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oracle3

agent
SUMMARY

Autonomous prediction market trading agent deploying Wang Transform pricing across Kalshi, Polymarket & Solana

README.md

Oracle3

Autonomous prediction market trading agent across Kalshi, Polymarket, and Solana.

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Python 3.10+
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This system deploys the exact $\lambda$ estimates and covariate model from prediction-market-pricing (Yang, 2026) as its real-time pricing engine.

Architecture

graph TD
    A[Wang Transform Pricing Engine<br/>MLE coefficients from paper] --> B[Fair Value Estimator<br/>Model Greeks · Kelly Sizing]
    B --> C[Strategy Layer]
    C --> D[8 Constraint-Based Arbitrage]
    C --> E[2 Model-Driven Strategies]
    C --> F[LLM Agent Strategies]
    D --> G[Trading Engine<br/>SpreadExecutor · Risk Manager · Position Tracker]
    E --> G
    F --> G
    G --> H[Kalshi]
    G --> I[Polymarket]
    G --> J[Solana / DFlow]

Strategies

Constraint-based arbitrage — each exploits a violated probability axiom:

Strategy Invariant
Cross-Market Same event, same price across exchanges
Exclusivity $P(A) + P(B) \leq 1$ for mutually exclusive events
Implication $P(A) \leq P(B)$ when A implies B
Conditional $P(A \mid B) \in [L, U]$ within derived bounds
Event Sum $\sum P(\text{outcome}_i) = 1$ within an event
Structural $P(A) = \beta \cdot P(B) + \alpha$ from calibrated model

Statistical arbitrage: cointegration spread (self-calibrating z-score), lead-lag (cross-correlation).

Model-driven: fair value divergence (Wang-model edge), premium decay (rides predictable premium lifecycle).

Pricing Engine

Deploys the Wang Transform from Yang (2026), calibrated on 291,309 contracts across 6 platforms:

$$p^{\text{mkt}} = \Phi\bigl(\Phi^{-1}(p^*) + \lambda\bigr), \quad \hat{\lambda} = 0.183 ; (p < 10^{-15})$$

  • Hierarchical model: $\lambda_i = 0.259 - 0.072 \ln(1+V) + 0.143 \ln(1+D) - 0.477 |p-0.5|$
  • Model Greeks: $\partial p / \partial \lambda$, Kelly fraction, edge decay rate
  • Online calibrator: hybrid batch MLE + streaming EWMA with category shrinkage
  • Correlation-aware risk: EWMA correlation matrix, effective exposure limits

Yang, Y. (2026). Pricing Prediction Markets: Risk Premiums, Incomplete Markets, and a Decomposition Framework. Working Paper, UIUC. [Replication package]

Quick Start

git clone https://github.com/YichengYang-Ethan/oracle3.git && cd oracle3
poetry install

oracle3 market list --exchange polymarket --limit 10
oracle3 dashboard --exchange solana --initial-capital 10000

See docs for full CLI reference.

Key Technical Choices

  • Event-driven async engine with snapshot persistence and Unix socket control (pause/resume/killswitch)
  • SpreadExecutor with automatic LIFO unwind on partial fills — no naked multi-leg positions
  • Dual-layer risk: local position/drawdown/exposure limits + Solana simulateTransaction pre-flight
  • On-chain audit trail via Solana Memo program; Jito bundle submission for MEV protection
  • 633 tests, ruff, mypy, codespell CI on every push

License

Apache 2.0 — see LICENSE for details.

This software is for research and educational purposes. Trading involves financial risk.

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