Structural#120

Anti-Martingale Conviction Scaling

Scale into a position only as your thesis is proven right (add when the price moves toward your model's fair value) and cut size when it moves against you, the opposite of averaging down a loser. The edge is convex exposure: you get big on the trades that are working and stay small on the ones that aren't, which suits prediction markets since every market eventually settles to a clear yes-or-no truth.

What you need to run it

  • Model fair-value estimate per market
  • Move-based add/trim ladder keyed to PnL/edge
  • Hard per-thesis loss cap
  • Execution that respects book depth on scale-ins

Where this applies

Markets on Polymarket where anti-martingale conviction scaling is the natural play:

  • Will the named incumbent win the 2026 US Senate race?
  • Will OpenAI release its next flagship model before December 31, 2026?
  • Will BTC close above $100k on December 31, 2026?

Capabilities this demands

Risk managementModel / quantPatienceCustom code / API

At a glance

CategoryStructural
Requirements4
CapabilitiesRisk management, Model / quant, Patience, Custom code / API
VenuePolymarket (CLOB, Polygon)

Build it

Related structural strategies

This is documentation, not advice. Poly Research & Robotics publishes how these strategies work because the method should be checkable — not as a recommendation to trade them. See the full strategy database (147 strategies) or the data resources directory.
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