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How to Build an AI Trading Bot for Frax on Kraken

Let’s keep it practical, not poetic. Focus: Frax contracts on Kraken.


Checklist

  • Trend is clear ✅
  • Signals agree (open interest + liquidation clusters) ✅
  • Stop planned (max daily loss limit) ✅
  • Position size is sane ✅
  • Costs considered ✅

ThingWhat to do
Choppy marketLower leverage, fewer trades, wait for clean levels.
Trending marketLet winners run, trail stop, don’t over-take-profit.
High fundingReduce hold time or wait for better entry.

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: If it feels like gambling, size down. Immediately.

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.