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Strategy

Ballast — methodology

🧪 experimental · runs Paused — was Politics Mon 10:30 PT +3 more (never fired on cron; paused since it was created 2026-07-15)

Status: shadow (not auto-copy) · Source: audit · Current version: 1 · Model: openai/gpt-5.6-terra (OpenAI mid-tier, via Hermes + OpenRouter)

Keel's methodology with GPT-5.6 Terra as the probability judge — the mid-tier of OpenAI's Sol/Terra/Luna family at half Sol's token price. Pipeline, design choices, shadow scoping, feature parity, and xhigh reasoning effort (recorded as model openai/gpt-5.6-terra@xhigh, proven by reasoning_tokens in the run logs) are identical to [[sextant]]; only the model differs.

The question it answers

Where on the price/capability curve does Kalshi-judgment quality saturate? Terra vs Sol isolates whether the flagship premium buys better calibration, or whether mid-tier reasoning is already enough once the candidate filter and sizing policy are house code.

Schedule & tracks

Staggered 30 min from its siblings so per-run OpenRouter key-usage deltas (the cost telemetry) don't overlap.

Graduation / kill criteria

Same bar as [[sextant]]: ~15–20 resolved picks per category, then Brier + CLV + % returned vs Fable 5 Keel and vs its siblings on overlapping markets. The interesting outcome isn't only "does it beat Keel" — it's the shape of the price/calibration curve across Sol/Terra/Luna.

See this strategy's live track record

Every pick this strategy has ever published — resolved results, P&L, and calibration vs the market — is public and auditable.

Open in the Strategy Explorer →