lead/lag analysis of polymarket oil prices and future prices
poly market implied prices vs future prices and the z score alpha signal
derivation of true price of oil from polymarket
polymarket snapshot
Agility Score VS Stress test Scenarios- agility scores is derived off how long a company takes to resolve a reported issue

Quantihack-ADA

Quantihack 2026 — Team ADA Theme: Alternative Data Alpha

TL;DR

We treat Polymarket prediction-market prices as real-time alternative data, reconstruct the crowd-implied True Price of WTI Crude Oil, and prove it leads NYMEX CL futures by ~2 hours — then backtest a trading strategy that exploits the lead. And we also use the github closed issue lifecycles of the major tech company as another unconventional data source for alpha generation. The entire workflow lives inside a full-stack product with a React dashboard, a Streamlit analytics console, and an AI-powered market assistant.

Why Polymarket Is Alternative Data

Traditional quant pipelines consume price feeds, filings, and order-book data. Polymarket binary options (Will crude oil hit $X by end of March?) encode discrete crowd probability assessments across 21 strike buckets

We do not just read prediction market prices. We reverse-engineer them into a continuous probability distribution, derive a model-free Expected Value, and use the spread vs futures as an alpha signal.

—

Why GitHub issues -

GitHub issue data is entirely non-financial, meaning it's uncorrelated with standard market signals and unlikely to be arbitraged. It acts as a direct window into internal team health before that health is reflected in stock price.

Core Research Pipeline

1. Data Ingestion

Source	What We Collect	Volume
Polymarket CLOB API	YES-token prices for 21 strike x 2 directions	69,342 rows
NYMEX CL Futures	30-min OHLCV bars	920 bars
News headlines	Scraped oil-related articles	37 articles
Reddit r/oil + r/energy	Posts with scores, comments	221 posts

GitHub Data Collection- Pulled closed issue lifecycles from 9 major tech repositories using the PyGitHub API, capturing open date, close date and resolution duration for every bug ticket within a 2-month window. We extracted data from multiple companies including Amazon, Apple, Meta, Microsoft, etc… Library- Used, a Python survival analysis library, to fit Kaplan-Meier curves and Weibull models directly on issue durations without requiring distributional assumptions. Agility Score- Compressed each company's survival curve into a single parameter: 1 / (median_survival + 1), bounding resolution speed between 0 and 1 where faster teams score higher. A higher agility score indicates how fast a reported issue was resolved by the company making it a safer investment.

2. True Price Derivation (Technical Complexity)

What price does the crowd actually believe in?

We transform 21 discrete binary option prices into a full continuous price distribution:

Binary Options -> Discrete CDF -> PCHIP Monotonic Spline -> Smooth CDF
                                  Log-Normal Tail Fitting
Smooth CDF -> Numerical Gradient -> PDF -> integral x*f(x) dx = Expected Value

Key techniques:

PCHIP Interpolation (Piecewise Cubic Hermite) — monotone cubic spline that preserves the shape of a CDF (no Runge oscillation, no negative probabilities)
Probit regression on ln(K) for log-normal tail estimation
Boundary anchoring at 0.4x and 1.6x strike range to handle unobserved tails
Trapezoid-normalised PDF with non-negativity enforcement
90% Credible Interval from the implied CDF

Result at Mar 20 16:00 UTC:

Metric	Value
NYMEX Futures	$97.54
PM Implied EV (True Price)	$104.91
Basis Gap	+$7.37
Log-Normal fit	mu=4.650, sigma=0.166
90% Credible Interval	[$81, $134]

3. Lead-Lag Discovery

Cross-correlation of first-differenced dEV vs dFutures reveals:

Metric	Value
Peak cross-correlation	r = +0.386 at lag +2h
95% CI band	+/-0.110
Verdict	PM Implied EV leads NYMEX by ~2 hours

The prediction market crowd prices in new information before the futures market converges. This 2-hour lead is statistically significant (3.5x the 95% CI).

4. Signal and Backtest

We tested four signal designs exploiting the 2-hour lead:

Signal	Return	Sharpe	Trades	Win Rate
Z-Score (baseline)	+0.10%	0.300	2	100%
EV Momentum	+0.06%	0.230	71	52%
Basis Gap Direction	+0.26%	0.590	4	75%
Combined (AND)	+0.24%	0.560	3	67%

The Basis Gap Direction strategy — simply buy when EV > Futures, sell when EV < Futures — is the best signal. The backtester 1-bar execution delay naturally matches the 2-hour lead.

5. Polymarket Leads the News Cycle

Separate analysis in polymarket_lead.ipynb confirms:

PM vs NYMEX Pearson r = -0.87 (strong inverse correlation for binary strike=65, direction=down)
Pre-news price movements are larger than post-news: Polymarket prices informationally before headlines break

The Product

React Dashboard (Vite + Chart.js)

A full-stack SPA with 5 pages:

Page	Function
Fetch Data	One-click ingestion from all 4 sources (Polymarket, News, Reddit, GitHub)
EDA	Auto-profiling of Polymarket dataset — row counts, strike distribution, liquidity snapshot
Signal Generator	Manual RSI-based signal tuning + AI-assisted parameter suggestion via GPT-4o
Backtest	Full equity curve, Sharpe/MaxDD/Win Rate KPIs, signal distribution chart, trade log
AI Summary	Aggregates live News + Reddit data -> OpenAI-powered market analysis with bullish/bearish sentiment detection

Key Findings

Polymarket is a leading indicator — the crowd-implied EV leads NYMEX CL futures by 2 hours (r = +0.386, p < 0.001)
The simplest signal wins — sign(EV - Futures) produces a Sharpe of 0.59, nearly 2x the Z-score baseline
Prediction markets price information before news breaks — pre-headline price change > post-headline price change
The True Price framework is generalizable — any set of binary options on a continuous outcome can be inverted into an implied distribution and expected value