Location
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
Document Type
Event
Start Date
24-11-2025 4:00 PM
Description
This work presents AutoTrader-AgentEdge, a human-in-loop trading system that positions AI agents as collaborative partners rather than autonomous replacements. We demonstrate that multi-indicator consensus voting combined with human approval achieves superior risk-adjusted returns while maintaining interpretability and control. Core Contribution: A production-ready VoterAgent implementing democratic voting between MACD momentum and RSI extremes, generating transparent trading signals for human evaluation. Unlike black-box automation, our interactive CLI augments trader expertise through interpretable consensus logic. The human retains final decision authority at all critical junctures. Validated Performance: Empirical validation demonstrates multi-indicator voting superiority over single-indicator automation: Sharpe ratio 0.856 vs 0.841, max drawdown -10.10% vs -10.58%, win rate 51.4% vs 31.9%. Extended testing shows 11.2% better relative performance in volatile markets, validating risk management focus. Implementation: Built on the Microsoft AutoGen framework with an extensible multi-agent architecture. SQLite caching achieves 8-10x performance improvement. Interactive CLI enables natural language trade discussion with human approval gates, ensuring trader control while reducing cognitive load. Alpaca broker integration for paper trading. Key Insight: Transparent, interpretable methods build trust and enable effective human-AI collaboration. By prioritizing augmentation over automation, we demonstrate that AI serves traders best as a decision support tool that preserves human judgment while systematically reducing errors. Implications: Contributes to augmented trading research - systems designed to enhance rather than replace human expertise. This work validates that human-in-loop architectures can achieve both superior risk-adjusted returns AND maintained trader control, challenging the "automation-at-all-costs" paradigm prevalent in algorithmic trading.
Included in
GRP-21155 AutoTrader-AgentEdge
https://www.kennesaw.edu/ccse/events/computing-showcase/fa25-cday-program.php
This work presents AutoTrader-AgentEdge, a human-in-loop trading system that positions AI agents as collaborative partners rather than autonomous replacements. We demonstrate that multi-indicator consensus voting combined with human approval achieves superior risk-adjusted returns while maintaining interpretability and control. Core Contribution: A production-ready VoterAgent implementing democratic voting between MACD momentum and RSI extremes, generating transparent trading signals for human evaluation. Unlike black-box automation, our interactive CLI augments trader expertise through interpretable consensus logic. The human retains final decision authority at all critical junctures. Validated Performance: Empirical validation demonstrates multi-indicator voting superiority over single-indicator automation: Sharpe ratio 0.856 vs 0.841, max drawdown -10.10% vs -10.58%, win rate 51.4% vs 31.9%. Extended testing shows 11.2% better relative performance in volatile markets, validating risk management focus. Implementation: Built on the Microsoft AutoGen framework with an extensible multi-agent architecture. SQLite caching achieves 8-10x performance improvement. Interactive CLI enables natural language trade discussion with human approval gates, ensuring trader control while reducing cognitive load. Alpaca broker integration for paper trading. Key Insight: Transparent, interpretable methods build trust and enable effective human-AI collaboration. By prioritizing augmentation over automation, we demonstrate that AI serves traders best as a decision support tool that preserves human judgment while systematically reducing errors. Implications: Contributes to augmented trading research - systems designed to enhance rather than replace human expertise. This work validates that human-in-loop architectures can achieve both superior risk-adjusted returns AND maintained trader control, challenging the "automation-at-all-costs" paradigm prevalent in algorithmic trading.