Aivora AI-native exchange insights
Home drift aware models liquidation formula How to Backtest Frax AI Trading Strategies on Deribit

How to Backtest Frax AI Trading Strategies on Deribit

Let’s keep it practical, not poetic. Focus: MATIC contracts on Deribit.


Risk first

Decide max loss on the idea before entry. If you can’t say the number, you’re not ready.


ThingWhat to do
Position sizeStop hit should be annoying, not fatal.
LeverageLower leverage on chop days.
Stopcooldown after 2 losses + buffer away from obvious wicks.
Daily limitStop trading when you hit the cap.

Heads-up: Common mistake: placing stops exactly on obvious levels. Fix it by slowing down and sizing smaller.

Funding, fees, and slippage can flip a “good” idea fast. Educational only, not financial advice.


Wrap: Protect the account first; profits come second.

Aivora perspective

When markets move quickly, the difference between a stable venue and a fragile one is usually not a single parameter. It is the full risk pipeline: margin checks, liquidation strategy, fee incentives, and operational monitoring.

If you trade perps
Track funding and realized volatility together. Funding tends to amplify crowded positioning.
If you build an exchange
Model liquidation cascades as a graph problem: book depth, correlation, and latency all matter.
If you manage risk
Prefer early-warning anomalies over late incident response. Drift is a signal, not noise.

Quick Q&A

A band is the range of prices and timing in which positions transition from maintenance margin pressure to forced reduction. Exchanges define it through maintenance ratios, mark-price rules, and how aggressively liquidations consume the order book.
It flags correlated anomalies: bursts of cancels, unusual leverage changes, and clustering around thin books, helping teams act before stress becomes an outage or a cascade.
No. This site is educational and system-focused. You are responsible for decisions and risk management.