Matrix-Based Long/Short Trading Platform

Customer

Challenge

• Beyond pair trading: Support basket-level synthetic ratios with overlapping components and frequent composition changes, without corrupting history or positions.
• Signal cadence & reliability: Evaluate signals on intraday windows while managing clock drift, partial data, and exchange events.
• Scale & performance: Compute large matrices of ratios and signals promptly after bar closes; avoid bottlenecks in data IO and order submission.
• Broker & data constraints: Respect connection limits, symbol entitlements, and account safeguards; reconcile expected vs. executed trades.
• Operational hardening: Provide guardrails for missed signals, symbol edge cases, user edits during trading hours, and connectivity changes.

Solution

Approach: A cross-functional Marsbridge squad—Quant Lead, Execution Engineer, Data/Infra Engineer, and QA/Ops Analyst—delivered the system in iterative drops: engine → GUI → integrations → scale-out → automation.

System architecture (cloud-hosted)

Multiple cloud nodes running Windows services; relational storage; consolidated real-time/interval feeds; and broker API sessions with position tracking and safety checks.

Synthetic ratio & portfolio controls

Ratios defined from long/short symbol groups with editable compositions. GUI supports bulk uploads and state-aware edits to preserve open state and reduce unintended trades.

Signal engines & performance

Generalized breakout-style and trend-following signal engines. System uses vectorization, batching, and a tuned latency budget for rapid compute and submission, with resilience checks and auto-restart.

Technologies & tools

Process

1. Kickoff & concept. Scope synthetic ratios, symbol lifecycle, and intraday evaluation approach; define entitlements and access.
2. Engine & GUI. Positions view, ratio editor, bulk upload, state-aware modify; initial paper-trade confirmation.
3. Signal formalization. Breakout-style and trend-following families with configurable parameters; fix edge cases (e.g., partial data and symbol quirks).
4. Cloud go-live. Provision nodes, configure secure access, and set up health checks and alerting.
5. Scale-out & hardening. Data-path optimization, staged exits, diagnostics for missed trades; matrix partitioning across nodes.
6. Automation & mobility. Optional scheduled symbol updates; continued tuning of position caps, partitions, and maintenance windows.
7. Ongoing operations. Parameter re-calibration, symbol refresh cadence, and account resets/rotations as needed.

Results

• End-to-end trading stack: End-to-end trading stack for synthetic long/short ratios: GUI, data, signals, brokerage, and audit trail.
• Large-scale coverage: Platform supports broad symbol universes and extensive ratio sets (precise counts omitted).
• Timely execution: Achieved compute-and-submit inside the targeted post-bar windows with diagnostics to prevent end-of-day misses.
• Operational resilience: Guarded edit windows, symbol automation, configurable position caps, and alerting/reporting for production operations.
• Capital constraints matter: Position caps and matrix partitioning maintain coverage while meeting account limits.
• Cadence vs. cost: Trend-following variants with fewer evaluations reduce churn while preserving trend capture.

Compliance note: All performance discussion reflects paper/live forward tests and internal analytics; no parameters, counts, or venue-specific tactics are disclosed.