Build a Crypto Trading Bot with AI Signals
An AI-powered crypto trading system that combines market data analysis, LLM-driven signal generation, multi-agent orchestration, and durable workflow execution for automated trading decisions.
Market Data & Research
Collect real-time market data, news, and on-chain signals for AI analysis
Scrapes crypto news sites, exchange announcements, and on-chain data sources into structured LLM-ready formats for signal generation
Open-source alternative for crawling crypto forums, social sentiment, and market data pages with LLM-friendly output
Autonomous deep research agent that can investigate market conditions, token fundamentals, and macro trends before trade decisions
AI Signal Generation & Reasoning
Use LLMs to analyze market data and generate buy/sell/hold signals with structured reasoning
Vercel AI SDK provides structured output via generateText + Output.object() for type-safe trade signals, plus multi-provider support through AI Gateway for model fallback
Programmatic framework for optimizing LLM prompts that generate trading signals — ensures consistent, high-quality structured outputs without manual prompt engineering
Mature agent framework with built-in tool-calling chains for combining technical indicators, sentiment analysis, and LLM reasoning into trading signals
Agent Orchestration & Decision Engine
Coordinate multiple specialized agents (analyst, risk manager, executor) to make and validate trading decisions
Graph-based agent framework ideal for modeling trading decision flows — analyst agent → risk check agent → execution agent — with conditional branching and state persistence
Role-based multi-agent framework where specialized agents (market analyst, risk manager, portfolio optimizer) collaborate on trading decisions with clear delegation
Purpose-built multi-agent financial trading framework with pre-defined analyst, trader, and risk manager roles specifically designed for trading workflows
Workflow Orchestration & Execution
Durable workflow engine that ensures trade execution survives failures and handles retries, scheduling, and state management
Production-grade durable execution engine — guarantees trade orders complete even through crashes, handles retries with idempotency, and supports scheduled recurring strategies
Python-native workflow orchestration with scheduling, retries, and observability — good for data pipeline-heavy trading systems that run periodic analysis and rebalancing
Visual workflow automation with native AI capabilities — enables rapid prototyping of trading pipelines connecting exchange APIs, signal generators, and notification systems
Observability & Evaluation
Monitor AI signal quality, track model performance, and evaluate trading strategy effectiveness over time
Tracks every LLM call in the signal generation pipeline — trace costs, latency, and output quality to ensure AI trading signals remain accurate and cost-effective
AI observability platform with built-in evaluation — monitor signal drift, compare model versions, and detect when market regime changes degrade AI performance
LLM evaluation framework to backtest AI signal quality against historical data — measures hallucination, faithfulness, and consistency of trading recommendations