Introduction
Understand how Statly moves from research to backtest, execution, and risk control.
Statly is a quantitative trading workspace for crypto perpetual markets. It gives a single operator or small team one surface for research, historical validation, supervised execution, and live risk control.
What Statly covers
- Research candidate features and market regimes before anything is promoted.
- Validate strategy variants against historical perpetual data.
- Route approved strategies into paper or live execution with the same operating approach.
- Enforce capital, drawdown, and exposure limits directly in the engine.
Start here
If you are new to Statly, start with the onboarding flow first and then move into the core workflows below.
Quickstart
Create a workspace, start your trial, and understand the initial onboarding path.
How Validation Works
Follow the ladder from backtest to paper and live, with warnings explained honestly.
Backtest Engine
Review strategy variants against historical perpetual data before risking capital.
Automated Execution
Deploy approved strategies into paper or live execution with supervision intact.
Risk Controls
Configure the capital, drawdown, and exposure rules enforced during execution.
Operating flow
- Start with a workspace and define the first strategy you want to evaluate.
- Use backtests to assess return quality, drawdown shape, and behavior across periods.
- Promote only validated ideas into paper first, then live execution when they remain trustworthy.
- Keep capital protection and exposure controls attached to the execution path at all times.
Commercial and rollout details
Use the supporting pages below when you need plan, billing, referral, or venue context around the operating workflow.
Plans & Billing
Review plan tiers, trial behavior, cadence choices, and billing lifecycle expectations.
Referral Program
Understand referral rewards, settlement timing, and how referral credits behave inside a workspace.
Supported Venues
See the venue focus list and how execution coverage is described across paper and live workflows.
Research section
Research is its own section because it carries the trust layer behind the product. It explains how evidence is produced, how candidate bots are framed, what warnings mean, and how customer choice stays separate from internal governance.
Use the research docs when you want the customer-facing explanation of:
- how validation works
- what a candidate bot actually represents
- what evidence is shown by default
- how launch warnings should be interpreted