Getting Started
Introduction
Use this page to understand what Stacking Sats covers, what it does not promise, and where to go next based on your goal.
Start with context
Know what Stacking Sats is before you optimize your workflow
These docs cover the app, the supporting model, and the companion Python package for Bitcoin accumulation research. They are designed to explain the AI-agent-first workflow first, then help you execute it safely.
Nothing on the Stacking Sats platform constitutes financial, investment, trading, legal, accounting, or tax advice.
New user flow: Sign in → open /profile → set plan (dates, budget) → generate a personal API token → your AI agent calls GET /api/v1/agent/signal (optional POST /api/v1/agent/heartbeat); you execute trades on your exchange or venue. Stacking Sats does not custody funds or place orders for you.
Auth: Personal API tokens from /profile with Authorization: Bearer <token>.
Who This Is For
Stacking Sats is for retail users and investors who want to accumulate bitcoin more effectively using optimized daily DCA weights. AI agents are the primary interface: you generate a personal API token from your profile, connect an AI agent you run or configure (for example self-hosted OpenClaw, a custom script, or a hosted service), and let that agent pull weights from the app while you execute trades on your preferred exchange or venue. Stacking Sats provides the daily signal only; it does not take custody of funds or execute trades for you.
What This Documentation Covers
- How to configure a profile plan and connect your AI agent using a personal API token.
- How to interpret daily DCA signals and understand model mechanics.
- How to evaluate validation metrics, including SPD and model score.
- How to work with the public agent-facing API and companion Python package.
Start With The Right Page
Do the workflow
Get to a live signal
Use the quick start when you want the shortest practical route from sign-in to signal retrieval.
Open quick startUnderstand the model
Learn the conceptual framing
Use the concepts section before reading model internals if you want to understand what the signal is meant to represent.
Open concepts overviewBuild or integrate
Inspect the public API and package
Jump straight to the agent API or StackSats package docs when you are integrating with the platform.
Open API guide