The 10× Research Gap in
AI Trading Systems
A comprehensive survey of 110+ papers exposing the critical imbalance between alpha generation research and deployment infrastructure in autonomous trading systems.
Vision
The field of LLM-powered trading systems has experienced explosive growth, with research output increasing 312.5% from 2023 to 2024. Yet this rapid expansion masks a critical structural problem: research priorities are misaligned with deployment requirements.
Our comprehensive analysis of 110+ papers reveals that 90.9% of research focuses on alpha generation (stages 1-4) while only 9.1% addresses deployment infrastructure (stages 5-7). This 10× research imbalance creates a systematic barrier to institutional adoption.
This survey provides the research community with the first systematic mapping between architectural patterns and pipeline requirements, quantifies the gap with empirical evidence, and identifies high-impact opportunities to bridge the divide between alpha generation research and production-ready systems.
The Problems We Solve
No Systematic Research Mapping
HighNo framework existed to map cognitive architectures to pipeline stages. We provide the first dual-axis taxonomy classifying six architecture families across seven trading stages, revealing where research concentrates and what's missing.
Hidden Research Imbalance
HighThe field lacked quantitative evidence of research gaps. We systematically analyzed 109 papers, exposing a 10× imbalance: 90.9% focus on alpha generation while only 9.1% address deployment-critical infrastructure (Stages 5-7).
Practitioner Decision Paralysis
MediumResearchers and institutions lack guidance for architecture selection. We provide reusable decision trees mapping latency requirements, regulatory constraints, and asset classes to optimal architecture families with empirical benchmarks.
Scattered Knowledge Landscape
MediumLLM trading research is fragmented across venues with no comprehensive catalog. We provide structured metadata for 109 papers including stage coverage, architecture patterns, code availability (49.5%), and dataset accessibility (54.1%).
Our Approach
Systematic analysis through dual-axis framework combining cognitive architectures and pipeline stages
Comprehensive Coverage
110+ papers spanning 2020-2025 with 99.1% Era 3 systems coverage
Architectural Taxonomy
Six cognitive families mapped to seven pipeline stages
Quantified Gap Analysis
Evidence-based documentation of 10× research imbalance
Practitioner Frameworks
Decision trees for optimal architecture selection
Pattern Identification
Novel patterns including dynamic routing and process-supervised RL
Research Roadmap
18 high-impact opportunities addressing identified gaps
The Problem We Identified
Our comprehensive survey of 109 papers (2020-2025) mapping LLM trading research across 7 pipeline stages and 6 agent architecture reveals a troubling reality: the research community has largely ignored the infrastructure needed to deploy AI trading systems in production.
This gap represents a fundamental disconnect between academic research and practical deployment. Rather than contributing to this imbalance, Tokalpha Labs is building integrated solutions that address the complete autonomous trading pipeline.
Current Status
The survey paper is currently being finalized for publication. We're preparing the manuscript for arXiv submission while continuing to refine our analysis and expand coverage.
RESEARCH & ANALYSIS
PUBLICATION & DISSEMINATION
Our Response: Integrated Research
Our four projects work together to fill the critical gaps we identified across all seven stages of the autonomous trading pipeline:
Future Direction
This survey is a living document that we continue to update as the field evolves. We're actively monitoring new research and tracking how the community responds to the infrastructure gap we've identified.
Collaboration Opportunities
We welcome collaboration from researchers working on any stage of the autonomous trading pipeline. If you're addressing these infrastructure challenges, we want to hear from you.
Research Institutions
Collaborate on survey expansion and validation
Trading Firms
Test infrastructure in production environments
Academic Researchers
Joint papers and methodology development