Forecast Blockchain Academy
“A data scientist is not a button pusher.” — Prof. Luiz Paulo Fávero - USP
A high-level overview of my modeling strategy for the Autonity Forecastathon — without revealing implementation details during the competition.
Completed — Forecastathon Season 3
This repository documents a statistically grounded forecasting framework used in Autonity Forecastathon Season 3, finishing 5th overall in an open, on-chain forecasting competition with real market incentives.
see official results: Forecastathon-Leaderboard
The Autonity Forecastathon operates in an on-chain forecasting market, where predictions are continuously priced, traded, and scored under real economic incentives.
Unlike offline benchmarks or static datasets, forecasts in this environment directly impact market outcomes, requiring disciplined modeling, calibration, and decision-making.
Forecasting in this environment is fundamentally different from offline benchmarks: models are continuously evaluated under market pressure, partial information, and adversarial pricing dynamics.
This repository documents the research, methodology, and modeling framework I use in the Autonity Forecastathon, a forecasting competition covering macroeconomic indicators such as:
- CPIZ25 – Inflation (MoM)
- GDPF26 – GDP (QoQ)
- UERF26 – Unemployment (MoM, forward)
- UERZ25 – Unemployment (MoM, near-term contract)
- BTCVOL – Short-Term Implied Bitcoin Volatility Contracts
The goal here is to present:
- A clear conceptual overview of the forecasting pipeline
- A professional reference describing the modeling architecture behind the strategy
Note:
Participation in the Forecastathon followed direct outreach within the Clearmatics ecosystem.
- Build a statistically grounded, explainable forecasting framework.
- Prioritize GLMs (Gaussian & Gamma) and classical statistical reasoning over blind black-box models.
- Explicitly incorporate lags, regimes, and economic structure into model design.
- Translate predictions into forecast prices aligned with Autonity’s mark-price mechanics.
- Keep the workflow reproducible and modular for future research or portfolio use.
Instead of defaulting to ARIMA/Prophet/neural networks, the approach emphasizes:
- GLMs (Gaussian link = identity, Gamma link = log)
- Regime-aware decomposition
- Diagnostics-driven selection
- Economic plausibility
This keeps the model understandable and stable — qualities that matter in real forecasting, not just competitions.
Macro signals rarely move instantly. The framework systematically evaluates:
- 1–3 month lags
- Regime-lag interactions
- Stability across rolling windows
Only lags that make statistical and economic sense are kept.
Economic time series change character during:
- COVID
- High inflation periods
- Post-pandemic normalization
Ignoring regime shifts tends to mislead models. This approach treats structural breaks as first-class modeling components.
Autonity uses mark prices; forecasts must be translated accordingly.
The mapping layer uses:
- Predicted percentage change
- Price-mapping formulas
- Adjustments for volatility or regime uncertainty
This layer is intentionally private until the competition ends.
- GLM-based modeling with Gaussian/Gamma alternatives
- Strong regime sensitivity
- Lag architecture to capture delayed drift
- Often close to consensus but improves turning-point detection
- Much smoother series
- Gaussian GLMs work well
- Lags matter less
- Focus is on stability rather than squeezing minor variance
- Noisy, revision-prone series
- Requires extremely parsimonious models
- Regime detection is important
- Gaussian vs Gamma tested for asymmetry and tail behavior
- More sensitive to immediate labor market shifts
- Helps cross-validate the UERF26 design
- Gamma (log link) often useful due to asymmetry and small-change clustering
- Final specification will be documented after the season
In addition to macroeconomic indicators, the strategy was extended to short-dated Bitcoin volatility contracts (weekly expiries).
These instruments require a fundamentally different treatment:
- No macro releases
- High-frequency price dynamics
- Expiry-driven behavior
A separate volatility-focused modeling layer was developed, intentionally kept private during the competition due to its sensitivity.
A key component of the framework is the separation between:
- Forecast generation
- Forecast submission
Forecasts are not submitted daily by default. Submissions are made only when new information is detected, such as:
- Structural regime changes
- New macroeconomic releases
- Material divergence between forecast and mark price
This avoids overtrading and aligns with the scoring dynamics of the Forecastathon.
When the Forecastathon ends, the repository will include:
forecastathon-modeling-framework/
src/ #R code
models/
results/
data/ # Automatic forecast CSVs
charts/ # Automatically generated charts
README.md
LICENSE
To preserve the integrity of ongoing and future Forecastathon seasons, implementation details are not publicly released during active participation.
This repository is intentionally focused on results, decision-making behavior, and modeling philosophy, rather than full code disclosure.
After the competition period, I’m open to discussing the modeling approach and selected implementation details in private, context-appropriate conversations with researchers, practitioners, students, or hiring teams interested in forecasting markets and applied statistical modeling.
Clearmatics is a London-based company focused on designing protocols and market infrastructure for decentralized and institutional financial systems.
Since the mid-2010s, Clearmatics has been cited in institutional initiatives involving major global banks, particularly in efforts related to blockchain-based settlement and financial market infrastructure.
References:
- CoinDesk (2018) – Blockchain Finance Startup Clearmatics Raises $12 Million in New Funding
- CoinDesk (2019) – Top banks invest $50 million to build blockchain settlement system
- CoinDesk (2019) – Barclays and Clearmatics Call on Coders to Help Blockchains Talk to Each Other
- CoinDesk (2025) – Clearmatics' New DeFi Derivatives Let Traders Bet on Anything, but It's Not a Prediction Market
This project was developed by an engineer and data scientist with a background in:
- Postgraduate degree in Data Science and Analytics (USP)
- Bachelor's degree in Computer Engineering (UERJ)
- Special interest in statistical models, interpretability, and applied AI