Oracle3

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

Why this exists

Prediction markets price binary contracts at systematically biased levels — a true 50/50 contract typically trades around 0.57 (favorite-longshot bias, $\hat{\lambda} \approx 0.183$). Most trading bots ignore this distortion entirely. Oracle3 operationalizes a peer-reviewed pricing model, calibrated on 291,309 resolved contracts across six venues, to systematically harvest the bias through arbitrage detection and Kelly-sized model trades.

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

How oracle3 differs from existing prediction-market tools

	Oracle3	polymarket-whales	prediction-market-maker	py-clob-client
Pricing model	Wang Transform (calibrated MLE)	None	Bid-ask MM	None
Constraint-based arbitrage	8 strategies	None	None	N/A
Multi-venue	Kalshi + Polymarket + Solana	Polymarket only	Polymarket only	Polymarket only
On-chain execution	Solana via DFlow + Jito	No	No	N/A (SDK)
Working paper	Yang (2026), SSRN	No	No	No
Tests	633	0	0	50+
License	Apache 2.0	MIT	MIT	MIT

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]

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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

Star History

Contributors

If oracle3 helps your research or trading, please ⭐ star the repo — it helps others find it.

License

Apache 2.0 — see LICENSE for details.

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