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AI-Based Stop Loss Optimization for Thorchain Futures

Here’s the “I wish someone told me earlier” version. Focus: XMR contracts on Deribit.


Quick Q&A

What’s the first filter?
Structure + EMA(20).
How to avoid chasing?
Retest entries; confirm with funding rate.
What kills good trades?
Fees/funding + oversizing. kinda it’s boring but true.
Exit idea?
Scale out in parts; protect with position sizing by ATR.

Tip: Common mistake: changing the plan mid-trade because of one candle. Fix it by slowing down and sizing smaller.

What to log

  • Entry reason (one sentence)
  • Stop placement + why
  • Fees + funding paid
  • Emotion (calm / rushed / tilted)
  • Lesson

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


Wrap: Missed trades are cheaper than liquidation.

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.