their profits.
problem Adverse selection of medium-frequency traders by high-frequency AI agents.
method Reinforcement learning in a Hawkes LOB model, with impulse control and PPO.
result AI agents can learn to capitalize on meta-orders, increasing their profits.
MacroHFT uses memory and context-aware reinforcement learning to improve HFT performance.
problem Overfitting and biased decisions in HFT due to rapid market changes.
method Memory Augmented Context-aware Reinforcement Learning (MacroHFT) that trains multiple sub-agents and a hyper-agent.
result MacroHFT achieves state-of-the-art performance on minute-level trading tasks.
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.
FinVision uses LLM agents to predict stock markets by processing various financial data types.
problem Challenges in integrating diverse financial data for accurate stock market prediction.
method Multi-agent framework with LLMs specialized in different financial data types and a reflection module.
result The reflection module enhances decision-making capabilities for financial trading.
Study shows market volatility affects optimal communication design for trading strategies.
problem Investigating how communication impacts trading strategy performance in multi-agent systems.
method 5-agent LLM-based trading systems across 450 experiments spanning 21 months, comparing 5 organizational structures.
result Communication improves performance but depends on market characteristics, with competitive conversation excelling in volatile tech stocks.
HedgeAgents boosts financial trading with balanced strategies.
problem Inefficient trading strategies under rapid market changes.
method Integrates LLMs with multi-agent system for robust decision-making.
result 70% annualized return and 400% total return over 3 years.
Improved investment performance with fine-grained LLM tasks.
problem Abstract financial trading systems often overlook real-world workflow intricacies, leading to degraded performance.
method Proposes a multi-agent LLM trading framework that decomposes investment analysis into fine-grained tasks.
result Fine-grained task decomposition significantly improves risk-adjusted returns compared to coarse-grained designs.
A minimal model of a market of myopic non-cooperative agents who trade bilaterally with random bids reproduces qualitative features of short-term electric power markets, such as those in California and New England. Each agent knows its own budget and preferences but not those of any other agent. The near-equilibrium pr…
Paper uses DDQN for trading assets, showing better performance than market benchmarks.
problem Improving financial trading strategies using AI.
method Double Deep Q-Network (DDQN) algorithm for trading multiple assets.
result Trading agent outperformed market benchmarks and achieved higher net asset value.
DRL agents learn to trade Intel stock with stable positive returns.
problem Active high frequency trading in the stock market.
method End-to-end DRL framework using Proximal Policy Optimization, Sequential Model Based Optimization, and LOB-based meta-features.
result DRL agents create dynamic trading strategies with stable positive returns.