Complete-Set Arbitrage (Multi-Engine with Bayesian Calibration)

How It Works This system runs multiple trading engines in parallel — including swing, scalp, Polymarket, altcoin farming, and complete-set arbitrage — under a meta-controller that dynamically allocates capital based on IF/AND/OR regime logic. The complete-set arbitrage engine specifically targets Polymarket markets where the sum of all outcome prices deviates from $1.00. A Bayesian calibration layer adjusts probability estimates and position sizes based on observed market behavior.

Entry/Exit Conditions The meta-controller determines which engines receive capital allocation based on current market regime (e.g., high volatility, trending, mean-reverting). The complete-set arb engine enters when sum-of-outcomes deviates beyond a calibrated threshold. The Bayesian layer continuously updates priors based on realized outcomes, refining entry thresholds over time. The neural plasticity network component suggests adaptive parameter updating.

Verified Results The system is described as 'local-first crypto trading research and paper-trading infrastructure,' indicating no live capital deployment is confirmed. The architecture is notably sophisticated for an individual project, raising questions about implementation completeness vs. design ambition.

Implementation Notes The multi-engine architecture with a Bayesian calibration layer and 'neural plasticity network' suggests a research-oriented system. The 'local-first' framing implies all computation runs on local hardware without cloud dependencies. The meta-controller using IF/AND/OR regime logic is a rule-based overlay, not a learned model, which provides interpretability. The combination of Bayesian calibration with neural components may indicate a hybrid frequentist/ML approach.

Risks System complexity is the primary risk — the more components interact, the harder it is to isolate the source of alpha or diagnose failures. 'Neural plasticity network' is vague and may represent overfitting risk. Paper-trading results may not transfer to live markets due to liquidity constraints. The multi-engine architecture requires robust orchestration to prevent correlated losses across engines in adverse regimes.