FinTech · Market Microstructure · Execution Quality

Testing executable edge in Polymarket BTC short-horizon markets.

A public market microstructure research project testing whether apparent prediction-market edge can survive the full path from signal detection to filled exposure and settlement.

Key lesson: short-horizon prediction-market edge is not only a pricing problem; it is an execution-quality problem. The public sample does not support a profitability claim.

Signal funnel showing execution stages
Execution funnel: where theoretical edge is lost before filled exposure.

Key findings

What the project shows

01

Simulation-to-live gap

Pure tick replay can show positive edge, but live-like ledger records become weaker after latency, quote staleness, order failure, fill probability, model gates, and settlement outcomes are included. This gap is the main research object.

02

Execution funnel dominates

The bottleneck is not a single cost such as spread. The harder question is whether a signal survives acceptance, fill probability, ML EV filtering, order submission, and settlement.

03

Extreme probabilities are fragile

Very low and very high probability buckets are less stable in the public sample, especially around the final resolution window where small price changes can cause large binary-outcome errors.

04

ML is an execution gate

The public workflow exports safe scalar ML EV and fill-probability diagnostics, but it does not ship private model artifacts, raw feature JSON, or production execution logic.

Visual summary

From signal edge to executable exposure

The dashboard and reports focus on the gap between apparent pricing edge and realized execution quality. The figures below summarize the public-sample execution funnel and observed status breakdown.

Signal funnel chart
Signal funnel
Execution status breakdown chart
Execution status breakdown

Research artifacts

Explore the public demo

Methodology

Public-safe research workflow

The project uses anonymized and downsampled public sample data to separate theoretical edge from executable edge. It keeps private raw ledgers, wallets, order IDs, model artifacts, and live execution systems out of the public repo.

1. Public sample dataAnonymized candidates, executions, settlements, and tick snapshots.
2. Fair probability modelReference-price proxy, volatility, and time-to-resolution assumptions.
3. Execution diagnosticsSpread, fill probability, latency, rejected orders, and settlement outcomes.
4. ML / risk checksML EV gate, fill-probability gate, calibration, and Monte Carlo simulation.
5. Public artifactsReports, notebooks, dashboard, and portfolio page.

Skills demonstrated

What this project is meant to show

Market microstructure reasoning

Separating theoretical pricing edge from executable exposure after spread, fill probability, latency, and settlement.

Research engineering

Turning private trading experiments into public-safe reports, notebooks, dashboards, and portfolio artifacts.

Risk and model evaluation

Using calibration, ML EV gates, fill-probability diagnostics, and Monte Carlo simulation without presenting them as profit guarantees.

Tech stack

Tools used

Scope and disclaimer

Research demo only

This is not financial advice, trading advice, investment advice, or a recommendation to participate in any market. The repository does not claim to provide a profitable strategy, does not claim to predict BTC, and does not provide a live trading or betting system.