r/alpacamarkets

I was tired of hunting for long-tail events; and creating bootstrapped event-generators was a hassle. If I'm focusing on a project, why would I want to maintain a sub-project? So I built a once-and-for-all market simulator with events on demand (completely free & open-source, available to everyone)

It's a fake market data API — realistic OHLCV for any ticker, a tick stream that runs 24/7, and reserved tickers (CRASH, GAP, HALT) that action on command. Point your existing client at it to test code paths that only fire on market edge-cases.

This is a testing tool, to test long-tail events like run-ups, halts, SSE loss, and intraday flash events.

No API Keys needed. Simple curl to test:
curl 'https://cuckootrade.com/api/v1/alpaca/v2/stocks/bars?symbols=AAPL,CRASH'

• Completely free, no API key, nothing to sign up for.
• Historical bars : OHLCV for any symbol, 1-minute through yearly, back to 1970, on the real NYSE calendar — no bars on weekends or holidays. (https://cuckootrade.com/docs#bars)
• Live SSE stream : SSE ticks that don't stop — nights, weekends, holidays included.
• Scenario tickers : Reserved symbols that misbehave on demand (https://cuckootrade.com/docs#magic)
  • CRASH drops ~25% mid-month
  • GAPPY gaps overnight
  • HALTS drops bars mid-session,
  • SPIKEY wicks and reverts
  • FLAT sits at exactly $100.00.
• Drop-in replacement for endpoints (switch alpaca.com/api -> cuckootrade.com/api)
  • Currently supports Alpaca, Polygon, & Alpha Vantage
• Deterministic & Reproducible: Every bar is a pure function of (symbol, timestamp, seed), computed per request, no database. Same request, same bytes, forever — results are consistent between local host, CI container, and hosted service.
• Live Sandbox to try it in a web browser ( https://cuckootrade.com/playground )

Worth saying: it's for testing, not backtesting. Synthetic data will make almost any strategy look profitable.

It's a portfolio project I'm still polishing, so feedback actually helps me. Tell me what's broken, or what provider or scenario you want next and I'll build it. Or you're welcome to contribute to the repository:

Open source Github: https://github.com/tj-miller-dev/stock_simulator

No paywalls. No fees. No login. Just the data.

Hey everyone,

AI is changing how people interact with financial markets, and we’re excited to see Alpaca featured in Bloomberg’s recent article on the rise of AI-powered retail trading.

What was once largely limited to developers is now becoming accessible to a much broader group of traders. Natural-language interfaces and AI-assisted tools are making it easier to build trading workflows and interact with APIs, helping users translate trading ideas and logic into executable trading workflows without building everything from scratch. We’re seeing that shift firsthand in the growth of API-driven trading on Alpaca.

But easier access does not remove the fundamentals of building a reliable trading system: high-quality data, rigorous testing, risk controls, ongoing monitoring, and human oversight.

At Alpaca, we’ve been building the infrastructure and tooling to make agentic trading streamlined, including our MCP Server, CLI, Skills Library, and new and improved SDKs. These tools give builders and AI systems more direct and efficient access to financial markets, helping reduce the friction between expressing a strategy and executing it.

We believe the future of finance will be agent-first, and we’re excited to support this movement by giving AI agents, and the humans behind them, the reliable, real-world access they need to turn new ideas and experimentation into financial products built on reliable infrastructure with appropriate controls.

We provide documentation, setup guides, authentication details, SDKs, example projects, and information on operational and risk considerations to help developers build trading applications responsibly.

Bloomberg article: https://www.bloomberg.com/news/features/2026-08-02/ai-powered-trading-bots-help-retail-investors-take-on-hedge-funds

Resources:

• MCP Server: https://alpaca.markets/mcp-server 
• Trading CLI: https://github.com/alpacahq/cli 
• Skills Library: https://alpaca.markets/blog/alpaca-launches-skills-library-for-ai-agents
• Building AI trading apps: https://alpaca.markets/blog/building-ai-trading-applications-with-alpaca
• New SDKs: https://alpaca.markets/blog/alpaca-rebuilds-its-node-js-sdk-with-broader-api-coverage-and-launches-its-first-supported-java-sdk/

If you’re building agentic workflows with Alpaca, we’d love to hear how you’re structuring research workflows, paper trading, and live guardrails. Drop a comment or post in this Subreddit or tag us on LinkedIn / X (@AlpacaHQ).

Disclosure: Alpaca’s Disclosure Library for additional information and disclosures: https://alpaca.markets/disclosures