Long-Horizon Futures Portfolio Optimization & Risk Management

Customer

Challenge

• Model credibility: Initial prototypes showed unusually strong results that required investigation for look-ahead and data-handling artifacts.
• Tradable data construction: Build continuous contract series and apply roll/adjustment logic suitable for long-horizon tests.
• Risk/exit policy: Specify stop-loss and trailing-exit approaches that retain trend participation while constraining adverse moves—without hard-coding client-specific thresholds.
• Robustness: Reduce sensitivity to single parameter settings; avoid overfitting to any one period or market.

Solution

Approach: A compact Risk & Research squad—Quant Lead, Data Engineer, and Research Analyst—delivered a clean test bed, generalized risk rules, and parameter-robust evaluation.

Bias diagnosis & corrected test bed

Recreated the strategy in code and isolated leakage pathways. Rebuilt the backtest on adjusted continuous series with open-interest/liquidity gating and clear timestamp causality.

Entry significance & exits (generalized)

Introduced entry-significance checks and designed stop/exit policies with configurable bands and trailing behavior; documented how to tune them.

Robustness & parameter risk

Ran grid/Monte-Carlo style sweeps across plausible ranges to observe envelopes of outcomes rather than single-point optima.

Technologies & tools

Process

1. Intake & replication: Reproduce the baseline strategy; verify data lineage and timestamp causality.
2. Data plane rebuild: Construct adjusted continuous series with roll logic and liquidity gates.
3. Leakage removal: Identify and eliminate look-ahead/peeking; re-run baselines on corrected data.
4. Risk-policy design: Specify generalized stop/trailing frameworks and entry-significance checks.
5. Parameter envelopes: Perform sweeps and summarize outcome bands; highlight robustness regions.
6. Handover: Provide code, methodology notes, and reviewer-ready reports.

Results

• Credible baselines: Removal of leakage and use of tradable series produced audit-ready results.
• Operationalized risk: Clear, generalized exit frameworks (stop/trailing) and entry-significance checks that can be tuned without exposing proprietary thresholds.
• Reduced overfit risk: Envelope-based evaluation highlighted stable parameter regions and realistic expectations.

All instrument lists, thresholds, sizing rules, and date windows have been omitted or generalized to protect client strategy. This document describes methodology, not trading performance.