From superior-skills
Fades overreactions on Polymarket by entering when probability deviates from a rolling median and exiting on reversion.
How this skill is triggered — by the user, by Claude, or both
Slash command
/superior-skills:probability-mean-reversionThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Use this when someone asks for fade, overreaction, mean reversion, range trading, panic buy, euphoria selloff, or probability jumps with weak follow-through.
Use this when someone asks for fade, overreaction, mean reversion, range trading, panic buy, euphoria selloff, or probability jumps with weak follow-through.
POST /v3/markets/search.Moderate fit. Filled TradeTick history can test whether probability moves have a measurable reversion pattern, while using actual fills as the execution proxy.
Limit: backtests cannot validate resting liquidity, spread paid, or what is missed in the maker queue.
Enter when the outcome trades below a lower band and exit near the rolling median. If your implementation permits, do the inverse for above-band conditions when downside overextension appears.
{
"window_ticks": 40,
"entry_deviation": 0.08,
"exit_deviation": 0.02,
"order_size": 10,
"max_holding_ticks": 80
}
| Knob | Effect |
|---|---|
window_ticks | Larger windows produce a smoother baseline. |
entry_deviation | Higher values wait for stronger overreactions. |
exit_deviation | Lower values demand tighter reversion before exit. |
max_holding_ticks | Prevents stale positions through stale conditions. |
"This is an overreaction fade. It works best in noisy markets without new decisive information. I’ll backtest it on filled prices first, then verify trade frequency and liquidity before suggesting live use."
npx claudepluginhub superior-trade/superior-skills --plugin superior-skillsImplements a probability momentum strategy for Polymarket markets, buying when outcome price and filled volume accelerate together. Backtestable with filled trade ticks.
Provides strategy, sizing, and backtesting methodology for binary prediction markets (Kalshi, Polymarket, ForecastEx). Covers durable edge thesis, fee-aware selection, fractional-Kelly sizing, and leak-free validation.
Scans live Polymarket markets, inspects event mispricing, evaluates trades with user-provided fair probability, and runs reproducible backtests.