arXiv AI
FORESIGHT-9: Prospective and Process-Aware Evaluation of Adaptive Trading Agents
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 14
NextFund: A Unified Performance Tracking Platform for Agentic Portfolio Management
arXiv:2607. 11141v1 Announce Type: new Abstract: Large language models (LLMs) based agents are beginning to participate in portfolio construction and market analysis, where decisions must be justified under evolving information and risk constraints.
arXiv AI
Sep 17
EvolveTrade: Experience-Driven Policy Refinement for Self-Evolving LLM Trading Agents
EvolveTrade is a self‑evolving framework that treats the system prompt of a tool‑using LLM trading agent as a text‑parameterized policy. After each update interval, a Policy Agent revises this policy using accumulated decision traces and portfolio feedback while keeping the backbone LLM fixed, allowing the agent to refine its information‑acquisition and portfolio‑construction procedures over time. Experiments across multiple market regimes and two LLM backbones show that EvolveTrade often improves Sharpe Ratio and Cumulative Return over fixed‑policy baselines, with behavioral analyses indicating increased code‑mediated analysis and regime‑relevant computations.
arXiv Computation and Language
Sep 22
Replayable Financial Agents: A Determinism-Faithfulness Assurance Harness for Tool-Using LLM Agents
arXiv:2601.15322v3 Announce Type: replace-cross Abstract: Tool-using agents can repeat a final decision while changing their recorded execution. We introduce the Determinism-Faithfulness Assurance Ha...
arXiv AI
Sep 25
Agent Memory with Episodic Retrieval for Financial Decision-Making
META (Memory Enhanced Trading Agent) is a new agent-based trading framework that augments large language models with episodic memory. It combines specialized indicator agents—such as Trend, MACD, Stochastic, RSI, SMA, AVWAP, and Heikin‑Ashi—with a Decision Agent that fuses their reports, while a Memory module retrieves and updates past trading episodes encoded as market state embeddings with outcomes and reflections. By recalling relevant experiences and adaptively reweighting signals under similar market regimes, META improves directional accuracy and robustness in short‑horizon evaluations, offering regime‑aware, interpretable, and low‑latency decision‑making for financial trading.
arXiv AI
Jul 24
DFAH-Bench: Benchmarking Observable Agent Instability in Financial Decision-Making
arXiv:2607. 20491v1 Announce Type: new Abstract: Standard evaluation benchmarks measure what a tool-using agent decides, not whether it arrives at that decision through the same process each time.
arXiv AI
Jun 30
CLQT: A Closed-Loop, Cost-Aware, Strategy-Consistent Benchmark for Diagnostic Evaluation of LLM Portfolio-Management Agents
arXiv:2606. 29771v1 Announce Type: new Abstract: LLM agents are increasingly cast as autonomous portfolio managers, and benchmarks have moved from financial question-answering to sequential trading.