Comparing LLM-Based Trading Bots: AI Agents, Techniques, and Results in Automated Trading

The rise of Large Language Models (LLMs) and AI agents has transformed the world of algorithmic trading. Today, trading bots built on advanced AI architectures can analyze market data, execute trades, and update portfolios autonomously. But with new projects emerging rapidly, how do these LLM-based bots actually compare? Which models and techniques deliver the best results, and what innovations are shaping the future of AI trading?

In this article, we provide a side-by-side comparison of the top LLM-driven trading bots, summarize the most effective quality-improvement techniques, and review real-world outcomes. We also highlight leading open-source projects that connect trading platforms to chatbot agents, and show how FlowHunt empowers daily, automated portfolio management with AI.

Top LLM-Based Trading Bots & Agent Frameworks (2025)

1. FinMem

• Model: LLM-based agent with layered memory and character design (repo )
• Techniques: Combines profiling (agent persona), layered memory (hierarchical context retention), and decision-making modules for human-like reasoning. Supports fine-tuning of perceptual span for improved trading.
• Results: Outperformed classic algorithmic agents in the 2024 IJCAI FinLLM challenge (stock trading). Notable for adaptability and interpretability of decisions.
• Integration: Modular Python framework—can connect to live market data sources and be further extended.

2. LLM_trader

• Model: Multi-model LLM architecture for crypto market analysis (repo )
• Techniques: Utilizes LLMs for chain-of-thought reasoning, technical analysis (over 20 indicators), and sentiment analysis. Features fallback models for reliability and stream processing for low-latency.
• Results: Provides real-time trading insights and position management, including automated stop-loss/take-profit. Evidence of practical utility for automated crypto trading.
• Integration: Built on Python, easily configurable for various LLM providers, connects to exchanges like Binance.

3. Freqtrade + FreqAI

• Model: Python trading bot with FreqAI ML module for adaptive prediction
• Techniques: Trains ML models (classifiers, regressors, neural nets), retrains on live data, and supports strategy optimization. LLMs or transformer models can be integrated for signal generation.
• Results: Large community, proven in live trading across multiple exchanges, rich feature set.
• Integration: Modular, supports live and dry-run trading, open-source.

4. AI-Hedge-Fund for Crypto (LLM-driven agents)

• Model: Ensemble of LLM agents, each specializing in different market aspects (technical, sentiment, news)
• Techniques: Uses LangChain-like agent orchestration, multi-agent reasoning, and strategy ensembling. Focus on explainable trades.
• Results: Highly experimental; demonstrates innovative agent collaboration but not yet proven in production.
• Integration: Flexible, intended for advanced experimentation.

5. Jesse with JesseGPT

• Model: Python backtesting and trading engine with GPT-powered assistant
• Techniques: Uses LLM for code generation, strategy optimization, and AI-assisted debugging. Users can iterate rapidly on strategies.
• Results: User-friendly, robust, especially for semi-automated development. Real AI-driven trading must be integrated manually.
• Integration: Supports live trading (paid plugin), open for custom AI integrations.

6. Other Notable Projects

• TensorTrade: Reinforcement learning framework for trading with modular RL environments. Good for research, requires manual live integration.
• Intelligent-Trading-Bot: Supervised learning with continuous model retraining for live trading signals.
• CryptoPredictions: Toolbox for ML model comparison and backtesting on crypto price data.
• AI-CryptoTrader: Ensemble learning bot combining indicators and ML models for robust signals, live on Binance.

Key Techniques for Improving AI Trading Quality

• Layered Memory & Profiling: As seen in FinMem, using hierarchical memory helps AI agents maintain long-term context, improving trade rationality and adaptability.
• Chain-of-Thought Reasoning: LLMs can explain their decisions step-by-step, making AI outputs more transparent and trustworthy.
• Continuous Model Retraining: Bots like Intelligent-Trading-Bot and Freqtrade’s FreqAI retrain on new data to avoid model drift and adapt to market shifts.
• Multi-Agent Collaboration: Some experimental bots use multiple specialized LLM agents, combining technical, sentiment, and news analysis for more holistic trading decisions.
• Feature Engineering & Ensemble Methods: Adding domain-specific features and combining multiple models (classical and deep learning) improves robustness.
• Fallback and Redundancy: Ensuring reliable operation by having backup models (as in LLM_trader).

Real-World Results & Practical Considerations

• Performance: FinMem’s agent led in academic trading challenges. Freqtrade and Intelligent-Trading-Bot have live trading track records. Ensemble and continuous retraining methods show resilience in volatile markets.
• Limitations: LLM-driven bots require careful prompt engineering and risk management. High-frequency trading is still best handled by non-LLM frameworks due to inference latency.
• Open-Source Availability: Most projects are open-source and extensible, allowing users to adapt them for stocks, crypto, and even traditional assets.

Leading Open-Source Projects Connecting Trading Platforms to Chatbots

• FinMem-LLM-StockTrading (GitHub ): Performance-Enhanced LLM Trading Agent
• LLM_trader (GitHub ): AI-powered LLM bot for real-time crypto market analysis
• Freqtrade (GitHub ): Modular trading bot with ML/AI integration
• AI-Hedge-Fund for Crypto: LLM-powered multi-agent trading framework

FlowHunt: AI Trading & Daily Portfolio Updates

FlowHunt enables users to create, automate, and monitor trading workflows using AI—including LLM-based agents. With FlowHunt, you can:

• Connect your trading platform and automate trade execution with no code
• Integrate LLMs for analysis, signal generation, or portfolio management
• Receive daily portfolio updates and rebalance automatically
• Use advanced AI pipelines for both crypto and traditional markets

FlowHunt’s flexible architecture means you can experiment with the latest open-source trading agents, or build your own workflows using AI and automation—all with daily performance reporting and actionable insights.

Conclusion

LLM-driven trading bots are rapidly advancing, with new agent architectures and techniques pushing the boundaries of automated trading. From layered memory models to multi-agent collaboration, the top projects demonstrate both academic rigor and real-world utility. By leveraging FlowHunt’s automation and AI integration, traders and quants can stay at the cutting edge, ensuring smarter, more adaptive portfolios—updated daily.

Ready to start? Explore FlowHunt’s AI trading features and automate your portfolio today.