Applied Quantitative Research & Open Data Suite
Reproducible empirical studies, 5,000-contract calibration benchmarks, and open-source datasets for binary prediction markets, scoring rules, and exchange fee architectures.
Empirical Calibration, Brier Decomposition & Fee Drag: A 5,000-Contract Benchmark
Exhaustive Murphy decomposition across politics, macroeconomics, and crypto markets, proving favourite-longshot bias and quantifying execution friction on Kelly compounding.
5,000-Contract Calibration Dataset
Individual contract records with clearing probabilities, binary outcomes, Brier loss, and volume across 5 event categories.
Cross-Venue Fee & Capital Preservation Matrix
Audited maker/taker fees, settlement redemption taxes, and annual drag on $100k volume across 7 global prediction exchanges.
Verification Test Runner (Python 3)
Automated test suite validating Murphy’s Brier score decomposition (REL - RES + UNC), dataset row checksums, and fee arithmetic.
Empirical Quantile Calibration Matrix (Audit Preview)
Partitioned 10-bin calibration verification showing clearing market probability vs realized event outcome frequency across 5,000 resolved contracts:
| Probability Bin | Contracts (Nk) | Mean Prob (f̄k) | Observed Freq (ōk) | Calibration Bias (Δ) | Dominant Category |
|---|---|---|---|---|---|
| [0.00 – 0.10) | 560 | 0.0491 | 0.0464 | +0.0027 | Macro / Geopolitics |
| [0.10 – 0.20) | 551 | 0.1500 | 0.1180 | +0.0320 | Crypto / Longshots |
| [0.20 – 0.30) | 527 | 0.2477 | 0.2353 | +0.0124 | General Policy |
| [0.30 – 0.40) | 430 | 0.3492 | 0.3488 | +0.0004 | Central Bank Rates |
| [0.40 – 0.50) | 389 | 0.4491 | 0.4602 | -0.0110 | Competitive Elections |
| [0.50 – 0.60) | 401 | 0.5493 | 0.5461 | +0.0032 | Debates / Polling |
| [0.60 – 0.70) | 441 | 0.6530 | 0.6463 | +0.0067 | Macro Indicators |
| [0.70 – 0.80) | 573 | 0.7521 | 0.7731 | -0.0210 | Incumbent Contests |
| [0.80 – 0.90) | 590 | 0.8498 | 0.8847 | -0.0349 | High-Certainty Bills |
| [0.90 – 1.00] | 538 | 0.9486 | 0.9591 | -0.0105 | Near Settlement |
Open Science Standards & Academic Reproducibility
In accordance with Open Science principles, all simulation datasets, probability matrices, and scoring algorithms are released under Creative Commons Attribution 4.0 International (CC-BY-4.0).
Clone the repository or download the CSV distribution suite.
Execute the Python audit suite to verify matrix completeness, Brier decomposition, and row sums.
Integrate the verified calibration vectors into your algorithmic execution strategies.