ANY LLM. ONE FRAMEWORK.

Build. Simulate. Analyze.Systematic research infrastructure.

WhaleRider brings systematic research infrastructure to the LLM and tools you already use.

Define your own systematic rules in your preferred environment. WhaleRider validates them, runs historical simulations using market and financial data, and helps you analyze simulated results.

Connect WhaleRider →View Documentation

↗ Works with any MCP-compatible LLM.

A quick introduction

See WhaleRider in 42 seconds.

From rules described in plain English to validated definitions, historical simulation, and trade-level analysis.

Explore real sessions →

One conversation.The complete research workflow.

Use the AI you already prefer. WhaleRider turns the conversation into validated, persistent research artifacts.

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Real PIVOT_SPRING conversationFrom the referenced Codex task

Validated workflow

You

Create a long-only trade plan named PIVOT_SPRING_TRADE_PLAN. Trade common stocks with point-in-time NASDAQ-100 membership and look for financially strong stocks that recover from monthly support.

WhaleRider

Created and validated. Definition ID: 6ab230c83b7850e53b6732f5

Completed simulation+764.08%Total return · 944 completed trades · 2016–2025

Everything you need for systematic strategy research.

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Works your way

ChatGPT, Claude, Gemini, Grok, or any MCP-compatible AI, online or desktop.

VS Code IntelliSense

AI-assisted, user-defined research artifacts link to an editable document with full IntelliSense support through the WhaleRider VS Code plugin.

CLI for advanced users

Validate, compile, simulate, and automate.

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Fully featured language

Indicators, signals, criteria, risk, state, and composition.

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Multi-domain data

Candles, fundamentals, ratios, financials, macro, and more.

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Historical simulation

Run historical simulations, inspect simulated trades, and analyze performance.