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]

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]](https://github.com/YichengYang-Ethan/prediction-market-pricing)

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