Zero-Cost Crypto Trading Bot with LLM
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Build an automated crypto trading bot using Freqtrade and LLM-powered AI co-processors. Complete blueprint from setup to deployment.
Episode Summary
Automated trading meets LLM intelligence. In this technical deep dive, we build a complete zero-cost crypto trading bot using Freqtrade—an open-source algorithmic trading framework—enhanced with LLM-powered AI co-processors for market analysis and strategy optimization.
This isn't theory. This is a production-grade system you can deploy today.
What We Cover
- Freqtrade fundamentals: Installation, configuration, and core trading strategies
- LLM integration: Using language models as real-time market analysis co-processors
- Strategy development: Building and backtesting custom trading algorithms
- Risk management: Stop-loss, position sizing, and portfolio optimization
- Deployment patterns: Running on free-tier cloud infrastructure (Oracle, Render, Railway)
- Monitoring & observability: Real-time alerts, performance tracking, and automated reporting
Architecture Overview
The bot architecture consists of three main components:
- Freqtrade Core: Handles exchange connectivity, order execution, and strategy orchestration
- LLM Co-Processor: Analyzes market sentiment, news, and on-chain data to inform trading decisions
- Monitoring Layer: Tracks performance metrics, manages alerts, and provides audit trails
Zero-Cost Infrastructure
This entire system runs on free-tier services:
- Oracle Cloud (Always Free): ARM-based compute instance for the Freqtrade bot
- Render/Railway (Free tier): LLM API endpoints and monitoring dashboard
- GitHub Actions: Automated backtesting and strategy validation
- Telegram/Discord: Real-time trade notifications and alerts
Key Topics
Resources & Links
- Freqtrade Official Documentation
- Freqtrade GitHub Repository
- Oracle Cloud Always Free Resources
- VoidCat Reasoning Core (for LLM integration patterns)
Important Disclaimers
⚠️ Risk Warning: Crypto trading carries significant financial risk. This episode is for educational purposes only and does not constitute financial advice. Always do your own research, never invest more than you can afford to lose, and consider consulting with a financial advisor before engaging in automated trading.
Code & Configuration
All configuration files, strategy templates, and deployment scripts referenced in this episode will be published to the VoidCat RDC GitHub. Check the Projects page for updates.
About the Host
Wykeve Freeman is the founder of VoidCat RDC, building agentic AI systems and automation platforms. With a background in algorithmic trading and AI systems engineering, Wykeve focuses on practical, production-grade implementations. Connect on GitHub or via email.