Seasonality Detection & Predictive Modeling in Global Futures

Customer

Challenge

• Define “robust seasonality” without hand-picking dates. Identify seasonal windows objectively across many histories and instruments without embedding proprietary heuristics.
• Continuation vs. mean-reversion. Determine when adherence to a seasonal template continues vs. when deviations revert—under clean, out-of-sample evaluation.
• ML without leakage. Add a supervised model that forecasts next-period behavior using de-biased features while avoiding forward-looking bias.
• Scale & auditability. Support large universes and deliver reproducible artifacts suited to investment reviews—without revealing strategy parameters.

Solution

Approach: A compact Marsbridge squad—Quant Lead, ML Engineer, Data Engineer, Research Analyst—implemented an objective seasonality engine, an ML prediction layer, and an evaluation harness with explainability and governance.

Seasonality engine (generalized & vendor-neutral)

Computed pattern similarity to a historical seasonal template and risk-aware scores that emphasize tradability over raw returns.

Machine-learning layer (supervised prediction)

Built out-of-sample targets for “probability of continuation vs. reversion” using de-biased features, tree-based learners, and temporal encoders.

Testing & governance

Enforced strict train/test chronology, ran parameter-robust sweeps, and produced feature-importance and reason codes (e.g., SHAP-style summaries).

Technologies & tools

Process

1. Discovery & scope: Define seasonal hypotheses and ML objectives; agree on governance and redaction boundaries.
2. Data readiness: Build a vendor-neutral historical panel; standardize calendars and rolls.
3. Seasonality engine: Compute pattern similarity and risk-aware scores over multiple window families (parameters generalized).
4. ML modeling: Train supervised models with time-aware validation; add calibration and abstention.
5. Evaluation: Summarize out-of-sample envelopes; produce explainability artifacts and reviewer-ready reports.
6. Handover: Deliver code, documentation, and sanitized dashboards—no strategy thresholds or entry/exit rules included.

Results

• Objective discovery: A repeatable framework for finding robust seasonal windows across assets, without hand-tuned dates.
• Predictive lift (sanitized): The ML layer provided generalizable signals for when seasonal adherence is likely to continue or reverse, under out-of-sample validation.
• Operational safety: Leakage controls, calibration, and abstention kept precision-first behavior in uncertain regimes.
• Governance-ready: Reproducible reports and explainability strengthened investment and compliance reviews.

All metrics, parameter values, window lengths, asset lists, and trade templates have been intentionally generalized or removed. This document describes methodology and tooling, not trading performance.